Parallel processing for digital picture comparison

NASA Technical Reports Server (NTRS)

Cheng, H. D.; Kou, L. T.

1987-01-01

In picture processing an important problem is to identify two digital pictures of the same scene taken under different lighting conditions. This kind of problem can be found in remote sensing, satellite signal processing and the related areas. The identification can be done by transforming the gray levels so that the gray level histograms of the two pictures are closely matched. The transformation problem can be solved by using the packing method. Researchers propose a VLSI architecture consisting of m x n processing elements with extensive parallel and pipelining computation capabilities to speed up the transformation with the time complexity 0(max(m,n)), where m and n are the numbers of the gray levels of the input picture and the reference picture respectively. If using uniprocessor and a dynamic programming algorithm, the time complexity will be 0(m(3)xn). The algorithm partition problem, as an important issue in VLSI design, is discussed. Verification of the proposed architecture is also given.

Cloud field classification based upon high spatial resolution textural features. II - Simplified vector approaches

NASA Technical Reports Server (NTRS)

Chen, D. W.; Sengupta, S. K.; Welch, R. M.

1989-01-01

This paper compares the results of cloud-field classification derived from two simplified vector approaches, the Sum and Difference Histogram (SADH) and the Gray Level Difference Vector (GLDV), with the results produced by the Gray Level Cooccurrence Matrix (GLCM) approach described by Welch et al. (1988). It is shown that the SADH method produces accuracies equivalent to those obtained using the GLCM method, while the GLDV method fails to resolve error clusters. Compared to the GLCM method, the SADH method leads to a 31 percent saving in run time and a 50 percent saving in storage requirements, while the GLVD approach leads to a 40 percent saving in run time and an 87 percent saving in storage requirements.

Modeling Early Postnatal Brain Growth and Development with CT: Changes in the Brain Radiodensity Histogram from Birth to 2 Years.

PubMed

Cauley, K A; Hu, Y; Och, J; Yorks, P J; Fielden, S W

2018-04-01

The majority of brain growth and development occur in the first 2 years of life. This study investigated these changes by analysis of the brain radiodensity histogram of head CT scans from the clinical population, 0-2 years of age. One hundred twenty consecutive head CTs with normal findings meeting the inclusion criteria from children from birth to 2 years were retrospectively identified from 3 different CT scan platforms. Histogram analysis was performed on brain-extracted images, and histogram mean, mode, full width at half maximum, skewness, kurtosis, and SD were correlated with subject age. The effects of scan platform were investigated. Normative curves were fitted by polynomial regression analysis. Average total brain volume was 360 cm 3 at birth, 948 cm 3 at 1 year, and 1072 cm 3 at 2 years. Total brain tissue density showed an 11% increase in mean density at 1 year and 19% at 2 years. Brain radiodensity histogram skewness was positive at birth, declining logarithmically in the first 200 days of life. The histogram kurtosis also decreased in the first 200 days to approach a normal distribution. Direct segmentation of CT images showed that changes in brain radiodensity histogram skewness correlated with, and can be explained by, a relative increase in gray matter volume and an increase in gray and white matter tissue density that occurs during this period of brain maturation. Normative metrics of the brain radiodensity histogram derived from routine clinical head CT images can be used to develop a model of normal brain development. © 2018 by American Journal of Neuroradiology.

An extensive analysis of various texture feature extractors to detect Diabetes Mellitus using facial specific regions.

PubMed

Shu, Ting; Zhang, Bob; Yan Tang, Yuan

2017-04-01

Researchers have recently discovered that Diabetes Mellitus can be detected through non-invasive computerized method. However, the focus has been on facial block color features. In this paper, we extensively study the effects of texture features extracted from facial specific regions at detecting Diabetes Mellitus using eight texture extractors. The eight methods are from four texture feature families: (1) statistical texture feature family: Image Gray-scale Histogram, Gray-level Co-occurance Matrix, and Local Binary Pattern, (2) structural texture feature family: Voronoi Tessellation, (3) signal processing based texture feature family: Gaussian, Steerable, and Gabor filters, and (4) model based texture feature family: Markov Random Field. In order to determine the most appropriate extractor with optimal parameter(s), various parameter(s) of each extractor are experimented. For each extractor, the same dataset (284 Diabetes Mellitus and 231 Healthy samples), classifiers (k-Nearest Neighbors and Support Vector Machines), and validation method (10-fold cross validation) are used. According to the experiments, the first and third families achieved a better outcome at detecting Diabetes Mellitus than the other two. The best texture feature extractor for Diabetes Mellitus detection is the Image Gray-scale Histogram with bin number=256, obtaining an accuracy of 99.02%, a sensitivity of 99.64%, and a specificity of 98.26% by using SVM. Copyright © 2017 Elsevier Ltd. All rights reserved.

Structure Size Enhanced Histogram

NASA Astrophysics Data System (ADS)

Wesarg, Stefan; Kirschner, Matthias

Direct volume visualization requires the definition of transfer functions (TFs) for the assignment of opacity and color. Multi-dimensional TFs are based on at least two image properties, and are specified by means of 2D histograms. In this work we propose a new type of a 2D histogram which combines gray value with information about the size of the structures. This structure size enhanced (SSE) histogram is an intuitive approach for representing anatomical features. Clinicians — the users we are focusing on — are much more familiar with selecting features by their size than by their gradient magnitude value. As a proof of concept, we employ the SSE histogram for the definition of two-dimensional TFs for the visualization of 3D MRI and CT image data.

