Ventriculogram segmentation using boosted decision trees

NASA Astrophysics Data System (ADS)

McDonald, John A.; Sheehan, Florence H.

2004-05-01

Left ventricular status, reflected in ejection fraction or end systolic volume, is a powerful prognostic indicator in heart disease. Quantitative analysis of these and other parameters from ventriculograms (cine xrays of the left ventricle) is infrequently performed due to the labor required for manual segmentation. None of the many methods developed for automated segmentation has achieved clinical acceptance. We present a method for semi-automatic segmentation of ventriculograms based on a very accurate two-stage boosted decision-tree pixel classifier. The classifier determines which pixels are inside the ventricle at key ED (end-diastole) and ES (end-systole) frames. The test misclassification rate is about 1%. The classifier is semi-automatic, requiring a user to select 3 points in each frame: the endpoints of the aortic valve and the apex. The first classifier stage is 2 boosted decision-trees, trained using features such as gray-level statistics (e.g. median brightness) and image geometry (e.g. coordinates relative to user supplied 3 points). Second stage classifiers are trained using the same features as the first, plus the output of the first stage. Border pixels are determined from the segmented images using dilation and erosion. A curve is then fit to the border pixels, minimizing a penalty function that trades off fidelity to the border pixels with smoothness. ED and ES volumes, and ejection fraction are estimated from border curves using standard area-length formulas. On independent test data, the differences between automatic and manual volumes (and ejection fractions) are similar in size to the differences between two human observers.

Clinical Utility of the Modified Segmental Boost Technique for Treatment of the Pelvis and Inguinal Nodes

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

Moran, M.S., E-mail: meena.moran@yale.ed; Yale New Haven Hospital, New Haven, Connecticut and William W. Backus Hospital, Norwich, Connecticut; Castrucci, W.A.

2010-03-15

Purpose: Low-lying pelvic malignancies often require simultaneous radiation to pelvis and inguinal nodes. We previously reported improved homogeneity with the modified segmental boost technique (MSBT) compared to that with traditional methods, using phantom models. Here we report our institutional clinical experience with MSBT. Methods and Materials: MSBT patients from May 2001 to March 2007 were evaluated. Parameters analyzed included isocenter/multileaf collimation shifts, time per fraction (four fields), monitor units (MU)/fraction, femoral doses, maximal dose relative to body mass index, and inguinal node depth. In addition, a dosimetric comparison of the MSBT versus intensity modulated radiation therapy (IMRT) was conducted. Results:more » Of the 37 MSBT patients identified, 32 were evaluable. Port film adjustments were required in 6% of films. Median values for each analyzed parameter were as follows: MU/fraction, 298 (range, 226-348); delivery time, 4 minutes; inguinal depth, 4.5 cm; volume receiving 45 Gy (V45), 7%; V27.5, 87%; body mass index, 25 (range, 16.0-33.8). Inguinal dose was 100% in all cases; in-field inhomogeneity ranged from 111% to 118%. IMRT resulted in significantly decreased dose to normal tissue but required more time for treatment planning and a higher number of MUs (1,184 vs. 313 MU). Conclusions: In our clinical experience, the mono-isocentric MSBT provides a high degree of accuracy, improved homogeneity compared with traditional techniques, ease of simulation, treatment planning, treatment delivery, and acceptable femoral doses for pelvic/inguinal radiation fields requiring 45 to 50.4 Gy. In addition, the MSBT delivers a relatively uniform dose distribution throughout the treatment volume, despite varying body habitus. Clinical scenarios for the use of MSBT vs. intensity-modulated radiation therapy are discussed. To our knowledge, this is the first study reporting the utility of MSBT in the clinical setting.« less

A boosted optimal linear learner for retinal vessel segmentation

NASA Astrophysics Data System (ADS)

Poletti, E.; Grisan, E.

2014-03-01

Ocular fundus images provide important information about retinal degeneration, which may be related to acute pathologies or to early signs of systemic diseases. An automatic and quantitative assessment of vessel morphological features, such as diameters and tortuosity, can improve clinical diagnosis and evaluation of retinopathy. At variance with available methods, we propose a data-driven approach, in which the system learns a set of optimal discriminative convolution kernels (linear learner). The set is progressively built based on an ADA-boost sample weighting scheme, providing seamless integration between linear learner estimation and classification. In order to capture the vessel appearance changes at different scales, the kernels are estimated on a pyramidal decomposition of the training samples. The set is employed as a rotating bank of matched filters, whose response is used by the boosted linear classifier to provide a classification of each image pixel into the two classes of interest (vessel/background). We tested the approach fundus images available from the DRIVE dataset. We show that the segmentation performance yields an accuracy of 0.94.

Breast conserving treatment for breast cancer: dosimetric comparison of sequential versus simultaneous integrated photon boost.

PubMed

Van Parijs, Hilde; Reynders, Truus; Heuninckx, Karina; Verellen, Dirk; Storme, Guy; De Ridder, Mark

2014-01-01

Breast conserving surgery followed by whole breast irradiation is widely accepted as standard of care for early breast cancer. Addition of a boost dose to the initial tumor area further reduces local recurrences. We investigated the dosimetric benefits of a simultaneously integrated boost (SIB) compared to a sequential boost to hypofractionate the boost volume, while maintaining normofractionation on the breast. For 10 patients 4 treatment plans were deployed, 1 with a sequential photon boost, and 3 with different SIB techniques: on a conventional linear accelerator, helical TomoTherapy, and static TomoDirect. Dosimetric comparison was performed. PTV-coverage was good in all techniques. Conformity was better with all SIB techniques compared to sequential boost (P = 0.0001). There was less dose spilling to the ipsilateral breast outside the PTVboost (P = 0.04). The dose to the organs at risk (OAR) was not influenced by SIB compared to sequential boost. Helical TomoTherapy showed a higher mean dose to the contralateral breast, but less than 5 Gy for each patient. SIB showed less dose spilling within the breast and equal dose to OAR compared to sequential boost. Both helical TomoTherapy and the conventional technique delivered acceptable dosimetry. SIB seems a safe alternative and can be implemented in clinical routine.

Breast Conserving Treatment for Breast Cancer: Dosimetric Comparison of Sequential versus Simultaneous Integrated Photon Boost

PubMed Central

Reynders, Truus; Heuninckx, Karina; Verellen, Dirk; Storme, Guy; De Ridder, Mark

2014-01-01

Background. Breast conserving surgery followed by whole breast irradiation is widely accepted as standard of care for early breast cancer. Addition of a boost dose to the initial tumor area further reduces local recurrences. We investigated the dosimetric benefits of a simultaneously integrated boost (SIB) compared to a sequential boost to hypofractionate the boost volume, while maintaining normofractionation on the breast. Methods. For 10 patients 4 treatment plans were deployed, 1 with a sequential photon boost, and 3 with different SIB techniques: on a conventional linear accelerator, helical TomoTherapy, and static TomoDirect. Dosimetric comparison was performed. Results. PTV-coverage was good in all techniques. Conformity was better with all SIB techniques compared to sequential boost (P = 0.0001). There was less dose spilling to the ipsilateral breast outside the PTVboost (P = 0.04). The dose to the organs at risk (OAR) was not influenced by SIB compared to sequential boost. Helical TomoTherapy showed a higher mean dose to the contralateral breast, but less than 5 Gy for each patient. Conclusions. SIB showed less dose spilling within the breast and equal dose to OAR compared to sequential boost. Both helical TomoTherapy and the conventional technique delivered acceptable dosimetry. SIB seems a safe alternative and can be implemented in clinical routine. PMID:25162031

Evaluating segmentation error without ground truth.

PubMed

Kohlberger, Timo; Singh, Vivek; Alvino, Chris; Bahlmann, Claus; Grady, Leo

2012-01-01

The automatic delineation of the boundaries of organs and other anatomical structures is a key component of many medical image processing systems. In this paper we present a generic learning approach based on a novel space of segmentation features, which can be trained to predict the overlap error and Dice coefficient of an arbitrary organ segmentation without knowing the ground truth delineation. We show the regressor to be much stronger a predictor of these error metrics than the responses of probabilistic boosting classifiers trained on the segmentation boundary. The presented approach not only allows us to build reliable confidence measures and fidelity checks, but also to rank several segmentation hypotheses against each other during online usage of the segmentation algorithm in clinical practice.

Use of nonlinear design optimization techniques in the comparison of battery discharger topologies for the space platform

NASA Technical Reports Server (NTRS)

Sable, Dan M.; Cho, Bo H.; Lee, Fred C.

1990-01-01

A detailed comparison of a boost converter, a voltage-fed, autotransformer converter, and a multimodule boost converter, designed specifically for the space platform battery discharger, is performed. Computer-based nonlinear optimization techniques are used to facilitate an objective comparison. The multimodule boost converter is shown to be the optimum topology at all efficiencies. The margin is greatest at 97 percent efficiency. The multimodule, multiphase boost converter combines the advantages of high efficiency, light weight, and ample margin on the component stresses, thus ensuring high reliability.

Automatic exudate detection by fusing multiple active contours and regionwise classification.

PubMed

Harangi, Balazs; Hajdu, Andras

2014-11-01

In this paper, we propose a method for the automatic detection of exudates in digital fundus images. Our approach can be divided into three stages: candidate extraction, precise contour segmentation and the labeling of candidates as true or false exudates. For candidate detection, we borrow a grayscale morphology-based method to identify possible regions containing these bright lesions. Then, to extract the precise boundary of the candidates, we introduce a complex active contour-based method. Namely, to increase the accuracy of segmentation, we extract additional possible contours by taking advantage of the diverse behavior of different pre-processing methods. After selecting an appropriate combination of the extracted contours, a region-wise classifier is applied to remove the false exudate candidates. For this task, we consider several region-based features, and extract an appropriate feature subset to train a Naïve-Bayes classifier optimized further by an adaptive boosting technique. Regarding experimental studies, the method was tested on publicly available databases both to measure the accuracy of the segmentation of exudate regions and to recognize their presence at image-level. In a proper quantitative evaluation on publicly available datasets the proposed approach outperformed several state-of-the-art exudate detector algorithms. Copyright © 2014 Elsevier Ltd. All rights reserved.

An improved method for pancreas segmentation using SLIC and interactive region merging

NASA Astrophysics Data System (ADS)

Zhang, Liyuan; Yang, Huamin; Shi, Weili; Miao, Yu; Li, Qingliang; He, Fei; He, Wei; Li, Yanfang; Zhang, Huimao; Mori, Kensaku; Jiang, Zhengang

2017-03-01

Considering the weak edges in pancreas segmentation, this paper proposes a new solution which integrates more features of CT images by combining SLIC superpixels and interactive region merging. In the proposed method, Mahalanobis distance is first utilized in SLIC method to generate better superpixel images. By extracting five texture features and one gray feature, the similarity measure between two superpixels becomes more reliable in interactive region merging. Furthermore, object edge blocks are accurately addressed by re-segmentation merging process. Applying the proposed method to four cases of abdominal CT images, we segment pancreatic tissues to verify the feasibility and effectiveness. The experimental results show that the proposed method can make segmentation accuracy increase to 92% on average. This study will boost the application process of pancreas segmentation for computer-aided diagnosis system.

An efficient ensemble learning method for gene microarray classification.

PubMed

Osareh, Alireza; Shadgar, Bita

2013-01-01

The gene microarray analysis and classification have demonstrated an effective way for the effective diagnosis of diseases and cancers. However, it has been also revealed that the basic classification techniques have intrinsic drawbacks in achieving accurate gene classification and cancer diagnosis. On the other hand, classifier ensembles have received increasing attention in various applications. Here, we address the gene classification issue using RotBoost ensemble methodology. This method is a combination of Rotation Forest and AdaBoost techniques which in turn preserve both desirable features of an ensemble architecture, that is, accuracy and diversity. To select a concise subset of informative genes, 5 different feature selection algorithms are considered. To assess the efficiency of the RotBoost, other nonensemble/ensemble techniques including Decision Trees, Support Vector Machines, Rotation Forest, AdaBoost, and Bagging are also deployed. Experimental results have revealed that the combination of the fast correlation-based feature selection method with ICA-based RotBoost ensemble is highly effective for gene classification. In fact, the proposed method can create ensemble classifiers which outperform not only the classifiers produced by the conventional machine learning but also the classifiers generated by two widely used conventional ensemble learning methods, that is, Bagging and AdaBoost.

International Students at Community Colleges: Challenges and Opportunities for This Unique Segment of U.S. Higher Education

ERIC Educational Resources Information Center

Jennings, Ross

2017-01-01

In unprecedented numbers, American community colleges are seeking to boost their recruitment and retention of international students. This chapter uncovers the challenges and opportunities based on successful experiences in community college efforts.

Computer simulations of optimum boost and buck-boost converters

NASA Technical Reports Server (NTRS)

Rahman, S.

1982-01-01

The development of mathematicl models suitable for minimum weight boost and buck-boost converter designs are presented. The facility of an augumented Lagrangian (ALAG) multiplier-based nonlinear programming technique is demonstrated for minimum weight design optimizations of boost and buck-boost power converters. ALAG-based computer simulation results for those two minimum weight designs are discussed. Certain important features of ALAG are presented in the framework of a comprehensive design example for boost and buck-boost power converter design optimization. The study provides refreshing design insight of power converters and presents such information as weight annd loss profiles of various semiconductor components and magnetics as a function of the switching frequency.

An optimized posterior axillary boost technique in radiation therapy to supraclavicular and axillary lymph nodes: A comparative study

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

Hernandez, Victor, E-mail: vhernandezmasgrau@gmail.com; Arenas, Meritxell; Müller, Katrin

2013-01-01

To assess the advantages of an optimized posterior axillary (AX) boost technique for the irradiation of supraclavicular (SC) and AX lymph nodes. Five techniques for the treatment of SC and levels I, II, and III AX lymph nodes were evaluated for 10 patients selected at random: a direct anterior field (AP); an anterior to posterior parallel pair (AP-PA); an anterior field with a posterior axillary boost (PAB); an anterior field with an anterior axillary boost (AAB); and an optimized PAB technique (OptPAB). The target coverage, hot spots, irradiated volume, and dose to organs at risk were evaluated and a statisticalmore » analysis comparison was performed. The AP technique delivered insufficient dose to the deeper AX nodes. The AP-PA technique produced larger irradiated volumes and higher mean lung doses than the other techniques. The PAB and AAB techniques originated excessive hot spots in most of the cases. The OptPAB technique produced moderate hot spots while maintaining a similar planning target volume (PTV) coverage, irradiated volume, and dose to organs at risk. This optimized technique combines the advantages of the PAB and AP-PA techniques, with moderate hot spots, sufficient target coverage, and adequate sparing of normal tissues. The presented technique is simple, fast, and easy to implement in routine clinical practice and is superior to the techniques historically used for the treatment of SC and AX lymph nodes.« less

Nonlinear program based optimization of boost and buck-boost converter designs

NASA Astrophysics Data System (ADS)

Rahman, S.; Lee, F. C.

The facility of an Augmented Lagrangian (ALAG) multiplier based nonlinear programming technique is demonstrated for minimum-weight design optimizations of boost and buck-boost power converters. Certain important features of ALAG are presented in the framework of a comprehensive design example for buck-boost power converter design optimization. The study provides refreshing design insight of power converters and presents such information as weight and loss profiles of various semiconductor components and magnetics as a function of the switching frequency.

Noninvasive differential diagnosis of dental periapical lesions in cone-beam CT scans

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

Okada, Kazunori, E-mail: kazokada@sfsu.edu; Rysavy, Steven; Flores, Arturo

Purpose: This paper proposes a novel application of computer-aided diagnosis (CAD) to an everyday clinical dental challenge: the noninvasive differential diagnosis of periapical lesions between periapical cysts and granulomas. A histological biopsy is the most reliable method currently available for this differential diagnosis; however, this invasive procedure prevents the lesions from healing noninvasively despite a report that they may heal without surgical treatment. A CAD using cone-beam computed tomography (CBCT) offers an alternative noninvasive diagnostic tool which helps to avoid potentially unnecessary surgery and to investigate the unknown healing process and rate for the lesions. Methods: The proposed semiautomatic solutionmore » combines graph-based random walks segmentation with machine learning-based boosted classifiers and offers a robust clinical tool with minimal user interaction. As part of this CAD framework, the authors provide two novel technical contributions: (1) probabilistic extension of the random walks segmentation with likelihood ratio test and (2) LDA-AdaBoost: a new integration of weighted linear discriminant analysis to AdaBoost. Results: A dataset of 28 CBCT scans is used to validate the approach and compare it with other popular segmentation and classification methods. The results show the effectiveness of the proposed method with 94.1% correct classification rate and an improvement of the performance by comparison with the Simon’s state-of-the-art method by 17.6%. The authors also compare classification performances with two independent ground-truth sets from the histopathology and CBCT diagnoses provided by endodontic experts. Conclusions: Experimental results of the authors show that the proposed CAD system behaves in clearer agreement with the CBCT ground-truth than with histopathology, supporting the Simon’s conjecture that CBCT diagnosis can be as accurate as histopathology for differentiating the periapical lesions.« less

Noninvasive differential diagnosis of dental periapical lesions in cone-beam CT scans.

PubMed

Okada, Kazunori; Rysavy, Steven; Flores, Arturo; Linguraru, Marius George

2015-04-01

This paper proposes a novel application of computer-aided diagnosis (CAD) to an everyday clinical dental challenge: the noninvasive differential diagnosis of periapical lesions between periapical cysts and granulomas. A histological biopsy is the most reliable method currently available for this differential diagnosis; however, this invasive procedure prevents the lesions from healing noninvasively despite a report that they may heal without surgical treatment. A CAD using cone-beam computed tomography (CBCT) offers an alternative noninvasive diagnostic tool which helps to avoid potentially unnecessary surgery and to investigate the unknown healing process and rate for the lesions. The proposed semiautomatic solution combines graph-based random walks segmentation with machine learning-based boosted classifiers and offers a robust clinical tool with minimal user interaction. As part of this CAD framework, the authors provide two novel technical contributions: (1) probabilistic extension of the random walks segmentation with likelihood ratio test and (2) LDA-AdaBoost: a new integration of weighted linear discriminant analysis to AdaBoost. A dataset of 28 CBCT scans is used to validate the approach and compare it with other popular segmentation and classification methods. The results show the effectiveness of the proposed method with 94.1% correct classification rate and an improvement of the performance by comparison with the Simon's state-of-the-art method by 17.6%. The authors also compare classification performances with two independent ground-truth sets from the histopathology and CBCT diagnoses provided by endodontic experts. Experimental results of the authors show that the proposed CAD system behaves in clearer agreement with the CBCT ground-truth than with histopathology, supporting the Simon's conjecture that CBCT diagnosis can be as accurate as histopathology for differentiating the periapical lesions.

Optimization of combined electron and photon beams for breast cancer

NASA Astrophysics Data System (ADS)

Xiong, W.; Li, J.; Chen, L.; Price, R. A.; Freedman, G.; Ding, M.; Qin, L.; Yang, J.; Ma, C.-M.

2004-05-01

Recently, intensity-modulated radiation therapy and modulated electron radiotherapy have gathered a growing interest for the treatment of breast and head and neck tumours. In this work, we carried out a study to combine electron and photon beams to achieve differential dose distributions for multiple target volumes simultaneously. A Monte Carlo based treatment planning system was investigated, which consists of a set of software tools to perform accurate dose calculation, treatment optimization, leaf sequencing and plan analysis. We compared breast treatment plans generated using this home-grown optimization and dose calculation software for different treatment techniques. Five different planning techniques have been developed for this study based on a standard photon beam whole breast treatment and an electron beam tumour bed cone down. Technique 1 includes two 6 MV tangential wedged photon beams followed by an anterior boost electron field. Technique 2 includes two 6 MV tangential intensity-modulated photon beams and the same boost electron field. Technique 3 optimizes two intensity-modulated photon beams based on a boost electron field. Technique 4 optimizes two intensity-modulated photon beams and the weight of the boost electron field. Technique 5 combines two intensity-modulated photon beams with an intensity-modulated electron field. Our results show that technique 2 can reduce hot spots both in the breast and the tumour bed compared to technique 1 (dose inhomogeneity is reduced from 34% to 28% for the target). Techniques 3, 4 and 5 can deliver a more homogeneous dose distribution to the target (with dose inhomogeneities for the target of 22%, 20% and 9%, respectively). In many cases techniques 3, 4 and 5 can reduce the dose to the lung and heart. It is concluded that combined photon and electron beam therapy may be advantageous for treating breast cancer compared to conventional treatment techniques using tangential wedged photon beams followed by a boost electron field.

68Ga-PSMA-PET/CT imaging of localized primary prostate cancer patients for intensity modulated radiation therapy treatment planning with integrated boost.

PubMed

Thomas, Lena; Kantz, Steffi; Hung, Arthur; Monaco, Debra; Gaertner, Florian C; Essler, Markus; Strunk, Holger; Laub, Wolfram; Bundschuh, Ralph A

2018-07-01

The purpose of our study was to show the feasibility and potential benefits of using 68 Ga-PSMA-PET/CT imaging for radiation therapy treatment planning of patients with primary prostate cancer using either integrated boost on the PET-positive volume or localized treatment of the PET-positive volume. The potential gain of such an approach, the improvement of tumor control, and reduction of the dose to organs-at-risk at the same time was analyzed using the QUANTEC biological model. Twenty-one prostate cancer patients (70 years average) without previous local therapy received 68 Ga-PSMA-PET/CT imaging. Organs-at-risk and standard prostate target volumes were manually defined on the obtained datasets. A PET active volume (PTV_PET) was segmented with a 40% of the maximum activity uptake in the lesion as threshold followed by manual adaption. Five different treatment plan variations were calculated for each patient. Analysis of derived treatment plans was done according to QUANTEC with in-house developed software. Tumor control probability (TCP) and normal tissue complication probability (NTCP) was calculated for all plan variations. Comparing the conventional plans to the plans with integrated boost and plans just treating the PET-positive tumor volume, we found that TCP increased to (95.2 ± 0.5%) for an integrated boost with 75.6 Gy, (98.1 ± 0.3%) for an integrated boost with 80 Gy, (94.7 ± 0.8%) for treatment of PET-positive volume with 75 Gy, and to (99.4 ± 0.1%) for treating PET-positive volume with 95 Gy (all p < 0.0001). For the integrated boost with 80 Gy, a significant increase of the median NTCP of the rectum was found, for all other plans no statistical significant increase in the NTCP neither of the rectum nor the bladder was found. Our study demonstrates that the use of 68 Ga-PSMA-PET/CT image information allows for more individualized prostate treatment planning. TCP values of identified active tumor volumes were increased, while rectum and bladder NTCP values either remained the same or were even lower. However, further studies need to clarify the clinical benefit for the patients applying these techniques.

Applying additive modeling and gradient boosting to assess the effects of watershed and reach characteristics on riverine assemblages

USGS Publications Warehouse

Maloney, Kelly O.; Schmid, Matthias; Weller, Donald E.

2012-01-01

Issues with ecological data (e.g. non-normality of errors, nonlinear relationships and autocorrelation of variables) and modelling (e.g. overfitting, variable selection and prediction) complicate regression analyses in ecology. Flexible models, such as generalized additive models (GAMs), can address data issues, and machine learning techniques (e.g. gradient boosting) can help resolve modelling issues. Gradient boosted GAMs do both. Here, we illustrate the advantages of this technique using data on benthic macroinvertebrates and fish from 1573 small streams in Maryland, USA.

A PIPO Boost Converter with Low Ripple and Medium Current Application

NASA Astrophysics Data System (ADS)

Bandri, S.; Sofian, A.; Ismail, F.

2018-04-01

This paper presents a Parallel Input Parallel Output (PIPO) boost converter is proposed to gain power ability of converter, and reduce current inductors. The proposed technique will distribute current for n-parallel inductor and switching component. Four parallel boost converters implement on input voltage 20.5Vdc to generate output voltage 28.8Vdc. The PIPO boost converter applied phase shift pulse width modulation which will compare with conventional PIPO boost converters by using a similar pulse for every switching component. The current ripple reduction shows an advantage PIPO boost converter then conventional boost converter. Varies loads and duty cycle will be simulated and analyzed to verify the performance of PIPO boost converter. Finally, the unbalance of current inductor is able to be verified on four area of duty cycle in less than 0.6.

Ship detection in panchromatic images: a new method and its DSP implementation

NASA Astrophysics Data System (ADS)

Yao, Yuan; Jiang, Zhiguo; Zhang, Haopeng; Wang, Mengfei; Meng, Gang

2016-03-01

In this paper, a new ship detection method is proposed after analyzing the characteristics of panchromatic remote sensing images and ship targets. Firstly, AdaBoost(Adaptive Boosting) classifiers trained by Haar features are utilized to make coarse detection of ship targets. Then LSD (Line Segment Detector) is adopted to extract the line features in target slices to make fine detection. Experimental results on a dataset of panchromatic remote sensing images with a spatial resolution of 2m show that the proposed algorithm can achieve high detection rate and low false alarm rate. Meanwhile, the algorithm can meet the needs of practical applications on DSP (Digital Signal Processor).

Quality of Life in Women Undergoing Breast Irradiation in a Randomized, Controlled Clinical Trial Evaluating Different Tumor Bed Boost Fractionations

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

Finkel, Morgan A.; Cooper, Benjamin T.; Li, Xiaochun

Purpose: To identify differences in breast cancer patient-reported quality of life (QOL) between 2 radiation tumor bed boost dose regimens. Methods and Materials: Four hundred patients with stage 0, I, or II breast cancer who underwent segmental mastectomy with sentinel node biopsy and/or axillary node dissection were treated with either a daily or weekly boost. Patients were treated prone to 40.5 Gy/15 fractions to the whole breast, 5 days per week. Patients were randomized to a concomitant daily boost to the tumor bed of 0.5 Gy, or a weekly boost of 2 Gy on Friday. Patients completed 6 validated QOL survey instruments at baseline,more » last week of treatment (3 weeks), 45-60 days from the completion of radiation treatment, and at 2-year follow-up. Results: There were no statistically significance differences in responses to the 6 QOL instruments between the daily and weekly radiation boost regimens, even after adjustment for important covariates. However, several changes in responses over time occurred in both arms, including worsening functional status, cosmetic status, and breast-specific pain at the end of treatment as compared with before and 45 to 60 days after the conclusion of treatment. Conclusions: Whole-breast, prone intensity modulated radiation has similar outcomes in QOL measures whether given with a daily or weekly boost. This trial has generated the foundation for a current study of weekly versus daily radiation boost in women with early breast cancer in which 3-dimensional conformal radiation is allowed as a prospective stratification factor.« less

Skin dose differences between intensity-modulated radiation therapy and volumetric-modulated arc therapy and between boost and integrated treatment regimens for treating head and neck and other cancer sites in patients

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

Penoncello, Gregory P.; Ding, George X., E-mail: george.ding@vanderbilt.edu

The purpose of this study was (1) to evaluate dose to skin between volumetric-modulated arc therapy (VMAT) and intensity-modulated radiation therapy (IMRT) treatment techniques for target sites in the head and neck, pelvis, and brain and (2) to determine if the treatment dose and fractionation regimen affect the skin dose between traditional sequential boost and integrated boost regimens for patients with head and neck cancer. A total of 19 patients and 48 plans were evaluated. The Eclipse (v11) treatment planning system was used to plan therapy in 9 patients with head and neck cancer, 5 patients with prostate cancer, andmore » 5 patients with brain cancer with VMAT and static-field IMRT. The mean skin dose and the maximum dose to a contiguous volume of 2 cm{sup 3} for head and neck plans and brain plans and a contiguous volume of 5 cm{sup 3} for pelvis plans were compared for each treatment technique. Of the 9 patients with head and neck cancer, 3 underwent an integrated boost regimen. One integrated boost plan was replanned with IMRT and VMAT using a traditional boost regimen. For target sites located in the head and neck, VMAT reduced the mean dose and contiguous hot spot most noticeably in the shoulder region by 5.6% and 5.4%, respectively. When using an integrated boost regimen, the contiguous hot spot skin dose in the shoulder was larger on average than a traditional boost pattern by 26.5% and the mean skin dose was larger by 1.7%. VMAT techniques largely decrease the contiguous hot spot in the skin in the pelvis by an average of 36% compared with IMRT. For the same target coverage, VMAT can reduce the skin dose in all the regions of the body, but more noticeably in the shoulders in patients with head and neck and pelvis cancer. We also found that using integrated boost regimens in patients with head and neck cancer leads to higher shoulder skin doses compared with traditional boost regimens.« less

Wastewater quality monitoring system using sensor fusion and machine learning techniques.

PubMed

Qin, Xusong; Gao, Furong; Chen, Guohua

2012-03-15

A multi-sensor water quality monitoring system incorporating an UV/Vis spectrometer and a turbidimeter was used to monitor the Chemical Oxygen Demand (COD), Total Suspended Solids (TSS) and Oil & Grease (O&G) concentrations of the effluents from the Chinese restaurant on campus and an electrocoagulation-electroflotation (EC-EF) pilot plant. In order to handle the noise and information unbalance in the fused UV/Vis spectra and turbidity measurements during the calibration model building, an improved boosting method, Boosting-Iterative Predictor Weighting-Partial Least Squares (Boosting-IPW-PLS), was developed in the present study. The Boosting-IPW-PLS method incorporates IPW into boosting scheme to suppress the quality-irrelevant variables by assigning small weights, and builds up the models for the wastewater quality predictions based on the weighted variables. The monitoring system was tested in the field with satisfactory results, underlying the potential of this technique for the online monitoring of water quality. Copyright © 2011 Elsevier Ltd. All rights reserved.

Automatic choroid cells segmentation and counting in fluorescence microscopic image

NASA Astrophysics Data System (ADS)

Fei, Jianjun; Zhu, Weifang; Shi, Fei; Xiang, Dehui; Lin, Xiao; Yang, Lei; Chen, Xinjian

2016-03-01

In this paper, we proposed a method to automatically segment and count the rhesus choroid-retinal vascular endothelial cells (RF/6A) in fluorescence microscopic images which is based on shape classification, bottleneck detection and accelerated Dijkstra algorithm. The proposed method includes four main steps. First, a thresholding filter and morphological operations are applied to reduce the noise. Second, a shape classifier is used to decide whether a connected component is needed to be segmented. In this step, the AdaBoost classifier is applied with a set of shape features. Third, the bottleneck positions are found based on the contours of the connected components. Finally, the cells segmentation and counting are completed based on the accelerated Dijkstra algorithm with the gradient information between the bottleneck positions. The results show the feasibility and efficiency of the proposed method.

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

Murphy, Colin; Anderson, Penny R.; Li Tianyu

Purpose: We examined the impact of radiation tumor bed boost parameters in early-stage breast cancer on local control and cosmetic outcomes. Methods and Materials: A total of 3,186 women underwent postlumpectomy whole-breast radiation with a tumor bed boost for Tis to T2 breast cancer from 1970 to 2008. Boost parameters analyzed included size, energy, dose, and technique. Endpoints were local control, cosmesis, and fibrosis. The Kaplan-Meier method was used to estimate actuarial incidence, and a Cox proportional hazard model was used to determine independent predictors of outcomes on multivariate analysis (MVA). The median follow-up was 78 months (range, 1-305 months).more » Results: The crude cosmetic results were excellent in 54%, good in 41%, and fair/poor in 5% of patients. The 10-year estimate of an excellent cosmesis was 66%. On MVA, independent predictors for excellent cosmesis were use of electron boost, lower electron energy, adjuvant systemic therapy, and whole-breast IMRT. Fibrosis was reported in 8.4% of patients. The actuarial incidence of fibrosis was 11% at 5 years and 17% at 10 years. On MVA, independent predictors of fibrosis were larger cup size and higher boost energy. The 10-year actuarial local failure was 6.3%. There was no significant difference in local control by boost method, cut-out size, dose, or energy. Conclusions: Likelihood of excellent cosmesis or fibrosis are associated with boost technique, electron energy, and cup size. However, because of high local control and rare incidence of fair/poor cosmesis with a boost, the anatomy of the patient and tumor cavity should ultimately determine the necessary boost parameters.« less

Cost-sensitive AdaBoost algorithm for ordinal regression based on extreme learning machine.

PubMed

Riccardi, Annalisa; Fernández-Navarro, Francisco; Carloni, Sante

2014-10-01

In this paper, the well known stagewise additive modeling using a multiclass exponential (SAMME) boosting algorithm is extended to address problems where there exists a natural order in the targets using a cost-sensitive approach. The proposed ensemble model uses an extreme learning machine (ELM) model as a base classifier (with the Gaussian kernel and the additional regularization parameter). The closed form of the derived weighted least squares problem is provided, and it is employed to estimate analytically the parameters connecting the hidden layer to the output layer at each iteration of the boosting algorithm. Compared to the state-of-the-art boosting algorithms, in particular those using ELM as base classifier, the suggested technique does not require the generation of a new training dataset at each iteration. The adoption of the weighted least squares formulation of the problem has been presented as an unbiased and alternative approach to the already existing ELM boosting techniques. Moreover, the addition of a cost model for weighting the patterns, according to the order of the targets, enables the classifier to tackle ordinal regression problems further. The proposed method has been validated by an experimental study by comparing it with already existing ensemble methods and ELM techniques for ordinal regression, showing competitive results.

Maximum power point tracking techniques for wind energy systems using three levels boost converter

NASA Astrophysics Data System (ADS)

Tran, Cuong Hung; Nollet, Frédéric; Essounbouli, Najib; Hamzaoui, Abdelaziz

2018-05-01

This paper presents modeling and simulation of three level Boost DC-DC converter in Wind Energy Conversion System (WECS). Three-level Boost converter has significant advantage compared to conventional Boost. A maximum power point tracking (MPPT) method for a variable speed wind turbine using permanent magnet synchronous generator (PMSG) is also presented. Simulation of three-level Boost converter topology with Perturb and Observe algorithm and Fuzzy Logic Control is implemented in MATLAB/SIMULINK. Results of this simulation show that the system with MPPT using fuzzy logic controller has better performance to the Perturb and Observe algorithm: fast response under changing conditions and small oscillation.

Cavity contour segmentation in chest radiographs using supervised learning and dynamic programming

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

Maduskar, Pragnya, E-mail: pragnya.maduskar@radboudumc.nl; Hogeweg, Laurens; Sánchez, Clara I.

Purpose: Efficacy of tuberculosis (TB) treatment is often monitored using chest radiography. Monitoring size of cavities in pulmonary tuberculosis is important as the size predicts severity of the disease and its persistence under therapy predicts relapse. The authors present a method for automatic cavity segmentation in chest radiographs. Methods: A two stage method is proposed to segment the cavity borders, given a user defined seed point close to the center of the cavity. First, a supervised learning approach is employed to train a pixel classifier using texture and radial features to identify the border pixels of the cavity. A likelihoodmore » value of belonging to the cavity border is assigned to each pixel by the classifier. The authors experimented with four different classifiers:k-nearest neighbor (kNN), linear discriminant analysis (LDA), GentleBoost (GB), and random forest (RF). Next, the constructed likelihood map was used as an input cost image in the polar transformed image space for dynamic programming to trace the optimal maximum cost path. This constructed path corresponds to the segmented cavity contour in image space. Results: The method was evaluated on 100 chest radiographs (CXRs) containing 126 cavities. The reference segmentation was manually delineated by an experienced chest radiologist. An independent observer (a chest radiologist) also delineated all cavities to estimate interobserver variability. Jaccard overlap measure Ω was computed between the reference segmentation and the automatic segmentation; and between the reference segmentation and the independent observer's segmentation for all cavities. A median overlap Ω of 0.81 (0.76 ± 0.16), and 0.85 (0.82 ± 0.11) was achieved between the reference segmentation and the automatic segmentation, and between the segmentations by the two radiologists, respectively. The best reported mean contour distance and Hausdorff distance between the reference and the automatic segmentation were, respectively, 2.48 ± 2.19 and 8.32 ± 5.66 mm, whereas these distances were 1.66 ± 1.29 and 5.75 ± 4.88 mm between the segmentations by the reference reader and the independent observer, respectively. The automatic segmentations were also visually assessed by two trained CXR readers as “excellent,” “adequate,” or “insufficient.” The readers had good agreement in assessing the cavity outlines and 84% of the segmentations were rated as “excellent” or “adequate” by both readers. Conclusions: The proposed cavity segmentation technique produced results with a good degree of overlap with manual expert segmentations. The evaluation measures demonstrated that the results approached the results of the experienced chest radiologists, in terms of overlap measure and contour distance measures. Automatic cavity segmentation can be employed in TB clinics for treatment monitoring, especially in resource limited settings where radiologists are not available.« less

A prospective case study of high boost, high frequency emphasis and two-way diffusion filters on MR images of glioblastoma multiforme.

PubMed

Anoop, B N; Joseph, Justin; Williams, J; Jayaraman, J Sivaraman; Sebastian, Ansa Maria; Sihota, Praveer

2018-06-01

Glioblastoma multiforme (GBM) appears undifferentiated and non-enhancing on magnetic resonance (MR) imagery. As MRI does not offer adequate image quality to allow visual discrimination of the boundary between GBM focus and perifocal vasogenic edema, surgical and radiotherapy planning become difficult. The presence of noise in MR images influences the computation of radiation dosage and precludes the edge based segmentation schemes in automated software for radiation treatment planning. The performance of techniques meant for simultaneous denoising and sharpening, like high boost filters, high frequency emphasize filters and two-way anisotropic diffusion is sensitive to the selection of their operational parameters. Improper selection may cause overshoot and saturation artefacts or noisy grey level transitions can be left unsuppressed. This paper is a prospective case study of the performance of high boost filters, high frequency emphasize filters and two-way anisotropic diffusion on MR images of GBM, for their ability to suppress noise from homogeneous regions and to selectively sharpen the true morphological edges. An objective method for determining the optimum value of the operational parameters of these techniques is also demonstrated. Saturation Evaluation Index (SEI), Perceptual Sharpness Index (PSI), Edge Model based Blur Metric (EMBM), Sharpness of Ridges (SOR), Structural Similarity Index Metric (SSIM), Peak Signal to Noise Ratio (PSNR) and Noise Suppression Ratio (NSR) are the objective functions used. They account for overshoot and saturation artefacts, sharpness of the image, width of salient edges (haloes), susceptibility of edge quality to noise, feature preservation and degree of noise suppression. Two-way diffusion is found to be superior to others in all these respects. The SEI, PSI, EMBM, SOR, SSIM, PSNR and NSR exhibited by two-way diffusion are 0.0016 ± 0.0012, 0.2049 ± 0.0187, 0.0905 ± 0.0408, 2.64 × 10 12  ± 1.6 × 10 12 , 0.9955 ± 0.0024, 38.214 ± 5.2145 and 0.3547 ± 0.0069, respectively.

Robust boosting via convex optimization

NASA Astrophysics Data System (ADS)

Rätsch, Gunnar

2001-12-01

In this work we consider statistical learning problems. A learning machine aims to extract information from a set of training examples such that it is able to predict the associated label on unseen examples. We consider the case where the resulting classification or regression rule is a combination of simple rules - also called base hypotheses. The so-called boosting algorithms iteratively find a weighted linear combination of base hypotheses that predict well on unseen data. We address the following issues: o The statistical learning theory framework for analyzing boosting methods. We study learning theoretic guarantees on the prediction performance on unseen examples. Recently, large margin classification techniques emerged as a practical result of the theory of generalization, in particular Boosting and Support Vector Machines. A large margin implies a good generalization performance. Hence, we analyze how large the margins in boosting are and find an improved algorithm that is able to generate the maximum margin solution. o How can boosting methods be related to mathematical optimization techniques? To analyze the properties of the resulting classification or regression rule, it is of high importance to understand whether and under which conditions boosting converges. We show that boosting can be used to solve large scale constrained optimization problems, whose solutions are well characterizable. To show this, we relate boosting methods to methods known from mathematical optimization, and derive convergence guarantees for a quite general family of boosting algorithms. o How to make Boosting noise robust? One of the problems of current boosting techniques is that they are sensitive to noise in the training sample. In order to make boosting robust, we transfer the soft margin idea from support vector learning to boosting. We develop theoretically motivated regularized algorithms that exhibit a high noise robustness. o How to adapt boosting to regression problems? Boosting methods are originally designed for classification problems. To extend the boosting idea to regression problems, we use the previous convergence results and relations to semi-infinite programming to design boosting-like algorithms for regression problems. We show that these leveraging algorithms have desirable theoretical and practical properties. o Can boosting techniques be useful in practice? The presented theoretical results are guided by simulation results either to illustrate properties of the proposed algorithms or to show that they work well in practice. We report on successful applications in a non-intrusive power monitoring system, chaotic time series analysis and a drug discovery process. --- Anmerkung: Der Autor ist Träger des von der Mathematisch-Naturwissenschaftlichen Fakultät der Universität Potsdam vergebenen Michelson-Preises für die beste Promotion des Jahres 2001/2002. In dieser Arbeit werden statistische Lernprobleme betrachtet. Lernmaschinen extrahieren Informationen aus einer gegebenen Menge von Trainingsmustern, so daß sie in der Lage sind, Eigenschaften von bisher ungesehenen Mustern - z.B. eine Klassenzugehörigkeit - vorherzusagen. Wir betrachten den Fall, bei dem die resultierende Klassifikations- oder Regressionsregel aus einfachen Regeln - den Basishypothesen - zusammengesetzt ist. Die sogenannten Boosting Algorithmen erzeugen iterativ eine gewichtete Summe von Basishypothesen, die gut auf ungesehenen Mustern vorhersagen. Die Arbeit behandelt folgende Sachverhalte: o Die zur Analyse von Boosting-Methoden geeignete Statistische Lerntheorie. Wir studieren lerntheoretische Garantien zur Abschätzung der Vorhersagequalität auf ungesehenen Mustern. Kürzlich haben sich sogenannte Klassifikationstechniken mit großem Margin als ein praktisches Ergebnis dieser Theorie herausgestellt - insbesondere Boosting und Support-Vektor-Maschinen. Ein großer Margin impliziert eine hohe Vorhersagequalität der Entscheidungsregel. Deshalb wird analysiert, wie groß der Margin bei Boosting ist und ein verbesserter Algorithmus vorgeschlagen, der effizient Regeln mit maximalem Margin erzeugt. o Was ist der Zusammenhang von Boosting und Techniken der konvexen Optimierung? Um die Eigenschaften der entstehenden Klassifikations- oder Regressionsregeln zu analysieren, ist es sehr wichtig zu verstehen, ob und unter welchen Bedingungen iterative Algorithmen wie Boosting konvergieren. Wir zeigen, daß solche Algorithmen benutzt werden koennen, um sehr große Optimierungsprobleme mit Nebenbedingungen zu lösen, deren Lösung sich gut charakterisieren laesst. Dazu werden Verbindungen zum Wissenschaftsgebiet der konvexen Optimierung aufgezeigt und ausgenutzt, um Konvergenzgarantien für eine große Familie von Boosting-ähnlichen Algorithmen zu geben. o Kann man Boosting robust gegenüber Meßfehlern und Ausreissern in den Daten machen? Ein Problem bisheriger Boosting-Methoden ist die relativ hohe Sensitivität gegenüber Messungenauigkeiten und Meßfehlern in der Trainingsdatenmenge. Um dieses Problem zu beheben, wird die sogenannte 'Soft-Margin' Idee, die beim Support-Vector Lernen schon benutzt wird, auf Boosting übertragen. Das führt zu theoretisch gut motivierten, regularisierten Algorithmen, die ein hohes Maß an Robustheit aufweisen. o Wie kann man die Anwendbarkeit von Boosting auf Regressionsprobleme erweitern? Boosting-Methoden wurden ursprünglich für Klassifikationsprobleme entwickelt. Um die Anwendbarkeit auf Regressionsprobleme zu erweitern, werden die vorherigen Konvergenzresultate benutzt und neue Boosting-ähnliche Algorithmen zur Regression entwickelt. Wir zeigen, daß diese Algorithmen gute theoretische und praktische Eigenschaften haben. o Ist Boosting praktisch anwendbar? Die dargestellten theoretischen Ergebnisse werden begleitet von Simulationsergebnissen, entweder, um bestimmte Eigenschaften von Algorithmen zu illustrieren, oder um zu zeigen, daß sie in der Praxis tatsächlich gut funktionieren und direkt einsetzbar sind. Die praktische Relevanz der entwickelten Methoden wird in der Analyse chaotischer Zeitreihen und durch industrielle Anwendungen wie ein Stromverbrauch-Überwachungssystem und bei der Entwicklung neuer Medikamente illustriert.

Arabidopsis phenotyping through Geometric Morphometrics.

PubMed

Manacorda, Carlos A; Asurmendi, Sebastian

2018-06-18

Recently, much technical progress was achieved in the field of plant phenotyping. High-throughput platforms and the development of improved algorithms for rosette image segmentation make it now possible to extract shape and size parameters for genetic, physiological and environmental studies on a large scale. The development of low-cost phenotyping platforms and freeware resources make it possible to widely expand phenotypic analysis tools for Arabidopsis. However, objective descriptors of shape parameters that could be used independently of platform and segmentation software used are still lacking and shape descriptions still rely on ad hoc or even sometimes contradictory descriptors, which could make comparisons difficult and perhaps inaccurate. Modern geometric morphometrics is a family of methods in quantitative biology proposed to be the main source of data and analytical tools in the emerging field of phenomics studies. Based on the location of landmarks (corresponding points) over imaged specimens and by combining geometry, multivariate analysis and powerful statistical techniques, these tools offer the possibility to reproducibly and accurately account for shape variations amongst groups and measure them in shape distance units. Here, a particular scheme of landmarks placement on Arabidopsis rosette images is proposed to study shape variation in the case of viral infection processes. Shape differences between controls and infected plants are quantified throughout the infectious process and visualized. Quantitative comparisons between two unrelated ssRNA+ viruses are shown and reproducibility issues are assessed. Combined with the newest automated platforms and plant segmentation procedures, geometric morphometric tools could boost phenotypic features extraction and processing in an objective, reproducible manner.

A closed curve is much more than an incomplete one: effect of closure in figure-ground segmentation.

PubMed

Kovács, I; Julesz, B

1993-08-15

Detection of fragmented closed contours against a cluttered background occurs much beyond the local coherence distance (maximal separation between segments) of nonclosed contours. This implies that the extent of interaction between locally connected detectors is boosted according to the global stimulus structure. We further show that detection of a target probe is facilitated when the probe is positioned inside a closed circle. To explain the striking contour segregation ability found here, and performance enhancement inside closed boundaries, we propose the existence of a synergetic process in early vision.

Skin dose differences between intensity-modulated radiation therapy and volumetric-modulated arc therapy and between boost and integrated treatment regimens for treating head and neck and other cancer sites in patients.

PubMed

Penoncello, Gregory P; Ding, George X

2016-01-01

The purpose of this study was (1) to evaluate dose to skin between volumetric-modulated arc therapy (VMAT) and intensity-modulated radiation therapy (IMRT) treatment techniques for target sites in the head and neck, pelvis, and brain and (2) to determine if the treatment dose and fractionation regimen affect the skin dose between traditional sequential boost and integrated boost regimens for patients with head and neck cancer. A total of 19 patients and 48 plans were evaluated. The Eclipse (v11) treatment planning system was used to plan therapy in 9 patients with head and neck cancer, 5 patients with prostate cancer, and 5 patients with brain cancer with VMAT and static-field IMRT. The mean skin dose and the maximum dose to a contiguous volume of 2cm(3) for head and neck plans and brain plans and a contiguous volume of 5cm(3) for pelvis plans were compared for each treatment technique. Of the 9 patients with head and neck cancer, 3 underwent an integrated boost regimen. One integrated boost plan was replanned with IMRT and VMAT using a traditional boost regimen. For target sites located in the head and neck, VMAT reduced the mean dose and contiguous hot spot most noticeably in the shoulder region by 5.6% and 5.4%, respectively. When using an integrated boost regimen, the contiguous hot spot skin dose in the shoulder was larger on average than a traditional boost pattern by 26.5% and the mean skin dose was larger by 1.7%. VMAT techniques largely decrease the contiguous hot spot in the skin in the pelvis by an average of 36% compared with IMRT. For the same target coverage, VMAT can reduce the skin dose in all the regions of the body, but more noticeably in the shoulders in patients with head and neck and pelvis cancer. We also found that using integrated boost regimens in patients with head and neck cancer leads to higher shoulder skin doses compared with traditional boost regimens. Copyright © 2016 American Association of Medical Dosimetrists. Published by Elsevier Inc. All rights reserved.

Object-Oriented Classification of Sugarcane Using Time-Series Middle-Resolution Remote Sensing Data Based on AdaBoost

PubMed Central

Zhou, Zhen; Huang, Jingfeng; Wang, Jing; Zhang, Kangyu; Kuang, Zhaomin; Zhong, Shiquan; Song, Xiaodong

2015-01-01

Most areas planted with sugarcane are located in southern China. However, remote sensing of sugarcane has been limited because useable remote sensing data are limited due to the cloudy climate of this region during the growing season and severe spectral mixing with other crops. In this study, we developed a methodology for automatically mapping sugarcane over large areas using time-series middle-resolution remote sensing data. For this purpose, two major techniques were used, the object-oriented method (OOM) and data mining (DM). In addition, time-series Chinese HJ-1 CCD images were obtained during the sugarcane growing period. Image objects were generated using a multi-resolution segmentation algorithm, and DM was implemented using the AdaBoost algorithm, which generated the prediction model. The prediction model was applied to the HJ-1 CCD time-series image objects, and then a map of the sugarcane planting area was produced. The classification accuracy was evaluated using independent field survey sampling points. The confusion matrix analysis showed that the overall classification accuracy reached 93.6% and that the Kappa coefficient was 0.85. Thus, the results showed that this method is feasible, efficient, and applicable for extrapolating the classification of other crops in large areas where the application of high-resolution remote sensing data is impractical due to financial considerations or because qualified images are limited. PMID:26528811

Object-Oriented Classification of Sugarcane Using Time-Series Middle-Resolution Remote Sensing Data Based on AdaBoost.

PubMed

Zhou, Zhen; Huang, Jingfeng; Wang, Jing; Zhang, Kangyu; Kuang, Zhaomin; Zhong, Shiquan; Song, Xiaodong

2015-01-01

Most areas planted with sugarcane are located in southern China. However, remote sensing of sugarcane has been limited because useable remote sensing data are limited due to the cloudy climate of this region during the growing season and severe spectral mixing with other crops. In this study, we developed a methodology for automatically mapping sugarcane over large areas using time-series middle-resolution remote sensing data. For this purpose, two major techniques were used, the object-oriented method (OOM) and data mining (DM). In addition, time-series Chinese HJ-1 CCD images were obtained during the sugarcane growing period. Image objects were generated using a multi-resolution segmentation algorithm, and DM was implemented using the AdaBoost algorithm, which generated the prediction model. The prediction model was applied to the HJ-1 CCD time-series image objects, and then a map of the sugarcane planting area was produced. The classification accuracy was evaluated using independent field survey sampling points. The confusion matrix analysis showed that the overall classification accuracy reached 93.6% and that the Kappa coefficient was 0.85. Thus, the results showed that this method is feasible, efficient, and applicable for extrapolating the classification of other crops in large areas where the application of high-resolution remote sensing data is impractical due to financial considerations or because qualified images are limited.

Dose Escalation to the Dominant Intraprostatic Lesion Defined by Sextant Biopsy in a Permanent Prostate I-125 Implant: A Prospective Comparative Toxicity Analysis

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

Gaudet, Marc; Vigneault, Eric; Aubin, Sylviane

2010-05-01

Purpose: Using real-time intraoperative inverse-planned permanent seed prostate implant (RTIOP/PSI), multiple core biopsy maps, and three-dimensional ultrasound guidance, we planned a boost volume (BV) within the prostate to which hyperdosage was delivered selectively. The aim of this study was to investigate the potential negative effects of such a procedure. Methods and Materials: Patients treated with RTIOP/PSI for localized prostate cancer with topographic biopsy results received an intraprostatic boost (boost group [BG]). They were compared with patients treated with a standard plan (reference group [RG]). Plans were generated using a simulated annealing inverse planning algorithm. Prospectively recorded urinary, rectal, and sexualmore » toxicities and dosimetric parameters were compared between groups. Results: The study included 120 patients treated with boost technique who were compared with 70 patients treated with a standard plan. Boost technique did not significantly change the number of seeds (55.1/RG vs. 53.6/BG). The intraoperative prostate V150 was slightly higher in BG (75.2/RG vs. 77.2/BG, p = 0.039). Urethra V100, urethra D90, and rectal D50 were significantly lower in the BG. No significant differences were seen in acute or late urinary, rectal, or sexual toxicities. Conclusions: Because there were no differences between the groups in acute and late toxicities, we believe that BV can be planned and delivered to the dominant intraprostatic lesion without increasing toxicity. It is too soon to say whether a boost technique will ultimately increase local control.« less

Segmentation of optic disc and optic cup in retinal fundus images using shape regression.

PubMed

Sedai, Suman; Roy, Pallab K; Mahapatra, Dwarikanath; Garnavi, Rahil

2016-08-01

Glaucoma is one of the leading cause of blindness. The manual examination of optic cup and disc is a standard procedure used for detecting glaucoma. This paper presents a fully automatic regression based method which accurately segments optic cup and disc in retinal colour fundus image. First, we roughly segment optic disc using circular hough transform. The approximated optic disc is then used to compute the initial optic disc and cup shapes. We propose a robust and efficient cascaded shape regression method which iteratively learns the final shape of the optic cup and disc from a given initial shape. Gradient boosted regression trees are employed to learn each regressor in the cascade. A novel data augmentation approach is proposed to improve the regressors performance by generating synthetic training data. The proposed optic cup and disc segmentation method is applied on an image set of 50 patients and demonstrate high segmentation accuracy for optic cup and disc with dice metric of 0.95 and 0.85 respectively. Comparative study shows that our proposed method outperforms state of the art optic cup and disc segmentation methods.

Multicenter, randomized study to optimize bowel preparation for colon capsule endoscopy

PubMed Central

Kastenberg, David; Jr, Wilmot C Burch; Romeo, David P; Kashyap, Pankaj K; Pound, David C; Papageorgiou, Neophytos; Sainz, Ignacio Fernández-Urien; Sokach, Carly E; Rex, Douglas K

2017-01-01

AIM To assess the cleansing efficacy and safety of a new Colon capsule endoscopy (CCE) bowel preparation regimen. METHODS This was a multicenter, prospective, randomized, controlled study comparing two CCE regimens. Subjects were asymptomatic and average risk for colorectal cancer. The second generation CCE system (PillCam® COLON 2; Medtronic, Yoqneam, Israel) was utilized. Preparation regimens differed in the 1st and 2nd boosts with the Study regimen using oral sulfate solution (89 mL) with diatrizoate meglumine and diatrizoate sodium solution (“diatrizoate solution”) (boost 1 = 60 mL, boost 2 = 30 mL) and the Control regimen oral sulfate solution (89 mL) alone. The primary outcome was overall and segmental colon cleansing. Secondary outcomes included safety, polyp detection, colonic transit, CCE completion and capsule excretion ≤ 12 h. RESULTS Both regimens had similar cleansing efficacy for the whole colon (Adequate: Study = 75.9%, Control = 77.3%; P = 0.88) and individual segments. In the Study group, CCE completion was superior (Study = 90.9%, Control = 76.9%; P = 0.048) and colonic transit was more often < 40 min (Study = 21.8%, Control = 4%; P = 0.0073). More Study regimen subjects experienced adverse events (Study = 19.4%, Control = 3.4%; P = 0.0061), and this difference did not appear related to diatrizoate solution. Adverse events were primarily gastrointestinal in nature and no serious adverse events related either to the bowel preparation regimen or the capsule were observed. There was a trend toward higher polyp detection with the Study regimen, but this did not achieve statistical significance for any size category. Mean transit time through the entire gastrointestinal tract, from ingestion to excretion, was shorter with the Study regimen while mean colonic transit times were similar for both study groups. CONCLUSION A CCE bowel preparation regimen using oral sulfate solution and diatrizoate solution as a boost agent is effective, safe, and achieved superior CCE completion. PMID:29358870

Bi-Frequency Modulated Quasi-Resonant Converters: Theory and Applications

NASA Astrophysics Data System (ADS)

Zhang, Yuefeng

1995-01-01

To avoid the variable frequency operation of quasi -resonant converters, many soft-switching PWM converters have been proposed, all of them require an auxiliary switch, which will increase the cost and complexity of the power supply system. In this thesis, a new kind of technique for quasi -resonant converters has been proposed, which is called the bi-frequency modulation technique. By operating the quasi-resonant converters at two switching frequencies, this technique enables quasi-resonant converters to achieve the soft-switching, at fixed switching frequencies, without an auxiliary switch. The steady-state analysis of four commonly used quasi-resonant converters, namely, ZVS buck, ZCS buck, ZVS boost, and ZCS boost converter has been presented. Using the concepts of equivalent sources, equivalent sinks, and resonant tank, the large signal models of these four quasi -resonant converters were developed. Based on these models, the steady-state control characteristics of BFM ZVS buck, BFM ZCS buck, BFM ZVS boost, and BFM ZCS boost converter have been derived. The functional block and design consideration of the bi-frequency controller were presented, and one of the implementations of the bi-frequency controller was given. A complete design example has been presented. Both computer simulations and experimental results have verified that the bi-frequency modulated quasi-resonant converters can achieve soft-switching, at fixed switching frequencies, without an auxiliary switch. One of the application of bi-frequency modulation technique is for EMI reduction. The basic principle of using BFM technique for EMI reduction was introduced. Based on the spectral analysis, the EMI performances of the PWM, variable-frequency, and bi-frequency modulated control signals was evaluated, and the BFM control signals show the lowest EMI emission. The bi-frequency modulated technique has also been applied to the power factor correction. A BFM zero -current switching boost converter has been designed for the power factor correction, and the simulation results show that the power factor has been improved.

FIVE-YEAR RESULTS OF ADJUVANT RADIOTHER

PubMed Central

Osa, Etin-Osa O.; DeWyngaert, Keith; Roses, Daniel; Speyer, James; Guth, Amber; Axelrod, Deborah; Kerimian, Maria Fenton; Goldberg, Judith D.; Formenti, Silvia C.

2015-01-01

Purpose/Objective A technique of prone breast radiotherapy delivered by a regimen of accelerated intensity modulated radiation therapy (IMRT) with a concurrent boost to the tumor bed, was developed at our institution. We report the five year results of this approach. Methods and Materials Between 2003–2006, 404 patients with Stage I–II breast cancer were prospectively enrolled into two consecutive protocols, institutional trials 03–30 and 05–181, that used the same regimen of 40.5Gy/15 fractions delivered to the index breast over 3 weeks, with a concomitant daily boost to the tumor bed of 0.5Gy (total dose=48Gy). All patients were treated after segmental mastectomy, had negative margins, and nodal assessment. Patients were set up prone: only if lung or heart volumes were in the field was a supine set-up attempted, and chosen if found to better spare these organs. Results 92% of patients were treated prone, 8% supine. 72% had stage I, 28% stage II invasive breast cancer. In-field lung volume ranged from 0 –228.27cc, mean: 19.65cc. In-field heart volume for left breast cancer patients ranged from 0–21.24cc, mean: 1.59cc. There was no heart in the field for right breast cancer patients. At a median follow-up of five years, the five-year cumulative incidence of isolated ipsilateral breast tumor recurrence was 0.82% (95% CI: 0.65–1.04). The five-year cumulative incidence of regional recurrence was 0.53% (95% CI:0.41–0.69) and the five-year overall cumulative death rate was 1.28% (95% CI: 0.48–3.38). 82% (95% CI: 77–85) of patients judged their final cosmetic result as excellent/good. Conclusions Prone accelerated IMRT with a concomitant boost results in excellent local control, optimal sparing of heart and lung, with good cosmesis. RTOG 10–05, a phase III, multi-institutional, randomized trial is ongoing and is evaluating the equivalence of a similar dose and fractionation approach to standard six weeks radiotherapy with a sequential boost. PMID:24867535

Optical Coherence Tomography and Stent Boost Imaging Guided Bioresorbable Vascular Scaffold Overlapping for Coronary Chronic Total Occlusion Lesion

PubMed Central

Li, Hu; Choi, Cheol Ung; Oh, Dong Joo

2017-01-01

We report herein the optical coherence tomography (OCT) and stent boost imaging guided bioresorbable vascular scaffold (BVS) implantation for right coronary artery (RCA) chronic total occlusion (CTO) lesion. The gold standard for evaluating BVS expansion after percutaneous coronary intervention is OCT. However, stent boost imaging is a new technique that improves fluoroscopy-based assessments of stent overlapping, and the present case shows clinical usefulness of OCT and stent boost imaging guided ‘overlapping’ BVS implantation via antegrade approach for a typical RCA CTO lesion. PMID:28792157

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

Cooper, Benjamin T.; Formenti-Ujlaki, George F.; Li, Xiaochun

Purpose: To report the results of a prospective randomized trial comparing a daily versus weekly boost to the tumor cavity during the course of accelerated radiation to the breast with patients in the prone position. Methods and Materials: From 2009 to 2012, 400 patients with stage 0 to II breast cancer who had undergone segmental mastectomy participated in an institutional review board–approved trial testing prone breast radiation therapy to 40.5 Gy in 15 fractions 5 d/wk to the whole breast, after randomization to a concomitant daily boost to the tumor bed of 0.5 Gy, or a weekly boost of 2 Gy, on Friday.more » The present noninferiority trial tested the primary hypothesis that a weekly boost produced no more acute toxicity than did a daily boost. The recurrence-free survival was estimated for both treatment arms using the Kaplan-Meier method; the relative risk of recurrence or death was estimated, and the 2 arms were compared using the log-rank test. Results: At a median follow-up period of 45 months, no deaths related to breast cancer had occurred. The weekly boost regimen produced no more grade ≥2 acute toxicity than did the daily boost regimen (8.1% vs 10.4%; noninferiority Z = −2.52; P=.006). No statistically significant difference was found in the cumulative incidence of long-term fibrosis or telangiectasia of grade ≥2 between the 2 arms (log-rank P=.923). Two local and two distant recurrences developed in the daily treatment arm and three local and one distant developed in the weekly arm. The 4-year recurrence-free survival rate was not different between the 2 treatment arms (98% for both arms). Conclusions: A tumor bed boost delivered either daily or weekly was tolerated similarly during accelerated prone breast radiation therapy, with excellent control of disease and comparable cosmetic results.« less

Vectorization of optically sectioned brain microvasculature: learning aids completion of vascular graphs by connecting gaps and deleting open-ended segments.

PubMed

Kaufhold, John P; Tsai, Philbert S; Blinder, Pablo; Kleinfeld, David

2012-08-01

A graph of tissue vasculature is an essential requirement to model the exchange of gasses and nutriments between the blood and cells in the brain. Such a graph is derived from a vectorized representation of anatomical data, provides a map of all vessels as vertices and segments, and may include the location of nonvascular components, such as neuronal and glial somata. Yet vectorized data sets typically contain erroneous gaps, spurious endpoints, and spuriously merged strands. Current methods to correct such defects only address the issue of connecting gaps and further require manual tuning of parameters in a high dimensional algorithm. To address these shortcomings, we introduce a supervised machine learning method that (1) connects vessel gaps by "learned threshold relaxation"; (2) removes spurious segments by "learning to eliminate deletion candidate strands"; and (3) enforces consistency in the joint space of learned vascular graph corrections through "consistency learning." Human operators are only required to label individual objects they recognize in a training set and are not burdened with tuning parameters. The supervised learning procedure examines the geometry and topology of features in the neighborhood of each vessel segment under consideration. We demonstrate the effectiveness of these methods on four sets of microvascular data, each with >800(3) voxels, obtained with all optical histology of mouse tissue and vectorization by state-of-the-art techniques in image segmentation. Through statistically validated sampling and analysis in terms of precision recall curves, we find that learning with bagged boosted decision trees reduces equal-error error rates for threshold relaxation by 5-21% and strand elimination performance by 18-57%. We benchmark generalization performance across datasets; while improvements vary between data sets, learning always leads to a useful reduction in error rates. Overall, learning is shown to more than halve the total error rate, and therefore, human time spent manually correcting such vectorizations. Copyright © 2012 Elsevier B.V. All rights reserved.

Vectorization of optically sectioned brain microvasculature: Learning aids completion of vascular graphs by connecting gaps and deleting open-ended segments

PubMed Central

Kaufhold, John P.; Tsai, Philbert S.; Blinder, Pablo; Kleinfeld, David

2012-01-01

A graph of tissue vasculature is an essential requirement to model the exchange of gasses and nutriments between the blood and cells in the brain. Such a graph is derived from a vectorized representation of anatomical data, provides a map of all vessels as vertices and segments, and may include the location of nonvascular components, such as neuronal and glial somata. Yet vectorized data sets typically contain erroneous gaps, spurious endpoints, and spuriously merged strands. Current methods to correct such defects only address the issue of connecting gaps and further require manual tuning of parameters in a high dimensional algorithm. To address these shortcomings, we introduce a supervised machine learning method that (1) connects vessel gaps by “learned threshold relaxation”; (2) removes spurious segments by “learning to eliminate deletion candidate strands”; and (3) enforces consistency in the joint space of learned vascular graph corrections through “consistency learning.” Human operators are only required to label individual objects they recognize in a training set and are not burdened with tuning parameters. The supervised learning procedure examines the geometry and topology of features in the neighborhood of each vessel segment under consideration. We demonstrate the effectiveness of these methods on four sets of microvascular data, each with > 8003 voxels, obtained with all optical histology of mouse tissue and vectorization by state-of-the-art techniques in image segmentation. Through statistically validated sampling and analysis in terms of precision recall curves, we find that learning with bagged boosted decision trees reduces equal-error error rates for threshold relaxation by 5 to 21 % and strand elimination performance by 18 to 57 %. We benchmark generalization performance across datasets; while improvements vary between data sets, learning always leads to a useful reduction in error rates. Overall, learning is shown to more than halve the total error rate, and therefore, human time spent manually correcting such vectorizations. PMID:22854035

Use of tomotherapy in treatment of synchronous bilateral breast cancer: dosimetric comparison study

PubMed Central

Wadasadawala, T; Sarin, R; Upreti, R R; Paul, S; Phurailatpam, R

2015-01-01

Objective: Synchronous malignancy in both breasts is a rare incidence. The present study aims at dosimetric comparison of conventional bitangential radiotherapy (RT) technique with conventional [field-in-field (FIF)] and rotational [Helical TomoTherapy® and TomoDirect™ (TD); Accuray Inc., Sunnyvale, CA] intensity-modulated RT for patients with synchronous bilateral breast cancer (SBBC). Methods: CT data sets of 10 patients with SBBC were selected for the present study. RT was planned for all patients on both sides to whole breast and/or chest wall using the above-mentioned techniques. Six females with breast conservation on at least one side also had a composite plan along with tumour bed (TB) boost using sequential electrons for bitangential and FIF techniques or sequential helical tomotherapy (HT) boost (for TD) or simultaneous integrated boost (SIB) for HT. Results: All techniques produced acceptable target coverage. The hotspot was significantly lower with FIF technique and HT but higher with TD. For the organs at risk doses, HT resulted in significant reduction of the higher dose volumes. Similarly, TD resulted in significant reduction of the mean dose to the heart and total lung by reducing the lower dose volumes. All techniques of delivering boost to the TB were comparable in terms of target coverage. HT-SIB markedly reduced mean doses to the total lung and heart by specifically lowering the higher dose volumes. Conclusion: This study demonstrates the cardiac and pulmonary sparing ability of tomotherapy in the setting of SBBC. Advances in knowledge: This is the first study demonstrating feasibility of treatment of SBBC using tomotherapy. PMID:25605345

Planning the Breast Boost: Comparison of Three Techniques and Evolution of Tumor Bed During Treatment

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

Hepel, Jaroslaw T.; Department of Radiation Oncology, Brown University, Rhode Island Hospital, Providence, RI; Evans, Suzanne B.

2009-06-01

Purpose: To evaluate the accuracy of two clinical techniques for electron boost planning compared with computed tomography (CT)-based planning. Additionally, we evaluated the tumor bed characteristics at whole breast planning and boost planning. Methods and Materials: A total of 30 women underwent tumor bed boost planning within 2 weeks of completing whole breast radiotherapy using three planning techniques: scar-based planning, palpation/clinical-based planning, and CT-based planning. The plans were analyzed for dosimetric coverage of the CT-delineated tumor bed. The cavity visualization score was used to define the CT-delineated tumor bed as well or poorly defined. Results: Scar-based planning resulted in inferiormore » tumor bed coverage compared with CT-based planning, with the minimal dose received by 90% of the target volume >90% in 53% and a geographic miss in 53%. The results of palpation/clinical-based planning were significantly better: 87% and 10% for the minimal dose received by 90% of the target volume >90% and geographic miss, respectively. Of the 30 tumor beds, 16 were poorly defined by the cavity visualization score. Of these 16, 8 were well demarcated by the surgical clips. The evaluation of the 22 well-defined tumor beds revealed similar results. A comparison of the tumor bed volume from the initial planning CT scan to the boost planning CT scan revealed a decrease in size in 77% of cases. The mean decrease in volume was 52%. Conclusion: The results of our study have shown that CT-based planning allows for optimal tumor bed coverage compared with clinical and scar-based approaches. However, in the setting of a poorly visualized cavity on CT without surgical clips, palpation/clinical-based planning can help delineate the appropriate target volumes and is superior to scar-based planning. CT simulation at boost planning could allow for a reduction in the boost volumes.« less

Application handbook for a Standardized Control Module (SCM) for DC-DC converters, volume 1

NASA Astrophysics Data System (ADS)

Lee, F. C.; Mahmoud, M. F.; Yu, Y.

1980-04-01

The standardized control module (SCM) was developed for application in the buck, boost and buck/boost DC-DC converters. The SCM used multiple feedback loops to provide improved input line and output load regulation, stable feedback control system, good dynamic transient response and adaptive compensation of the control loop for changes in open loop gain and output filter time constraints. The necessary modeling and analysis tools to aid the design engineer in the application of the SCM to DC-DC Converters were developed. The SCM functional block diagram and the different analysis techniques were examined. The average time domain analysis technique was chosen as the basic analytical tool. The power stage transfer functions were developed for the buck, boost and buck/boost converters. The analog signal and digital signal processor transfer functions were developed for the three DC-DC Converter types using the constant on time, constant off time and constant frequency control laws.

Application handbook for a Standardized Control Module (SCM) for DC-DC converters, volume 1

NASA Technical Reports Server (NTRS)

Lee, F. C.; Mahmoud, M. F.; Yu, Y.

1980-01-01

The standardized control module (SCM) was developed for application in the buck, boost and buck/boost DC-DC converters. The SCM used multiple feedback loops to provide improved input line and output load regulation, stable feedback control system, good dynamic transient response and adaptive compensation of the control loop for changes in open loop gain and output filter time constraints. The necessary modeling and analysis tools to aid the design engineer in the application of the SCM to DC-DC Converters were developed. The SCM functional block diagram and the different analysis techniques were examined. The average time domain analysis technique was chosen as the basic analytical tool. The power stage transfer functions were developed for the buck, boost and buck/boost converters. The analog signal and digital signal processor transfer functions were developed for the three DC-DC Converter types using the constant on time, constant off time and constant frequency control laws.

Space Shuttle booster thrust imbalance analysis

NASA Technical Reports Server (NTRS)

Bailey, W. R.; Blackwell, D. L.

1985-01-01

An analysis of the Shuttle SRM thrust imbalance during the steady-state and tailoff portions of the boost phase of flight are presented. Results from flights STS-1 through STS-13 are included. A statistical analysis of the observed thrust imbalance data is presented. A 3 sigma thrust imbalance history versus time was generated from the observed data and is compared to the vehicle design requirements. The effect on Shuttle thrust imbalance from the use of replacement SRM segments is predicted. Comparisons of observed thrust imbalances with respect to predicted imbalances are presented for the two space shuttle flights which used replacement aft segments (STS-9 and STS-13).

Effect of pole zero location on system dynamics of boost converter for micro grid

NASA Astrophysics Data System (ADS)

Lavanya, A.; Vijayakumar, K.; Navamani, J. D.; Jayaseelan, N.

2018-04-01

Green clean energy like photo voltaic, wind energy, fuel cell can be brought together by microgrid.For low voltage sources like photovoltaic cell boost converter is very much essential. This paper explores the dynamic analysis of boost converter in a continuous conduction mode (CCM). The transient performance and stability analysis is carried out in this paper using time domain analysis and frequency domain analysis techniques. Boost converter is simulated using both PSIM and MATLAB software. Furthermore, state space model obtained and the transfer function is derived. The converter behaviour when a step input is applied is analyzed and stability of the converter is analyzed from bode plot frequency for open loop. Effect of the locations of poles and zeros in the transfer function of boost converter and how the performance parameters are affected is discussed in this paper. Closed loop performance with PI controller is also analyzed for boost converter.

Nucleus detection using gradient orientation information and linear least squares regression

NASA Astrophysics Data System (ADS)

Kwak, Jin Tae; Hewitt, Stephen M.; Xu, Sheng; Pinto, Peter A.; Wood, Bradford J.

2015-03-01

Computerized histopathology image analysis enables an objective, efficient, and quantitative assessment of digitized histopathology images. Such analysis often requires an accurate and efficient detection and segmentation of histological structures such as glands, cells and nuclei. The segmentation is used to characterize tissue specimens and to determine the disease status or outcomes. The segmentation of nuclei, in particular, is challenging due to the overlapping or clumped nuclei. Here, we propose a nuclei seed detection method for the individual and overlapping nuclei that utilizes the gradient orientation or direction information. The initial nuclei segmentation is provided by a multiview boosting approach. The angle of the gradient orientation is computed and traced for the nuclear boundaries. Taking the first derivative of the angle of the gradient orientation, high concavity points (junctions) are discovered. False junctions are found and removed by adopting a greedy search scheme with the goodness-of-fit statistic in a linear least squares sense. Then, the junctions determine boundary segments. Partial boundary segments belonging to the same nucleus are identified and combined by examining the overlapping area between them. Using the final set of the boundary segments, we generate the list of seeds in tissue images. The method achieved an overall precision of 0.89 and a recall of 0.88 in comparison to the manual segmentation.