GPU accelerated edge-region based level set evolution constrained by 2D gray-scale histogram.

PubMed

Balla-Arabé, Souleymane; Gao, Xinbo; Wang, Bin

2013-07-01

Due to its intrinsic nature which allows to easily handle complex shapes and topological changes, the level set method (LSM) has been widely used in image segmentation. Nevertheless, LSM is computationally expensive, which limits its applications in real-time systems. For this purpose, we propose a new level set algorithm, which uses simultaneously edge, region, and 2D histogram information in order to efficiently segment objects of interest in a given scene. The computational complexity of the proposed LSM is greatly reduced by using the highly parallelizable lattice Boltzmann method (LBM) with a body force to solve the level set equation (LSE). The body force is the link with image data and is defined from the proposed LSE. The proposed LSM is then implemented using an NVIDIA graphics processing units to fully take advantage of the LBM local nature. The new algorithm is effective, robust against noise, independent to the initial contour, fast, and highly parallelizable. The edge and region information enable to detect objects with and without edges, and the 2D histogram information enable the effectiveness of the method in a noisy environment. Experimental results on synthetic and real images demonstrate subjectively and objectively the performance of the proposed method.

Generalized image contrast enhancement technique based on the Heinemann contrast discrimination model

NASA Astrophysics Data System (ADS)

Liu, Hong; Nodine, Calvin F.

1996-07-01

This paper presents a generalized image contrast enhancement technique, which equalizes the perceived brightness distribution based on the Heinemann contrast discrimination model. It is based on the mathematically proven existence of a unique solution to a nonlinear equation, and is formulated with easily tunable parameters. The model uses a two-step log-log representation of luminance contrast between targets and surround in a luminous background setting. The algorithm consists of two nonlinear gray scale mapping functions that have seven parameters, two of which are adjustable Heinemann constants. Another parameter is the background gray level. The remaining four parameters are nonlinear functions of the gray-level distribution of the given image, and can be uniquely determined once the previous three are set. Tests have been carried out to demonstrate the effectiveness of the algorithm for increasing the overall contrast of radiology images. The traditional histogram equalization can be reinterpreted as an image enhancement technique based on the knowledge of human contrast perception. In fact, it is a special case of the proposed algorithm.

Correlates of mammographic density in B-mode ultrasound and real time elastography.

PubMed

Jud, Sebastian Michael; Häberle, Lothar; Fasching, Peter A; Heusinger, Katharina; Hack, Carolin; Faschingbauer, Florian; Uder, Michael; Wittenberg, Thomas; Wagner, Florian; Meier-Meitinger, Martina; Schulz-Wendtland, Rüdiger; Beckmann, Matthias W; Adamietz, Boris R

2012-07-01

The aim of our study involved the assessment of B-mode imaging and elastography with regard to their ability to predict mammographic density (MD) without X-rays. Women, who underwent routine mammography, were prospectively examined with additional B-mode ultrasound and elastography. MD was assessed quantitatively with a computer-assisted method (Madena). The B-mode and elastography images were assessed by histograms with equally sized gray-level intervals. Regression models were built and cross validated to examine the ability to predict MD. The results of this study showed that B-mode imaging and elastography were able to predict MD. B-mode seemed to give a more accurate prediction. R for B-mode image and elastography were 0.67 and 0.44, respectively. Areas in the B-mode images that correlated with mammographic dense areas were either dark gray or of intermediate gray levels. Concerning elastography only the gray levels that represent extremely stiff tissue correlated positively with MD. In conclusion, ultrasound seems to be able to predict MD. Easy and cheap utilization of regular breast ultrasound machines encourages the use of ultrasound in larger case-control studies to validate this method as a breast cancer risk predictor. Furthermore, the application of ultrasound for breast tissue characterization could enable comprehensive research concerning breast cancer risk and breast density in young and pregnant women.

Texture Feature Analysis for Different Resolution Level of Kidney Ultrasound Images

NASA Astrophysics Data System (ADS)

Kairuddin, Wan Nur Hafsha Wan; Mahmud, Wan Mahani Hafizah Wan

2017-08-01

Image feature extraction is a technique to identify the characteristic of the image. The objective of this work is to discover the texture features that best describe a tissue characteristic of a healthy kidney from ultrasound (US) image. Three ultrasound machines that have different specifications are used in order to get a different quality (different resolution) of the image. Initially, the acquired images are pre-processed to de-noise the speckle to ensure the image preserve the pixels in a region of interest (ROI) for further extraction. Gaussian Low- pass Filter is chosen as the filtering method in this work. 150 of enhanced images then are segmented by creating a foreground and background of image where the mask is created to eliminate some unwanted intensity values. Statistical based texture features method is used namely Intensity Histogram (IH), Gray-Level Co-Occurance Matrix (GLCM) and Gray-level run-length matrix (GLRLM).This method is depends on the spatial distribution of intensity values or gray levels in the kidney region. By using One-Way ANOVA in SPSS, the result indicated that three features (Contrast, Difference Variance and Inverse Difference Moment Normalized) from GLCM are not statistically significant; this concludes that these three features describe a healthy kidney characteristics regardless of the ultrasound image quality.

In vivo skin imaging for hydration and micro relief-measurement.

PubMed

Kardosova, Z; Hegyi, V

2013-01-01

We present the results of our work with device used for measurement of skin capacitance before and after application of moisturizing creams and results of experiment performed on cellulose filter papers soaked with different solvents. The measurements were performed by a device built on capacitance sensor, which provides an investigator with a capacitance image of the skin. The capacitance values are coded in a range of 256 gray levels then the skin hydration can be characterized using parameters derived from gray level histogram by specific software. The images obtained by device allow a highly precise observation of skin topography. Measuring of skin capacitance brings new, objective, reliable information about topographical, physical and chemical parameters of the skin. The study shows that there is a good correlation between the average grayscale values and skin hydration. In future works we need to complete more comparison studies, interpret the average grayscale values to skin hydration levels and use it for follow-up of dynamics of skin micro-relief and hydration changes (Fig. 6, Ref. 15).

Analysis of dose heterogeneity using a subvolume-DVH

NASA Astrophysics Data System (ADS)

Said, M.; Nilsson, P.; Ceberg, C.

2017-11-01

The dose-volume histogram (DVH) is universally used in radiation therapy for its highly efficient way of summarizing three-dimensional dose distributions. An apparent limitation that is inherent to standard histograms is the loss of spatial information, e.g. it is no longer possible to tell where low- and high-dose regions are, and whether they are connected or disjoint. Two methods for overcoming the spatial fragmentation of low- and high-dose regions are presented, both based on the gray-level size zone matrix, which is a two-dimensional histogram describing the frequencies of connected regions of similar intensities. The first approach is a quantitative metric which can be likened to a homogeneity index. The large cold spot metric (LCS) is here defined to emphasize large contiguous regions receiving too low a dose; emphasis is put on both size, and deviation from the prescribed dose. In contrast, the subvolume-DVH (sDVH) is an extension to the standard DVH and allows for a qualitative evaluation of the degree of dose heterogeneity. The information retained from the two-dimensional histogram is overlaid on top of the DVH and the two are presented simultaneously. Both methods gauge the underlying heterogeneity in ways that the DVH alone cannot, and both have their own merits—the sDVH being more intuitive and the LCS being quantitative.

Digital enhancement of computerized axial tomograms

NASA Technical Reports Server (NTRS)

Roberts, E., Jr.

1978-01-01

A systematic evaluation was conducted of certain digital image enhancement techniques performed in image space. Three types of images were used, computer generated phantoms, tomograms of a synthetic phantom, and axial tomograms of human anatomy containing images of lesions, artificially introduced into the tomograms. Several types of smoothing, sharpening, and histogram modification were explored. It was concluded that the most useful enhancement techniques are a selective smoothing of singular picture elements, combined with contrast manipulation. The most useful tool in applying these techniques is the gray-scale histogram.

Application of texture analysis method for mammogram density classification

NASA Astrophysics Data System (ADS)

Nithya, R.; Santhi, B.

2017-07-01

Mammographic density is considered a major risk factor for developing breast cancer. This paper proposes an automated approach to classify breast tissue types in digital mammogram. The main objective of the proposed Computer-Aided Diagnosis (CAD) system is to investigate various feature extraction methods and classifiers to improve the diagnostic accuracy in mammogram density classification. Texture analysis methods are used to extract the features from the mammogram. Texture features are extracted by using histogram, Gray Level Co-Occurrence Matrix (GLCM), Gray Level Run Length Matrix (GLRLM), Gray Level Difference Matrix (GLDM), Local Binary Pattern (LBP), Entropy, Discrete Wavelet Transform (DWT), Wavelet Packet Transform (WPT), Gabor transform and trace transform. These extracted features are selected using Analysis of Variance (ANOVA). The features selected by ANOVA are fed into the classifiers to characterize the mammogram into two-class (fatty/dense) and three-class (fatty/glandular/dense) breast density classification. This work has been carried out by using the mini-Mammographic Image Analysis Society (MIAS) database. Five classifiers are employed namely, Artificial Neural Network (ANN), Linear Discriminant Analysis (LDA), Naive Bayes (NB), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM). Experimental results show that ANN provides better performance than LDA, NB, KNN and SVM classifiers. The proposed methodology has achieved 97.5% accuracy for three-class and 99.37% for two-class density classification.

A psychophysical comparison of two methods for adaptive histogram equalization.