A boosted negative bit-line SRAM with write-assisted cell in 45 nm CMOS technology

NASA Astrophysics Data System (ADS)

Bhatnagar, Vipul; Kumar, Pradeep; Pandey, Neeta; Pandey, Sujata

2018-02-01

A new 11 T SRAM cell with write-assist is proposed to improve operation at low supply voltage. In this technique, a negative bit-line voltage is applied to one of the write bit-lines, while a boosted voltage is applied to the other write bit-line where transmission gate access is used in proposed 11 T cell. Supply voltage to one of the inverters is interrupted to weaken the feedback. Improved write feature is attributed to strengthened write access devices and weakened feedback loop of cell at the same time. Amount of boosting required for write performance improvement is also reduced due to feedback weakening, solving the persistent problem of half-selected cells and reliability reduction of access devices with the other suggested boosted and negative bit-line techniques. The proposed design improves write time by 79%, 63% and slower by 52% with respect to LP 10 T, WRE 8 T and 6 T cells respectively. It is found that write margin for the proposed cell is improved by about 4×, 2.4× and 5.37× compared to WRE8 T, LP10 T and 6 T respectively. The proposed cell with boosted negative bit line (BNBL) provides 47%, 31%, and 68.4% improvement in write margin with respect to no write-assist, negative bit line (NBL) and boosted bit line (BBL) write-assist respectively. Also, new sensing circuit with replica bit-line is proposed to give a more precise timing of applying boosted voltages for improved results. All simulations are done on TSMC 45 nm CMOS technology.

A cosmetic evaluation of breast cancer treatment: A randomized study of radiotherapy boost technique

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

Vass, Sylvie; Bairati, Isabelle

2005-08-01

Purpose: To compare cosmetic results of two different radiotherapy (RT) boost techniques used in the treatment of breast cancer after whole breast radiotherapy and to identify factors affecting cosmetic outcomes. Methods and Materials: Between 1996 and 1998, 142 patients with Stage I and II breast cancer were treated with breast conservative surgery and adjuvant RT. Patients were then randomly assigned to receive a boost dose of 15 Gy delivered to the tumor bed either by iridium 192, or a combination of photons and electrons. Cosmetic evaluations were done on a 6-month basis, with a final evaluation at 36 months aftermore » RT. The evaluations were done using a panel of global and specific subjective scores, a digitized scoring system using the breast retraction assessment (BRA) measurement, and a patient's self-assessment evaluation. As cosmetic results were graded according to severity, the comparison of boost techniques was done using the ordinal logistic regression model. Adjusted odds ratios (OR) and their 95% confidence intervals (CI) are presented. Results: At 36 months of follow-up, there was no significant difference between the two groups with respect to the global subjective cosmetic outcome (OR = 1.40; 95%CI = 0.69-2.85, p = 0.35). Good to excellent scores were observed in 65% of implant patients and 62% of photon/electron patients. At 24 months and beyond, telangiectasia was more severe in the implant group with an OR of 9.64 (95%CI = 4.05-22.92, p < 0.0001) at 36 months. The only variable associated with a worse global cosmetic outcome was the presence of concomitant chemotherapy (OR = 3.87; 95%CI = 1.74-8.62). The BRA value once adjusted for age, concomitant chemotherapy, and boost volume showed a positive association with the boost technique. The BRA value was significantly greater in the implant group (p 0.03). There was no difference in the patient's final self-assessment score between the two groups. Three variables were statistically associated with an adverse self-evaluation: an inferior quadrant tumor localization, postoperative hematoma, and concomitant chemotherapy. Conclusions: Although this trial showed that at 36 months of follow-up, there were no significant differences in the overall global cosmetic scores between the implant boost group and the photon/electron boost group, telangiectasia was more severe and the BRA value was greater in the implant group.« less

Automated framework for intraretinal cystoid macular edema segmentation in three-dimensional optical coherence tomography images with macular hole

NASA Astrophysics Data System (ADS)

Zhu, Weifang; Zhang, Li; Shi, Fei; Xiang, Dehui; Wang, Lirong; Guo, Jingyun; Yang, Xiaoling; Chen, Haoyu; Chen, Xinjian

2017-07-01

Cystoid macular edema (CME) and macular hole (MH) are the leading causes for visual loss in retinal diseases. The volume of the CMEs can be an accurate predictor for visual prognosis. This paper presents an automatic method to segment the CMEs from the abnormal retina with coexistence of MH in three-dimensional-optical coherence tomography images. The proposed framework consists of preprocessing and CMEs segmentation. The preprocessing part includes denoising, intraretinal layers segmentation and flattening, and MH and vessel silhouettes exclusion. In the CMEs segmentation, a three-step strategy is applied. First, an AdaBoost classifier trained with 57 features is employed to generate the initialization results. Second, an automated shape-constrained graph cut algorithm is applied to obtain the refined results. Finally, cyst area information is used to remove false positives (FPs). The method was evaluated on 19 eyes with coexistence of CMEs and MH from 18 subjects. The true positive volume fraction, FP volume fraction, dice similarity coefficient, and accuracy rate for CMEs segmentation were 81.0%±7.8%, 0.80%±0.63%, 80.9%±5.7%, and 99.7%±0.1%, respectively.

High-Dose Split-Course Radiation Therapy for Anal Cancer: Outcome Analysis Regarding the Boost Strategy (CORS-03 Study)

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

Hannoun-Levi, Jean-Michel, E-mail: jean-michel.hannoun-levi@nice.fnclcc.fr; Cercle des Oncologues Radiotherapeutes du Sud; Ortholan, Cecile

2011-07-01

Purpose: To retrospectively assess the clinical outcome in anal cancer patients treated with split-course radiation therapy and boosted through external-beam radiation therapy (EBRT) or brachytherapy (BCT). Methods and Materials: From January 2000 to December 2004, a selected group (162 patients) with invasive nonmetastatic anal squamous cell carcinoma was studied. Tumor staging reported was T1 = 31 patients (19%), T2 = 77 patients (48%), T3 = 42 patients (26%), and T4= 12 patients (7%). Lymph node status was N0-1 (86%) and N2-3 (14%). Patients underwent a first course of EBRT: mean dose 45.1 Gy (range, 39.5-50) followed by a boost: meanmore » dose 17.9 Gy (range, 8-25) using EBRT (76 patients, 47%) or BCT (86 patients, 53%). All characteristics of patients and tumors were well balanced between the BCT and EBRT groups. Results: The mean overall treatment time (OTT) was 82 days (range, 45-143) and 67 days (range, 37-128) for the EBRT and BCT groups, respectively (p < 0.001). The median follow-up was 62 months (range, 2-108). The 5-year cumulative rate of local recurrence (CRLR) was 21%. In the univariate analysis, the prognostic factors for CRLR were as follows: T stage (T1-2 = 15% vs. T3-4 = 36%, p = 0.03), boost technique (BCT = 12% vs. EBRT = 33%, p = 0.002) and OTT (OTT <80 days = 14%, OTT {>=}80 days = 34%, p = 0.005). In the multivariate analysis, BCT boost was the unique prognostic factor (hazard ratio = 0.62 (0.41-0.92). In the subgroup of patients with OTT <80 days, the 5-year CRLR was significantly increased with the BCT boost (BC = 9% vs. EBRT = 28%, p = 0.03). In the case of OTT {>=}80 days, the 5-year CRLR was not affected by the boost technique (BCT = 29% vs. EBRT = 38%, p = 0.21). Conclusion: In anal cancer, when OTT is <80 days, BCT boost is superior to EBRT boost for CRLR. These results suggest investigating the benefit of BCT boost in prospective trials.« less

The influence of simultaneous integrated boost, hypofractionation and oncoplastic surgery on cosmetic outcome and PROMs after breast conserving therapy.

PubMed

Lansu, J T P; Essers, M; Voogd, A C; Luiten, E J T; Buijs, C; Groenendaal, N; Poortmans, P M H

2015-10-01

We retrospectively investigated the possible influence of a simultaneous integrated boost (SIB), hypofractionation and oncoplastic surgery on cosmetic outcome in 125 patients with stage I-II breast cancer treated with breast conserving therapy (BCT). The boost was given sequentially (55%) or by SIB (45%); fractionation was conventional (83%) or hypofractionated (17%); the surgical technique was a conventional lumpectomy (74%) or an oncoplastic technique (26%). We compared cosmetic results subjectively using a questionnaire independently completed by the patient and by the physician and objectively with the BCCT.core software. Independent-samples T-tests were used to compare outcome in different groups. Patients also completed the EORTC QLQ C30 and BR23. Univariate analyses indicated no significant differences of the cosmetic results (P ≤ 0.05) for the type of boost or fractionation. However, the conventional lumpectomy group scored significantly better than the oncoplastic group in the BCCT.core evaluation, without a significant difference in the subjective cosmetic evaluation. Quality of life outcome was in favour of SIB, hypofractionation and conventional surgery. Our study indicates that the current RT techniques seem to be safe for cosmetic outcome and quality of life. Further investigation is needed to verify the possible negative influence of oncoplastic surgery on the cosmetic outcome and the quality of life as this technique is especially indicated for patients with an unfavourable tumour/breast volume ratio. Copyright © 2015 Elsevier Ltd. All rights reserved.

Modeling of switching regulator power stages with and without zero-inductor-current dwell time

NASA Technical Reports Server (NTRS)

Lee, F. C. Y.; Yu, Y.

1979-01-01

State-space techniques are employed to derive accurate models for the three basic switching converter power stages: buck, boost, and buck/boost operating with and without zero-inductor-current dwell time. A generalized procedure is developed which treats the continuous-inductor-current mode without dwell time as a special case of the discontinuous-current mode when the dwell time vanishes. Abrupt changes of system behavior, including a reduction of the system order when the dwell time appears, are shown both analytically and experimentally. Merits resulting from the present modeling technique in comparison with existing modeling techniques are illustrated.

CMOS single-stage input-powered bridge rectifier with boost switch and duty cycle control

NASA Astrophysics Data System (ADS)

Radzuan, Roskhatijah; Mohd Salleh, Mohd Khairul; Hamzah, Mustafar Kamal; Ab Wahab, Norfishah

2017-06-01

This paper presents a single-stage input-powered bridge rectifier with boost switch for wireless-powered devices such as biomedical implants and wireless sensor nodes. Realised using CMOS process technology, it employs a duty cycle switch control to achieve high output voltage using boost technique, leading to a high output power conversion. It has only six external connections with the boost inductance. The input frequency of the bridge rectifier is set at 50 Hz, while the switching frequency is 100 kHz. The proposed circuit is fabricated on a single 0.18-micron CMOS die with a space area of 0.024 mm2. The simulated and measured results show good agreement.

KSC-2009-5065

NASA Image and Video Library

2009-08-22

CAPE CANAVERAL, Fla. – At the Astrotech payload processing facility in Titusville, Fla., workers attach the upper segment of the transportation canister to the lower segments around the Space Tracking and Surveillance System – Demonstrators, or STSS Demo, spacecraft. The STSS Demo is a space-based sensor component of a layered Ballistic Missile Defense System designed for the overall mission of detecting, tracking and discriminating ballistic missiles. STSS is capable of tracking objects after boost phase and provides trajectory information to other sensors. It will be launched by NASA for the Missile Defense Agency between 8 and 8:58 a.m. EDT Sept. 18. Approved for Public Release 09-MDA-04886 (10 SEPT 09) Photo credit: NASA/Kim Shiflett

Impact of the Educational Boost Your Brain and Memory Program Among Senior Living Residents.

PubMed

Nicholson, Roscoe; O'Brien, Catherine

2017-12-01

This random assignment waitlist control intervention study examined an implementation of the educational Boost Your Brain and Memory cognitive fitness intervention in 12 senior living organizations. Older adult participants ( n = 166) completed measures of brain health knowledge, use of memory techniques, physical and intellectual activity, and mindfulness, at baseline and after the intervention group's completion of the course. Changes in knowledge scores and in self-reported physical and intellectual activity increased significantly more for intervention participants than for waitlist controls at the conclusion of the course. There were no significant changes between the groups in mindfulness or use of memory techniques. This suggests that in senior living settings Boost Your Brain and Memory is effective in educating participants about brain healthy behaviors and in motivating behavioral change in the areas of physical and intellectual activity.

Sleep-Driven Computations in Speech Processing

PubMed Central

Frost, Rebecca L. A.; Monaghan, Padraic

2017-01-01

Acquiring language requires segmenting speech into individual words, and abstracting over those words to discover grammatical structure. However, these tasks can be conflicting—on the one hand requiring memorisation of precise sequences that occur in speech, and on the other requiring a flexible reconstruction of these sequences to determine the grammar. Here, we examine whether speech segmentation and generalisation of grammar can occur simultaneously—with the conflicting requirements for these tasks being over-come by sleep-related consolidation. After exposure to an artificial language comprising words containing non-adjacent dependencies, participants underwent periods of consolidation involving either sleep or wake. Participants who slept before testing demonstrated a sustained boost to word learning and a short-term improvement to grammatical generalisation of the non-adjacencies, with improvements after sleep outweighing gains seen after an equal period of wake. Thus, we propose that sleep may facilitate processing for these conflicting tasks in language acquisition, but with enhanced benefits for speech segmentation. PMID:28056104

Sleep-Driven Computations in Speech Processing.

PubMed

Frost, Rebecca L A; Monaghan, Padraic

2017-01-01

Acquiring language requires segmenting speech into individual words, and abstracting over those words to discover grammatical structure. However, these tasks can be conflicting-on the one hand requiring memorisation of precise sequences that occur in speech, and on the other requiring a flexible reconstruction of these sequences to determine the grammar. Here, we examine whether speech segmentation and generalisation of grammar can occur simultaneously-with the conflicting requirements for these tasks being over-come by sleep-related consolidation. After exposure to an artificial language comprising words containing non-adjacent dependencies, participants underwent periods of consolidation involving either sleep or wake. Participants who slept before testing demonstrated a sustained boost to word learning and a short-term improvement to grammatical generalisation of the non-adjacencies, with improvements after sleep outweighing gains seen after an equal period of wake. Thus, we propose that sleep may facilitate processing for these conflicting tasks in language acquisition, but with enhanced benefits for speech segmentation.

Boosting the discriminative power of color models for feature detection

NASA Astrophysics Data System (ADS)

Stokman, Harro M. G.; Gevers, Theo

2005-01-01

We consider the well-known problem of segmenting a color image into foreground-background pixels. Such result can be obtained by segmenting the red, green and blue channels directly. Alternatively, the result may be obtained through the transformation of the color image into other color spaces, such as HSV or normalized colors. The problem then is how to select the color space or color channel that produces the best segmentation result. Furthermore, if more than one channels are equally good candidates, the next problem is how to combine the results. In this article, we investigate if the principles of the formal model for diversification of Markowitz (1952) can be applied to solve the problem. We verify, in theory and in practice, that the proposed diversification model can be applied effectively to determine the most appropriate combination of color spaces for the application at hand.

Adjuvant-Specific Genetic and Immune Signatures Associated with Lower SIV Infection Risk in Animal Models | Center for Cancer Research

Cancer.gov

The Human Immunodeficiency Virus (HIV) vaccine trial, RV144, employed a priming Canarypox-based vector, ALVAC-HIV, along with a boost composed of segments of the HIV envelope protein, gp120, with the adjuvant alum.  Results from the trial suggested the vaccine provided protection and, because of the importance of antibodies to that protection, using an adjuvant that could

RBOOST: RIEMANNIAN DISTANCE BASED REGULARIZED BOOSTING

PubMed Central

Liu, Meizhu; Vemuri, Baba C.

2011-01-01

Boosting is a versatile machine learning technique that has numerous applications including but not limited to image processing, computer vision, data mining etc. It is based on the premise that the classification performance of a set of weak learners can be boosted by some weighted combination of them. There have been a number of boosting methods proposed in the literature, such as the AdaBoost, LPBoost, SoftBoost and their variations. However, the learning update strategies used in these methods usually lead to overfitting and instabilities in the classification accuracy. Improved boosting methods via regularization can overcome such difficulties. In this paper, we propose a Riemannian distance regularized LPBoost, dubbed RBoost. RBoost uses Riemannian distance between two square-root densities (in closed form) – used to represent the distribution over the training data and the classification error respectively – to regularize the error distribution in an iterative update formula. Since this distance is in closed form, RBoost requires much less computational cost compared to other regularized Boosting algorithms. We present several experimental results depicting the performance of our algorithm in comparison to recently published methods, LP-Boost and CAVIAR, on a variety of datasets including the publicly available OASIS database, a home grown Epilepsy database and the well known UCI repository. Results depict that the RBoost algorithm performs better than the competing methods in terms of accuracy and efficiency. PMID:21927643

Trajectory tracking and backfitting techniques against theater ballistic missiles

NASA Astrophysics Data System (ADS)

Hutchins, Robert G.; Britt, Patrick T.

1999-10-01

Since the SCUD launches in the Gulf War, theater ballistic missile (TBM) systems have become a growing concern for the US military. Detection, fast track initiation, backfitting for launch point determination, and tracking and engagement during boost phase or shortly after booster cutoff are goals that grow in importance with the proliferation of weapons of mass destruction. This paper focuses on track initiation and backfitting techniques, as well as extending some earlier results on tracking a TBM during boost phase cutoff. Results indicate that Kalman techniques are superior to third order polynomial extrapolations in estimating the launch point, and that some knowledge of missile parameters, especially thrust, is extremely helpful in track initiation.

Energy Harvesting from Salinity Gradient

NASA Astrophysics Data System (ADS)

Muhthassim, B.; Thian, X. K.; Hasan, K. N. Md

2018-04-01

Abstract: Energy harvesting from salt water received attention started back in 1970s’, but due to varying interests in the field and the growing potentials of other more promising sources, more work was required to fully establish it. This paper aims at identifying existing techniques of energy harvesting and the methodology involved determining an effective technique for small scale applications of the method. Capacitive deionization technique which involves electrochemical reaction was chosen for further analysis. The experiment was conducted to analyze factors affecting its performance including the electrode and the electrolyte. Combination electrode of carbon/aluminium, copper/aluminium and carbon/copper were selected and tested with different concentration of salty water. From the experiment, copper and aluminum electrodes were found to be the most effective among the rest. A DC-DC boost converter was used to step-up the voltage. Physical implementation of the circuit was done and the circuit was tested in which an input voltage of 1.022 V was boosted to 1.255 V. The efficiency of the boost converter was 38.17 % based on input power and output power obtained.

Lovelock action with nonsmooth boundaries

NASA Astrophysics Data System (ADS)

Cano, Pablo A.

2018-05-01

We examine the variational problem in Lovelock gravity when the boundary contains timelike and spacelike segments nonsmoothly glued. We show that two kinds of contributions have to be added to the action. The first one is associated with the presence of a boundary in every segment and it depends on intrinsic and extrinsic curvatures. We can think of this contribution as adding a total derivative to the usual surface term of Lovelock gravity. The second one appears in every joint between two segments and it involves the integral along the joint of the Jacobson-Myers entropy density weighted by the Lorentz boost parameter, which relates the orthonormal frames in each segment. We argue that this term can be straightforwardly extended to the case of joints involving null boundaries. As an application, we compute the contribution of these terms to the complexity of global anti-de Sitter space in Lovelock gravity by using the "complexity =action " proposal and we identify possible universal terms for arbitrary values of the Lovelock couplings. We find that they depend on the charge a* controlling the holographic entanglement entropy and on a new constant that we characterize.

Deep learning approach for the detection and quantification of intraretinal cystoid fluid in multivendor optical coherence tomography.

PubMed

Venhuizen, Freerk G; van Ginneken, Bram; Liefers, Bart; van Asten, Freekje; Schreur, Vivian; Fauser, Sascha; Hoyng, Carel; Theelen, Thomas; Sánchez, Clara I

2018-04-01

We developed a deep learning algorithm for the automatic segmentation and quantification of intraretinal cystoid fluid (IRC) in spectral domain optical coherence tomography (SD-OCT) volumes independent of the device used for acquisition. A cascade of neural networks was introduced to include prior information on the retinal anatomy, boosting performance significantly. The proposed algorithm approached human performance reaching an overall Dice coefficient of 0.754 ± 0.136 and an intraclass correlation coefficient of 0.936, for the task of IRC segmentation and quantification, respectively. The proposed method allows for fast quantitative IRC volume measurements that can be used to improve patient care, reduce costs, and allow fast and reliable analysis in large population studies.

Deep learning approach for the detection and quantification of intraretinal cystoid fluid in multivendor optical coherence tomography

PubMed Central

Venhuizen, Freerk G.; van Ginneken, Bram; Liefers, Bart; van Asten, Freekje; Schreur, Vivian; Fauser, Sascha; Hoyng, Carel; Theelen, Thomas; Sánchez, Clara I.

2018-01-01

We developed a deep learning algorithm for the automatic segmentation and quantification of intraretinal cystoid fluid (IRC) in spectral domain optical coherence tomography (SD-OCT) volumes independent of the device used for acquisition. A cascade of neural networks was introduced to include prior information on the retinal anatomy, boosting performance significantly. The proposed algorithm approached human performance reaching an overall Dice coefficient of 0.754 ± 0.136 and an intraclass correlation coefficient of 0.936, for the task of IRC segmentation and quantification, respectively. The proposed method allows for fast quantitative IRC volume measurements that can be used to improve patient care, reduce costs, and allow fast and reliable analysis in large population studies. PMID:29675301

KSC-2009-5062

NASA Image and Video Library

2009-08-22

CAPE CANAVERAL, Fla. – At the Astrotech payload processing facility in Titusville, Fla., the upper segment of the transportation canister is lowered toward the Space Tracking and Surveillance System – Demonstrators, or STSS Demo, spacecraft. It will be installed onto the lower segments already in place. The STSS Demo is a space-based sensor component of a layered Ballistic Missile Defense System designed for the overall mission of detecting, tracking and discriminating ballistic missiles. STSS is capable of tracking objects after boost phase and provides trajectory information to other sensors. It will be launched by NASA for the Missile Defense Agency between 8 and 8:58 a.m. EDT Sept. 18. Approved for Public Release 09-MDA-04886 (10 SEPT 09) Photo credit: NASA/Kim Shiflett

KSC-2009-5063

NASA Image and Video Library

2009-08-22

CAPE CANAVERAL, Fla. – At the Astrotech payload processing facility in Titusville, Fla., the upper segment of the transportation canister is lowered over the Space Tracking and Surveillance System – Demonstrators, or STSS Demo, spacecraft. It will be installed onto the lower segments already in place. The STSS Demo is a space-based sensor component of a layered Ballistic Missile Defense System designed for the overall mission of detecting, tracking and discriminating ballistic missiles. STSS is capable of tracking objects after boost phase and provides trajectory information to other sensors. It will be launched by NASA for the Missile Defense Agency between 8 and 8:58 a.m. EDT Sept. 18. Approved for Public Release 09-MDA-04886 (10 SEPT 09) Photo credit: NASA/Kim Shiflett

KSC-2009-5064

NASA Image and Video Library

2009-08-22

CAPE CANAVERAL, Fla. – At the Astrotech payload processing facility in Titusville, Fla., the upper segment of the transportation canister is lowered over the Space Tracking and Surveillance System – Demonstrators, or STSS Demo, spacecraft. It will be installed onto the lower segments already in place. The STSS Demo is a space-based sensor component of a layered Ballistic Missile Defense System designed for the overall mission of detecting, tracking and discriminating ballistic missiles. STSS is capable of tracking objects after boost phase and provides trajectory information to other sensors. It will be launched by NASA for the Missile Defense Agency between 8 and 8:58 a.m. EDT Sept. 18. Approved for Public Release 09-MDA-04886 (10 SEPT 09) Photo credit: NASA/Kim Shiflett

Parity-expanded variational analysis for nonzero momentum

NASA Astrophysics Data System (ADS)

Stokes, Finn M.; Kamleh, Waseem; Leinweber, Derek B.; Mahbub, M. Selim; Menadue, Benjamin J.; Owen, Benjamin J.

2015-12-01

In recent years, the use of variational analysis techniques in lattice QCD has been demonstrated to be successful in the investigation of the rest-mass spectrum of many hadrons. However, due to parity mixing, more care must be taken for investigations of boosted states to ensure that the projected correlation functions provided by the variational analysis correspond to the same states at zero momentum. In this paper we present the parity-expanded variational analysis (PEVA) technique, a novel method for ensuring the successful and consistent isolation of boosted baryons through a parity expansion of the operator basis used to construct the correlation matrix.

Dynamics of multirate sampled data control systems. [for space shuttle boost vehicle

NASA Technical Reports Server (NTRS)

Naylor, J. R.; Hynes, R. J.; Molnar, D. O.

1974-01-01

The effect was investigated of the synthesis approach (single or multirate) on the machine requirements for a digital control system for the space shuttle boost vehicle. The study encompassed four major work areas: synthesis approach trades, machine requirements trades, design analysis requirements and multirate adaptive control techniques. The primary results are two multirate autopilot designs for the low Q and maximum Q flight conditions that exhibits equal or better performance than the analog and single rate system designs. Also, a preferred technique for analyzing and synthesizing multirate digital control systems is included.

External beam boost versus interstitial high-dose-rate brachytherapy boost in the adjuvant radiotherapy following breast-conserving therapy in early-stage breast cancer: a dosimetric comparison

PubMed Central

Melchert, Corinna; Kovács, György

2016-01-01

Purpose This study aims to compare the dosimetric data of local tumor's bed dose escalation (boost) with photon beams (external beam radiation therapy – EBRT) versus high-dose-rate interstitial brachytherapy (HDR-BT) after breast-conserving treatment in women with early-stage breast cancer. Material and methods We analyzed the treatment planning data of 136 irradiated patients, treated between 2006 and 2013, who underwent breast-conserving surgery and adjuvant whole breast irradiation (WBI; 50.4 Gy) and boost (HDR-BT: 10 Gy in one fraction [n = 36]; EBRT: 10 Gy in five fractions [n = 100]). Organs at risk (OAR; heart, ipsilateral lung, skin, most exposed rib segment) were delineated. Dosimetric parameters were calculated with the aid of dose-volume histograms (DVH). A non-parametric test was performed to compare the two different boost forms. Results There was no difference for left-sided cancers regarding the maximum dose to the heart (HDR-BT 29.8% vs. EBRT 29.95%, p = 0.34). The maximum doses to the other OAR were significantly lower for HDR-BT (Dmax lung 47.12% vs. 87.7%, p < 0.01; rib 61.17% vs. 98.5%, p < 0.01; skin 57.1% vs. 94.75%, p < 0.01; in the case of right-sided breast irradiation, dose of the heart 6.00% vs. 16.75%, p < 0.01). Conclusions Compared to EBRT, local dose escalation with HDR-BT presented a significant dose reduction to the investigated OAR. Only left-sided irradiation showed no difference regarding the maximum dose to the heart. Reducing irradiation exposure to OAR could result in a reduction of long-term side effects. Therefore, from a dosimetric point of view, an interstitial boost complementary to WBI via EBRT seems to be more advantageous in the adjuvant radiotherapy of breast cancer. PMID:27648082

The importance of calorimetry for highly-boosted jet substructure

DOE PAGES

Coleman, Evan; Freytsis, Marat; Hinzmann, Andreas; ...

2018-01-09

Here, jet substructure techniques are playing an essential role in exploring the TeV scale at the Large Hadron Collider (LHC), since they facilitate the efficient reconstruction and identification of highly-boosted objects. Both for the LHC and for future colliders, there is a growing interest in using jet substructure methods based only on charged-particle information. The reason is that silicon-based tracking detectors offer excellent granularity and precise vertexing, which can improve the angular resolution on highly-collimated jets and mitigate the impact of pileup. In this paper, we assess how much jet substructure performance degrades by using track-only information, and we demonstratemore » physics contexts in which calorimetry is most beneficial. Specifically, we consider five different hadronic final states - W bosons, Z bosons, top quarks, light quarks, gluons - and test the pairwise discrimination power with a multi-variate combination of substructure observables. In the idealized case of perfect reconstruction, we quantify the loss in discrimination performance when using just charged particles compared to using all detected particles. We also consider the intermediate case of using charged particles plus photons, which provides valuable information about neutral pions. In the more realistic case of a segmented calorimeter, we assess the potential performance gains from improving calorimeter granularity and resolution, comparing a CMS-like detector to more ambitious future detector concepts. Broadly speaking, we find large performance gains from neutral-particle information and from improved calorimetry in cases where jet mass resolution drives the discrimination power, whereas the gains are more modest if an absolute mass scale calibration is not required.« less

The importance of calorimetry for highly-boosted jet substructure

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

Coleman, Evan; Freytsis, Marat; Hinzmann, Andreas

2017-09-25

Jet substructure techniques are playing an essential role in exploring the TeV scale at the Large Hadron Collider (LHC), since they facilitate the efficient reconstruction and identification of highly-boosted objects. Both for the LHC and for future colliders, there is a growing interest in using jet substructure methods based only on charged-particle information. The reason is that silicon-based tracking detectors offer excellent granularity and precise vertexing, which can improve the angular resolution on highly-collimated jets and mitigate the impact of pileup. In this paper, we assess how much jet substructure performance degrades by using track-only information, and we demonstrate physicsmore » contexts in which calorimetry is most beneficial. Specifically, we consider five different hadronic final states - W bosons, Z bosons, top quarks, light quarks, gluons - and test the pairwise discrimination power with a multi-variate combination of substructure observables. In the idealized case of perfect reconstruction, we quantify the loss in discrimination performance when using just charged particles compared to using all detected particles. We also consider the intermediate case of using charged particles plus photons, which provides valuable information about neutral pions. In the more realistic case of a segmented calorimeter, we assess the potential performance gains from improving calorimeter granularity and resolution, comparing a CMS-like detector to more ambitious future detector concepts. Broadly speaking, we find large performance gains from neutral-particle information and from improved calorimetry in cases where jet mass resolution drives the discrimination power, whereas the gains are more modest if an absolute mass scale calibration is not required.« less

The importance of calorimetry for highly-boosted jet substructure

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

Coleman, Evan; Freytsis, Marat; Hinzmann, Andreas

Here, jet substructure techniques are playing an essential role in exploring the TeV scale at the Large Hadron Collider (LHC), since they facilitate the efficient reconstruction and identification of highly-boosted objects. Both for the LHC and for future colliders, there is a growing interest in using jet substructure methods based only on charged-particle information. The reason is that silicon-based tracking detectors offer excellent granularity and precise vertexing, which can improve the angular resolution on highly-collimated jets and mitigate the impact of pileup. In this paper, we assess how much jet substructure performance degrades by using track-only information, and we demonstratemore » physics contexts in which calorimetry is most beneficial. Specifically, we consider five different hadronic final states - W bosons, Z bosons, top quarks, light quarks, gluons - and test the pairwise discrimination power with a multi-variate combination of substructure observables. In the idealized case of perfect reconstruction, we quantify the loss in discrimination performance when using just charged particles compared to using all detected particles. We also consider the intermediate case of using charged particles plus photons, which provides valuable information about neutral pions. In the more realistic case of a segmented calorimeter, we assess the potential performance gains from improving calorimeter granularity and resolution, comparing a CMS-like detector to more ambitious future detector concepts. Broadly speaking, we find large performance gains from neutral-particle information and from improved calorimetry in cases where jet mass resolution drives the discrimination power, whereas the gains are more modest if an absolute mass scale calibration is not required.« less

Boosting production yield of biomedical peptides

NASA Technical Reports Server (NTRS)

Manatt, S. L.

1978-01-01

Nuclear magnetic resonance (NMR) technique is employed to monitor synthesis of biomedical peptides. Application of NMR technique may improve production yields of insulin, ACTH, and growth hormones, as well as other synthesized biomedical peptides.

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

Moureau-Zabotto, Laurence, E-mail: moureaul@ipc.unicancer.fr; Ortholan, Cecile; Hannoun-Levi, Jean-Michel

Purpose: To assess retrospectively the clinical outcome in anal cancer patients, with lymph node involvement, treated with split-course radiation therapy and receiving a boost through external beam radiation therapy (EBRT) or brachytherapy (BCT). Methods and Materials: From 2000 to 2005, among 229 patients with invasive nonmetastatic anal squamous cell carcinoma, a selected group of 99 patients, with lymph node involvement, was studied. Tumor staging reported was T1 in 4 patients, T2 in 16 patients, T3 in 49 patients, T4 in 16 patients, and T unknown in 14 patients and as N1 in 67 patients and N2/N3 in 32 patients. Patientsmore » underwent a first course of EBRT (mean dose, 45.1 Gy) followed by a boost (mean dose, 18 Gy) using EBRT (50 patients) or BCT (49 patients). All characteristics of patients and tumors were well balanced between the BCT and EBRT groups. Prognostic factors of cumulative rate of local recurrence (CRLR), cumulative rate of distant (including nodal) recurrence (CRDR), colostomy-free survival (CFS) rate, and overall survival (OS) rate were analyzed for the overall population and according to the nodal status classification. Results: The median follow-up was 71.5 months. The 5-year CRLR, CRDR, CFS rate, and OS rate were 21%, 19%, 63%, and 74.4%, respectively. In the overall population, the type of node involvement (N1 vs N2/N3) was the unique independent prognostic factor for CRLR. In N1 patients, by use of multivariate analysis, BCT boost was the unique prognostic factor for CRLR (4% for BCT vs 31% for EBRT; hazard ratio, 0.08; P=.042). No studied factors were significantly associated with CRDR, CFS, and OS. No difference with regard to boost technique and any other factor studied was observed in N2/N3 patients for any kind of recurrence. Conclusion: In anal cancer, even in the case of initial perirectal node invasion, BCT boost is superior to EBRT boost for CRLR, without an influence on OS, suggesting that N1 status should not be a contraindication to use of a BCT boost technique, as well as emphasizing the important of investigating the benefit of BCT boost in prospective randomized trials.« less

A Comprehensive Texture Segmentation Framework for Segmentation of Capillary Non-Perfusion Regions in Fundus Fluorescein Angiograms

PubMed Central

Zheng, Yalin; Kwong, Man Ting; MacCormick, Ian J. C.; Beare, Nicholas A. V.; Harding, Simon P.

2014-01-01

Capillary non-perfusion (CNP) in the retina is a characteristic feature used in the management of a wide range of retinal diseases. There is no well-established computation tool for assessing the extent of CNP. We propose a novel texture segmentation framework to address this problem. This framework comprises three major steps: pre-processing, unsupervised total variation texture segmentation, and supervised segmentation. It employs a state-of-the-art multiphase total variation texture segmentation model which is enhanced by new kernel based region terms. The model can be applied to texture and intensity-based multiphase problems. A supervised segmentation step allows the framework to take expert knowledge into account, an AdaBoost classifier with weighted cost coefficient is chosen to tackle imbalanced data classification problems. To demonstrate its effectiveness, we applied this framework to 48 images from malarial retinopathy and 10 images from ischemic diabetic maculopathy. The performance of segmentation is satisfactory when compared to a reference standard of manual delineations: accuracy, sensitivity and specificity are 89.0%, 73.0%, and 90.8% respectively for the malarial retinopathy dataset and 80.8%, 70.6%, and 82.1% respectively for the diabetic maculopathy dataset. In terms of region-wise analysis, this method achieved an accuracy of 76.3% (45 out of 59 regions) for the malarial retinopathy dataset and 73.9% (17 out of 26 regions) for the diabetic maculopathy dataset. This comprehensive segmentation framework can quantify capillary non-perfusion in retinopathy from two distinct etiologies, and has the potential to be adopted for wider applications. PMID:24747681

Adjuvant-Specific Genetic and Immune Signatures Associated with Lower SIV Infection Risk in Animal Models | Center for Cancer Research

Cancer.gov

The Human Immunodeficiency Virus (HIV) vaccine trial, RV144, employed a priming Canarypox-based vector, ALVAC-HIV, along with a boost composed of segments of the HIV envelope protein, gp120, with the adjuvant alum.  Results from the trial suggested the vaccine provided protection and, because of the importance of antibodies to that protection, using an adjuvant that could elicit a stronger immune response might improve efficacy.

Threshold responses of Blackside Dace (Chrosomus cumberlandensis) and Kentucky Arrow Darter (Etheostoma spilotum) to stream conductivity

USGS Publications Warehouse

Hitt, Nathaniel P.; Floyd, Michael; Compton, Michael; McDonald, Kenneth

2016-01-01

Chrosomus cumberlandensis (Blackside Dace [BSD]) and Etheostoma spilotum (Kentucky Arrow Darter [KAD]) are fish species of conservation concern due to their fragmented distributions, their low population sizes, and threats from anthropogenic stressors in the southeastern United States. We evaluated the relationship between fish abundance and stream conductivity, an index of environmental quality and potential physiological stressor. We modeled occurrence and abundance of KAD in the upper Kentucky River basin (208 samples) and BSD in the upper Cumberland River basin (294 samples) for sites sampled between 2003 and 2013. Segmented regression indicated a conductivity change-point for BSD abundance at 343 μS/cm (95% CI: 123–563 μS/cm) and for KAD abundance at 261 μS/cm (95% CI: 151–370 μS/cm). In both cases, abundances were negligible above estimated conductivity change-points. Post-hoc randomizations accounted for variance in estimated change points due to unequal sample sizes across the conductivity gradients. Boosted regression-tree analysis indicated stronger effects of conductivity than other natural and anthropogenic factors known to influence stream fishes. Boosted regression trees further indicated threshold responses of BSD and KAD occurrence to conductivity gradients in support of segmented regression results. We suggest that the observed conductivity relationship may indicate energetic limitations for insectivorous fishes due to changes in benthic macroinvertebrate community composition.

Application of Boosting Regression Trees to Preliminary Cost Estimation in Building Construction Projects

PubMed Central

2015-01-01

Among the recent data mining techniques available, the boosting approach has attracted a great deal of attention because of its effective learning algorithm and strong boundaries in terms of its generalization performance. However, the boosting approach has yet to be used in regression problems within the construction domain, including cost estimations, but has been actively utilized in other domains. Therefore, a boosting regression tree (BRT) is applied to cost estimations at the early stage of a construction project to examine the applicability of the boosting approach to a regression problem within the construction domain. To evaluate the performance of the BRT model, its performance was compared with that of a neural network (NN) model, which has been proven to have a high performance in cost estimation domains. The BRT model has shown results similar to those of NN model using 234 actual cost datasets of a building construction project. In addition, the BRT model can provide additional information such as the importance plot and structure model, which can support estimators in comprehending the decision making process. Consequently, the boosting approach has potential applicability in preliminary cost estimations in a building construction project. PMID:26339227

Application of Boosting Regression Trees to Preliminary Cost Estimation in Building Construction Projects.

PubMed

Shin, Yoonseok

2015-01-01

Among the recent data mining techniques available, the boosting approach has attracted a great deal of attention because of its effective learning algorithm and strong boundaries in terms of its generalization performance. However, the boosting approach has yet to be used in regression problems within the construction domain, including cost estimations, but has been actively utilized in other domains. Therefore, a boosting regression tree (BRT) is applied to cost estimations at the early stage of a construction project to examine the applicability of the boosting approach to a regression problem within the construction domain. To evaluate the performance of the BRT model, its performance was compared with that of a neural network (NN) model, which has been proven to have a high performance in cost estimation domains. The BRT model has shown results similar to those of NN model using 234 actual cost datasets of a building construction project. In addition, the BRT model can provide additional information such as the importance plot and structure model, which can support estimators in comprehending the decision making process. Consequently, the boosting approach has potential applicability in preliminary cost estimations in a building construction project.

The Series Connected Buck Boost Regulator Concept for High Efficiency Light Weight DC Voltage Regulation

NASA Technical Reports Server (NTRS)

Birchenough, Arthur G.

2003-01-01

Improvements in the efficiency and size of DC-DC converters have resulted from advances in components, primarily semiconductors, and improved topologies. One topology, which has shown very high potential in limited applications, is the Series Connected Boost Unit (SCBU), wherein a small DC-DC converter output is connected in series with the input bus to provide an output voltage equal to or greater than the input voltage. Since the DC-DC converter switches only a fraction of the power throughput, the overall system efficiency is very high. But this technique is limited to applications where the output is always greater than the input. The Series Connected Buck Boost Regulator (SCBBR) concept extends partial power processing technique used in the SCBU to operation when the desired output voltage is higher or lower than the input voltage, and the implementation described can even operate as a conventional buck converter to operate at very low output to input voltage ratios. This paper describes the operation and performance of an SCBBR configured as a bus voltage regulator providing 50 percent voltage regulation range, bus switching, and overload limiting, operating above 98 percent efficiency. The technique does not provide input-output isolation.

Towards Automatic Image Segmentation Using Optimised Region Growing Technique

NASA Astrophysics Data System (ADS)

Alazab, Mamoun; Islam, Mofakharul; Venkatraman, Sitalakshmi

Image analysis is being adopted extensively in many applications such as digital forensics, medical treatment, industrial inspection, etc. primarily for diagnostic purposes. Hence, there is a growing interest among researches in developing new segmentation techniques to aid the diagnosis process. Manual segmentation of images is labour intensive, extremely time consuming and prone to human errors and hence an automated real-time technique is warranted in such applications. There is no universally applicable automated segmentation technique that will work for all images as the image segmentation is quite complex and unique depending upon the domain application. Hence, to fill the gap, this paper presents an efficient segmentation algorithm that can segment a digital image of interest into a more meaningful arrangement of regions and objects. Our algorithm combines region growing approach with optimised elimination of false boundaries to arrive at more meaningful segments automatically. We demonstrate this using X-ray teeth images that were taken for real-life dental diagnosis.

Activity Detection and Retrieval for Image and Video Data with Limited Training

DTIC Science & Technology

2015-06-10

applications. Here we propose two techniques for image segmentation. The first involves an automata based multiple threshold selection scheme, where a... automata . For our second approach to segmentation, we employ a region based segmentation technique that is capable of handling intensity inhomogeneity...techniques for image segmentation. The first involves an automata based multiple threshold selection scheme, where a mixture of Gaussian is fitted to the

Component Pin Recognition Using Algorithms Based on Machine Learning

NASA Astrophysics Data System (ADS)

Xiao, Yang; Hu, Hong; Liu, Ze; Xu, Jiangchang

2018-04-01

The purpose of machine vision for a plug-in machine is to improve the machine’s stability and accuracy, and recognition of the component pin is an important part of the vision. This paper focuses on component pin recognition using three different techniques. The first technique involves traditional image processing using the core algorithm for binary large object (BLOB) analysis. The second technique uses the histogram of oriented gradients (HOG), to experimentally compare the effect of the support vector machine (SVM) and the adaptive boosting machine (AdaBoost) learning meta-algorithm classifiers. The third technique is the use of an in-depth learning method known as convolution neural network (CNN), which involves identifying the pin by comparing a sample to its training. The main purpose of the research presented in this paper is to increase the knowledge of learning methods used in the plug-in machine industry in order to achieve better results.

Contralateral Breast Dose After Whole-Breast Irradiation: An Analysis by Treatment Technique

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

Williams, Terence M.; Moran, Jean M., E-mail: jmmoran@med.umich.edu; Hsu, Shu-Hui

2012-04-01

Purpose: To investigate the contralateral breast dose (CBD) across a continuum of breast-conservation therapy techniques. Methods and Materials: An anthropomorphic phantom was CT-simulated, and six treatment plans were generated: open tangents, tangents with an external wedge on the lateral beam, tangents with lateral and medial external wedges, a simple segment plan (three segments per tangent), a complex segmental intensity-modulated radiotherapy (IMRT) plan (five segments per tangent), and a beamlet IMRT plan (>100 segments). For all techniques, the breast on the phantom was irradiated to 5000 cGy. Contralateral breast dose was measured at a uniform depth at the center and eachmore » quadrant using thermoluminescent detectors. Results: Contralateral breast dose varied with position and was 50 {+-} 7.3 cGy in the inner half, 24 {+-} 4.1 cGy at the center, and 16 {+-} 2.2 cGy in the outer half for the open tangential plan. Compared with an average dose of 31 cGy across all points for the open field, the average doses were simple segment 32 cGy (range, 99-105% compared with open technique), complex segment 34 cGy (range, 103-117% compared with open technique), beamlet IMRT 34 cGy (range, 103-124% compared with open technique), lateral wedge only 46 cGy (range, 133-175% compared with open technique), and medial and lateral wedge 96 cGy (range, 282-370% compared with open technique). Conclusions: Single or dual wedge techniques resulted in the highest CBD increases compared with open tangents. To obtain the desired homogeneity to the treated breast while minimizing CBD, segmental and IMRT techniques should be encouraged over external physical compensators.« less

Leaf position optimization for step-and-shoot IMRT.

PubMed

De Gersem, W; Claus, F; De Wagter, C; Van Duyse, B; De Neve, W

2001-12-01

To describe the theoretical basis, the algorithm, and implementation of a tool that optimizes segment shapes and weights for step-and-shoot intensity-modulated radiation therapy delivered by multileaf collimators. The tool, called SOWAT (Segment Outline and Weight Adapting Tool) is applied to a set of segments, segment weights, and corresponding dose distribution, computed by an external dose computation engine. SOWAT evaluates the effects of changing the position of each collimating leaf of each segment on an objective function, as follows. Changing a leaf position causes a change in the segment-specific dose matrix, which is calculated by a fast dose computation algorithm. A weighted sum of all segment-specific dose matrices provides the dose distribution and allows computation of the value of the objective function. Only leaf position changes that comply with the multileaf collimator constraints are evaluated. Leaf position changes that tend to decrease the value of the objective function are retained. After several possible positions have been evaluated for all collimating leaves of all segments, an external dose engine recomputes the dose distribution, based on the adapted leaf positions and weights. The plan is evaluated. If the plan is accepted, a segment sequencer is used to make the prescription files for the treatment machine. Otherwise, the user can restart SOWAT using the new set of segments, segment weights, and corresponding dose distribution. The implementation was illustrated using two example cases. The first example is a T1N0M0 supraglottic cancer case that was distributed as a multicenter planning exercise by investigators from Rotterdam, The Netherlands. The exercise involved a two-phase plan. Phase 1 involved the delivery of 46 Gy to a concave-shaped planning target volume (PTV) consisting of the primary tumor volume and the elective lymph nodal regions II-IV on both sides of the neck. Phase 2 involved a boost of 24 Gy to the primary tumor region only. SOWAT was applied to the Phase 1 plan. Parotid sparing was a planning goal. The second implementation example is an ethmoid sinus cancer case, planned with the intent of bilateral visus sparing. The median PTV prescription dose was 70 Gy with a maximum dose constraint to the optic pathway structures of 60 Gy. The initial set of segments, segment weights, and corresponding dose distribution were obtained, respectively, by an anatomy-based segmentation tool, a segment weight optimization tool, and a differential scatter-air ratio dose computation algorithm as external dose engine. For the supraglottic case, this resulted in a plan that proved to be comparable to the plans obtained at the other institutes by forward or inverse planning techniques. After using SOWAT, the minimum PTV dose and PTV dose homogeneity increased; the maximum dose to the spinal cord decreased from 38 Gy to 32 Gy. The left parotid mean dose decreased from 22 Gy to 19 Gy and the right parotid mean dose from 20 to 18 Gy. For the ethmoid sinus case, the target homogeneity increased by leaf position optimization, together with a better sparing of the optical tracts. By using SOWAT, the plans improved with respect to all plan evaluation end points. Compliance with the multileaf collimator constraints is guaranteed. The treatment delivery time remains almost unchanged, because no additional segments are created.

Crack detection in oak flooring lamellae using ultrasound-excited thermography

NASA Astrophysics Data System (ADS)

Pahlberg, Tobias; Thurley, Matthew; Popovic, Djordje; Hagman, Olle

2018-01-01

Today, a large number of people are manually grading and detecting defects in wooden lamellae in the parquet flooring industry. This paper investigates the possibility of using the ensemble methods random forests and boosting to automatically detect cracks using ultrasound-excited thermography and a variety of predictor variables. When friction occurs in thin cracks, they become warm and thus visible to a thermographic camera. Several image processing techniques have been used to suppress the noise and enhance probable cracks in the images. The most successful predictor variables captured the upper part of the heat distribution, such as the maximum temperature, kurtosis and percentile values 92-100 of the edge pixels. The texture in the images was captured by Completed Local Binary Pattern histograms and cracks were also segmented by background suppression and thresholding. The classification accuracy was significantly improved from previous research through added image processing, introduction of more predictors, and by using automated machine learning. The best ensemble methods reach an average classification accuracy of 0.8, which is very close to the authors' own manual attempt at separating the images (0.83).

A prior feature SVM – MRF based method for mouse brain segmentation

PubMed Central

Wu, Teresa; Bae, Min Hyeok; Zhang, Min; Pan, Rong; Badea, Alexandra

2012-01-01

We introduce an automated method, called prior feature Support Vector Machine- Markov Random Field (pSVMRF), to segment three-dimensional mouse brain Magnetic Resonance Microscopy (MRM) images. Our earlier work, extended MRF (eMRF) integrated Support Vector Machine (SVM) and Markov Random Field (MRF) approaches, leading to improved segmentation accuracy; however, the computation of eMRF is very expensive, which may limit its performance on segmentation and robustness. In this study pSVMRF reduces training and testing time for SVM, while boosting segmentation performance. Unlike the eMRF approach, where MR intensity information and location priors are linearly combined, pSVMRF combines this information in a nonlinear fashion, and enhances the discriminative ability of the algorithm. We validate the proposed method using MR imaging of unstained and actively stained mouse brain specimens, and compare segmentation accuracy with two existing methods: eMRF and MRF. C57BL/6 mice are used for training and testing, using cross validation. For formalin fixed C57BL/6 specimens, pSVMRF outperforms both eMRF and MRF. The segmentation accuracy for C57BL/6 brains, stained or not, was similar for larger structures like hippocampus and caudate putamen, (~87%), but increased substantially for smaller regions like susbtantia nigra (from 78.36% to 91.55%), and anterior commissure (from ~50% to ~80%). To test segmentation robustness against increased anatomical variability we add two strains, BXD29 and a transgenic mouse model of Alzheimer’s Disease. Segmentation accuracy for new strains is 80% for hippocampus, and caudate putamen, indicating that pSVMRF is a promising approach for phenotyping mouse models of human brain disorders. PMID:21988893

A prior feature SVM-MRF based method for mouse brain segmentation.

PubMed

Wu, Teresa; Bae, Min Hyeok; Zhang, Min; Pan, Rong; Badea, Alexandra

2012-02-01

We introduce an automated method, called prior feature Support Vector Machine-Markov Random Field (pSVMRF), to segment three-dimensional mouse brain Magnetic Resonance Microscopy (MRM) images. Our earlier work, extended MRF (eMRF) integrated Support Vector Machine (SVM) and Markov Random Field (MRF) approaches, leading to improved segmentation accuracy; however, the computation of eMRF is very expensive, which may limit its performance on segmentation and robustness. In this study pSVMRF reduces training and testing time for SVM, while boosting segmentation performance. Unlike the eMRF approach, where MR intensity information and location priors are linearly combined, pSVMRF combines this information in a nonlinear fashion, and enhances the discriminative ability of the algorithm. We validate the proposed method using MR imaging of unstained and actively stained mouse brain specimens, and compare segmentation accuracy with two existing methods: eMRF and MRF. C57BL/6 mice are used for training and testing, using cross validation. For formalin fixed C57BL/6 specimens, pSVMRF outperforms both eMRF and MRF. The segmentation accuracy for C57BL/6 brains, stained or not, was similar for larger structures like hippocampus and caudate putamen, (~87%), but increased substantially for smaller regions like susbtantia nigra (from 78.36% to 91.55%), and anterior commissure (from ~50% to ~80%). To test segmentation robustness against increased anatomical variability we add two strains, BXD29 and a transgenic mouse model of Alzheimer's disease. Segmentation accuracy for new strains is 80% for hippocampus, and caudate putamen, indicating that pSVMRF is a promising approach for phenotyping mouse models of human brain disorders. Copyright © 2011 Elsevier Inc. All rights reserved.

Impact of tumour bed boost integration on acute and late toxicity in patients with breast cancer: A systematic review.

PubMed

Hamilton, Daniel George; Bale, Rebecca; Jones, Claire; Fitzgerald, Emma; Khor, Richard; Knight, Kellie; Wasiak, Jason

2016-06-01

The purpose of this systematic review was to summarise the evidence from studies investigating the integration of tumour bed boosts into whole breast irradiation for patients with Stage 0-III breast cancer, with a focus on its impact on acute and late toxicities. A comprehensive systematic electronic search through the Ovid MEDLINE, EMBASE and PubMed databases from January 2000 to January 2015 was conducted. Studies were considered eligible if they investigated the efficacy of hypo- or normofractionated whole breast irradiation with the inclusion of a daily concurrent boost. The primary outcomes of interest were the degree of observed acute and late toxicity following radiotherapy treatment. Methodological quality assessment was performed on all included studies using either the Newcastle-Ottawa Scale or a previously published investigator-derived quality instrument. The search identified 35 articles, of which 17 satisfied our eligibility criteria. Thirteen and eleven studies reported on acute and late toxicities respectively. Grade 3 acute skin toxicity ranged from 1 to 7% whilst moderate to severe fibrosis and telangiectasia were both limited to 9%. Reported toxicity profiles were comparable to historical data at similar time-points. Studies investigating the delivery of concurrent boosts with whole breast radiotherapy courses report safe short to medium-term toxicity profiles and cosmesis rates. Whilst the quality of evidence and length of follow-up supporting these findings is low, sufficient evidence has been generated to consider concurrent boost techniques as an alternative to conventional sequential techniques. Copyright © 2016 Elsevier Ltd. All rights reserved.

Methods, safety, and early clinical outcomes of dose escalation using simultaneous integrated and sequential boosts in patients with locally advanced gynecologic malignancies.

PubMed

Boyle, John; Craciunescu, Oana; Steffey, Beverly; Cai, Jing; Chino, Junzo

2014-11-01

To evaluate the safety of dose escalated radiotherapy using a simultaneous integrated boost technique in patients with locally advanced gynecological malignancies. Thirty-nine women with locally advanced gynecological malignancies were treated with intensity modulated radiation therapy utilizing a simultaneous integrated boost (SIB) technique for gross disease in the para-aortic and/or pelvic nodal basins, sidewall extension, or residual primary disease. Women were treated to 45Gy in 1.8Gy fractions to elective nodal regions. Gross disease was simultaneously treated to 55Gy in 2.2Gy fractions (n=44 sites). An additional sequential boost of 10Gy in 2Gy fractions was delivered if deemed appropriate (n=29 sites). Acute and late toxicity, local control in the treated volumes (LC), overall survival (OS), and distant metastases (DM) were assessed. All were treated with a SIB to a dose of 55Gy. Twenty-four patients were subsequently treated with a sequential boost to a median dose of 65Gy. Median follow-up was 18months. Rates of acute>grade 2 gastrointestinal (GI), genitourinary (GU), and hematologic (heme) toxicities were 2.5%, 0%, and 30%, respectively. There were no grade 4 acute toxicities. At one year, grade 1-2 late GI toxicities were 24.5%. There were no grade 3 or 4 late GI toxicities. Rates of grade 1-2 late GU toxicities were 12.7%. There were no grade 3 or 4 late GU toxicities. Dose escalated radiotherapy using a SIB results in acceptable rates of acute toxicity. Copyright © 2014 Elsevier Inc. All rights reserved.

Three-dimensional segmentation of luminal and adventitial borders in serial intravascular ultrasound images

NASA Technical Reports Server (NTRS)

Shekhar, R.; Cothren, R. M.; Vince, D. G.; Chandra, S.; Thomas, J. D.; Cornhill, J. F.

1999-01-01

Intravascular ultrasound (IVUS) provides exact anatomy of arteries, allowing accurate quantitative analysis. Automated segmentation of IVUS images is a prerequisite for routine quantitative analyses. We present a new three-dimensional (3D) segmentation technique, called active surface segmentation, which detects luminal and adventitial borders in IVUS pullback examinations of coronary arteries. The technique was validated against expert tracings by computing correlation coefficients (range 0.83-0.97) and William's index values (range 0.37-0.66). The technique was statistically accurate, robust to image artifacts, and capable of segmenting a large number of images rapidly. Active surface segmentation enabled geometrically accurate 3D reconstruction and visualization of coronary arteries and volumetric measurements.

G and C boost and abort study summary, exhibit B

NASA Technical Reports Server (NTRS)

Backman, H. D.

1972-01-01

A six degree of freedom simulation of rigid vehicles was developed to study space shuttle vehicle boost-abort guidance and control techniques. The simulation was described in detail as an all digital program and as a hybrid program. Only the digital simulation was implemented. The equations verified in the digital simulation were adapted for use in the hybrid simulation. Study results were obtained from four abort cases using the digital program.

An Innovative Technique to Assess Spontaneous Baroreflex Sensitivity with Short Data Segments: Multiple Trigonometric Regressive Spectral Analysis.

PubMed

Li, Kai; Rüdiger, Heinz; Haase, Rocco; Ziemssen, Tjalf

2018-01-01

Objective: As the multiple trigonometric regressive spectral (MTRS) analysis is extraordinary in its ability to analyze short local data segments down to 12 s, we wanted to evaluate the impact of the data segment settings by applying the technique of MTRS analysis for baroreflex sensitivity (BRS) estimation using a standardized data pool. Methods: Spectral and baroreflex analyses were performed on the EuroBaVar dataset (42 recordings, including lying and standing positions). For this analysis, the technique of MTRS was used. We used different global and local data segment lengths, and chose the global data segments from different positions. Three global data segments of 1 and 2 min and three local data segments of 12, 20, and 30 s were used in MTRS analysis for BRS. Results: All the BRS-values calculated on the three global data segments were highly correlated, both in the supine and standing positions; the different global data segments provided similar BRS estimations. When using different local data segments, all the BRS-values were also highly correlated. However, in the supine position, using short local data segments of 12 s overestimated BRS compared with those using 20 and 30 s. In the standing position, the BRS estimations using different local data segments were comparable. There was no proportional bias for the comparisons between different BRS estimations. Conclusion: We demonstrate that BRS estimation by the MTRS technique is stable when using different global data segments, and MTRS is extraordinary in its ability to evaluate BRS in even short local data segments (20 and 30 s). Because of the non-stationary character of most biosignals, the MTRS technique would be preferable for BRS analysis especially in conditions when only short stationary data segments are available or when dynamic changes of BRS should be monitored.

Lorentz boosted frame simulation technique in Particle-in-cell methods

NASA Astrophysics Data System (ADS)

Yu, Peicheng

In this dissertation, we systematically explore the use of a simulation method for modeling laser wakefield acceleration (LWFA) using the particle-in-cell (PIC) method, called the Lorentz boosted frame technique. In the lab frame the plasma length is typically four orders of magnitude larger than the laser pulse length. Using this technique, simulations are performed in a Lorentz boosted frame in which the plasma length, which is Lorentz contracted, and the laser length, which is Lorentz expanded, are now comparable. This technique has the potential to reduce the computational needs of a LWFA simulation by more than four orders of magnitude, and is useful if there is no or negligible reflection of the laser in the lab frame. To realize the potential of Lorentz boosted frame simulations for LWFA, the first obstacle to overcome is a robust and violent numerical instability, called the Numerical Cerenkov Instability (NCI), that leads to unphysical energy exchange between relativistically drifting particles and their radiation. This leads to unphysical noise that dwarfs the real physical processes. In this dissertation, we first present a theoretical analysis of this instability, and show that the NCI comes from the unphysical coupling of the electromagnetic (EM) modes and Langmuir modes (both main and aliasing) of the relativistically drifting plasma. We then discuss the methods to eliminate them. However, the use of FFTs can lead to parallel scalability issues when there are many more cells along the drifting direction than in the transverse direction(s). We then describe an algorithm that has the potential to address this issue by using a higher order finite difference operator for the derivative in the plasma drifting direction, while using the standard second order operators in the transverse direction(s). The NCI for this algorithm is analyzed, and it is shown that the NCI can be eliminated using the same strategies that were used for the hybrid FFT/Finite Difference solver. This scheme also requires a current correction and filtering which require FFTs. However, we show that in this case the FFTs can be done locally on each parallel partition. We also describe how the use of the hybrid FFT/Finite Difference or the hybrid higher order finite difference/second order finite difference methods permit combining the Lorentz boosted frame simulation technique with another "speed up" technique, called the quasi-3D algorithm, to gain unprecedented speed up for the LWFA simulations. In the quasi-3D algorithm the fields and currents are defined on an r--z PIC grid and expanded in azimuthal harmonics. The expansion is truncated with only a few modes so it has similar computational needs of a 2D r--z PIC code. We show that NCI has similar properties in r--z as in z-x slab geometry and show that the same strategies for eliminating the NCI in Cartesian geometry can be effective for the quasi-3D algorithm leading to the possibility of unprecedented speed up. We also describe a new code called UPIC-EMMA that is based on fully spectral (FFT) solver. The new code includes implementation of a moving antenna that can launch lasers in the boosted frame. We also describe how the new hybrid algorithms were implemented into OSIRIS. Examples of LWFA using the boosted frame using both UPIC-EMMA and OSIRIS are given, including the comparisons against the lab frame results. We also describe how to efficiently obtain the boosted frame simulations data that are needed to generate the transformed lab frame data, as well as how to use a moving window in the boosted frame. The NCI is also a major issue for modeling relativistic shocks with PIC algorithm. In relativistic shock simulations two counter-propagating plasmas drifting at relativistic speeds are colliding against each other. We show that the strategies for eliminating the NCI developed in this dissertation are enabling such simulations being run for much longer simulation times, which should open a path for major advances in relativistic shock research. (Abstract shortened by ProQuest.).

A discriminative model-constrained graph cuts approach to fully automated pediatric brain tumor segmentation in 3-D MRI.

PubMed

Wels, Michael; Carneiro, Gustavo; Aplas, Alexander; Huber, Martin; Hornegger, Joachim; Comaniciu, Dorin

2008-01-01

In this paper we present a fully automated approach to the segmentation of pediatric brain tumors in multi-spectral 3-D magnetic resonance images. It is a top-down segmentation approach based on a Markov random field (MRF) model that combines probabilistic boosting trees (PBT) and lower-level segmentation via graph cuts. The PBT algorithm provides a strong discriminative observation model that classifies tumor appearance while a spatial prior takes into account the pair-wise homogeneity in terms of classification labels and multi-spectral voxel intensities. The discriminative model relies not only on observed local intensities but also on surrounding context for detecting candidate regions for pathology. A mathematically sound formulation for integrating the two approaches into a unified statistical framework is given. The proposed method is applied to the challenging task of detection and delineation of pediatric brain tumors. This segmentation task is characterized by a high non-uniformity of both the pathology and the surrounding non-pathologic brain tissue. A quantitative evaluation illustrates the robustness of the proposed method. Despite dealing with more complicated cases of pediatric brain tumors the results obtained are mostly better than those reported for current state-of-the-art approaches to 3-D MR brain tumor segmentation in adult patients. The entire processing of one multi-spectral data set does not require any user interaction, and takes less time than previously proposed methods.

Investigating existing medical CT segmentation techniques within automated baggage and package inspection

NASA Astrophysics Data System (ADS)

Megherbi, Najla; Breckon, Toby P.; Flitton, Greg T.

2013-10-01

3D Computed Tomography (CT) image segmentation is already well established tool in medical research and in routine daily clinical practice. However, such techniques have not been used in the context of 3D CT image segmentation for baggage and package security screening using CT imagery. CT systems are increasingly used in airports for security baggage examination. We propose in this contribution an investigation of the current 3D CT medical image segmentation methods for use in this new domain. Experimental results of 3D segmentation on real CT baggage security imagery using a range of techniques are presented and discussed.

'Designing Ambient Interactions - Pervasive Ergonomic Interfaces for Ageing Well' (DAI'10)

NASA Astrophysics Data System (ADS)

Geven, Arjan; Prost, Sebastian; Tscheligi, Manfred; Soldatos, John; Gonzalez, Mari Feli

The workshop will focus on novel computer based interaction mechanisms and interfaces, which boost natural interactivity and obviate the need for conventional tedious interfaces. Such interfaces are increasingly used in ambient intelligence environments and related applications, including application boosting elderly cognitive support, cognitive rehabilitation and Ambient Assisted Living (AAL). The aim of the workshop is to provide insights on the technological underpinnings of such interfaces, along with tools and techniques for their design and evaluation.

Using Boosting Decision Trees in Gravitational Wave Searches triggered by Gamma-ray Bursts

NASA Astrophysics Data System (ADS)

Zuraw, Sarah; LIGO Collaboration

2015-04-01

The search for gravitational wave bursts requires the ability to distinguish weak signals from background detector noise. Gravitational wave bursts are characterized by their transient nature, making them particularly difficult to detect as they are similar to non-Gaussian noise fluctuations in the detector. The Boosted Decision Tree method is a powerful machine learning algorithm which uses Multivariate Analysis techniques to explore high-dimensional data sets in order to distinguish between gravitational wave signal and background detector noise. It does so by training with known noise events and simulated gravitational wave events. The method is tested using waveform models and compared with the performance of the standard gravitational wave burst search pipeline for Gamma-ray Bursts. It is shown that the method is able to effectively distinguish between signal and background events under a variety of conditions and over multiple Gamma-ray Burst events. This example demonstrates the usefulness and robustness of the Boosted Decision Tree and Multivariate Analysis techniques as a detection method for gravitational wave bursts. LIGO, UMass, PREP, NEGAP.

Apparatus and method for compensating for electron beam emittance in synchronizing light sources

DOEpatents

Neil, George R.

1996-01-01

A focused optical beam is used to change the path length of the core electrons in electron light sources thereby boosting their efficiency of conversion of electron beam energy to light. Both coherent light in the free electron laser and incoherent light in the synchrotron is boosted by this technique. By changing the path length of the core electrons by the proper amount, the core electrons are caused to stay in phase with the electrons in the outer distribution of the electron beam. This increases the fraction of the electron beam energy that is converted to light thereby improving the efficiency of conversion of energy to light and therefore boosting the power output of the free electron laser and synchrotron.

Apparatus and method for compensating for electron beam emittance in synchronizing light sources

DOEpatents

Neil, G.R.

1996-07-30

A focused optical beam is used to change the path length of the core electrons in electron light sources thereby boosting their efficiency of conversion of electron beam energy to light. Both coherent light in the free electron laser and incoherent light in the synchrotron is boosted by this technique. By changing the path length of the core electrons by the proper amount, the core electrons are caused to stay in phase with the electrons in the outer distribution of the electron beam. This increases the fraction of the electron beam energy that is converted to light thereby improving the efficiency of conversion of energy to light and therefore boosting the power output of the free electron laser and synchrotron. 4 figs.

Evaluation of segmentation algorithms for optical coherence tomography images of ovarian tissue

NASA Astrophysics Data System (ADS)

Sawyer, Travis W.; Rice, Photini F. S.; Sawyer, David M.; Koevary, Jennifer W.; Barton, Jennifer K.

2018-02-01

Ovarian cancer has the lowest survival rate among all gynecologic cancers due to predominantly late diagnosis. Early detection of ovarian cancer can increase 5-year survival rates from 40% up to 92%, yet no reliable early detection techniques exist. Optical coherence tomography (OCT) is an emerging technique that provides depthresolved, high-resolution images of biological tissue in real time and demonstrates great potential for imaging of ovarian tissue. Mouse models are crucial to quantitatively assess the diagnostic potential of OCT for ovarian cancer imaging; however, due to small organ size, the ovaries must rst be separated from the image background using the process of segmentation. Manual segmentation is time-intensive, as OCT yields three-dimensional data. Furthermore, speckle noise complicates OCT images, frustrating many processing techniques. While much work has investigated noise-reduction and automated segmentation for retinal OCT imaging, little has considered the application to the ovaries, which exhibit higher variance and inhomogeneity than the retina. To address these challenges, we evaluated a set of algorithms to segment OCT images of mouse ovaries. We examined ve preprocessing techniques and six segmentation algorithms. While all pre-processing methods improve segmentation, Gaussian filtering is most effective, showing an improvement of 32% +/- 1.2%. Of the segmentation algorithms, active contours performs best, segmenting with an accuracy of 0.948 +/- 0.012 compared with manual segmentation (1.0 being identical). Nonetheless, further optimization could lead to maximizing the performance for segmenting OCT images of the ovaries.

Definitive radiotherapy for locally advanced squamous cell carcinoma of the vulva and technical issues: a case report

NASA Astrophysics Data System (ADS)

Kumar, Gokula; Norhafizah, I.; Shazril, I.; Nursyatina, AR; Aziz, MZ Abdul; Zin, Hafiz M.; Zakir, MK; Norjayadi; Norliza, AS; Ismail, A.; Khairun, N.

2017-05-01

This case report describes a complex radical 3D-Conformal Radiotherapy treatment planning, dosimetric issues and outcome of definitive treatment of un-resectable carcinoma of the vulvar in a 42-year old lady. The patient presented with large fungating mass of the vulva which was biopsy confirmed as Keratinizing Squamous Cell Carcinoma. Further staging investigation revealed locally advanced disease (T4), with bilateral inguinal lymph nodes involvement. There is no systemic metastasis or intra-pelvic nodes. The patient was seen by Gynae-Oncology team and the disease was deemed un-resectable without significant morbidity. She was treated to a total dose of 64.8Gy in 36 fractions over 7 weeks with concurrent weekly Cisplatinum in 2 phases. 3D-Conformal radiotherapy technique using the modified segmental boost technique (MSBT, large PA and small AP photon fields with inguinal electron matching) was used. TLD chips were used for in-vivo dose verification in phase 1 and 2 of the treatment. At completion of planned radiotherapy, patient had a complete clinical response, grade 2-3 skin toxicity, grade 2 rectal toxicity, and grade 2 dysuria Vulval Squamous Cell Carcinomas are very radiosensitive tumours and the skills of the treating Radiation Oncologist, Dosimetrists, Physicist, Radiation Therapist and also nurses is of foremost importance is ensuring good clinical outcomes.

[Management of locally advanced anal canal carcinoma with modulated arctherapy and concurrent chemotherapy].

PubMed

Troussier, I; Huguet, F; Servagi-Vernat, S; Benahim, C; Khalifa, J; Darmon, I; Ortholan, C; Krebs, L; Dejean, C; Fenoglietto, P; Vieillot, S; Bensadoun, R-J; Thariat, J

2015-04-01

The standard treatment of locally advanced (stage II and III) squamous cell carcinoma of the anal canal consists of concurrent chemoradiotherapy (two cycles of 5-fluoro-uracil, mitomycin C, on a 28-day cycle), with a dose of 45 Gy in 1.8 Gy per fraction in the prophylactic planning target volume and additional 14 to 20 Gy in the boost planning target volume (5 days per week) with a possibility of 15 days gap period between the two sequences. While conformal irradiation may only yield suboptimal tumor coverage using complex photon/electron field junctions (especially on nodal areas), intensity modulated radiation therapy techniques (segmented static, dynamic, volumetric modulated arc therapy and helical tomotherapy) allow better tumour coverage while sparing organs at risk from intermediate/high doses (small intestine, perineum/genitalia, bladder, pelvic bone, etc.). Such dosimetric advantages result in fewer severe acute toxicities and better potential to avoid a prolonged treatment break that increases risk of local failure. These techniques also allow a reduction in late gastrointestinal and skin toxicities of grade 3 or above, as well as better functional conservation of anorectal sphincter. The technical achievements (simulation, contouring, prescription dose, treatment planning, control quality) of volumetric modulated arctherapy are discussed. Copyright © 2015 Société française de radiothérapie oncologique (SFRO). Published by Elsevier SAS. All rights reserved.

Identification of boosted, hadronically decaying W bosons and comparisons with ATLAS data taken at √s = 8 TeV

DOE PAGES

Aad, G.; Abbott, B.; Abdallah, J.; ...

2016-03-17

This paper reports a detailed study of techniques for identifying boosted, hadronically decaying W bosons using 20.3 fb –1 of proton–proton collision data collected by the ATLAS detector at the LHC at a centre-of-mass energy √s = 8 TeV. A range of techniques for optimising the signal jet mass resolution are combined with various jet substructure variables. The results of these studies in Monte Carlo simulations show that a simple pairwise combination of groomed jet mass and one substructure variable can provide a 50 % efficiency for identifying W bosons with transverse momenta larger than 200 GeV while maintaining multijetmore » background efficiencies of 2–4 % for jets with the same transverse momentum. As a result, these signal and background efficiencies are confirmed in data for a selection of tagging techniques.« less

Identification of boosted, hadronically decaying W bosons and comparisons with ATLAS data taken at [Formula: see text] TeV.