PubMed

Zimmerman, J B; Cousins, S B; Hartzell, K M; Frisse, M E; Kahn, M G

1989-05-01

Adaptive histogram equalization (AHE) is a method for adaptive contrast enhancement of digital images. It is an automatic, reproducible method for the simultaneous viewing of contrast within a digital image with a large dynamic range. Recent experiments have shown that in specific cases, there is no significant difference in the ability of AHE and linear intensity windowing to display gray-scale contrast. More recently, a variant of AHE which limits the allowed contrast enhancement of the image has been proposed. This contrast-limited adaptive histogram equalization (CLAHE) produces images in which the noise content of an image is not excessively enhanced, but in which sufficient contrast is provided for the visualization of structures within the image. Images processed with CLAHE have a more natural appearance and facilitate the comparison of different areas of an image. However, the reduced contrast enhancement of CLAHE may hinder the ability of an observer to detect the presence of some significant gray-scale contrast. In this report, a psychophysical observer experiment was performed to determine if there is a significant difference in the ability of AHE and CLAHE to depict gray-scale contrast. Observers were presented with computed tomography (CT) images of the chest processed with AHE and CLAHE. Subtle artificial lesions were introduced into some images. The observers were asked to rate their confidence regarding the presence of the lesions; this rating-scale data was analyzed using receiver operating characteristic (ROC) curve techniques. These ROC curves were compared for significant differences in the observers' performances. In this report, no difference was found in the abilities of AHE and CLAHE to depict contrast information.

LEDs as light source: examining quality of acquired images

NASA Astrophysics Data System (ADS)

Bachnak, Rafic; Funtanilla, Jeng; Hernandez, Jose

2004-05-01

Recent advances in technology have made light emitting diodes (LEDs) viable in a number of applications, including vehicle stoplights, traffic lights, machine-vision-inspection, illumination, and street signs. This paper presents the results of comparing images taken by a videoscope using two different light sources. One of the sources is the internal metal halide lamp and the other is a LED placed at the tip of the insertion tube. Images acquired using these two light sources were quantitatively compared using their histogram, intensity profile along a line segment, and edge detection. Also, images were qualitatively compared using image registration and transformation. The gray-level histogram, edge detection, image profile and image registration do not offer conclusive results. The LED light source, however, produces good images for visual inspection by an operator. The paper will present the results and discuss the usefulness and shortcomings of various comparison methods.

A threshold selection method based on edge preserving

NASA Astrophysics Data System (ADS)

Lou, Liantang; Dan, Wei; Chen, Jiaqi

2015-12-01

A method of automatic threshold selection for image segmentation is presented. An optimal threshold is selected in order to preserve edge of image perfectly in image segmentation. The shortcoming of Otsu's method based on gray-level histograms is analyzed. The edge energy function of bivariate continuous function is expressed as the line integral while the edge energy function of image is simulated by discretizing the integral. An optimal threshold method by maximizing the edge energy function is given. Several experimental results are also presented to compare with the Otsu's method.

Near-affine-invariant texture learning for lung tissue analysis using isotropic wavelet frames.

PubMed

Depeursinge, Adrien; Van de Ville, Dimitri; Platon, Alexandra; Geissbuhler, Antoine; Poletti, Pierre-Alexandre; Müller, Henning

2012-07-01

We propose near-affine-invariant texture descriptors derived from isotropic wavelet frames for the characterization of lung tissue patterns in high-resolution computed tomography (HRCT) imaging. Affine invariance is desirable to enable learning of nondeterministic textures without a priori localizations, orientations, or sizes. When combined with complementary gray-level histograms, the proposed method allows a global classification accuracy of 76.9% with balanced precision among five classes of lung tissue using a leave-one-patient-out cross validation, in accordance with clinical practice.

Digital enhancement of computerized axial tomograms

NASA Technical Reports Server (NTRS)

Roberts, E., Jr.

1978-01-01

A systematic evaluation has been conducted of certain digital image enhancement techniques performed in image space. Three types of images have been used, computer generated phantoms, tomograms of a synthetic phantom, and axial tomograms of human anatomy containing images of lesions, artificially introduced into the tomograms. Several types of smoothing, sharpening, and histogram modification have been explored. It has been concluded that the most useful enhancement techniques are a selective smoothing of singular picture elements, combined with contrast manipulation. The most useful tool in applying these techniques is the gray-scale histogram.

Assessing clutter reduction in parallel coordinates using image processing techniques

NASA Astrophysics Data System (ADS)

Alhamaydh, Heba; Alzoubi, Hussein; Almasaeid, Hisham

2018-01-01

Information visualization has appeared as an important research field for multidimensional data and correlation analysis in recent years. Parallel coordinates (PCs) are one of the popular techniques to visual high-dimensional data. A problem with the PCs technique is that it suffers from crowding, a clutter which hides important data and obfuscates the information. Earlier research has been conducted to reduce clutter without loss in data content. We introduce the use of image processing techniques as an approach for assessing the performance of clutter reduction techniques in PC. We use histogram analysis as our first measure, where the mean feature of the color histograms of the possible alternative orderings of coordinates for the PC images is calculated and compared. The second measure is the extracted contrast feature from the texture of PC images based on gray-level co-occurrence matrices. The results show that the best PC image is the one that has the minimal mean value of the color histogram feature and the maximal contrast value of the texture feature. In addition to its simplicity, the proposed assessment method has the advantage of objectively assessing alternative ordering of PC visualization.

Illumination normalization of face image based on illuminant direction estimation and improved Retinex.