PubMed

Aad, G; Abbott, B; Abdallah, J; Abdinov, O; Aben, R; Abolins, M; AbouZeid, O S; Abramowicz, H; Abreu, H; Abreu, R; Abulaiti, Y; Acharya, B S; Adamczyk, L; Adams, D L; Adelman, J; Adomeit, S; Adye, T; Affolder, A A; Agatonovic-Jovin, T; Agricola, J; Aguilar-Saavedra, J A; Ahlen, S P; Ahmadov, F; Aielli, G; Akerstedt, H; Åkesson, T P A; Akimov, A V; Alberghi, G L; Albert, J; Albrand, S; Alconada Verzini, M J; Aleksa, M; Aleksandrov, I N; Alexa, C; Alexander, G; Alexopoulos, T; Alhroob, M; Alimonti, G; Alio, L; Alison, J; Alkire, S P; Allbrooke, B M M; Allport, P P; Aloisio, A; Alonso, A; Alonso, F; Alpigiani, C; Altheimer, A; Alvarez Gonzalez, B; Álvarez Piqueras, D; Alviggi, M G; Amadio, B T; Amako, K; Amaral Coutinho, Y; Amelung, C; Amidei, D; Amor Dos Santos, S P; Amorim, A; Amoroso, S; Amram, N; Amundsen, G; Anastopoulos, C; Ancu, L S; Andari, N; Andeen, T; Anders, C F; Anders, G; Anders, J K; Anderson, K J; Andreazza, A; Andrei, V; Angelidakis, S; Angelozzi, I; Anger, P; Angerami, A; Anghinolfi, F; Anisenkov, A V; Anjos, N; Annovi, A; Antonelli, M; Antonov, A; Antos, J; Anulli, F; Aoki, M; Aperio Bella, L; Arabidze, G; Arai, Y; Araque, J P; Arce, A T H; Arduh, F A; Arguin, J-F; Argyropoulos, S; Arik, M; Armbruster, A J; Arnaez, O; Arnold, H; Arratia, M; Arslan, O; Artamonov, A; Artoni, G; Asai, S; Asbah, N; Ashkenazi, A; Åsman, B; Asquith, L; Assamagan, K; Astalos, R; Atkinson, M; Atlay, N B; Augsten, K; Aurousseau, M; Avolio, G; Axen, B; Ayoub, M K; Azuelos, G; Baak, M A; Baas, A E; Baca, M J; Bacci, C; Bachacou, H; Bachas, K; Backes, M; Backhaus, M; Bagiacchi, P; Bagnaia, P; Bai, Y; Bain, T; Baines, J T; Baker, O K; Baldin, E M; Balek, P; Balestri, T; Balli, F; Balunas, W K; Banas, E; Banerjee, Sw; Bannoura, A A E; Barak, L; Barberio, E L; Barberis, D; Barbero, M; Barillari, T; Barisonzi, M; Barklow, T; Barlow, N; Barnes, S L; Barnett, B M; Barnett, R M; Barnovska, Z; Baroncelli, A; Barone, G; Barr, A J; Barreiro, F; Barreiro Guimarães da Costa, J; Bartoldus, R; Barton, A E; Bartos, P; Basalaev, A; Bassalat, A; Basye, A; Bates, R L; Batista, S J; Batley, J R; Battaglia, M; Bauce, M; Bauer, F; Bawa, H S; Beacham, J B; Beattie, M D; Beau, T; Beauchemin, P H; Beccherle, R; Bechtle, P; Beck, H P; Becker, K; Becker, M; Beckingham, M; Becot, C; Beddall, A J; Beddall, A; Bednyakov, V A; Bee, C P; Beemster, L J; Beermann, T A; Begel, M; Behr, J K; Belanger-Champagne, C; Bell, W H; Bella, G; Bellagamba, L; Bellerive, A; Bellomo, M; Belotskiy, K; Beltramello, O; Benary, O; Benchekroun, D; Bender, M; Bendtz, K; Benekos, N; Benhammou, Y; Benhar Noccioli, E; Benitez Garcia, J A; Benjamin, D P; Bensinger, J R; Bentvelsen, S; Beresford, L; Beretta, M; Berge, D; Bergeaas Kuutmann, E; Berger, N; Berghaus, F; Beringer, J; Bernard, C; Bernard, N R; Bernius, C; Bernlochner, F U; Berry, T; Berta, P; Bertella, C; Bertoli, G; Bertolucci, F; Bertsche, C; Bertsche, D; Besana, M I; Besjes, G J; Bessidskaia Bylund, O; Bessner, M; Besson, N; Betancourt, C; Bethke, S; Bevan, A J; 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Winklmeier, F; Winter, B T; Wittgen, M; Wittkowski, J; Wollstadt, S J; Wolter, M W; Wolters, H; Wosiek, B K; Wotschack, J; Woudstra, M J; Wozniak, K W; Wu, M; Wu, M; Wu, S L; Wu, X; Wu, Y; Wyatt, T R; Wynne, B M; Xella, S; Xu, D; Xu, L; Yabsley, B; Yacoob, S; Yakabe, R; Yamada, M; Yamaguchi, D; Yamaguchi, Y; Yamamoto, A; Yamamoto, S; Yamanaka, T; Yamauchi, K; Yamazaki, Y; Yan, Z; Yang, H; Yang, H; Yang, Y; Yao, W-M; Yap, Y C; Yasu, Y; Yatsenko, E; Yau Wong, K H; Ye, J; Ye, S; Yeletskikh, I; Yen, A L; Yildirim, E; Yorita, K; Yoshida, R; Yoshihara, K; Young, C; Young, C J S; Youssef, S; Yu, D R; Yu, J; Yu, J M; Yu, J; Yuan, L; Yuen, S P Y; Yurkewicz, A; Yusuff, I; Zabinski, B; Zaidan, R; Zaitsev, A M; Zalieckas, J; Zaman, A; Zambito, S; Zanello, L; Zanzi, D; Zeitnitz, C; Zeman, M; Zemla, A; Zeng, Q; Zengel, K; Zenin, O; Ženiš, T; Zerwas, D; Zhang, D; Zhang, F; Zhang, G; Zhang, H; Zhang, J; Zhang, L; Zhang, R; Zhang, X; Zhang, Z; Zhao, X; Zhao, Y; Zhao, Z; Zhemchugov, A; Zhong, J; Zhou, B; Zhou, C; Zhou, L; Zhou, L; Zhou, M; Zhou, N; Zhu, C G; Zhu, H; Zhu, J; Zhu, Y; Zhuang, X; Zhukov, K; Zibell, A; Zieminska, D; Zimine, N I; Zimmermann, C; Zimmermann, S; Zinonos, Z; Zinser, M; Ziolkowski, M; Živković, L; Zobernig, G; Zoccoli, A; Zur Nedden, M; Zurzolo, G; Zwalinski, L

This paper reports a detailed study of techniques for identifying boosted, hadronically decaying W bosons using 20.3 fb[Formula: see text] of proton-proton collision data collected by the ATLAS detector at the LHC at a centre-of-mass energy [Formula: see text]. A range of techniques for optimising the signal jet mass resolution are combined with various jet substructure variables. The results of these studies in Monte Carlo simulations show that a simple pairwise combination of groomed jet mass and one substructure variable can provide a 50 % efficiency for identifying W bosons with transverse momenta larger than 200 GeV while maintaining multijet background efficiencies of 2-4 % for jets with the same transverse momentum. These signal and background efficiencies are confirmed in data for a selection of tagging techniques.

Learning Compositional Shape Models of Multiple Distance Metrics by Information Projection.

PubMed

Luo, Ping; Lin, Liang; Liu, Xiaobai

2016-07-01

This paper presents a novel compositional contour-based shape model by incorporating multiple distance metrics to account for varying shape distortions or deformations. Our approach contains two key steps: 1) contour feature generation and 2) generative model pursuit. For each category, we first densely sample an ensemble of local prototype contour segments from a few positive shape examples and describe each segment using three different types of distance metrics. These metrics are diverse and complementary with each other to capture various shape deformations. We regard the parameterized contour segment plus an additive residual ϵ as a basic subspace, namely, ϵ -ball, in the sense that it represents local shape variance under the certain distance metric. Using these ϵ -balls as features, we then propose a generative learning algorithm to pursue the compositional shape model, which greedily selects the most representative features under the information projection principle. In experiments, we evaluate our model on several public challenging data sets, and demonstrate that the integration of multiple shape distance metrics is capable of dealing various shape deformations, articulations, and background clutter, hence boosting system performance.

Constrained Deep Weak Supervision for Histopathology Image Segmentation.

PubMed

Jia, Zhipeng; Huang, Xingyi; Chang, Eric I-Chao; Xu, Yan

2017-11-01

In this paper, we develop a new weakly supervised learning algorithm to learn to segment cancerous regions in histopathology images. This paper is under a multiple instance learning (MIL) framework with a new formulation, deep weak supervision (DWS); we also propose an effective way to introduce constraints to our neural networks to assist the learning process. The contributions of our algorithm are threefold: 1) we build an end-to-end learning system that segments cancerous regions with fully convolutional networks (FCNs) in which image-to-image weakly-supervised learning is performed; 2) we develop a DWS formulation to exploit multi-scale learning under weak supervision within FCNs; and 3) constraints about positive instances are introduced in our approach to effectively explore additional weakly supervised information that is easy to obtain and enjoy a significant boost to the learning process. The proposed algorithm, abbreviated as DWS-MIL, is easy to implement and can be trained efficiently. Our system demonstrates the state-of-the-art results on large-scale histopathology image data sets and can be applied to various applications in medical imaging beyond histopathology images, such as MRI, CT, and ultrasound images.

Denoising and segmentation of retinal layers in optical coherence tomography images

NASA Astrophysics Data System (ADS)

Dash, Puspita; Sigappi, A. N.

2018-04-01

Optical Coherence Tomography (OCT) is an imaging technique used to localize the intra-retinal boundaries for the diagnostics of macular diseases. Due to speckle noise, low image contrast and accurate segmentation of individual retinal layers is difficult. Due to this, a method for retinal layer segmentation from OCT images is presented. This paper proposes a pre-processing filtering approach for denoising and segmentation methods for segmenting retinal layers OCT images using graph based segmentation technique. These techniques are used for segmentation of retinal layers for normal as well as patients with Diabetic Macular Edema. The algorithm based on gradient information and shortest path search is applied to optimize the edge selection. In this paper the four main layers of the retina are segmented namely Internal limiting membrane (ILM), Retinal pigment epithelium (RPE), Inner nuclear layer (INL) and Outer nuclear layer (ONL). The proposed method is applied on a database of OCT images of both ten normal and twenty DME affected patients and the results are found to be promising.

Treatment plan comparison between Tri-Co-60 magnetic-resonance image-guided radiation therapy and volumetric modulated arc therapy for prostate cancer

PubMed Central

Park, Jong Min; Park, So-Yeon; Choi, Chang Heon; Chun, Minsoo; Kim, Jin Ho; Kim, Jung-In

2017-01-01

To investigate the plan quality of tri-Co-60 intensity-modulated radiation therapy (IMRT) with magnetic-resonance image-guided radiation therapy compared with volumetric-modulated arc therapy (VMAT) for prostate cancer. Twenty patients with intermediate-risk prostate cancer, who received radical VMAT were selected. Additional tri-Co-60 IMRT plans were generated for each patient. Both primary and boost plans were generated with tri-Co-60 IMRT and VMAT techniques. The prescription doses of the primary and boost plans were 50.4 Gy and 30.6 Gy, respectively. The primary and boost planning target volumes (PTVs) of the tri-Co-60 IMRT were generated with 3 mm margins from the primary clinical target volume (CTV, prostate + seminal vesicle) and a boost CTV (prostate), respectively. VMAT had a primary planning target volume (primary CTV + 1 cm or 2 cm margins) and a boost PTV (boost CTV + 0.7 cm margins), respectively. For both tri-Co-60 IMRT and VMAT, all the primary and boost plans were generated that 95% of the target volumes would be covered by the 100% of the prescription doses. Sum plans were generated by summation of primary and boost plans. In sum plans, the average values of V70 Gy of the bladder of tri-Co-60 IMRT vs. VMAT were 4.0% ± 3.1% vs. 10.9% ± 6.7%, (p < 0.001). Average values of V70 Gy of the rectum of tri-Co-60 IMRT vs. VMAT were 5.2% ± 1.8% vs. 19.1% ± 4.0% (p < 0.001). The doses of tri-Co-60 IMRT delivered to the bladder and rectum were smaller than those of VMAT while maintaining identical target coverage in both plans. PMID:29207634

Improving LHC searches for dark photons using lepton-jet substructure

NASA Astrophysics Data System (ADS)

Barello, G.; Chang, Spencer; Newby, Christopher A.; Ostdiek, Bryan

2017-03-01

Collider signals of dark photons are an exciting probe for new gauge forces and are characterized by events with boosted lepton jets. Existing techniques are efficient in searching for muonic lepton jets but due to substantial backgrounds have difficulty constraining lepton jets containing only electrons. This is unfortunate since upcoming intensity frontier experiments are sensitive to dark photon masses which only allow electron decays. Analyzing a recently proposed model of kinetic mixing, with new scalar particles decaying into dark photons, we find that existing techniques for electron jets can be substantially improved. We show that using lepton-jet-substructure variables, in association with a boosted decision tree, improves background rejection, significantly increasing the LHC's reach for dark photons in this region of parameter space.

Paraesthesia during the needle-through-needle and the double segment technique for combined spinal epidural anaesthesia.

PubMed

Ahn, H J; Choi, D H; Kim, C S

2006-07-01

Paraesthesia during regional anaesthesia is an unpleasant sensation for patients and, more importantly, in some cases it is related to neurological injury. Relatively few studies have been conducted on the frequency of paraesthesia during combined spinal epidural anaesthesia. We compared two combined spinal epidural anaesthesia techniques: the needle-through-needle technique and the double segment technique in this respect. We randomly allocated 116 parturients undergoing elective Caesarean section to receive anaesthesia using one of these techniques. Both techniques were performed using a 27G pencil point needle, an 18G Tuohy needle, and a 20G multiport epidural catheter from the same manufacturer. The overall frequency of paraesthesia was higher in the needle-through-needle technique group (56.9% vs. 31.6%, p = 0.011). The frequency of paraesthesia at spinal needle insertion was 20.7% in the needle-through-needle technique group and 8.8% in the double segment technique group; whereas the frequency of paraesthesia at epidural catheter insertion was 46.6% in the needle-through-needle technique group and 24.6% in the double segment technique group.

Simulation comparison of proportional integral derivative and fuzzy logic in controlling AC-DC buck boost converter

NASA Astrophysics Data System (ADS)

Faisal, A.; Hasan, S.; Suherman

2018-03-01

AC-DC converter is widely used in the commercial industry even for daily purposes. The AC-DC converter is used to convert AC voltage into DC. In order to obtain the desired output voltage, the converter usually has a controllable regulator. This paper discusses buck boost regulator with a power MOSFET as switching component which is adjusted based on the duty cycle of pulse width modulation (PWM). The main problems of the buck boost converter at start up are the high overshoot, the long peak time and rise time. This paper compares the effectiveness of two control techniques: proportional integral derivative (PID) and fuzzy logic control in controlling the buck boost converter through simulations. The results show that the PID is more sensitive to voltage change than fuzzy logic. However, PID generates higher overshoot, long peak time and rise time. On the other hand, fuzzy logic generates no overshoot and shorter rise time.

State-plane analysis of zero-voltage-switching resonant dc/dc power converters

NASA Astrophysics Data System (ADS)

Kazimierczuk, Marian K.; Morse, William D.

The state-plane analysis technique for the zero-voltage-switching resonant dc/dc power converter family of topologies, namely the buck, boost, buck-boost, and Cuk converters is established. The state plane provides a compression of information that allows the designer to uniquely examine the nonlinear dynamics of resonant converter operation. Utilizing the state plane, resonant converter modes of operation are examined and the switching frequencies are derived for the boundaries between these modes, including the boundary of energy conversion.

Modeling of switching regulator power stages with and without zero-inductor-current dwell time

NASA Technical Reports Server (NTRS)

Lee, F. C.; Yu, Y.; Triner, J. E.

1976-01-01

State space techniques are employed to derive accurate models for buck, boost, and buck/boost converter power stages operating with and without zero-inductor-current dwell time. A generalized procedure is developed which treats the continuous-inductor-current mode without the dwell time as a special case of the discontinuous-current mode, when the dwell time vanishes. An abrupt change of system behavior including a reduction of the system order when the dwell time appears is shown both analytically and experimentally.

Dynamics of action potential backpropagation in basal dendrites of prefrontal cortical pyramidal neurons.

PubMed

Zhou, Wen-Liang; Yan, Ping; Wuskell, Joseph P; Loew, Leslie M; Antic, Srdjan D

2008-02-01

Basal dendrites of neocortical pyramidal neurons are relatively short and directly attached to the cell body. This allows electrical signals arising in basal dendrites to strongly influence the neuronal output. Likewise, somatic action potentials (APs) should readily propagate back into the basilar dendritic tree to influence synaptic plasticity. Two recent studies, however, determined that sodium APs are severely attenuated in basal dendrites of cortical pyramidal cells, so that they completely fail in distal dendritic segments. Here we used the latest improvements in the voltage-sensitive dye imaging technique (Zhou et al., 2007) to study AP backpropagation in basal dendrites of layer 5 pyramidal neurons of the rat prefrontal cortex. With a signal-to-noise ratio of > 15 and minimal temporal averaging (only four sweeps) we were able to sample AP waveforms from the very last segments of individual dendritic branches (dendritic tips). We found that in short- (< 150 microm) and medium (150-200 microm in length)-range basal dendrites APs backpropagated with modest changes in AP half-width or AP rise-time. The lack of substantial changes in AP shape and dynamics of rise is inconsistent with the AP-failure model. The lack of substantial amplitude boosting of the third AP in the high-frequency burst also suggests that in short- and medium-range basal dendrites backpropagating APs were not severely attenuated. Our results show that the AP-failure concept does not apply in all basal dendrites of the rat prefrontal cortex. The majority of synaptic contacts in the basilar dendritic tree actually received significant AP-associated electrical and calcium transients.

Tracking down hyper-boosted top quarks

DOE PAGES

Larkoski, Andrew J.; Maltoni, Fabio; Selvaggi, Michele

2015-06-05

The identification of hadronically decaying heavy states, such as vector bosons, the Higgs, or the top quark, produced with large transverse boosts has been and will continue to be a central focus of the jet physics program at the Large Hadron Collider (LHC). At a future hadron collider working at an order-of-magnitude larger energy than the LHC, these heavy states would be easily produced with transverse boosts of several TeV. At these energies, their decay products will be separated by angular scales comparable to individual calorimeter cells, making the current jet substructure identification techniques for hadronic decay modes not directlymore » employable. In addition, at the high energy and luminosity projected at a future hadron collider, there will be numerous sources for contamination including initial- and final-state radiation, underlying event, or pile-up which must be mitigated. We propose a simple strategy to tag such "hyper-boosted" objects that defines jets with radii that scale inversely proportional to their transverse boost and combines the standard calorimetric information with charged track-based observables. By means of a fast detector simulation, we apply it to top quark identification and demonstrate that our method efficiently discriminates hadronically decaying top quarks from light QCD jets up to transverse boosts of 20 TeV. Lastly, our results open the way to tagging heavy objects with energies in the multi-TeV range at present and future hadron colliders.« less

A hybrid approach of using symmetry technique for brain tumor segmentation.

PubMed

Saddique, Mubbashar; Kazmi, Jawad Haider; Qureshi, Kalim

2014-01-01

Tumor and related abnormalities are a major cause of disability and death worldwide. Magnetic resonance imaging (MRI) is a superior modality due to its noninvasiveness and high quality images of both the soft tissues and bones. In this paper we present two hybrid segmentation techniques and their results are compared with well-recognized techniques in this area. The first technique is based on symmetry and we call it a hybrid algorithm using symmetry and active contour (HASA). In HASA, we take refection image, calculate the difference image, and then apply the active contour on the difference image to segment the tumor. To avoid unimportant segmented regions, we improve the results by proposing an enhancement in the form of the second technique, EHASA. In EHASA, we also take reflection of the original image, calculate the difference image, and then change this image into a binary image. This binary image is mapped onto the original image followed by the application of active contouring to segment the tumor region.

Strain analysis in CRT candidates using the novel segment length in cine (SLICE) post-processing technique on standard CMR cine images.

PubMed

Zweerink, Alwin; Allaart, Cornelis P; Kuijer, Joost P A; Wu, LiNa; Beek, Aernout M; van de Ven, Peter M; Meine, Mathias; Croisille, Pierre; Clarysse, Patrick; van Rossum, Albert C; Nijveldt, Robin

2017-12-01

Although myocardial strain analysis is a potential tool to improve patient selection for cardiac resynchronization therapy (CRT), there is currently no validated clinical approach to derive segmental strains. We evaluated the novel segment length in cine (SLICE) technique to derive segmental strains from standard cardiovascular MR (CMR) cine images in CRT candidates. Twenty-seven patients with left bundle branch block underwent CMR examination including cine imaging and myocardial tagging (CMR-TAG). SLICE was performed by measuring segment length between anatomical landmarks throughout all phases on short-axis cines. This measure of frame-to-frame segment length change was compared to CMR-TAG circumferential strain measurements. Subsequently, conventional markers of CRT response were calculated. Segmental strains showed good to excellent agreement between SLICE and CMR-TAG (septum strain, intraclass correlation coefficient (ICC) 0.76; lateral wall strain, ICC 0.66). Conventional markers of CRT response also showed close agreement between both methods (ICC 0.61-0.78). Reproducibility of SLICE was excellent for intra-observer testing (all ICC ≥0.76) and good for interobserver testing (all ICC ≥0.61). The novel SLICE post-processing technique on standard CMR cine images offers both accurate and robust segmental strain measures compared to the 'gold standard' CMR-TAG technique, and has the advantage of being widely available. • Myocardial strain analysis could potentially improve patient selection for CRT. • Currently a well validated clinical approach to derive segmental strains is lacking. • The novel SLICE technique derives segmental strains from standard CMR cine images. • SLICE-derived strain markers of CRT response showed close agreement with CMR-TAG. • Future studies will focus on the prognostic value of SLICE in CRT candidates.

Learning to rank atlases for multiple-atlas segmentation.

PubMed

Sanroma, Gerard; Wu, Guorong; Gao, Yaozong; Shen, Dinggang

2014-10-01

Recently, multiple-atlas segmentation (MAS) has achieved a great success in the medical imaging area. The key assumption is that multiple atlases have greater chances of correctly labeling a target image than a single atlas. However, the problem of atlas selection still remains unexplored. Traditionally, image similarity is used to select a set of atlases. Unfortunately, this heuristic criterion is not necessarily related to the final segmentation performance. To solve this seemingly simple but critical problem, we propose a learning-based atlas selection method to pick up the best atlases that would lead to a more accurate segmentation. Our main idea is to learn the relationship between the pairwise appearance of observed instances (i.e., a pair of atlas and target images) and their final labeling performance (e.g., using the Dice ratio). In this way, we select the best atlases based on their expected labeling accuracy. Our atlas selection method is general enough to be integrated with any existing MAS method. We show the advantages of our atlas selection method in an extensive experimental evaluation in the ADNI, SATA, IXI, and LONI LPBA40 datasets. As shown in the experiments, our method can boost the performance of three widely used MAS methods, outperforming other learning-based and image-similarity-based atlas selection methods.

The Lateral Decubitus Breast Boost: Description, Rationale, and Efficacy

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

Ludwig, Michelle S., E-mail: mludwig@mdanderson.or; McNeese, Marsha D.; Buchholz, Thomas A.

2010-01-15

Purpose: To describe and evaluate the modified lateral decubitus boost, a breast irradiation technique. Patients are repositioned and resimulated for electron boost to minimize the necessary depth for the electron beam and optimize target volume coverage. Methods and Materials: A total of 2,606 patients were treated with post-lumpectomy radiation at our institution between January 1, 2000, and February 1, 2008. Of these, 231 patients underwent resimulation in the lateral decubitus position with electron boost. Distance from skin to the maximal depth of target volume was measured in both the original and boost plans. Age, body mass index (BMI), boost electronmore » energy, and skin reaction were evaluated. Results: Resimulation in the lateral decubitus position reduced the distance from skin to maximal target volume depth in all patients. Average depth reduction by repositioning was 2.12 cm, allowing for an average electron energy reduction of approximately 7 MeV. Mean skin entrance dose was reduced from about 90% to about 85% (p < 0.001). Only 14 patients (6%) experienced moist desquamation in the boost field at the end of treatment. Average BMI of these patients was 30.4 (range, 17.8-50.7). BMI greater than 30 was associated with more depth reduction by repositioning and increased risk of moist desquamation. Conclusions: The lateral decubitus position allows for a decrease in the distance from the skin to the target volume depth, improving electron coverage of the tumor bed while reducing skin entrance dose. This is a well-tolerated regimen for a patient population with a high BMI or deep tumor location.« less

Multi-Sectional Views Textural Based SVM for MS Lesion Segmentation in Multi-Channels MRIs

PubMed Central

Abdullah, Bassem A; Younis, Akmal A; John, Nigel M

2012-01-01

In this paper, a new technique is proposed for automatic segmentation of multiple sclerosis (MS) lesions from brain magnetic resonance imaging (MRI) data. The technique uses a trained support vector machine (SVM) to discriminate between the blocks in regions of MS lesions and the blocks in non-MS lesion regions mainly based on the textural features with aid of the other features. The classification is done on each of the axial, sagittal and coronal sectional brain view independently and the resultant segmentations are aggregated to provide more accurate output segmentation. The main contribution of the proposed technique described in this paper is the use of textural features to detect MS lesions in a fully automated approach that does not rely on manually delineating the MS lesions. In addition, the technique introduces the concept of the multi-sectional view segmentation to produce verified segmentation. The proposed textural-based SVM technique was evaluated using three simulated datasets and more than fifty real MRI datasets. The results were compared with state of the art methods. The obtained results indicate that the proposed method would be viable for use in clinical practice for the detection of MS lesions in MRI. PMID:22741026

Digital image enhancement techniques used in some ERTS application problems. [geology, geomorphology, and oceanography

NASA Technical Reports Server (NTRS)

Goetz, A. F. H.; Billingsley, F. C.

1974-01-01

Enhancements discussed include contrast stretching, multiratio color displays, Fourier plane operations to remove striping and boosting MTF response to enhance high spatial frequency content. The use of each technique in a specific application in the fields of geology, geomorphology and oceanography is demonstrated.

Understanding and Mitigating Forum Spam

ERIC Educational Resources Information Center

Shin, Youngsang

2011-01-01

The Web is large and expanding, making it challenging to attract new visitors to websites. Website operators often use Search Engine Optimization (SEO) techniques to boost the search engine rankings of their sites, thereby maximizing the inflow of visitors. Malicious operators take SEO to the extreme through many unsavory techniques that are often…

Maximization of the usage of coronary CTA derived plaque information using a machine learning based algorithm to improve risk stratification; insights from the CONFIRM registry.

PubMed

van Rosendael, Alexander R; Maliakal, Gabriel; Kolli, Kranthi K; Beecy, Ashley; Al'Aref, Subhi J; Dwivedi, Aeshita; Singh, Gurpreet; Panday, Mohit; Kumar, Amit; Ma, Xiaoyue; Achenbach, Stephan; Al-Mallah, Mouaz H; Andreini, Daniele; Bax, Jeroen J; Berman, Daniel S; Budoff, Matthew J; Cademartiri, Filippo; Callister, Tracy Q; Chang, Hyuk-Jae; Chinnaiyan, Kavitha; Chow, Benjamin J W; Cury, Ricardo C; DeLago, Augustin; Feuchtner, Gudrun; Hadamitzky, Martin; Hausleiter, Joerg; Kaufmann, Philipp A; Kim, Yong-Jin; Leipsic, Jonathon A; Maffei, Erica; Marques, Hugo; Pontone, Gianluca; Raff, Gilbert L; Rubinshtein, Ronen; Shaw, Leslee J; Villines, Todd C; Gransar, Heidi; Lu, Yao; Jones, Erica C; Peña, Jessica M; Lin, Fay Y; Min, James K

Machine learning (ML) is a field in computer science that demonstrated to effectively integrate clinical and imaging data for the creation of prognostic scores. The current study investigated whether a ML score, incorporating only the 16 segment coronary tree information derived from coronary computed tomography angiography (CCTA), provides enhanced risk stratification compared with current CCTA based risk scores. From the multi-center CONFIRM registry, patients were included with complete CCTA risk score information and ≥3 year follow-up for myocardial infarction and death (primary endpoint). Patients with prior coronary artery disease were excluded. Conventional CCTA risk scores (conventional CCTA approach, segment involvement score, duke prognostic index, segment stenosis score, and the Leaman risk score) and a score created using ML were compared for the area under the receiver operating characteristic curve (AUC). Only 16 segment based coronary stenosis (0%, 1-24%, 25-49%, 50-69%, 70-99% and 100%) and composition (calcified, mixed and non-calcified plaque) were provided to the ML model. A boosted ensemble algorithm (extreme gradient boosting; XGBoost) was used and the entire data was randomly split into a training set (80%) and testing set (20%). First, tuned hyperparameters were used to generate a trained model from the training data set (80% of data). Second, the performance of this trained model was independently tested on the unseen test set (20% of data). In total, 8844 patients (mean age 58.0 ± 11.5 years, 57.7% male) were included. During a mean follow-up time of 4.6 ± 1.5 years, 609 events occurred (6.9%). No CAD was observed in 48.7% (3.5% event), non-obstructive CAD in 31.8% (6.8% event), and obstructive CAD in 19.5% (15.6% event). Discrimination of events as expressed by AUC was significantly better for the ML based approach (0.771) vs the other scores (ranging from 0.685 to 0.701), P < 0.001. Net reclassification improvement analysis showed that the improved risk stratification was the result of down-classification of risk among patients that did not experience events (non-events). A risk score created by a ML based algorithm, that utilizes standard 16 coronary segment stenosis and composition information derived from detailed CCTA reading, has greater prognostic accuracy than current CCTA integrated risk scores. These findings indicate that a ML based algorithm can improve the integration of CCTA derived plaque information to improve risk stratification. Published by Elsevier Inc.

Breast conserving treatment for breast cancer: dosimetric comparison of different non-invasive techniques for additional boost delivery

PubMed Central

2014-01-01

Background Today it is unclear which technique for delivery of an additional boost after whole breast radiotherapy for breast conserved patients should be state of the art. We present a dosimetric comparison of different non-invasive treatment techniques for additional boost delivery. Methods For 10 different tumor bed localizations, 7 different non-invasive treatment plans were made. Dosimetric comparison of PTV-coverage and dose to organs at risk was performed. Results The Vero system achieved an excellent PTV-coverage and at the same time could minimize the dose to the organs at risk with an average near-maximum-dose (D2) to the heart of 0.9 Gy and the average volume of ipsilateral lung receiving 5 Gy (V5) of 1.5%. The TomoTherapy modalities delivered an average D2 to the heart of 0.9 Gy for the rotational and of 2.3 Gy for the static modality and an average V5 to the ipsilateral lung of 7.3% and 2.9% respectively. A rotational technique offers an adequate conformity at the cost of more low dose spread and a larger build-up area. In most cases a 2-field technique showed acceptable PTV-coverage, but a bad conformity. Electrons often delivered a worse PTV-coverage than photons, with the planning requirements achieved only in 2 patients and with an average D2 to the heart of 2.8 Gy and an average V5 to the ipsilateral lung of 5.8%. Conclusions We present advices which can be used as guidelines for the selection of the best individualized treatment. PMID:24467916

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

Jabbari, Siavash; Weinberg, Vivian K.; Kaprealian, Tania

Purpose: High dose rate (HDR) brachytherapy has been established as an excellent monotherapy or after external-beam radiotherapy (EBRT) boost treatment for prostate cancer (PCa). Recently, dosimetric studies have demonstrated the potential for achieving similar dosimetry with stereotactic body radiotherapy (SBRT) compared with HDR brachytherapy. Here, we report our technique, PSA nadir, and acute and late toxicity with SBRT as monotherapy and post-EBRT boost for PCa using HDR brachytherapy fractionation. Patients and Methods: To date, 38 patients have been treated with SBRT at University of California-San Francisco with a minimum follow-up of 12 months. Twenty of 38 patients were treated withmore » SBRT monotherapy (9.5 Gy Multiplication-Sign 4 fractions), and 18 were treated with SBRT boost (9.5 Gy Multiplication-Sign 2 fractions) post-EBRT and androgen deprivation therapy. PSA nadir to date for 44 HDR brachytherapy boost patients with disease characteristics similar to the SBRT boost cohort was also analyzed as a descriptive comparison. Results: SBRT was well tolerated. With a median follow-up of 18.3 months (range, 12.6-43.5), 42% and 11% of patients had acute Grade 2 gastrourinary and gastrointestinal toxicity, respectively, with no Grade 3 or higher acute toxicity to date. Two patients experienced late Grade 3 GU toxicity. All patients are without evidence of biochemical or clinical progression to date, and favorably low PSA nadirs have been observed with a current median PSA nadir of 0.35 ng/mL (range, <0.01-2.1) for all patients (0.47 ng/mL, range, 0.2-2.1 for the monotherapy cohort; 0.10 ng/mL, range, 0.01-0.5 for the boost cohort). With a median follow-up of 48.6 months (range, 16.4-87.8), the comparable HDR brachytherapy boost cohort has achieved a median PSA nadir of 0.09 ng/mL (range, 0.0-3.3). Conclusions: Early results with SBRT monotherapy and post-EBRT boost for PCa demonstrate acceptable PSA response and minimal toxicity. PSA nadir with SBRT boost appears comparable to those achieved with HDR brachytherapy boost.« less

Using wavelet denoising and mathematical morphology in the segmentation technique applied to blood cells images.

PubMed

Boix, Macarena; Cantó, Begoña

2013-04-01

Accurate image segmentation is used in medical diagnosis since this technique is a noninvasive pre-processing step for biomedical treatment. In this work we present an efficient segmentation method for medical image analysis. In particular, with this method blood cells can be segmented. For that, we combine the wavelet transform with morphological operations. Moreover, the wavelet thresholding technique is used to eliminate the noise and prepare the image for suitable segmentation. In wavelet denoising we determine the best wavelet that shows a segmentation with the largest area in the cell. We study different wavelet families and we conclude that the wavelet db1 is the best and it can serve for posterior works on blood pathologies. The proposed method generates goods results when it is applied on several images. Finally, the proposed algorithm made in MatLab environment is verified for a selected blood cells.

Segmentation of deformable organs from medical images using particle swarm optimization and nonlinear shape priors

NASA Astrophysics Data System (ADS)

Afifi, Ahmed; Nakaguchi, Toshiya; Tsumura, Norimichi

2010-03-01

In many medical applications, the automatic segmentation of deformable organs from medical images is indispensable and its accuracy is of a special interest. However, the automatic segmentation of these organs is a challenging task according to its complex shape. Moreover, the medical images usually have noise, clutter, or occlusion and considering the image information only often leads to meager image segmentation. In this paper, we propose a fully automated technique for the segmentation of deformable organs from medical images. In this technique, the segmentation is performed by fitting a nonlinear shape model with pre-segmented images. The kernel principle component analysis (KPCA) is utilized to capture the complex organs deformation and to construct the nonlinear shape model. The presegmentation is carried out by labeling each pixel according to its high level texture features extracted using the overcomplete wavelet packet decomposition. Furthermore, to guarantee an accurate fitting between the nonlinear model and the pre-segmented images, the particle swarm optimization (PSO) algorithm is employed to adapt the model parameters for the novel images. In this paper, we demonstrate the competence of proposed technique by implementing it to the liver segmentation from computed tomography (CT) scans of different patients.

Semi-automated Neuron Boundary Detection and Nonbranching Process Segmentation in Electron Microscopy Images

PubMed Central

Jurrus, Elizabeth; Watanabe, Shigeki; Giuly, Richard J.; Paiva, Antonio R. C.; Ellisman, Mark H.; Jorgensen, Erik M.; Tasdizen, Tolga

2013-01-01

Neuroscientists are developing new imaging techniques and generating large volumes of data in an effort to understand the complex structure of the nervous system. The complexity and size of this data makes human interpretation a labor-intensive task. To aid in the analysis, new segmentation techniques for identifying neurons in these feature rich datasets are required. This paper presents a method for neuron boundary detection and nonbranching process segmentation in electron microscopy images and visualizing them in three dimensions. It combines both automated segmentation techniques with a graphical user interface for correction of mistakes in the automated process. The automated process first uses machine learning and image processing techniques to identify neuron membranes that deliniate the cells in each two-dimensional section. To segment nonbranching processes, the cell regions in each two-dimensional section are connected in 3D using correlation of regions between sections. The combination of this method with a graphical user interface specially designed for this purpose, enables users to quickly segment cellular processes in large volumes. PMID:22644867

Semi-Automated Neuron Boundary Detection and Nonbranching Process Segmentation in Electron Microscopy Images

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

Jurrus, Elizabeth R.; Watanabe, Shigeki; Giuly, Richard J.

2013-01-01

Neuroscientists are developing new imaging techniques and generating large volumes of data in an effort to understand the complex structure of the nervous system. The complexity and size of this data makes human interpretation a labor-intensive task. To aid in the analysis, new segmentation techniques for identifying neurons in these feature rich datasets are required. This paper presents a method for neuron boundary detection and nonbranching process segmentation in electron microscopy images and visualizing them in three dimensions. It combines both automated segmentation techniques with a graphical user interface for correction of mistakes in the automated process. The automated processmore » first uses machine learning and image processing techniques to identify neuron membranes that deliniate the cells in each two-dimensional section. To segment nonbranching processes, the cell regions in each two-dimensional section are connected in 3D using correlation of regions between sections. The combination of this method with a graphical user interface specially designed for this purpose, enables users to quickly segment cellular processes in large volumes.« less

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.

Detection and measurement of fetal anatomies from ultrasound images using a constrained probabilistic boosting tree.

PubMed

Carneiro, Gustavo; Georgescu, Bogdan; Good, Sara; Comaniciu, Dorin

2008-09-01

We propose a novel method for the automatic detection and measurement of fetal anatomical structures in ultrasound images. This problem offers a myriad of challenges, including: difficulty of modeling the appearance variations of the visual object of interest, robustness to speckle noise and signal dropout, and large search space of the detection procedure. Previous solutions typically rely on the explicit encoding of prior knowledge and formulation of the problem as a perceptual grouping task solved through clustering or variational approaches. These methods are constrained by the validity of the underlying assumptions and usually are not enough to capture the complex appearances of fetal anatomies. We propose a novel system for fast automatic detection and measurement of fetal anatomies that directly exploits a large database of expert annotated fetal anatomical structures in ultrasound images. Our method learns automatically to distinguish between the appearance of the object of interest and background by training a constrained probabilistic boosting tree classifier. This system is able to produce the automatic segmentation of several fetal anatomies using the same basic detection algorithm. We show results on fully automatic measurement of biparietal diameter (BPD), head circumference (HC), abdominal circumference (AC), femur length (FL), humerus length (HL), and crown rump length (CRL). Notice that our approach is the first in the literature to deal with the HL and CRL measurements. Extensive experiments (with clinical validation) show that our system is, on average, close to the accuracy of experts in terms of segmentation and obstetric measurements. Finally, this system runs under half second on a standard dual-core PC computer.

Automatic segmentation of lung parenchyma based on curvature of ribs using HRCT images in scleroderma studies

NASA Astrophysics Data System (ADS)

Prasad, M. N.; Brown, M. S.; Ahmad, S.; Abtin, F.; Allen, J.; da Costa, I.; Kim, H. J.; McNitt-Gray, M. F.; Goldin, J. G.

2008-03-01

Segmentation of lungs in the setting of scleroderma is a major challenge in medical image analysis. Threshold based techniques tend to leave out lung regions that have increased attenuation, for example in the presence of interstitial lung disease or in noisy low dose CT scans. The purpose of this work is to perform segmentation of the lungs using a technique that selects an optimal threshold for a given scleroderma patient by comparing the curvature of the lung boundary to that of the ribs. Our approach is based on adaptive thresholding and it tries to exploit the fact that the curvature of the ribs and the curvature of the lung boundary are closely matched. At first, the ribs are segmented and a polynomial is used to represent the ribs' curvature. A threshold value to segment the lungs is selected iteratively such that the deviation of the lung boundary from the polynomial is minimized. A Naive Bayes classifier is used to build the model for selection of the best fitting lung boundary. The performance of the new technique was compared against a standard approach using a simple fixed threshold of -400HU followed by regiongrowing. The two techniques were evaluated against manual reference segmentations using a volumetric overlap fraction (VOF) and the adaptive threshold technique was found to be significantly better than the fixed threshold technique.

Comprehensive evaluation of an image segmentation technique for measuring tumor volume from CT images

NASA Astrophysics Data System (ADS)

Deng, Xiang; Huang, Haibin; Zhu, Lei; Du, Guangwei; Xu, Xiaodong; Sun, Yiyong; Xu, Chenyang; Jolly, Marie-Pierre; Chen, Jiuhong; Xiao, Jie; Merges, Reto; Suehling, Michael; Rinck, Daniel; Song, Lan; Jin, Zhengyu; Jiang, Zhaoxia; Wu, Bin; Wang, Xiaohong; Zhang, Shuai; Peng, Weijun

2008-03-01

Comprehensive quantitative evaluation of tumor segmentation technique on large scale clinical data sets is crucial for routine clinical use of CT based tumor volumetry for cancer diagnosis and treatment response evaluation. In this paper, we present a systematic validation study of a semi-automatic image segmentation technique for measuring tumor volume from CT images. The segmentation algorithm was tested using clinical data of 200 tumors in 107 patients with liver, lung, lymphoma and other types of cancer. The performance was evaluated using both accuracy and reproducibility. The accuracy was assessed using 7 commonly used metrics that can provide complementary information regarding the quality of the segmentation results. The reproducibility was measured by the variation of the volume measurements from 10 independent segmentations. The effect of disease type, lesion size and slice thickness of image data on the accuracy measures were also analyzed. Our results demonstrate that the tumor segmentation algorithm showed good correlation with ground truth for all four lesion types (r = 0.97, 0.99, 0.97, 0.98, p < 0.0001 for liver, lung, lymphoma and other respectively). The segmentation algorithm can produce relatively reproducible volume measurements on all lesion types (coefficient of variation in the range of 10-20%). Our results show that the algorithm is insensitive to lesion size (coefficient of determination close to 0) and slice thickness of image data(p > 0.90). The validation framework used in this study has the potential to facilitate the development of new tumor segmentation algorithms and assist large scale evaluation of segmentation techniques for other clinical applications.

Multiple sclerosis lesion segmentation using an automatic multimodal graph cuts.

PubMed

García-Lorenzo, Daniel; Lecoeur, Jeremy; Arnold, Douglas L; Collins, D Louis; Barillot, Christian

2009-01-01

Graph Cuts have been shown as a powerful interactive segmentation technique in several medical domains. We propose to automate the Graph Cuts in order to automatically segment Multiple Sclerosis (MS) lesions in MRI. We replace the manual interaction with a robust EM-based approach in order to discriminate between MS lesions and the Normal Appearing Brain Tissues (NABT). Evaluation is performed in synthetic and real images showing good agreement between the automatic segmentation and the target segmentation. We compare our algorithm with the state of the art techniques and with several manual segmentations. An advantage of our algorithm over previously published ones is the possibility to semi-automatically improve the segmentation due to the Graph Cuts interactive feature.

Boosting safety behaviour: Descriptive norms encourage child booster seat usage amongst low involvement parents.

PubMed

Jeffrey, Jennifer; Whelan, Jodie; Pirouz, Dante M; Snowdon, Anne W

2016-07-01

Campaigns advocating behavioural changes often employ social norms as a motivating technique, favouring injunctive norms (what is typically approved or disapproved) over descriptive norms (what is typically done). Here, we investigate an upside to including descriptive norms in health and safety appeals. Because descriptive norms are easy to process and understand, they should provide a heuristic to guide behaviour in those individuals who lack the interest or motivation to reflect on the advocated behaviour more deeply. When those descriptive norms are positive - suggesting that what is done is consistent with what ought to be done - including them in campaigns should be particularly beneficial at influencing this low-involvement segment. We test this proposition via research examining booster seat use amongst parents with children of booster seat age, and find that incorporating positive descriptive norms into a related campaign is particularly impactful for parents who report low involvement in the topic of booster seat safety. Descriptive norms are easy to state and easy to understand, and our research suggests that these norms resonate with low involvement individuals. As a result, we recommend incorporating descriptive norms when possible into health and safety campaigns. Copyright © 2016. Published by Elsevier Ltd.

Improved normal tissue sparing in head and neck radiotherapy using biological cost function based-IMRT.

PubMed

Anderson, N; Lawford, C; Khoo, V; Rolfo, M; Joon, D L; Wada, M

2011-12-01

Intensity-modulated radiotherapy (IMRT) has reduced the impact of acute and late toxicities associated with head and neck radiotherapy. Treatment planning system (TPS) advances in biological cost function based optimization (BBO) and improved segmentation techniques have increased organ at risk (OAR) sparing compared to conventional dose-based optimization (DBO). A planning study was undertaken to compare OAR avoidance in DBO and BBO treatment planning. Simultaneous integrated boost treatment plans were produced for 10 head and neck patients using both planning systems. Plans were compared for tar get coverage and OAR avoidance. Comparisons were made using the BBO TPS Monte Carlo dose engine to eliminate differences due to inherent algorithms. Target coverage (V95%) was maintained for both solutions. BBO produced lower OAR doses, with statistically significant improvement to left (12.3%, p = 0.005) and right parotid mean dose (16.9%, p = 0.004), larynx V50_Gy (71.0%, p = 0.005), spinal cord (21.9%, p < 0.001) and brain stem dose maximums (31.5%, p = 0.002). This study observed improved OAR avoidance with BBO planning. Further investigations will be undertaken to review any clinical benefit of this improved planned dosimetry.

Improved Normal Tissue Sparing in Head and Neck Radiotherapy Using Biological Cost Function Based-IMRT

PubMed Central

Anderson, N.; Lawford, C.; Khoo, V.; Rolfo, M.; Joon, D. Lim; Wada, M.

2011-01-01

Intensity-modulated radiotherapy (IMRT) has reduced the impact of acute and late toxicities associated with head and neck radiotherapy. Treatment planning system (TPS) advances in biological cost function based optimization (BBO) and improved segmentation techniques have increased organ at risk (OAR) sparing compared to conventional dose-based optimization (DBO). A planning study was undertaken to compare OAR avoidance in DBO and BBO treatment planning. Simultaneous integrated boost treatment plans were produced for 10 head and neck patients using both planning systems. Plans were compared for tar get coverage and OAR avoidance. Comparisons were made using the BBO TPS Monte Carlo dose engine to eliminate differences due to inherent algorithms. Target coverage (V95%) was maintained for both solutions. BBO produced lower OAR doses, with statistically significant improvement to left (12.3%, p = 0.005) and right parotid mean dose (16.9%, p = 0.004), larynx V50 Gy (71.0%, p = 0.005), spinal cord (21.9%, p < 0.001) and brain stem dose maximums (31.5%, p = 0.002). This study observed improved OAR avoidance with BBO planning. Further investigations will be undertaken to review any clinical benefit of this improved planned dosimetry. PMID:22066597

Success of segmentation in a sequence of images tracking the growth of endogenously fluorescent kidneys

NASA Astrophysics Data System (ADS)

Goldberg, Robert R.; Goldberg, Michael R.

1999-05-01

A previous paper by the authors presented an algorithm that successfully segmented organs grown in vitro from their surroundings. It was noticed that one difficulty in standard dyeing techniques for the analysis of contours in organs was due to the fact that the antigen necessary to bind with the fluorescent dye was not uniform throughout the cell borders. To address these concerns, a new fluorescent technique was utilized. A transgenic mouse line was genetically engineered utilizing the hoxb7/gfp (green fluorescent protein). Whereas the original technique (fixed and blocking) required a numerous number of noise removal filtering and sophisticated segmentation techniques, segmentation on the GFP kidney required only an adaptive binary threshold technique which yielded excellent results without the need for specific noise reduction. This is important for tracking the growth of kidney development through time.

Development of a semi-automated combined PET and CT lung lesion segmentation framework

NASA Astrophysics Data System (ADS)

Rossi, Farli; Mokri, Siti Salasiah; Rahni, Ashrani Aizzuddin Abd.

2017-03-01

Segmentation is one of the most important steps in automated medical diagnosis applications, which affects the accuracy of the overall system. In this paper, we propose a semi-automated segmentation method for extracting lung lesions from thoracic PET/CT images by combining low level processing and active contour techniques. The lesions are first segmented in PET images which are first converted to standardised uptake values (SUVs). The segmented PET images then serve as an initial contour for subsequent active contour segmentation of corresponding CT images. To evaluate its accuracy, the Jaccard Index (JI) was used as a measure of the accuracy of the segmented lesion compared to alternative segmentations from the QIN lung CT segmentation challenge, which is possible by registering the whole body PET/CT images to the corresponding thoracic CT images. The results show that our proposed technique has acceptable accuracy in lung lesion segmentation with JI values of around 0.8, especially when considering the variability of the alternative segmentations.

Ascent Guidance for a Winged Boost Vehicle. M.S. Thesis

NASA Technical Reports Server (NTRS)

Corvin, Michael Alexander

1988-01-01

The objective of the advanced ascent guidance study was to investigate guidance concepts which could contribute to increased autonomy during ascent operations in a winged boost vehicle such as the proposed Shuttle II. The guidance scheme was required to yield near a full-optimal ascent in the presence of vehicle system and environmental dispersions. The study included consideration of trajectory shaping issues, trajectory design, closed loop and predictive adaptive guidance techniques and control of dynamic pressure by throttling. An extensive ascent vehicle simulation capability was developed for use in the study.

Clinical results of conformal versus intensity-modulated radiotherapy using a focal simultaneous boost for muscle-invasive bladder cancer in elderly or medically unfit patients.

PubMed

Lutkenhaus, Lotte J; van Os, Rob M; Bel, Arjan; Hulshof, Maarten C C M

2016-03-18

For elderly or medically unfit patients with muscle-invasive bladder cancer, cystectomy or chemotherapy are contraindicated. This leaves radical radiotherapy as the only treatment option. It was the aim of this study to retrospectively analyze the treatment outcome and associated toxicity of conformal versus intensity-modulated radiotherapy (IMRT) using a focal simultaneous tumor boost for muscle-invasive bladder cancer in patients not suitable for cystectomy. One hundred eighteen patients with T2-4 N0-1 M0 bladder cancer were analyzed retrospectively. Median age was 80 years. Treatment consisted of either a conformal box technique or IMRT and included a simultaneous boost to the tumor. To enable an accurate boost delivery, fiducial markers were placed around the tumor. Patients were treated with 40 Gy in 20 fractions to the elective treatment volumes, and a daily tumor boost up to 55-60 Gy. Clinical complete response was seen in 87 % of patients. Three-year overall survival was 44 %, with a locoregional control rate of 73 % at 3 years. Toxicity was low, with late urinary and intestinal toxicity rates grade ≥ 2 of 14 and 5 %, respectively. The use of IMRT reduced late intestinal toxicity, whereas fiducial markers reduced acute urinary toxicity. Radical radiotherapy using a focal boost is feasible and effective for elderly or unfit patients, with a 3-year locoregional control of 73 %. Toxicity rates were low, and were reduced by the use of IMRT and fiducial markers.

Shot boundary detection and label propagation for spatio-temporal video segmentation

NASA Astrophysics Data System (ADS)

Piramanayagam, Sankaranaryanan; Saber, Eli; Cahill, Nathan D.; Messinger, David

2015-02-01

This paper proposes a two stage algorithm for streaming video segmentation. In the first stage, shot boundaries are detected within a window of frames by comparing dissimilarity between 2-D segmentations of each frame. In the second stage, the 2-D segments are propagated across the window of frames in both spatial and temporal direction. The window is moved across the video to find all shot transitions and obtain spatio-temporal segments simultaneously. As opposed to techniques that operate on entire video, the proposed approach consumes significantly less memory and enables segmentation of lengthy videos. We tested our segmentation based shot detection method on the TRECVID 2007 video dataset and compared it with block-based technique. Cut detection results on the TRECVID 2007 dataset indicate that our algorithm has comparable results to the best of the block-based methods. The streaming video segmentation routine also achieves promising results on a challenging video segmentation benchmark database.

Cophasing techniques for extremely large telescopes

NASA Astrophysics Data System (ADS)

Devaney, Nicholas; Schumacher, Achim

2004-07-01

The current designs of the majority of ELTs envisage that at least the primary mirror will be segmented. Phasing of the segments is therefore a major concern, and a lot of work is underway to determine the most suitable techniques. The techniques which have been developed are either wave optics generalizations of classical geometric optics tests (e.g. Shack-Hartmann and curvature sensing) or direct interferometric measurements. We present a review of the main techniques proposed for phasing and outline their relative merits. We consider problems which are specific to ELTs, e.g. vignetting of large parts of the primary mirror by the secondary mirror spiders, and the need to disentangle phase errors arising in different segmented mirrors. We present improvements in the Shack-Hartmann and curvature sensing techniques which allow greater precision and range. Finally, we describe a piston plate which simulates segment phasing errors and show the results of laboratory experiments carried out to verify the precision of the Shack-Hartmann technique.

Dosimetric evaluation of simultaneous integrated boost during stereotactic body radiation therapy for pancreatic cancer

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

Yang, Wensha, E-mail: wensha.yang@cshs.org; Reznik, Robert; Fraass, Benedick A.

Stereotactic body radiation therapy (SBRT) provides a promising way to treat locally advanced pancreatic cancer and borderline resectable pancreatic cancer. A simultaneous integrated boost (SIB) to the region of vessel abutment or encasement during SBRT has the potential to downstage otherwise likely positive surgical margins. Despite the potential benefit of using SIB-SBRT, the ability to boost is limited by the local geometry of the organs at risk (OARs), such as stomach, duodenum, and bowel (SDB), relative to tumor. In this study, we have retrospectively replanned 20 patients with 25 Gy prescribed to the planning target volume (PTV) and 33~80 Gymore » to the boost target volume (BTV) using an SIB technique for all patients. The number of plans and patients able to satisfy a set of clinically established constraints is analyzed. The ability to boost vessels (within the gross target volume [GTV]) is shown to correlate with the overlap volume (OLV), defined to be the overlap between the GTV + a 1(OLV1)- or 2(OLV2)-cm margin with the union of SDB. Integral dose, boost dose contrast (BDC), biologically effective BDC, tumor control probability for BTV, and normal tissue complication probabilities are used to analyze the dosimetric results. More than 65% of the cases can deliver a boost to 40 Gy while satisfying all OAR constraints. An OLV2 of 100 cm{sup 3} is identified as the cutoff volume: for cases with OLV2 larger than 100 cm{sup 3}, it is very unlikely the case could achieve 25 Gy to the PTV while successfully meeting all the OAR constraints.« less

Dosimetric evaluation of simultaneous integrated boost during stereotactic body radiation therapy for pancreatic cancer.

PubMed

Yang, Wensha; Reznik, Robert; Fraass, Benedick A; Nissen, Nicholas; Hendifar, Andrew; Wachsman, Ashley; Sandler, Howard; Tuli, Richard

2015-01-01

Stereotactic body radiation therapy (SBRT) provides a promising way to treat locally advanced pancreatic cancer and borderline resectable pancreatic cancer. A simultaneous integrated boost (SIB) to the region of vessel abutment or encasement during SBRT has the potential to downstage otherwise likely positive surgical margins. Despite the potential benefit of using SIB-SBRT, the ability to boost is limited by the local geometry of the organs at risk (OARs), such as stomach, duodenum, and bowel (SDB), relative to tumor. In this study, we have retrospectively replanned 20 patients with 25Gy prescribed to the planning target volume (PTV) and 33~80Gy to the boost target volume (BTV) using an SIB technique for all patients. The number of plans and patients able to satisfy a set of clinically established constraints is analyzed. The ability to boost vessels (within the gross target volume [GTV]) is shown to correlate with the overlap volume (OLV), defined to be the overlap between the GTV + a 1(OLV1)- or 2(OLV2)-cm margin with the union of SDB. Integral dose, boost dose contrast (BDC), biologically effective BDC, tumor control probability for BTV, and normal tissue complication probabilities are used to analyze the dosimetric results. More than 65% of the cases can deliver a boost to 40Gy while satisfying all OAR constraints. An OLV2 of 100cm(3) is identified as the cutoff volume: for cases with OLV2 larger than 100cm(3), it is very unlikely the case could achieve 25Gy to the PTV while successfully meeting all the OAR constraints. Copyright © 2015 American Association of Medical Dosimetrists. Published by Elsevier Inc. All rights reserved.

Stereotactic Body Radiotherapy: A Promising Treatment Option for the Boost of Oropharyngeal Cancers Not Suitable for Brachytherapy: A Single-Institutional Experience

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

Al-Mamgani, Abrahim, E-mail: a.al-mamgani@erasmusmc.nl; Tans, Lisa; Teguh, David N.

2012-03-15

Purpose: To prospectively assess the outcome and toxicity of frameless stereotactic body radiotherapy (SBRT) as a treatment option for boosting primary oropharyngeal cancers (OPC) in patients who not suitable for the standard brachytherapy boost (BTB). Methods and Materials: Between 2005 and 2010, 51 patients with Stage I to IV biopsy-proven OPC who were not suitable for BTB received boosts by means of SBRT (3 times 5.5 Gy, prescribed to the 80% isodose line), after 46 Gy of IMRT to the primary tumor and neck (when indicated). Endpoints of the study were local control (LC), disease-free survival (DFS), overall survival (OS),more » and acute and late toxicity. Results: After a median follow-up of 18 months (range, 6-65 months), the 2-year actuarial rates of LC, DFS, and OS were 86%, 80%, and 82%, respectively, and the 3-year rates were 70%, 66%, and 54%, respectively. The treatment was well tolerated, as there were no treatment breaks and no Grade 4 or 5 toxicity reported, either acute or chronic. The overall 2-year cumulative incidence of Grade {>=}2 late toxicity was 28%. Of the patients with 2 years with no evidence of disease (n = 20), only 1 patient was still feeding tube dependent and 2 patients had Grade 3 xerostomia. Conclusions: According to our knowledge, this study is the first report of patients with primary OPC who received boosts by means of SBRT. Patients with OPC who are not suitable for the standard BTB can safely and effectively receive boosts by SBRT. With this radiation technique, an excellent outcome was achieved. Furthermore, the SBRT boost did not have a negative impact regarding acute and late side effects.« less

All Internal Segmental Bone Transport and Optional Lengthening With a Newly Developed Universal Cylinder-Kombi-Tube Module for Motorized Nails-Description of a Surgical Technique.

PubMed

Krettek, Christian; El Naga, Ashraf

2017-10-01

Segmental transport is an effective method of treatment for segmental defects, but the need for external fixation during the transport phase is a disadvantage. To avoid external fixation, we have developed a Cylinder-Kombi-Tube Segmental Transport (CKTST) module for combination with a commercially available motorized lengthening nail. This CKTST module allows for an all-internal segmental bone transport and also allows for optional lengthening if needed. The concept and surgical technique of CKTST are described and illustrated with a clinical case.

Alternative Distraction Osteogenesis Technique After Implant Placement for Alveolar Ridge Augmentation of the Maxilla.

PubMed

Nogueira, Renato Luiz Maia; Osterne, Rafael Lima Verde; Abreu, Ricardo Teixeira; Araújo, Phelype Maia

2017-07-01

An alternative technique to reconstruct atrophic alveolar vertical bone after implant placement is presented. The technique consists of distraction osteogenesis or direct surgical repositioning of an implant-and-bone block segment after segmental osteotomies that can be used in esthetic or unesthetic cases. Initially, casts indicating the implant position are obtained and the future ideal prosthetic position is determined to guide the model surgery. After the model surgery, a new provisional prosthesis is fabricated, and an occlusal splint, which is used as a surgical guide and a device for distraction osteogenesis, is custom fabricated. Then, the surgery is performed. For mobilization of the implant-and-bone block segment, 2 vertical osteotomies are performed and then joined by a horizontal osteotomy. The implant-and-bone block segment is moved to the planned position. If a small movement is planned, then the implant-and-bone segment is stabilized; for larger movements, the implant-and-bone segment can be gradually moved to the final position by distraction osteogenesis. This technique has good predictability of the final position of the implant-and-bone segment and relatively fast esthetic rehabilitation. It can be considered for dental implants in regions of vertical bone atrophy. Copyright © 2017 American Association of Oral and Maxillofacial Surgeons. Published by Elsevier Inc. All rights reserved.

Centaur propellant acquisition system study

NASA Technical Reports Server (NTRS)

Blatt, M. H.; Walter, M. D.

1975-01-01

A study was performed to determine the desirability of replacing the hydrogen peroxide settling system on the Centaur D-1S with a capillary acquisition system. A comprehensive screening was performed to select the most promising capillary device fluid acquisition, thermal conditioning, and fabrication techniques. Refillable start baskets and bypass feed start tanks were selected for detailed design. Critical analysis areas were settling and refilling, start sequence development with an initially dry boost pump, and cooling the fluid delivered to the boost pump in order to provide necessary net position suction head (NPSH). Design drawings were prepared for the start basket and start tank concepts for both LO2 and LH2 tanks. System comparisons indicated that the start baskets using wicking for thermal conditioning, and thermal subcooling for boost pump NPSH, are the most desirable systems for future development.

Centaur propellant acquisition system

NASA Technical Reports Server (NTRS)

Blatt, M. H.; Aydelott, J. C.

1975-01-01

The desirability of replacing the hydrogen peroxide settling system of the Centaur D-1S with a capillary acquisition system was evaluated. A comprehensive screening was performed to select the most promising capillary device fluid acquisition, thermal conditioning, and fabrication techniques. Refillable start baskets and bypass feed start tanks were selected for detailed design. Critical analysis areas were settling and refilling, start sequence development with an initially dry boost pump, and cooling the fluid delivered to the boost pump to provide the necessary net positive suction head (NPSH). Design drawings were prepared for start basket and start tank concepts for both the liquid oxygen and liquid hydrogen tanks. System comparisons indicated that the start baskets using wicking flow for thermal conditioning, and thermal subcooling for providing boost pump NPSH, are the most desirable systems for future Centaur acquisition system development.

Clustering cancer gene expression data by projective clustering ensemble

PubMed Central

Yu, Xianxue; Yu, Guoxian

2017-01-01

Gene expression data analysis has paramount implications for gene treatments, cancer diagnosis and other domains. Clustering is an important and promising tool to analyze gene expression data. Gene expression data is often characterized by a large amount of genes but with limited samples, thus various projective clustering techniques and ensemble techniques have been suggested to combat with these challenges. However, it is rather challenging to synergy these two kinds of techniques together to avoid the curse of dimensionality problem and to boost the performance of gene expression data clustering. In this paper, we employ a projective clustering ensemble (PCE) to integrate the advantages of projective clustering and ensemble clustering, and to avoid the dilemma of combining multiple projective clusterings. Our experimental results on publicly available cancer gene expression data show PCE can improve the quality of clustering gene expression data by at least 4.5% (on average) than other related techniques, including dimensionality reduction based single clustering and ensemble approaches. The empirical study demonstrates that, to further boost the performance of clustering cancer gene expression data, it is necessary and promising to synergy projective clustering with ensemble clustering. PCE can serve as an effective alternative technique for clustering gene expression data. PMID:28234920

Protecting High Energy Barriers: A New Equation to Regulate Boost Energy in Accelerated Molecular Dynamics Simulations.

PubMed

Sinko, William; de Oliveira, César Augusto F; Pierce, Levi C T; McCammon, J Andrew

2012-01-10

Molecular dynamics (MD) is one of the most common tools in computational chemistry. Recently, our group has employed accelerated molecular dynamics (aMD) to improve the conformational sampling over conventional molecular dynamics techniques. In the original aMD implementation, sampling is greatly improved by raising energy wells below a predefined energy level. Recently, our group presented an alternative aMD implementation where simulations are accelerated by lowering energy barriers of the potential energy surface. When coupled with thermodynamic integration simulations, this implementation showed very promising results. However, when applied to large systems, such as proteins, the simulation tends to be biased to high energy regions of the potential landscape. The reason for this behavior lies in the boost equation used since the highest energy barriers are dramatically more affected than the lower ones. To address this issue, in this work, we present a new boost equation that prevents oversampling of unfavorable high energy conformational states. The new boost potential provides not only better recovery of statistics throughout the simulation but also enhanced sampling of statistically relevant regions in explicit solvent MD simulations.

Building Roof Segmentation from Aerial Images Using a Line-and Region-Based Watershed Segmentation Technique

PubMed Central

Merabet, Youssef El; Meurie, Cyril; Ruichek, Yassine; Sbihi, Abderrahmane; Touahni, Raja

2015-01-01

In this paper, we present a novel strategy for roof segmentation from aerial images (orthophotoplans) based on the cooperation of edge- and region-based segmentation methods. The proposed strategy is composed of three major steps. The first one, called the pre-processing step, consists of simplifying the acquired image with an appropriate couple of invariant and gradient, optimized for the application, in order to limit illumination changes (shadows, brightness, etc.) affecting the images. The second step is composed of two main parallel treatments: on the one hand, the simplified image is segmented by watershed regions. Even if the first segmentation of this step provides good results in general, the image is often over-segmented. To alleviate this problem, an efficient region merging strategy adapted to the orthophotoplan particularities, with a 2D modeling of roof ridges technique, is applied. On the other hand, the simplified image is segmented by watershed lines. The third step consists of integrating both watershed segmentation strategies into a single cooperative segmentation scheme in order to achieve satisfactory segmentation results. Tests have been performed on orthophotoplans containing 100 roofs with varying complexity, and the results are evaluated with the VINETcriterion using ground-truth image segmentation. A comparison with five popular segmentation techniques of the literature demonstrates the effectiveness and the reliability of the proposed approach. Indeed, we obtain a good segmentation rate of 96% with the proposed method compared to 87.5% with statistical region merging (SRM), 84% with mean shift, 82% with color structure code (CSC), 80% with efficient graph-based segmentation algorithm (EGBIS) and 71% with JSEG. PMID:25648706

Pressure ulcer image segmentation technique through synthetic frequencies generation and contrast variation using toroidal geometry.

PubMed

David, Ortiz P; Sierra-Sosa, Daniel; Zapirain, Begoña García

2017-01-06

Pressure ulcers have become subject of study in recent years due to the treatment high costs and decreased life quality from patients. These chronic wounds are related to the global life expectancy increment, being the geriatric and physical disable patients the principal affected by this condition. Injuries diagnosis and treatment usually takes weeks or even months by medical personel. Using non-invasive techniques, such as image processing techniques, it is possible to conduct an analysis from ulcers and aid in its diagnosis. This paper proposes a novel technique for image segmentation based on contrast changes by using synthetic frequencies obtained from the grayscale value available in each pixel of the image. These synthetic frequencies are calculated using the model of energy density over an electric field to describe a relation between a constant density and the image amplitude in a pixel. A toroidal geometry is used to decompose the image into different contrast levels by variating the synthetic frequencies. Then, the decomposed image is binarized applying Otsu's threshold allowing for obtaining the contours that describe the contrast variations. Morphological operations are used to obtain the desired segment of the image. The proposed technique is evaluated by synthesizing a Data Base with 51 images of pressure ulcers, provided by the Centre IGURCO. With the segmentation of these pressure ulcer images it is possible to aid in its diagnosis and treatment. To provide evidences of technique performance, digital image correlation was used as a measure, where the segments obtained using the methodology are compared with the real segments. The proposed technique is compared with two benchmarked algorithms. The results over the technique present an average correlation of 0.89 with a variation of ±0.1 and a computational time of 9.04 seconds. The methodology presents better segmentation results than the benchmarked algorithms using less computational time and without the need of an initial condition.

Maximum-likelihood techniques for joint segmentation-classification of multispectral chromosome images.

PubMed

Schwartzkopf, Wade C; Bovik, Alan C; Evans, Brian L

2005-12-01

Traditional chromosome imaging has been limited to grayscale images, but recently a 5-fluorophore combinatorial labeling technique (M-FISH) was developed wherein each class of chromosomes binds with a different combination of fluorophores. This results in a multispectral image, where each class of chromosomes has distinct spectral components. In this paper, we develop new methods for automatic chromosome identification by exploiting the multispectral information in M-FISH chromosome images and by jointly performing chromosome segmentation and classification. We (1) develop a maximum-likelihood hypothesis test that uses multispectral information, together with conventional criteria, to select the best segmentation possibility; (2) use this likelihood function to combine chromosome segmentation and classification into a robust chromosome identification system; and (3) show that the proposed likelihood function can also be used as a reliable indicator of errors in segmentation, errors in classification, and chromosome anomalies, which can be indicators of radiation damage, cancer, and a wide variety of inherited diseases. We show that the proposed multispectral joint segmentation-classification method outperforms past grayscale segmentation methods when decomposing touching chromosomes. We also show that it outperforms past M-FISH classification techniques that do not use segmentation information.

Using deep learning in image hyper spectral segmentation, classification, and detection

NASA Astrophysics Data System (ADS)

Zhao, Xiuying; Su, Zhenyu

2018-02-01

Recent years have shown that deep learning neural networks are a valuable tool in the field of computer vision. Deep learning method can be used in applications like remote sensing such as Land cover Classification, Detection of Vehicle in Satellite Images, Hyper spectral Image classification. This paper addresses the use of the deep learning artificial neural network in Satellite image segmentation. Image segmentation plays an important role in image processing. The hue of the remote sensing image often has a large hue difference, which will result in the poor display of the images in the VR environment. Image segmentation is a pre processing technique applied to the original images and splits the image into many parts which have different hue to unify the color. Several computational models based on supervised, unsupervised, parametric, probabilistic region based image segmentation techniques have been proposed. Recently, one of the machine learning technique known as, deep learning with convolution neural network has been widely used for development of efficient and automatic image segmentation models. In this paper, we focus on study of deep neural convolution network and its variants for automatic image segmentation rather than traditional image segmentation strategies.

Comparison of segmentation algorithms for fluorescence microscopy images of cells.

PubMed

Dima, Alden A; Elliott, John T; Filliben, James J; Halter, Michael; Peskin, Adele; Bernal, Javier; Kociolek, Marcin; Brady, Mary C; Tang, Hai C; Plant, Anne L

2011-07-01

The analysis of fluorescence microscopy of cells often requires the determination of cell edges. This is typically done using segmentation techniques that separate the cell objects in an image from the surrounding background. This study compares segmentation results from nine different segmentation techniques applied to two different cell lines and five different sets of imaging conditions. Significant variability in the results of segmentation was observed that was due solely to differences in imaging conditions or applications of different algorithms. We quantified and compared the results with a novel bivariate similarity index metric that evaluates the degree of underestimating or overestimating a cell object. The results show that commonly used threshold-based segmentation techniques are less accurate than k-means clustering with multiple clusters. Segmentation accuracy varies with imaging conditions that determine the sharpness of cell edges and with geometric features of a cell. Based on this observation, we propose a method that quantifies cell edge character to provide an estimate of how accurately an algorithm will perform. The results of this study will assist the development of criteria for evaluating interlaboratory comparability. Published 2011 Wiley-Liss, Inc.

Segmentation and pulse shape discrimination techniques for rejecting background in germanium detectors

NASA Technical Reports Server (NTRS)

Roth, J.; Primbsch, J. H.; Lin, R. P.

1984-01-01

The possibility of rejecting the internal beta-decay background in coaxial germanium detectors by distinguishing between the multi-site energy losses characteristic of photons and the single-site energy losses of electrons in the range 0.2 - 2 MeV is examined. The photon transport was modeled with a Monte Carlo routine. Background rejection by both multiple segmentation and pulse shape discrimination techniques is investigated. The efficiency of a six 1 cm-thick segment coaxial detector operating in coincidence mode alone is compared to that of a two-segment (1 cm and 5 cm) detector employing both front-rear coincidence and PSD in the rear segment to isolate photon events. Both techniques can provide at least 95 percent rejection of single-site events while accepting at least 80 percent of the multi-site events above 500 keV.

SU-E-T-29: A Dosimetric Study of Volumetric Modulated Arc Therapy with Simultaneous Integrated Boost for Rectal Cancer

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

Sun, T; Lin, X; Yin, Y

Purpose: To compare the dosimetric differences among fixed field intensity-modulated radiotherapy (IMRT) and double-arc volumetricmodulated arc therapy (VMAT) plans with simultaneous integrated boost in rectal cancer. Methods: Ten patients with rectal cancer previously treated with IMRT were included in this analysis. For each patient, two treatment techniques were designed for each patient: the fixed 7 fields IMRT and double-arc VMAT with RapidArc technique. The treatment plan was designed to deliver in one process with simultaneous integrated boost (SIB). The prescribed doses to the planning target volume of the subclinical disease (PTV1) and the gross disease (PTV2) were 45 Gy andmore » 55 Gy in 25 fractions, respectively. The dose distribution in the target, the dose to the organs at risk, total MU and the delivery time in two techniques were compared to explore the dosimetric differences. Results: For the target dose and homogeneity in PTV1 and PTV2, no statistically differences were observed in the two plans. VMAT plans showed a better conformity in PTV1. VMAT plans reduced the mean dose to bladder, small bowel, femur heads and iliac wings. For iliac wings, VMAT plans resulted in a statistically significant reduction in irradiated volume of 15 Gy, 20 Gy, 30 Gy but increased the 10 Gy irradiated volume. VMAT plans reduced the small bowel irradiated volume of 20 Gy and 30 Gy. Compared with IMRT plans, VMAT plans showed a significant reduction of monitor units by nearly 30% and reduced treatment time by an average of 70% Conclusion: Compared to IMRT plans, VMAT plans showed the similar target dose and reduced the dose of the organs at risk, especially for small bowel and iliac wings. For rectal cancer, VMAT with simultaneous integrated boost can be carried out with high quality and efficiency.« less

Wellness at work. Boost wellness center participation with target marketing strategies.

PubMed

DeMoranville, C W; Schoenbachler, D D; Przytulski, J

1998-01-01

By using target marketing strategies, corporate wellness programs can increase employee participation rates and tailor activities to meet employee needs. The authors examined this issue through a research survey that segmented a university's staff and employee population into three wellness program groups: High Participators, Moderate Participators, and Low Participators. Participators' views on the following issues were analyzed: health management programs, exercise programs, wellness center use inhibitors, wellness center use incentives, wellness center communications, and willingness to pay for the wellness center. The results identified unique lifestyle characteristics for each group that can help make target marketing strategies effective.

Using CART to segment road images

NASA Astrophysics Data System (ADS)

Davies, Bob; Lienhart, Rainer

2006-01-01

The 2005 DARPA Grand Challenge is a 132 mile race through the desert with autonomous robotic vehicles. Lasers mounted on the car roof provide a map of the road up to 20 meters ahead of the car but the car needs to see further in order to go fast enough to win the race. Computer vision can extend that map of the road ahead but desert road is notoriously similar to the surrounding desert. The CART algorithm (Classification and Regression Trees) provided a machine learning boost to find road while at the same time measuring when that road could not be distinguished from surrounding desert.

Risk-adaptive radiotherapy

NASA Astrophysics Data System (ADS)

Kim, Yusung

Currently, there is great interest in integrating biological information into intensity-modulated radiotherapy (IMRT) treatment planning with the aim of boosting high-risk tumor subvolumes. Selective boosting of tumor subvolumes can be accomplished without violating normal tissue complication constraints using information from functional imaging. In this work we have developed a risk-adaptive optimization-framework that utilizes a nonlinear biological objective function. Employing risk-adaptive radiotherapy for prostate cancer, it is possible to increase the equivalent uniform dose (EUD) by up to 35.4 Gy in tumor subvolumes having the highest risk classification without increasing normal tissue complications. Subsequently, we have studied the impact of functional imaging accuracy, and found on the one hand that loss in sensitivity had a large impact on expected local tumor control, which was maximal when a low-risk classification for the remaining low risk PTV was chosen. While on the other hand loss in specificity appeared to have a minimal impact on normal tissue sparing. Therefore, it appears that in order to improve the therapeutic ratio a functional imaging technique with a high sensitivity, rather than specificity, is needed. Last but not least a comparison study between selective boosting IMRT strategies and uniform-boosting IMRT strategies yielding the same EUD to the overall PTV was carried out, and found that selective boosting IMRT considerably improves expected TCP compared to uniform-boosting IMRT, especially when lack of control of the high-risk tumor subvolumes is the cause of expected therapy failure. Furthermore, while selective boosting IMRT, using physical dose-volume objectives, did yield similar rectal and bladder sparing when compared its equivalent uniform-boosting IMRT plan, risk-adaptive radiotherapy, utilizing biological objective functions, did yield a 5.3% reduction in NTCP for the rectum. Hence, in risk-adaptive radiotherapy the therapeutic ratio can be increased over that which can be achieved with conventional selective boosting IMRT using physical dose-volume objectives. In conclusion, a novel risk-adaptive radiotherapy strategy is proposed and promises increased expected local control for locoregionally advanced tumors with equivalent or better normal tissue sparing.

ACM-based automatic liver segmentation from 3-D CT images by combining multiple atlases and improved mean-shift techniques.

PubMed

Ji, Hongwei; He, Jiangping; Yang, Xin; Deklerck, Rudi; Cornelis, Jan

2013-05-01

In this paper, we present an autocontext model(ACM)-based automatic liver segmentation algorithm, which combines ACM, multiatlases, and mean-shift techniques to segment liver from 3-D CT images. Our algorithm is a learning-based method and can be divided into two stages. At the first stage, i.e., the training stage, ACM is performed to learn a sequence of classifiers in each atlas space (based on each atlas and other aligned atlases). With the use of multiple atlases, multiple sequences of ACM-based classifiers are obtained. At the second stage, i.e., the segmentation stage, the test image will be segmented in each atlas space by applying each sequence of ACM-based classifiers. The final segmentation result will be obtained by fusing segmentation results from all atlas spaces via a multiclassifier fusion technique. Specially, in order to speed up segmentation, given a test image, we first use an improved mean-shift algorithm to perform over-segmentation and then implement the region-based image labeling instead of the original inefficient pixel-based image labeling. The proposed method is evaluated on the datasets of MICCAI 2007 liver segmentation challenge. The experimental results show that the average volume overlap error and the average surface distance achieved by our method are 8.3% and 1.5 m, respectively, which are comparable to the results reported in the existing state-of-the-art work on liver segmentation.

Atlas-based fuzzy connectedness segmentation and intensity nonuniformity correction applied to brain MRI.

PubMed

Zhou, Yongxin; Bai, Jing

2007-01-01

A framework that combines atlas registration, fuzzy connectedness (FC) segmentation, and parametric bias field correction (PABIC) is proposed for the automatic segmentation of brain magnetic resonance imaging (MRI). First, the atlas is registered onto the MRI to initialize the following FC segmentation. Original techniques are proposed to estimate necessary initial parameters of FC segmentation. Further, the result of the FC segmentation is utilized to initialize a following PABIC algorithm. Finally, we re-apply the FC technique on the PABIC corrected MRI to get the final segmentation. Thus, we avoid expert human intervention and provide a fully automatic method for brain MRI segmentation. Experiments on both simulated and real MRI images demonstrate the validity of the method, as well as the limitation of the method. Being a fully automatic method, it is expected to find wide applications, such as three-dimensional visualization, radiation therapy planning, and medical database construction.

Cartilage segmentation of 3D MRI scans of the osteoarthritic knee combining user knowledge and active contours

NASA Astrophysics Data System (ADS)

Lynch, John A.; Zaim, Souhil; Zhao, Jenny; Stork, Alexander; Peterfy, Charles G.; Genant, Harry K.

2000-06-01

A technique for segmentation of articular cartilage from 3D MRI scans of the knee has been developed. It overcomes the limitations of the conventionally used region growing techniques, which are prone to inter- and intra-observer variability, and which can require much manual intervention. We describe a hybrid segmentation method combining expert knowledge with directionally oriented Canny filters, cost functions and cubic splines. After manual initialization, the technique utilized 3 cost functions which aided automated detection of cartilage and its boundaries. Using the sign of the edge strength, and the local direction of the boundary, this technique is more reliable than conventional 'snakes,' and the user had little control over smoothness of boundaries. This means that the automatically detected boundary can conform to the true shape of the real boundary, also allowing reliable detection of subtle local lesions on the normally smooth cartilage surface. Manual corrections, with possible re-optimization were sometimes needed. When compared to the conventionally used region growing techniques, this newly described technique measured local cartilage volume with 3 times better reproducibility, and involved two thirds less human interaction. Combined with the use of 3D image registration, the new technique should also permit unbiased segmentation of followup scans by automated initialization from a baseline segmentation of an earlier scan of the same patient.

Analyzing spatial and temporal trends in Aboveground Biomass within the Acadian New England Forests using the complete Landsat Archive

NASA Astrophysics Data System (ADS)

Kilbride, J. B.; Fraver, S.; Ayrey, E.; Weiskittel, A.; Braaten, J.; Hughes, J. M.; Hayes, D. J.

2017-12-01

Forests within the New England states and Canadian Maritime provinces, here described as the Acadian New England (ANE) forests, have undergone substantial disturbances due to insect, fire, and anthropogenic factors. Through repeated satellite observations captures by USGS's Landsat program, 45 years of disturbance information can be incorporated into modeling efforts to better understand the spatial and temporal trends in forest above ground biomass (AGB). Using Google's Earth Engine, annual mosaics were developed for the ANE study area and then disturbance and recovery metrics were developed using the temporal segmentation algorithm VeRDET. Normalization procedures were developed to incorporate the Landsat Multispectral Scanner (MSS, 1972 - 1985) data alongside the modern era of Landsat Thematic Mapper (TM, 1984-2013), Enhanced Thematic Mapper plus (ETM+, 1999 - present), and Operational Land Imager (OLI, 2013- present) data products. This has enabled the creation of a dataset with an unprecedented spatial and temporal view of forest landscape change. Model training was performed using was the Forest Inventory Analysis (FIA) and New Brunswick Permanent Sample Plot data datasets. Modeling was performed using parametric techniques such as mixed effects models and non-parametric techniques such as k-NN imputation and generalized boosted regression. We compare the biomass estimate and model accuracy to other inventory and modeling studies produced within this study area. The spatial and temporal patterns of stock changes are analyzed against resource policy, land ownership changes, and forest management.

Reducing "Math Anxiety" in College Algebra Courses Including Comparisons with Elementary Statistics Courses.

ERIC Educational Resources Information Center

Bankhead, Mike

The high levels of anxiety, apprehension, and apathy of students in college algebra courses caused the instructor to create and test a variety of math teaching techniques designed to boost student confidence and enthusiasm in the subject. Overall, this proposal covers several different techniques, which have been evaluated by both students and the…

Automatic segmentation of closed-contour features in ophthalmic images using graph theory and dynamic programming.

PubMed

Chiu, Stephanie J; Toth, Cynthia A; Bowes Rickman, Catherine; Izatt, Joseph A; Farsiu, Sina

2012-05-01

This paper presents a generalized framework for segmenting closed-contour anatomical and pathological features using graph theory and dynamic programming (GTDP). More specifically, the GTDP method previously developed for quantifying retinal and corneal layer thicknesses is extended to segment objects such as cells and cysts. The presented technique relies on a transform that maps closed-contour features in the Cartesian domain into lines in the quasi-polar domain. The features of interest are then segmented as layers via GTDP. Application of this method to segment closed-contour features in several ophthalmic image types is shown. Quantitative validation experiments for retinal pigmented epithelium cell segmentation in confocal fluorescence microscopy images attests to the accuracy of the presented technique.

Automatic segmentation of closed-contour features in ophthalmic images using graph theory and dynamic programming

PubMed Central

Chiu, Stephanie J.; Toth, Cynthia A.; Bowes Rickman, Catherine; Izatt, Joseph A.; Farsiu, Sina

2012-01-01

This paper presents a generalized framework for segmenting closed-contour anatomical and pathological features using graph theory and dynamic programming (GTDP). More specifically, the GTDP method previously developed for quantifying retinal and corneal layer thicknesses is extended to segment objects such as cells and cysts. The presented technique relies on a transform that maps closed-contour features in the Cartesian domain into lines in the quasi-polar domain. The features of interest are then segmented as layers via GTDP. Application of this method to segment closed-contour features in several ophthalmic image types is shown. Quantitative validation experiments for retinal pigmented epithelium cell segmentation in confocal fluorescence microscopy images attests to the accuracy of the presented technique. PMID:22567602

User-guided segmentation for volumetric retinal optical coherence tomography images

PubMed Central

Yin, Xin; Chao, Jennifer R.; Wang, Ruikang K.

2014-01-01

Abstract. Despite the existence of automatic segmentation techniques, trained graders still rely on manual segmentation to provide retinal layers and features from clinical optical coherence tomography (OCT) images for accurate measurements. To bridge the gap between this time-consuming need of manual segmentation and currently available automatic segmentation techniques, this paper proposes a user-guided segmentation method to perform the segmentation of retinal layers and features in OCT images. With this method, by interactively navigating three-dimensional (3-D) OCT images, the user first manually defines user-defined (or sketched) lines at regions where the retinal layers appear very irregular for which the automatic segmentation method often fails to provide satisfactory results. The algorithm is then guided by these sketched lines to trace the entire 3-D retinal layer and anatomical features by the use of novel layer and edge detectors that are based on robust likelihood estimation. The layer and edge boundaries are finally obtained to achieve segmentation. Segmentation of retinal layers in mouse and human OCT images demonstrates the reliability and efficiency of the proposed user-guided segmentation method. PMID:25147962

User-guided segmentation for volumetric retinal optical coherence tomography images.

PubMed

Yin, Xin; Chao, Jennifer R; Wang, Ruikang K

2014-08-01

Despite the existence of automatic segmentation techniques, trained graders still rely on manual segmentation to provide retinal layers and features from clinical optical coherence tomography (OCT) images for accurate measurements. To bridge the gap between this time-consuming need of manual segmentation and currently available automatic segmentation techniques, this paper proposes a user-guided segmentation method to perform the segmentation of retinal layers and features in OCT images. With this method, by interactively navigating three-dimensional (3-D) OCT images, the user first manually defines user-defined (or sketched) lines at regions where the retinal layers appear very irregular for which the automatic segmentation method often fails to provide satisfactory results. The algorithm is then guided by these sketched lines to trace the entire 3-D retinal layer and anatomical features by the use of novel layer and edge detectors that are based on robust likelihood estimation. The layer and edge boundaries are finally obtained to achieve segmentation. Segmentation of retinal layers in mouse and human OCT images demonstrates the reliability and efficiency of the proposed user-guided segmentation method.

Spatially aggregated multiclass pattern classification in functional MRI using optimally selected functional brain areas.

PubMed

Zheng, Weili; Ackley, Elena S; Martínez-Ramón, Manel; Posse, Stefan

2013-02-01

In previous works, boosting aggregation of classifier outputs from discrete brain areas has been demonstrated to reduce dimensionality and improve the robustness and accuracy of functional magnetic resonance imaging (fMRI) classification. However, dimensionality reduction and classification of mixed activation patterns of multiple classes remain challenging. In the present study, the goals were (a) to reduce dimensionality by combining feature reduction at the voxel level and backward elimination of optimally aggregated classifiers at the region level, (b) to compare region selection for spatially aggregated classification using boosting and partial least squares regression methods and (c) to resolve mixed activation patterns using probabilistic prediction of individual tasks. Brain activation maps from interleaved visual, motor, auditory and cognitive tasks were segmented into 144 functional regions. Feature selection reduced the number of feature voxels by more than 50%, leaving 95 regions. The two aggregation approaches further reduced the number of regions to 30, resulting in more than 75% reduction of classification time and misclassification rates of less than 3%. Boosting and partial least squares (PLS) were compared to select the most discriminative and the most task correlated regions, respectively. Successful task prediction in mixed activation patterns was feasible within the first block of task activation in real-time fMRI experiments. This methodology is suitable for sparsifying activation patterns in real-time fMRI and for neurofeedback from distributed networks of brain activation. Copyright © 2013 Elsevier Inc. All rights reserved.

SU-F-T-349: Dosimetric Comparison of Three Different Simultaneous Integrated Boost Irradiation Techniques for Multiple Brain Metastases: Intensity-Modulatedradiotherapy, Hybrid Intensity-Modulated Radiotherapy and Volumetric Modulated Arc Therapy

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

Lin, X; Sun, T; Yin, Y

Purpose: To study the dosimetric impact of intensity-modulated radiotherapy (IMRT), hybrid intensity-modulated radiotherapy (h-IMRT) and volumetric modulated arc therapy(VMAT) for whole-brain radiotherapy (WBRT) with simultaneous integrated boost in patients with multiple brain metastases. Methods: Ten patients with multiple brain metastases were included in this analysis. The prescribed dose was 45 Gy to the whole brain (PTVWBRT) and 55 Gy to individual brain metastases (PTVboost) delivered simultaneously in 25 fractions. Three treatment techniques were designed: the 7 equal spaced fields IMRT plan, hybrid IMRT plan and VMAT with two 358°arcs. In hybrid IMRT plan, two fields(90°and 270°) were planned to themore » whole brain. This was used as a base dose plan. Then 5 fields IMRT plan was optimized based on the two fields plan. The dose distribution in the target, the dose to the organs at risk and total MU in three techniques were compared. Results: For the target dose, conformity and homogeneity in PTV, no statistically differences were observed in the three techniques. For the maximum dose in bilateral lens and the mean dose in bilateral eyes, IMRT and h-IMRT plans showed the highest and lowest value respectively. No statistically significant differences were observed in the dose of optic nerve and brainstem. For the monitor units, IMRT and VMAT plans showed the highest and lowest value respectively. Conclusion: For WBRT with simultaneous integrated boost in patients with multiple brain metastases, hybrid IMRT could reduce the doses to lens and eyes. It is feasible for patients with brain metastases.« less

Segmenting the Adult Education Market.

ERIC Educational Resources Information Center

Aurand, Tim

1994-01-01

Describes market segmentation and how the principles of segmentation can be applied to the adult education market. Indicates that applying segmentation techniques to adult education programs results in programs that are educationally and financially satisfying and serve an appropriate population. (JOW)

Inhomogeneity compensation for MR brain image segmentation using a multi-stage FCM-based approach.

PubMed

Szilágyi, László; Szilágyi, Sándor M; Dávid, László; Benyó, Zoltán

2008-01-01

Intensity inhomogeneity or intensity non-uniformity (INU) is an undesired phenomenon that represents the main obstacle for MR image segmentation and registration methods. Various techniques have been proposed to eliminate or compensate the INU, most of which are embedded into clustering algorithms. This paper proposes a multiple stage fuzzy c-means (FCM) based algorithm for the estimation and compensation of the slowly varying additive or multiplicative noise, supported by a pre-filtering technique for Gaussian and impulse noise elimination. The slowly varying behavior of the bias or gain field is assured by a smoothening filter that performs a context dependent averaging, based on a morphological criterion. The experiments using 2-D synthetic phantoms and real MR images show, that the proposed method provides accurate segmentation. The produced segmentation and fuzzy membership values can serve as excellent support for 3-D registration and segmentation techniques.

A fully automatic three-step liver segmentation method on LDA-based probability maps for multiple contrast MR images.

PubMed

Gloger, Oliver; Kühn, Jens; Stanski, Adam; Völzke, Henry; Puls, Ralf

2010-07-01

Automatic 3D liver segmentation in magnetic resonance (MR) data sets has proven to be a very challenging task in the domain of medical image analysis. There exist numerous approaches for automatic 3D liver segmentation on computer tomography data sets that have influenced the segmentation of MR images. In contrast to previous approaches to liver segmentation in MR data sets, we use all available MR channel information of different weightings and formulate liver tissue and position probabilities in a probabilistic framework. We apply multiclass linear discriminant analysis as a fast and efficient dimensionality reduction technique and generate probability maps then used for segmentation. We develop a fully automatic three-step 3D segmentation approach based upon a modified region growing approach and a further threshold technique. Finally, we incorporate characteristic prior knowledge to improve the segmentation results. This novel 3D segmentation approach is modularized and can be applied for normal and fat accumulated liver tissue properties. Copyright 2010 Elsevier Inc. All rights reserved.

A semi-automatic technique to quantify complex tuberculous lung lesions on 18F-fluorodeoxyglucose positron emission tomography/computerised tomography images.

PubMed

Malherbe, Stephanus T; Dupont, Patrick; Kant, Ilse; Ahlers, Petri; Kriel, Magdalena; Loxton, André G; Chen, Ray Y; Via, Laura E; Thienemann, Friedrich; Wilkinson, Robert J; Barry, Clifton E; Griffith-Richards, Stephanie; Ellman, Annare; Ronacher, Katharina; Winter, Jill; Walzl, Gerhard; Warwick, James M

2018-06-25

There is a growing interest in the use of 18 F-FDG PET-CT to monitor tuberculosis (TB) treatment response. However, TB causes complex and widespread pathology, which is challenging to segment and quantify in a reproducible manner. To address this, we developed a technique to standardise uptake (Z-score), segment and quantify tuberculous lung lesions on PET and CT concurrently, in order to track changes over time. We used open source tools and created a MATLAB script. The technique was optimised on a training set of five pulmonary tuberculosis (PTB) cases after standard TB therapy and 15 control patients with lesion-free lungs. We compared the proposed method to a fixed threshold (SUV > 1) and manual segmentation by two readers and piloted the technique successfully on scans of five control patients and five PTB cases (four cured and one failed treatment case), at diagnosis and after 1 and 6 months of treatment. There was a better correlation between the Z-score-based segmentation and manual segmentation than SUV > 1 and manual segmentation in terms of overall spatial overlap (measured in Dice similarity coefficient) and specificity (1 minus false positive volume fraction). However, SUV > 1 segmentation appeared more sensitive. Both the Z-score and SUV > 1 showed very low variability when measuring change over time. In addition, total glycolytic activity, calculated using segmentation by Z-score and lesion-to-background ratio, correlated well with traditional total glycolytic activity calculations. The technique quantified various PET and CT parameters, including the total glycolytic activity index, metabolic lesion volume, lesion volumes at different CT densities and combined PET and CT parameters. The quantified metrics showed a marked decrease in the cured cases, with changes already apparent at month one, but remained largely unchanged in the failed treatment case. Our technique is promising to segment and quantify the lung scans of pulmonary tuberculosis patients in a semi-automatic manner, appropriate for measuring treatment response. Further validation is required in larger cohorts.

Intensity modulated radiotherapy with simultaneous integrated boost vs. conventional radiotherapy with sequential boost for breast cancer - A preliminary result.

PubMed

Lee, Hsin-Hua; Hou, Ming-Feng; Chuang, Hung-Yi; Huang, Ming-Yii; Tsuei, Le-Ping; Chen, Fang-Ming; Ou-Yang, Fu; Huang, Chih-Jen

2015-10-01

This study was aimed to assess the acute dermatological adverse effect from two distinct RT techniques for breast cancer patients. We compared intensity-modulated radiotherapy with simultaneous integrated boost (IMRT-SIB) and conventional radiotherapy followed by sequential boost (CRT-SB). The study population was composed of 126 consecutive female breast cancer patients treated with breast conserving surgery. Sixty-six patients received IMRT-SIB to 2 dose levels simultaneously. They received 50.4 Gy at 1.8 Gy per fraction to the whole breast and 60.2 Gy at 2.15 Gy per fraction to the tumor bed by integral boost. Sixty patients in the CRT-SB group received 50 Gy in 25 fractions to the whole breast followed by a boost irradiation to tumor bed in 5-7 fractions to a total dose of 60-64 Gy. Acute skin toxicities were documented in agreement with the Common Terminology Criteria for Adverse Events version 3 (CTCAE v.3.0). Ninety-eight patients had grade 1 radiation dermatitis while 14 patients had grade 2. Among those with grade 2, there were 3 patients in IMRT-SIB group (4.5%) while 11 in CRT-SB group (18.3%). (P = 0.048) There was no patient with higher than grade 2 toxicity. Three year local control was 99.2%, 3-year disease free survival was 97.5% and 3-year overall survival was 99.2%. A significant reduction in the severity of acute radiation dermatitis from IMRT-SIB comparing with CRT-SB is demonstrated. Copyright © 2015 Elsevier Ltd. All rights reserved.

Segmentation Techniques for Expanding a Library Instruction Market: Evaluating and Brainstorming.

ERIC Educational Resources Information Center

Warren, Rebecca; Hayes, Sherman; Gunter, Donna

2001-01-01

Describes a two-part segmentation technique applied to an instruction program for an academic library during a strategic planning process. Discusses a brainstorming technique used to create a list of existing and potential audiences, and then describes a follow-up review session that evaluated the past years' efforts. (Author/LRW)

High Step-Up DC—DC Converter for AC Photovoltaic Module with MPPT Control

NASA Astrophysics Data System (ADS)

Sundar, Govindasamy; Karthick, Narashiman; Rama Reddy, Sasi

2014-08-01

This paper presents the high gain step-up BOOST converter which is essential to step up the low output voltage from PV panel to the high voltage according to the requirement of the application. In this paper a high gain BOOST converter with coupled inductor technique is proposed with the MPPT control. Without extreme duty ratios and the numerous turns-ratios of a coupled inductor this converter achieves a high step-up voltage-conversion ratio and the leakage energy of the coupled inductor is efficiently recycled to the load. MPPT control used to extract the maximum power from PV panel by controlling the Duty ratio of the converter. The PV panel, BOOST converter and the MPPT are modeled using Sim Power System blocks in MATLAB/SIMULINK environment. The prototype model of the proposed converter has been implemented with the maximum measured efficiency is up to 95.4% and full-load efficiency is 93.1%.

KENNEDY SPACE CENTER, FLA. - Seen from below and through a solid rocket booster segment mockup, Jeff Thon, an SRB mechanic with United Space Alliance, tests the feasibility of a vertical solid rocket booster propellant grain inspection technique. The inspection of segments is required as part of safety analysis.

NASA Image and Video Library

2003-09-11

KENNEDY SPACE CENTER, FLA. - Seen from below and through a solid rocket booster segment mockup, Jeff Thon, an SRB mechanic with United Space Alliance, tests the feasibility of a vertical solid rocket booster propellant grain inspection technique. The inspection of segments is required as part of safety analysis.

Bayesian Fusion of Color and Texture Segmentations

NASA Technical Reports Server (NTRS)

Manduchi, Roberto

2000-01-01

In many applications one would like to use information from both color and texture features in order to segment an image. We propose a novel technique to combine "soft" segmentations computed for two or more features independently. Our algorithm merges models according to a mean entropy criterion, and allows to choose the appropriate number of classes for the final grouping. This technique also allows to improve the quality of supervised classification based on one feature (e.g. color) by merging information from unsupervised segmentation based on another feature (e.g., texture.)

The SRS (Segmented Rail Surface) railgun: A new approach to restrike control. [Segmented rail surface

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

Parker J.V.

1988-01-01

A Segmented Rail Surface (SRS) structure is described that eliminates restrike arcs by progressively disconnecting segments of the rail surface after the plasma armature has passed. This technique has been demonstrated using the Los Alamos MIDI-2 railgun. Restrike was eliminated in a plasma armature acceleration experiment using metal-foil fuses as opening switches. A plasma velocity increase from 11 to 16 km/s was demonstrated using the SRS technique to eliminate the viscous drag losses associated with the restrike plasma. This technique appears to be a practical option for a laboratory launcher at present and for future multi-shot launchers if appropriate switchesmore » can be developed. 5 refs., 8 figs.« less

Step-by-Step Technique for Segmental Reconstruction of Reverse Hill-Sachs Lesions Using Homologous Osteochondral Allograft.

PubMed

Alkaduhimi, Hassanin; van den Bekerom, Michel P J; van Deurzen, Derek F P

2017-06-01

Posterior shoulder dislocations are accompanied by high forces and can result in an anteromedial humeral head impression fracture called a reverse Hill-Sachs lesion. This reverse Hill-Sachs lesion can result in serious complications including posttraumatic osteoarthritis, posterior dislocations, osteonecrosis, persistent joint stiffness, and loss of shoulder function. Treatment is challenging and depends on the amount of bone loss. Several techniques have been reported to describe the surgical treatment of lesions larger than 20%. However, there is still limited evidence with regard to the optimal procedure. Favorable results have been reported by performing segmental reconstruction of the reverse Hill-Sachs lesion with bone allograft. Although the procedure of segmental reconstruction has been used in several studies, its technique has not yet been well described in detail. In this report we propose a step-by-step description of the technique how to perform a segmental reconstruction of a reverse Hill-Sachs defect.

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

Larkoski, Andrew J.; Maltoni, Fabio; Selvaggi, Michele

The identification of hadronically decaying heavy states, such as vector bosons, the Higgs, or the top quark, produced with large transverse boosts has been and will continue to be a central focus of the jet physics program at the Large Hadron Collider (LHC). At a future hadron collider working at an order-of-magnitude larger energy than the LHC, these heavy states would be easily produced with transverse boosts of several TeV. At these energies, their decay products will be separated by angular scales comparable to individual calorimeter cells, making the current jet substructure identification techniques for hadronic decay modes not directlymore » employable. In addition, at the high energy and luminosity projected at a future hadron collider, there will be numerous sources for contamination including initial- and final-state radiation, underlying event, or pile-up which must be mitigated. We propose a simple strategy to tag such "hyper-boosted" objects that defines jets with radii that scale inversely proportional to their transverse boost and combines the standard calorimetric information with charged track-based observables. By means of a fast detector simulation, we apply it to top quark identification and demonstrate that our method efficiently discriminates hadronically decaying top quarks from light QCD jets up to transverse boosts of 20 TeV. Lastly, our results open the way to tagging heavy objects with energies in the multi-TeV range at present and future hadron colliders.« less

Segmentation of lung nodules in computed tomography images using dynamic programming and multidirection fusion techniques.

PubMed

Wang, Qian; Song, Enmin; Jin, Renchao; Han, Ping; Wang, Xiaotong; Zhou, Yanying; Zeng, Jianchao

2009-06-01

The aim of this study was to develop a novel algorithm for segmenting lung nodules on three-dimensional (3D) computed tomographic images to improve the performance of computer-aided diagnosis (CAD) systems. The database used in this study consists of two data sets obtained from the Lung Imaging Database Consortium. The first data set, containing 23 nodules (22% irregular nodules, 13% nonsolid nodules, 17% nodules attached to other structures), was used for training. The second data set, containing 64 nodules (37% irregular nodules, 40% nonsolid nodules, 62% nodules attached to other structures), was used for testing. Two key techniques were developed in the segmentation algorithm: (1) a 3D extended dynamic programming model, with a newly defined internal cost function based on the information between adjacent slices, allowing parameters to be adapted to each slice, and (2) a multidirection fusion technique, which makes use of the complementary relationships among different directions to improve the final segmentation accuracy. The performance of this approach was evaluated by the overlap criterion, complemented by the true-positive fraction and the false-positive fraction criteria. The mean values of the overlap, true-positive fraction, and false-positive fraction for the first data set achieved using the segmentation scheme were 66%, 75%, and 15%, respectively, and the corresponding values for the second data set were 58%, 71%, and 22%, respectively. The experimental results indicate that this segmentation scheme can achieve better performance for nodule segmentation than two existing algorithms reported in the literature. The proposed 3D extended dynamic programming model is an effective way to segment sequential images of lung nodules. The proposed multidirection fusion technique is capable of reducing segmentation errors especially for no-nodule and near-end slices, thus resulting in better overall performance.

Interleaved segment correction achieves higher improvement factors in using genetic algorithm to optimize light focusing through scattering media

NASA Astrophysics Data System (ADS)

Li, Runze; Peng, Tong; Liang, Yansheng; Yang, Yanlong; Yao, Baoli; Yu, Xianghua; Min, Junwei; Lei, Ming; Yan, Shaohui; Zhang, Chunmin; Ye, Tong

2017-10-01

Focusing and imaging through scattering media has been proved possible with high resolution wavefront shaping. A completely scrambled scattering field can be corrected by applying a correction phase mask on a phase only spatial light modulator (SLM) and thereby the focusing quality can be improved. The correction phase is often found by global searching algorithms, among which Genetic Algorithm (GA) stands out for its parallel optimization process and high performance in noisy environment. However, the convergence of GA slows down gradually with the progression of optimization, causing the improvement factor of optimization to reach a plateau eventually. In this report, we propose an interleaved segment correction (ISC) method that can significantly boost the improvement factor with the same number of iterations comparing with the conventional all segment correction method. In the ISC method, all the phase segments are divided into a number of interleaved groups; GA optimization procedures are performed individually and sequentially among each group of segments. The final correction phase mask is formed by applying correction phases of all interleaved groups together on the SLM. The ISC method has been proved significantly useful in practice because of its ability to achieve better improvement factors when noise is present in the system. We have also demonstrated that the imaging quality is improved as better correction phases are found and applied on the SLM. Additionally, the ISC method lowers the demand of dynamic ranges of detection devices. The proposed method holds potential in applications, such as high-resolution imaging in deep tissue.

Multi-scales region segmentation for ROI separation in digital mammograms

NASA Astrophysics Data System (ADS)

Zhang, Dapeng; Zhang, Di; Li, Yue; Wang, Wei

2017-02-01

Mammography is currently the most effective imaging modality used by radiologists for the screening of breast cancer. Segmentation is one of the key steps in the process of developing anatomical models for calculation of safe medical dose of radiation. This paper explores the potential of the statistical region merging segmentation technique for Breast segmentation in digital mammograms. First, the mammograms are pre-processing for regions enhancement, then the enhanced images are segmented using SRM with multi scales, finally these segmentations are combined for region of interest (ROI) separation and edge detection. The proposed algorithm uses multi-scales region segmentation in order to: separate breast region from background region, region edge detection and ROIs separation. The experiments are performed using a data set of mammograms from different patients, demonstrating the validity of the proposed criterion. Results show that, the statistical region merging segmentation algorithm actually can work on the segmentation of medical image and more accurate than another methods. And the outcome shows that the technique has a great potential to become a method of choice for segmentation of mammograms.

Optimization of segmented thermoelectric generator using Taguchi and ANOVA techniques.

PubMed

Kishore, Ravi Anant; Sanghadasa, Mohan; Priya, Shashank

2017-12-01

Recent studies have demonstrated that segmented thermoelectric generators (TEGs) can operate over large thermal gradient and thus provide better performance (reported efficiency up to 11%) as compared to traditional TEGs, comprising of single thermoelectric (TE) material. However, segmented TEGs are still in early stages of development due to the inherent complexity in their design optimization and manufacturability. In this study, we demonstrate physics based numerical techniques along with Analysis of variance (ANOVA) and Taguchi optimization method for optimizing the performance of segmented TEGs. We have considered comprehensive set of design parameters, such as geometrical dimensions of p-n legs, height of segmentation, hot-side temperature, and load resistance, in order to optimize output power and efficiency of segmented TEGs. Using the state-of-the-art TE material properties and appropriate statistical tools, we provide near-optimum TEG configuration with only 25 experiments as compared to 3125 experiments needed by the conventional optimization methods. The effect of environmental factors on the optimization of segmented TEGs is also studied. Taguchi results are validated against the results obtained using traditional full factorial optimization technique and a TEG configuration for simultaneous optimization of power and efficiency is obtained.

Multi-atlas segmentation with joint label fusion and corrective learning—an open source implementation

PubMed Central

Wang, Hongzhi; Yushkevich, Paul A.

2013-01-01

Label fusion based multi-atlas segmentation has proven to be one of the most competitive techniques for medical image segmentation. This technique transfers segmentations from expert-labeled images, called atlases, to a novel image using deformable image registration. Errors produced by label transfer are further reduced by label fusion that combines the results produced by all atlases into a consensus solution. Among the proposed label fusion strategies, weighted voting with spatially varying weight distributions derived from atlas-target intensity similarity is a simple and highly effective label fusion technique. However, one limitation of most weighted voting methods is that the weights are computed independently for each atlas, without taking into account the fact that different atlases may produce similar label errors. To address this problem, we recently developed the joint label fusion technique and the corrective learning technique, which won the first place of the 2012 MICCAI Multi-Atlas Labeling Challenge and was one of the top performers in 2013 MICCAI Segmentation: Algorithms, Theory and Applications (SATA) challenge. To make our techniques more accessible to the scientific research community, we describe an Insight-Toolkit based open source implementation of our label fusion methods. Our implementation extends our methods to work with multi-modality imaging data and is more suitable for segmentation problems with multiple labels. We demonstrate the usage of our tools through applying them to the 2012 MICCAI Multi-Atlas Labeling Challenge brain image dataset and the 2013 SATA challenge canine leg image dataset. We report the best results on these two datasets so far. PMID:24319427

A Randomized Crossover Study Comparing Cervical Spine Motion During Intubation Between Two Lightwand Intubation Techniques in Patients With Simulated Cervical Immobilization: Laryngoscope-Assisted Versus Conventional Lightwand Intubation.

PubMed

Kim, Tae Kyong; Son, Je-Do; Seo, Hyungseok; Lee, Yun-Seok; Bae, Jinyoung; Park, Hee-Pyoung

2017-08-01

In patients with cervical immobilization, jaw thrust can cause cervical spine movement. Concurrent use of a laryngoscope may facilitate lightwand intubation, allowing midline placement and free movement of the lightwand in the oral cavity without jaw thrust. We compared the effects of laryngoscope-assisted lightwand intubation (LALI) versus conventional lightwand intubation (CLI) on cervical spine motion during intubation in patients with simulated cervical immobilization. In this randomized crossover study, the cervical spine angle was measured before and during intubation at the occiput-C1, C1-C2, and C2-C5 segments in 20 patients with simulated cervical immobilization who underwent intubation using both the LALI and CLI techniques. Cervical spine motion was defined as the change from baseline in angle measured at each cervical segment during intubation. Cervical spine motion at the occiput-C1 segment was 5.6° (4.3) and 9.3° (4.5) when we used the LALI and CLI techniques, respectively (mean difference [98.33% CI]; -3.8° [-7.2 to -0.3]; P = .007). At other cervical segments, it was not significantly different between the 2 techniques (-0.1° [-2.6 to 2.5]; P = .911 in the C1-C2 segment and -0.2° [-2.8 to 2.5]; P = .795 in the C2-C5 segment). The LALI technique produces less upper cervical spine motion during intubation than the CLI technique in patients with simulated cervical immobilization.

Automatic identification of epileptic seizures from EEG signals using linear programming boosting.

PubMed

Hassan, Ahnaf Rashik; Subasi, Abdulhamit

2016-11-01

Computerized epileptic seizure detection is essential for expediting epilepsy diagnosis and research and for assisting medical professionals. Moreover, the implementation of an epilepsy monitoring device that has low power and is portable requires a reliable and successful seizure detection scheme. In this work, the problem of automated epilepsy seizure detection using singe-channel EEG signals has been addressed. At first, segments of EEG signals are decomposed using a newly proposed signal processing scheme, namely complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). Six spectral moments are extracted from the CEEMDAN mode functions and train and test matrices are formed afterward. These matrices are fed into the classifier to identify epileptic seizures from EEG signal segments. In this work, we implement an ensemble learning based machine learning algorithm, namely linear programming boosting (LPBoost) to perform classification. The efficacy of spectral features in the CEEMDAN domain is validated by graphical and statistical analyses. The performance of CEEMDAN is compared to those of its predecessors to further inspect its suitability. The effectiveness and the appropriateness of LPBoost are demonstrated as opposed to the commonly used classification models. Resubstitution and 10 fold cross-validation error analyses confirm the superior algorithm performance of the proposed scheme. The algorithmic performance of our epilepsy seizure identification scheme is also evaluated against state-of-the-art works in the literature. Experimental outcomes manifest that the proposed seizure detection scheme performs better than the existing works in terms of accuracy, sensitivity, specificity, and Cohen's Kappa coefficient. It can be anticipated that owing to its use of only one channel of EEG signal, the proposed method will be suitable for device implementation, eliminate the onus of clinicians for analyzing a large bulk of data manually, and expedite epilepsy diagnosis. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

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.

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

Qiu, J; Zheng, X; Liu, H

Purpose: This study is to evaluate the feasibility of simultaneously integrated boost (SIB) to hypoxic subvolume (HTV) in nasopharyngeal carcinomas under the guidance of 18F-Fluoromisonidazole (FMISO) PET/CT using a novel non-uniform volumetric modulated arc therapy (VMAT)technique. Methods: Eight nasopharyngeal carcinoma patients treated with conventional uniform VMAT were retrospectively analyzed. For each treatment, actual conventional uniform VMAT plan with two or more arcs (2–2.5 arcs, totally rotating angle < 1000o) was designed with dose boost to hopxic subvolume (total dose, 84Gy) in the gross tumor volme (GTV) under the guidance of 18F- FMISO PET/CT. Based on the same dataset, experimental singlemore » arc non-uniform VAMT plans were generated with the same dose prescription using customized software tools. Dosimetric parameters, quality assurance and the efficiency of the treatment delivery were compared between the uniform and non-uniform VMAT plans. Results: To develop the non-uniform VMAT technique, a specific optimization model was successfully established. Both techniques generate high-quality plans with pass rate (>98%) with the 3mm, 3% criterion. HTV received dose of 84.1±0.75Gy and 84.1±1.2Gy from uniform and non-uniform VMAT plans, respectively. In terms of target coverage and dose homogeneity, there was no significant statistical difference between actual and experimental plans for each case. However, for critical organs at risk (OAR), including the parotids, oral cavity and larynx, dosimetric difference was significant with better dose sparing form experimental plans. Regarding plan implementation efficiency, the average machine time was 3.5 minutes for the actual VMAT plans and 3.7 minutes for the experimental nonuniform VMAT plans (p>0.050). Conclusion: Compared to conventional VMAT technique, the proposed non-uniform VMAT technique has the potential to produce efficient and safe treatment plans, especially in cases with complicated anatomical structures and demanding dose boost to subvolumes.« less

Multiclass feature selection for improved pediatric brain tumor segmentation

NASA Astrophysics Data System (ADS)

Ahmed, Shaheen; Iftekharuddin, Khan M.

2012-03-01

In our previous work, we showed that fractal-based texture features are effective in detection, segmentation and classification of posterior-fossa (PF) pediatric brain tumor in multimodality MRI. We exploited an information theoretic approach such as Kullback-Leibler Divergence (KLD) for feature selection and ranking different texture features. We further incorporated the feature selection technique with segmentation method such as Expectation Maximization (EM) for segmentation of tumor T and non tumor (NT) tissues. In this work, we extend the two class KLD technique to multiclass for effectively selecting the best features for brain tumor (T), cyst (C) and non tumor (NT). We further obtain segmentation robustness for each tissue types by computing Bay's posterior probabilities and corresponding number of pixels for each tissue segments in MRI patient images. We evaluate improved tumor segmentation robustness using different similarity metric for 5 patients in T1, T2 and FLAIR modalities.

Identification techniques for highly boosted W bosons that decay into hadrons

DOE PAGES

Khachatryan, Vardan

2014-12-02

In searches for new physics in the energy regime of the LHC, it is becoming increasingly important to distinguish single-jet objects that originate from the merging of the decay products of W bosons produced with high transverse momenta from jets initiated by single partons. Algorithms are defined to identify such W jets for different signals of interest, using techniques that are also applicable to other decays of bosons to hadrons that result in a single jet, such as those from highly boosted Z and Higgs bosons. The efficiency for tagging W jets is measured in data collected with the CMSmore » detector at a center-of-mass energy of 8 TeV, corresponding to an integrated luminosity of 19.7 fb -1. The performance of W tagging in data is compared with predictions from several Monte Carlo simulators.« less

Cloud Detection by Fusing Multi-Scale Convolutional Features

NASA Astrophysics Data System (ADS)

Li, Zhiwei; Shen, Huanfeng; Wei, Yancong; Cheng, Qing; Yuan, Qiangqiang

2018-04-01

Clouds detection is an important pre-processing step for accurate application of optical satellite imagery. Recent studies indicate that deep learning achieves best performance in image segmentation tasks. Aiming at boosting the accuracy of cloud detection for multispectral imagery, especially for those that contain only visible and near infrared bands, in this paper, we proposed a deep learning based cloud detection method termed MSCN (multi-scale cloud net), which segments cloud by fusing multi-scale convolutional features. MSCN was trained on a global cloud cover validation collection, and was tested in more than ten types of optical images with different resolution. Experiment results show that MSCN has obvious advantages over the traditional multi-feature combined cloud detection method in accuracy, especially when in snow and other areas covered by bright non-cloud objects. Besides, MSCN produced more detailed cloud masks than the compared deep cloud detection convolution network. The effectiveness of MSCN make it promising for practical application in multiple kinds of optical imagery.

A comparative study on preprocessing techniques in diabetic retinopathy retinal images: illumination correction and contrast enhancement.

PubMed

Rasta, Seyed Hossein; Partovi, Mahsa Eisazadeh; Seyedarabi, Hadi; Javadzadeh, Alireza

2015-01-01

To investigate the effect of preprocessing techniques including contrast enhancement and illumination correction on retinal image quality, a comparative study was carried out. We studied and implemented a few illumination correction and contrast enhancement techniques on color retinal images to find out the best technique for optimum image enhancement. To compare and choose the best illumination correction technique we analyzed the corrected red and green components of color retinal images statistically and visually. The two contrast enhancement techniques were analyzed using a vessel segmentation algorithm by calculating the sensitivity and specificity. The statistical evaluation of the illumination correction techniques were carried out by calculating the coefficients of variation. The dividing method using the median filter to estimate background illumination showed the lowest Coefficients of variations in the red component. The quotient and homomorphic filtering methods after the dividing method presented good results based on their low Coefficients of variations. The contrast limited adaptive histogram equalization increased the sensitivity of the vessel segmentation algorithm up to 5% in the same amount of accuracy. The contrast limited adaptive histogram equalization technique has a higher sensitivity than the polynomial transformation operator as a contrast enhancement technique for vessel segmentation. Three techniques including the dividing method using the median filter to estimate background, quotient based and homomorphic filtering were found as the effective illumination correction techniques based on a statistical evaluation. Applying the local contrast enhancement technique, such as CLAHE, for fundus images presented good potentials in enhancing the vasculature segmentation.

Evaluation of segmental left ventricular wall motion by equilibrium gated radionuclide ventriculography.

PubMed

Van Nostrand, D; Janowitz, W R; Holmes, D R; Cohen, H A

1979-01-01

The ability of equilibrium gated radionuclide ventriculography to detect segmental left ventricular (LV) wall motion abnormalities was determined in 26 patients undergoing cardiac catheterization. Multiple gated studies obtained in 30 degrees right anterior oblique and 45 degrees left anterior oblique projections, played back in a movie format, were compared to the corresponding LV ventriculograms. The LV wall in the two projections was divided into eight segments. Each segment was graded as normal, hypokinetic, akinetic, dyskinetic, or indeterminate. Thirteen percent of the segments in the gated images were indeterminate; 24 out of 27 of these were proximal or distal inferior wall segments. There was exact agreement in 86% of the remaining segments. The sensitivity of the radionuclide technique for detecting normal versus any abnormal wall motion was 71%, with a specificity of 99%. Equilibrium gated ventriculography is an excellent noninvasive technique for evaluating segmental LV wall motion. It is least reliable in assessing the proximal inferior wall and interventricular septum.

Automatic 2D and 3D segmentation of liver from Computerised Tomography

NASA Astrophysics Data System (ADS)

Evans, Alun

As part of the diagnosis of liver disease, a Computerised Tomography (CT) scan is taken of the patient, which the clinician then uses for assistance in determining the presence and extent of the disease. This thesis presents the background, methodology, results and future work of a project that employs automated methods to segment liver tissue. The clinical motivation behind this work is the desire to facilitate the diagnosis of liver disease such as cirrhosis or cancer, assist in volume determination for liver transplantation, and possibly assist in measuring the effect of any treatment given to the liver. Previous attempts at automatic segmentation of liver tissue have relied on 2D, low-level segmentation techniques, such as thresholding and mathematical morphology, to obtain the basic liver structure. The derived boundary can then be smoothed or refined using more advanced methods. The 2D results presented in this thesis improve greatly on this previous work by using a topology adaptive active contour model to accurately segment liver tissue from CT images. The use of conventional snakes for liver segmentation is difficult due to the presence of other organs closely surrounding the liver this new technique avoids this problem by adding an inflationary force to the basic snake equation, and initialising the snake inside the liver. The concepts underlying the 2D technique are extended to 3D, and results of full 3D segmentation of the liver are presented. The 3D technique makes use of an inflationary active surface model which is adaptively reparameterised, according to its size and local curvature, in order that it may more accurately segment the organ. Statistical analysis of the accuracy of the segmentation is presented for 18 healthy liver datasets, and results of the segmentation of unhealthy livers are also shown. The novel work developed during the course of this project has possibilities for use in other areas of medical imaging research, for example the segmentation of internal liver structures, and the segmentation and classification of unhealthy tissue. The possibilities of this future work are discussed towards the end of the report.

Knee cartilage segmentation using active shape models and local binary patterns

NASA Astrophysics Data System (ADS)

González, Germán.; Escalante-Ramírez, Boris

2014-05-01

Segmentation of knee cartilage has been useful for opportune diagnosis and treatment of osteoarthritis (OA). This paper presents a semiautomatic segmentation technique based on Active Shape Models (ASM) combined with Local Binary Patterns (LBP) and its approaches to describe the surrounding texture of femoral cartilage. The proposed technique is tested on a 16-image database of different patients and it is validated through Leave- One-Out method. We compare different segmentation techniques: ASM-LBP, ASM-medianLBP, and ASM proposed by Cootes. The ASM-LBP approaches are tested with different ratios to decide which of them describes the cartilage texture better. The results show that ASM-medianLBP has better performance than ASM-LBP and ASM. Furthermore, we add a routine which improves the robustness versus two principal problems: oversegmentation and initialization.

KENNEDY SPACE CENTER, FLA. - Jeff Thon, an SRB mechanic with United Space Alliance, is fitted with a harness to test a vertical solid rocket booster propellant grain inspection technique. Thon will be lowered inside a mockup of two segments of the SRBs. The inspection of segments is required as part of safety analysis.

NASA Image and Video Library

2003-09-11

KENNEDY SPACE CENTER, FLA. - Jeff Thon, an SRB mechanic with United Space Alliance, is fitted with a harness to test a vertical solid rocket booster propellant grain inspection technique. Thon will be lowered inside a mockup of two segments of the SRBs. The inspection of segments is required as part of safety analysis.

Training the Recurrent neural network by the Fuzzy Min-Max algorithm for fault prediction

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

Zemouri, Ryad; Racoceanu, Daniel; Zerhouni, Noureddine

2009-03-05

In this paper, we present a training technique of a Recurrent Radial Basis Function neural network for fault prediction. We use the Fuzzy Min-Max technique to initialize the k-center of the RRBF neural network. The k-means algorithm is then applied to calculate the centers that minimize the mean square error of the prediction task. The performances of the k-means algorithm are then boosted by the Fuzzy Min-Max technique.

Sci-Thur AM: YIS – 03: Combining sagittally-reconstructed 3D and live-2D ultrasound for high-dose-rate prostate brachytherapy needle segmentation

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

Hrinivich, Thomas; Hoover, Douglas; Surry, Kathlee

Ultrasound-guided high-dose-rate prostate brachytherapy (HDR-BT) needle segmentation is performed clinically using live-2D sagittal images. Organ segmentation is then performed using axial images, introducing a source of geometric uncertainty. Sagittally-reconstructed 3D (SR3D) ultrasound enables both needle and organ segmentation, but suffers from shadow artifacts. We present a needle segmentation technique augmenting SR3D with live-2D sagittal images using mechanical probe tracking to mitigate image artifacts and compare it to the clinical standard. Seven prostate cancer patients underwent TRUS-guided HDR-BT during which the clinical and proposed segmentation techniques were completed in parallel using dual ultrasound video outputs. Calibrated needle end-length measurements were usedmore » to calculate insertion depth errors (IDEs), and the dosimetric impact of IDEs was evaluated by perturbing clinical treatment plan source positions. The proposed technique provided smaller IDEs than the clinical approach, with mean±SD of −0.3±2.2 mm and −0.5±3.7mm respectively. The proposed and clinical techniques resulted in 84% and 43% of needles with IDEs within ±3mm, and IDE ranges across all needles of [−7.7mm, 5.9mm] and [−9.3mm, 7.7mm] respectively. The proposed and clinical IDEs lead to mean±SD changes in the volume of the prostate receiving the prescription dose of −0.6±0.9% and −2.0±5.3% respectively. The proposed technique provides improved HDR-BT needle segmentation accuracy over the clinical technique leading to decreased dosimetric uncertainty by eliminating the axial-to-sagittal registration, and mitigates the effect of shadow artifacts by incorporating mechanically registered live-2D sagittal images.« less

A comparison study of atlas-based 3D cardiac MRI segmentation: global versus global and local transformations

NASA Astrophysics Data System (ADS)

Daryanani, Aditya; Dangi, Shusil; Ben-Zikri, Yehuda Kfir; Linte, Cristian A.

2016-03-01

Magnetic Resonance Imaging (MRI) is a standard-of-care imaging modality for cardiac function assessment and guidance of cardiac interventions thanks to its high image quality and lack of exposure to ionizing radiation. Cardiac health parameters such as left ventricular volume, ejection fraction, myocardial mass, thickness, and strain can be assessed by segmenting the heart from cardiac MRI images. Furthermore, the segmented pre-operative anatomical heart models can be used to precisely identify regions of interest to be treated during minimally invasive therapy. Hence, the use of accurate and computationally efficient segmentation techniques is critical, especially for intra-procedural guidance applications that rely on the peri-operative segmentation of subject-specific datasets without delaying the procedure workflow. Atlas-based segmentation incorporates prior knowledge of the anatomy of interest from expertly annotated image datasets. Typically, the ground truth atlas label is propagated to a test image using a combination of global and local registration. The high computational cost of non-rigid registration motivated us to obtain an initial segmentation using global transformations based on an atlas of the left ventricle from a population of patient MRI images and refine it using well developed technique based on graph cuts. Here we quantitatively compare the segmentations obtained from the global and global plus local atlases and refined using graph cut-based techniques with the expert segmentations according to several similarity metrics, including Dice correlation coefficient, Jaccard coefficient, Hausdorff distance, and Mean absolute distance error.

An empirical comparison of several recent epistatic interaction detection methods.

PubMed

Wang, Yue; Liu, Guimei; Feng, Mengling; Wong, Limsoon

2011-11-01

Many new methods have recently been proposed for detecting epistatic interactions in GWAS data. There is, however, no in-depth independent comparison of these methods yet. Five recent methods-TEAM, BOOST, SNPHarvester, SNPRuler and Screen and Clean (SC)-are evaluated here in terms of power, type-1 error rate, scalability and completeness. In terms of power, TEAM performs best on data with main effect and BOOST performs best on data without main effect. In terms of type-1 error rate, TEAM and BOOST have higher type-1 error rates than SNPRuler and SNPHarvester. SC does not control type-1 error rate well. In terms of scalability, we tested the five methods using a dataset with 100 000 SNPs on a 64 bit Ubuntu system, with Intel (R) Xeon(R) CPU 2.66 GHz, 16 GB memory. TEAM takes ~36 days to finish and SNPRuler reports heap allocation problems. BOOST scales up to 100 000 SNPs and the cost is much lower than that of TEAM. SC and SNPHarvester are the most scalable. In terms of completeness, we study how frequently the pruning techniques employed by these methods incorrectly prune away the most significant epistatic interactions. We find that, on average, 20% of datasets without main effect and 60% of datasets with main effect are pruned incorrectly by BOOST, SNPRuler and SNPHarvester. The software for the five methods tested are available from the URLs below. TEAM: http://csbio.unc.edu/epistasis/download.php BOOST: http://ihome.ust.hk/~eeyang/papers.html. SNPHarvester: http://bioinformatics.ust.hk/SNPHarvester.html. SNPRuler: http://bioinformatics.ust.hk/SNPRuler.zip. Screen and Clean: http://wpicr.wpic.pitt.edu/WPICCompGen/. wangyue@nus.edu.sg.

Influence of Route of Administration on Immediate and Extended Protection in Rats Immunized with Escherichia coli Heat-Labile Enterotoxin

PubMed Central

Klipstein, Frederick A.; Engert, Richard F.

1980-01-01

The effect of route of administration, dosage, and number of boosts employed during immunization with the polymyxin-release form of Escherichia coli heat-labile (LT) enterotoxin on the degree and duration of protection afforded was evaluated in rats which were challenged by the ligated loop technique. Increasing the boosting dosage by fivefold from 50 to 250 μg resulted in a marked increase in protection against challenge with toxin in rats immunized either just by the parenteral route (i.p./i.p.) or by a parenteral prime, followed by peroral boosts (i.p./p.o.) in rats pretreated with cimetidine to ablate gastric secretions; such was not the case, however, even with a 50-fold increase in dosage in rats immunized just by the peroral route (p.o./p.o.). Four weekly peroral boosts were required to achieve the strongest degree of protection. Increasing the boosting dosage also increased the degree of protection against challenge with viable LT+/ST− and LT+/ST+ strains (ST indicates heat-stable enterotoxin) in rats immunized by the i.p./p.o., but not by the i.p./i.p., route; no protection was evident against an LT−/ST+ strain. Protection was lost within 3 weeks after immunization in rats immunized by the i.p./i.p. route. In contrast, protection was extended over the 3-month observation period in those immunized by the i.p./p.o. route; the degree of protection was enhanced in rats which received an additional boost at 2 months. These observations establish the fact that immunization with LT is similar to that with cholera toxin in that arousal of the local immune intestinal response by means of peroral immunization provides maximal extended protection. PMID:6987180

Semi-automatic segmentation of nonviable cardiac tissue using cine and delayed enhancement magnetic resonance images

NASA Astrophysics Data System (ADS)

O'Donnell, Thomas P.; Xu, Ning; Setser, Randolph M.; White, Richard D.

2003-05-01

Post myocardial infarction, the identification and assessment of non-viable (necrotic) tissues is necessary for effective development of intervention strategies and treatment plans. Delayed Enhancement Magnetic Resonance (DEMR) imaging is a technique whereby non-viable cardiac tissue appears with increased signal intensity. Radiologists typically acquire these images in conjunction with other functional modalities (e.g., MR Cine), and use domain knowledge and experience to isolate the non-viable tissues. In this paper, we present a technique for automatically segmenting these tissues given the delineation of myocardial borders in the DEMR and in the End-systolic and End-diastolic MR Cine images. Briefly, we obtain a set of segmentations furnished by an expert and employ an artificial intelligence technique, Support Vector Machines (SVMs), to "learn" the segmentations based on features culled from the images. Using those features we then allow the SVM to predict the segmentations the expert would provide on previously unseen images.

Performance of an Artificial Multi-observer Deep Neural Network for Fully Automated Segmentation of Polycystic Kidneys.

PubMed

Kline, Timothy L; Korfiatis, Panagiotis; Edwards, Marie E; Blais, Jaime D; Czerwiec, Frank S; Harris, Peter C; King, Bernard F; Torres, Vicente E; Erickson, Bradley J

2017-08-01

Deep learning techniques are being rapidly applied to medical imaging tasks-from organ and lesion segmentation to tissue and tumor classification. These techniques are becoming the leading algorithmic approaches to solve inherently difficult image processing tasks. Currently, the most critical requirement for successful implementation lies in the need for relatively large datasets that can be used for training the deep learning networks. Based on our initial studies of MR imaging examinations of the kidneys of patients affected by polycystic kidney disease (PKD), we have generated a unique database of imaging data and corresponding reference standard segmentations of polycystic kidneys. In the study of PKD, segmentation of the kidneys is needed in order to measure total kidney volume (TKV). Automated methods to segment the kidneys and measure TKV are needed to increase measurement throughput and alleviate the inherent variability of human-derived measurements. We hypothesize that deep learning techniques can be leveraged to perform fast, accurate, reproducible, and fully automated segmentation of polycystic kidneys. Here, we describe a fully automated approach for segmenting PKD kidneys within MR images that simulates a multi-observer approach in order to create an accurate and robust method for the task of segmentation and computation of TKV for PKD patients. A total of 2000 cases were used for training and validation, and 400 cases were used for testing. The multi-observer ensemble method had mean ± SD percent volume difference of 0.68 ± 2.2% compared with the reference standard segmentations. The complete framework performs fully automated segmentation at a level comparable with interobserver variability and could be considered as a replacement for the task of segmentation of PKD kidneys by a human.

Segmentation of hepatic artery in multi-phase liver CT using directional dilation and connectivity analysis

NASA Astrophysics Data System (ADS)

Wang, Lei; Schnurr, Alena-Kathrin; Zidowitz, Stephan; Georgii, Joachim; Zhao, Yue; Razavi, Mohammad; Schwier, Michael; Hahn, Horst K.; Hansen, Christian

2016-03-01

Segmentation of hepatic arteries in multi-phase computed tomography (CT) images is indispensable in liver surgery planning. During image acquisition, the hepatic artery is enhanced by the injection of contrast agent. The enhanced signals are often not stably acquired due to non-optimal contrast timing. Other vascular structure, such as hepatic vein or portal vein, can be enhanced as well in the arterial phase, which can adversely affect the segmentation results. Furthermore, the arteries might suffer from partial volume effects due to their small diameter. To overcome these difficulties, we propose a framework for robust hepatic artery segmentation requiring a minimal amount of user interaction. First, an efficient multi-scale Hessian-based vesselness filter is applied on the artery phase CT image, aiming to enhance vessel structures with specified diameter range. Second, the vesselness response is processed using a Bayesian classifier to identify the most probable vessel structures. Considering the vesselness filter normally performs not ideally on the vessel bifurcations or the segments corrupted by noise, two vessel-reconnection techniques are proposed. The first technique uses a directional morphological operator to dilate vessel segments along their centerline directions, attempting to fill the gap between broken vascular segments. The second technique analyzes the connectivity of vessel segments and reconnects disconnected segments and branches. Finally, a 3D vessel tree is reconstructed. The algorithm has been evaluated using 18 CT images of the liver. To quantitatively measure the similarities between segmented and reference vessel trees, the skeleton coverage and mean symmetric distance are calculated to quantify the agreement between reference and segmented vessel skeletons, resulting in an average of 0:55+/-0:27 and 12:7+/-7:9 mm (mean standard deviation), respectively.

Computed tomography landmark-based semi-automated mesh morphing and mapping techniques: generation of patient specific models of the human pelvis without segmentation.

PubMed

Salo, Zoryana; Beek, Maarten; Wright, David; Whyne, Cari Marisa

2015-04-13

Current methods for the development of pelvic finite element (FE) models generally are based upon specimen specific computed tomography (CT) data. This approach has traditionally required segmentation of CT data sets, which is time consuming and necessitates high levels of user intervention due to the complex pelvic anatomy. The purpose of this research was to develop and assess CT landmark-based semi-automated mesh morphing and mapping techniques to aid the generation and mechanical analysis of specimen-specific FE models of the pelvis without the need for segmentation. A specimen-specific pelvic FE model (source) was created using traditional segmentation methods and morphed onto a CT scan of a different (target) pelvis using a landmark-based method. The morphed model was then refined through mesh mapping by moving the nodes to the bone boundary. A second target model was created using traditional segmentation techniques. CT intensity based material properties were assigned to the morphed/mapped model and to the traditionally segmented target models. Models were analyzed to evaluate their geometric concurrency and strain patterns. Strains generated in a double-leg stance configuration were compared to experimental strain gauge data generated from the same target cadaver pelvis. CT landmark-based morphing and mapping techniques were efficiently applied to create a geometrically multifaceted specimen-specific pelvic FE model, which was similar to the traditionally segmented target model and better replicated the experimental strain results (R(2)=0.873). This study has shown that mesh morphing and mapping represents an efficient validated approach for pelvic FE model generation without the need for segmentation. Copyright © 2015 Elsevier Ltd. All rights reserved.

Tumor segmentation on FDG-PET: usefulness of locally connected conditional random fields

NASA Astrophysics Data System (ADS)

Nishio, Mizuho; Kono, Atsushi K.; Koyama, Hisanobu; Nishii, Tatsuya; Sugimura, Kazuro

2015-03-01

This study aimed to develop software for tumor segmentation on 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET). To segment the tumor from the background, we used graph cut, whose segmentation energy was generally divided into two terms: the unary and pairwise terms. Locally connected conditional random fields (LCRF) was proposed for the pairwise term. In LCRF, a three-dimensional cubic window with length L was set for each voxel, and voxels within the window were considered for the pairwise term. To evaluate our method, 64 clinically suspected metastatic bone tumors were tested, which were revealed by FDG-PET. To obtain ground truth, the tumors were manually delineated via consensus of two board-certified radiologists. To compare the LCRF accuracy, other types of segmentation were also applied such as region-growing based on 35%, 40%, and 45% of the tumor maximum standardized uptake value (RG35, RG40, and RG45, respectively), SLIC superpixels (SS), and region-based active contour models (AC). To validate the tumor segmentation accuracy, a dice similarity coefficient (DSC) was calculated between manual segmentation and result of each technique. The DSC difference was tested using the Wilcoxon signed rank test. The mean DSCs of LCRF at L = 3, 5, 7, and 9 were 0.784, 0.801, 0.809, and 0.812, respectively. The mean DSCs of other techniques were RG35, 0.633; RG40, 0.675; RG45, 0.689; SS, 0.709; and AC, 0.758. The DSC differences between LCRF and other techniques were statistically significant (p <0.05). In conclusion, tumor segmentation was more reliably performed with LCRF relative to other techniques.

Automated segmentation of synchrotron radiation micro-computed tomography biomedical images using Graph Cuts and neural networks

NASA Astrophysics Data System (ADS)

Alvarenga de Moura Meneses, Anderson; Giusti, Alessandro; de Almeida, André Pereira; Parreira Nogueira, Liebert; Braz, Delson; Cely Barroso, Regina; deAlmeida, Carlos Eduardo

2011-12-01

Synchrotron Radiation (SR) X-ray micro-Computed Tomography (μCT) enables magnified images to be used as a non-invasive and non-destructive technique with a high space resolution for the qualitative and quantitative analyses of biomedical samples. The research on applications of segmentation algorithms to SR-μCT is an open problem, due to the interesting and well-known characteristics of SR images for visualization, such as the high resolution and the phase contrast effect. In this article, we describe and assess the application of the Energy Minimization via Graph Cuts (EMvGC) algorithm for the segmentation of SR-μCT biomedical images acquired at the Synchrotron Radiation for MEdical Physics (SYRMEP) beam line at the Elettra Laboratory (Trieste, Italy). We also propose a method using EMvGC with Artificial Neural Networks (EMANNs) for correcting misclassifications due to intensity variation of phase contrast, which are important effects and sometimes indispensable in certain biomedical applications, although they impair the segmentation provided by conventional techniques. Results demonstrate considerable success in the segmentation of SR-μCT biomedical images, with average Dice Similarity Coefficient 99.88% for bony tissue in Wistar Rats rib samples (EMvGC), as well as 98.95% and 98.02% for scans of Rhodnius prolixus insect samples (Chagas's disease vector) with EMANNs, in relation to manual segmentation. The techniques EMvGC and EMANNs cope with the task of performing segmentation in images with the intensity variation due to phase contrast effects, presenting a superior performance in comparison to conventional segmentation techniques based on thresholding and linear/nonlinear image filtering, which is also discussed in the present article.

A new user-assisted segmentation and tracking technique for an object-based video editing system

NASA Astrophysics Data System (ADS)

Yu, Hong Y.; Hong, Sung-Hoon; Lee, Mike M.; Choi, Jae-Gark

2004-03-01

This paper presents a semi-automatic segmentation method which can be used to generate video object plane (VOP) for object based coding scheme and multimedia authoring environment. Semi-automatic segmentation can be considered as a user-assisted segmentation technique. A user can initially mark objects of interest around the object boundaries and then the user-guided and selected objects are continuously separated from the unselected areas through time evolution in the image sequences. The proposed segmentation method consists of two processing steps: partially manual intra-frame segmentation and fully automatic inter-frame segmentation. The intra-frame segmentation incorporates user-assistance to define the meaningful complete visual object of interest to be segmentation and decides precise object boundary. The inter-frame segmentation involves boundary and region tracking to obtain temporal coherence of moving object based on the object boundary information of previous frame. The proposed method shows stable efficient results that could be suitable for many digital video applications such as multimedia contents authoring, content based coding and indexing. Based on these results, we have developed objects based video editing system with several convenient editing functions.

Post processing for offline Chinese handwritten character string recognition

NASA Astrophysics Data System (ADS)

Wang, YanWei; Ding, XiaoQing; Liu, ChangSong

2012-01-01

Offline Chinese handwritten character string recognition is one of the most important research fields in pattern recognition. Due to the free writing style, large variability in character shapes and different geometric characteristics, Chinese handwritten character string recognition is a challenging problem to deal with. However, among the current methods over-segmentation and merging method which integrates geometric information, character recognition information and contextual information, shows a promising result. It is found experimentally that a large part of errors are segmentation error and mainly occur around non-Chinese characters. In a Chinese character string, there are not only wide characters namely Chinese characters, but also narrow characters like digits and letters of the alphabet. The segmentation error is mainly caused by uniform geometric model imposed on all segmented candidate characters. To solve this problem, post processing is employed to improve recognition accuracy of narrow characters. On one hand, multi-geometric models are established for wide characters and narrow characters respectively. Under multi-geometric models narrow characters are not prone to be merged. On the other hand, top rank recognition results of candidate paths are integrated to boost final recognition of narrow characters. The post processing method is investigated on two datasets, in total 1405 handwritten address strings. The wide character recognition accuracy has been improved lightly and narrow character recognition accuracy has been increased up by 10.41% and 10.03% respectively. It indicates that the post processing method is effective to improve recognition accuracy of narrow characters.

MR PROSTATE SEGMENTATION VIA DISTRIBUTED DISCRIMINATIVE DICTIONARY (DDD) LEARNING.

PubMed

Guo, Yanrong; Zhan, Yiqiang; Gao, Yaozong; Jiang, Jianguo; Shen, Dinggang

2013-01-01

Segmenting prostate from MR images is important yet challenging. Due to non-Gaussian distribution of prostate appearances in MR images, the popular active appearance model (AAM) has its limited performance. Although the newly developed sparse dictionary learning method[1, 2] can model the image appearance in a non-parametric fashion, the learned dictionaries still lack the discriminative power between prostate and non-prostate tissues, which is critical for accurate prostate segmentation. In this paper, we propose to integrate deformable model with a novel learning scheme, namely the Distributed Discriminative Dictionary ( DDD ) learning, which can capture image appearance in a non-parametric and discriminative fashion. In particular, three strategies are designed to boost the tissue discriminative power of DDD. First , minimum Redundancy Maximum Relevance (mRMR) feature selection is performed to constrain the dictionary learning in a discriminative feature space. Second , linear discriminant analysis (LDA) is employed to assemble residuals from different dictionaries for optimal separation between prostate and non-prostate tissues. Third , instead of learning the global dictionaries, we learn a set of local dictionaries for the local regions (each with small appearance variations) along prostate boundary, thus achieving better tissue differentiation locally. In the application stage, DDDs will provide the appearance cues to robustly drive the deformable model onto the prostate boundary. Experiments on 50 MR prostate images show that our method can yield a Dice Ratio of 88% compared to the manual segmentations, and have 7% improvement over the conventional AAM.

Segmentation, Splitting, and Classification of Overlapping Bacteria in Microscope Images for Automatic Bacterial Vaginosis Diagnosis.

PubMed

Song, Youyi; He, Liang; Zhou, Feng; Chen, Siping; Ni, Dong; Lei, Baiying; Wang, Tianfu

2017-07-01

Quantitative analysis of bacterial morphotypes in the microscope images plays a vital role in diagnosis of bacterial vaginosis (BV) based on the Nugent score criterion. However, there are two main challenges for this task: 1) It is quite difficult to identify the bacterial regions due to various appearance, faint boundaries, heterogeneous shapes, low contrast with the background, and small bacteria sizes with regards to the image. 2) There are numerous bacteria overlapping each other, which hinder us to conduct accurate analysis on individual bacterium. To overcome these challenges, we propose an automatic method in this paper to diagnose BV by quantitative analysis of bacterial morphotypes, which consists of a three-step approach, i.e., bacteria regions segmentation, overlapping bacteria splitting, and bacterial morphotypes classification. Specifically, we first segment the bacteria regions via saliency cut, which simultaneously evaluates the global contrast and spatial weighted coherence. And then Markov random field model is applied for high-quality unsupervised segmentation of small object. We then decompose overlapping bacteria clumps into markers, and associate a pixel with markers to identify evidence for eventual individual bacterium splitting. Next, we extract morphotype features from each bacterium to learn the descriptors and to characterize the types of bacteria using an Adaptive Boosting machine learning framework. Finally, BV diagnosis is implemented based on the Nugent score criterion. Experiments demonstrate that our proposed method achieves high accuracy and efficiency in computation for BV diagnosis.

Automated skin lesion segmentation with kernel density estimation

NASA Astrophysics Data System (ADS)

Pardo, A.; Real, E.; Fernandez-Barreras, G.; Madruga, F. J.; López-Higuera, J. M.; Conde, O. M.

2017-07-01

Skin lesion segmentation is a complex step for dermoscopy pathological diagnosis. Kernel density estimation is proposed as a segmentation technique based on the statistic distribution of color intensities in the lesion and non-lesion regions.

Tumor or abnormality identification from magnetic resonance images using statistical region fusion based segmentation.

PubMed

Subudhi, Badri Narayan; Thangaraj, Veerakumar; Sankaralingam, Esakkirajan; Ghosh, Ashish

2016-11-01

In this article, a statistical fusion based segmentation technique is proposed to identify different abnormality in magnetic resonance images (MRI). The proposed scheme follows seed selection, region growing-merging and fusion of multiple image segments. In this process initially, an image is divided into a number of blocks and for each block we compute the phase component of the Fourier transform. The phase component of each block reflects the gray level variation among the block but contains a large correlation among them. Hence a singular value decomposition (SVD) technique is adhered to generate a singular value of each block. Then a thresholding procedure is applied on these singular values to identify edgy and smooth regions and some seed points are selected for segmentation. By considering each seed point we perform a binary segmentation of the complete MRI and hence with all seed points we get an equal number of binary images. A parcel based statistical fusion process is used to fuse all the binary images into multiple segments. Effectiveness of the proposed scheme is tested on identifying different abnormalities: prostatic carcinoma detection, tuberculous granulomas identification and intracranial neoplasm or brain tumor detection. The proposed technique is established by comparing its results against seven state-of-the-art techniques with six performance evaluation measures. Copyright © 2016 Elsevier Inc. All rights reserved.

Interactive segmentation of tongue contours in ultrasound video sequences using quality maps

NASA Astrophysics Data System (ADS)

Ghrenassia, Sarah; Ménard, Lucie; Laporte, Catherine

2014-03-01

Ultrasound (US) imaging is an effective and non invasive way of studying the tongue motions involved in normal and pathological speech, and the results of US studies are of interest for the development of new strategies in speech therapy. State-of-the-art tongue shape analysis techniques based on US images depend on semi-automated tongue segmentation and tracking techniques. Recent work has mostly focused on improving the accuracy of the tracking techniques themselves. However, occasional errors remain inevitable, regardless of the technique used, and the tongue tracking process must thus be supervised by a speech scientist who will correct these errors manually or semi-automatically. This paper proposes an interactive framework to facilitate this process. In this framework, the user is guided towards potentially problematic portions of the US image sequence by a segmentation quality map that is based on the normalized energy of an active contour model and automatically produced during tracking. When a problematic segmentation is identified, corrections to the segmented contour can be made on one image and propagated both forward and backward in the problematic subsequence, thereby improving the user experience. The interactive tools were tested in combination with two different tracking algorithms. Preliminary results illustrate the potential of the proposed framework, suggesting that the proposed framework generally improves user interaction time, with little change in segmentation repeatability.

Computation of parton distributions from the quasi-PDF approach at the physical point

NASA Astrophysics Data System (ADS)

Alexandrou, Constantia; Bacchio, Simone; Cichy, Krzysztof; Constantinou, Martha; Hadjiyiannakou, Kyriakos; Jansen, Karl; Koutsou, Giannis; Scapellato, Aurora; Steffens, Fernanda

2018-03-01

We show the first results for parton distribution functions within the proton at the physical pion mass, employing the method of quasi-distributions. In particular, we present the matrix elements for the iso-vector combination of the unpolarized, helicity and transversity quasi-distributions, obtained with Nf = 2 twisted mass cloverimproved fermions and a proton boosted with momentum |p→| = 0.83 GeV. The momentum smearing technique has been applied to improve the overlap with the proton boosted state. Moreover, we present the renormalized helicity matrix elements in the RI' scheme, following the non-perturbative renormalization prescription recently developed by our group.

Responsivity boosting in FIR TiN LEKIDs using phonon recycling: simulations and array design

NASA Astrophysics Data System (ADS)

Fyhrie, Adalyn; McKenney, Christopher; Glenn, Jason; LeDuc, Henry G.; Gao, Jiansong; Day, Peter; Zmuidzinas, Jonas

2016-07-01

To characterize further the cosmic star formation history at high redshifts, a large-area survey by a cryogenic 4-6 meter class telescope with a focal plane populated by tens of thousands of far-infrared (FIR, 30-300 μm) detectors with broadband detector noise equivalent powers (NEPs) on the order of 3×10-9 W/√ Hz is needed. Ideal detectors for such a surveyor do not yet exist. As a demonstration of one technique for approaching the ultra-low NEPs required by this surveyor, we present the design of an array of 96 350 µm KIDs that utilize phonon recycling to boost responsivity. Our KID array is fabricated with TiN deposited on a silicon-on-insulator (SOI) wafer, which is a 2 μm thick layer of silicon bonded to a thicker slab of silicon by a thin oxide layer. The backside thick slab is etched away underneath the absorbers so that the inductors are suspended on just the 2 μm membrane. The intent is that quasiparticle recombination phonons are trapped in the thin membrane, thereby increasing their likelihood of being re-absorbed by the KID to break additional Cooper pairs and boost responsivity. We also present a Monte-Carlo simulation that predicts the amount of signal boost expected from phonon recycling given different detector geometries and illumination strategies. For our current array geometry, the simulation predicts a measurable 50% boost in responsivity.