PubMed

Yi, Jizheng; Mao, Xia; Chen, Lijiang; Xue, Yuli; Rovetta, Alberto; Caleanu, Catalin-Daniel

2015-01-01

Illumination normalization of face image for face recognition and facial expression recognition is one of the most frequent and difficult problems in image processing. In order to obtain a face image with normal illumination, our method firstly divides the input face image into sixteen local regions and calculates the edge level percentage in each of them. Secondly, three local regions, which meet the requirements of lower complexity and larger average gray value, are selected to calculate the final illuminant direction according to the error function between the measured intensity and the calculated intensity, and the constraint function for an infinite light source model. After knowing the final illuminant direction of the input face image, the Retinex algorithm is improved from two aspects: (1) we optimize the surround function; (2) we intercept the values in both ends of histogram of face image, determine the range of gray levels, and stretch the range of gray levels into the dynamic range of display device. Finally, we achieve illumination normalization and get the final face image. Unlike previous illumination normalization approaches, the method proposed in this paper does not require any training step or any knowledge of 3D face and reflective surface model. The experimental results using extended Yale face database B and CMU-PIE show that our method achieves better normalization effect comparing with the existing techniques.

Patient dose, gray level and exposure index with a computed radiography system

NASA Astrophysics Data System (ADS)

Silva, T. R.; Yoshimura, E. M.

2014-02-01

Computed radiography (CR) is gradually replacing conventional screen-film system in Brazil. To assess image quality, manufactures provide the calculation of an exposure index through the acquisition software of the CR system. The objective of this study is to verify if the CR image can be used as an evaluator of patient absorbed dose too, through a relationship between the entrance skin dose and the exposure index or the gray level values obtained in the image. The CR system used for this study (Agfa model 30-X with NX acquisition software) calculates an exposure index called Log of the Median (lgM), related to the absorbed dose to the IP. The lgM value depends on the average gray level (called Scan Average Level (SAL)) of the segmented pixel value histogram of the whole image. A Rando male phantom was used to simulate a human body (chest and head), and was irradiated with an X-ray equipment, using usual radiologic techniques for chest exams. Thermoluminescent dosimeters (LiF, TLD100) were used to evaluate entrance skin dose and exit dose. The results showed a logarithm relation between entrance dose and SAL in the image center, regardless of the beam filtration. The exposure index varies linearly with the entrance dose, but the angular coefficient is beam quality dependent. We conclude that, with an adequate calibration, the CR system can be used to evaluate the patient absorbed dose.

Automatic detection method for mura defects on display film surface using modified Weber's law

NASA Astrophysics Data System (ADS)

Kim, Myung-Muk; Lee, Seung-Ho

2014-07-01

We propose a method that automatically detects mura defects on display film surfaces using a modified version of Weber's law. The proposed method detects mura defects regardless of their properties and shapes by identifying regions perceived by human vision as mura using the brightness of pixel and image distribution ratio of mura in an image histogram. The proposed detection method comprises five stages. In the first stage, the display film surface image is acquired and a gray-level shift performed. In the second and third stages, the image histogram is acquired and analyzed, respectively. In the fourth stage, the mura range is acquired. This is followed by postprocessing in the fifth stage. Evaluations of the proposed method conducted using 200 display film mura image samples indicate a maximum detection rate of ˜95.5%. Further, the results of application of the Semu index for luminance mura in flat panel display (FPD) image quality inspection indicate that the proposed method is more reliable than a popular conventional method.

Predicting neo-adjuvant chemotherapy response and progression-free survival of locally advanced breast cancer using textural features of intratumoral heterogeneity on F-18 FDG PET/CT and diffusion-weighted MR imaging.

PubMed

Yoon, Hai-Jeon; Kim, Yemi; Chung, Jin; Kim, Bom Sahn

2018-03-30

Predicting response to neo-adjuvant chemotherapy (NAC) and survival in locally advanced breast cancer (LABC) is important. This study investigated the prognostic value of tumor heterogeneity evaluated with textural analysis through F-18 fluorodeoxyglucose (FDG) positron emission tomography (PET) and diffusion-weighted imaging (DWI). We enrolled 83 patients with LABC who had completed NAC and curative surgery. Tumor texture indices from pretreatment FDG PET and DWI were extracted from histogram analysis and 7 different parent matrices: co-occurrence matrix, the voxel-alignment matrix, neighborhood intensity difference matrix, intensity size-zone matrix (ISZM), normalized gray-level co-occurrence matrix (NGLCM), neighboring gray-level dependence matrix (NGLDM), and texture spectrum matrix. The predictive values of textural features were tested regarding both pathologic NAC response and progression-free survival. Among 83 patients, 46 were pathologic responders, while 37 were nonresponders. The PET texture indices from 7 parent matrices, DWI texture indices from histogram, and 1 parent matrix (NGLCM) showed significant differences according to NAC response. On multivariable analysis, number nonuniformity of PET extracted from the NGLDM was an independent predictor of pathologic response (P = .009). During a median follow-up period of 17.3 months, 14 patients experienced recurrence. High-intensity zone emphasis (HIZE) and high-intensity short-zone emphasis (HISZE) from PET extracted from ISZM were significant textural predictors (P = .011 and P = .033). On Cox regression analysis, only HIZE was a significant predictor of recurrence (P = .027), while HISZE showed borderline significance (P = .107). Tumor texture indices are useful for NAC response prediction in LABC. Moreover, PET texture indices can help to predict disease recurrence. © 2018 Wiley Periodicals, Inc.