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

Turley, Jessica; Claridge Mackonis, Elizabeth

To evaluate in-field megavoltage (MV) imaging of simultaneously integrated boost (SIB) breast fields to determine its feasibility in treatment verification for the SIB breast radiotherapy technique, and to assess whether the current-imaging protocol and treatment margins are sufficient. For nine patients undergoing SIB breast radiotherapy, in-field MV images of the SIB fields were acquired on days that regular treatment verification imaging was performed. The in-field images were matched offline according to the scar wire on digitally reconstructed radiographs. The offline image correction results were then applied to a margin recipe formula to calculate safe margins that account for random andmore » systematic uncertainties in the position of the boost volume when an offline correction protocol has been applied. After offline assessment of the acquired images, 96% were within the tolerance set in the current department-imaging protocol. Retrospectively performing the maximum position deviations on the Eclipse™ treatment planning system demonstrated that the clinical target volume (CTV) boost received a minimum dose difference of 0.4% and a maximum dose difference of 1.4% less than planned. Furthermore, applying our results to the Van Herk margin formula to ensure that 90% of patients receive 95% of the prescribed dose, the calculated CTV margins were comparable to the current departmental procedure used. Based on the in-field boost images acquired and the feasible application of these results to the margin formula the current CTV-planning target volume margins used are appropriate for the accurate treatment of the SIB boost volume without additional imaging.« less

The 'respect rather than resect' principle in mitral valve repair: the lateral dislocation of the P2 technique.

PubMed

Zanobini, Marco; Ricciardi, Gabriella; Mammana, Francesco Liborio; Kassem, Samer; Poggio, Paolo; Di Minno, Alessandro; Cavallotti, L; Saccocci, Matteo

2017-09-01

Leaflet resection represents the reference standard for surgical treatment of mitral valve (MV) regurgitation. New approaches recently proposed place emphasis on respecting, rather than resecting, the leaflet tissue to avoid the drawbacks of the 'resection' approach. The lateral dislocation of mid portion of mitral posterior leaflet (P2) technique for MV repair is a nonresectional technique in which the prolapsed P2 segment is sutured to normal P1 segment. Our study evaluates the effectiveness of this technique. We performed the procedure on seven patients. Once ring annular sutures were placed, the prolapsed P2 segment was dislocated toward the normal P1 segment with a rotation of 90° and without any resection. If present, residual clefts between P2 and P3 segments were closed. Once the absence of residual mitral regurgitation is confirmed by saline pressure test, ring annuloplasty was completed. The valve was evaluated using transesophageal echocardiography in the operating room and by transthoracic echocardiography before discharge. At the last follow-up visit, transthoracic echocardiography revealed no mitral regurgitation and normal TRANSVALVULAR gradients. The lateral dislocation of P2 is an easily fine-tuned technique for isolated P2 prolapse, with the advantage of short aortic cross-clamp and cardiopulmonary bypass times. We think it might be very favorable in older and frail patients. Long-term follow-up is necessary to assess the durability of this technique.

Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans

NASA Astrophysics Data System (ADS)

Cheng, Jie-Zhi; Ni, Dong; Chou, Yi-Hong; Qin, Jing; Tiu, Chui-Mei; Chang, Yeun-Chung; Huang, Chiun-Sheng; Shen, Dinggang; Chen, Chung-Ming

2016-04-01

This paper performs a comprehensive study on the deep-learning-based computer-aided diagnosis (CADx) for the differential diagnosis of benign and malignant nodules/lesions by avoiding the potential errors caused by inaccurate image processing results (e.g., boundary segmentation), as well as the classification bias resulting from a less robust feature set, as involved in most conventional CADx algorithms. Specifically, the stacked denoising auto-encoder (SDAE) is exploited on the two CADx applications for the differentiation of breast ultrasound lesions and lung CT nodules. The SDAE architecture is well equipped with the automatic feature exploration mechanism and noise tolerance advantage, and hence may be suitable to deal with the intrinsically noisy property of medical image data from various imaging modalities. To show the outperformance of SDAE-based CADx over the conventional scheme, two latest conventional CADx algorithms are implemented for comparison. 10 times of 10-fold cross-validations are conducted to illustrate the efficacy of the SDAE-based CADx algorithm. The experimental results show the significant performance boost by the SDAE-based CADx algorithm over the two conventional methods, suggesting that deep learning techniques can potentially change the design paradigm of the CADx systems without the need of explicit design and selection of problem-oriented features.

Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans.

PubMed

Cheng, Jie-Zhi; Ni, Dong; Chou, Yi-Hong; Qin, Jing; Tiu, Chui-Mei; Chang, Yeun-Chung; Huang, Chiun-Sheng; Shen, Dinggang; Chen, Chung-Ming

2016-04-15

This paper performs a comprehensive study on the deep-learning-based computer-aided diagnosis (CADx) for the differential diagnosis of benign and malignant nodules/lesions by avoiding the potential errors caused by inaccurate image processing results (e.g., boundary segmentation), as well as the classification bias resulting from a less robust feature set, as involved in most conventional CADx algorithms. Specifically, the stacked denoising auto-encoder (SDAE) is exploited on the two CADx applications for the differentiation of breast ultrasound lesions and lung CT nodules. The SDAE architecture is well equipped with the automatic feature exploration mechanism and noise tolerance advantage, and hence may be suitable to deal with the intrinsically noisy property of medical image data from various imaging modalities. To show the outperformance of SDAE-based CADx over the conventional scheme, two latest conventional CADx algorithms are implemented for comparison. 10 times of 10-fold cross-validations are conducted to illustrate the efficacy of the SDAE-based CADx algorithm. The experimental results show the significant performance boost by the SDAE-based CADx algorithm over the two conventional methods, suggesting that deep learning techniques can potentially change the design paradigm of the CADx systems without the need of explicit design and selection of problem-oriented features.

Comments on the 2015 ESC Guidelines for the Management of Acute Coronary Syndromes in Patients Presenting Without Persistent ST-segment Elevation.

PubMed

Barrabes, J

2015-12-01

Patients who have undergone angioplasty with stenting can be reintegrated into normal life at an early stage, thanks to the absence of sequelae associated with the procedure itself. Consequently, these patients can be involved earlier in the second stage of cardiac rehabilitation. Although rehabilitation for coronary patients follows the general guidelines used for all patients, which were developed with the secondary prevention of coronary artery atherosclerosis in mind, the specific form of rehabilitation adopted for each individual with ischemic heart disease will depend on the patient's circumstances, including the revascularization technique used. Regular physical exercise (i.e. physical training), in itself, has substantial cardiovascular benefits for both primary and secondary cardiovascular prevention. In patients who have had a myocardial infarction, training decreases mortality, increases functional capacity and improves ventricular function and remodeling. It is also thought to boost the collateral circulation. In addition, training improves endothelial function and stimulates the circulation of stem cells. It has been shown that physical training after percutaneous revascularization decreases the number of cardiac events. Moreover, in patients with stable angina, it results in fewer events than percutaneous revascularization. Copyright © 2015 Sociedad Española de Cardiología. Published by Elsevier España, S.L.U. All rights reserved.

Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans

PubMed Central

Cheng, Jie-Zhi; Ni, Dong; Chou, Yi-Hong; Qin, Jing; Tiu, Chui-Mei; Chang, Yeun-Chung; Huang, Chiun-Sheng; Shen, Dinggang; Chen, Chung-Ming

2016-01-01

This paper performs a comprehensive study on the deep-learning-based computer-aided diagnosis (CADx) for the differential diagnosis of benign and malignant nodules/lesions by avoiding the potential errors caused by inaccurate image processing results (e.g., boundary segmentation), as well as the classification bias resulting from a less robust feature set, as involved in most conventional CADx algorithms. Specifically, the stacked denoising auto-encoder (SDAE) is exploited on the two CADx applications for the differentiation of breast ultrasound lesions and lung CT nodules. The SDAE architecture is well equipped with the automatic feature exploration mechanism and noise tolerance advantage, and hence may be suitable to deal with the intrinsically noisy property of medical image data from various imaging modalities. To show the outperformance of SDAE-based CADx over the conventional scheme, two latest conventional CADx algorithms are implemented for comparison. 10 times of 10-fold cross-validations are conducted to illustrate the efficacy of the SDAE-based CADx algorithm. The experimental results show the significant performance boost by the SDAE-based CADx algorithm over the two conventional methods, suggesting that deep learning techniques can potentially change the design paradigm of the CADx systems without the need of explicit design and selection of problem-oriented features. PMID:27079888

Automated kidney morphology measurements from ultrasound images using texture and edge analysis

NASA Astrophysics Data System (ADS)

Ravishankar, Hariharan; Annangi, Pavan; Washburn, Michael; Lanning, Justin

2016-04-01

In a typical ultrasound scan, a sonographer measures Kidney morphology to assess renal abnormalities. Kidney morphology can also help to discriminate between chronic and acute kidney failure. The caliper placements and volume measurements are often time consuming and an automated solution will help to improve accuracy, repeatability and throughput. In this work, we developed an automated Kidney morphology measurement solution from long axis Ultrasound scans. Automated kidney segmentation is challenging due to wide variability in kidney shape, size, weak contrast of the kidney boundaries and presence of strong edges like diaphragm, fat layers. To address the challenges and be able to accurately localize and detect kidney regions, we present a two-step algorithm that makes use of edge and texture information in combination with anatomical cues. First, we use an edge analysis technique to localize kidney region by matching the edge map with predefined templates. To accurately estimate the kidney morphology, we use textural information in a machine learning algorithm framework using Haar features and Gradient boosting classifier. We have tested the algorithm on 45 unseen cases and the performance against ground truth is measured by computing Dice overlap, % error in major and minor axis of kidney. The algorithm shows successful performance on 80% cases.

Contour Tracking in Echocardiographic Sequences via Sparse Representation and Dictionary Learning

PubMed Central

Huang, Xiaojie; Dione, Donald P.; Compas, Colin B.; Papademetris, Xenophon; Lin, Ben A.; Bregasi, Alda; Sinusas, Albert J.; Staib, Lawrence H.; Duncan, James S.

2013-01-01

This paper presents a dynamical appearance model based on sparse representation and dictionary learning for tracking both endocardial and epicardial contours of the left ventricle in echocardiographic sequences. Instead of learning offline spatiotemporal priors from databases, we exploit the inherent spatiotemporal coherence of individual data to constraint cardiac contour estimation. The contour tracker is initialized with a manual tracing of the first frame. It employs multiscale sparse representation of local image appearance and learns online multiscale appearance dictionaries in a boosting framework as the image sequence is segmented frame-by-frame sequentially. The weights of multiscale appearance dictionaries are optimized automatically. Our region-based level set segmentation integrates a spectrum of complementary multilevel information including intensity, multiscale local appearance, and dynamical shape prediction. The approach is validated on twenty-six 4D canine echocardiographic images acquired from both healthy and post-infarct canines. The segmentation results agree well with expert manual tracings. The ejection fraction estimates also show good agreement with manual results. Advantages of our approach are demonstrated by comparisons with a conventional pure intensity model, a registration-based contour tracker, and a state-of-the-art database-dependent offline dynamical shape model. We also demonstrate the feasibility of clinical application by applying the method to four 4D human data sets. PMID:24292554

A Comparative Study on Preprocessing Techniques in Diabetic Retinopathy Retinal Images: Illumination Correction and Contrast Enhancement

PubMed Central

Rasta, Seyed Hossein; Partovi, Mahsa Eisazadeh; Seyedarabi, Hadi; Javadzadeh, Alireza

2015-01-01

To investigate the effect of preprocessing techniques including contrast enhancement and illumination correction on retinal image quality, a comparative study was carried out. We studied and implemented a few illumination correction and contrast enhancement techniques on color retinal images to find out the best technique for optimum image enhancement. To compare and choose the best illumination correction technique we analyzed the corrected red and green components of color retinal images statistically and visually. The two contrast enhancement techniques were analyzed using a vessel segmentation algorithm by calculating the sensitivity and specificity. The statistical evaluation of the illumination correction techniques were carried out by calculating the coefficients of variation. The dividing method using the median filter to estimate background illumination showed the lowest Coefficients of variations in the red component. The quotient and homomorphic filtering methods after the dividing method presented good results based on their low Coefficients of variations. The contrast limited adaptive histogram equalization increased the sensitivity of the vessel segmentation algorithm up to 5% in the same amount of accuracy. The contrast limited adaptive histogram equalization technique has a higher sensitivity than the polynomial transformation operator as a contrast enhancement technique for vessel segmentation. Three techniques including the dividing method using the median filter to estimate background, quotient based and homomorphic filtering were found as the effective illumination correction techniques based on a statistical evaluation. Applying the local contrast enhancement technique, such as CLAHE, for fundus images presented good potentials in enhancing the vasculature segmentation. PMID:25709940

The slip-and-slide algorithm: a refinement protocol for detector geometry

PubMed Central

Ginn, Helen Mary; Stuart, David Ian

2017-01-01

Geometry correction is traditionally plagued by mis-fitting of correlated parameters, leading to local minima which prevent further improvements. Segmented detectors pose an enhanced risk of mis-fitting: even a minor confusion of detector distance and panel separation can prevent improvement in data quality. The slip-and-slide algorithm breaks down effects of the correlated parameters and their associated target functions in a fundamental shift in the approach to the problem. Parameters are never refined against the components of the data to which they are insensitive, providing a dramatic boost in the exploitation of information from a very small number of diffraction patterns. This algorithm can be applied to exploit the adherence of the spot-finding results prior to indexing to a given lattice using unit-cell dimensions as a restraint. Alternatively, it can be applied to the predicted spot locations and the observed reflection positions after indexing from a smaller number of images. Thus, the indexing rate can be boosted by 5.8% using geometry refinement from only 125 indexed patterns or 500 unindexed patterns. In one example of cypovirus type 17 polyhedrin diffraction at the Linac Coherent Light Source, this geometry refinement reveals a detector tilt of 0.3° (resulting in a maximal Z-axis error of ∼0.5 mm from an average detector distance of ∼90 mm) whilst treating all panels independently. Re-indexing and integrating with updated detector geometry reduces systematic errors providing a boost in anomalous signal of sulfur atoms by 20%. Due to the refinement of decoupled parameters, this geometry method also reaches convergence. PMID:29091058

Internal bone transport using a cannulated screw as a mounting device in the treatment of a post-infective ulnar defect.

PubMed

Tsitskaris, Konstantinos; Havard, Heledd; Bijlsma, Paulien; Hill, Robert A

2016-04-01

Bone transport techniques can be used to address the segmental bone loss occurring after debridement for infection. Secure fixation of the bone transport construct to the bone transport segment can be challenging, particularly if the bone is small and osteopenic. We report a case of a segmental ulnar bone defect in a young child treated with internal bone transport using a cannulated screw as the mounting device. We found this technique particularly useful in the treatment of bone loss secondary to infection, where previous treatment and prolonged immobilisation had led to osteopenia. This technique has not been previously reported.

A Dataset and a Technique for Generalized Nuclear Segmentation for Computational Pathology.

PubMed

Kumar, Neeraj; Verma, Ruchika; Sharma, Sanuj; Bhargava, Surabhi; Vahadane, Abhishek; Sethi, Amit

2017-07-01

Nuclear segmentation in digital microscopic tissue images can enable extraction of high-quality features for nuclear morphometrics and other analysis in computational pathology. Conventional image processing techniques, such as Otsu thresholding and watershed segmentation, do not work effectively on challenging cases, such as chromatin-sparse and crowded nuclei. In contrast, machine learning-based segmentation can generalize across various nuclear appearances. However, training machine learning algorithms requires data sets of images, in which a vast number of nuclei have been annotated. Publicly accessible and annotated data sets, along with widely agreed upon metrics to compare techniques, have catalyzed tremendous innovation and progress on other image classification problems, particularly in object recognition. Inspired by their success, we introduce a large publicly accessible data set of hematoxylin and eosin (H&E)-stained tissue images with more than 21000 painstakingly annotated nuclear boundaries, whose quality was validated by a medical doctor. Because our data set is taken from multiple hospitals and includes a diversity of nuclear appearances from several patients, disease states, and organs, techniques trained on it are likely to generalize well and work right out-of-the-box on other H&E-stained images. We also propose a new metric to evaluate nuclear segmentation results that penalizes object- and pixel-level errors in a unified manner, unlike previous metrics that penalize only one type of error. We also propose a segmentation technique based on deep learning that lays a special emphasis on identifying the nuclear boundaries, including those between the touching or overlapping nuclei, and works well on a diverse set of test images.

Efficient multi-atlas abdominal segmentation on clinically acquired CT with SIMPLE context learning.

PubMed

Xu, Zhoubing; Burke, Ryan P; Lee, Christopher P; Baucom, Rebeccah B; Poulose, Benjamin K; Abramson, Richard G; Landman, Bennett A

2015-08-01

Abdominal segmentation on clinically acquired computed tomography (CT) has been a challenging problem given the inter-subject variance of human abdomens and complex 3-D relationships among organs. Multi-atlas segmentation (MAS) provides a potentially robust solution by leveraging label atlases via image registration and statistical fusion. We posit that the efficiency of atlas selection requires further exploration in the context of substantial registration errors. The selective and iterative method for performance level estimation (SIMPLE) method is a MAS technique integrating atlas selection and label fusion that has proven effective for prostate radiotherapy planning. Herein, we revisit atlas selection and fusion techniques for segmenting 12 abdominal structures using clinically acquired CT. Using a re-derived SIMPLE algorithm, we show that performance on multi-organ classification can be improved by accounting for exogenous information through Bayesian priors (so called context learning). These innovations are integrated with the joint label fusion (JLF) approach to reduce the impact of correlated errors among selected atlases for each organ, and a graph cut technique is used to regularize the combined segmentation. In a study of 100 subjects, the proposed method outperformed other comparable MAS approaches, including majority vote, SIMPLE, JLF, and the Wolz locally weighted vote technique. The proposed technique provides consistent improvement over state-of-the-art approaches (median improvement of 7.0% and 16.2% in DSC over JLF and Wolz, respectively) and moves toward efficient segmentation of large-scale clinically acquired CT data for biomarker screening, surgical navigation, and data mining. Copyright © 2015 Elsevier B.V. All rights reserved.

Techniques on semiautomatic segmentation using the Adobe Photoshop

NASA Astrophysics Data System (ADS)

Park, Jin Seo; Chung, Min Suk; Hwang, Sung Bae

2005-04-01

The purpose of this research is to enable anybody to semiautomatically segment the anatomical structures in the MRIs, CTs, and other medical images on the personal computer. The segmented images are used for making three-dimensional images, which are helpful in medical education and research. To achieve this purpose, the following trials were performed. The entire body of a volunteer was MR scanned to make 557 MRIs, which were transferred to a personal computer. On Adobe Photoshop, contours of 19 anatomical structures in the MRIs were semiautomatically drawn using MAGNETIC LASSO TOOL; successively, manually corrected using either LASSO TOOL or DIRECT SELECTION TOOL to make 557 segmented images. In a likewise manner, 11 anatomical structures in the 8,500 anatomcial images were segmented. Also, 12 brain and 10 heart anatomical structures in anatomical images were segmented. Proper segmentation was verified by making and examining the coronal, sagittal, and three-dimensional images from the segmented images. During semiautomatic segmentation on Adobe Photoshop, suitable algorithm could be used, the extent of automatization could be regulated, convenient user interface could be used, and software bugs rarely occurred. The techniques of semiautomatic segmentation using Adobe Photoshop are expected to be widely used for segmentation of the anatomical structures in various medical images.

Detection and tracking of gas plumes in LWIR hyperspectral video sequence data

NASA Astrophysics Data System (ADS)

Gerhart, Torin; Sunu, Justin; Lieu, Lauren; Merkurjev, Ekaterina; Chang, Jen-Mei; Gilles, Jérôme; Bertozzi, Andrea L.

2013-05-01

Automated detection of chemical plumes presents a segmentation challenge. The segmentation problem for gas plumes is difficult due to the diffusive nature of the cloud. The advantage of considering hyperspectral images in the gas plume detection problem over the conventional RGB imagery is the presence of non-visual data, allowing for a richer representation of information. In this paper we present an effective method of visualizing hyperspectral video sequences containing chemical plumes and investigate the effectiveness of segmentation techniques on these post-processed videos. Our approach uses a combination of dimension reduction and histogram equalization to prepare the hyperspectral videos for segmentation. First, Principal Components Analysis (PCA) is used to reduce the dimension of the entire video sequence. This is done by projecting each pixel onto the first few Principal Components resulting in a type of spectral filter. Next, a Midway method for histogram equalization is used. These methods redistribute the intensity values in order to reduce icker between frames. This properly prepares these high-dimensional video sequences for more traditional segmentation techniques. We compare the ability of various clustering techniques to properly segment the chemical plume. These include K-means, spectral clustering, and the Ginzburg-Landau functional.

Gray matter segmentation of the spinal cord with active contours in MR images.

PubMed

Datta, Esha; Papinutto, Nico; Schlaeger, Regina; Zhu, Alyssa; Carballido-Gamio, Julio; Henry, Roland G

2017-02-15

Fully or partially automated spinal cord gray matter segmentation techniques for spinal cord gray matter segmentation will allow for pivotal spinal cord gray matter measurements in the study of various neurological disorders. The objective of this work was multi-fold: (1) to develop a gray matter segmentation technique that uses registration methods with an existing delineation of the cord edge along with Morphological Geodesic Active Contour (MGAC) models; (2) to assess the accuracy and reproducibility of the newly developed technique on 2D PSIR T1 weighted images; (3) to test how the algorithm performs on different resolutions and other contrasts; (4) to demonstrate how the algorithm can be extended to 3D scans; and (5) to show the clinical potential for multiple sclerosis patients. The MGAC algorithm was developed using a publicly available implementation of a morphological geodesic active contour model and the spinal cord segmentation tool of the software Jim (Xinapse Systems) for initial estimate of the cord boundary. The MGAC algorithm was demonstrated on 2D PSIR images of the C2/C3 level with two different resolutions, 2D T2* weighted images of the C2/C3 level, and a 3D PSIR image. These images were acquired from 45 healthy controls and 58 multiple sclerosis patients selected for the absence of evident lesions at the C2/C3 level. Accuracy was assessed though visual assessment, Hausdorff distances, and Dice similarity coefficients. Reproducibility was assessed through interclass correlation coefficients. Validity was assessed through comparison of segmented gray matter areas in images with different resolution for both manual and MGAC segmentations. Between MGAC and manual segmentations in healthy controls, the mean Dice similarity coefficient was 0.88 (0.82-0.93) and the mean Hausdorff distance was 0.61 (0.46-0.76) mm. The interclass correlation coefficient from test and retest scans of healthy controls was 0.88. The percent change between the manual segmentations from high and low-resolution images was 25%, while the percent change between the MGAC segmentations from high and low resolution images was 13%. Between MGAC and manual segmentations in MS patients, the average Dice similarity coefficient was 0.86 (0.8-0.92) and the average Hausdorff distance was 0.83 (0.29-1.37) mm. We demonstrate that an automatic segmentation technique, based on a morphometric geodesic active contours algorithm, can provide accurate and precise spinal cord gray matter segmentations on 2D PSIR images. We have also shown how this automated technique can potentially be extended to other imaging protocols. Copyright © 2016 Elsevier Inc. All rights reserved.

48 CFR 9904.410-50 - Techniques for application.

Code of Federal Regulations, 2012 CFR

2012-10-01

... segment incurred by another segment shall be removed from the incurring segment's G&A expense pool. They... whole, shall be included in the receiving segment's G&A expense pool. (2) Any separate allocation of the... from the G&A expense pool required by 9904.410-40(a), and the particular final cost objective's cost...

48 CFR 9904.410-50 - Techniques for application.

Code of Federal Regulations, 2014 CFR

2014-10-01

... segment incurred by another segment shall be removed from the incurring segment's G&A expense pool. They... whole, shall be included in the receiving segment's G&A expense pool. (2) Any separate allocation of the... from the G&A expense pool required by 9904.410-40(a), and the particular final cost objective's cost...

48 CFR 9904.410-50 - Techniques for application.

Code of Federal Regulations, 2013 CFR

2013-10-01

... segment incurred by another segment shall be removed from the incurring segment's G&A expense pool. They... whole, shall be included in the receiving segment's G&A expense pool. (2) Any separate allocation of the... from the G&A expense pool required by 9904.410-40(a), and the particular final cost objective's cost...

Segmentation of pulmonary nodules in computed tomography using a regression neural network approach and its application to the Lung Image Database Consortium and Image Database Resource Initiative dataset.

PubMed

Messay, Temesguen; Hardie, Russell C; Tuinstra, Timothy R

2015-05-01

We present new pulmonary nodule segmentation algorithms for computed tomography (CT). These include a fully-automated (FA) system, a semi-automated (SA) system, and a hybrid system. Like most traditional systems, the new FA system requires only a single user-supplied cue point. On the other hand, the SA system represents a new algorithm class requiring 8 user-supplied control points. This does increase the burden on the user, but we show that the resulting system is highly robust and can handle a variety of challenging cases. The proposed hybrid system starts with the FA system. If improved segmentation results are needed, the SA system is then deployed. The FA segmentation engine has 2 free parameters, and the SA system has 3. These parameters are adaptively determined for each nodule in a search process guided by a regression neural network (RNN). The RNN uses a number of features computed for each candidate segmentation. We train and test our systems using the new Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) data. To the best of our knowledge, this is one of the first nodule-specific performance benchmarks using the new LIDC-IDRI dataset. We also compare the performance of the proposed methods with several previously reported results on the same data used by those other methods. Our results suggest that the proposed FA system improves upon the state-of-the-art, and the SA system offers a considerable boost over the FA system. Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.

DeepNAT: Deep convolutional neural network for segmenting neuroanatomy.

PubMed

Wachinger, Christian; Reuter, Martin; Klein, Tassilo

2018-04-15

We introduce DeepNAT, a 3D Deep convolutional neural network for the automatic segmentation of NeuroAnaTomy in T1-weighted magnetic resonance images. DeepNAT is an end-to-end learning-based approach to brain segmentation that jointly learns an abstract feature representation and a multi-class classification. We propose a 3D patch-based approach, where we do not only predict the center voxel of the patch but also neighbors, which is formulated as multi-task learning. To address a class imbalance problem, we arrange two networks hierarchically, where the first one separates foreground from background, and the second one identifies 25 brain structures on the foreground. Since patches lack spatial context, we augment them with coordinates. To this end, we introduce a novel intrinsic parameterization of the brain volume, formed by eigenfunctions of the Laplace-Beltrami operator. As network architecture, we use three convolutional layers with pooling, batch normalization, and non-linearities, followed by fully connected layers with dropout. The final segmentation is inferred from the probabilistic output of the network with a 3D fully connected conditional random field, which ensures label agreement between close voxels. The roughly 2.7million parameters in the network are learned with stochastic gradient descent. Our results show that DeepNAT compares favorably to state-of-the-art methods. Finally, the purely learning-based method may have a high potential for the adaptation to young, old, or diseased brains by fine-tuning the pre-trained network with a small training sample on the target application, where the availability of larger datasets with manual annotations may boost the overall segmentation accuracy in the future. Copyright © 2017 Elsevier Inc. All rights reserved.

Multi-object segmentation using coupled nonparametric shape and relative pose priors

NASA Astrophysics Data System (ADS)

Uzunbas, Mustafa Gökhan; Soldea, Octavian; Çetin, Müjdat; Ünal, Gözde; Erçil, Aytül; Unay, Devrim; Ekin, Ahmet; Firat, Zeynep

2009-02-01

We present a new method for multi-object segmentation in a maximum a posteriori estimation framework. Our method is motivated by the observation that neighboring or coupling objects in images generate configurations and co-dependencies which could potentially aid in segmentation if properly exploited. Our approach employs coupled shape and inter-shape pose priors that are computed using training images in a nonparametric multi-variate kernel density estimation framework. The coupled shape prior is obtained by estimating the joint shape distribution of multiple objects and the inter-shape pose priors are modeled via standard moments. Based on such statistical models, we formulate an optimization problem for segmentation, which we solve by an algorithm based on active contours. Our technique provides significant improvements in the segmentation of weakly contrasted objects in a number of applications. In particular for medical image analysis, we use our method to extract brain Basal Ganglia structures, which are members of a complex multi-object system posing a challenging segmentation problem. We also apply our technique to the problem of handwritten character segmentation. Finally, we use our method to segment cars in urban scenes.

In vivo quantitative evaluation of vascular parameters for angiogenesis based on sparse principal component analysis and aggregated boosted trees

NASA Astrophysics Data System (ADS)

Zhao, Fengjun; Liu, Junting; Qu, Xiaochao; Xu, Xianhui; Chen, Xueli; Yang, Xiang; Cao, Feng; Liang, Jimin; Tian, Jie

2014-12-01

To solve the multicollinearity issue and unequal contribution of vascular parameters for the quantification of angiogenesis, we developed a quantification evaluation method of vascular parameters for angiogenesis based on in vivo micro-CT imaging of hindlimb ischemic model mice. Taking vascular volume as the ground truth parameter, nine vascular parameters were first assembled into sparse principal components (PCs) to reduce the multicolinearity issue. Aggregated boosted trees (ABTs) were then employed to analyze the importance of vascular parameters for the quantification of angiogenesis via the loadings of sparse PCs. The results demonstrated that vascular volume was mainly characterized by vascular area, vascular junction, connectivity density, segment number and vascular length, which indicated they were the key vascular parameters for the quantification of angiogenesis. The proposed quantitative evaluation method was compared with both the ABTs directly using the nine vascular parameters and Pearson correlation, which were consistent. In contrast to the ABTs directly using the vascular parameters, the proposed method can select all the key vascular parameters simultaneously, because all the key vascular parameters were assembled into the sparse PCs with the highest relative importance.

An integrated method for atherosclerotic carotid plaque segmentation in ultrasound image.

PubMed

Qian, Chunjun; Yang, Xiaoping

2018-01-01

Carotid artery atherosclerosis is an important cause of stroke. Ultrasound imaging has been widely used in the diagnosis of atherosclerosis. Therefore, segmenting atherosclerotic carotid plaque in ultrasound image is an important task. Accurate plaque segmentation is helpful for the measurement of carotid plaque burden. In this paper, we propose and evaluate a novel learning-based integrated framework for plaque segmentation. In our study, four different classification algorithms, along with the auto-context iterative algorithm, were employed to effectively integrate features from ultrasound images and later also the iteratively estimated and refined probability maps together for pixel-wise classification. The four classification algorithms were support vector machine with linear kernel, support vector machine with radial basis function kernel, AdaBoost and random forest. The plaque segmentation was implemented in the generated probability map. The performance of the four different learning-based plaque segmentation methods was tested on 29 B-mode ultrasound images. The evaluation indices for our proposed methods were consisted of sensitivity, specificity, Dice similarity coefficient, overlap index, error of area, absolute error of area, point-to-point distance, and Hausdorff point-to-point distance, along with the area under the ROC curve. The segmentation method integrated the random forest and an auto-context model obtained the best results (sensitivity 80.4 ± 8.4%, specificity 96.5 ± 2.0%, Dice similarity coefficient 81.0 ± 4.1%, overlap index 68.3 ± 5.8%, error of area -1.02 ± 18.3%, absolute error of area 14.7 ± 10.9%, point-to-point distance 0.34 ± 0.10 mm, Hausdorff point-to-point distance 1.75 ± 1.02 mm, and area under the ROC curve 0.897), which were almost the best, compared with that from the existed methods. Our proposed learning-based integrated framework investigated in this study could be useful for atherosclerotic carotid plaque segmentation, which will be helpful for the measurement of carotid plaque burden. Copyright © 2017 Elsevier B.V. All rights reserved.

A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain

NASA Technical Reports Server (NTRS)

Hall, Lawrence O.; Bensaid, Amine M.; Clarke, Laurence P.; Velthuizen, Robert P.; Silbiger, Martin S.; Bezdek, James C.

1992-01-01

Magnetic resonance (MR) brain section images are segmented and then synthetically colored to give visual representations of the original data with three approaches: the literal and approximate fuzzy c-means unsupervised clustering algorithms and a supervised computational neural network, a dynamic multilayered perception trained with the cascade correlation learning algorithm. Initial clinical results are presented on both normal volunteers and selected patients with brain tumors surrounded by edema. Supervised and unsupervised segmentation techniques provide broadly similar results. Unsupervised fuzzy algorithms were visually observed to show better segmentation when compared with raw image data for volunteer studies. However, for a more complex segmentation problem with tumor/edema or cerebrospinal fluid boundary, where the tissues have similar MR relaxation behavior, inconsistency in rating among experts was observed.

Novel multiresolution mammographic density segmentation using pseudo 3D features and adaptive cluster merging

NASA Astrophysics Data System (ADS)

He, Wenda; Juette, Arne; Denton, Erica R. E.; Zwiggelaar, Reyer

2015-03-01

Breast cancer is the most frequently diagnosed cancer in women. Early detection, precise identification of women at risk, and application of appropriate disease prevention measures are by far the most effective ways to overcome the disease. Successful mammographic density segmentation is a key aspect in deriving correct tissue composition, ensuring an accurate mammographic risk assessment. However, mammographic densities have not yet been fully incorporated with non-image based risk prediction models, (e.g. the Gail and the Tyrer-Cuzick model), because of unreliable segmentation consistency and accuracy. This paper presents a novel multiresolution mammographic density segmentation, a concept of stack representation is proposed, and 3D texture features were extracted by adapting techniques based on classic 2D first-order statistics. An unsupervised clustering technique was employed to achieve mammographic segmentation, in which two improvements were made; 1) consistent segmentation by incorporating an optimal centroids initialisation step, and 2) significantly reduced the number of missegmentation by using an adaptive cluster merging technique. A set of full field digital mammograms was used in the evaluation. Visual assessment indicated substantial improvement on segmented anatomical structures and tissue specific areas, especially in low mammographic density categories. The developed method demonstrated an ability to improve the quality of mammographic segmentation via clustering, and results indicated an improvement of 26% in segmented image with good quality when compared with the standard clustering approach. This in turn can be found useful in early breast cancer detection, risk-stratified screening, and aiding radiologists in the process of decision making prior to surgery and/or treatment.

Cellular image segmentation using n-agent cooperative game theory

NASA Astrophysics Data System (ADS)

Dimock, Ian B.; Wan, Justin W. L.

2016-03-01

Image segmentation is an important problem in computer vision and has significant applications in the segmentation of cellular images. Many different imaging techniques exist and produce a variety of image properties which pose difficulties to image segmentation routines. Bright-field images are particularly challenging because of the non-uniform shape of the cells, the low contrast between cells and background, and imaging artifacts such as halos and broken edges. Classical segmentation techniques often produce poor results on these challenging images. Previous attempts at bright-field imaging are often limited in scope to the images that they segment. In this paper, we introduce a new algorithm for automatically segmenting cellular images. The algorithm incorporates two game theoretic models which allow each pixel to act as an independent agent with the goal of selecting their best labelling strategy. In the non-cooperative model, the pixels choose strategies greedily based only on local information. In the cooperative model, the pixels can form coalitions, which select labelling strategies that benefit the entire group. Combining these two models produces a method which allows the pixels to balance both local and global information when selecting their label. With the addition of k-means and active contour techniques for initialization and post-processing purposes, we achieve a robust segmentation routine. The algorithm is applied to several cell image datasets including bright-field images, fluorescent images and simulated images. Experiments show that the algorithm produces good segmentation results across the variety of datasets which differ in cell density, cell shape, contrast, and noise levels.

A High Efficiency Boost Converter with MPPT Scheme for Low Voltage Thermoelectric Energy Harvesting

NASA Astrophysics Data System (ADS)

Guan, Mingjie; Wang, Kunpeng; Zhu, Qingyuan; Liao, Wei-Hsin

2016-11-01

Using thermoelectric elements to harvest energy from heat has been of great interest during the last decade. This paper presents a direct current-direct current (DC-DC) boost converter with a maximum power point tracking (MPPT) scheme for low input voltage thermoelectric energy harvesting applications. Zero current switch technique is applied in the proposed MPPT scheme. Theoretical analysis on the converter circuits is explored to derive the equations for parameters needed in the design of the boost converter. Simulations and experiments are carried out to verify the theoretical analysis and equations. A prototype of the designed converter is built using discrete components and a low-power microcontroller. The results show that the designed converter can achieve a high efficiency at low input voltage. The experimental efficiency of the designed converter is compared with a commercial converter solution. It is shown that the designed converter has a higher efficiency than the commercial solution in the considered voltage range.

Co-occurrence frequency evaluated with large language corpora boosts semantic priming effects.

PubMed

Brunellière, Angèle; Perre, Laetitia; Tran, ThiMai; Bonnotte, Isabelle

2017-09-01

In recent decades, many computational techniques have been developed to analyse the contextual usage of words in large language corpora. The present study examined whether the co-occurrence frequency obtained from large language corpora might boost purely semantic priming effects. Two experiments were conducted: one with conscious semantic priming, the other with subliminal semantic priming. Both experiments contrasted three semantic priming contexts: an unrelated priming context and two related priming contexts with word pairs that are semantically related and that co-occur either frequently or infrequently. In the conscious priming presentation (166-ms stimulus-onset asynchrony, SOA), a semantic priming effect was recorded in both related priming contexts, which was greater with higher co-occurrence frequency. In the subliminal priming presentation (66-ms SOA), no significant priming effect was shown, regardless of the related priming context. These results show that co-occurrence frequency boosts pure semantic priming effects and are discussed with reference to models of semantic network.

Health Lifestyles: Audience Segmentation Analysis for Public Health Interventions.

ERIC Educational Resources Information Center

Slater, Michael D.; Flora, June A.

This paper is concerned with the application of market research techniques to segment large populations into homogeneous units in order to improve the reach, utilization, and effectiveness of health programs. The paper identifies seven distinctive patterns of health attitudes, social influences, and behaviors using cluster analytic techniques in a…

Estimating A Reference Standard Segmentation With Spatially Varying Performance Parameters: Local MAP STAPLE

PubMed Central

Commowick, Olivier; Akhondi-Asl, Alireza; Warfield, Simon K.

2012-01-01

We present a new algorithm, called local MAP STAPLE, to estimate from a set of multi-label segmentations both a reference standard segmentation and spatially varying performance parameters. It is based on a sliding window technique to estimate the segmentation and the segmentation performance parameters for each input segmentation. In order to allow for optimal fusion from the small amount of data in each local region, and to account for the possibility of labels not being observed in a local region of some (or all) input segmentations, we introduce prior probabilities for the local performance parameters through a new Maximum A Posteriori formulation of STAPLE. Further, we propose an expression to compute confidence intervals in the estimated local performance parameters. We carried out several experiments with local MAP STAPLE to characterize its performance and value for local segmentation evaluation. First, with simulated segmentations with known reference standard segmentation and spatially varying performance, we show that local MAP STAPLE performs better than both STAPLE and majority voting. Then we present evaluations with data sets from clinical applications. These experiments demonstrate that spatial adaptivity in segmentation performance is an important property to capture. We compared the local MAP STAPLE segmentations to STAPLE, and to previously published fusion techniques and demonstrate the superiority of local MAP STAPLE over other state-of-the- art algorithms. PMID:22562727

Measurement of gamma' precipitates in a nickel-based superalloy using energy-filtered transmission electron microscopy coupled with automated segmenting techniques.

PubMed

Tiley, J S; Viswanathan, G B; Shiveley, A; Tschopp, M; Srinivasan, R; Banerjee, R; Fraser, H L

2010-08-01

Precipitates of the ordered L1(2) gamma' phase (dispersed in the face-centered cubic or FCC gamma matrix) were imaged in Rene 88 DT, a commercial multicomponent Ni-based superalloy, using energy-filtered transmission electron microscopy (EFTEM). Imaging was performed using the Cr, Co, Ni, Ti and Al elemental L-absorption edges in the energy loss spectrum. Manual and automated segmentation procedures were utilized for identification of precipitate boundaries and measurement of precipitate sizes. The automated region growing technique for precipitate identification in images was determined to measure accurately precipitate diameters. In addition, the region growing technique provided a repeatable method for optimizing segmentation techniques for varying EFTEM conditions. (c) 2010 Elsevier Ltd. All rights reserved.

Segmentation of pulmonary nodules in three-dimensional CT images by use of a spiral-scanning technique

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

Wang Jiahui; Engelmann, Roger; Li Qiang

2007-12-15

Accurate segmentation of pulmonary nodules in computed tomography (CT) is an important and difficult task for computer-aided diagnosis of lung cancer. Therefore, the authors developed a novel automated method for accurate segmentation of nodules in three-dimensional (3D) CT. First, a volume of interest (VOI) was determined at the location of a nodule. To simplify nodule segmentation, the 3D VOI was transformed into a two-dimensional (2D) image by use of a key 'spiral-scanning' technique, in which a number of radial lines originating from the center of the VOI spirally scanned the VOI from the 'north pole' to the 'south pole'. Themore » voxels scanned by the radial lines provided a transformed 2D image. Because the surface of a nodule in the 3D image became a curve in the transformed 2D image, the spiral-scanning technique considerably simplified the segmentation method and enabled reliable segmentation results to be obtained. A dynamic programming technique was employed to delineate the 'optimal' outline of a nodule in the 2D image, which corresponded to the surface of the nodule in the 3D image. The optimal outline was then transformed back into 3D image space to provide the surface of the nodule. An overlap between nodule regions provided by computer and by the radiologists was employed as a performance metric for evaluating the segmentation method. The database included two Lung Imaging Database Consortium (LIDC) data sets that contained 23 and 86 CT scans, respectively, with 23 and 73 nodules that were 3 mm or larger in diameter. For the two data sets, six and four radiologists manually delineated the outlines of the nodules as reference standards in a performance evaluation for nodule segmentation. The segmentation method was trained on the first and was tested on the second LIDC data sets. The mean overlap values were 66% and 64% for the nodules in the first and second LIDC data sets, respectively, which represented a higher performance level than those of two existing segmentation methods that were also evaluated by use of the LIDC data sets. The segmentation method provided relatively reliable results for pulmonary nodule segmentation and would be useful for lung cancer quantification, detection, and diagnosis.« less

Segmentation of DTI based on tensorial morphological gradient

NASA Astrophysics Data System (ADS)

Rittner, Leticia; de Alencar Lotufo, Roberto

2009-02-01

This paper presents a segmentation technique for diffusion tensor imaging (DTI). This technique is based on a tensorial morphological gradient (TMG), defined as the maximum dissimilarity over the neighborhood. Once this gradient is computed, the tensorial segmentation problem becomes an scalar one, which can be solved by conventional techniques, such as watershed transform and thresholding. Similarity functions, namely the dot product, the tensorial dot product, the J-divergence and the Frobenius norm, were compared, in order to understand their differences regarding the measurement of tensor dissimilarities. The study showed that the dot product and the tensorial dot product turned out to be inappropriate for computation of the TMG, while the Frobenius norm and the J-divergence were both capable of measuring tensor dissimilarities, despite the distortion of Frobenius norm, since it is not an affine invariant measure. In order to validate the TMG as a solution for DTI segmentation, its computation was performed using distinct similarity measures and structuring elements. TMG results were also compared to fractional anisotropy. Finally, synthetic and real DTI were used in the method validation. Experiments showed that the TMG enables the segmentation of DTI by watershed transform or by a simple choice of a threshold. The strength of the proposed segmentation method is its simplicity and robustness, consequences of TMG computation. It enables the use, not only of well-known algorithms and tools from the mathematical morphology, but also of any other segmentation method to segment DTI, since TMG computation transforms tensorial images in scalar ones.

Leading with the Heart.

ERIC Educational Resources Information Center

Linik, Joyce Riha

1998-01-01

Describes techniques used in a multi-age class at Coupeville Elementary School, Washington, to boost reading comprehension and inspire students' love of books: access to an abundance of books, challenges to students, skills reinforcement, combined phonics and whole-language instruction, running-record assessment, paired reading, independent…

Statistical segmentation of multidimensional brain datasets

NASA Astrophysics Data System (ADS)

Desco, Manuel; Gispert, Juan D.; Reig, Santiago; Santos, Andres; Pascau, Javier; Malpica, Norberto; Garcia-Barreno, Pedro

2001-07-01

This paper presents an automatic segmentation procedure for MRI neuroimages that overcomes part of the problems involved in multidimensional clustering techniques like partial volume effects (PVE), processing speed and difficulty of incorporating a priori knowledge. The method is a three-stage procedure: 1) Exclusion of background and skull voxels using threshold-based region growing techniques with fully automated seed selection. 2) Expectation Maximization algorithms are used to estimate the probability density function (PDF) of the remaining pixels, which are assumed to be mixtures of gaussians. These pixels can then be classified into cerebrospinal fluid (CSF), white matter and grey matter. Using this procedure, our method takes advantage of using the full covariance matrix (instead of the diagonal) for the joint PDF estimation. On the other hand, logistic discrimination techniques are more robust against violation of multi-gaussian assumptions. 3) A priori knowledge is added using Markov Random Field techniques. The algorithm has been tested with a dataset of 30 brain MRI studies (co-registered T1 and T2 MRI). Our method was compared with clustering techniques and with template-based statistical segmentation, using manual segmentation as a gold-standard. Our results were more robust and closer to the gold-standard.

Evaluation of velocity-sensitized and acceleration-sensitized NCE-MRA for below-knee peripheral arterial disease.

PubMed

Shaida, Nadeem; Priest, Andrew N; See, T C; Winterbottom, Andrew P; Graves, Martin J; Lomas, David J

2017-06-01

To evaluate the diagnostic performance of velocity- and acceleration-sensitized noncontrast-enhanced magnetic resonance angiography (NCE-MRA) of the infrageniculate arteries using contrast-enhanced MRA (CE-MRA) as a reference standard. Twenty-four patients with symptoms of peripheral arterial disease were recruited. Each patient's infrageniculate arterial tree was examined using a velocity-dependent flow-sensitized dephasing (VEL-FSD) technique, an acceleration-dependent (ACC-FSD) technique, and our conventional CE-MRA technique performed at 1.5T. The images were independently reviewed by two experienced vascular radiologists, who evaluated each vessel segment to assess visibility, diagnostic confidence, venous contamination, and detection of pathology. In all, 432 segments were evaluated by each of the three techniques by each reader in total. Overall diagnostic confidence was rated as moderate or high in 98.5% of segments with CE-MRA, 92.1% with VEL-FSD, and 79.9% with ACC-FSD. No venous contamination was seen in 96% of segments with CE-MRA, 72.2% with VEL-FSD, and 85.8% with ACC-FSD. Per-segment, per-limb, and per-patient sensitivities for detecting significant stenotic disease were 63.4%, 73%, and 92%, respectively, for ACC-FSD, and 65.3%, 87.2%, and 96% for VEL-FSD, and as such no significant statistical change was detected using McNemar's chi-squared test with P-values of 1.00, 0.13, and 0.77 obtained, respectively. Flow-dependent NCE-MRA techniques may have a role to play in evaluation of patients with peripheral vascular disease. Increased sensitivity of a velocity-based technique compared to an acceleration-based technique comes at the expense of greater venous contamination. 2J. Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;45:1846-1853. © 2016 International Society for Magnetic Resonance in Medicine.

Interactive tele-radiological segmentation systems for treatment and diagnosis.

PubMed

Zimeras, S; Gortzis, L G

2012-01-01

Telehealth is the exchange of health information and the provision of health care services through electronic information and communications technology, where participants are separated by geographic, time, social and cultural barriers. The shift of telemedicine from desktop platforms to wireless and mobile technologies is likely to have a significant impact on healthcare in the future. It is therefore crucial to develop a general information exchange e-medical system to enables its users to perform online and offline medical consultations through diagnosis. During the medical diagnosis, image analysis techniques combined with doctor's opinions could be useful for final medical decisions. Quantitative analysis of digital images requires detection and segmentation of the borders of the object of interest. In medical images, segmentation has traditionally been done by human experts. Even with the aid of image processing software (computer-assisted segmentation tools), manual segmentation of 2D and 3D CT images is tedious, time-consuming, and thus impractical, especially in cases where a large number of objects must be specified. Substantial computational and storage requirements become especially acute when object orientation and scale have to be considered. Therefore automated or semi-automated segmentation techniques are essential if these software applications are ever to gain widespread clinical use. The main purpose of this work is to analyze segmentation techniques for the definition of anatomical structures under telemedical systems.

Automated localization and segmentation techniques for B-mode ultrasound images: A review.

PubMed

Meiburger, Kristen M; Acharya, U Rajendra; Molinari, Filippo

2018-01-01

B-mode ultrasound imaging is used extensively in medicine. Hence, there is a need to have efficient segmentation tools to aid in computer-aided diagnosis, image-guided interventions, and therapy. This paper presents a comprehensive review on automated localization and segmentation techniques for B-mode ultrasound images. The paper first describes the general characteristics of B-mode ultrasound images. Then insight on the localization and segmentation of tissues is provided, both in the case in which the organ/tissue localization provides the final segmentation and in the case in which a two-step segmentation process is needed, due to the desired boundaries being too fine to locate from within the entire ultrasound frame. Subsequenly, examples of some main techniques found in literature are shown, including but not limited to shape priors, superpixel and classification, local pixel statistics, active contours, edge-tracking, dynamic programming, and data mining. Ten selected applications (abdomen/kidney, breast, cardiology, thyroid, liver, vascular, musculoskeletal, obstetrics, gynecology, prostate) are then investigated in depth, and the performances of a few specific applications are compared. In conclusion, future perspectives for B-mode based segmentation, such as the integration of RF information, the employment of higher frequency probes when possible, the focus on completely automatic algorithms, and the increase in available data are discussed. Copyright © 2017 Elsevier Ltd. All rights reserved.

A prospective pilot study on early toxicity from a simultaneously integrated boost technique for canine sinonasal tumours using image-guided intensity-modulated radiation therapy.

PubMed

Soukup, A; Meier, V; Pot, S; Voelter, K; Rohrer Bley, C

2018-05-14

In order to overcome the common local treatment failure of canine sinonasal tumours, integrated boost techniques were tried in the cobalt/orthovoltage era, but dismissed because of unacceptable early (acute) toxicity. Intriguingly, a recent calculation study of a simultaneously integrated boost (SIB) technique for sinonasal irradiation using intensity-modulated radiation therapy (IMRT) predicted theoretical feasibility. In this prospective pilot study we applied a commonly used protocol of 10 × 4.2 Gy to the planning target volume (PTV) with a 20%-SIB dose to the gross tumour volume (GTV). Our hypothesis expected this dose escalation to be clinically tolerable if applied with image-guided IMRT. We included 9 dogs diagnosed with sinonasal tumours without local/distant metastases. For treatment planning, organs at risk were contoured according to strict anatomical guidelines. Planning volume extensions (GTV/CTV/PTV) were standardized to minimize interplanner variability. Treatments were applied with rigid patient positioning and verified daily with image guidance. After radiation therapy, we set focus on early ophthalmologic complications as well as mucosal and cutaneous toxicity. Early toxicity was evaluated at week 1, 2, 3, 8 and 12 after radiotherapy. Only mild ophthalmologic complications were found. Three patients (33%) had self-limiting moderate to severe early toxicity (grade 3 mucositis) which was managed medically. No patient developed ulcerations/haemorrhage/necrosis of skin/mucosa. The SIB protocol applied with image-guided IMRT to treat canine sinonasal tumours led to clinically acceptable side effects. The suspected increased tumour control probability and the risk of late toxicity with the used dose escalation of 20% has to be further investigated. © 2018 John Wiley & Sons Ltd.

Traumatic laryngotracheal stenosis--an alternative surgical technique.

PubMed

Syal, Rajan; Tyagi, Isha; Goyal, Amit

2006-02-01

Reconstruction of combined laryngotracheal stenosis requires complex techniques including resection and incorporation of grafts and stents that can be performed as single or multistaged procedure. A complicated case of traumatic laryngotracheal stenosis was managed by us, surgical technique is discussed. A 16-year-old male presented with Stage-3 laryngotracheal stenosis of grade-3 to 4 (>70% of the complete obstruction of tracheal lumen) of 5 cm segment of the larynx and trachea. Restoration of the critical functions of respiration and phonation was achieved in this patient by resection anastomosis of the trachea and with subglottic remodeling. Resection of 5 cm long segment of trachea and primary anastomosis in this case would have created tension at the site of anastomosis. So we did tracheal resection of 3 cm segment of trachea along with subglottic remodeling instead of removing the 5 cm segment of stenosed laryngotracheal region and doing thyrotracheal anastomosis. In complicated long segment, laryngotracheal stenosis, tracheal resection and subglottic remodeling with primary anastomosis can be an alternative approach. Fibrin glue can be used to support free bone/cartilage grafts in laryngotracheal reconstructions.

Statistical optimisation techniques in fatigue signal editing problem

NASA Astrophysics Data System (ADS)

Nopiah, Z. M.; Osman, M. H.; Baharin, N.; Abdullah, S.

2015-02-01

Success in fatigue signal editing is determined by the level of length reduction without compromising statistical constraints. A great reduction rate can be achieved by removing small amplitude cycles from the recorded signal. The long recorded signal sometimes renders the cycle-to-cycle editing process daunting. This has encouraged researchers to focus on the segment-based approach. This paper discusses joint application of the Running Damage Extraction (RDE) technique and single constrained Genetic Algorithm (GA) in fatigue signal editing optimisation.. In the first section, the RDE technique is used to restructure and summarise the fatigue strain. This technique combines the overlapping window and fatigue strain-life models. It is designed to identify and isolate the fatigue events that exist in the variable amplitude strain data into different segments whereby the retention of statistical parameters and the vibration energy are considered. In the second section, the fatigue data editing problem is formulated as a constrained single optimisation problem that can be solved using GA method. The GA produces the shortest edited fatigue signal by selecting appropriate segments from a pool of labelling segments. Challenges arise due to constraints on the segment selection by deviation level over three signal properties, namely cumulative fatigue damage, root mean square and kurtosis values. Experimental results over several case studies show that the idea of solving fatigue signal editing within a framework of optimisation is effective and automatic, and that the GA is robust for constrained segment selection.

Statistical optimisation techniques in fatigue signal editing problem

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

Nopiah, Z. M.; Osman, M. H.; Baharin, N.

Success in fatigue signal editing is determined by the level of length reduction without compromising statistical constraints. A great reduction rate can be achieved by removing small amplitude cycles from the recorded signal. The long recorded signal sometimes renders the cycle-to-cycle editing process daunting. This has encouraged researchers to focus on the segment-based approach. This paper discusses joint application of the Running Damage Extraction (RDE) technique and single constrained Genetic Algorithm (GA) in fatigue signal editing optimisation.. In the first section, the RDE technique is used to restructure and summarise the fatigue strain. This technique combines the overlapping window andmore » fatigue strain-life models. It is designed to identify and isolate the fatigue events that exist in the variable amplitude strain data into different segments whereby the retention of statistical parameters and the vibration energy are considered. In the second section, the fatigue data editing problem is formulated as a constrained single optimisation problem that can be solved using GA method. The GA produces the shortest edited fatigue signal by selecting appropriate segments from a pool of labelling segments. Challenges arise due to constraints on the segment selection by deviation level over three signal properties, namely cumulative fatigue damage, root mean square and kurtosis values. Experimental results over several case studies show that the idea of solving fatigue signal editing within a framework of optimisation is effective and automatic, and that the GA is robust for constrained segment selection.« less

Localized Statistics for DW-MRI Fiber Bundle Segmentation

PubMed Central

Lankton, Shawn; Melonakos, John; Malcolm, James; Dambreville, Samuel; Tannenbaum, Allen

2013-01-01

We describe a method for segmenting neural fiber bundles in diffusion-weighted magnetic resonance images (DWMRI). As these bundles traverse the brain to connect regions, their local orientation of diffusion changes drastically, hence a constant global model is inaccurate. We propose a method to compute localized statistics on orientation information and use it to drive a variational active contour segmentation that accurately models the non-homogeneous orientation information present along the bundle. Initialized from a single fiber path, the proposed method proceeds to capture the entire bundle. We demonstrate results using the technique to segment the cingulum bundle and describe several extensions making the technique applicable to a wide range of tissues. PMID:23652079

Finding a good segmentation strategy for tree crown transparency estimation

Treesearch

Neil A. Clark; Sang-Mook Lee; Philip A. Araman

2003-01-01

Image segmentation is a general term for delineating image areas into informational categories. A wide variety of general techniques exist depending on application and the image data specifications. Specialized algorithms, utilizing components of several techniques, usually are needed to meet the rigors for a specific application. This paper considers automated color...

Market Segmentation: An Instructional Module.

ERIC Educational Resources Information Center

Wright, Peter H.

A concept-based introduction to market segmentation is provided in this instructional module for undergraduate and graduate transportation-related courses. The material can be used in many disciplines including engineering, business, marketing, and technology. The concept of market segmentation is primarily a transportation planning technique by…

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

Catheter ablation therapy has become increasingly popular for the treatment of left atrial fibrillation. The effect of this treatment on left atrial morphology, however, has not yet been completely quantified. Initial studies have indicated a decrease in left atrial size with a concomitant decrease in pulmonary vein diameter. In order to effectively study if catheter based therapies affect left atrial geometry, robust segmentations with minimal user interaction are required. In this work, we validate a method to semi-automatically segment the left atrium from computed-tomography scans. The first step of the technique utilizes seeded region growing to extract the entire blood pool including the four chambers of the heart, the pulmonary veins, aorta, superior vena cava, inferior vena cava, and other surrounding structures. Next, the left atrium and pulmonary veins are separated from the rest of the blood pool using an algorithm that searches for thin connections between user defined points in the volumetric data or on a surface rendering. Finally, pulmonary veins are separated from the left atrium using a three dimensional tracing tool. A single user segmented three datasets three times using both the semi-automatic technique as well as manual tracing. The user interaction time for the semi-automatic technique was approximately forty-five minutes per dataset and the manual tracing required between four and eight hours per dataset depending on the number of slices. A truth model was generated using a simple voting scheme on the repeated manual segmentations. A second user segmented each of the nine datasets using the semi-automatic technique only. Several metrics were computed to assess the agreement between the semi-automatic technique and the truth model including percent differences in left atrial volume, DICE overlap, and mean distance between the boundaries of the segmented left atria. Overall, the semi-automatic approach was demonstrated to be repeatable within 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.

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

Xu, Xingchen; Peng, Xuan; Sumption, Michael

The internal oxidation technique can generate ZrO2 nano particles in Nb3Sn strands, which markedly refine the Nb3Sn grain size and boost the high-field critical current density (Jc). This article summarizes recent efforts on implementing this technique in practical Nb3Sn wires and adding Ti as a dopant. It is demonstrated that this technique can be readily incorporated into the present Nb3Sn conductor manufacturing technology. Powder-in-tube (PIT) strands with fine subelements (~25 µm) based on this technique were successfully fabricated, and proper heat treatments for oxygen transfer were explored. Future work for producing strands ready for applications is proposed.

Fetal brain volumetry through MRI volumetric reconstruction and segmentation

PubMed Central

Estroff, Judy A.; Barnewolt, Carol E.; Connolly, Susan A.; Warfield, Simon K.

2013-01-01

Purpose Fetal MRI volumetry is a useful technique but it is limited by a dependency upon motion-free scans, tedious manual segmentation, and spatial inaccuracy due to thick-slice scans. An image processing pipeline that addresses these limitations was developed and tested. Materials and methods The principal sequences acquired in fetal MRI clinical practice are multiple orthogonal single-shot fast spin echo scans. State-of-the-art image processing techniques were used for inter-slice motion correction and super-resolution reconstruction of high-resolution volumetric images from these scans. The reconstructed volume images were processed with intensity non-uniformity correction and the fetal brain extracted by using supervised automated segmentation. Results Reconstruction, segmentation and volumetry of the fetal brains for a cohort of twenty-five clinically acquired fetal MRI scans was done. Performance metrics for volume reconstruction, segmentation and volumetry were determined by comparing to manual tracings in five randomly chosen cases. Finally, analysis of the fetal brain and parenchymal volumes was performed based on the gestational age of the fetuses. Conclusion The image processing pipeline developed in this study enables volume rendering and accurate fetal brain volumetry by addressing the limitations of current volumetry techniques, which include dependency on motion-free scans, manual segmentation, and inaccurate thick-slice interpolation. PMID:20625848

Segmentation of dermatoscopic images by frequency domain filtering and k-means clustering algorithms.

PubMed

Rajab, Maher I

2011-11-01

Since the introduction of epiluminescence microscopy (ELM), image analysis tools have been extended to the field of dermatology, in an attempt to algorithmically reproduce clinical evaluation. Accurate image segmentation of skin lesions is one of the key steps for useful, early and non-invasive diagnosis of coetaneous melanomas. This paper proposes two image segmentation algorithms based on frequency domain processing and k-means clustering/fuzzy k-means clustering. The two methods are capable of segmenting and extracting the true border that reveals the global structure irregularity (indentations and protrusions), which may suggest excessive cell growth or regression of a melanoma. As a pre-processing step, Fourier low-pass filtering is applied to reduce the surrounding noise in a skin lesion image. A quantitative comparison of the techniques is enabled by the use of synthetic skin lesion images that model lesions covered with hair to which Gaussian noise is added. The proposed techniques are also compared with an established optimal-based thresholding skin-segmentation method. It is demonstrated that for lesions with a range of different border irregularity properties, the k-means clustering and fuzzy k-means clustering segmentation methods provide the best performance over a range of signal to noise ratios. The proposed segmentation techniques are also demonstrated to have similar performance when tested on real skin lesions representing high-resolution ELM images. This study suggests that the segmentation results obtained using a combination of low-pass frequency filtering and k-means or fuzzy k-means clustering are superior to the result that would be obtained by using k-means or fuzzy k-means clustering segmentation methods alone. © 2011 John Wiley & Sons A/S.

Interactive vs. automatic ultrasound image segmentation methods for staging hepatic lipidosis.

PubMed

Weijers, Gert; Starke, Alexander; Haudum, Alois; Thijssen, Johan M; Rehage, Jürgen; De Korte, Chris L

2010-07-01

The aim of this study was to test the hypothesis that automatic segmentation of vessels in ultrasound (US) images can produce similar or better results in grading fatty livers than interactive segmentation. A study was performed in postpartum dairy cows (N=151), as an animal model of human fatty liver disease, to test this hypothesis. Five transcutaneous and five intraoperative US liver images were acquired in each animal and a liverbiopsy was taken. In liver tissue samples, triacylglycerol (TAG) was measured by biochemical analysis and hepatic diseases other than hepatic lipidosis were excluded by histopathologic examination. Ultrasonic tissue characterization (UTC) parameters--Mean echo level, standard deviation (SD) of echo level, signal-to-noise ratio (SNR), residual attenuation coefficient (ResAtt) and axial and lateral speckle size--were derived using a computer-aided US (CAUS) protocol and software package. First, the liver tissue was interactively segmented by two observers. With increasing fat content, fewer hepatic vessels were visible in the ultrasound images and, therefore, a smaller proportion of the liver needed to be excluded from these images. Automatic-segmentation algorithms were implemented and it was investigated whether better results could be achieved than with the subjective and time-consuming interactive-segmentation procedure. The automatic-segmentation algorithms were based on both fixed and adaptive thresholding techniques in combination with a 'speckle'-shaped moving-window exclusion technique. All data were analyzed with and without postprocessing as contained in CAUS and with different automated-segmentation techniques. This enabled us to study the effect of the applied postprocessing steps on single and multiple linear regressions ofthe various UTC parameters with TAG. Improved correlations for all US parameters were found by using automatic-segmentation techniques. Stepwise multiple linear-regression formulas where derived and used to predict TAG level in the liver. Receiver-operating-characteristics (ROC) analysis was applied to assess the performance and area under the curve (AUC) of predicting TAG and to compare the sensitivity and specificity of the methods. Best speckle-size estimates and overall performance (R2 = 0.71, AUC = 0.94) were achieved by using an SNR-based adaptive automatic-segmentation method (used TAG threshold: 50 mg/g liver wet weight). Automatic segmentation is thus feasible and profitable.

A 540-[Formula: see text] Duty Controlled RSSI With Current Reusing Technique for Human Body Communication.

PubMed

Jang, Jaeeun; Lee, Yongsu; Cho, Hyunwoo; Yoo, Hoi-Jun

2016-08-01

An ultra-low-power duty controlled received signal strength indicator (RSSI) is implemented for human body communication (HBC) in 180 nm CMOS technology under 1.5 V supply. The proposed RSSI adopted 3 following key features for low-power consumption; 1) current reusing technique (CR-RSSI) with replica bias circuit and calibration unit, 2) duty controller, and 3) reconfigurable gm-boosting LNA. The CR-RSSI utilizes stacked amplifier-rectifier-cell (AR-cell) to reuse the supply current of each blocks. As a result, the power consumption becomes 540 [Formula: see text] with +/-2 dB accuracy and 75 dB dynamic range. The replica bias circuit and calibration unit are adopted to increase the reliability of CR-RSSI. In addition, the duty controller turns off the RSSI when it is not required, and this function leads 70% power reduction. At last, the gm-boosting reconfigurable LNA can adaptively vary its noise and linearity performance with respect to input signal strength. Fro current reusing technique m this feature, we achieve 62% power reduction in the LNA. Thanks to these schemes, compared to the previous works, we can save 70% of power in RSSI and LNA.

Alignment and Integration Techniques for Mirror Segment Pairs on the Constellation X Telescope

NASA Technical Reports Server (NTRS)

Hadjimichael, Theo; Lehan, John; Olsen, Larry; Owens, Scott; Saha, Timo; Wallace, Tom; Zhang, Will

2007-01-01

We present the concepts behind current alignment and integration techniques for testing a Constellation-X primary-secondary mirror segment pair in an x-ray beam line test. We examine the effects of a passive mount on thin glass x-ray mirror segments, and the issues of mount shape and environment on alignment. We also investigate how bonding and transfer to a permanent housing affects the quality of the final image, comparing predicted results to a full x-ray test on a primary secondary pair.

Lung tumor segmentation in PET images using graph cuts.

PubMed

Ballangan, Cherry; Wang, Xiuying; Fulham, Michael; Eberl, Stefan; Feng, David Dagan

2013-03-01

The aim of segmentation of tumor regions in positron emission tomography (PET) is to provide more accurate measurements of tumor size and extension into adjacent structures, than is possible with visual assessment alone and hence improve patient management decisions. We propose a segmentation energy function for the graph cuts technique to improve lung tumor segmentation with PET. Our segmentation energy is based on an analysis of the tumor voxels in PET images combined with a standardized uptake value (SUV) cost function and a monotonic downhill SUV feature. The monotonic downhill feature avoids segmentation leakage into surrounding tissues with similar or higher PET tracer uptake than the tumor and the SUV cost function improves the boundary definition and also addresses situations where the lung tumor is heterogeneous. We evaluated the method in 42 clinical PET volumes from patients with non-small cell lung cancer (NSCLC). Our method improves segmentation and performs better than region growing approaches, the watershed technique, fuzzy-c-means, region-based active contour and tumor customized downhill. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

New Embedded Denotes Fuzzy C-Mean Application for Breast Cancer Density Segmentation in Digital Mammograms

NASA Astrophysics Data System (ADS)

Othman, Khairulnizam; Ahmad, Afandi

2016-11-01

In this research we explore the application of normalize denoted new techniques in advance fast c-mean in to the problem of finding the segment of different breast tissue regions in mammograms. The goal of the segmentation algorithm is to see if new denotes fuzzy c- mean algorithm could separate different densities for the different breast patterns. The new density segmentation is applied with multi-selection of seeds label to provide the hard constraint, whereas the seeds labels are selected based on user defined. New denotes fuzzy c- mean have been explored on images of various imaging modalities but not on huge format digital mammograms just yet. Therefore, this project is mainly focused on using normalize denoted new techniques employed in fuzzy c-mean to perform segmentation to increase visibility of different breast densities in mammography images. Segmentation of the mammogram into different mammographic densities is useful for risk assessment and quantitative evaluation of density changes. Our proposed methodology for the segmentation of mammograms on the basis of their region into different densities based categories has been tested on MIAS database and Trueta Database.

Automatic layer segmentation of H&E microscopic images of mice skin

NASA Astrophysics Data System (ADS)

Hussein, Saif; Selway, Joanne; Jassim, Sabah; Al-Assam, Hisham

2016-05-01

Mammalian skin is a complex organ composed of a variety of cells and tissue types. The automatic detection and quantification of changes in skin structures has a wide range of applications for biological research. To accurately segment and quantify nuclei, sebaceous gland, hair follicles, and other skin structures, there is a need for a reliable segmentation of different skin layers. This paper presents an efficient segmentation algorithm to segment the three main layers of mice skin, namely epidermis, dermis, and subcutaneous layers. It also segments the epidermis layer into two sub layers, basal and cornified layers. The proposed algorithm uses adaptive colour deconvolution technique on H&E stain images to separate different tissue structures, inter-modes and Otsu thresholding techniques were effectively combined to segment the layers. It then uses a set of morphological and logical operations on each layer to removing unwanted objects. A dataset of 7000 H&E microscopic images of mutant and wild type mice were used to evaluate the effectiveness of the algorithm. Experimental results examined by domain experts have confirmed the viability of the proposed algorithms.

A Unified Framework for Brain Segmentation in MR Images

PubMed Central

Yazdani, S.; Yusof, R.; Karimian, A.; Riazi, A. H.; Bennamoun, M.

2015-01-01

Brain MRI segmentation is an important issue for discovering the brain structure and diagnosis of subtle anatomical changes in different brain diseases. However, due to several artifacts brain tissue segmentation remains a challenging task. The aim of this paper is to improve the automatic segmentation of brain into gray matter, white matter, and cerebrospinal fluid in magnetic resonance images (MRI). We proposed an automatic hybrid image segmentation method that integrates the modified statistical expectation-maximization (EM) method and the spatial information combined with support vector machine (SVM). The combined method has more accurate results than what can be achieved with its individual techniques that is demonstrated through experiments on both real data and simulated images. Experiments are carried out on both synthetic and real MRI. The results of proposed technique are evaluated against manual segmentation results and other methods based on real T1-weighted scans from Internet Brain Segmentation Repository (IBSR) and simulated images from BrainWeb. The Kappa index is calculated to assess the performance of the proposed framework relative to the ground truth and expert segmentations. The results demonstrate that the proposed combined method has satisfactory results on both simulated MRI and real brain datasets. PMID:26089978

Colour image segmentation using unsupervised clustering technique for acute leukemia images

NASA Astrophysics Data System (ADS)

Halim, N. H. Abd; Mashor, M. Y.; Nasir, A. S. Abdul; Mustafa, N.; Hassan, R.

2015-05-01

Colour image segmentation has becoming more popular for computer vision due to its important process in most medical analysis tasks. This paper proposes comparison between different colour components of RGB(red, green, blue) and HSI (hue, saturation, intensity) colour models that will be used in order to segment the acute leukemia images. First, partial contrast stretching is applied on leukemia images to increase the visual aspect of the blast cells. Then, an unsupervised moving k-means clustering algorithm is applied on the various colour components of RGB and HSI colour models for the purpose of segmentation of blast cells from the red blood cells and background regions in leukemia image. Different colour components of RGB and HSI colour models have been analyzed in order to identify the colour component that can give the good segmentation performance. The segmented images are then processed using median filter and region growing technique to reduce noise and smooth the images. The results show that segmentation using saturation component of HSI colour model has proven to be the best in segmenting nucleus of the blast cells in acute leukemia image as compared to the other colour components of RGB and HSI colour models.

State of the art survey on MRI brain tumor segmentation.

PubMed

Gordillo, Nelly; Montseny, Eduard; Sobrevilla, Pilar

2013-10-01

Brain tumor segmentation consists of separating the different tumor tissues (solid or active tumor, edema, and necrosis) from normal brain tissues: gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). In brain tumor studies, the existence of abnormal tissues may be easily detectable most of the time. However, accurate and reproducible segmentation and characterization of abnormalities are not straightforward. In the past, many researchers in the field of medical imaging and soft computing have made significant survey in the field of brain tumor segmentation. Both semiautomatic and fully automatic methods have been proposed. Clinical acceptance of segmentation techniques has depended on the simplicity of the segmentation, and the degree of user supervision. Interactive or semiautomatic methods are likely to remain dominant in practice for some time, especially in these applications where erroneous interpretations are unacceptable. This article presents an overview of the most relevant brain tumor segmentation methods, conducted after the acquisition of the image. Given the advantages of magnetic resonance imaging over other diagnostic imaging, this survey is focused on MRI brain tumor segmentation. Semiautomatic and fully automatic techniques are emphasized. Copyright © 2013 Elsevier Inc. All rights reserved.

A summary of image segmentation techniques

NASA Technical Reports Server (NTRS)

Spirkovska, Lilly

1993-01-01

Machine vision systems are often considered to be composed of two subsystems: low-level vision and high-level vision. Low level vision consists primarily of image processing operations performed on the input image to produce another image with more favorable characteristics. These operations may yield images with reduced noise or cause certain features of the image to be emphasized (such as edges). High-level vision includes object recognition and, at the highest level, scene interpretation. The bridge between these two subsystems is the segmentation system. Through segmentation, the enhanced input image is mapped into a description involving regions with common features which can be used by the higher level vision tasks. There is no theory on image segmentation. Instead, image segmentation techniques are basically ad hoc and differ mostly in the way they emphasize one or more of the desired properties of an ideal segmenter and in the way they balance and compromise one desired property against another. These techniques can be categorized in a number of different groups including local vs. global, parallel vs. sequential, contextual vs. noncontextual, interactive vs. automatic. In this paper, we categorize the schemes into three main groups: pixel-based, edge-based, and region-based. Pixel-based segmentation schemes classify pixels based solely on their gray levels. Edge-based schemes first detect local discontinuities (edges) and then use that information to separate the image into regions. Finally, region-based schemes start with a seed pixel (or group of pixels) and then grow or split the seed until the original image is composed of only homogeneous regions. Because there are a number of survey papers available, we will not discuss all segmentation schemes. Rather than a survey, we take the approach of a detailed overview. We focus only on the more common approaches in order to give the reader a flavor for the variety of techniques available yet present enough details to facilitate implementation and experimentation.

Intra- and interoperator variability of lobar pulmonary volumes and emphysema scores in patients with chronic obstructive pulmonary disease and emphysema: comparison of manual and semi-automated segmentation techniques.