SU-F-R-36: Validating Quantitative Radiomic Texture Features for Oncologic PET: A Digital Phantom Study

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

Yang, F; Yang, Y; Young, L

Purpose: Radiomic texture features derived from the oncologic PET have recently been brought under intense investigation within the context of patient stratification and treatment outcome prediction in a variety of cancer types; however, their validity has not yet been examined. This work is aimed to validate radiomic PET texture metrics through the use of realistic simulations in the ground truth setting. Methods: Simulation of FDG-PET was conducted by applying the Zubal phantom as an attenuation map to the SimSET software package that employs Monte Carlo techniques to model the physical process of emission imaging. A total of 15 irregularly-shaped lesionsmore » featuring heterogeneous activity distribution were simulated. For each simulated lesion, 28 texture features in relation to the intensity histograms (GLIH), grey-level co-occurrence matrices (GLCOM), neighborhood difference matrices (GLNDM), and zone size matrices (GLZSM) were evaluated and compared with their respective values extracted from the ground truth activity map. Results: In reference to the values from the ground truth images, texture parameters appearing on the simulated data varied with a range of 0.73–3026.2% for GLIH-based, 0.02–100.1% for GLCOM-based, 1.11–173.8% for GLNDM-based, and 0.35–66.3% for GLZSM-based. For majority of the examined texture metrics (16/28), their values on the simulated data differed significantly from those from the ground truth images (P-value ranges from <0.0001 to 0.04). Features not exhibiting significant difference comprised of GLIH-based standard deviation, GLCO-based energy and entropy, GLND-based coarseness and contrast, and GLZS-based low gray-level zone emphasis, high gray-level zone emphasis, short zone low gray-level emphasis, long zone low gray-level emphasis, long zone high gray-level emphasis, and zone size nonuniformity. Conclusion: The extent to which PET imaging disturbs texture appearance is feature-dependent and could be substantial. It is thus advised that use of PET texture parameters for predictive and prognostic measurements in oncologic setting awaits further systematic and critical evaluation.« less

A scalable method to improve gray matter segmentation at ultra high field MRI.

PubMed

Gulban, Omer Faruk; Schneider, Marian; Marquardt, Ingo; Haast, Roy A M; De Martino, Federico

2018-01-01

High-resolution (functional) magnetic resonance imaging (MRI) at ultra high magnetic fields (7 Tesla and above) enables researchers to study how anatomical and functional properties change within the cortical ribbon, along surfaces and across cortical depths. These studies require an accurate delineation of the gray matter ribbon, which often suffers from inclusion of blood vessels, dura mater and other non-brain tissue. Residual segmentation errors are commonly corrected by browsing the data slice-by-slice and manually changing labels. This task becomes increasingly laborious and prone to error at higher resolutions since both work and error scale with the number of voxels. Here we show that many mislabeled, non-brain voxels can be corrected more efficiently and semi-automatically by representing three-dimensional anatomical images using two-dimensional histograms. We propose both a uni-modal (based on first spatial derivative) and multi-modal (based on compositional data analysis) approach to this representation and quantify the benefits in 7 Tesla MRI data of nine volunteers. We present an openly accessible Python implementation of these approaches and demonstrate that editing cortical segmentations using two-dimensional histogram representations as an additional post-processing step aids existing algorithms and yields improved gray matter borders. By making our data and corresponding expert (ground truth) segmentations openly available, we facilitate future efforts to develop and test segmentation algorithms on this challenging type of data.

A scalable method to improve gray matter segmentation at ultra high field MRI

PubMed Central

De Martino, Federico

2018-01-01

High-resolution (functional) magnetic resonance imaging (MRI) at ultra high magnetic fields (7 Tesla and above) enables researchers to study how anatomical and functional properties change within the cortical ribbon, along surfaces and across cortical depths. These studies require an accurate delineation of the gray matter ribbon, which often suffers from inclusion of blood vessels, dura mater and other non-brain tissue. Residual segmentation errors are commonly corrected by browsing the data slice-by-slice and manually changing labels. This task becomes increasingly laborious and prone to error at higher resolutions since both work and error scale with the number of voxels. Here we show that many mislabeled, non-brain voxels can be corrected more efficiently and semi-automatically by representing three-dimensional anatomical images using two-dimensional histograms. We propose both a uni-modal (based on first spatial derivative) and multi-modal (based on compositional data analysis) approach to this representation and quantify the benefits in 7 Tesla MRI data of nine volunteers. We present an openly accessible Python implementation of these approaches and demonstrate that editing cortical segmentations using two-dimensional histogram representations as an additional post-processing step aids existing algorithms and yields improved gray matter borders. By making our data and corresponding expert (ground truth) segmentations openly available, we facilitate future efforts to develop and test segmentation algorithms on this challenging type of data. PMID:29874295

Reproducibility of F18-FDG PET radiomic features for different cervical tumor segmentation methods, gray-level discretization, and reconstruction algorithms.