PubMed

Molinari, Francesco; Pirronti, Tommaso; Sverzellati, Nicola; Diciotti, Stefano; Amato, Michele; Paolantonio, Guglielmo; Gentile, Luigia; Parapatt, George K; D'Argento, Francesco; Kuhnigk, Jan-Martin

2013-01-01

We aimed to compare the intra- and interoperator variability of lobar volumetry and emphysema scores obtained by semi-automated and manual segmentation techniques in lung emphysema patients. In two sessions held three months apart, two operators performed lobar volumetry of unenhanced chest computed tomography examinations of 47 consecutive patients with chronic obstructive pulmonary disease and lung emphysema. Both operators used the manual and semi-automated segmentation techniques. The intra- and interoperator variability of the volumes and emphysema scores obtained by semi-automated segmentation was compared with the variability obtained by manual segmentation of the five pulmonary lobes. The intra- and interoperator variability of the lobar volumes decreased when using semi-automated lobe segmentation (coefficients of repeatability for the first operator: right upper lobe, 147 vs. 96.3; right middle lobe, 137.7 vs. 73.4; right lower lobe, 89.2 vs. 42.4; left upper lobe, 262.2 vs. 54.8; and left lower lobe, 260.5 vs. 56.5; coefficients of repeatability for the second operator: right upper lobe, 61.4 vs. 48.1; right middle lobe, 56 vs. 46.4; right lower lobe, 26.9 vs. 16.7; left upper lobe, 61.4 vs. 27; and left lower lobe, 63.6 vs. 27.5; coefficients of reproducibility in the interoperator analysis: right upper lobe, 191.3 vs. 102.9; right middle lobe, 219.8 vs. 126.5; right lower lobe, 122.6 vs. 90.1; left upper lobe, 166.9 vs. 68.7; and left lower lobe, 168.7 vs. 71.6). The coefficients of repeatability and reproducibility of emphysema scores also decreased when using semi-automated segmentation and had ranges that varied depending on the target lobe and selected threshold of emphysema. Semi-automated segmentation reduces the intra- and interoperator variability of lobar volumetry and provides a more objective tool than manual technique for quantifying lung volumes and severity of emphysema.

Dialog detection in narrative video by shot and face analysis

NASA Astrophysics Data System (ADS)

Kroon, B.; Nesvadba, J.; Hanjalic, A.

2007-01-01

The proliferation of captured personal and broadcast content in personal consumer archives necessitates comfortable access to stored audiovisual content. Intuitive retrieval and navigation solutions require however a semantic level that cannot be reached by generic multimedia content analysis alone. A fusion with film grammar rules can help to boost the reliability significantly. The current paper describes the fusion of low-level content analysis cues including face parameters and inter-shot similarities to segment commercial content into film grammar rule-based entities and subsequently classify those sequences into so-called shot reverse shots, i.e. dialog sequences. Moreover shot reverse shot specific mid-level cues are analyzed augmenting the shot reverse shot information with dialog specific descriptions.

KSC-2009-5060

NASA Image and Video Library

2009-08-22

CAPE CANAVERAL, Fla. – At the Astrotech payload processing facility in Titusville, Fla., the upper segment of the transportation canister is moved toward the Space Tracking and Surveillance System – Demonstrators, or STSS Demo, spacecraft, at left. The STSS Demo is a space-based sensor component of a layered Ballistic Missile Defense System designed for the overall mission of detecting, tracking and discriminating ballistic missiles. STSS is capable of tracking objects after boost phase and provides trajectory information to other sensors. It will be launched by NASA for the Missile Defense Agency between 8 and 8:58 a.m. EDT Sept. 18. Approved for Public Release 09-MDA-04886 (10 SEPT 09) Photo credit: NASA/Kim Shiflett

KSC-2009-5059

NASA Image and Video Library

2009-08-22

CAPE CANAVERAL, Fla. – At the Astrotech payload processing facility in Titusville, Fla., the upper segment of the transportation canister is lifted to be placed on the top of the Space Tracking and Surveillance System – Demonstrators, or STSS Demo, spacecraft. The STSS Demo is a space-based sensor component of a layered Ballistic Missile Defense System designed for the overall mission of detecting, tracking and discriminating ballistic missiles. STSS is capable of tracking objects after boost phase and provides trajectory information to other sensors. It will be launched by NASA for the Missile Defense Agency between 8 and 8:58 a.m. EDT Sept. 18. Approved for Public Release 09-MDA-04886 (10 SEPT 09) Photo credit: NASA/Kim Shiflett

KSC-2009-5057

NASA Image and Video Library

2009-08-22

CAPE CANAVERAL, Fla. – At the Astrotech payload processing facility in Titusville, Fla., workers maneuver one of the second-row segments of the transportation canister that will be placed around the Space Tracking and Surveillance System – Demonstrators, or STSS Demo, spacecraft. The STSS Demo is a space-based sensor component of a layered Ballistic Missile Defense System designed for the overall mission of detecting, tracking and discriminating ballistic missiles. STSS is capable of tracking objects after boost phase and provides trajectory information to other sensors. It will be launched by NASA for the Missile Defense Agency between 8 and 8:58 a.m. EDT Sept. 18. Approved for Public Release 09-MDA-04886 (10 SEPT 09) Photo credit: NASA/Kim Shiflett

KSC-2009-5061

NASA Image and Video Library

2009-08-22

CAPE CANAVERAL, Fla. – At the Astrotech payload processing facility in Titusville, Fla., the upper segment of the transportation canister is moved toward the Space Tracking and Surveillance System – Demonstrators, or STSS Demo, spacecraft, at bottom left. The STSS Demo is a space-based sensor component of a layered Ballistic Missile Defense System designed for the overall mission of detecting, tracking and discriminating ballistic missiles. STSS is capable of tracking objects after boost phase and provides trajectory information to other sensors. It will be launched by NASA for the Missile Defense Agency between 8 and 8:58 a.m. EDT Sept. 18. Approved for Public Release 09-MDA-04886 (10 SEPT 09) Photo credit: NASA/Kim Shiflett

KSC-2009-5056

NASA Image and Video Library

2009-08-22

CAPE CANAVERAL, Fla. – At the Astrotech payload processing facility in Titusville, Fla., workers place the second row of segments of the transportation canister around the Space Tracking and Surveillance System – Demonstrators, or STSS Demo, spacecraft. The STSS Demo is a space-based sensor component of a layered Ballistic Missile Defense System designed for the overall mission of detecting, tracking and discriminating ballistic missiles. STSS is capable of tracking objects after boost phase and provides trajectory information to other sensors. It will be launched by NASA for the Missile Defense Agency between 8 and 8:58 a.m. EDT Sept. 18. Approved for Public Release 09-MDA-04886 (10 SEPT 09) Photo credit: NASA/Kim Shiflett

KSC-2009-5055

NASA Image and Video Library

2009-08-22

CAPE CANAVERAL, Fla. – At the Astrotech payload processing facility in Titusville, Fla., workers place the first segments of the transportation canister around the base of the Space Tracking and Surveillance System – Demonstrators, or STSS Demo, spacecraft. The STSS Demo is a space-based sensor component of a layered Ballistic Missile Defense System designed for the overall mission of detecting, tracking and discriminating ballistic missiles. STSS is capable of tracking objects after boost phase and provides trajectory information to other sensors. It will be launched by NASA for the Missile Defense Agency between 8 and 8:58 a.m. EDT Sept. 18. Approved for Public Release 09-MDA-04886 (10 SEPT 09) Photo credit: NASA/Kim Shiflett

KSC-2009-5058

NASA Image and Video Library

2009-08-22

CAPE CANAVERAL, Fla. – At the Astrotech payload processing facility in Titusville, Fla., workers maneuver one of the second-row segments of the transportation canister that will be placed around the Space Tracking and Surveillance System – Demonstrators, or STSS Demo, spacecraft. The STSS Demo is a space-based sensor component of a layered Ballistic Missile Defense System designed for the overall mission of detecting, tracking and discriminating ballistic missiles. STSS is capable of tracking objects after boost phase and provides trajectory information to other sensors. It will be launched by NASA for the Missile Defense Agency between 8 and 8:58 a.m. EDT Sept. 18. Approved for Public Release 09-MDA-04886 (10 SEPT 09) Photo credit: NASA/Kim Shiflett

Five-year Results of Whole Breast Intensity Modulated Radiation Therapy for the Treatment of Early Stage Breast Cancer: The Fox Chase Cancer Center Experience

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

Keller, Lanea M.M., E-mail: Lanea.Keller@fccc.edu; Sopka, Dennis M.; Li Tianyu

Purpose: To report the 5-year outcomes using whole-breast intensity-modulated radiation therapy (IMRT) for the treatment of early-stage-breast cancer at the Fox Chase Cancer Center. Methods and Materials: A total of 946 women with early-stage breast cancer (stage 0, I, or II) were treated with IMRT after surgery with or without systemic therapy from 2003-2010. Whole-breast radiation was delivered via an IMRT technique with a median whole-breast radiation dose of 46 Gy and median tumor bed boost of 14 Gy. Endpoints included local-regional recurrence, cosmesis, and late complications. Results: With a median follow-up of 31 months (range, 1-97 months), there weremore » 12 ipsilateral breast tumor recurrences (IBTR) and one locoregional recurrence. The 5-year actuarial IBTR and locoregional recurrence rates were 2.0% and 2.4%. Physician-reported cosmestic outcomes were available for 645 patients: 63% were considered 'excellent', 33% 'good', and <1.5% 'fair/poor'. For physician-reported cosmesis, boost doses {>=}16 Gy, breast size >900 cc, or boost volumes >34 cc were significantly associated with a 'fair/poor' cosmetic outcome. Fibrosis, edema, erythema, and telangectasia were also associated with 'fair/poor' physician-reported cosmesis; erythema and telangectasia remained significant on multivariate analysis. Patient-reported cosmesis was available for 548 patients, and 33%, 50%, and 17% of patients reported 'excellent', 'good', and 'fair/poor' cosmesis, respectively. The use of a boost and increased boost volume: breast volume ratio were significantly associated with 'fair/poor' outcomes. No parameter for patient-reported cosmesis was significant on multivariate analysis. The chances of experiencing a treatment related effect was significantly associated with a boost dose {>=}16 Gy, receipt of chemotherapy and endocrine therapy, large breast size, and electron boost energy. Conclusions: Whole-breast IMRT is associated with very low rates of local recurrence at 5 years, 83%-98% 'good/excellent' cosmetic outcomes, and minimal chronic toxicity, including late fibrosis.« less

L2-Boosting algorithm applied to high-dimensional problems in genomic selection.

PubMed

González-Recio, Oscar; Weigel, Kent A; Gianola, Daniel; Naya, Hugo; Rosa, Guilherme J M

2010-06-01

The L(2)-Boosting algorithm is one of the most promising machine-learning techniques that has appeared in recent decades. It may be applied to high-dimensional problems such as whole-genome studies, and it is relatively simple from a computational point of view. In this study, we used this algorithm in a genomic selection context to make predictions of yet to be observed outcomes. Two data sets were used: (1) productive lifetime predicted transmitting abilities from 4702 Holstein sires genotyped for 32 611 single nucleotide polymorphisms (SNPs) derived from the Illumina BovineSNP50 BeadChip, and (2) progeny averages of food conversion rate, pre-corrected by environmental and mate effects, in 394 broilers genotyped for 3481 SNPs. Each of these data sets was split into training and testing sets, the latter comprising dairy or broiler sires whose ancestors were in the training set. Two weak learners, ordinary least squares (OLS) and non-parametric (NP) regression were used for the L2-Boosting algorithm, to provide a stringent evaluation of the procedure. This algorithm was compared with BL [Bayesian LASSO (least absolute shrinkage and selection operator)] and BayesA regression. Learning tasks were carried out in the training set, whereas validation of the models was performed in the testing set. Pearson correlations between predicted and observed responses in the dairy cattle (broiler) data set were 0.65 (0.33), 0.53 (0.37), 0.66 (0.26) and 0.63 (0.27) for OLS-Boosting, NP-Boosting, BL and BayesA, respectively. The smallest bias and mean-squared errors (MSEs) were obtained with OLS-Boosting in both the dairy cattle (0.08 and 1.08, respectively) and broiler (-0.011 and 0.006) data sets, respectively. In the dairy cattle data set, the BL was more accurate (bias=0.10 and MSE=1.10) than BayesA (bias=1.26 and MSE=2.81), whereas no differences between these two methods were found in the broiler data set. L2-Boosting with a suitable learner was found to be a competitive alternative for genomic selection applications, providing high accuracy and low bias in genomic-assisted evaluations with a relatively short computational time.

A low-cost three-dimensional laser surface scanning approach for defining body segment parameters.

PubMed

Pandis, Petros; Bull, Anthony Mj

2017-11-01

Body segment parameters are used in many different applications in ergonomics as well as in dynamic modelling of the musculoskeletal system. Body segment parameters can be defined using different methods, including techniques that involve time-consuming manual measurements of the human body, used in conjunction with models or equations. In this study, a scanning technique for measuring subject-specific body segment parameters in an easy, fast, accurate and low-cost way was developed and validated. The scanner can obtain the body segment parameters in a single scanning operation, which takes between 8 and 10 s. The results obtained with the system show a standard deviation of 2.5% in volumetric measurements of the upper limb of a mannequin and 3.1% difference between scanning volume and actual volume. Finally, the maximum mean error for the moment of inertia by scanning a standard-sized homogeneous object was 2.2%. This study shows that a low-cost system can provide quick and accurate subject-specific body segment parameter estimates.

Fast Appearance Modeling for Automatic Primary Video Object Segmentation.

PubMed

Yang, Jiong; Price, Brian; Shen, Xiaohui; Lin, Zhe; Yuan, Junsong

2016-02-01

Automatic segmentation of the primary object in a video clip is a challenging problem as there is no prior knowledge of the primary object. Most existing techniques thus adapt an iterative approach for foreground and background appearance modeling, i.e., fix the appearance model while optimizing the segmentation and fix the segmentation while optimizing the appearance model. However, these approaches may rely on good initialization and can be easily trapped in local optimal. In addition, they are usually time consuming for analyzing videos. To address these limitations, we propose a novel and efficient appearance modeling technique for automatic primary video object segmentation in the Markov random field (MRF) framework. It embeds the appearance constraint as auxiliary nodes and edges in the MRF structure, and can optimize both the segmentation and appearance model parameters simultaneously in one graph cut. The extensive experimental evaluations validate the superiority of the proposed approach over the state-of-the-art methods, in both efficiency and effectiveness.

Graphic/symbol segmentation for Group 4 facsimile systems

NASA Astrophysics Data System (ADS)

Deutermann, A. R.

1982-04-01

The purpose of this study was to examine possible techniques for and symbol areas, and assemble a code that represents the entire document. Parameters to be considered include compression, commonality with facsimile and TELETEX* transmissions, and complexity of implementation. Six segmentation technique were selected for analysis. The techniques were designed to differ from each other as much as possible, so as to display a wide variety of characteristics. For each technique, many minor modifications would be possible, but it is not expected that these modifications would alter the conclusions drawn from the study.

A goal bias in action: The boundaries adults perceive in events align with sites of actor intent.

PubMed

Levine, Dani; Hirsh-Pasek, Kathy; Pace, Amy; Michnick Golinkoff, Roberta

2017-06-01

We live in a dynamic world comprised of continuous events. Remembering our past and predicting future events, however, requires that we segment these ongoing streams of information in a consistent manner. How is this segmentation achieved? This research examines whether the boundaries adults perceive in events, such as the Olympic figure skating routine used in these studies, align with the beginnings (sources) and endings (goals) of human goal-directed actions. Study 1 showed that a group of experts, given an explicit task with unlimited time to rewatch the event, identified the same subevents as one another, but with greater agreement as to the timing of goals than sources. In Study 2, experts, novices familiarized with the figure skating sequence, and unfamiliarized novices performed an online event segmentation task, marking boundaries as the video progressed in real time. The online boundaries of all groups corresponded with the sources and goals offered by Study 1's experts, with greater alignment of goals than sources. Additionally, expertise, but not mere perceptual familiarity, boosted the alignment of sources and goals. Finally, Study 3, which presented novices with the video played in reverse, indicated, unexpectedly, that even when spatiotemporal cues were disrupted, viewers' perceived event boundaries still aligned with their perception of the actors' intended sources and goals. This research extends the goal bias to event segmentation, and suggests that our spontaneous sensitivity toward goals may allow us to transform even relatively complex and unfamiliar event streams into structured and meaningful representations. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Image Processing Strategies Based on a Visual Saliency Model for Object Recognition Under Simulated Prosthetic Vision.

PubMed

Wang, Jing; Li, Heng; Fu, Weizhen; Chen, Yao; Li, Liming; Lyu, Qing; Han, Tingting; Chai, Xinyu

2016-01-01

Retinal prostheses have the potential to restore partial vision. Object recognition in scenes of daily life is one of the essential tasks for implant wearers. Still limited by the low-resolution visual percepts provided by retinal prostheses, it is important to investigate and apply image processing methods to convey more useful visual information to the wearers. We proposed two image processing strategies based on Itti's visual saliency map, region of interest (ROI) extraction, and image segmentation. Itti's saliency model generated a saliency map from the original image, in which salient regions were grouped into ROI by the fuzzy c-means clustering. Then Grabcut generated a proto-object from the ROI labeled image which was recombined with background and enhanced in two ways--8-4 separated pixelization (8-4 SP) and background edge extraction (BEE). Results showed that both 8-4 SP and BEE had significantly higher recognition accuracy in comparison with direct pixelization (DP). Each saliency-based image processing strategy was subject to the performance of image segmentation. Under good and perfect segmentation conditions, BEE and 8-4 SP obtained noticeably higher recognition accuracy than DP, and under bad segmentation condition, only BEE boosted the performance. The application of saliency-based image processing strategies was verified to be beneficial to object recognition in daily scenes under simulated prosthetic vision. They are hoped to help the development of the image processing module for future retinal prostheses, and thus provide more benefit for the patients. Copyright © 2015 International Center for Artificial Organs and Transplantation and Wiley Periodicals, Inc.

A single-layer network unsupervised feature learning method for white matter hyperintensity segmentation

NASA Astrophysics Data System (ADS)

Vijverberg, Koen; Ghafoorian, Mohsen; van Uden, Inge W. M.; de Leeuw, Frank-Erik; Platel, Bram; Heskes, Tom

2016-03-01

Cerebral small vessel disease (SVD) is a disorder frequently found among the old people and is associated with deterioration in cognitive performance, parkinsonism, motor and mood impairments. White matter hyperintensities (WMH) as well as lacunes, microbleeds and subcortical brain atrophy are part of the spectrum of image findings, related to SVD. Accurate segmentation of WMHs is important for prognosis and diagnosis of multiple neurological disorders such as MS and SVD. Almost all of the published (semi-)automated WMH detection models employ multiple complex hand-crafted features, which require in-depth domain knowledge. In this paper we propose to apply a single-layer network unsupervised feature learning (USFL) method to avoid hand-crafted features, but rather to automatically learn a more efficient set of features. Experimental results show that a computer aided detection system with a USFL system outperforms a hand-crafted approach. Moreover, since the two feature sets have complementary properties, a hybrid system that makes use of both hand-crafted and unsupervised learned features, shows a significant performance boost compared to each system separately, getting close to the performance of an independent human expert.

The antioxidant melatonin boosts recovery of cryopreserved shoot tips

USDA-ARS?s Scientific Manuscript database

Many useful plant species found in Canada are of conservation concern. In vitro storage and cryopreservation techniques guarantee safety of these species and have potential applications which may result in sustainable agriculture. Shoot tips of in vitro-grown plantlets of American elm, St John’s Wor...

Comparison of Post-Processing Techniques for the Detection of Perfusion Defects by Cardiac Computed Tomography in Patients Presenting with Acute ST Segment Elevation Myocardial Infarction

PubMed Central

Rogers, Ian S.; Cury, Ricardo C.; Blankstein, Ron; Shapiro, Michael D.; Nieman, Koen; Hoffmann, Udo; Brady, Thomas J.; Abbara, Suhny

2010-01-01

Background Despite rapid advances in cardiac computed tomography (CT), a strategy for optimal visualization of perfusion abnormalities on CT has yet to be validated. Objective To evaluate the performance of several post-processing techniques of source data sets to detect and characterize perfusion defects in acute myocardial infarctions with cardiac CT. Methods Twenty-one subjects (18 men; 60 ± 13 years) that were successfully treated with percutaneous coronary intervention for ST-segment myocardial infarction underwent 64-slice cardiac CT and 1.5 Tesla cardiac MRI scans following revascularization. Delayed enhancement MRI images were analyzed to identify the location of infarcted myocardium. Contiguous short axis images of the left ventricular myocardium were created from the CT source images using 0.75mm multiplanar reconstruction (MPR), 5mm MPR, 5mm maximal intensity projection (MIP), and 5mm minimum intensity projection (MinIP) techniques. Segments already confirmed to contain infarction by MRI were then evaluated qualitatively and quantitatively with CT. Results Overall, 143 myocardial segments were analyzed. On qualitative analysis, the MinIP and thick MPR techniques had greater visibility and definition than the thin MPR and MIP techniques (p < 0.001). On quantitative analysis, the absolute difference in Hounsfield Unit (HU) attenuation between normal and infarcted segments was significantly greater for the MinIP (65.4 HU) and thin MPR (61.2 HU) techniques. However, the relative difference in HU attenuation was significantly greatest for the MinIP technique alone (95%, p < 0.001). Contrast to noise was greatest for the MinIP (4.2) and thick MPR (4.1) techniques (p < 0.001). Conclusion The results of our current investigation found that MinIP and thick MPR detected infarcted myocardium with greater visibility and definition than MIP and thin MPR. PMID:20579617

Application of an enhanced fuzzy algorithm for MR brain tumor image segmentation

NASA Astrophysics Data System (ADS)

Hemanth, D. Jude; Vijila, C. Kezi Selva; Anitha, J.

2010-02-01

Image segmentation is one of the significant digital image processing techniques commonly used in the medical field. One of the specific applications is tumor detection in abnormal Magnetic Resonance (MR) brain images. Fuzzy approaches are widely preferred for tumor segmentation which generally yields superior results in terms of accuracy. But most of the fuzzy algorithms suffer from the drawback of slow convergence rate which makes the system practically non-feasible. In this work, the application of modified Fuzzy C-means (FCM) algorithm to tackle the convergence problem is explored in the context of brain image segmentation. This modified FCM algorithm employs the concept of quantization to improve the convergence rate besides yielding excellent segmentation efficiency. This algorithm is experimented on real time abnormal MR brain images collected from the radiologists. A comprehensive feature vector is extracted from these images and used for the segmentation technique. An extensive feature selection process is performed which reduces the convergence time period and improve the segmentation efficiency. After segmentation, the tumor portion is extracted from the segmented image. Comparative analysis in terms of segmentation efficiency and convergence rate is performed between the conventional FCM and the modified FCM. Experimental results show superior results for the modified FCM algorithm in terms of the performance measures. Thus, this work highlights the application of the modified algorithm for brain tumor detection in abnormal MR brain images.

Estimating propagation velocity through a surface acoustic wave sensor

DOEpatents

Xu, Wenyuan; Huizinga, John S.

2010-03-16

Techniques are described for estimating the propagation velocity through a surface acoustic wave sensor. In particular, techniques which measure and exploit a proper segment of phase frequency response of the surface acoustic wave sensor are described for use as a basis of bacterial detection by the sensor. As described, use of velocity estimation based on a proper segment of phase frequency response has advantages over conventional techniques that use phase shift as the basis for detection.

Development and validation of automatic tools for interactive recurrence analysis in radiation therapy: optimization of treatment algorithms for locally advanced pancreatic cancer.

PubMed

Kessel, Kerstin A; Habermehl, Daniel; Jäger, Andreas; Floca, Ralf O; Zhang, Lanlan; Bendl, Rolf; Debus, Jürgen; Combs, Stephanie E

2013-06-07

In radiation oncology recurrence analysis is an important part in the evaluation process and clinical quality assurance of treatment concepts. With the example of 9 patients with locally advanced pancreatic cancer we developed and validated interactive analysis tools to support the evaluation workflow. After an automatic registration of the radiation planning CTs with the follow-up images, the recurrence volumes are segmented manually. Based on these volumes the DVH (dose volume histogram) statistic is calculated, followed by the determination of the dose applied to the region of recurrence and the distance between the boost and recurrence volume. We calculated the percentage of the recurrence volume within the 80%-isodose volume and compared it to the location of the recurrence within the boost volume, boost + 1 cm, boost + 1.5 cm and boost + 2 cm volumes. Recurrence analysis of 9 patients demonstrated that all recurrences except one occurred within the defined GTV/boost volume; one recurrence developed beyond the field border/outfield. With the defined distance volumes in relation to the recurrences, we could show that 7 recurrent lesions were within the 2 cm radius of the primary tumor. Two large recurrences extended beyond the 2 cm, however, this might be due to very rapid growth and/or late detection of the tumor progression. The main goal of using automatic analysis tools is to reduce time and effort conducting clinical analyses. We showed a first approach and use of a semi-automated workflow for recurrence analysis, which will be continuously optimized. In conclusion, despite the limitations of the automatic calculations we contributed to in-house optimization of subsequent study concepts based on an improved and validated target volume definition.

Measurement of segmental lumbar spine flexion and extension using ultrasound imaging.

PubMed

Chleboun, Gary S; Amway, Matthew J; Hill, Jesse G; Root, Kara J; Murray, Hugh C; Sergeev, Alexander V

2012-10-01

Clinical measurement, technical note. To describe a technique to measure interspinous process distance using ultrasound (US) imaging, to assess the reliability of the technique, and to compare the US imaging measurements to magnetic resonance imaging (MRI) measurements in 3 different positions of the lumbar spine. Segmental spinal motion has been assessed using various imaging techniques, as well as surgically inserted pins. However, some imaging techniques are costly (MRI) and some require ionizing radiation (radiographs and fluoroscopy), and surgical procedures have limited use because of the invasive nature of the technique. Therefore, it is important to have an easily accessible and inexpensive technique for measuring lumbar segmental motion to more fully understand spine motion in vivo, to evaluate the changes that occur with various interventions, and to be able to accurately relate the changes in symptoms to changes in motion of individual vertebral segments. Six asymptomatic subjects participated. The distance between spinous processes at each lumbar segment (L1-2, L2-3, L3-4, L4-5) was measured digitally using MRI and US imaging. The interspinous distance was measured with subjects supine and the lumbar spine in 3 different positions (resting, lumbar flexion, and lumbar extension) for both MRI and US imaging. The differences in distance from neutral to extension, neutral to flexion, and extension to flexion were calculated. The measurement methods had excellent reliability for US imaging (intraclass correlation coefficient [ICC3,3] = 0.94; 95% confidence interval: 0.85, 0.97) and MRI (ICC3,3 = 0.98; 95% confidence interval: 0.95, 0.99). The distance measured was similar between US imaging and MRI (P>.05), except at L3-4 flexion-extension (P = .003). On average, the MRI measurements were 1.3 mm greater than the US imaging measurements. This study describes a new method for the measurement of lumbar spine segmental flexion and extension motion using US imaging. The US method may offer an alternative to other imaging techniques to monitor clinical outcomes because of its ease of use and the consistency of measurements compared to MRI.

Segmental Refinement: A Multigrid Technique for Data Locality

DOE PAGES

Adams, Mark F.; Brown, Jed; Knepley, Matt; ...

2016-08-04

In this paper, we investigate a domain decomposed multigrid technique, termed segmental refinement, for solving general nonlinear elliptic boundary value problems. We extend the method first proposed in 1994 by analytically and experimentally investigating its complexity. We confirm that communication of traditional parallel multigrid is eliminated on fine grids, with modest amounts of extra work and storage, while maintaining the asymptotic exactness of full multigrid. We observe an accuracy dependence on the segmental refinement subdomain size, which was not considered in the original analysis. Finally, we present a communication complexity analysis that quantifies the communication costs ameliorated by segmental refinementmore » and report performance results with up to 64K cores on a Cray XC30.« less

Comparison of MRI segmentation techniques for measuring liver cyst volumes in autosomal dominant polycystic kidney disease.

PubMed

Farooq, Zerwa; Behzadi, Ashkan Heshmatzadeh; Blumenfeld, Jon D; Zhao, Yize; Prince, Martin R

To compare MRI segmentation methods for measuring liver cyst volumes in autosomal dominant polycystic kidney disease (ADPKD). Liver cyst volumes in 42 ADPKD patients were measured using region growing, thresholding and cyst diameter techniques. Manual segmentation was the reference standard. Root mean square deviation was 113, 155, and 500 for cyst diameter, thresholding and region growing respectively. Thresholding error for cyst volumes below 500ml was 550% vs 17% for cyst volumes above 500ml (p<0.001). For measuring volume of a small number of cysts, cyst diameter and manual segmentation methods are recommended. For severe disease with numerous, large hepatic cysts, thresholding is an acceptable alternative. Copyright © 2017 Elsevier Inc. All rights reserved.

Whole-Brain Radiotherapy With Simultaneous Integrated Boost to Multiple Brain Metastases Using Volumetric Modulated Arc Therapy

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

Lagerwaard, Frank J.; Hoorn, Elles A.P. van der; Verbakel, Wilko

2009-09-01

Purpose: Volumetric modulated arc therapy (RapidArc [RA]; Varian Medical Systems, Palo Alto, CA) allows for the generation of intensity-modulated dose distributions by use of a single gantry rotation. We used RA to plan and deliver whole-brain radiotherapy (WBRT) with a simultaneous integrated boost in patients with multiple brain metastases. Methods and Materials: Composite RA plans were generated for 8 patients, consisting of WBRT (20 Gy in 5 fractions) with an integrated boost, also 20 Gy in 5 fractions, to Brain metastases, and clinically delivered in 3 patients. Summated gross tumor volumes were 1.0 to 37.5 cm{sup 3}. RA plans weremore » measured in a solid water phantom by use of Gafchromic films (International Specialty Products, Wayne, NJ). Results: Composite RA plans could be generated within 1 hour. Two arcs were needed to deliver the mean of 1,600 monitor units with a mean 'beam-on' time of 180 seconds. RA plans showed excellent coverage of planning target volume for WBRT and planning target volume for the boost, with mean volumes receiving at least 95% of the prescribed dose of 100% and 99.8%, respectively. The mean conformity index was 1.36. Composite plans showed much steeper dose gradients outside Brain metastases than plans with a conventional summation of WBRT and radiosurgery. Comparison of calculated and measured doses showed a mean gamma for double-arc plans of 0.30, and the area with a gamma larger than 1 was 2%. In-room times for clinical RA sessions were approximately 20 minutes for each patient. Conclusions: RA treatment planning and delivery of integrated plans of WBRT and boosts to multiple brain metastases is a rapid and accurate technique that has a higher conformity index than conventional summation of WBRT and radiosurgery boost.« less

SEGMENTATION OF MITOCHONDRIA IN ELECTRON MICROSCOPY IMAGES USING ALGEBRAIC CURVES.

PubMed

Seyedhosseini, Mojtaba; Ellisman, Mark H; Tasdizen, Tolga

2013-01-01

High-resolution microscopy techniques have been used to generate large volumes of data with enough details for understanding the complex structure of the nervous system. However, automatic techniques are required to segment cells and intracellular structures in these multi-terabyte datasets and make anatomical analysis possible on a large scale. We propose a fully automated method that exploits both shape information and regional statistics to segment irregularly shaped intracellular structures such as mitochondria in electron microscopy (EM) images. The main idea is to use algebraic curves to extract shape features together with texture features from image patches. Then, these powerful features are used to learn a random forest classifier, which can predict mitochondria locations precisely. Finally, the algebraic curves together with regional information are used to segment the mitochondria at the predicted locations. We demonstrate that our method outperforms the state-of-the-art algorithms in segmentation of mitochondria in EM images.

A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain.

PubMed

Hall, L O; Bensaid, A M; Clarke, L P; Velthuizen, R P; Silbiger, M S; Bezdek, J C

1992-01-01

Magnetic resonance (MR) brain section images are segmented and then synthetically colored to give visual representations of the original data with three approaches: the literal and approximate fuzzy c-means unsupervised clustering algorithms, and a supervised computational neural network. Initial clinical results are presented on normal volunteers and selected patients with brain tumors surrounded by edema. Supervised and unsupervised segmentation techniques provide broadly similar results. Unsupervised fuzzy algorithms were visually observed to show better segmentation when compared with raw image data for volunteer studies. For a more complex segmentation problem with tumor/edema or cerebrospinal fluid boundary, where the tissues have similar MR relaxation behavior, inconsistency in rating among experts was observed, with fuzz-c-means approaches being slightly preferred over feedforward cascade correlation results. Various facets of both approaches, such as supervised versus unsupervised learning, time complexity, and utility for the diagnostic process, are compared.

Genetic and Psychosocial Predictors of Aggression: Variable Selection and Model Building With Component-Wise Gradient Boosting.

PubMed

Suchting, Robert; Gowin, Joshua L; Green, Charles E; Walss-Bass, Consuelo; Lane, Scott D

2018-01-01

Rationale : Given datasets with a large or diverse set of predictors of aggression, machine learning (ML) provides efficient tools for identifying the most salient variables and building a parsimonious statistical model. ML techniques permit efficient exploration of data, have not been widely used in aggression research, and may have utility for those seeking prediction of aggressive behavior. Objectives : The present study examined predictors of aggression and constructed an optimized model using ML techniques. Predictors were derived from a dataset that included demographic, psychometric and genetic predictors, specifically FK506 binding protein 5 (FKBP5) polymorphisms, which have been shown to alter response to threatening stimuli, but have not been tested as predictors of aggressive behavior in adults. Methods : The data analysis approach utilized component-wise gradient boosting and model reduction via backward elimination to: (a) select variables from an initial set of 20 to build a model of trait aggression; and then (b) reduce that model to maximize parsimony and generalizability. Results : From a dataset of N = 47 participants, component-wise gradient boosting selected 8 of 20 possible predictors to model Buss-Perry Aggression Questionnaire (BPAQ) total score, with R 2 = 0.66. This model was simplified using backward elimination, retaining six predictors: smoking status, psychopathy (interpersonal manipulation and callous affect), childhood trauma (physical abuse and neglect), and the FKBP5_13 gene (rs1360780). The six-factor model approximated the initial eight-factor model at 99.4% of R 2 . Conclusions : Using an inductive data science approach, the gradient boosting model identified predictors consistent with previous experimental work in aggression; specifically psychopathy and trauma exposure. Additionally, allelic variants in FKBP5 were identified for the first time, but the relatively small sample size limits generality of results and calls for replication. This approach provides utility for the prediction of aggression behavior, particularly in the context of large multivariate datasets.

Improving the segmentation of therapy-induced leukoencephalopathy using apriori information and a gradient magnitude threshold

NASA Astrophysics Data System (ADS)

Glass, John O.; Reddick, Wilburn E.; Reeves, Cara; Pui, Ching-Hon

2004-05-01

Reliably quantifying therapy-induced leukoencephalopathy in children treated for cancer is a challenging task due to its varying MR properties and similarity to normal tissues and imaging artifacts. T1, T2, PD, and FLAIR images were analyzed for a subset of 15 children from an institutional protocol for the treatment of acute lymphoblastic leukemia. Three different analysis techniques were compared to examine improvements in the segmentation accuracy of leukoencephalopathy versus manual tracings by two expert observers. The first technique utilized no apriori information and a white matter mask based on the segmentation of the first serial examination of each patient. MR images were then segmented with a Kohonen Self-Organizing Map. The other two techniques combine apriori maps from the ICBM atlas spatially normalized to each patient and resliced using SPM99 software. The apriori maps were included as input and a gradient magnitude threshold calculated on the FLAIR images was also utilized. The second technique used a 2-dimensional threshold, while the third algorithm utilized a 3-dimensional threshold. Kappa values were compared for the three techniques to each observer, and improvements were seen with each addition to the original algorithm (Observer 1: 0.651, 0.653, 0.744; Observer 2: 0.603, 0.615, 0.699).

Efficient use of mobile devices for quantification of pressure injury images.

PubMed

Garcia-Zapirain, Begonya; Sierra-Sosa, Daniel; Ortiz, David; Isaza-Monsalve, Mariano; Elmaghraby, Adel

2018-01-01

Pressure Injuries are chronic wounds that are formed due to the constriction of the soft tissues against bone prominences. In order to assess these injuries, the medical personnel carry out the evaluation and diagnosis using visual methods and manual measurements, which can be inaccurate and may generate discomfort in the patients. By using segmentation techniques, the Pressure Injuries can be extracted from an image and accurately parameterized, leading to a correct diagnosis. In general, these techniques are based on the solution of differential equations and the involved numerical methods are demanding in terms of computational resources. In previous work, we proposed a technique developed using toroidal parametric equations for image decomposition and segmentation without solving differential equations. In this paper, we present the development of a mobile application useful for the non-contact assessment of Pressure Injuries based on the toroidal decomposition from images. The usage of this technique allows us to achieve an accurate segmentation almost 8 times faster than Active Contours without Edges (ACWE) and Dynamic Contours methods. We describe the techniques and the implementation for Android devices using Python and Kivy. This application allows for the segmentation and parameterization of injuries, obtain relevant information for the diagnosis and tracking the evolution of patient's injuries.

Dosimetric effects of patient rotational setup errors on prostate IMRT treatments

NASA Astrophysics Data System (ADS)

Fu, Weihua; Yang, Yong; Li, Xiang; Heron, Dwight E.; Saiful Huq, M.; Yue, Ning J.

2006-10-01

The purpose of this work is to determine dose delivery errors that could result from systematic rotational setup errors (ΔΦ) for prostate cancer patients treated with three-phase sequential boost IMRT. In order to implement this, different rotational setup errors around three Cartesian axes were simulated for five prostate patients and dosimetric indices, such as dose-volume histogram (DVH), tumour control probability (TCP), normal tissue complication probability (NTCP) and equivalent uniform dose (EUD), were employed to evaluate the corresponding dosimetric influences. Rotational setup errors were simulated by adjusting the gantry, collimator and horizontal couch angles of treatment beams and the dosimetric effects were evaluated by recomputing the dose distributions in the treatment planning system. Our results indicated that, for prostate cancer treatment with the three-phase sequential boost IMRT technique, the rotational setup errors do not have significant dosimetric impacts on the cumulative plan. Even in the worst-case scenario with ΔΦ = 3°, the prostate EUD varied within 1.5% and TCP decreased about 1%. For seminal vesicle, slightly larger influences were observed. However, EUD and TCP changes were still within 2%. The influence on sensitive structures, such as rectum and bladder, is also negligible. This study demonstrates that the rotational setup error degrades the dosimetric coverage of target volume in prostate cancer treatment to a certain degree. However, the degradation was not significant for the three-phase sequential boost prostate IMRT technique and for the margin sizes used in our institution.

On the application of accelerated molecular dynamics to liquid water simulations.

PubMed

de Oliveira, César Augusto F; Hamelberg, Donald; McCammon, J Andrew

2006-11-16

Our group recently proposed a robust bias potential function that can be used in an efficient all-atom accelerated molecular dynamics (MD) approach to simulate the transition of high energy barriers without any advance knowledge of the potential-energy landscape. The main idea is to modify the potential-energy surface by adding a bias, or boost, potential in regions close to the local minima, such that all transitions rates are increased. By applying the accelerated MD simulation method to liquid water, we observed that this new simulation technique accelerates the molecular motion without losing its microscopic structure and equilibrium properties. Our results showed that the application of a small boost energy on the potential-energy surface significantly reduces the statistical inefficiency of the simulation while keeping all the other calculated properties unchanged. On the other hand, although aggressive acceleration of the dynamics simulation increases the self-diffusion coefficient of water molecules greatly and dramatically reduces the correlation time of the simulation, configurations representative of the true structure of liquid water are poorly sampled. Our results also showed the strength and robustness of this simulation technique, which confirm this approach as a very useful and promising tool to extend the time scale of the all-atom simulations of biological system with explicit solvent models. However, we should keep in mind that there is a compromise between the strength of the boost applied in the simulation and the reproduction of the ensemble average properties.

Improving cerebellar segmentation with statistical fusion

NASA Astrophysics Data System (ADS)

Plassard, Andrew J.; Yang, Zhen; Prince, Jerry L.; Claassen, Daniel O.; Landman, Bennett A.

2016-03-01

The cerebellum is a somatotopically organized central component of the central nervous system well known to be involved with motor coordination and increasingly recognized roles in cognition and planning. Recent work in multiatlas labeling has created methods that offer the potential for fully automated 3-D parcellation of the cerebellar lobules and vermis (which are organizationally equivalent to cortical gray matter areas). This work explores the trade offs of using different statistical fusion techniques and post hoc optimizations in two datasets with distinct imaging protocols. We offer a novel fusion technique by extending the ideas of the Selective and Iterative Method for Performance Level Estimation (SIMPLE) to a patch-based performance model. We demonstrate the effectiveness of our algorithm, Non- Local SIMPLE, for segmentation of a mixed population of healthy subjects and patients with severe cerebellar anatomy. Under the first imaging protocol, we show that Non-Local SIMPLE outperforms previous gold-standard segmentation techniques. In the second imaging protocol, we show that Non-Local SIMPLE outperforms previous gold standard techniques but is outperformed by a non-locally weighted vote with the deeper population of atlases available. This work advances the state of the art in open source cerebellar segmentation algorithms and offers the opportunity for routinely including cerebellar segmentation in magnetic resonance imaging studies that acquire whole brain T1-weighted volumes with approximately 1 mm isotropic resolution.

Comparison of competing segmentation standards for X-ray computed topographic imaging using Lattice Boltzmann techniques

NASA Astrophysics Data System (ADS)

Larsen, J. D.; Schaap, M. G.

2013-12-01

Recent advances in computing technology and experimental techniques have made it possible to observe and characterize fluid dynamics at the micro-scale. Many computational methods exist that can adequately simulate fluid flow in porous media. Lattice Boltzmann methods provide the distinct advantage of tracking particles at the microscopic level and returning macroscopic observations. While experimental methods can accurately measure macroscopic fluid dynamics, computational efforts can be used to predict and gain insight into fluid dynamics by utilizing thin sections or computed micro-tomography (CMT) images of core sections. Although substantial effort have been made to advance non-invasive imaging methods such as CMT, fluid dynamics simulations, and microscale analysis, a true three dimensional image segmentation technique has not been developed until recently. Many competing segmentation techniques are utilized in industry and research settings with varying results. In this study lattice Boltzmann method is used to simulate stokes flow in a macroporous soil column. Two dimensional CMT images were used to reconstruct a three dimensional representation of the original sample. Six competing segmentation standards were used to binarize the CMT volumes which provide distinction between solid phase and pore space. The permeability of the reconstructed samples was calculated, with Darcy's Law, from lattice Boltzmann simulations of fluid flow in the samples. We compare simulated permeability from differing segmentation algorithms to experimental findings.

Unsupervised color image segmentation using a lattice algebra clustering technique

NASA Astrophysics Data System (ADS)

Urcid, Gonzalo; Ritter, Gerhard X.

2011-08-01

In this paper we introduce a lattice algebra clustering technique for segmenting digital images in the Red-Green- Blue (RGB) color space. The proposed technique is a two step procedure. Given an input color image, the first step determines the finite set of its extreme pixel vectors within the color cube by means of the scaled min-W and max-M lattice auto-associative memory matrices, including the minimum and maximum vector bounds. In the second step, maximal rectangular boxes enclosing each extreme color pixel are found using the Chebychev distance between color pixels; afterwards, clustering is performed by assigning each image pixel to its corresponding maximal box. The two steps in our proposed method are completely unsupervised or autonomous. Illustrative examples are provided to demonstrate the color segmentation results including a brief numerical comparison with two other non-maximal variations of the same clustering technique.

Automated brain tumor segmentation in magnetic resonance imaging based on sliding-window technique and symmetry analysis.

PubMed

Lian, Yanyun; Song, Zhijian

2014-01-01

Brain tumor segmentation from magnetic resonance imaging (MRI) is an important step toward surgical planning, treatment planning, monitoring of therapy. However, manual tumor segmentation commonly used in clinic is time-consuming and challenging, and none of the existed automated methods are highly robust, reliable and efficient in clinic application. An accurate and automated tumor segmentation method has been developed for brain tumor segmentation that will provide reproducible and objective results close to manual segmentation results. Based on the symmetry of human brain, we employed sliding-window technique and correlation coefficient to locate the tumor position. At first, the image to be segmented was normalized, rotated, denoised, and bisected. Subsequently, through vertical and horizontal sliding-windows technique in turn, that is, two windows in the left and the right part of brain image moving simultaneously pixel by pixel in two parts of brain image, along with calculating of correlation coefficient of two windows, two windows with minimal correlation coefficient were obtained, and the window with bigger average gray value is the location of tumor and the pixel with biggest gray value is the locating point of tumor. At last, the segmentation threshold was decided by the average gray value of the pixels in the square with center at the locating point and 10 pixels of side length, and threshold segmentation and morphological operations were used to acquire the final tumor region. The method was evaluated on 3D FSPGR brain MR images of 10 patients. As a result, the average ratio of correct location was 93.4% for 575 slices containing tumor, the average Dice similarity coefficient was 0.77 for one scan, and the average time spent on one scan was 40 seconds. An fully automated, simple and efficient segmentation method for brain tumor is proposed and promising for future clinic use. Correlation coefficient is a new and effective feature for tumor location.

Arc Second Alignment of International X-Ray Observatory Mirror Segments in a Fixed Structure

NASA Technical Reports Server (NTRS)

Evans, Tyler C.; Chan, Kai-Wing

2009-01-01

The optics for the International X-Ray Observatory (IXO) require alignment and integration of about fourteen thousand thin mirror segments to achieve the mission goal of 3.0 square meters of effective area at 1.25 keV with an angular resolution of five arc seconds. These mirror segments are 0.4mm thick, and 200 to 400mm in size, which makes it hard not to impart distortion at the subarc second level. This paper outlines the precise alignment, verification testing, and permanent bonding techniques developed at NASA's Goddard Space Flight Center (GSFC). These techniques are used to overcome the challenge of transferring thin mirror segments from a temporary mount to a fixed structure with arc second alignment and minimal figure distortion. Recent advances in technology development in addition to the automation of several processes have produced significant results. This paper will highlight the recent advances in alignment, testing, and permanent bonding techniques as well as the results they have produced.

Arc-Second Alignment of International X-Ray Observatory Mirror Segments in a Fixed Structure

NASA Technical Reports Server (NTRS)

Evans, Tyler C.; Chan, Kai-Wing; Saha, Timo T.

2010-01-01

The optics for the International X-Ray Observatory (IXO) require alignment and integration of about fourteen thousand thin mirror segments to achieve the mission goal of 3.0 square meters of effective area at 1.25 keV with an angular resolution of five arc-seconds. These mirror segments are 0.4 mm thick, and 200 to 400 mm in size, which makes it hard not to impart distortion at the subare- second level. This paper outlines the precise alignment, verification testing, and permanent bonding techniques developed at NASA's Goddard Space Flight Center (GSFC). These techniques are used to overcome the challenge of transferring thin mirror segments from a temporary mount to a fixed structure with arc-second alignment and minimal figure distortion. Recent advances in technology development in addition to the automation of several processes have produced significant results. This paper will highlight the recent advances in alignment, testing, and permanent bonding techniques as well as the results they have produced.

Multiple Vehicle Detection and Segmentation in Malaysia Traffic Flow

NASA Astrophysics Data System (ADS)

Fariz Hasan, Ahmad; Fikri Che Husin, Mohd; Affendi Rosli, Khairul; Norhafiz Hashim, Mohd; Faiz Zainal Abidin, Amar

2018-03-01

Vision based system are widely used in the field of Intelligent Transportation System (ITS) to extract a large amount of information to analyze traffic scenes. By rapid number of vehicles on the road as well as significant increase on cameras dictated the need for traffic surveillance systems. This system can take over the burden some task was performed by human operator in traffic monitoring centre. The main technique proposed by this paper is concentrated on developing a multiple vehicle detection and segmentation focusing on monitoring through Closed Circuit Television (CCTV) video. The system is able to automatically segment vehicle extracted from heavy traffic scene by optical flow estimation alongside with blob analysis technique in order to detect the moving vehicle. Prior to segmentation, blob analysis technique will compute the area of interest region corresponding to moving vehicle which will be used to create bounding box on that particular vehicle. Experimental validation on the proposed system was performed and the algorithm is demonstrated on various set of traffic scene.

Marketing Education Through Benefit Segmentation. AIR Forum 1981 Paper.

ERIC Educational Resources Information Center

Goodnow, Wilma Elizabeth

The applicability of the "benefit segmentation" marketing technique to education was tested at the College of DuPage in 1979. Benefit segmentation identified target markets homogeneous in benefits expected from a program offering and may be useful in combatting declining enrollments. The 487 randomly selected students completed the 223…

Strategies for Dual-Career Couples. [Second Edition.

ERIC Educational Resources Information Center

Stevens, Paul

This booklet focuses on interpersonal techniques that two-career couples can use to boost their careers, organize their lives, and enhance their relationships. The guide begins by outlining expected traits of career-oriented women and commenting on the social changes that have made careers desirable for more women. It mentions the differences…

Best Practices in Preparing Students for Mock Interviews

ERIC Educational Resources Information Center

Hansen, Katharine; Oliphant, Gary C.; Oliphant, Becky J.; Hansen, Randall S.

2009-01-01

Studies have shown the importance of employment interview preparation in boosting the confidence and performance of students and jobseekers when they interview. This article reviews several techniques for preparing students for mock job interviews and, hence, actual job interviews. For instructors who would like to enhance the learning value of…

Fission gas bubble identification using MATLAB's image processing toolbox

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

Collette, R.

Automated image processing routines have the potential to aid in the fuel performance evaluation process by eliminating bias in human judgment that may vary from person-to-person or sample-to-sample. This study presents several MATLAB based image analysis routines designed for fission gas void identification in post-irradiation examination of uranium molybdenum (U–Mo) monolithic-type plate fuels. Frequency domain filtration, enlisted as a pre-processing technique, can eliminate artifacts from the image without compromising the critical features of interest. This process is coupled with a bilateral filter, an edge-preserving noise removal technique aimed at preparing the image for optimal segmentation. Adaptive thresholding proved to bemore » the most consistent gray-level feature segmentation technique for U–Mo fuel microstructures. The Sauvola adaptive threshold technique segments the image based on histogram weighting factors in stable contrast regions and local statistics in variable contrast regions. Once all processing is complete, the algorithm outputs the total fission gas void count, the mean void size, and the average porosity. The final results demonstrate an ability to extract fission gas void morphological data faster, more consistently, and at least as accurately as manual segmentation methods. - Highlights: •Automated image processing can aid in the fuel qualification process. •Routines are developed to characterize fission gas bubbles in irradiated U–Mo fuel. •Frequency domain filtration effectively eliminates FIB curtaining artifacts. •Adaptive thresholding proved to be the most accurate segmentation method. •The techniques established are ready to be applied to large scale data extraction testing.« less

A cost comparison analysis of adjuvant radiation therapy techniques after breast-conserving surgery.

PubMed

Lanni, Thomas; Keisch, Martin; Shah, Chirag; Wobb, Jessica; Kestin, Larry; Vicini, Frank

2013-01-01

The aim of this study is to perform a cost analysis to compare adjuvant radiation therapy schedules following breast conserving surgery. Treatment planning and delivery utilization data were modeled for a series of 10 different breast RT techniques. The whole breast (WB) regimens consisted of: (1) Wedge based WB (25 fractions [fx]), (2) WB using IMRT, (3) WBRT with a boost (B), (4) WBRT using IMRT with a B, (5) Canadian WB (16 fx) with 3D-CRT, and (6) Canadian using IMRT. The accelerated partial breast irradiation (APBI) regimens included (7): APBI using 3D-CRT, (8) IMRT, (9) single channel balloon, and (10) multi-channel balloon. Costs incurred by the payer (i.e., direct medical costs) were taken from the 2011 Medicare Fee Schedule. Among all the different regimens examined, Canadian 3D-CRT and APBI 3D-CRT were the least costly whereas WB using IMRT with a B was the most expensive. Both APBI brachytherapy techniques were less costly than conventional WB with a B. In terms of direct medical costs, the technical component accounted for most, if not all, of the disparity among the various treatments. A general trend of decreasing RT costs was observed with further reductions in overall treatment time for WBRT techniques, but not all of the alternative treatment regimens led to similar total cost savings. APBI using brachytherapy techniques was less costly than conventional WBRT with a standard boost. © 2013 Wiley Periodicals, Inc.

Segmentation of Unstructured Datasets

NASA Technical Reports Server (NTRS)

Bhat, Smitha

1996-01-01

Datasets generated by computer simulations and experiments in Computational Fluid Dynamics tend to be extremely large and complex. It is difficult to visualize these datasets using standard techniques like Volume Rendering and Ray Casting. Object Segmentation provides a technique to extract and quantify regions of interest within these massive datasets. This thesis explores basic algorithms to extract coherent amorphous regions from two-dimensional and three-dimensional scalar unstructured grids. The techniques are applied to datasets from Computational Fluid Dynamics and from Finite Element Analysis.

Evaluation of a native vegetation masking technique

NASA Technical Reports Server (NTRS)

Kinsler, M. C.

1984-01-01

A crop masking technique based on Ashburn's vegetative index (AVI) was used to evaluate native vegetation as an indicator of crop moisture condition. A mask of the range areas (native vegetation) was generated for each of thirteen Great Plains LANDSAT MSS sample segments. These masks were compared to the digitized ground truth and accuracies were computed. An analysis of the types of errors indicates a consistency in errors among the segments. The mask represents a simple quick-look technique for evaluating vegetative cover.

KENNEDY SPACE CENTER, FLA. - Jeff Thon, an SRB mechanic with United Space Alliance, tests a technique for vertical solid rocket booster propellant grain inspection. The inspection of segments is required as part of safety analysis.

NASA Image and Video Library

2003-09-11

KENNEDY SPACE CENTER, FLA. - Jeff Thon, an SRB mechanic with United Space Alliance, tests a technique for vertical solid rocket booster propellant grain inspection. The inspection of segments is required as part of safety analysis.

Fission gas bubble identification using MATLAB's image processing toolbox

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

Collette, R.; King, J.; Keiser, Jr., D.

Automated image processing routines have the potential to aid in the fuel performance evaluation process by eliminating bias in human judgment that may vary from person-to-person or sample-to-sample. In addition, this study presents several MATLAB based image analysis routines designed for fission gas void identification in post-irradiation examination of uranium molybdenum (U–Mo) monolithic-type plate fuels. Frequency domain filtration, enlisted as a pre-processing technique, can eliminate artifacts from the image without compromising the critical features of interest. This process is coupled with a bilateral filter, an edge-preserving noise removal technique aimed at preparing the image for optimal segmentation. Adaptive thresholding provedmore » to be the most consistent gray-level feature segmentation technique for U–Mo fuel microstructures. The Sauvola adaptive threshold technique segments the image based on histogram weighting factors in stable contrast regions and local statistics in variable contrast regions. Once all processing is complete, the algorithm outputs the total fission gas void count, the mean void size, and the average porosity. The final results demonstrate an ability to extract fission gas void morphological data faster, more consistently, and at least as accurately as manual segmentation methods.« less

Fission gas bubble identification using MATLAB's image processing toolbox

DOE PAGES

Collette, R.; King, J.; Keiser, Jr., D.; ...

2016-06-08

Automated image processing routines have the potential to aid in the fuel performance evaluation process by eliminating bias in human judgment that may vary from person-to-person or sample-to-sample. In addition, this study presents several MATLAB based image analysis routines designed for fission gas void identification in post-irradiation examination of uranium molybdenum (U–Mo) monolithic-type plate fuels. Frequency domain filtration, enlisted as a pre-processing technique, can eliminate artifacts from the image without compromising the critical features of interest. This process is coupled with a bilateral filter, an edge-preserving noise removal technique aimed at preparing the image for optimal segmentation. Adaptive thresholding provedmore » to be the most consistent gray-level feature segmentation technique for U–Mo fuel microstructures. The Sauvola adaptive threshold technique segments the image based on histogram weighting factors in stable contrast regions and local statistics in variable contrast regions. Once all processing is complete, the algorithm outputs the total fission gas void count, the mean void size, and the average porosity. The final results demonstrate an ability to extract fission gas void morphological data faster, more consistently, and at least as accurately as manual segmentation methods.« less

Fuzzy Logic Controlled Solar Module for Driving Three- Phase Induction Motor

NASA Astrophysics Data System (ADS)

Afiqah Zainal, Nurul; Sooi Tat, Chan; Ajisman

2016-02-01

Renewable energy produced by solar module gives advantages for generated three- phase induction motor in remote area. But, solar module's ou tput is uncertain and complex. Fuzzy logic controller is one of controllers that can handle non-linear system and maximum power of solar module. Fuzzy logic controller used for Maximum Power Point Tracking (MPPT) technique to control Pulse-Width Modulation (PWM) for switching power electronics circuit. DC-DC boost converter used to boost up photovoltaic voltage to desired output and supply voltage source inverter which controlled by three-phase PWM generated by microcontroller. IGBT switched Voltage source inverter (VSI) produced alternating current (AC) voltage from direct current (DC) source to control speed of three-phase induction motor from boost converter output. Results showed that, the output power of solar module is optimized and controlled by using fuzzy logic controller. Besides that, the three-phase induction motor can be drive and control using VSI switching by the PWM signal generated by the fuzzy logic controller. This concluded that the non-linear system can be controlled and used in driving three-phase induction motor.

Arc-Second Alignment of International X-Ray Observatory Mirror Segments in a Fixed Structure

NASA Technical Reports Server (NTRS)

Evans, Tyler, C.; Chan, Kai-Wing; Saha, Timo T.

2010-01-01

The optics for the International X-Ray Observatory (IXO) require alignment and integration of about fourteen thousand thin mirror segments to achieve the mission goal of 3.0 square meters of effective area at 1.25 keV with an angular resolution of five arc-seconds. These mirror segments are 0.4 mm thick, and 200 to 400 mm in size, which makes it hard to meet the strict angular resolution requirement of 5 arc-seconds for the telescope. This paper outlines the precise alignment, verification testing, and permanent bonding techniques developed at NASA's Goddard Space Flight Center (GSFC). These techniques are used to overcome the challenge of transferring thin mirror segments from a temporary mount to a fixed structure with arc-second alignment and minimal figure distortion. Recent advances in technology development in addition to the automation of several processes have produced significant results. Recent advances in the mirror fixture process known as the suspension mount has allowed for a mirror to be mounted to a fixture with minimal distortion. Once on the fixture, mirror segments have been aligned to around 5 arc-seconds which is halfway to the goal of 2.5 arc-seconds per mirror segment. This paper will highlight the recent advances in alignment, testing, and permanent bonding techniques as well as the results they have produced.

ADMET Evaluation in Drug Discovery. 18. Reliable Prediction of Chemical-Induced Urinary Tract Toxicity by Boosting Machine Learning Approaches.

PubMed

Lei, Tailong; Sun, Huiyong; Kang, Yu; Zhu, Feng; Liu, Hui; Zhou, Wenfang; Wang, Zhe; Li, Dan; Li, Youyong; Hou, Tingjun

2017-11-06

Xenobiotic chemicals and their metabolites are mainly excreted out of our bodies by the urinary tract through the urine. Chemical-induced urinary tract toxicity is one of the main reasons that cause failure during drug development, and it is a common adverse event for medications, natural supplements, and environmental chemicals. Despite its importance, there are only a few in silico models for assessing urinary tract toxicity for a large number of compounds with diverse chemical structures. Here, we developed a series of qualitative and quantitative structure-activity relationship (QSAR) models for predicting urinary tract toxicity. In our study, the recursive feature elimination method incorporated with random forests (RFE-RF) was used for dimension reduction, and then eight machine learning approaches were used for QSAR modeling, i.e., relevance vector machine (RVM), support vector machine (SVM), regularized random forest (RRF), C5.0 trees, eXtreme gradient boosting (XGBoost), AdaBoost.M1, SVM boosting (SVMBoost), and RVM boosting (RVMBoost). For building classification models, the synthetic minority oversampling technique was used to handle the imbalance data set problem. Among all the machine learning approaches, SVMBoost based on the RBF kernel achieves both the best quantitative (q ext 2 = 0.845) and qualitative predictions for the test set (MCC of 0.787, AUC of 0.893, sensitivity of 89.6%, specificity of 94.1%, and global accuracy of 90.8%). The application domains were then analyzed, and all of the tested chemicals fall within the application domain coverage. We also examined the structure features of the chemicals with large prediction errors. In brief, both the regression and classification models developed by the SVMBoost approach have reliable prediction capability for assessing chemical-induced urinary tract toxicity.

Bioimpedance Measurement of Segmental Fluid Volumes and Hemodynamics

NASA Technical Reports Server (NTRS)

Montgomery, Leslie D.; Wu, Yi-Chang; Ku, Yu-Tsuan E.; Gerth, Wayne A.; DeVincenzi, D. (Technical Monitor)

2000-01-01

Bioimpedance has become a useful tool to measure changes in body fluid compartment volumes. An Electrical Impedance Spectroscopic (EIS) system is described that extends the capabilities of conventional fixed frequency impedance plethysmographic (IPG) methods to allow examination of the redistribution of fluids between the intracellular and extracellular compartments of body segments. The combination of EIS and IPG techniques was evaluated in the human calf, thigh, and torso segments of eight healthy men during 90 minutes of six degree head-down tilt (HDT). After 90 minutes HDT the calf and thigh segments significantly (P < 0.05) lost conductive volume (eight and four percent, respectively) while the torso significantly (P < 0.05) gained volume (approximately three percent). Hemodynamic responses calculated from pulsatile IPG data also showed a segmental pattern consistent with vascular fluid loss from the lower extremities and vascular engorgement in the torso. Lumped-parameter equivalent circuit analyses of EIS data for the calf and thigh indicated that the overall volume decreases in these segments arose from reduced extracellular volume that was not completely balanced by increased intracellular volume. The combined use of IPG and EIS techniques enables noninvasive tracking of multi-segment volumetric and hemodynamic responses to environmental and physiological stresses.

Transfer learning improves supervised image segmentation across imaging protocols.

PubMed

van Opbroek, Annegreet; Ikram, M Arfan; Vernooij, Meike W; de Bruijne, Marleen

2015-05-01

The variation between images obtained with different scanners or different imaging protocols presents a major challenge in automatic segmentation of biomedical images. This variation especially hampers the application of otherwise successful supervised-learning techniques which, in order to perform well, often require a large amount of labeled training data that is exactly representative of the target data. We therefore propose to use transfer learning for image segmentation. Transfer-learning techniques can cope with differences in distributions between training and target data, and therefore may improve performance over supervised learning for segmentation across scanners and scan protocols. We present four transfer classifiers that can train a classification scheme with only a small amount of representative training data, in addition to a larger amount of other training data with slightly different characteristics. The performance of the four transfer classifiers was compared to that of standard supervised classification on two magnetic resonance imaging brain-segmentation tasks with multi-site data: white matter, gray matter, and cerebrospinal fluid segmentation; and white-matter-/MS-lesion segmentation. The experiments showed that when there is only a small amount of representative training data available, transfer learning can greatly outperform common supervised-learning approaches, minimizing classification errors by up to 60%.

Segmentation of fluorescence microscopy cell images using unsupervised mining.

PubMed

Du, Xian; Dua, Sumeet

2010-05-28

The accurate measurement of cell and nuclei contours are critical for the sensitive and specific detection of changes in normal cells in several medical informatics disciplines. Within microscopy, this task is facilitated using fluorescence cell stains, and segmentation is often the first step in such approaches. Due to the complex nature of cell issues and problems inherent to microscopy, unsupervised mining approaches of clustering can be incorporated in the segmentation of cells. In this study, we have developed and evaluated the performance of multiple unsupervised data mining techniques in cell image segmentation. We adapt four distinctive, yet complementary, methods for unsupervised learning, including those based on k-means clustering, EM, Otsu's threshold, and GMAC. Validation measures are defined, and the performance of the techniques is evaluated both quantitatively and qualitatively using synthetic and recently published real data. Experimental results demonstrate that k-means, Otsu's threshold, and GMAC perform similarly, and have more precise segmentation results than EM. We report that EM has higher recall values and lower precision results from under-segmentation due to its Gaussian model assumption. We also demonstrate that these methods need spatial information to segment complex real cell images with a high degree of efficacy, as expected in many medical informatics applications.

Abdomen and spinal cord segmentation with augmented active shape models.

PubMed

Xu, Zhoubing; Conrad, Benjamin N; Baucom, Rebeccah B; Smith, Seth A; Poulose, Benjamin K; Landman, Bennett A

2016-07-01

Active shape models (ASMs) have been widely used for extracting human anatomies in medical images given their capability for shape regularization of topology preservation. However, sensitivity to model initialization and local correspondence search often undermines their performances, especially around highly variable contexts in computed-tomography (CT) and magnetic resonance (MR) images. In this study, we propose an augmented ASM (AASM) by integrating the multiatlas label fusion (MALF) and level set (LS) techniques into the traditional ASM framework. Using AASM, landmark updates are optimized globally via a region-based LS evolution applied on the probability map generated from MALF. This augmentation effectively extends the searching range of correspondent landmarks while reducing sensitivity to the image contexts and improves the segmentation robustness. We propose the AASM framework as a two-dimensional segmentation technique targeting structures with one axis of regularity. We apply AASM approach to abdomen CT and spinal cord (SC) MR segmentation challenges. On 20 CT scans, the AASM segmentation of the whole abdominal wall enables the subcutaneous/visceral fat measurement, with high correlation to the measurement derived from manual segmentation. On 28 3T MR scans, AASM yields better performances than other state-of-the-art approaches in segmenting white/gray matter in SC.

Energy flow during Olympic weight lifting.

PubMed

Garhammer, J

1982-01-01

Data obtained from 16-mm film of world caliber Olympic weight lifters performing at major competitions were analyzed to study energy changes during body segment and barbell movements, energy transfer to the barbell, and energy transfer between segments during the lifting movements contested. Determination of barbell and body segment kinematics and use of rigid-link modeling and energy flow techniques permitted the calculation of segment energy content and energy transfer between segments. Energy generation within and transfer to and from segments were determined at 0.04-s intervals by comparing mechanical energy changes of a segment with energy transfer at the joints, calculated from the scalar product of net joint force with absolute joint velocity, and the product of net joint torque due to muscular activity with absolute segment angular velocity. The results provided a detailed understanding of the magnitude and temporal input of energy from dominant muscle groups during a lift. This information also provided a means of quantifying lifting technique. Comparison of segment energy changes determined by the two methods were satisfactory but could likely be improved by employing more sophisticated data smoothing methods. The procedures used in this study could easily be applied to weight training and rehabilitative exercises to help determine their efficacy in producing desired results or to ergonomic situations where a more detailed understanding of the demands made on the body during lifting tasks would be useful.

Contextually guided very-high-resolution imagery classification with semantic segments

NASA Astrophysics Data System (ADS)

Zhao, Wenzhi; Du, Shihong; Wang, Qiao; Emery, William J.

2017-10-01

Contextual information, revealing relationships and dependencies between image objects, is one of the most important information for the successful interpretation of very-high-resolution (VHR) remote sensing imagery. Over the last decade, geographic object-based image analysis (GEOBIA) technique has been widely used to first divide images into homogeneous parts, and then to assign semantic labels according to the properties of image segments. However, due to the complexity and heterogeneity of VHR images, segments without semantic labels (i.e., semantic-free segments) generated with low-level features often fail to represent geographic entities (such as building roofs usually be partitioned into chimney/antenna/shadow parts). As a result, it is hard to capture contextual information across geographic entities when using semantic-free segments. In contrast to low-level features, "deep" features can be used to build robust segments with accurate labels (i.e., semantic segments) in order to represent geographic entities at higher levels. Based on these semantic segments, semantic graphs can be constructed to capture contextual information in VHR images. In this paper, semantic segments were first explored with convolutional neural networks (CNN) and a conditional random field (CRF) model was then applied to model the contextual information between semantic segments. Experimental results on two challenging VHR datasets (i.e., the Vaihingen and Beijing scenes) indicate that the proposed method is an improvement over existing image classification techniques in classification performance (overall accuracy ranges from 82% to 96%).

Automated identification of sleep states from EEG signals by means of ensemble empirical mode decomposition and random under sampling boosting.

PubMed

Hassan, Ahnaf Rashik; Bhuiyan, Mohammed Imamul Hassan

2017-03-01

Automatic sleep staging is essential for alleviating the burden of the physicians of analyzing a large volume of data by visual inspection. It is also a precondition for making an automated sleep monitoring system feasible. Further, computerized sleep scoring will expedite large-scale data analysis in sleep research. Nevertheless, most of the existing works on sleep staging are either multichannel or multiple physiological signal based which are uncomfortable for the user and hinder the feasibility of an in-home sleep monitoring device. So, a successful and reliable computer-assisted sleep staging scheme is yet to emerge. In this work, we propose a single channel EEG based algorithm for computerized sleep scoring. In the proposed algorithm, we decompose EEG signal segments using Ensemble Empirical Mode Decomposition (EEMD) and extract various statistical moment based features. The effectiveness of EEMD and statistical features are investigated. Statistical analysis is performed for feature selection. A newly proposed classification technique, namely - Random under sampling boosting (RUSBoost) is introduced for sleep stage classification. This is the first implementation of EEMD in conjunction with RUSBoost to the best of the authors' knowledge. The proposed feature extraction scheme's performance is investigated for various choices of classification models. The algorithmic performance of our scheme is evaluated against contemporary works in the literature. The performance of the proposed method is comparable or better than that of the state-of-the-art ones. The proposed algorithm gives 88.07%, 83.49%, 92.66%, 94.23%, and 98.15% for 6-state to 2-state classification of sleep stages on Sleep-EDF database. Our experimental outcomes reveal that RUSBoost outperforms other classification models for the feature extraction framework presented in this work. Besides, the algorithm proposed in this work demonstrates high detection accuracy for the sleep states S1 and REM. Statistical moment based features in the EEMD domain distinguish the sleep states successfully and efficaciously. The automated sleep scoring scheme propounded herein can eradicate the onus of the clinicians, contribute to the device implementation of a sleep monitoring system, and benefit sleep research. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Exploring the Dynamics of Dyadic Interactions via Hierarchical Segmentation

ERIC Educational Resources Information Center

Hsieh, Fushing; Ferrer, Emilio; Chen, Shu-Chun; Chow, Sy-Miin

2010-01-01

In this article we present an exploratory tool for extracting systematic patterns from multivariate data. The technique, hierarchical segmentation (HS), can be used to group multivariate time series into segments with similar discrete-state recurrence patterns and it is not restricted by the stationarity assumption. We use a simulation study to…

Increasing Enrollment by Better Serving Your Institution's Target Audiences through Benefit Segmentation.

ERIC Educational Resources Information Center

Goodnow, Betsy

The marketing technique of benefit segmentation may be effective in increasing enrollment in adult educational programs, according to a study at College of DuPage, Glen Ellyn, Illinois. The study was conducted to test applicability of benefit segmentation to enrollment generation. The measuring instrument used in this study--the course improvement…

The Uses of Student Market Segmentation Techniques in College Recruitment. AIR Forum Paper 1978.

ERIC Educational Resources Information Center

Spiro, Louis M.

Student market segmentation separates prospective college students into subgroups with similar characteristics, the most commonly used being geography, demography, attitudes, and behavior. Recruitment efforts can then focus on student segments similar to the present student body or on other students that might be attracted. The goal is to make…

Brain tumor segmentation in multi-spectral MRI using convolutional neural networks (CNN).

PubMed

Iqbal, Sajid; Ghani, M Usman; Saba, Tanzila; Rehman, Amjad

2018-04-01

A tumor could be found in any area of the brain and could be of any size, shape, and contrast. There may exist multiple tumors of different types in a human brain at the same time. Accurate tumor area segmentation is considered primary step for treatment of brain tumors. Deep Learning is a set of promising techniques that could provide better results as compared to nondeep learning techniques for segmenting timorous part inside a brain. This article presents a deep convolutional neural network (CNN) to segment brain tumors in MRIs. The proposed network uses BRATS segmentation challenge dataset which is composed of images obtained through four different modalities. Accordingly, we present an extended version of existing network to solve segmentation problem. The network architecture consists of multiple neural network layers connected in sequential order with the feeding of Convolutional feature maps at the peer level. Experimental results on BRATS 2015 benchmark data thus show the usability of the proposed approach and its superiority over the other approaches in this area of research. © 2018 Wiley Periodicals, Inc.

Automatic cell detection and segmentation from H and E stained pathology slides using colorspace decorrelation stretching

NASA Astrophysics Data System (ADS)

Peikari, Mohammad; Martel, Anne L.

2016-03-01

Purpose: Automatic cell segmentation plays an important role in reliable diagnosis and prognosis of patients. Most of the state-of-the-art cell detection and segmentation techniques focus on complicated methods to subtract foreground cells from the background. In this study, we introduce a preprocessing method which leads to a better detection and segmentation results compared to a well-known state-of-the-art work. Method: We transform the original red-green-blue (RGB) space into a new space defined by the top eigenvectors of the RGB space. Stretching is done by manipulating the contrast of each pixel value to equalize the color variances. New pixel values are then inverse transformed to the original RGB space. This altered RGB image is then used to segment cells. Result: The validation of our method with a well-known state-of-the-art technique revealed a statistically significant improvement on an identical validation set. We achieved a mean F1-score of 0.901. Conclusion: Preprocessing steps to decorrelate colorspaces may improve cell segmentation performances.

Excluded segmental duct bile leakage: the case for bilio-enteric anastomosis.

PubMed

Patrono, Damiano; Tandoi, Francesco; Romagnoli, Renato; Salizzoni, Mauro

2014-06-01

Excluded segmental duct bile leak is the rarest type of post-hepatectomy bile leak and presents unique diagnostic and management features. Classical management strategies invariably entail a significant loss of functioning hepatic parenchyma. The aim of this study is to report a new liver-sparing technique to handle excluded segmental duct bile leakage. Two cases of excluded segmental duct bile leak occurring after major hepatic resection were managed by a Roux-en-Y hepatico-jejunostomy on the excluded segmental duct, avoiding the sacrifice of the liver parenchyma origin of the fistula. In both cases, classical management strategies would have led to the functional loss of roughly 50 % of the liver remnant. Diagnostic and management implications are thoroughly discussed. Both cases had an uneventful postoperative course. The timing of repair was associated with a different outcome: the patient who underwent surgical repair in the acute phase developed no long-term complications, whereas the patient who underwent delayed repair developed a late stenosis requiring percutaneous dilatation. Roux-en-Y hepatico-jejunostomy on the excluded bile duct is a valuable technique in selected cases of excluded segmental duct bile leakage.