PubMed

Altazi, Baderaldeen A; Zhang, Geoffrey G; Fernandez, Daniel C; Montejo, Michael E; Hunt, Dylan; Werner, Joan; Biagioli, Matthew C; Moros, Eduardo G

2017-11-01

Site-specific investigations of the role of radiomics in cancer diagnosis and therapy are emerging. We evaluated the reproducibility of radiomic features extracted from 18 Flourine-fluorodeoxyglucose ( 18 F-FDG) PET images for three parameters: manual versus computer-aided segmentation methods, gray-level discretization, and PET image reconstruction algorithms. Our cohort consisted of pretreatment PET/CT scans from 88 cervical cancer patients. Two board-certified radiation oncologists manually segmented the metabolic tumor volume (MTV 1 and MTV 2 ) for each patient. For comparison, we used a graphical-based method to generate semiautomated segmented volumes (GBSV). To address any perturbations in radiomic feature values, we down-sampled the tumor volumes into three gray-levels: 32, 64, and 128 from the original gray-level of 256. Finally, we analyzed the effect on radiomic features on PET images of eight patients due to four PET 3D-reconstruction algorithms: maximum likelihood-ordered subset expectation maximization (OSEM) iterative reconstruction (IR) method, fourier rebinning-ML-OSEM (FOREIR), FORE-filtered back projection (FOREFBP), and 3D-Reprojection (3DRP) analytical method. We extracted 79 features from all segmentation method, gray-levels of down-sampled volumes, and PET reconstruction algorithms. The features were extracted using gray-level co-occurrence matrices (GLCM), gray-level size zone matrices (GLSZM), gray-level run-length matrices (GLRLM), neighborhood gray-tone difference matrices (NGTDM), shape-based features (SF), and intensity histogram features (IHF). We computed the Dice coefficient between each MTV and GBSV to measure segmentation accuracy. Coefficient values close to one indicate high agreement, and values close to zero indicate low agreement. We evaluated the effect on radiomic features by calculating the mean percentage differences (d¯) between feature values measured from each pair of parameter elements (i.e. segmentation methods: MTV 1 -MTV 2 , MTV 1 -GBSV, MTV 2 -GBSV; gray-levels: 64-32, 64-128, and 64-256; reconstruction algorithms: OSEM-FORE-OSEM, OSEM-FOREFBP, and OSEM-3DRP). We used |d¯| as a measure of radiomic feature reproducibility level, where any feature scored |d¯| ±SD ≤ |25|% ± 35% was considered reproducible. We used Bland-Altman analysis to evaluate the mean, standard deviation (SD), and upper/lower reproducibility limits (U/LRL) for radiomic features in response to variation in each testing parameter. Furthermore, we proposed U/LRL as a method to classify the level of reproducibility: High- ±1% ≤ U/LRL ≤ ±30%; Intermediate- ±30% < U/LRL ≤ ±45%; Low- ±45 < U/LRL ≤ ±50%. We considered any feature below the low level as nonreproducible (NR). Finally, we calculated the interclass correlation coefficient (ICC) to evaluate the reliability of radiomic feature measurements for each parameter. The segmented volumes of 65 patients (81.3%) scored Dice coefficient >0.75 for all three volumes. The result outcomes revealed a tendency of higher radiomic feature reproducibility among segmentation pair MTV 1 -GBSV than MTV 2 -GBSV, gray-level pairs of 64-32 and 64-128 than 64-256, and reconstruction algorithm pairs of OSEM-FOREIR and OSEM-FOREFBP than OSEM-3DRP. Although the choice of cervical tumor segmentation method, gray-level value, and reconstruction algorithm may affect radiomic features, some features were characterized by high reproducibility through all testing parameters. The number of radiomic features that showed insensitivity to variations in segmentation methods, gray-level discretization, and reconstruction algorithms was 10 (13%), 4 (5%), and 1 (1%), respectively. These results suggest that a careful analysis of the effects of these parameters is essential prior to any radiomics clinical application. © 2017 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.

Adaptive histogram equalization in digital radiography of destructive skeletal lesions.

PubMed

Braunstein, E M; Capek, P; Buckwalter, K; Bland, P; Meyer, C R

1988-03-01

Adaptive histogram equalization, an image-processing technique that distributes pixel values of an image uniformly throughout the gray scale, was applied to 28 plain radiographs of bone lesions, after they had been digitized. The non-equalized and equalized digital images were compared by two skeletal radiologists with respect to lesion margins, internal matrix, soft-tissue mass, cortical breakthrough, and periosteal reaction. Receiver operating characteristic (ROC) curves were constructed on the basis of the responses. Equalized images were superior to nonequalized images in determination of cortical breakthrough and presence or absence of periosteal reaction. ROC analysis showed no significant difference in determination of margins, matrix, or soft-tissue masses.

Graphical user interface to optimize image contrast parameters used in object segmentation - biomed 2009.