Unsupervised sputum color image segmentation for lung cancer diagnosis based on a Hopfield neural network

NASA Astrophysics Data System (ADS)

Sammouda, Rachid; Niki, Noboru; Nishitani, Hiroshi; Nakamura, S.; Mori, Shinichiro

1997-04-01

The paper presents a method for automatic segmentation of sputum cells with color images, to develop an efficient algorithm for lung cancer diagnosis based on a Hopfield neural network. We formulate the segmentation problem as a minimization of an energy function constructed with two terms, the cost-term as a sum of squared errors, and the second term a temporary noise added to the network as an excitation to escape certain local minima with the result of being closer to the global minimum. To increase the accuracy in segmenting the regions of interest, a preclassification technique is used to extract the sputum cell regions within the color image and remove those of the debris cells. The former is then given with the raw image to the input of Hopfield neural network to make a crisp segmentation by assigning each pixel to label such as background, cytoplasm, and nucleus. The proposed technique has yielded correct segmentation of complex scene of sputum prepared by ordinary manual staining method in most of the tested images selected from our database containing thousands of sputum color images.

Residential roof condition assessment system using deep learning

NASA Astrophysics Data System (ADS)

Wang, Fan; Kerekes, John P.; Xu, Zhuoyi; Wang, Yandong

2018-01-01

The emergence of high resolution (HR) and ultra high resolution (UHR) airborne remote sensing imagery is enabling humans to move beyond traditional land cover analysis applications to the detailed characterization of surface objects. A residential roof condition assessment method using techniques from deep learning is presented. The proposed method operates on individual roofs and divides the task into two stages: (1) roof segmentation, followed by (2) condition classification of the segmented roof regions. As the first step in this process, a self-tuning method is proposed to segment the images into small homogeneous areas. The segmentation is initialized with simple linear iterative clustering followed by deep learned feature extraction and region merging, with the optimal result selected by an unsupervised index, Q. After the segmentation, a pretrained residual network is fine-tuned on the augmented roof segments using a proposed k-pixel extension technique for classification. The effectiveness of the proposed algorithm was demonstrated on both HR and UHR imagery collected by EagleView over different study sites. The proposed algorithm has yielded promising results and has outperformed traditional machine learning methods using hand-crafted features.

Computer aided detection of tumor and edema in brain FLAIR magnetic resonance image using ANN

NASA Astrophysics Data System (ADS)

Pradhan, Nandita; Sinha, A. K.

2008-03-01

This paper presents an efficient region based segmentation technique for detecting pathological tissues (Tumor & Edema) of brain using fluid attenuated inversion recovery (FLAIR) magnetic resonance (MR) images. This work segments FLAIR brain images for normal and pathological tissues based on statistical features and wavelet transform coefficients using k-means algorithm. The image is divided into small blocks of 4×4 pixels. The k-means algorithm is used to cluster the image based on the feature vectors of blocks forming different classes representing different regions in the whole image. With the knowledge of the feature vectors of different segmented regions, supervised technique is used to train Artificial Neural Network using fuzzy back propagation algorithm (FBPA). Segmentation for detecting healthy tissues and tumors has been reported by several researchers by using conventional MRI sequences like T1, T2 and PD weighted sequences. This work successfully presents segmentation of healthy and pathological tissues (both Tumors and Edema) using FLAIR images. At the end pseudo coloring of segmented and classified regions are done for better human visualization.

Augmenting atlas-based liver segmentation for radiotherapy treatment planning by incorporating image features proximal to the atlas contours

NASA Astrophysics Data System (ADS)

Li, Dengwang; Liu, Li; Chen, Jinhu; Li, Hongsheng; Yin, Yong; Ibragimov, Bulat; Xing, Lei

2017-01-01

Atlas-based segmentation utilizes a library of previously delineated contours of similar cases to facilitate automatic segmentation. The problem, however, remains challenging because of limited information carried by the contours in the library. In this studying, we developed a narrow-shell strategy to enhance the information of each contour in the library and to improve the accuracy of the exiting atlas-based approach. This study presented a new concept of atlas based segmentation method. Instead of using the complete volume of the target organs, only information along the organ contours from the atlas images was used for guiding segmentation of the new image. In setting up an atlas-based library, we included not only the coordinates of contour points, but also the image features adjacent to the contour. In this work, 139 CT images with normal appearing livers collected for radiotherapy treatment planning were used to construct the library. The CT images within the library were first registered to each other using affine registration. The nonlinear narrow shell was generated alongside the object contours of registered images. Matching voxels were selected inside common narrow shell image features of a library case and a new case using a speed-up robust features (SURF) strategy. A deformable registration was then performed using a thin plate splines (TPS) technique. The contour associated with the library case was propagated automatically onto the new image by exploiting the deformation field vectors. The liver contour was finally obtained by employing level set based energy optimization within the narrow shell. The performance of the proposed method was evaluated by comparing quantitatively the auto-segmentation results with that delineated by physicians. A novel atlas-based segmentation technique with inclusion of neighborhood image features through the introduction of a narrow-shell surrounding the target objects was established. Application of the technique to 30 liver cases suggested that the technique was capable to reliably segment liver cases from CT, 4D-CT, and CBCT images with little human interaction. The accuracy and speed of the proposed method are quantitatively validated by comparing automatic segmentation results with the manual delineation results. The Jaccard similarity metric between the automatically generated liver contours obtained by the proposed method and the physician delineated results are on an average 90%-96% for planning images. Incorporation of image features into the library contours improves the currently available atlas-based auto-contouring techniques and provides a clinically practical solution for auto-segmentation. The proposed mountainous narrow shell atlas based method can achieve efficient automatic liver propagation for CT, 4D-CT and CBCT images with following treatment planning and should find widespread application in future treatment planning systems.

Augmenting atlas-based liver segmentation for radiotherapy treatment planning by incorporating image features proximal to the atlas contours.

PubMed

Li, Dengwang; Liu, Li; Chen, Jinhu; Li, Hongsheng; Yin, Yong; Ibragimov, Bulat; Xing, Lei

2017-01-07

Atlas-based segmentation utilizes a library of previously delineated contours of similar cases to facilitate automatic segmentation. The problem, however, remains challenging because of limited information carried by the contours in the library. In this studying, we developed a narrow-shell strategy to enhance the information of each contour in the library and to improve the accuracy of the exiting atlas-based approach. This study presented a new concept of atlas based segmentation method. Instead of using the complete volume of the target organs, only information along the organ contours from the atlas images was used for guiding segmentation of the new image. In setting up an atlas-based library, we included not only the coordinates of contour points, but also the image features adjacent to the contour. In this work, 139 CT images with normal appearing livers collected for radiotherapy treatment planning were used to construct the library. The CT images within the library were first registered to each other using affine registration. The nonlinear narrow shell was generated alongside the object contours of registered images. Matching voxels were selected inside common narrow shell image features of a library case and a new case using a speed-up robust features (SURF) strategy. A deformable registration was then performed using a thin plate splines (TPS) technique. The contour associated with the library case was propagated automatically onto the new image by exploiting the deformation field vectors. The liver contour was finally obtained by employing level set based energy optimization within the narrow shell. The performance of the proposed method was evaluated by comparing quantitatively the auto-segmentation results with that delineated by physicians. A novel atlas-based segmentation technique with inclusion of neighborhood image features through the introduction of a narrow-shell surrounding the target objects was established. Application of the technique to 30 liver cases suggested that the technique was capable to reliably segment liver cases from CT, 4D-CT, and CBCT images with little human interaction. The accuracy and speed of the proposed method are quantitatively validated by comparing automatic segmentation results with the manual delineation results. The Jaccard similarity metric between the automatically generated liver contours obtained by the proposed method and the physician delineated results are on an average 90%-96% for planning images. Incorporation of image features into the library contours improves the currently available atlas-based auto-contouring techniques and provides a clinically practical solution for auto-segmentation. The proposed mountainous narrow shell atlas based method can achieve efficient automatic liver propagation for CT, 4D-CT and CBCT images with following treatment planning and should find widespread application in future treatment planning systems.

The effect of a collagen-elastin matrix on adhesion formation after flexor tendon repair in a rabbit model.

PubMed

Wichelhaus, Dagmar Alice; Beyersdoerfer, Sascha Tobias; Gierer, Philip; Vollmar, Brigitte; Mittlmeier, Th

2016-07-01

The outcome of flexor tendon surgery is negatively affected by the formation of adhesions which can occur during the healing of the tendon repair. In this experimental study, we sought to prevent adhesion formation by wrapping a collagen-elastin scaffold around the repaired tendon segment. In 28 rabbit hind legs, the flexor tendons of the third and fourth digits were cut and then repaired using a two-strand suture technique on the fourth digit and a four-strand technique on the third digit. Rabbits were randomly assigned to study and control groups. In the control group, the operation ended by closing the tendon sheath and the skin. In the study group, a collagen-elastin scaffold was wrapped around the repaired tendon segment in both digits. After 3 and 8 weeks, the tendons were harvested and processed histologically. The range of motion of the digits and the gap formation between the repaired tendon ends were measured. The formation of adhesions, infiltration of leucocytes and extracellular inflammatory response were quantified. At the time of tendon harvesting, all joints of the operated toes showed free range of motion. Four-strand core sutures lead to significantly less diastasis between the repaired tendon ends than two-strand core suture repairs. The collagen-elastin scaffold leads to greater gapping after 3 weeks compared to the controls treated without the matrix. Within the tendons treated with the collagen-elastin matrix, a significant boost of cellular and extracellular inflammation could be stated after 3 weeks which was reflected by a higher level of CAE positive cells and more formation of myofibroblasts in the αSMA stain in the study group. The inflammatory response subsided gradually and significantly until the late stage of the study. Both the cellular and extracellular inflammatory response was emphasized with the amount of material used for the repair. The use of a collagen-elastin matrix cannot be advised for the prevention of adhesion formation in flexor tendon surgery, because it enhances both cellular and extracellular inflammation. Four-strand core sutures lead to less gapping than two-strand core sutures, but at the same time, the cellular and extracellular inflammatory response is more pronounced.

Rigid shape matching by segmentation averaging.

PubMed

Wang, Hongzhi; Oliensis, John

2010-04-01

We use segmentations to match images by shape. The new matching technique does not require point-to-point edge correspondence and is robust to small shape variations and spatial shifts. To address the unreliability of segmentations computed bottom-up, we give a closed form approximation to an average over all segmentations. Our method has many extensions, yielding new algorithms for tracking, object detection, segmentation, and edge-preserving smoothing. For segmentation, instead of a maximum a posteriori approach, we compute the "central" segmentation minimizing the average distance to all segmentations of an image. For smoothing, instead of smoothing images based on local structures, we smooth based on the global optimal image structures. Our methods for segmentation, smoothing, and object detection perform competitively, and we also show promising results in shape-based tracking.

Laryngotracheal reconstruction with resorbable microplate buttressing.

PubMed

Javia, Luv R; Zur, Karen B

2012-04-01

In patients undergoing laryngotracheal reconstruction (LTR), malacic segments of trachea can pose challenges to successful reconstruction. Malacic segments may inadequately support cartilage grafts used in augmentation surgery, sometimes requiring cricotracheal or tracheal resections. We describe a novel technique of LTR with resorbable microplate buttressing of malacic lateral tracheal segments. Retrospective case series. Review of technique, treatment outcomes, and complications of seven children with subglottic stenosis and tracheomalacia requiring a microplate-augmented LTR technique. Seven infants ranging from 26 months to 9 years of age successfully underwent LTR for subglottic stenosis. Six children had a grade III subglottic stenosis. The seventh child had grade II subglottic stenosis, bilateral vocal fold paralysis, an elliptical cricoid, and an obstructing giant suprastomal fibroma. Five children underwent a double-stage LTR with resorbable microplates sutured bilaterally to support severely malacic lateral tracheal segments. A cricotracheal resection would not have been feasible in one child due to the resection length and inadequate tracheal mobilization. Two children underwent a single-stage LTR with unilateral application of a microplate. Six children were decannulated within 3 months and continue without airway symptoms or complications. One child, who is just over 2 months from reconstructive surgery, is being setup for decannulation. No complications were encountered. LTR with resorbable microplate buttressing of malacic lateral tracheal segments is technically feasible, safe, and can avoid more extensive surgery requiring tracheal resection. Further experience may support the use of this technique in challenging airway reconstructions. Copyright © 2012 The American Laryngological, Rhinological, and Otological Society, Inc.

Inferring the most probable maps of underground utilities using Bayesian mapping model

NASA Astrophysics Data System (ADS)

Bilal, Muhammad; Khan, Wasiq; Muggleton, Jennifer; Rustighi, Emiliano; Jenks, Hugo; Pennock, Steve R.; Atkins, Phil R.; Cohn, Anthony

2018-03-01

Mapping the Underworld (MTU), a major initiative in the UK, is focused on addressing social, environmental and economic consequences raised from the inability to locate buried underground utilities (such as pipes and cables) by developing a multi-sensor mobile device. The aim of MTU device is to locate different types of buried assets in real time with the use of automated data processing techniques and statutory records. The statutory records, even though typically being inaccurate and incomplete, provide useful prior information on what is buried under the ground and where. However, the integration of information from multiple sensors (raw data) with these qualitative maps and their visualization is challenging and requires the implementation of robust machine learning/data fusion approaches. An approach for automated creation of revised maps was developed as a Bayesian Mapping model in this paper by integrating the knowledge extracted from sensors raw data and available statutory records. The combination of statutory records with the hypotheses from sensors was for initial estimation of what might be found underground and roughly where. The maps were (re)constructed using automated image segmentation techniques for hypotheses extraction and Bayesian classification techniques for segment-manhole connections. The model consisting of image segmentation algorithm and various Bayesian classification techniques (segment recognition and expectation maximization (EM) algorithm) provided robust performance on various simulated as well as real sites in terms of predicting linear/non-linear segments and constructing refined 2D/3D maps.

Laboratory Preparation in the Ocular Therapy Curriculum.

ERIC Educational Resources Information Center

Cummings, Roger W.

1986-01-01

Aspects of laboratory preparation necessary for undergraduate or graduate optometric training in the use of therapeutic drugs are discussed, including glaucoma therapy, anterior segment techniques, posterior segment, and systemic procedures. (MSE)

Effects of Deregulation and Vertical Unbundling on the Performance of China's Electricity Generation Sector†

PubMed Central

Gao, Hang; Van Biesebroeck, Johannes

2014-01-01

The restructuring of the Chinese electricity sector in 2002 reshaped the market structure by vertically unbundling the dominant integrated firm and started the process of wholesale price liberalization. We estimate factor demands to study whether these reforms boosted productivity in the generation segment of the industry. Controlling explicitly for price‐heterogeneity across firms and unobservable productivity shocks, we find that the reforms are associated with reductions in labor and material use of 7 and 5 per cent, respectively. These effects only appear two years after the reforms and are robust to many specification checks. The absolute magnitudes of the estimated restructuring effects vary in intuitive ways by location, firm size or age, and for different definitions of restructured firms. PMID:27076686

Comparison of k-means related clustering methods for nuclear medicine images segmentation

NASA Astrophysics Data System (ADS)

Borys, Damian; Bzowski, Pawel; Danch-Wierzchowska, Marta; Psiuk-Maksymowicz, Krzysztof

2017-03-01

In this paper, we evaluate the performance of SURF descriptor for high resolution satellite imagery (HRSI) retrieval through a BoVW model on a land-use/land-cover (LULC) dataset. Local feature approaches such as SIFT and SURF descriptors can deal with a large variation of scale, rotation and illumination of the images, providing, therefore, a better discriminative power and retrieval efficiency than global features, especially for HRSI which contain a great range of objects and spatial patterns. Moreover, we combine SURF and color features to improve the retrieval accuracy, and we propose to learn a category-specific dictionary for each image category which results in a more discriminative image representation and boosts the image retrieval performance.

Detecting Weak Spectral Lines in Interferometric Data through Matched Filtering

NASA Astrophysics Data System (ADS)

Loomis, Ryan A.; Öberg, Karin I.; Andrews, Sean M.; Walsh, Catherine; Czekala, Ian; Huang, Jane; Rosenfeld, Katherine A.

2018-04-01

Modern radio interferometers enable observations of spectral lines with unprecedented spatial resolution and sensitivity. In spite of these technical advances, many lines of interest are still at best weakly detected and therefore necessitate detection and analysis techniques specialized for the low signal-to-noise ratio (S/N) regime. Matched filters can leverage knowledge of the source structure and kinematics to increase sensitivity of spectral line observations. Application of the filter in the native Fourier domain improves S/N while simultaneously avoiding the computational cost and ambiguities associated with imaging, making matched filtering a fast and robust method for weak spectral line detection. We demonstrate how an approximate matched filter can be constructed from a previously observed line or from a model of the source, and we show how this filter can be used to robustly infer a detection significance for weak spectral lines. When applied to ALMA Cycle 2 observations of CH3OH in the protoplanetary disk around TW Hya, the technique yields a ≈53% S/N boost over aperture-based spectral extraction methods, and we show that an even higher boost will be achieved for observations at higher spatial resolution. A Python-based open-source implementation of this technique is available under the MIT license at http://github.com/AstroChem/VISIBLE.

Clusius-Dickel Separations (CDS): A new look at an old technique

NASA Technical Reports Server (NTRS)

Grodzka, P. G.

1975-01-01

The history, applications, and theoretical basis of the CDS technique are reviewed. The advantage to be realized by conduction of CDSs in low-g, space environments are deduced. The results are reported of investigations aimed at further improving CDS efficiencies by altering convective flow patterns. The question of whether multicellular flow or turbulence can introduce a new separation mechanism which would boost separation efficiencies at least an order of magnitude is considered. Results are presented and discussed.

Intravesical markers for delineation of target volume during external focal irradiation of bladder carcinomas.

PubMed

Hulshof, Maarten C C M; van Andel, George; Bel, Arjen; Gangel, Pieter; van de Kamer, Jeroen B

2007-07-01

A clip forceps was developed which can insert markers at the border of a bladder tumour through a rigid cystoscope. This technique proved to be simple and safe and is of help for delineation of the target volume during CT simulation for focal boost irradiation of bladder cancer.

An Alternative Educational Method in Early Childhood: Museum Education

ERIC Educational Resources Information Center

Akamca, Güzin Özyilmaz; Yildirim, R. Gunseli; Ellez, A. Murat

2017-01-01

According to the preschool education program that came into effect by Turkish Ministry of Education in Turkey in 2013, teaching should be offered not only in classrooms but also in places outside classrooms likely to boost learning. The program required utilizing learning techniques, and environments different from conventional ones. The aim of…

Wavelet-enhanced convolutional neural network: a new idea in a deep learning paradigm.

PubMed

Savareh, Behrouz Alizadeh; Emami, Hassan; Hajiabadi, Mohamadreza; Azimi, Seyed Majid; Ghafoori, Mahyar

2018-05-29

Manual brain tumor segmentation is a challenging task that requires the use of machine learning techniques. One of the machine learning techniques that has been given much attention is the convolutional neural network (CNN). The performance of the CNN can be enhanced by combining other data analysis tools such as wavelet transform. In this study, one of the famous implementations of CNN, a fully convolutional network (FCN), was used in brain tumor segmentation and its architecture was enhanced by wavelet transform. In this combination, a wavelet transform was used as a complementary and enhancing tool for CNN in brain tumor segmentation. Comparing the performance of basic FCN architecture against the wavelet-enhanced form revealed a remarkable superiority of enhanced architecture in brain tumor segmentation tasks. Using mathematical functions and enhancing tools such as wavelet transform and other mathematical functions can improve the performance of CNN in any image processing task such as segmentation and classification.

Improved inference in Bayesian segmentation using Monte Carlo sampling: application to hippocampal subfield volumetry.

PubMed

Iglesias, Juan Eugenio; Sabuncu, Mert Rory; Van Leemput, Koen

2013-10-01

Many segmentation algorithms in medical image analysis use Bayesian modeling to augment local image appearance with prior anatomical knowledge. Such methods often contain a large number of free parameters that are first estimated and then kept fixed during the actual segmentation process. However, a faithful Bayesian analysis would marginalize over such parameters, accounting for their uncertainty by considering all possible values they may take. Here we propose to incorporate this uncertainty into Bayesian segmentation methods in order to improve the inference process. In particular, we approximate the required marginalization over model parameters using computationally efficient Markov chain Monte Carlo techniques. We illustrate the proposed approach using a recently developed Bayesian method for the segmentation of hippocampal subfields in brain MRI scans, showing a significant improvement in an Alzheimer's disease classification task. As an additional benefit, the technique also allows one to compute informative "error bars" on the volume estimates of individual structures. Copyright © 2013 Elsevier B.V. All rights reserved.

Improved Inference in Bayesian Segmentation Using Monte Carlo Sampling: Application to Hippocampal Subfield Volumetry

PubMed Central

Iglesias, Juan Eugenio; Sabuncu, Mert Rory; Leemput, Koen Van

2013-01-01

Many segmentation algorithms in medical image analysis use Bayesian modeling to augment local image appearance with prior anatomical knowledge. Such methods often contain a large number of free parameters that are first estimated and then kept fixed during the actual segmentation process. However, a faithful Bayesian analysis would marginalize over such parameters, accounting for their uncertainty by considering all possible values they may take. Here we propose to incorporate this uncertainty into Bayesian segmentation methods in order to improve the inference process. In particular, we approximate the required marginalization over model parameters using computationally efficient Markov chain Monte Carlo techniques. We illustrate the proposed approach using a recently developed Bayesian method for the segmentation of hippocampal subfields in brain MRI scans, showing a significant improvement in an Alzheimer’s disease classification task. As an additional benefit, the technique also allows one to compute informative “error bars” on the volume estimates of individual structures. PMID:23773521

A human visual based binarization technique for histological images

NASA Astrophysics Data System (ADS)

Shreyas, Kamath K. M.; Rajendran, Rahul; Panetta, Karen; Agaian, Sos

2017-05-01

In the field of vision-based systems for object detection and classification, thresholding is a key pre-processing step. Thresholding is a well-known technique for image segmentation. Segmentation of medical images, such as Computed Axial Tomography (CAT), Magnetic Resonance Imaging (MRI), X-Ray, Phase Contrast Microscopy, and Histological images, present problems like high variability in terms of the human anatomy and variation in modalities. Recent advances made in computer-aided diagnosis of histological images help facilitate detection and classification of diseases. Since most pathology diagnosis depends on the expertise and ability of the pathologist, there is clearly a need for an automated assessment system. Histological images are stained to a specific color to differentiate each component in the tissue. Segmentation and analysis of such images is problematic, as they present high variability in terms of color and cell clusters. This paper presents an adaptive thresholding technique that aims at segmenting cell structures from Haematoxylin and Eosin stained images. The thresholded result can further be used by pathologists to perform effective diagnosis. The effectiveness of the proposed method is analyzed by visually comparing the results to the state of art thresholding methods such as Otsu, Niblack, Sauvola, Bernsen, and Wolf. Computer simulations demonstrate the efficiency of the proposed method in segmenting critical information.

New Software for Market Segmentation Analysis: A Chi-Square Interaction Detector. AIR 1983 Annual Forum Paper.

ERIC Educational Resources Information Center

Lay, Robert S.

The advantages and disadvantages of new software for market segmentation analysis are discussed, and the application of this new, chi-square based procedure (CHAID), is illustrated. A comparison is presented of an earlier, binary segmentation technique (THAID) and a multiple discriminant analysis. It is suggested that CHAID is superior to earlier…

Mathematical models used in segmentation and fractal methods of 2-D ultrasound images

NASA Astrophysics Data System (ADS)

Moldovanu, Simona; Moraru, Luminita; Bibicu, Dorin

2012-11-01

Mathematical models are widely used in biomedical computing. The extracted data from images using the mathematical techniques are the "pillar" achieving scientific progress in experimental, clinical, biomedical, and behavioural researches. This article deals with the representation of 2-D images and highlights the mathematical support for the segmentation operation and fractal analysis in ultrasound images. A large number of mathematical techniques are suitable to be applied during the image processing stage. The addressed topics cover the edge-based segmentation, more precisely the gradient-based edge detection and active contour model, and the region-based segmentation namely Otsu method. Another interesting mathematical approach consists of analyzing the images using the Box Counting Method (BCM) to compute the fractal dimension. The results of the paper provide explicit samples performed by various combination of methods.

An Automatic Segmentation Method Combining an Active Contour Model and a Classification Technique for Detecting Polycomb-group Proteinsin High-Throughput Microscopy Images.

PubMed

Gregoretti, Francesco; Cesarini, Elisa; Lanzuolo, Chiara; Oliva, Gennaro; Antonelli, Laura

2016-01-01

The large amount of data generated in biological experiments that rely on advanced microscopy can be handled only with automated image analysis. Most analyses require a reliable cell image segmentation eventually capable of detecting subcellular structures.We present an automatic segmentation method to detect Polycomb group (PcG) proteins areas isolated from nuclei regions in high-resolution fluorescent cell image stacks. It combines two segmentation algorithms that use an active contour model and a classification technique serving as a tool to better understand the subcellular three-dimensional distribution of PcG proteins in live cell image sequences. We obtained accurate results throughout several cell image datasets, coming from different cell types and corresponding to different fluorescent labels, without requiring elaborate adjustments to each dataset.

Rule-based fuzzy vector median filters for 3D phase contrast MRI segmentation

NASA Astrophysics Data System (ADS)

Sundareswaran, Kartik S.; Frakes, David H.; Yoganathan, Ajit P.

2008-02-01

Recent technological advances have contributed to the advent of phase contrast magnetic resonance imaging (PCMRI) as standard practice in clinical environments. In particular, decreased scan times have made using the modality more feasible. PCMRI is now a common tool for flow quantification, and for more complex vector field analyses that target the early detection of problematic flow conditions. Segmentation is one component of this type of application that can impact the accuracy of the final product dramatically. Vascular segmentation, in general, is a long-standing problem that has received significant attention. Segmentation in the context of PCMRI data, however, has been explored less and can benefit from object-based image processing techniques that incorporate fluids specific information. Here we present a fuzzy rule-based adaptive vector median filtering (FAVMF) algorithm that in combination with active contour modeling facilitates high-quality PCMRI segmentation while mitigating the effects of noise. The FAVMF technique was tested on 111 synthetically generated PC MRI slices and on 15 patients with congenital heart disease. The results were compared to other multi-dimensional filters namely the adaptive vector median filter, the adaptive vector directional filter, and the scalar low pass filter commonly used in PC MRI applications. FAVMF significantly outperformed the standard filtering methods (p < 0.0001). Two conclusions can be drawn from these results: a) Filtering should be performed after vessel segmentation of PC MRI; b) Vector based filtering methods should be used instead of scalar techniques.

Esophageal cancer dose escalation using a simultaneous integrated boost technique.

PubMed

Welsh, James; Palmer, Matthew B; Ajani, Jaffer A; Liao, Zhongxing; Swisher, Steven G; Hofstetter, Wayne L; Allen, Pamela K; Settle, Steven H; Gomez, Daniel; Likhacheva, Anna; Cox, James D; Komaki, Ritsuko

2012-01-01

We previously showed that 75% of radiation therapy (RT) failures in patients with unresectable esophageal cancer are in the gross tumor volume (GTV). We performed a planning study to evaluate if a simultaneous integrated boost (SIB) technique could selectively deliver a boost dose of radiation to the GTV in patients with esophageal cancer. Treatment plans were generated using four different approaches (two-dimensional conformal radiotherapy [2D-CRT] to 50.4 Gy, 2D-CRT to 64.8 Gy, intensity-modulated RT [IMRT] to 50.4 Gy, and SIB-IMRT to 64.8 Gy) and optimized for 10 patients with distal esophageal cancer. All plans were constructed to deliver the target dose in 28 fractions using heterogeneity corrections. Isodose distributions were evaluated for target coverage and normal tissue exposure. The 50.4 Gy IMRT plan was associated with significant reductions in mean cardiac, pulmonary, and hepatic doses relative to the 50.4 Gy 2D-CRT plan. The 64.8 Gy SIB-IMRT plan produced a 28% increase in GTV dose and comparable normal tissue doses as the 50.4 Gy IMRT plan; compared with the 50.4 Gy 2D-CRT plan, the 64.8 Gy SIB-IMRT produced significant dose reductions to all critical structures (heart, lung, liver, and spinal cord). The use of SIB-IMRT allowed us to selectively increase the dose to the GTV, the area at highest risk of failure, while simultaneously reducing the dose to the normal heart, lung, and liver. Clinical implications warrant systematic evaluation. Copyright © 2012 Elsevier Inc. All rights reserved.

Esophageal Cancer Dose Escalation using a Simultaneous Integrated Boost Technique

PubMed Central

Welsh, James; Palmer, Matthew B.; Ajani, Jaffer A.; Liao, Zhongxing; Swisher, Steven G.; Hofstetter, Wayne L.; Allen, Pamela K.; Settle, Steven H.; Gomez, Daniel; Likhacheva, Anna; Cox, James D.; Komaki, Ritsuko

2014-01-01

Purpose We previously showed that 75% of radiation therapy (RT) failures in patients with unresectable esophageal cancer are in the gross tumor volume (GTV). We performed a planning study to evaluate if a simultaneous integrated boost (SIB) technique could selectively deliver a boost dose of radiation to the GTV in patients with esophageal cancer. Methods and Materials Treatment plans were generated using four different approaches (two-dimensional conformal RT [2D-CRT] to 50.4 Gy or 64.8 Gy, intensity-modulated RT [IMRT] to 50.4 Gy, and SIB-IMRT to 64.8 Gy) and optimized for 10 patients with distal esophageal cancer. All plans were constructed to deliver the target dose in 28 fractions using heterogeneity corrections. Isodose distributions were evaluated for target coverage and normal tissue exposure. Results The 50.4-Gy IMRT plan was associated with significant reductions in mean cardiac, pulmonary, and hepatic doses relative to the 50.4-Gy 2D-CRT plan. The 64.8-Gy SIB-IMRT plan produced a 28% increase in GTV dose and the same normal tissue doses as the 50.4-Gy IMRT plan; compared with the 50.4-Gy 2D-CRT plan, the 64.8-Gy SIB-IMRT produced significant dose reductions to all critical structures (heart, lung, liver, and spinal cord). Conclusions The use of SIB-IMRT allowed us to selectively increase the dose to the GTV, the area at highest risk of failure, while simultaneously reducing the dose to the normal heart, lung, and liver. Clinical implications warrant systematic evaluation. PMID:21123005

Iris segmentation using an edge detector based on fuzzy sets theory and cellular learning automata.

PubMed

Ghanizadeh, Afshin; Abarghouei, Amir Atapour; Sinaie, Saman; Saad, Puteh; Shamsuddin, Siti Mariyam

2011-07-01

Iris-based biometric systems identify individuals based on the characteristics of their iris, since they are proven to remain unique for a long time. An iris recognition system includes four phases, the most important of which is preprocessing in which the iris segmentation is performed. The accuracy of an iris biometric system critically depends on the segmentation system. In this paper, an iris segmentation system using edge detection techniques and Hough transforms is presented. The newly proposed edge detection system enhances the performance of the segmentation in a way that it performs much more efficiently than the other conventional iris segmentation methods.

A Fast Method for the Segmentation of Synaptic Junctions and Mitochondria in Serial Electron Microscopic Images of the Brain.

PubMed

Márquez Neila, Pablo; Baumela, Luis; González-Soriano, Juncal; Rodríguez, Jose-Rodrigo; DeFelipe, Javier; Merchán-Pérez, Ángel

2016-04-01

Recent electron microscopy (EM) imaging techniques permit the automatic acquisition of a large number of serial sections from brain samples. Manual segmentation of these images is tedious, time-consuming and requires a high degree of user expertise. Therefore, there is considerable interest in developing automatic segmentation methods. However, currently available methods are computationally demanding in terms of computer time and memory usage, and to work properly many of them require image stacks to be isotropic, that is, voxels must have the same size in the X, Y and Z axes. We present a method that works with anisotropic voxels and that is computationally efficient allowing the segmentation of large image stacks. Our approach involves anisotropy-aware regularization via conditional random field inference and surface smoothing techniques to improve the segmentation and visualization. We have focused on the segmentation of mitochondria and synaptic junctions in EM stacks from the cerebral cortex, and have compared the results to those obtained by other methods. Our method is faster than other methods with similar segmentation results. Our image regularization procedure introduces high-level knowledge about the structure of labels. We have also reduced memory requirements with the introduction of energy optimization in overlapping partitions, which permits the regularization of very large image stacks. Finally, the surface smoothing step improves the appearance of three-dimensional renderings of the segmented volumes.

A two-stage approach for fully automatic segmentation of venous vascular structures in liver CT images

NASA Astrophysics Data System (ADS)

Kaftan, Jens N.; Tek, Hüseyin; Aach, Til

2009-02-01

The segmentation of the hepatic vascular tree in computed tomography (CT) images is important for many applications such as surgical planning of oncological resections and living liver donations. In surgical planning, vessel segmentation is often used as basis to support the surgeon in the decision about the location of the cut to be performed and the extent of the liver to be removed, respectively. We present a novel approach to hepatic vessel segmentation that can be divided into two stages. First, we detect and delineate the core vessel components efficiently with a high specificity. Second, smaller vessel branches are segmented by a robust vessel tracking technique based on a medialness filter response, which starts from the terminal points of the previously segmented vessels. Specifically, in the first phase major vessels are segmented using the globally optimal graphcuts algorithm in combination with foreground and background seed detection, while the computationally more demanding tracking approach needs to be applied only locally in areas of smaller vessels within the second stage. The method has been evaluated on contrast-enhanced liver CT scans from clinical routine showing promising results. In addition to the fully-automatic instance of this method, the vessel tracking technique can also be used to easily add missing branches/sub-trees to an already existing segmentation result by adding single seed-points.

Blood vessel segmentation algorithms - Review of methods, datasets and evaluation metrics.

PubMed

Moccia, Sara; De Momi, Elena; El Hadji, Sara; Mattos, Leonardo S

2018-05-01

Blood vessel segmentation is a topic of high interest in medical image analysis since the analysis of vessels is crucial for diagnosis, treatment planning and execution, and evaluation of clinical outcomes in different fields, including laryngology, neurosurgery and ophthalmology. Automatic or semi-automatic vessel segmentation can support clinicians in performing these tasks. Different medical imaging techniques are currently used in clinical practice and an appropriate choice of the segmentation algorithm is mandatory to deal with the adopted imaging technique characteristics (e.g. resolution, noise and vessel contrast). This paper aims at reviewing the most recent and innovative blood vessel segmentation algorithms. Among the algorithms and approaches considered, we deeply investigated the most novel blood vessel segmentation including machine learning, deformable model, and tracking-based approaches. This paper analyzes more than 100 articles focused on blood vessel segmentation methods. For each analyzed approach, summary tables are presented reporting imaging technique used, anatomical region and performance measures employed. Benefits and disadvantages of each method are highlighted. Despite the constant progress and efforts addressed in the field, several issues still need to be overcome. A relevant limitation consists in the segmentation of pathological vessels. Unfortunately, not consistent research effort has been addressed to this issue yet. Research is needed since some of the main assumptions made for healthy vessels (such as linearity and circular cross-section) do not hold in pathological tissues, which on the other hand require new vessel model formulations. Moreover, image intensity drops, noise and low contrast still represent an important obstacle for the achievement of a high-quality enhancement. This is particularly true for optical imaging, where the image quality is usually lower in terms of noise and contrast with respect to magnetic resonance and computer tomography angiography. No single segmentation approach is suitable for all the different anatomical region or imaging modalities, thus the primary goal of this review was to provide an up to date source of information about the state of the art of the vessel segmentation algorithms so that the most suitable methods can be chosen according to the specific task. Copyright © 2018 Elsevier B.V. All rights reserved.

Joint level-set and spatio-temporal motion detection for cell segmentation.

PubMed

Boukari, Fatima; Makrogiannis, Sokratis

2016-08-10

Cell segmentation is a critical step for quantification and monitoring of cell cycle progression, cell migration, and growth control to investigate cellular immune response, embryonic development, tumorigenesis, and drug effects on live cells in time-lapse microscopy images. In this study, we propose a joint spatio-temporal diffusion and region-based level-set optimization approach for moving cell segmentation. Moving regions are initially detected in each set of three consecutive sequence images by numerically solving a system of coupled spatio-temporal partial differential equations. In order to standardize intensities of each frame, we apply a histogram transformation approach to match the pixel intensities of each processed frame with an intensity distribution model learned from all frames of the sequence during the training stage. After the spatio-temporal diffusion stage is completed, we compute the edge map by nonparametric density estimation using Parzen kernels. This process is followed by watershed-based segmentation and moving cell detection. We use this result as an initial level-set function to evolve the cell boundaries, refine the delineation, and optimize the final segmentation result. We applied this method to several datasets of fluorescence microscopy images with varying levels of difficulty with respect to cell density, resolution, contrast, and signal-to-noise ratio. We compared the results with those produced by Chan and Vese segmentation, a temporally linked level-set technique, and nonlinear diffusion-based segmentation. We validated all segmentation techniques against reference masks provided by the international Cell Tracking Challenge consortium. The proposed approach delineated cells with an average Dice similarity coefficient of 89 % over a variety of simulated and real fluorescent image sequences. It yielded average improvements of 11 % in segmentation accuracy compared to both strictly spatial and temporally linked Chan-Vese techniques, and 4 % compared to the nonlinear spatio-temporal diffusion method. Despite the wide variation in cell shape, density, mitotic events, and image quality among the datasets, our proposed method produced promising segmentation results. These results indicate the efficiency and robustness of this method especially for mitotic events and low SNR imaging, enabling the application of subsequent quantification tasks.

Breast tumor segmentation in high resolution x-ray phase contrast analyzer based computed tomography.

PubMed

Brun, E; Grandl, S; Sztrókay-Gaul, A; Barbone, G; Mittone, A; Gasilov, S; Bravin, A; Coan, P

2014-11-01

Phase contrast computed tomography has emerged as an imaging method, which is able to outperform present day clinical mammography in breast tumor visualization while maintaining an equivalent average dose. To this day, no segmentation technique takes into account the specificity of the phase contrast signal. In this study, the authors propose a new mathematical framework for human-guided breast tumor segmentation. This method has been applied to high-resolution images of excised human organs, each of several gigabytes. The authors present a segmentation procedure based on the viscous watershed transform and demonstrate the efficacy of this method on analyzer based phase contrast images. The segmentation of tumors inside two full human breasts is then shown as an example of this procedure's possible applications. A correct and precise identification of the tumor boundaries was obtained and confirmed by manual contouring performed independently by four experienced radiologists. The authors demonstrate that applying the watershed viscous transform allows them to perform the segmentation of tumors in high-resolution x-ray analyzer based phase contrast breast computed tomography images. Combining the additional information provided by the segmentation procedure with the already high definition of morphological details and tissue boundaries offered by phase contrast imaging techniques, will represent a valuable multistep procedure to be used in future medical diagnostic applications.

Segmentation of cortical bone using fast level sets

NASA Astrophysics Data System (ADS)

Chowdhury, Manish; Jörgens, Daniel; Wang, Chunliang; Smedby, Årjan; Moreno, Rodrigo

2017-02-01

Cortical bone plays a big role in the mechanical competence of bone. The analysis of cortical bone requires accurate segmentation methods. Level set methods are usually in the state-of-the-art for segmenting medical images. However, traditional implementations of this method are computationally expensive. This drawback was recently tackled through the so-called coherent propagation extension of the classical algorithm which has decreased computation times dramatically. In this study, we assess the potential of this technique for segmenting cortical bone in interactive time in 3D images acquired through High Resolution peripheral Quantitative Computed Tomography (HR-pQCT). The obtained segmentations are used to estimate cortical thickness and cortical porosity of the investigated images. Cortical thickness and Cortical porosity is computed using sphere fitting and mathematical morphological operations respectively. Qualitative comparison between the segmentations of our proposed algorithm and a previously published approach on six images volumes reveals superior smoothness properties of the level set approach. While the proposed method yields similar results to previous approaches in regions where the boundary between trabecular and cortical bone is well defined, it yields more stable segmentations in challenging regions. This results in more stable estimation of parameters of cortical bone. The proposed technique takes few seconds to compute, which makes it suitable for clinical settings.

3D TEM reconstruction and segmentation process of laminar bio-nanocomposites

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

Iturrondobeitia, M., E-mail: maider.iturrondobeitia@ehu.es; Okariz, A.; Fernandez-Martinez, R.

2015-03-30

The microstructure of laminar bio-nanocomposites (Poly (lactic acid)(PLA)/clay) depends on the amount of clay platelet opening after integration with the polymer matrix and determines the final properties of the material. Transmission electron microscopy (TEM) technique is the only one that can provide a direct observation of the layer dispersion and the degree of exfoliation. However, the orientation of the clay platelets, which affects the final properties, is practically immeasurable from a single 2D TEM image. This issue can be overcome using transmission electron tomography (ET), a technique that allows the complete 3D characterization of the structure, including the measurement ofmore » the orientation of clay platelets, their morphology and their 3D distribution. ET involves a 3D reconstruction of the study volume and a subsequent segmentation of the study object. Currently, accurate segmentation is performed manually, which is inefficient and tedious. The aim of this work is to propose an objective/automated segmentation methodology process of a 3D TEM tomography reconstruction. In this method the segmentation threshold is optimized by minimizing the variation of the dimensions of the segmented objects and matching the segmented V{sub clay} (%) and the actual one. The method is first validated using a fictitious set of objects, and then applied on a nanocomposite.« less

Abdomen and spinal cord segmentation with augmented active shape models

PubMed Central

Xu, Zhoubing; Conrad, Benjamin N.; Baucom, Rebeccah B.; Smith, Seth A.; Poulose, Benjamin K.; Landman, Bennett A.

2016-01-01

Abstract. Active shape models (ASMs) have been widely used for extracting human anatomies in medical images given their capability for shape regularization of topology preservation. However, sensitivity to model initialization and local correspondence search often undermines their performances, especially around highly variable contexts in computed-tomography (CT) and magnetic resonance (MR) images. In this study, we propose an augmented ASM (AASM) by integrating the multiatlas label fusion (MALF) and level set (LS) techniques into the traditional ASM framework. Using AASM, landmark updates are optimized globally via a region-based LS evolution applied on the probability map generated from MALF. This augmentation effectively extends the searching range of correspondent landmarks while reducing sensitivity to the image contexts and improves the segmentation robustness. We propose the AASM framework as a two-dimensional segmentation technique targeting structures with one axis of regularity. We apply AASM approach to abdomen CT and spinal cord (SC) MR segmentation challenges. On 20 CT scans, the AASM segmentation of the whole abdominal wall enables the subcutaneous/visceral fat measurement, with high correlation to the measurement derived from manual segmentation. On 28 3T MR scans, AASM yields better performances than other state-of-the-art approaches in segmenting white/gray matter in SC. PMID:27610400

A new method to distinguish hadronically decaying boosted Z bosons from W bosons using the ATLAS detector

NASA Astrophysics Data System (ADS)

Aad, G.; Abbott, B.; Abdallah, J.; Abdinov, O.; Aben, R.; Abolins, M.; AbouZeid, O. S.; Abramowicz, H.; Abreu, H.; Abreu, R.; Abulaiti, Y.; Acharya, B. S.; Adamczyk, L.; Adams, D. L.; Adelman, J.; Adomeit, S.; Adye, T.; Affolder, A. A.; Agatonovic-Jovin, T.; Agricola, J.; Aguilar-Saavedra, J. A.; Ahlen, S. P.; Ahmadov, F.; Aielli, G.; Akerstedt, H.; Åkesson, T. P. A.; Akimov, A. V.; Alberghi, G. L.; Albert, J.; Albrand, S.; Alconada Verzini, M. J.; Aleksa, M.; Aleksandrov, I. N.; Alexa, C.; Alexander, G.; Alexopoulos, T.; Alhroob, M.; Alimonti, G.; Alio, L.; Alison, J.; Alkire, S. P.; Allbrooke, B. M. M.; Allport, P. P.; Aloisio, A.; Alonso, A.; Alonso, F.; Alpigiani, C.; Altheimer, A.; Alvarez Gonzalez, B.; Álvarez Piqueras, D.; Alviggi, M. G.; Amadio, B. T.; Amako, K.; Amaral Coutinho, Y.; Amelung, C.; Amidei, D.; Amor Dos Santos, S. P.; Amorim, A.; Amoroso, S.; Amram, N.; Amundsen, G.; Anastopoulos, C.; Ancu, L. S.; Andari, N.; Andeen, T.; Anders, C. F.; Anders, G.; Anders, J. K.; Anderson, K. J.; Andreazza, A.; Andrei, V.; Angelidakis, S.; Angelozzi, I.; Anger, P.; Angerami, A.; Anghinolfi, F.; Anisenkov, A. V.; Anjos, N.; Annovi, A.; Antonelli, M.; Antonov, A.; Antos, J.; Anulli, F.; Aoki, M.; Aperio Bella, L.; Arabidze, G.; Arai, Y.; Araque, J. P.; Arce, A. T. H.; Arduh, F. A.; Arguin, J.-F.; Argyropoulos, S.; Arik, M.; Armbruster, A. J.; Arnaez, O.; Arnal, V.; Arnold, H.; Arratia, M.; Arslan, O.; Artamonov, A.; Artoni, G.; Asai, S.; Asbah, N.; Ashkenazi, A.; Åsman, B.; Asquith, L.; Assamagan, K.; Astalos, R.; Atkinson, M.; Atlay, N. B.; Augsten, K.; Aurousseau, M.; Avolio, G.; Axen, B.; Ayoub, M. K.; Azuelos, G.; Baak, M. A.; Baas, A. E.; Baca, M. J.; Bacci, C.; Bachacou, H.; Bachas, K.; Backes, M.; Backhaus, M.; Bagiacchi, P.; Bagnaia, P.; Bai, Y.; Bain, T.; Baines, J. T.; Baker, O. K.; Baldin, E. M.; Balek, P.; Balestri, T.; Balli, F.; Balunas, W. K.; Banas, E.; Banerjee, Sw.; Bannoura, A. A. E.; Bansil, H. S.; Barak, L.; Barberio, E. L.; Barberis, D.; Barbero, M.; Barillari, T.; Barisonzi, M.; Barklow, T.; Barlow, N.; Barnes, S. L.; Barnett, B. M.; Barnett, R. M.; Barnovska, Z.; Baroncelli, A.; Barone, G.; Barr, A. J.; Barreiro, F.; Barreiro Guimarães da Costa, J.; Bartoldus, R.; Barton, A. E.; Bartos, P.; Basalaev, A.; Bassalat, A.; Basye, A.; Bates, R. L.; Batista, S. J.; Batley, J. R.; Battaglia, M.; Bauce, M.; Bauer, F.; Bawa, H. S.; Beacham, J. B.; Beattie, M. D.; Beau, T.; Beauchemin, P. H.; Beccherle, R.; Bechtle, P.; Beck, H. P.; Becker, K.; Becker, M.; Beckingham, M.; Becot, C.; Beddall, A. J.; Beddall, A.; Bednyakov, V. A.; Bee, C. P.; Beemster, L. J.; Beermann, T. A.; Begel, M.; Behr, J. K.; Belanger-Champagne, C.; Bell, W. H.; Bella, G.; Bellagamba, L.; Bellerive, A.; Bellomo, M.; Belotskiy, K.; Beltramello, O.; Benary, O.; Benchekroun, D.; Bender, M.; Bendtz, K.; Benekos, N.; Benhammou, Y.; Benhar Noccioli, E.; Benitez Garcia, J. A.; Benjamin, D. P.; Bensinger, J. R.; Bentvelsen, S.; Beresford, L.; Beretta, M.; Berge, D.; Bergeaas Kuutmann, E.; Berger, N.; Berghaus, F.; Beringer, J.; Bernard, C.; Bernard, N. R.; Bernius, C.; Bernlochner, F. U.; Berry, T.; Berta, P.; Bertella, C.; Bertoli, G.; Bertolucci, F.; Bertsche, C.; Bertsche, D.; Besana, M. I.; Besjes, G. J.; Bessidskaia Bylund, O.; Bessner, M.; Besson, N.; Betancourt, C.; Bethke, S.; Bevan, A. J.; Bhimji, W.; Bianchi, R. M.; Bianchini, L.; Bianco, M.; Biebel, O.; Biedermann, D.; Bieniek, S. P.; Biglietti, M.; Bilbao De Mendizabal, J.; Bilokon, H.; Bindi, M.; Binet, S.; Bingul, A.; Bini, C.; Biondi, S.; Black, C. W.; Black, J. E.; Black, K. M.; Blackburn, D.; Blair, R. E.; Blanchard, J.-B.; Blanco, J. E.; Blazek, T.; Bloch, I.; Blocker, C.; Blum, W.; Blumenschein, U.; Bobbink, G. J.; Bobrovnikov, V. S.; Bocchetta, S. S.; Bocci, A.; Bock, C.; Boehler, M.; Bogaerts, J. A.; Bogavac, D.; Bogdanchikov, A. G.; Bohm, C.; Boisvert, V.; Bold, T.; Boldea, V.; Boldyrev, A. 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R.; Suzuki, S.; Svatos, M.; Swiatlowski, M.; Sykora, I.; Sykora, T.; Ta, D.; Taccini, C.; Tackmann, K.; Taenzer, J.; Taffard, A.; Tafirout, R.; Taiblum, N.; Takai, H.; Takashima, R.; Takeda, H.; Takeshita, T.; Takubo, Y.; Talby, M.; Talyshev, A. A.; Tam, J. Y. C.; Tan, K. G.; Tanaka, J.; Tanaka, R.; Tanaka, S.; Tannenwald, B. B.; Tannoury, N.; Tapprogge, S.; Tarem, S.; Tarrade, F.; Tartarelli, G. F.; Tas, P.; Tasevsky, M.; Tashiro, T.; Tassi, E.; Tavares Delgado, A.; Tayalati, Y.; Taylor, F. E.; Taylor, G. N.; Taylor, P. T. E.; Taylor, W.; Teischinger, F. A.; Teixeira Dias Castanheira, M.; Teixeira-Dias, P.; Temming, K. K.; Temple, D.; Ten Kate, H.; Teng, P. K.; Teoh, J. J.; Tepel, F.; Terada, S.; Terashi, K.; Terron, J.; Terzo, S.; Testa, M.; Teuscher, R. J.; Theveneaux-Pelzer, T.; Thomas, J. P.; Thomas-Wilsker, J.; Thompson, E. N.; Thompson, P. D.; Thompson, R. J.; Thompson, A. S.; Thomsen, L. A.; Thomson, E.; Thomson, M.; Thun, R. P.; Tibbetts, M. J.; Ticse Torres, R. 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G.; Zhu, H.; Zhu, J.; Zhu, Y.; Zhuang, X.; Zhukov, K.; Zibell, A.; Zieminska, D.; Zimine, N. I.; Zimmermann, C.; Zimmermann, S.; Zinonos, Z.; Zinser, M.; Ziolkowski, M.; Živković, L.; Zobernig, G.; Zoccoli, A.; zur Nedden, M.; Zurzolo, G.; Zwalinski, L.

2016-05-01

The distribution of particles inside hadronic jets produced in the decay of boosted W and Z bosons can be used to discriminate such jets from the continuum background. Given that a jet has been identified as likely resulting from the hadronic decay of a boosted W or Z boson, this paper presents a technique for further differentiating Z bosons from W bosons. The variables used are jet mass, jet charge, and a b-tagging discriminant. A likelihood tagger is constructed from these variables and tested in the simulation of W'→ WZ for bosons in the transverse momentum range 200 GeV

A new method to distinguish hadronically decaying boosted Z bosons from W bosons using the ATLAS detector

DOE PAGES

Aad, G.; Abbott, B.; Abdallah, J.; ...

2016-04-28

The distribution of particles inside hadronic jets produced in the decay of boosted W and Z bosons can be used to discriminate such jets from the continuum background. Given that a jet has been identified as likely resulting from the hadronic decay of a boosted W or Z boson, this paper presents a technique for further differentiating Z bosons from W bosons. The variables used are jet mass, jet charge, and a b-tagging discriminant. A likelihood tagger is constructed from these variables and tested in the simulation of W' → WZ for bosons in the transverse momentum range 200 GeV 

Simultaneous integrated vs. sequential boost in VMAT radiotherapy of high-grade gliomas.

PubMed

Farzin, Mostafa; Molls, Michael; Astner, Sabrina; Rondak, Ina-Christine; Oechsner, Markus

2015-12-01

In 20 patients with high-grade gliomas, we compared two methods of planning for volumetric-modulated arc therapy (VMAT): simultaneous integrated boost (SIB) vs. sequential boost (SEB). The investigation focused on the analysis of dose distributions in the target volumes and the organs at risk (OARs). After contouring the target volumes [planning target volumes (PTVs) and boost volumes (BVs)] and OARs, SIB planning and SEB planning were performed. The SEB method consisted of two plans: in the first plan the PTV received 50 Gy in 25 fractions with a 2-Gy dose per fraction. In the second plan the BV received 10 Gy in 5 fractions with a dose per fraction of 2 Gy. The doses of both plans were summed up to show the total doses delivered. In the SIB method the PTV received 54 Gy in 30 fractions with a dose per fraction of 1.8 Gy, while the BV received 60 Gy in the same fraction number but with a dose per fraction of 2 Gy. All of the OARs showed higher doses (Dmax and Dmean) in the SEB method when compared with the SIB technique. The differences between the two methods were statistically significant in almost all of the OARs. Analysing the total doses of the target volumes we found dose distributions with similar homogeneities and comparable total doses. Our analysis shows that the SIB method offers advantages over the SEB method in terms of sparing OARs.

A Sensorless Predictive Current Controlled Boost Converter by Using an EKF with Load Variation Effect Elimination Function

PubMed Central

Tong, Qiaoling; Chen, Chen; Zhang, Qiao; Zou, Xuecheng

2015-01-01

To realize accurate current control for a boost converter, a precise measurement of the inductor current is required to achieve high resolution current regulating. Current sensors are widely used to measure the inductor current. However, the current sensors and their processing circuits significantly contribute extra hardware cost, delay and noise to the system. They can also harm the system reliability. Therefore, current sensorless control techniques can bring cost effective and reliable solutions for various boost converter applications. According to the derived accurate model, which contains a number of parasitics, the boost converter is a nonlinear system. An Extended Kalman Filter (EKF) is proposed for inductor current estimation and output voltage filtering. With this approach, the system can have the same advantages as sensored current control mode. To implement EKF, the load value is necessary. However, the load may vary from time to time. This can lead to errors of current estimation and filtered output voltage. To solve this issue, a load variation elimination effect elimination (LVEE) module is added. In addition, a predictive average current controller is used to regulate the current. Compared with conventional voltage controlled system, the transient response is greatly improved since it only takes two switching cycles for the current to reach its reference. Finally, experimental results are presented to verify the stable operation and output tracking capability for large-signal transients of the proposed algorithm. PMID:25928061

A new method to distinguish hadronically decaying boosted Z bosons from W bosons using the ATLAS detector.

PubMed

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2016-01-01

The distribution of particles inside hadronic jets produced in the decay of boosted W and Z bosons can be used to discriminate such jets from the continuum background. Given that a jet has been identified as likely resulting from the hadronic decay of a boosted W or Z boson, this paper presents a technique for further differentiating Z bosons from W bosons. The variables used are jet mass, jet charge, and a b -tagging discriminant. A likelihood tagger is constructed from these variables and tested in the simulation of [Formula: see text] for bosons in the transverse momentum range 200 GeV [Formula: see text] 400 GeV in [Formula: see text] TeV pp collisions with the ATLAS detector at the LHC. For Z -boson tagging efficiencies of [Formula: see text], 50, and [Formula: see text], one can achieve [Formula: see text]-boson tagging rejection factors ([Formula: see text]) of 1.7, 8.3 and 1000, respectively. It is not possible to measure these efficiencies in the data due to the lack of a pure sample of high [Formula: see text], hadronically decaying Z bosons. However, the modelling of the tagger inputs for boosted W bosons is studied in data using a [Formula: see text]-enriched sample of events in 20.3 fb[Formula: see text] of data at [Formula: see text] TeV. The inputs are well modelled within uncertainties, which builds confidence in the expected tagger performance.

Segmentation Fusion Techniques with Application to Plenoptic Images: A Survey.

NASA Astrophysics Data System (ADS)

Evin, D.; Hadad, A.; Solano, A.; Drozdowicz, B.

2016-04-01

The segmentation of anatomical and pathological structures plays a key role in the characterization of clinically relevant evidence from digital images. Recently, plenoptic imaging has emerged as a new promise to enrich the diagnostic potential of conventional photography. Since the plenoptic images comprises a set of slightly different versions of the target scene, we propose to make use of those images to improve the segmentation quality in relation to the scenario of a single image segmentation. The problem of finding a segmentation solution from multiple images of a single scene, is called segmentation fusion. This paper reviews the issue of segmentation fusion in order to find solutions that can be applied to plenoptic images, particularly images from the ophthalmological domain.

Helical Tomotherapy With Simultaneous Integrated Boost After Laparoscopic Staging in Patients With Cervical Cancer: Analysis of Feasibility and Early Toxicity

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

Marnitz, Simone, E-mail: simone.marnitz@charite.de; Koehler, Christhardt; Burova, Elena

Purpose: To demonstrate the feasibility and safety of the simultaneous integrated boost technique for dose escalation in combination with helical tomotherapy in patients with cervical cancer. Methods and Materials: Forty patients (International Federation of Gynecology and Obstetrics Stage IB1 pN1-IVA) underwent primary chemoradiation with helical tomotherapy. Before therapy, 29/40 patients underwent laparoscopic pelvic and para-aortic lymphadenectomy. In 21%, 31%, and 3% of the patients, pelvic, pelvic and para-aortic, and skip metastases in the para-aortic region could be confirmed. All patients underwent radiation with 1.8-50.4 Gy to the tumor region and the pelvic (para-aortic) lymph node region (planning target volume-A), andmore » a simultaneous boost with 2.12-59.36 Gy to the boost region (planning target volume-B). The boost region was defined using titan clips during laparoscopic staging. In all other patients, standardized borders for the planning target volume-B were defined. High-dose-rate brachytherapy was performed in 39/40 patients. The mean biologic effective dose to the macroscopic tumor ranged from 87.5 to 97.5 Gy. Chemotherapy consisted of weekly cisplatin 40 mg/m{sup 2}. Dose-volume histograms and acute gastrointestinal, genitourinary, and hematologic toxicity were evaluated. Results: The mean treatment time was 45 days. The mean doses to the small bowel, rectum, and bladder were 28.5 {+-} 6.1 Gy, 47.9 {+-} 3.8 Gy, and 48 {+-} 3 Gy, respectively. Hematologic toxicity Grade 3 occurred in 20% of patients, diarrhea Grade 2 in 5%, and diarrhea Grade 3 in 2.5%. There was no Grade 3 genitourinary toxicity. All patients underwent curettage 3 months after chemoradiation, which confirmed complete pathologic response in 38/40 patients. Conclusions: The concept of simultaneous integrated boost for dose escalation in patients with cervical cancer is feasible, with a low rate of acute gastrointestinal and genitourinary toxicity. Whether dose escalation can be translated into improved outcome will be assessed after a longer follow-up time.« less

The gradient boosting algorithm and random boosting for genome-assisted evaluation in large data sets.

PubMed

González-Recio, O; Jiménez-Montero, J A; Alenda, R

2013-01-01

In the next few years, with the advent of high-density single nucleotide polymorphism (SNP) arrays and genome sequencing, genomic evaluation methods will need to deal with a large number of genetic variants and an increasing sample size. The boosting algorithm is a machine-learning technique that may alleviate the drawbacks of dealing with such large data sets. This algorithm combines different predictors in a sequential manner with some shrinkage on them; each predictor is applied consecutively to the residuals from the committee formed by the previous ones to form a final prediction based on a subset of covariates. Here, a detailed description is provided and examples using a toy data set are included. A modification of the algorithm called "random boosting" was proposed to increase predictive ability and decrease computation time of genome-assisted evaluation in large data sets. Random boosting uses a random selection of markers to add a subsequent weak learner to the predictive model. These modifications were applied to a real data set composed of 1,797 bulls genotyped for 39,714 SNP. Deregressed proofs of 4 yield traits and 1 type trait from January 2009 routine evaluations were used as dependent variables. A 2-fold cross-validation scenario was implemented. Sires born before 2005 were used as a training sample (1,576 and 1,562 for production and type traits, respectively), whereas younger sires were used as a testing sample to evaluate predictive ability of the algorithm on yet-to-be-observed phenotypes. Comparison with the original algorithm was provided. The predictive ability of the algorithm was measured as Pearson correlations between observed and predicted responses. Further, estimated bias was computed as the average difference between observed and predicted phenotypes. The results showed that the modification of the original boosting algorithm could be run in 1% of the time used with the original algorithm and with negligible differences in accuracy and bias. This modification may be used to speed the calculus of genome-assisted evaluation in large data sets such us those obtained from consortiums. Copyright © 2013 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Retinal slit lamp video mosaicking.

PubMed

De Zanet, Sandro; Rudolph, Tobias; Richa, Rogerio; Tappeiner, Christoph; Sznitman, Raphael

2016-06-01

To this day, the slit lamp remains the first tool used by an ophthalmologist to examine patient eyes. Imaging of the retina poses, however, a variety of problems, namely a shallow depth of focus, reflections from the optical system, a small field of view and non-uniform illumination. For ophthalmologists, the use of slit lamp images for documentation and analysis purposes, however, remains extremely challenging due to large image artifacts. For this reason, we propose an automatic retinal slit lamp video mosaicking, which enlarges the field of view and reduces amount of noise and reflections, thus enhancing image quality. Our method is composed of three parts: (i) viable content segmentation, (ii) global registration and (iii) image blending. Frame content is segmented using gradient boosting with custom pixel-wise features. Speeded-up robust features are used for finding pair-wise translations between frames with robust random sample consensus estimation and graph-based simultaneous localization and mapping for global bundle adjustment. Foreground-aware blending based on feathering merges video frames into comprehensive mosaics. Foreground is segmented successfully with an area under the curve of the receiver operating characteristic curve of 0.9557. Mosaicking results and state-of-the-art methods were compared and rated by ophthalmologists showing a strong preference for a large field of view provided by our method. The proposed method for global registration of retinal slit lamp images of the retina into comprehensive mosaics improves over state-of-the-art methods and is preferred qualitatively.

Automatic brain tumor detection in MRI: methodology and statistical validation

NASA Astrophysics Data System (ADS)

Iftekharuddin, Khan M.; Islam, Mohammad A.; Shaik, Jahangheer; Parra, Carlos; Ogg, Robert

2005-04-01

Automated brain tumor segmentation and detection are immensely important in medical diagnostics because it provides information associated to anatomical structures as well as potential abnormal tissue necessary to delineate appropriate surgical planning. In this work, we propose a novel automated brain tumor segmentation technique based on multiresolution texture information that combines fractal Brownian motion (fBm) and wavelet multiresolution analysis. Our wavelet-fractal technique combines the excellent multiresolution localization property of wavelets to texture extraction of fractal. We prove the efficacy of our technique by successfully segmenting pediatric brain MR images (MRIs) from St. Jude Children"s Research Hospital. We use self-organizing map (SOM) as our clustering tool wherein we exploit both pixel intensity and multiresolution texture features to obtain segmented tumor. Our test results show that our technique successfully segments abnormal brain tissues in a set of T1 images. In the next step, we design a classifier using Feed-Forward (FF) neural network to statistically validate the presence of tumor in MRI using both the multiresolution texture and the pixel intensity features. We estimate the corresponding receiver operating curve (ROC) based on the findings of true positive fractions and false positive fractions estimated from our classifier at different threshold values. An ROC, which can be considered as a gold standard to prove the competence of a classifier, is obtained to ascertain the sensitivity and specificity of our classifier. We observe that at threshold 0.4 we achieve true positive value of 1.0 (100%) sacrificing only 0.16 (16%) false positive value for the set of 50 T1 MRI analyzed in this experiment.

Integrated circuit layer image segmentation

NASA Astrophysics Data System (ADS)

Masalskis, Giedrius; Petrauskas, Romas

2010-09-01

In this paper we present IC layer image segmentation techniques which are specifically created for precise metal layer feature extraction. During our research we used many samples of real-life de-processed IC metal layer images which were obtained using optical light microscope. We have created sequence of various image processing filters which provides segmentation results of good enough precision for our application. Filter sequences were fine tuned to provide best possible results depending on properties of IC manufacturing process and imaging technology. Proposed IC image segmentation filter sequences were experimentally tested and compared with conventional direct segmentation algorithms.

Elimination of RF inhomogeneity effects in segmentation.

PubMed

Agus, Onur; Ozkan, Mehmed; Aydin, Kubilay

2007-01-01

There are various methods proposed for the segmentation and analysis of MR images. However the efficiency of these techniques is effected by various artifacts that occur in the imaging system. One of the most encountered problems is the intensity variation across an image. To overcome this problem different methods are used. In this paper we propose a method for the elimination of intensity artifacts in segmentation of MRI images. Inter imager variations are also minimized to produce the same tissue segmentation for the same patient. A well-known multivariate classification algorithm, maximum likelihood is employed to illustrate the enhancement in segmentation.

Segmentation of radiologic images with self-organizing maps: the segmentation problem transformed into a classification task

NASA Astrophysics Data System (ADS)

Pelikan, Erich; Vogelsang, Frank; Tolxdorff, Thomas

1996-04-01

The texture-based segmentation of x-ray images of focal bone lesions using topological maps is introduced. Texture characteristics are described by image-point correlation of feature images to feature vectors. For the segmentation, the topological map is labeled using an improved labeling strategy. Results of the technique are demonstrated on original and synthetic x-ray images and quantified with the aid of quality measures. In addition, a classifier-specific contribution analysis is applied for assessing the feature space.

Segmented-field radiography in scoliosis

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

Daniel, W.W.; Barnes, G.T.; Nasca, R.J.

1985-02-01

A method of scoliosis imaging using segmented fields is presented. The method is advantageous for patients requiring serial radiographic monitoring, as it results in markedly reduced radiation doses to critical organs, particularly the breast. Absorbed dose to the breast was measured to be 8.8 mrad (88 ..mu..Gy) for a full-field examination and 0.051 mrad (5.1 ..mu..Gy) for the segmented-field study. The segmented-field technique also results in improved image quality. Experience with 53 studies in 23 patients is reported.

Improving bone repair of femoral and radial defects in rabbit by incorporating PRP into PLGA/CPC composite scaffold with unidirectional pore structure.

PubMed

He, Fupo; Chen, Yan; Li, Jiyan; Lin, Bomiao; Ouyang, Yi; Yu, Bo; Xia, Yuanyou; Yu, Bo; Ye, Jiandong

2015-04-01

In this study, a platelet-rich plasma poly(lactic-co-glycolic acid) (PRP-PLGA)/calcium phosphate cement (CPC) composite scaffold was prepared by incorporating PRP into PLGA/CPC scaffold with unidirectional pore structure, which was fabricated by the unidirectional freeze casting of CPC slurry and the following infiltration of PLGA. The results from in vitro cell experiments and in vivo implantation in femoral defects manifested that incorporation of PRP into PLGA/CPC scaffold improved in vitro cell response (cell attachment, proliferation, and differentiation), and markedly boosted bone formation, angiogenesis and material degradation. The incorporation of PRP into scaffold showed more outstanding improvement in osteogenesis as the scaffolds were used to repair the segmental radial defects, especially at the early stage. The new bone tissues grew along the unidirectional lamellar pores of scaffold. At 12 weeks postimplantation, the segmental radial defects treated with PRP-PLGA/CPC scaffold had almost recuperated, whereas treated with the scaffold without PRP was far from healed. Taken together, the PRP-PLGA/CPC scaffold with unidirectional pore structure is a promising candidate to repair bone defects at various sites. © 2014 Wiley Periodicals, Inc.

A new feature extraction method for signal classification applied to cord dorsum potentials detection

PubMed Central

Vidaurre, D.; Rodríguez, E. E.; Bielza, C.; Larrañaga, P.; Rudomin, P.

2012-01-01

In the spinal cord of the anesthetized cat, spontaneous cord dorsum potentials (CDPs) appear synchronously along the lumbo-sacral segments. These CDPs have different shapes and magnitudes. Previous work has indicated that some CDPs appear to be specially associated with the activation of spinal pathways that lead to primary afferent depolarization and presynaptic inhibition. Visual detection and classification of these CDPs provides relevant information on the functional organization of the neural networks involved in the control of sensory information and allows the characterization of the changes produced by acute nerve and spinal lesions. We now present a novel feature extraction approach for signal classification, applied to CDP detection. The method is based on an intuitive procedure. We first remove by convolution the noise from the CDPs recorded in each given spinal segment. Then, we assign a coefficient for each main local maximum of the signal using its amplitude and distance to the most important maximum of the signal. These coefficients will be the input for the subsequent classification algorithm. In particular, we employ gradient boosting classification trees. This combination of approaches allows a faster and more accurate discrimination of CDPs than is obtained by other methods. PMID:22929924

A new feature extraction method for signal classification applied to cord dorsum potential detection.

PubMed

Vidaurre, D; Rodríguez, E E; Bielza, C; Larrañaga, P; Rudomin, P

2012-10-01

In the spinal cord of the anesthetized cat, spontaneous cord dorsum potentials (CDPs) appear synchronously along the lumbo-sacral segments. These CDPs have different shapes and magnitudes. Previous work has indicated that some CDPs appear to be specially associated with the activation of spinal pathways that lead to primary afferent depolarization and presynaptic inhibition. Visual detection and classification of these CDPs provides relevant information on the functional organization of the neural networks involved in the control of sensory information and allows the characterization of the changes produced by acute nerve and spinal lesions. We now present a novel feature extraction approach for signal classification, applied to CDP detection. The method is based on an intuitive procedure. We first remove by convolution the noise from the CDPs recorded in each given spinal segment. Then, we assign a coefficient for each main local maximum of the signal using its amplitude and distance to the most important maximum of the signal. These coefficients will be the input for the subsequent classification algorithm. In particular, we employ gradient boosting classification trees. This combination of approaches allows a faster and more accurate discrimination of CDPs than is obtained by other methods.

Lumen-based detection of prostate cancer via convolutional neural networks

NASA Astrophysics Data System (ADS)

Kwak, Jin Tae; Hewitt, Stephen M.

2017-03-01

We present a deep learning approach for detecting prostate cancers. The approach consists of two steps. In the first step, we perform tissue segmentation that identifies lumens within digitized prostate tissue specimen images. Intensity- and texture-based image features are computed at five different scales, and a multiview boosting method is adopted to cooperatively combine the image features from differing scales and to identify lumens. In the second step, we utilize convolutional neural networks (CNN) to automatically extract high-level image features of lumens and to predict cancers. The segmented lumens are rescaled to reduce computational complexity and data augmentation by scaling, rotating, and flipping the rescaled image is applied to avoid overfitting. We evaluate the proposed method using two tissue microarrays (TMA) - TMA1 includes 162 tissue specimens (73 Benign and 89 Cancer) and TMA2 comprises 185 tissue specimens (70 Benign and 115 Cancer). In cross-validation on TMA1, the proposed method achieved an AUC of 0.95 (CI: 0.93-0.98). Trained on TMA1 and tested on TMA2, CNN obtained an AUC of 0.95 (CI: 0.92-0.98). This demonstrates that the proposed method can potentially improve prostate cancer pathology.

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.

Visualization of 3D CT-based anatomical models

NASA Astrophysics Data System (ADS)

Alaytsev, Innokentiy K.; Danilova, Tatyana V.; Manturov, Alexey O.; Mareev, Gleb O.; Mareev, Oleg V.

2018-04-01

Biomedical volumetric data visualization techniques for the exploration purposes are well developed. Most of the known methods are inappropriate for surgery simulation systems due to lack of realism. A segmented data visualization is a well-known approach for the visualization of the structured volumetric data. The research is focused on improvement of the segmented data visualization technique by the aliasing problems resolution and the use of material transparency modeling for better semitransparent structures rendering.

Reimplantation of the resected tumour-bearing segment after recycling using liquid nitrogen for osteosarcoma.

PubMed

Abdel Rahman, Mohamed; Bassiony, Ayman; Shalaby, Hisham

2009-10-01

Reconstruction after en block resection of malignant tumours is still the subject of debate. We questioned the effectiveness of reconstruction by reimplanting the tumour-bearing segment after recycling in liquid nitrogen. Ten patients with osteosarcoma around the knee were included, with a mean age of 21 years. The operative technique included wide en bloc excision, debridement, and management of the resected segment with liquid nitrogen followed by reimplantation and internal fixation. At a mean follow-up of 4.5 years there was no local or systemic recurrence and the mean functional score was 82.4%. The frozen graft united proximally and distally in all but one patient in a period ranging from six to ten months. The effectiveness of this reconstruction technique in properly selected patients with osteosarcoma is comparable to other techniques of biological reconstruction with the added benefit of being simple, cheap and durable.

An exploration of the applicability of situational segmentation in the health care market: development of a situational taxonomy.

PubMed

Gehrt, K C; Pinto, M B

1990-01-01

Competition in the health care market has intensified in recent years. Health care providers are increasingly adopting innovative marketing techniques to secure their positions in the marketplace. This paper examines an innovative marketing technique, situational segmentation, and assesses its applicability to the health care market. Situational segmentation has proven useful in many consumer goods markets but has received little attention in the context of health care marketing. A two-stage research process is used to develop a taxonomy of situational factors pertinent to health care choice. In stage one, focus group interviews are used to gather information which is instrumental to questionnaire development. In stage two, the responses of 151 subjects to a 51 item questionnaire are factor analyzed. The results demonstrate that situational segmentation is a viable strategy in the health care market.

Japanese migration in contemporary Japan: economic segmentation and interprefectural migration.

PubMed

Fukurai, H

1991-01-01

This paper examines the economic segmentation model in explaining 1985-86 Japanese interregional migration. The analysis takes advantage of statistical graphic techniques to illustrate the following substantive issues of interregional migration: (1) to examine whether economic segmentation significantly influences Japanese regional migration and (2) to explain socioeconomic characteristics of prefectures for both in- and out-migration. Analytic techniques include a latent structural equation (LISREL) methodology and statistical residual mapping. The residual dispersion patterns, for instance, suggest the extent to which socioeconomic and geopolitical variables explain migration differences by showing unique clusters of unexplained residuals. The analysis further points out that extraneous factors such as high residential land values, significant commuting populations, and regional-specific cultures and traditions need to be incorporated in the economic segmentation model in order to assess the extent of the model's reliability in explaining the pattern of interprefectural migration.

Quantification and Segmentation of Brain Tissues from MR Images: A Probabilistic Neural Network Approach

PubMed Central

Wang, Yue; Adalý, Tülay; Kung, Sun-Yuan; Szabo, Zsolt

2007-01-01

This paper presents a probabilistic neural network based technique for unsupervised quantification and segmentation of brain tissues from magnetic resonance images. It is shown that this problem can be solved by distribution learning and relaxation labeling, resulting in an efficient method that may be particularly useful in quantifying and segmenting abnormal brain tissues where the number of tissue types is unknown and the distributions of tissue types heavily overlap. The new technique uses suitable statistical models for both the pixel and context images and formulates the problem in terms of model-histogram fitting and global consistency labeling. The quantification is achieved by probabilistic self-organizing mixtures and the segmentation by a probabilistic constraint relaxation network. The experimental results show the efficient and robust performance of the new algorithm and that it outperforms the conventional classification based approaches. PMID:18172510

A discriminative model-constrained EM approach to 3D MRI brain tissue classification and intensity non-uniformity correction

NASA Astrophysics Data System (ADS)

Wels, Michael; Zheng, Yefeng; Huber, Martin; Hornegger, Joachim; Comaniciu, Dorin

2011-06-01

We describe a fully automated method for tissue classification, which is the segmentation into cerebral gray matter (GM), cerebral white matter (WM), and cerebral spinal fluid (CSF), and intensity non-uniformity (INU) correction in brain magnetic resonance imaging (MRI) volumes. It combines supervised MRI modality-specific discriminative modeling and unsupervised statistical expectation maximization (EM) segmentation into an integrated Bayesian framework. While both the parametric observation models and the non-parametrically modeled INUs are estimated via EM during segmentation itself, a Markov random field (MRF) prior model regularizes segmentation and parameter estimation. Firstly, the regularization takes into account knowledge about spatial and appearance-related homogeneity of segments in terms of pairwise clique potentials of adjacent voxels. Secondly and more importantly, patient-specific knowledge about the global spatial distribution of brain tissue is incorporated into the segmentation process via unary clique potentials. They are based on a strong discriminative model provided by a probabilistic boosting tree (PBT) for classifying image voxels. It relies on the surrounding context and alignment-based features derived from a probabilistic anatomical atlas. The context considered is encoded by 3D Haar-like features of reduced INU sensitivity. Alignment is carried out fully automatically by means of an affine registration algorithm minimizing cross-correlation. Both types of features do not immediately use the observed intensities provided by the MRI modality but instead rely on specifically transformed features, which are less sensitive to MRI artifacts. Detailed quantitative evaluations on standard phantom scans and standard real-world data show the accuracy and robustness of the proposed method. They also demonstrate relative superiority in comparison to other state-of-the-art approaches to this kind of computational task: our method achieves average Dice coefficients of 0.93 ± 0.03 (WM) and 0.90 ± 0.05 (GM) on simulated mono-spectral and 0.94 ± 0.02 (WM) and 0.92 ± 0.04 (GM) on simulated multi-spectral data from the BrainWeb repository. The scores are 0.81 ± 0.09 (WM) and 0.82 ± 0.06 (GM) and 0.87 ± 0.05 (WM) and 0.83 ± 0.12 (GM) for the two collections of real-world data sets—consisting of 20 and 18 volumes, respectively—provided by the Internet Brain Segmentation Repository.

A discriminative model-constrained EM approach to 3D MRI brain tissue classification and intensity non-uniformity correction.

PubMed

Wels, Michael; Zheng, Yefeng; Huber, Martin; Hornegger, Joachim; Comaniciu, Dorin

2011-06-07

We describe a fully automated method for tissue classification, which is the segmentation into cerebral gray matter (GM), cerebral white matter (WM), and cerebral spinal fluid (CSF), and intensity non-uniformity (INU) correction in brain magnetic resonance imaging (MRI) volumes. It combines supervised MRI modality-specific discriminative modeling and unsupervised statistical expectation maximization (EM) segmentation into an integrated Bayesian framework. While both the parametric observation models and the non-parametrically modeled INUs are estimated via EM during segmentation itself, a Markov random field (MRF) prior model regularizes segmentation and parameter estimation. Firstly, the regularization takes into account knowledge about spatial and appearance-related homogeneity of segments in terms of pairwise clique potentials of adjacent voxels. Secondly and more importantly, patient-specific knowledge about the global spatial distribution of brain tissue is incorporated into the segmentation process via unary clique potentials. They are based on a strong discriminative model provided by a probabilistic boosting tree (PBT) for classifying image voxels. It relies on the surrounding context and alignment-based features derived from a probabilistic anatomical atlas. The context considered is encoded by 3D Haar-like features of reduced INU sensitivity. Alignment is carried out fully automatically by means of an affine registration algorithm minimizing cross-correlation. Both types of features do not immediately use the observed intensities provided by the MRI modality but instead rely on specifically transformed features, which are less sensitive to MRI artifacts. Detailed quantitative evaluations on standard phantom scans and standard real-world data show the accuracy and robustness of the proposed method. They also demonstrate relative superiority in comparison to other state-of-the-art approaches to this kind of computational task: our method achieves average Dice coefficients of 0.93 ± 0.03 (WM) and 0.90 ± 0.05 (GM) on simulated mono-spectral and 0.94 ± 0.02 (WM) and 0.92 ± 0.04 (GM) on simulated multi-spectral data from the BrainWeb repository. The scores are 0.81 ± 0.09 (WM) and 0.82 ± 0.06 (GM) and 0.87 ± 0.05 (WM) and 0.83 ± 0.12 (GM) for the two collections of real-world data sets-consisting of 20 and 18 volumes, respectively-provided by the Internet Brain Segmentation Repository.

Fractionated stereotactic radiotherapy boost for gynecologic tumors: An alternative to brachytherapy?

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

Molla, Meritxell; Escude, Lluis D.; Nouet, Philippe

2005-05-01

Purpose: A brachytherapy (BT) boost to the vaginal vault is considered standard treatment for many endometrial or cervical cancers. We aimed to challenge this treatment standard by using stereotactic radiotherapy (SRT) with a linac-based micromultileaf collimator technique. Methods and Materials: Since January 2002, 16 patients with either endometrial (9) or cervical (7) cancer have been treated with a final boost to the areas at higher risk for relapse. In 14 patients, the target volume included the vaginal vault, the upper vagina, the parametria, or (if not operated) the uterus (clinical target volume [CTV]). In 2 patients with local relapse, themore » CTV was the tumor in the vaginal stump. Margins of 6-10 mm were added to the CTV to define the planning target volume (PTV). Hypofractionated dynamic-arc or intensity-modulated radiotherapy techniques were used. Postoperative treatment was delivered in 12 patients (2 x 7 Gy to the PTV with a 4-7-day interval between fractions). In the 4 nonoperated patients, a dose of 4 Gy/fraction in 5 fractions with 2 to 3 days' interval was delivered. Patients were immobilized in a customized vacuum body cast and optimally repositioned with an infrared-guided system developed for extracranial SRT. To further optimize daily repositioning and target immobilization, an inflated rectal balloon was used during each treatment fraction. In 10 patients, CT resimulation was performed before the last boost fraction to assess for repositioning reproducibility via CT-to-CT registration and to estimate PTV safety margins around the CTV. Finally, a comparative treatment planning study between BT and SRT was performed in 2 patients with an operated endometrial Stage I cancer. Results: No patient developed severe acute urinary or low-intestinal toxicity. No patient developed urinary late effects (>6 months). One patient with a vaginal relapse previously irradiated to the pelvic region presented with Grade 3 rectal bleeding 18 months after retreatment. A second patient known to suffer from irritable bowel syndrome presented with Grade 1 abdominal pain after treatment. The estimated PTV margins around the CTV were 9-10 mm with infrared marker registration. External SRT succeeded in improving dose homogeneity to the PTV and in reducing the maximum dose to the rectum, when compared to BT. Conclusion: These results suggest that the use of external SRT to deliver a final boost to the areas at higher risk for relapse in endometrial or cervical cancer is feasible, well tolerated, and may well be considered an acceptable alternative to BT.« less

Comparative Approach of MRI-Based Brain Tumor Segmentation and Classification Using Genetic Algorithm.

PubMed

Bahadure, Nilesh Bhaskarrao; Ray, Arun Kumar; Thethi, Har Pal

2018-01-17

The detection of a brain tumor and its classification from modern imaging modalities is a primary concern, but a time-consuming and tedious work was performed by radiologists or clinical supervisors. The accuracy of detection and classification of tumor stages performed by radiologists is depended on their experience only, so the computer-aided technology is very important to aid with the diagnosis accuracy. In this study, to improve the performance of tumor detection, we investigated comparative approach of different segmentation techniques and selected the best one by comparing their segmentation score. Further, to improve the classification accuracy, the genetic algorithm is employed for the automatic classification of tumor stage. The decision of classification stage is supported by extracting relevant features and area calculation. The experimental results of proposed technique are evaluated and validated for performance and quality analysis on magnetic resonance brain images, based on segmentation score, accuracy, sensitivity, specificity, and dice similarity index coefficient. The experimental results achieved 92.03% accuracy, 91.42% specificity, 92.36% sensitivity, and an average segmentation score between 0.82 and 0.93 demonstrating the effectiveness of the proposed technique for identifying normal and abnormal tissues from brain MR images. The experimental results also obtained an average of 93.79% dice similarity index coefficient, which indicates better overlap between the automated extracted tumor regions with manually extracted tumor region by radiologists.

Color edges extraction using statistical features and automatic threshold technique: application to the breast cancer cells.

PubMed

Ben Chaabane, Salim; Fnaiech, Farhat

2014-01-23

Color image segmentation has been so far applied in many areas; hence, recently many different techniques have been developed and proposed. In the medical imaging area, the image segmentation may be helpful to provide assistance to doctor in order to follow-up the disease of a certain patient from the breast cancer processed images. The main objective of this work is to rebuild and also to enhance each cell from the three component images provided by an input image. Indeed, from an initial segmentation obtained using the statistical features and histogram threshold techniques, the resulting segmentation may represent accurately the non complete and pasted cells and enhance them. This allows real help to doctors, and consequently, these cells become clear and easy to be counted. A novel method for color edges extraction based on statistical features and automatic threshold is presented. The traditional edge detector, based on the first and the second order neighborhood, describing the relationship between the current pixel and its neighbors, is extended to the statistical domain. Hence, color edges in an image are obtained by combining the statistical features and the automatic threshold techniques. Finally, on the obtained color edges with specific primitive color, a combination rule is used to integrate the edge results over the three color components. Breast cancer cell images were used to evaluate the performance of the proposed method both quantitatively and qualitatively. Hence, a visual and a numerical assessment based on the probability of correct classification (PC), the false classification (Pf), and the classification accuracy (Sens(%)) are presented and compared with existing techniques. The proposed method shows its superiority in the detection of points which really belong to the cells, and also the facility of counting the number of the processed cells. Computer simulations highlight that the proposed method substantially enhances the segmented image with smaller error rates better than other existing algorithms under the same settings (patterns and parameters). Moreover, it provides high classification accuracy, reaching the rate of 97.94%. Additionally, the segmentation method may be extended to other medical imaging types having similar properties.

Automatic segmentation of airway tree based on local intensity filter and machine learning technique in 3D chest CT volume.

PubMed

Meng, Qier; Kitasaka, Takayuki; Nimura, Yukitaka; Oda, Masahiro; Ueno, Junji; Mori, Kensaku

2017-02-01

Airway segmentation plays an important role in analyzing chest computed tomography (CT) volumes for computerized lung cancer detection, emphysema diagnosis and pre- and intra-operative bronchoscope navigation. However, obtaining a complete 3D airway tree structure from a CT volume is quite a challenging task. Several researchers have proposed automated airway segmentation algorithms basically based on region growing and machine learning techniques. However, these methods fail to detect the peripheral bronchial branches, which results in a large amount of leakage. This paper presents a novel approach for more accurate extraction of the complex airway tree. This proposed segmentation method is composed of three steps. First, Hessian analysis is utilized to enhance the tube-like structure in CT volumes; then, an adaptive multiscale cavity enhancement filter is employed to detect the cavity-like structure with different radii. In the second step, support vector machine learning will be utilized to remove the false positive (FP) regions from the result obtained in the previous step. Finally, the graph-cut algorithm is used to refine the candidate voxels to form an integrated airway tree. A test dataset including 50 standard-dose chest CT volumes was used for evaluating our proposed method. The average extraction rate was about 79.1 % with the significantly decreased FP rate. A new method of airway segmentation based on local intensity structure and machine learning technique was developed. The method was shown to be feasible for airway segmentation in a computer-aided diagnosis system for a lung and bronchoscope guidance system.

Segmentation of lung fields using Chan-Vese active contour model in chest radiographs

NASA Astrophysics Data System (ADS)

Sohn, Kiwon

2011-03-01

A CAD tool for chest radiographs consists of several procedures and the very first step is segmentation of lung fields. We develop a novel methodology for segmentation of lung fields in chest radiographs that can satisfy the following two requirements. First, we aim to develop a segmentation method that does not need a training stage with manual estimation of anatomical features in a large training dataset of images. Secondly, for the ease of implementation, it is desirable to apply a well established model that is widely used for various image-partitioning practices. The Chan-Vese active contour model, which is based on Mumford-Shah functional in the level set framework, is applied for segmentation of lung fields. With the use of this model, segmentation of lung fields can be carried out without detailed prior knowledge on the radiographic anatomy of the chest, yet in some chest radiographs, the trachea regions are unfavorably segmented out in addition to the lung field contours. To eliminate artifacts from the trachea, we locate the upper end of the trachea, find a vertical center line of the trachea and delineate it, and then brighten the trachea region to make it less distinctive. The segmentation process is finalized by subsequent morphological operations. We randomly select 30 images from the Japanese Society of Radiological Technology image database to test the proposed methodology and the results are shown. We hope our segmentation technique can help to promote of CAD tools, especially for emerging chest radiographic imaging techniques such as dual energy radiography and chest tomosynthesis.

MIMoSA: An Automated Method for Intermodal Segmentation Analysis of Multiple Sclerosis Brain Lesions.

PubMed

Valcarcel, Alessandra M; Linn, Kristin A; Vandekar, Simon N; Satterthwaite, Theodore D; Muschelli, John; Calabresi, Peter A; Pham, Dzung L; Martin, Melissa Lynne; Shinohara, Russell T

2018-03-08

Magnetic resonance imaging (MRI) is crucial for in vivo detection and characterization of white matter lesions (WMLs) in multiple sclerosis. While WMLs have been studied for over two decades using MRI, automated segmentation remains challenging. Although the majority of statistical techniques for the automated segmentation of WMLs are based on single imaging modalities, recent advances have used multimodal techniques for identifying WMLs. Complementary modalities emphasize different tissue properties, which help identify interrelated features of lesions. Method for Inter-Modal Segmentation Analysis (MIMoSA), a fully automatic lesion segmentation algorithm that utilizes novel covariance features from intermodal coupling regression in addition to mean structure to model the probability lesion is contained in each voxel, is proposed. MIMoSA was validated by comparison with both expert manual and other automated segmentation methods in two datasets. The first included 98 subjects imaged at Johns Hopkins Hospital in which bootstrap cross-validation was used to compare the performance of MIMoSA against OASIS and LesionTOADS, two popular automatic segmentation approaches. For a secondary validation, a publicly available data from a segmentation challenge were used for performance benchmarking. In the Johns Hopkins study, MIMoSA yielded average Sørensen-Dice coefficient (DSC) of .57 and partial AUC of .68 calculated with false positive rates up to 1%. This was superior to performance using OASIS and LesionTOADS. The proposed method also performed competitively in the segmentation challenge dataset. MIMoSA resulted in statistically significant improvements in lesion segmentation performance compared with LesionTOADS and OASIS, and performed competitively in an additional validation study. Copyright © 2018 by the American Society of Neuroimaging.

The cascaded moving k-means and fuzzy c-means clustering algorithms for unsupervised segmentation of malaria images

NASA Astrophysics Data System (ADS)

Abdul-Nasir, Aimi Salihah; Mashor, Mohd Yusoff; Halim, Nurul Hazwani Abd; Mohamed, Zeehaida

2015-05-01

Malaria is a life-threatening parasitic infectious disease that corresponds for nearly one million deaths each year. Due to the requirement of prompt and accurate diagnosis of malaria, the current study has proposed an unsupervised pixel segmentation based on clustering algorithm in order to obtain the fully segmented red blood cells (RBCs) infected with malaria parasites based on the thin blood smear images of P. vivax species. In order to obtain the segmented infected cell, the malaria images are first enhanced by using modified global contrast stretching technique. Then, an unsupervised segmentation technique based on clustering algorithm has been applied on the intensity component of malaria image in order to segment the infected cell from its blood cells background. In this study, cascaded moving k-means (MKM) and fuzzy c-means (FCM) clustering algorithms has been proposed for malaria slide image segmentation. After that, median filter algorithm has been applied to smooth the image as well as to remove any unwanted regions such as small background pixels from the image. Finally, seeded region growing area extraction algorithm has been applied in order to remove large unwanted regions that are still appeared on the image due to their size in which cannot be cleaned by using median filter. The effectiveness of the proposed cascaded MKM and FCM clustering algorithms has been analyzed qualitatively and quantitatively by comparing the proposed cascaded clustering algorithm with MKM and FCM clustering algorithms. Overall, the results indicate that segmentation using the proposed cascaded clustering algorithm has produced the best segmentation performances by achieving acceptable sensitivity as well as high specificity and accuracy values compared to the segmentation results provided by MKM and FCM algorithms.

Automated MRI segmentation for individualized modeling of current flow in the human head.

PubMed

Huang, Yu; Dmochowski, Jacek P; Su, Yuzhuo; Datta, Abhishek; Rorden, Christopher; Parra, Lucas C

2013-12-01

High-definition transcranial direct current stimulation (HD-tDCS) and high-density electroencephalography require accurate models of current flow for precise targeting and current source reconstruction. At a minimum, such modeling must capture the idiosyncratic anatomy of the brain, cerebrospinal fluid (CSF) and skull for each individual subject. Currently, the process to build such high-resolution individualized models from structural magnetic resonance images requires labor-intensive manual segmentation, even when utilizing available automated segmentation tools. Also, accurate placement of many high-density electrodes on an individual scalp is a tedious procedure. The goal was to develop fully automated techniques to reduce the manual effort in such a modeling process. A fully automated segmentation technique based on Statical Parametric Mapping 8, including an improved tissue probability map and an automated correction routine for segmentation errors, was developed, along with an automated electrode placement tool for high-density arrays. The performance of these automated routines was evaluated against results from manual segmentation on four healthy subjects and seven stroke patients. The criteria include segmentation accuracy, the difference of current flow distributions in resulting HD-tDCS models and the optimized current flow intensities on cortical targets. The segmentation tool can segment out not just the brain but also provide accurate results for CSF, skull and other soft tissues with a field of view extending to the neck. Compared to manual results, automated segmentation deviates by only 7% and 18% for normal and stroke subjects, respectively. The predicted electric fields in the brain deviate by 12% and 29% respectively, which is well within the variability observed for various modeling choices. Finally, optimized current flow intensities on cortical targets do not differ significantly. Fully automated individualized modeling may now be feasible for large-sample EEG research studies and tDCS clinical trials.

Subband-Based Group Delay Segmentation of Spontaneous Speech into Syllable-Like Units

NASA Astrophysics Data System (ADS)

Nagarajan, T.; Murthy, H. A.

2004-12-01

In the development of a syllable-centric automatic speech recognition (ASR) system, segmentation of the acoustic signal into syllabic units is an important stage. Although the short-term energy (STE) function contains useful information about syllable segment boundaries, it has to be processed before segment boundaries can be extracted. This paper presents a subband-based group delay approach to segment spontaneous speech into syllable-like units. This technique exploits the additive property of the Fourier transform phase and the deconvolution property of the cepstrum to smooth the STE function of the speech signal and make it suitable for syllable boundary detection. By treating the STE function as a magnitude spectrum of an arbitrary signal, a minimum-phase group delay function is derived. This group delay function is found to be a better representative of the STE function for syllable boundary detection. Although the group delay function derived from the STE function of the speech signal contains segment boundaries, the boundaries are difficult to determine in the context of long silences, semivowels, and fricatives. In this paper, these issues are specifically addressed and algorithms are developed to improve the segmentation performance. The speech signal is first passed through a bank of three filters, corresponding to three different spectral bands. The STE functions of these signals are computed. Using these three STE functions, three minimum-phase group delay functions are derived. By combining the evidence derived from these group delay functions, the syllable boundaries are detected. Further, a multiresolution-based technique is presented to overcome the problem of shift in segment boundaries during smoothing. Experiments carried out on the Switchboard and OGI-MLTS corpora show that the error in segmentation is at most 25 milliseconds for 67% and 76.6% of the syllable segments, respectively.

A hierarchical 3D segmentation method and the definition of vertebral body coordinate systems for QCT of the lumbar spine.

PubMed

Mastmeyer, André; Engelke, Klaus; Fuchs, Christina; Kalender, Willi A

2006-08-01

We have developed a new hierarchical 3D technique to segment the vertebral bodies in order to measure bone mineral density (BMD) with high trueness and precision in volumetric CT datasets. The hierarchical approach starts with a coarse separation of the individual vertebrae, applies a variety of techniques to segment the vertebral bodies with increasing detail and ends with the definition of an anatomic coordinate system for each vertebral body, relative to which up to 41 trabecular and cortical volumes of interest are positioned. In a pre-segmentation step constraints consisting of Boolean combinations of simple geometric shapes are determined that enclose each individual vertebral body. Bound by these constraints viscous deformable models are used to segment the main shape of the vertebral bodies. Volume growing and morphological operations then capture the fine details of the bone-soft tissue interface. In the volumes of interest bone mineral density and content are determined. In addition, in the segmented vertebral bodies geometric parameters such as volume or the length of the main axes of inertia can be measured. Intra- and inter-operator precision errors of the segmentation procedure were analyzed using existing clinical patient datasets. Results for segmented volume, BMD, and coordinate system position were below 2.0%, 0.6%, and 0.7%, respectively. Trueness was analyzed using phantom scans. The bias of the segmented volume was below 4%; for BMD it was below 1.5%. The long-term goal of this work is improved fracture prediction and patient monitoring in the field of osteoporosis. A true 3D segmentation also enables an accurate measurement of geometrical parameters that may augment the clinical value of a pure BMD analysis.

WE-EF-210-08: BEST IN PHYSICS (IMAGING): 3D Prostate Segmentation in Ultrasound Images Using Patch-Based Anatomical Feature

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

Yang, X; Rossi, P; Jani, A

Purpose: Transrectal ultrasound (TRUS) is the standard imaging modality for the image-guided prostate-cancer interventions (e.g., biopsy and brachytherapy) due to its versatility and real-time capability. Accurate segmentation of the prostate plays a key role in biopsy needle placement, treatment planning, and motion monitoring. As ultrasound images have a relatively low signal-to-noise ratio (SNR), automatic segmentation of the prostate is difficult. However, manual segmentation during biopsy or radiation therapy can be time consuming. We are developing an automated method to address this technical challenge. Methods: The proposed segmentation method consists of two major stages: the training stage and the segmentation stage.more » During the training stage, patch-based anatomical features are extracted from the registered training images with patient-specific information, because these training images have been mapped to the new patient’ images, and the more informative anatomical features are selected to train the kernel support vector machine (KSVM). During the segmentation stage, the selected anatomical features are extracted from newly acquired image as the input of the well-trained KSVM and the output of this trained KSVM is the segmented prostate of this patient. Results: This segmentation technique was validated with a clinical study of 10 patients. The accuracy of our approach was assessed using the manual segmentation. The mean volume Dice Overlap Coefficient was 89.7±2.3%, and the average surface distance was 1.52 ± 0.57 mm between our and manual segmentation, which indicate that the automatic segmentation method works well and could be used for 3D ultrasound-guided prostate intervention. Conclusion: We have developed a new prostate segmentation approach based on the optimal feature learning framework, demonstrated its clinical feasibility, and validated its accuracy with manual segmentation (gold standard). This segmentation technique could be a useful tool for image-guided interventions in prostate-cancer diagnosis and treatment. This research is supported in part by DOD PCRP Award W81XWH-13-1-0269, and National Cancer Institute (NCI) Grant CA114313.« less

The cure for employee malaise--motivation.

PubMed

Dawson, K M; Dawson, S N

1991-01-01

Although working conditions, hours, pay, and advancement opportunities are better now than in the 1950s--the "golden age" of American business--today's workers are significantly less satisfied. Why? The authors believe the cause of this malaise is lack of motivation. This article examines several techniques to cure employee malaise and discusses the long-term benefits of these techniques, which include empowerment, recognition, career development, the Pygmalion effect, incentives, and rewards. By making a commitment to these motivational techniques, managers will boost the morale and enthusiasm of their employees and their organization. This motivational process is not quick and easy; developing your employees is an ongoing process.

Localized Segment Based Processing for Automatic Building Extraction from LiDAR Data

NASA Astrophysics Data System (ADS)

Parida, G.; Rajan, K. S.

2017-05-01

The current methods of object segmentation and extraction and classification of aerial LiDAR data is manual and tedious task. This work proposes a technique for object segmentation out of LiDAR data. A bottom-up geometric rule based approach was used initially to devise a way to segment buildings out of the LiDAR datasets. For curved wall surfaces, comparison of localized surface normals was done to segment buildings. The algorithm has been applied to both synthetic datasets as well as real world dataset of Vaihingen, Germany. Preliminary results show successful segmentation of the buildings objects from a given scene in case of synthetic datasets and promissory results in case of real world data. The advantages of the proposed work is non-dependence on any other form of data required except LiDAR. It is an unsupervised method of building segmentation, thus requires no model training as seen in supervised techniques. It focuses on extracting the walls of the buildings to construct the footprint, rather than focussing on roof. The focus on extracting the wall to reconstruct the buildings from a LiDAR scene is crux of the method proposed. The current segmentation approach can be used to get 2D footprints of the buildings, with further scope to generate 3D models. Thus, the proposed method can be used as a tool to get footprints of buildings in urban landscapes, helping in urban planning and the smart cities endeavour.

SRB Safety Analysis

NASA Image and Video Library

2003-09-11

Jeff Thon, an SRB mechanic with United Space Alliance, is lowered into a mockup of a segment of a solid rocket booster. He is testing a technique for vertical SRB propellant grain inspection. The inspection of segments is required as part of safety analysis.

A new bioimpedance research device (BIRD) for measuring the electrical impedance of acupuncture meridians.

PubMed

Wong, Felix Wu Shun; Lim, Chi Eung Danforn; Smith, Warren

2010-03-01

The aim of this article is to introduce an electrical bioimpedance device that uses an old and little-known impedance measuring technique to study the impedance of the meridian and nonmeridian tissue segments. Three (3) pilot experimental studies involving both a tissue phantom (a cucumber) and 3 human subjects were performed using this BIRD-I (Bioimpedance Research Device) device. This device consists of a Fluke RCL meter, a multiplexer box, a laptop computer, and a medical-grade isolation transformer. Segment and surface sheath (or local) impedances were estimated using formulae first published in the 1930s, in an approach that differs from that of the standard four-electrode technique used in most meridian studies to date. Our study found that, when using a quasilinear four-electrode arrangement, the reference electrodes should be positioned at least 10 cm from the test electrodes to ensure that the segment (or core) impedance estimation is not affected by the proximity of the reference electrodes. A tissue phantom was used to determine the repeatability of segment (core) impedance measurement by the device. An applied frequency of 100 kHz was found to produce the best repeatability among the various frequencies tested. In another preliminary study, with a segment of the triple energizer meridian on the lower arm selected as reference segment, core resistance-based profiles around the lower arm showed three of the other five meridians to exist as local resistance minima relative to neighboring nonmeridian segments. The profiles of the 2 subjects tested were very similar, suggesting that the results are unlikely to be spurious. In electrical bioimpedance studies, it is recommended that the measuring technique and device be clearly defined and standardized to provide optimal working conditions. In our study using the BIRD I device, we defined our standard experimental conditions as a test frequency of 100 kHz and the position of the reference electrodes of at least 10 cm from the test electrodes. Our device has demonstrated potential for use in quantifying the degree of electrical interconnection between any two surface-defined test meridian or nonmeridian segments. Issues arising from use of this device and the measurement Horton and van Ravenswaay technique were also presented.

Prosthetic component segmentation with blur compensation: a fast method for 3D fluoroscopy.

PubMed

Tarroni, Giacomo; Tersi, Luca; Corsi, Cristiana; Stagni, Rita

2012-06-01

A new method for prosthetic component segmentation from fluoroscopic images is presented. The hybrid approach we propose combines diffusion filtering, region growing and level-set techniques without exploiting any a priori knowledge of the analyzed geometry. The method was evaluated on a synthetic dataset including 270 images of knee and hip prosthesis merged to real fluoroscopic data simulating different conditions of blurring and illumination gradient. The performance of the method was assessed by comparing estimated contours to references using different metrics. Results showed that the segmentation procedure is fast, accurate, independent on the operator as well as on the specific geometrical characteristics of the prosthetic component, and able to compensate for amount of blurring and illumination gradient. Importantly, the method allows a strong reduction of required user interaction time when compared to traditional segmentation techniques. Its effectiveness and robustness in different image conditions, together with simplicity and fast implementation, make this prosthetic component segmentation procedure promising and suitable for multiple clinical applications including assessment of in vivo joint kinematics in a variety of cases.

Retinal blood vessel segmentation using fully convolutional network with transfer learning.

PubMed

Jiang, Zhexin; Zhang, Hao; Wang, Yi; Ko, Seok-Bum

2018-04-26

Since the retinal blood vessel has been acknowledged as an indispensable element in both ophthalmological and cardiovascular disease diagnosis, the accurate segmentation of the retinal vessel tree has become the prerequisite step for automated or computer-aided diagnosis systems. In this paper, a supervised method is presented based on a pre-trained fully convolutional network through transfer learning. This proposed method has simplified the typical retinal vessel segmentation problem from full-size image segmentation to regional vessel element recognition and result merging. Meanwhile, additional unsupervised image post-processing techniques are applied to this proposed method so as to refine the final result. Extensive experiments have been conducted on DRIVE, STARE, CHASE_DB1 and HRF databases, and the accuracy of the cross-database test on these four databases is state-of-the-art, which also presents the high robustness of the proposed approach. This successful result has not only contributed to the area of automated retinal blood vessel segmentation but also supports the effectiveness of transfer learning when applying deep learning technique to medical imaging. Copyright © 2018 Elsevier Ltd. All rights reserved.

Automated segmentation and feature extraction of product inspection items

NASA Astrophysics Data System (ADS)

Talukder, Ashit; Casasent, David P.

1997-03-01

X-ray film and linescan images of pistachio nuts on conveyor trays for product inspection are considered. The final objective is the categorization of pistachios into good, blemished and infested nuts. A crucial step before classification is the separation of touching products and the extraction of features essential for classification. This paper addresses new detection and segmentation algorithms to isolate touching or overlapping items. These algorithms employ a new filter, a new watershed algorithm, and morphological processing to produce nutmeat-only images. Tests on a large database of x-ray film and real-time x-ray linescan images of around 2900 small, medium and large nuts showed excellent segmentation results. A new technique to detect and segment dark regions in nutmeat images is also presented and tested on approximately 300 x-ray film and approximately 300 real-time linescan x-ray images with 95-97 percent detection and correct segmentation. New algorithms are described that determine nutmeat fill ratio and locate splits in nutmeat. The techniques formulated in this paper are of general use in many different product inspection and computer vision problems.

A Q-Ising model application for linear-time image segmentation

NASA Astrophysics Data System (ADS)

Bentrem, Frank W.

2010-10-01

A computational method is presented which efficiently segments digital grayscale images by directly applying the Q-state Ising (or Potts) model. Since the Potts model was first proposed in 1952, physicists have studied lattice models to gain deep insights into magnetism and other disordered systems. For some time, researchers have realized that digital images may be modeled in much the same way as these physical systems ( i.e., as a square lattice of numerical values). A major drawback in using Potts model methods for image segmentation is that, with conventional methods, it processes in exponential time. Advances have been made via certain approximations to reduce the segmentation process to power-law time. However, in many applications (such as for sonar imagery), real-time processing requires much greater efficiency. This article contains a description of an energy minimization technique that applies four Potts (Q-Ising) models directly to the image and processes in linear time. The result is analogous to partitioning the system into regions of four classes of magnetism. This direct Potts segmentation technique is demonstrated on photographic, medical, and acoustic images.

Neural and Decision Theoretic Approaches for the Automated Segmentation of Radiodense Tissue in Digitized Mammograms

NASA Astrophysics Data System (ADS)

Eckert, R.; Neyhart, J. T.; Burd, L.; Polikar, R.; Mandayam, S. A.; Tseng, M.

2003-03-01

Mammography is the best method available as a non-invasive technique for the early detection of breast cancer. The radiographic appearance of the female breast consists of radiolucent (dark) regions due to fat and radiodense (light) regions due to connective and epithelial tissue. The amount of radiodense tissue can be used as a marker for predicting breast cancer risk. Previously, we have shown that the use of statistical models is a reliable technique for segmenting radiodense tissue. This paper presents improvements in the model that allow for further development of an automated system for segmentation of radiodense tissue. The segmentation algorithm employs a two-step process. In the first step, segmentation of tissue and non-tissue regions of a digitized X-ray mammogram image are identified using a radial basis function neural network. The second step uses a constrained Neyman-Pearson algorithm, developed especially for this research work, to determine the amount of radiodense tissue. Results obtained using the algorithm have been validated by comparing with estimates provided by a radiologist employing previously established methods.

Automatic phase aberration compensation for digital holographic microscopy based on deep learning background detection.

PubMed

Nguyen, Thanh; Bui, Vy; Lam, Van; Raub, Christopher B; Chang, Lin-Ching; Nehmetallah, George

2017-06-26

We propose a fully automatic technique to obtain aberration free quantitative phase imaging in digital holographic microscopy (DHM) based on deep learning. The traditional DHM solves the phase aberration compensation problem by manually detecting the background for quantitative measurement. This would be a drawback in real time implementation and for dynamic processes such as cell migration phenomena. A recent automatic aberration compensation approach using principle component analysis (PCA) in DHM avoids human intervention regardless of the cells' motion. However, it corrects spherical/elliptical aberration only and disregards the higher order aberrations. Traditional image segmentation techniques can be employed to spatially detect cell locations. Ideally, automatic image segmentation techniques make real time measurement possible. However, existing automatic unsupervised segmentation techniques have poor performance when applied to DHM phase images because of aberrations and speckle noise. In this paper, we propose a novel method that combines a supervised deep learning technique with convolutional neural network (CNN) and Zernike polynomial fitting (ZPF). The deep learning CNN is implemented to perform automatic background region detection that allows for ZPF to compute the self-conjugated phase to compensate for most aberrations.

The effect of the heights and thicknesses of the remaining root segments on buccal bone resorption in the socket-shield technique: An experimental study in dogs.

PubMed

Tan, Zhen; Kang, Jian; Liu, Wenjia; Wang, Hang

2018-06-01

To date only a few studies have been done on the use of the socket-shield technique for preserving the resorption of the buccal bone in aesthetically sensitive sites. Besides, there have been no further studies on the effect of the heights and thicknesses of the remaining root segments on buccal bone resorption when using this method. The aim of this study was to evaluate the effect of different heights and thicknesses of the remaining root segments on bone resorption in the socket-shield technique. Four healthy female beagle dogs were used in this study. The third premolar (P3) and the fourth premolar (P4) on both sides of the mandible were hemisected in the buccal-lingual direction, and the clinical crown of the distal root was beheaded. In the experimental groups, the roots were worn down in the apical direction until they were located at the buccal crestal level (Group A) or 1 mm higher than that level (Group B). In the control group, the distal root segments were extracted. Then, implant placement was performed into the distal root. After 3 months of healing, the specimens were prepared for histological diagnosis. There was no difference between Group A and Group B when using the socket-shield technique, but the results of both groups were better than those of the control group. The height of the root segments has little effect on the bone absorption of alveolar bone, while the bone absorption was strongly influenced by the thickness of the root segments. More precisely, the absorption may decrease if the thickness of the root fragment increases, when the thickness of the root plate is in the 0.5-1.5 mm range. © 2018 Wiley Periodicals, Inc.

Arabic OCR: toward a complete system

NASA Astrophysics Data System (ADS)

El-Bialy, Ahmed M.; Kandil, Ahmed H.; Hashish, Mohamed; Yamany, Sameh M.

1999-12-01

Latin and Chinese OCR systems have been studied extensively in the literature. Yet little work was performed for Arabic character recognition. This is due to the technical challenges found in the Arabic text. Due to its cursive nature, a powerful and stable text segmentation is needed. Also; features capturing the characteristics of the rich Arabic character representation are needed to build the Arabic OCR. In this paper a novel segmentation technique which is font and size independent is introduced. This technique can segment the cursive written text line even if the line suffers from small skewness. The technique is not sensitive to the location of the centerline of the text line and can segment different font sizes and type (for different character sets) occurring on the same line. Features extraction is considered one of the most important phases of the text reading system. Ideally, the features extracted from a character image should capture the essential characteristics of this character that are independent of the font type and size. In such ideal case, the classifier stores a single prototype per character. However, it is practically challenging to find such ideal set of features. In this paper, a set of features that reflect the topological aspects of Arabia characters is proposed. These proposed features integrated with a topological matching technique introduce an Arabic text reading system that is semi Omni.

Pupil Tracking for Real-Time Motion Corrected Anterior Segment Optical Coherence Tomography

PubMed Central

Carrasco-Zevallos, Oscar M.; Nankivil, Derek; Viehland, Christian; Keller, Brenton; Izatt, Joseph A.

2016-01-01

Volumetric acquisition with anterior segment optical coherence tomography (ASOCT) is necessary to obtain accurate representations of the tissue structure and to account for asymmetries of the anterior eye anatomy. Additionally, recent interest in imaging of anterior segment vasculature and aqueous humor flow resulted in application of OCT angiography techniques to generate en face and 3D micro-vasculature maps of the anterior segment. Unfortunately, ASOCT structural and vasculature imaging systems do not capture volumes instantaneously and are subject to motion artifacts due to involuntary eye motion that may hinder their accuracy and repeatability. Several groups have demonstrated real-time tracking for motion-compensated in vivo OCT retinal imaging, but these techniques are not applicable in the anterior segment. In this work, we demonstrate a simple and low-cost pupil tracking system integrated into a custom swept-source OCT system for real-time motion-compensated anterior segment volumetric imaging. Pupil oculography hardware coaxial with the swept-source OCT system enabled fast detection and tracking of the pupil centroid. The pupil tracking ASOCT system with a field of view of 15 x 15 mm achieved diffraction-limited imaging over a lateral tracking range of +/- 2.5 mm and was able to correct eye motion at up to 22 Hz. Pupil tracking ASOCT offers a novel real-time motion compensation approach that may facilitate accurate and reproducible anterior segment imaging. PMID:27574800

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

Up and down or down and up? The process of change in constructive couple behavior during Traditional and Integrative Behavioral Couple Therapy.

PubMed

Sevier, Mia; Atkins, David C; Doss, Brian D; Christensen, Andrew

2015-01-01

Observed positive and negative spouse behavior during sessions of Traditional (TBCT) and Integrative Behavioral Couples Therapy (IBCT) were compared for couples with successful outcomes and their unsuccessful counterparts. One hundred and thirty-four married chronically and seriously distressed couples (on average in their forties and 80% Caucasian) were randomly assigned to TBCT or IBCT. Trained observers made ratings of 1224 segments from approximately 956 sessions sampled from the course of up to 26 sessions. Multilevel modeling was used to examine change over time. TBCT treatment responders demonstrated a boost-drop pattern, increasing in constructive behaviors early (more positive behaviors and less negative behaviors) but decreasing later. IBCT responders demonstrated an opposite, drop-boost pattern, decreasing in constructive behaviors early and increasing later. Patterns were significant for positive behaviors (p < .05) and approached significance for negative behaviors (p = .05). In both treatments, nonresponders showed a significant pattern of decline in positive and increase in negative behaviors over time, although a trend (p = .05) indicates that TBCT nonresponders initially declined in negative behaviors. This study helps clarify the different process of change in two behavioral couple therapies, which may assist in treatment development and provide a guide for therapists in considering behavioral markers of change during treatment. © 2013 American Association for Marriage and Family Therapy.

Automatic Seizure Detection in Rats Using Laplacian EEG and Verification with Human Seizure Signals

PubMed Central

Feltane, Amal; Boudreaux-Bartels, G. Faye; Besio, Walter

2012-01-01

Automated detection of seizures is still a challenging problem. This study presents an approach to detect seizure segments in Laplacian electroencephalography (tEEG) recorded from rats using the tripolar concentric ring electrode (TCRE) configuration. Three features, namely, median absolute deviation, approximate entropy, and maximum singular value were calculated and used as inputs into two different classifiers: support vector machines and adaptive boosting. The relative performance of the extracted features on TCRE tEEG was examined. Results are obtained with an overall accuracy between 84.81 and 96.51%. In addition to using TCRE tEEG data, the seizure detection algorithm was also applied to the recorded EEG signals from Andrzejak et al. database to show the efficiency of the proposed method for seizure detection. PMID:23073989

Modification of earth-satellite orbits using medium-energy pulsed lasers

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

Phipps, C.R.

1992-01-01

Laser Impulse Space Propulsion (LISP) has become an attractive concept, due to recent advances in gas laser technology, high-speed segmented mirrors and improved coeffici-ents for momentum coupling to targets in pulsed laser ablation. There are numerous specialized applications of the basic concept to space science-ranging from far-future and high capital cost to the immediate and inexpensive, such as: LEO-LISP (launch of massive objects into low-Earth-Orbit at dramatically improved cost-per-kg relative to present practice); LEGO-LISP (LEO to geosynchronous transfers); LO-LISP) (periodic re-boost of decaying LEO orbits); and LISK (geosynchronous satellite station-keeping). It is unlikely that one type of laser will bemore » best for all scenarios. In this paper, we will focus on the last two applications.« less

Modification of earth-satellite orbits using medium-energy pulsed lasers

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

Phipps, C.R.

1992-10-01

Laser Impulse Space Propulsion (LISP) has become an attractive concept, due to recent advances in gas laser technology, high-speed segmented mirrors and improved coeffici-ents for momentum coupling to targets in pulsed laser ablation. There are numerous specialized applications of the basic concept to space science-ranging from far-future and high capital cost to the immediate and inexpensive, such as: LEO-LISP (launch of massive objects into low-Earth-Orbit at dramatically improved cost-per-kg relative to present practice); LEGO-LISP (LEO to geosynchronous transfers); LO-LISP) (periodic re-boost of decaying LEO orbits); and LISK (geosynchronous satellite station-keeping). It is unlikely that one type of laser will bemore » best for all scenarios. In this paper, we will focus on the last two applications.« less

Multivariate statistical model for 3D image segmentation with application to medical images.

PubMed

John, Nigel M; Kabuka, Mansur R; Ibrahim, Mohamed O

2003-12-01

In this article we describe a statistical model that was developed to segment brain magnetic resonance images. The statistical segmentation algorithm was applied after a pre-processing stage involving the use of a 3D anisotropic filter along with histogram equalization techniques. The segmentation algorithm makes use of prior knowledge and a probability-based multivariate model designed to semi-automate the process of segmentation. The algorithm was applied to images obtained from the Center for Morphometric Analysis at Massachusetts General Hospital as part of the Internet Brain Segmentation Repository (IBSR). The developed algorithm showed improved accuracy over the k-means, adaptive Maximum Apriori Probability (MAP), biased MAP, and other algorithms. Experimental results showing the segmentation and the results of comparisons with other algorithms are provided. Results are based on an overlap criterion against expertly segmented images from the IBSR. The algorithm produced average results of approximately 80% overlap with the expertly segmented images (compared with 85% for manual segmentation and 55% for other algorithms).

Segmenting overlapping nano-objects in atomic force microscopy image

NASA Astrophysics Data System (ADS)

Wang, Qian; Han, Yuexing; Li, Qing; Wang, Bing; Konagaya, Akihiko

2018-01-01

Recently, techniques for nanoparticles have rapidly been developed for various fields, such as material science, medical, and biology. In particular, methods of image processing have widely been used to automatically analyze nanoparticles. A technique to automatically segment overlapping nanoparticles with image processing and machine learning is proposed. Here, two tasks are necessary: elimination of image noises and action of the overlapping shapes. For the first task, mean square error and the seed fill algorithm are adopted to remove noises and improve the quality of the original image. For the second task, four steps are needed to segment the overlapping nanoparticles. First, possibility split lines are obtained by connecting the high curvature pixels on the contours. Second, the candidate split lines are classified with a machine learning algorithm. Third, the overlapping regions are detected with the method of density-based spatial clustering of applications with noise (DBSCAN). Finally, the best split lines are selected with a constrained minimum value. We give some experimental examples and compare our technique with two other methods. The results can show the effectiveness of the proposed technique.

Treatment Techniques and Site Considerations Regarding Dysphagia-Related Quality of Life in Cancer of the Oropharynx and Nasopharynx

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

Teguh, David N.; Levendag, Peter C.; Noever, Inge

2008-11-15

Purpose: To assess the relationship for oropharyngeal (OP) cancer and nasopharyngeal (NP) cancer between the dose received by the swallowing structures and the dysphagia related quality of life (QoL). Methods and Materials: Between 2000 and 2005, 85 OP and 47 NP cancer patients were treated by radiation therapy. After 46 Gy, OP cancer is boosted by intensity-modulated radiation therapy (IMRT), brachytherapy (BT), or frameless stereotactic radiation/cyberknife (CBK). After 46 Gy, the NP cancer was boosted with parallel-opposed fields or IMRT to a total dose of 70 Gy; subsequently, a second boost was given by either BT (11 Gy) or stereotacticmore » radiation (SRT)/CBK (11.2 Gy). Sixty OP and 21 NP cancer patients responded to functional and QoL questionnaires (i.e., the Performance Status Scales, European Organization for Research and Treatment of Cancer H and N35, and M.D. Anderson Dysphagia Inventory). The swallowing muscles were delineated and the mean dose calculated using the original three-dimensional computed tomography-based treatment plans. Univariate analyses were performed using logistic regression analysis. Results: Most dysphagia problems were observed in the base of tongue tumors. For OP cancer, boosting with IMRT resulted in more dysphagia as opposed to BT or SRT/CBK. For NPC patients, in contrast to the first booster dose (46-70 Gy), no additional increase of dysphagia by the second boost was observed. Conclusions: The lowest mean doses of radiation to the swallowing muscles were achieved when using BT as opposed to SRT/CBK or IMRT. For the 81 patients alive with no evidence of disease for at least 1 year, a dose-effect relationship was observed between the dose in the superior constrictor muscle and the 'normalcy of diet' (Performance Status Scales) or 'swallowing scale' (H and N35) scores (p < 0.01)« less

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

Balik, S; Weiss, E; Sleeman, W

Purpose: To evaluate the potential impact of several setup error correction strategies on a proposed image-guided adaptive radiotherapy strategy for locally advanced lung cancer. Methods: Daily 4D cone-beam CT and weekly 4D fan-beam CT images were acquired from 9 lung cancer patients undergoing concurrent chemoradiation therapy. Initial planning CT was deformably registered to daily CBCT images to generate synthetic treatment courses. An adaptive radiation therapy course was simulated using the weekly CT images with replanning twice and a hypofractionated, simultaneous integrated boost to a total dose of 66 Gy to the original PTV and either a 66 Gy (no boost)more » or 82 Gy (boost) dose to the boost PTV (ITV + 3mm) in 33 fractions with IMRT or VMAT. Lymph nodes (LN) were not boosted (prescribed to 66 Gy in both plans). Synthetic images were rigidly, bony (BN) or tumor and carina (TC), registered to the corresponding plan CT, dose was computed on these from adaptive replans (PLAN) and deformably accumulated back to the original planning CT. Cumulative D98% of CTV of PT (ITV for 82Gy) and LN, and normal tissue dose changes were analyzed. Results: Two patients were removed from the study due to large registration errors. For the remaining 7 patients, D98% for CTV-PT (ITV-PT for 82 Gy) and CTV-LN was within 1 Gy of PLAN for both 66 Gy and 82 Gy plans with both setup techniques. Overall, TC based setup provided better results, especially for LN coverage (p = 0.1 for 66Gy plan and p = 0.2 for 82 Gy plan, comparison of BN and TC), though not significant. Normal tissue dose constraints violated for some patients if constraint was barely achieved in PLAN. Conclusion: The hypofractionated adaptive strategy appears to be deliverable with soft tissue alignment for the evaluated margins and planning parameters. Research was supported by NIH P01CA116602.« less

Facial Affect Recognition Using Regularized Discriminant Analysis-Based Algorithms

NASA Astrophysics Data System (ADS)

Lee, Chien-Cheng; Huang, Shin-Sheng; Shih, Cheng-Yuan

2010-12-01

This paper presents a novel and effective method for facial expression recognition including happiness, disgust, fear, anger, sadness, surprise, and neutral state. The proposed method utilizes a regularized discriminant analysis-based boosting algorithm (RDAB) with effective Gabor features to recognize the facial expressions. Entropy criterion is applied to select the effective Gabor feature which is a subset of informative and nonredundant Gabor features. The proposed RDAB algorithm uses RDA as a learner in the boosting algorithm. The RDA combines strengths of linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA). It solves the small sample size and ill-posed problems suffered from QDA and LDA through a regularization technique. Additionally, this study uses the particle swarm optimization (PSO) algorithm to estimate optimal parameters in RDA. Experiment results demonstrate that our approach can accurately and robustly recognize facial expressions.

Synchronous prostate and rectal adenocarcinomas irradiation utilising volumetric modulated arc therapy.

PubMed

Ng, Sweet Ping; Tran, Thu; Moloney, Philip; Sale, Charlotte; Mathlum, Maitham; Ong, Grace; Lynch, Rod

2015-12-01

Cases of synchronous prostate and colorectal adenocarcinomas have been sporadically reported. There are case reports on patients with synchronous prostate and rectal cancers treated with external beam radiotherapy alone or combined with high-dose rate brachytherapy boost to the prostate. Here, we illustrate a patient with synchronous prostate and rectal cancers treated using the volumetric arc therapy (VMAT) technique. The patient was treated with radical radiotherapy to 50.4 Gy in 28 fractions to the pelvis, incorporating the involved internal iliac node and the prostate. A boost of 24 Gy in 12 fractions was delivered to the prostate only, using VMAT. Treatment-related toxicities and follow-up prostate-specific antigen and carcinoembryonic antigen were collected for data analysis. At 12 months, the patient achieved complete response for both rectal and prostate cancers without significant treatment-related toxicities.

Mixture of Segmenters with Discriminative Spatial Regularization and Sparse Weight Selection*

PubMed Central

Chen, Ting; Rangarajan, Anand; Eisenschenk, Stephan J.

2011-01-01

This paper presents a novel segmentation algorithm which automatically learns the combination of weak segmenters and builds a strong one based on the assumption that the locally weighted combination varies w.r.t. both the weak segmenters and the training images. We learn the weighted combination during the training stage using a discriminative spatial regularization which depends on training set labels. A closed form solution to the cost function is derived for this approach. In the testing stage, a sparse regularization scheme is imposed to avoid overfitting. To the best of our knowledge, such a segmentation technique has never been reported in literature and we empirically show that it significantly improves on the performances of the weak segmenters. After showcasing the performance of the algorithm in the context of atlas-based segmentation, we present comparisons to the existing weak segmenter combination strategies on a hippocampal data set. PMID:22003748

Hemorrhage Detection and Segmentation in Traumatic Pelvic Injuries

PubMed Central

Davuluri, Pavani; Wu, Jie; Tang, Yang; Cockrell, Charles H.; Ward, Kevin R.; Najarian, Kayvan; Hargraves, Rosalyn H.

2012-01-01

Automated hemorrhage detection and segmentation in traumatic pelvic injuries is vital for fast and accurate treatment decision making. Hemorrhage is the main cause of deaths in patients within first 24 hours after the injury. It is very time consuming for physicians to analyze all Computed Tomography (CT) images manually. As time is crucial in emergence medicine, analyzing medical images manually delays the decision-making process. Automated hemorrhage detection and segmentation can significantly help physicians to analyze these images and make fast and accurate decisions. Hemorrhage segmentation is a crucial step in the accurate diagnosis and treatment decision-making process. This paper presents a novel rule-based hemorrhage segmentation technique that utilizes pelvic anatomical information to segment hemorrhage accurately. An evaluation measure is used to quantify the accuracy of hemorrhage segmentation. The results show that the proposed method is able to segment hemorrhage very well, and the results are promising. PMID:22919433

Target marketing strategies for occupational therapy entrepreneurs.

PubMed

Kautzmann, L N; Kautzmann, F N; Navarro, F H

1989-01-01

Understanding marketing techniques is one of the skills needed by successful entre renews. Target marketing is an effective method for occupational therapy entrepreneurs to use in determining when and where to enter the marketplace. The two components of target marketing, market segmentation and the development of marketing mix strategies for each identified market segment, are described. The Profife of Attitudes Toward Health Care (PATH) method of psychographic market segmentation of health care consumers is presented. Occupational therapy marketing mix strategies for each PATH consumer group are delineated and compatible groupings of market segments are suggested.

A novel sub-shot segmentation method for user-generated video

NASA Astrophysics Data System (ADS)

Lei, Zhuo; Zhang, Qian; Zheng, Chi; Qiu, Guoping

2018-04-01

With the proliferation of the user-generated videos, temporal segmentation is becoming a challengeable problem. Traditional video temporal segmentation methods like shot detection are not able to work on unedited user-generated videos, since they often only contain one single long shot. We propose a novel temporal segmentation framework for user-generated video. It finds similar frames with a tree partitioning min-Hash technique, constructs sparse temporal constrained affinity sub-graphs, and finally divides the video into sub-shot-level segments with a dense-neighbor-based clustering method. Experimental results show that our approach outperforms all the other related works. Furthermore, it is indicated that the proposed approach is able to segment user-generated videos at an average human level.

Sensitivity analysis of brain morphometry based on MRI-derived surface models

NASA Astrophysics Data System (ADS)

Klein, Gregory J.; Teng, Xia; Schoenemann, P. T.; Budinger, Thomas F.

1998-07-01

Quantification of brain structure is important for evaluating changes in brain size with growth and aging and for characterizing neurodegeneration disorders. Previous quantification efforts using ex vivo techniques suffered considerable error due to shrinkage of the cerebrum after extraction from the skull, deformation of slices during sectioning, and numerous other factors. In vivo imaging studies of brain anatomy avoid these problems and allow repetitive studies following progression of brain structure changes due to disease or natural processes. We have developed a methodology for obtaining triangular mesh models of the cortical surface from MRI brain datasets. The cortex is segmented from nonbrain tissue using a 2D region-growing technique combined with occasional manual edits. Once segmented, thresholding and image morphological operations (erosions and openings) are used to expose the regions between adjacent surfaces in deep cortical folds. A 2D region- following procedure is then used to find a set of contours outlining the cortical boundary on each slice. The contours on all slices are tiled together to form a closed triangular mesh model approximating the cortical surface. This model can be used for calculation of cortical surface area and volume, as well as other parameters of interest. Except for the initial segmentation of the cortex from the skull, the technique is automatic and requires only modest computation time on modern workstations. Though the use of image data avoids many of the pitfalls of ex vivo and sectioning techniques, our MRI-based technique is still vulnerable to errors that may impact the accuracy of estimated brain structure parameters. Potential inaccuracies include segmentation errors due to incorrect thresholding, missed deep sulcal surfaces, falsely segmented holes due to image noise and surface tiling artifacts. The focus of this paper is the characterization of these errors and how they affect measurements of cortical surface area and volume.

Seed robustness of oriented relative fuzzy connectedness: core computation and its applications

NASA Astrophysics Data System (ADS)

Tavares, Anderson C. M.; Bejar, Hans H. C.; Miranda, Paulo A. V.

2017-02-01

In this work, we present a formal definition and an efficient algorithm to compute the cores of Oriented Relative Fuzzy Connectedness (ORFC), a recent seed-based segmentation technique. The core is a region where the seed can be moved without altering the segmentation, an important aspect for robust techniques and reduction of user effort. We show how ORFC cores can be used to build a powerful hybrid image segmentation approach. We also provide some new theoretical relations between ORFC and Oriented Image Foresting Transform (OIFT), as well as their cores. Experimental results among several methods show that the hybrid approach conserves high accuracy, avoids the shrinking problem and provides robustness to seed placement inside the desired object due to the cores properties.

Image segmentation for enhancing symbol recognition in prosthetic vision.

PubMed

Horne, Lachlan; Barnes, Nick; McCarthy, Chris; He, Xuming

2012-01-01

Current and near-term implantable prosthetic vision systems offer the potential to restore some visual function, but suffer from poor resolution and dynamic range of induced phosphenes. This can make it difficult for users of prosthetic vision systems to identify symbolic information (such as signs) except in controlled conditions. Using image segmentation techniques from computer vision, we show it is possible to improve the clarity of such symbolic information for users of prosthetic vision implants in uncontrolled conditions. We use image segmentation to automatically divide a natural image into regions, and using a fixation point controlled by the user, select a region to phosphenize. This technique improves the apparent contrast and clarity of symbolic information over traditional phosphenization approaches.

Automatic video segmentation and indexing

NASA Astrophysics Data System (ADS)

Chahir, Youssef; Chen, Liming

1999-08-01

Indexing is an important aspect of video database management. Video indexing involves the analysis of video sequences, which is a computationally intensive process. However, effective management of digital video requires robust indexing techniques. The main purpose of our proposed video segmentation is twofold. Firstly, we develop an algorithm that identifies camera shot boundary. The approach is based on the use of combination of color histograms and block-based technique. Next, each temporal segment is represented by a color reference frame which specifies the shot similarities and which is used in the constitution of scenes. Experimental results using a variety of videos selected in the corpus of the French Audiovisual National Institute are presented to demonstrate the effectiveness of performing shot detection, the content characterization of shots and the scene constitution.

Optimal design strategy of switching converters employing current injected control

NASA Astrophysics Data System (ADS)

Lee, F. C.; Fang, Z. D.; Lee, T. H.

1985-01-01

This paper analyzes a buck/boost regulator employing current-injected control (CIC). It reveals the complex interactions between the dc loop and the current-injected loop and underlines the fundamental principle that governs the loop gain determination. Three commonly used compensation techniques are compared. The integral and lead/lag compensation are shown to be most desirable for performance optimization and stability.

Strength in Numbers: Data-Driven Collaboration May Not Sound Sexy, But it Could Save Your Job

ERIC Educational Resources Information Center

Buzzeo, Toni

2010-01-01

This article describes a practical, sure-fire way for media specialists to boost student achievement. The method is called data-driven collaboration, and it's a practical, easy-to-use technique in which media specialists and teachers work together to pinpoint kids' instructional needs and improve their essential skills. The author discusses the…

Implementing the patient circle. Call on patients to help improve perceptions of health care quality.

PubMed

Ostasiewski, P; Fugate, D L

1994-01-01

Adapting the quality-circle concept to a health care setting helped one hospital solve a problem and boosted its image among patients. The "patient circle" technique is one step health care providers can take toward delivering "total customer value," a quality perception that can mean the difference between surviving and thriving in the future.

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

Dolen, James; Harris, Philip; Marzani, Simone

Here, we explore the scale-dependence and correlations of jet substructure observables to improve upon existing techniques in the identification of highly Lorentz-boosted objects. Modified observables are designed to remove correlations from existing theoretically well-understood observables, providing practical advantages for experimental measurements and searches for new phenomena. We study such observables in W jet tagging and provide recommendations for observables based on considerations beyond signal and background efficiencies.

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.

An improved survivability prognosis of breast cancer by using sampling and feature selection technique to solve imbalanced patient classification data.

PubMed

Wang, Kung-Jeng; Makond, Bunjira; Wang, Kung-Min

2013-11-09

Breast cancer is one of the most critical cancers and is a major cause of cancer death among women. It is essential to know the survivability of the patients in order to ease the decision making process regarding medical treatment and financial preparation. Recently, the breast cancer data sets have been imbalanced (i.e., the number of survival patients outnumbers the number of non-survival patients) whereas the standard classifiers are not applicable for the imbalanced data sets. The methods to improve survivability prognosis of breast cancer need for study. Two well-known five-year prognosis models/classifiers [i.e., logistic regression (LR) and decision tree (DT)] are constructed by combining synthetic minority over-sampling technique (SMOTE), cost-sensitive classifier technique (CSC), under-sampling, bagging, and boosting. The feature selection method is used to select relevant variables, while the pruning technique is applied to obtain low information-burden models. These methods are applied on data obtained from the Surveillance, Epidemiology, and End Results database. The improvements of survivability prognosis of breast cancer are investigated based on the experimental results. Experimental results confirm that the DT and LR models combined with SMOTE, CSC, and under-sampling generate higher predictive performance consecutively than the original ones. Most of the time, DT and LR models combined with SMOTE and CSC use less informative burden/features when a feature selection method and a pruning technique are applied. LR is found to have better statistical power than DT in predicting five-year survivability. CSC is superior to SMOTE, under-sampling, bagging, and boosting to improve the prognostic performance of DT and LR.

Hydrodynamic boost: a novel re-entry technique in subintimal angioplasty of below-the-knee vessels.

PubMed

Ferraresi, Roberto; Hamade, Meneme; Gallicchio, Vito; Troisi, Nicola; Mauri, Giovanni

2016-08-01

To describe the hydrodynamic boost (HB) technique and report our preliminary results with this technique in the subintimal angioplasty of below-the-knee vessels. HB was used in 23 cases (14 males, mean age 73 ± 12 years) of critical limb ischemia, with long chronic total occlusion of tibial arteries extended to the ankle level. The operator performs a manual injection of diluted contrast dye through a 4 F catheter into the subintimal space, close to the patent true distal lumen, in order to achieve a tear in the intimal flap and a connection with the true lumen. In 19/23 (83 %) cases, the HB was effective in creating a connection between the subintimal space and the true distal lumen and it was possible to advance a wire and to conclude the procedure. In 4/23 (17 %) lesions, the HB failed and the procedure was successfully completed by retrograde approach. No major complications occurred. Mean length between catheter tip and re-entry point was 8 ± 5 mm. HB seems to be a feasible, safe and effective re-entry technique in distal below-the-knee vessels. This method represents an easy option for re-entry that extends the possibility of antegrade approach to obtain a successful revascularization. • In subintimal angioplasty of below-the-knee vessel re-entry can represent a challenge. • Inability to re-enter may determine the failure of the revascularization procedure. • HB is a novel re-entry technique feasible in distal below-the-knee vessels. • HB may increase the success rate of antegrade approach. • In case of failure, retrograde approach remains feasible.

An improved survivability prognosis of breast cancer by using sampling and feature selection technique to solve imbalanced patient classification data

PubMed Central

2013-01-01

Background Breast cancer is one of the most critical cancers and is a major cause of cancer death among women. It is essential to know the survivability of the patients in order to ease the decision making process regarding medical treatment and financial preparation. Recently, the breast cancer data sets have been imbalanced (i.e., the number of survival patients outnumbers the number of non-survival patients) whereas the standard classifiers are not applicable for the imbalanced data sets. The methods to improve survivability prognosis of breast cancer need for study. Methods Two well-known five-year prognosis models/classifiers [i.e., logistic regression (LR) and decision tree (DT)] are constructed by combining synthetic minority over-sampling technique (SMOTE) ,cost-sensitive classifier technique (CSC), under-sampling, bagging, and boosting. The feature selection method is used to select relevant variables, while the pruning technique is applied to obtain low information-burden models. These methods are applied on data obtained from the Surveillance, Epidemiology, and End Results database. The improvements of survivability prognosis of breast cancer are investigated based on the experimental results. Results Experimental results confirm that the DT and LR models combined with SMOTE, CSC, and under-sampling generate higher predictive performance consecutively than the original ones. Most of the time, DT and LR models combined with SMOTE and CSC use less informative burden/features when a feature selection method and a pruning technique are applied. Conclusions LR is found to have better statistical power than DT in predicting five-year survivability. CSC is superior to SMOTE, under-sampling, bagging, and boosting to improve the prognostic performance of DT and LR. PMID:24207108

Fully automated, real-time 3D ultrasound segmentation to estimate first trimester placental volume using deep learning.

PubMed

Looney, Pádraig; Stevenson, Gordon N; Nicolaides, Kypros H; Plasencia, Walter; Molloholli, Malid; Natsis, Stavros; Collins, Sally L

2018-06-07

We present a new technique to fully automate the segmentation of an organ from 3D ultrasound (3D-US) volumes, using the placenta as the target organ. Image analysis tools to estimate organ volume do exist but are too time consuming and operator dependant. Fully automating the segmentation process would potentially allow the use of placental volume to screen for increased risk of pregnancy complications. The placenta was segmented from 2,393 first trimester 3D-US volumes using a semiautomated technique. This was quality controlled by three operators to produce the "ground-truth" data set. A fully convolutional neural network (OxNNet) was trained using this ground-truth data set to automatically segment the placenta. OxNNet delivered state-of-the-art automatic segmentation. The effect of training set size on the performance of OxNNet demonstrated the need for large data sets. The clinical utility of placental volume was tested by looking at predictions of small-for-gestational-age babies at term. The receiver-operating characteristics curves demonstrated almost identical results between OxNNet and the ground-truth). Our results demonstrated good similarity to the ground-truth and almost identical clinical results for the prediction of SGA.

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

Brun, E., E-mail: emmanuel.brun@esrf.fr; Grandl, S.; Sztrókay-Gaul, A.

Purpose: Phase contrast computed tomography has emerged as an imaging method, which is able to outperform present day clinical mammography in breast tumor visualization while maintaining an equivalent average dose. To this day, no segmentation technique takes into account the specificity of the phase contrast signal. In this study, the authors propose a new mathematical framework for human-guided breast tumor segmentation. This method has been applied to high-resolution images of excised human organs, each of several gigabytes. Methods: The authors present a segmentation procedure based on the viscous watershed transform and demonstrate the efficacy of this method on analyzer basedmore » phase contrast images. The segmentation of tumors inside two full human breasts is then shown as an example of this procedure’s possible applications. Results: A correct and precise identification of the tumor boundaries was obtained and confirmed by manual contouring performed independently by four experienced radiologists. Conclusions: The authors demonstrate that applying the watershed viscous transform allows them to perform the segmentation of tumors in high-resolution x-ray analyzer based phase contrast breast computed tomography images. Combining the additional information provided by the segmentation procedure with the already high definition of morphological details and tissue boundaries offered by phase contrast imaging techniques, will represent a valuable multistep procedure to be used in future medical diagnostic applications.« less

Search for a massive resonance decaying into a Higgs boson and a W or Z boson in hadronic final states in proton-proton collisions at √s = 8 TeV

DOE PAGES

Khachatryan, Vardan

2015-06-05

A search for a massive resonance decaying into a standard-model-like Higgs boson (H) and a W or Z boson is reported. The analysis is performed on a data sample corresponding to an integrated luminosity of 19.7 fb –1, collected in proton-proton collisions at a centre-of-mass energy of 8 TeV with the CMS detector at the LHC. Signal events, in which the decay products of Higgs, W, or Z bosons at high Lorentz boost are contained within single reconstructed jets, are identified using jet substructure techniques, including the tagging of b hadrons. This is the first search for heavy resonances decayingmore » in HW or HZ resulting in an all-jet final state, as well as the first application of jet substructure techniques to identify H → WW* → 4q decays at high Lorentz boost. Furthermore, no significant signal is observed and limits are set at 95% confidence level on the production cross section of W' and Z' in a model with mass-degenerate charged and neutral spin-1 resonances.« less

Detection of chewing from piezoelectric film sensor signals using ensemble classifiers.

PubMed

Farooq, Muhammad; Sazonov, Edward

2016-08-01

Selection and use of pattern recognition algorithms is application dependent. In this work, we explored the use of several ensembles of weak classifiers to classify signals captured from a wearable sensor system to detect food intake based on chewing. Three sensor signals (Piezoelectric sensor, accelerometer, and hand to mouth gesture) were collected from 12 subjects in free-living conditions for 24 hrs. Sensor signals were divided into 10 seconds epochs and for each epoch combination of time and frequency domain features were computed. In this work, we present a comparison of three different ensemble techniques: boosting (AdaBoost), bootstrap aggregation (bagging) and stacking, each trained with 3 different weak classifiers (Decision Trees, Linear Discriminant Analysis (LDA) and Logistic Regression). Type of feature normalization used can also impact the classification results. For each ensemble method, three feature normalization techniques: (no-normalization, z-score normalization, and minmax normalization) were tested. A 12 fold cross-validation scheme was used to evaluate the performance of each model where the performance was evaluated in terms of precision, recall, and accuracy. Best results achieved here show an improvement of about 4% over our previous algorithms.

Boosting the FM-Index on the GPU: Effective Techniques to Mitigate Random Memory Access.

PubMed

Chacón, Alejandro; Marco-Sola, Santiago; Espinosa, Antonio; Ribeca, Paolo; Moure, Juan Carlos

2015-01-01

The recent advent of high-throughput sequencing machines producing big amounts of short reads has boosted the interest in efficient string searching techniques. As of today, many mainstream sequence alignment software tools rely on a special data structure, called the FM-index, which allows for fast exact searches in large genomic references. However, such searches translate into a pseudo-random memory access pattern, thus making memory access the limiting factor of all computation-efficient implementations, both on CPUs and GPUs. Here, we show that several strategies can be put in place to remove the memory bottleneck on the GPU: more compact indexes can be implemented by having more threads work cooperatively on larger memory blocks, and a k-step FM-index can be used to further reduce the number of memory accesses. The combination of those and other optimisations yields an implementation that is able to process about two Gbases of queries per second on our test platform, being about 8 × faster than a comparable multi-core CPU version, and about 3 × to 5 × faster than the FM-index implementation on the GPU provided by the recently announced Nvidia NVBIO bioinformatics library.

Rapid solution casting under vacuum of very thick sheets of a segmented polyurethane elastomer

NASA Technical Reports Server (NTRS)

Cuddihy, E. F.; Moacanin, J.

1981-01-01

A technique has been developed for rapidly casting from solution under vacuum smooth, bubble-free, clear-white and uniformly thick (about 0.20 cm) sheets of a segmented polyurethane elastomer. The casting is carried out from dimethylformamide solutions inside temperature-controlled air-circulated ovens in order to minimize the establishment of thermal gradients throughout the casting solution. The technique produces quality sheets in 9 days, compared with 40-45 days for an inferior film produced in open pans.

Power spectral ensity of markov texture fields

NASA Technical Reports Server (NTRS)

Shanmugan, K. S.; Holtzman, J. C.

1984-01-01

Texture is an important image characteristic. A variety of spatial domain techniques were proposed for extracting and utilizing textural features for segmenting and classifying images. for the most part, these spatial domain techniques are ad hos in nature. A markov random field model for image texture is discussed. A frequency domain description of image texture is derived in terms of the power spectral density. This model is used for designing optimum frequency domain filters for enhancing, restoring and segmenting images based on their textural properties.

Increasing Enrollment through Benefit Segmentation.

ERIC Educational Resources Information Center

Goodnow, Betty

1982-01-01

The applicability of benefit segmentation, a market research technique which groups people according to benefits expected from a program offering, was tested at the College of DuPage. Preferences and demographic characteristics were analyzed and program improvements adopted, increasing enrollment by 20 percent. (Author/SK)

[Evaluation of Image Quality of Readout Segmented EPI with Readout Partial Fourier Technique].

PubMed

Yoshimura, Yuuki; Suzuki, Daisuke; Miyahara, Kanae

Readout segmented EPI (readout segmentation of long variable echo-trains: RESOLVE) segmented k-space in the readout direction. By using the partial Fourier method in the readout direction, the imaging time was shortened. However, the influence on image quality due to insufficient data sampling is concerned. The setting of the partial Fourier method in the readout direction in each segment was changed. Then, we examined signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and distortion ratio for changes in image quality due to differences in data sampling. As the number of sampling segments decreased, SNR and CNR showed a low value. In addition, the distortion ratio did not change. The image quality of minimum sampling segments is greatly different from full data sampling, and caution is required when using it.

Flexible methods for segmentation evaluation: results from CT-based luggage screening.

PubMed

Karimi, Seemeen; Jiang, Xiaoqian; Cosman, Pamela; Martz, Harry

2014-01-01

Imaging systems used in aviation security include segmentation algorithms in an automatic threat recognition pipeline. The segmentation algorithms evolve in response to emerging threats and changing performance requirements. Analysis of segmentation algorithms' behavior, including the nature of errors and feature recovery, facilitates their development. However, evaluation methods from the literature provide limited characterization of the segmentation algorithms. To develop segmentation evaluation methods that measure systematic errors such as oversegmentation and undersegmentation, outliers, and overall errors. The methods must measure feature recovery and allow us to prioritize segments. We developed two complementary evaluation methods using statistical techniques and information theory. We also created a semi-automatic method to define ground truth from 3D images. We applied our methods to evaluate five segmentation algorithms developed for CT luggage screening. We validated our methods with synthetic problems and an observer evaluation. Both methods selected the same best segmentation algorithm. Human evaluation confirmed the findings. The measurement of systematic errors and prioritization helped in understanding the behavior of each segmentation algorithm. Our evaluation methods allow us to measure and explain the accuracy of segmentation algorithms.

The SRS railgun

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

Parker, J.V.

1989-01-01

A Segmented Rail Surface (SRS) structure is described that eliminates restrike arcs by progressively disconnecting segments of the rail surface after the plasma armature has passed. This technique has been demonstrated using the Los Alamos MIDI-2 railgun. Restrike was eliminated in a plasma armature acceleration experiment using metal-foil fuses as opening switches. A plasma velocity increase from 11 to 16 km/s was demonstrated using the SRS technique to eliminate the viscous drag losses associated with the restrike plasma. This technique appears to be a practical option for a laboratory launcher at present and for future multi-shot launchers if appropriate switchesmore » can be developed.« less