PubMed

Anderson, Jeffrey R; Barrett, Steven F

2009-01-01

Image segmentation is the process of isolating distinct objects within an image. Computer algorithms have been developed to aid in the process of object segmentation, but a completely autonomous segmentation algorithm has yet to be developed [1]. This is because computers do not have the capability to understand images and recognize complex objects within the image. However, computer segmentation methods [2], requiring user input, have been developed to quickly segment objects in serial sectioned images, such as magnetic resonance images (MRI) and confocal laser scanning microscope (CLSM) images. In these cases, the segmentation process becomes a powerful tool in visualizing the 3D nature of an object. The user input is an important part of improving the performance of many segmentation methods. A double threshold segmentation method has been investigated [3] to separate objects in gray scaled images, where the gray level of the object is among the gray levels of the background. In order to best determine the threshold values for this segmentation method the image must be manipulated for optimal contrast. The same is true of other segmentation and edge detection methods as well. Typically, the better the image contrast, the better the segmentation results. This paper describes a graphical user interface (GUI) that allows the user to easily change image contrast parameters that will optimize the performance of subsequent object segmentation. This approach makes use of the fact that the human brain is extremely effective in object recognition and understanding. The GUI provides the user with the ability to define the gray scale range of the object of interest. These lower and upper bounds of this range are used in a histogram stretching process to improve image contrast. Also, the user can interactively modify the gamma correction factor that provides a non-linear distribution of gray scale values, while observing the corresponding changes to the image. This interactive approach gives the user the power to make optimal choices in the contrast enhancement parameters.

Finger vein recognition with personalized feature selection.

PubMed

Xi, Xiaoming; Yang, Gongping; Yin, Yilong; Meng, Xianjing

2013-08-22

Finger veins are a promising biometric pattern for personalized identification in terms of their advantages over existing biometrics. Based on the spatial pyramid representation and the combination of more effective information such as gray, texture and shape, this paper proposes a simple but powerful feature, called Pyramid Histograms of Gray, Texture and Orientation Gradients (PHGTOG). For a finger vein image, PHGTOG can reflect the global spatial layout and local details of gray, texture and shape. To further improve the recognition performance and reduce the computational complexity, we select a personalized subset of features from PHGTOG for each subject by using the sparse weight vector, which is trained by using LASSO and called PFS-PHGTOG. We conduct extensive experiments to demonstrate the promise of the PHGTOG and PFS-PHGTOG, experimental results on our databases show that PHGTOG outperforms the other existing features. Moreover, PFS-PHGTOG can further boost the performance in comparison with PHGTOG.

Finger Vein Recognition with Personalized Feature Selection

PubMed Central

Xi, Xiaoming; Yang, Gongping; Yin, Yilong; Meng, Xianjing

2013-01-01

Finger veins are a promising biometric pattern for personalized identification in terms of their advantages over existing biometrics. Based on the spatial pyramid representation and the combination of more effective information such as gray, texture and shape, this paper proposes a simple but powerful feature, called Pyramid Histograms of Gray, Texture and Orientation Gradients (PHGTOG). For a finger vein image, PHGTOG can reflect the global spatial layout and local details of gray, texture and shape. To further improve the recognition performance and reduce the computational complexity, we select a personalized subset of features from PHGTOG for each subject by using the sparse weight vector, which is trained by using LASSO and called PFS-PHGTOG. We conduct extensive experiments to demonstrate the promise of the PHGTOG and PFS-PHGTOG, experimental results on our databases show that PHGTOG outperforms the other existing features. Moreover, PFS-PHGTOG can further boost the performance in comparison with PHGTOG. PMID:23974154

Brain segmentation and the generation of cortical surfaces

NASA Technical Reports Server (NTRS)

Joshi, M.; Cui, J.; Doolittle, K.; Joshi, S.; Van Essen, D.; Wang, L.; Miller, M. I.

1999-01-01

This paper describes methods for white matter segmentation in brain images and the generation of cortical surfaces from the segmentations. We have developed a system that allows a user to start with a brain volume, obtained by modalities such as MRI or cryosection, and constructs a complete digital representation of the cortical surface. The methodology consists of three basic components: local parametric modeling and Bayesian segmentation; surface generation and local quadratic coordinate fitting; and surface editing. Segmentations are computed by parametrically fitting known density functions to the histogram of the image using the expectation maximization algorithm [DLR77]. The parametric fits are obtained locally rather than globally over the whole volume to overcome local variations in gray levels. To represent the boundary of the gray and white matter we use triangulated meshes generated using isosurface generation algorithms [GH95]. A complete system of local parametric quadratic charts [JWM+95] is superimposed on the triangulated graph to facilitate smoothing and geodesic curve tracking. Algorithms for surface editing include extraction of the largest closed surface. Results for several macaque brains are presented comparing automated and hand surface generation. Copyright 1999 Academic Press.

SU-F-R-20: Image Texture Features Correlate with Time to Local Failure in Lung SBRT Patients

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

Andrews, M; Abazeed, M; Woody, N

Purpose: To explore possible correlation between CT image-based texture and histogram features and time-to-local-failure in early stage non-small cell lung cancer (NSCLC) patients treated with stereotactic body radiotherapy (SBRT).Methods and Materials: From an IRB-approved lung SBRT registry for patients treated between 2009–2013 we selected 48 (20 male, 28 female) patients with local failure. Median patient age was 72.3±10.3 years. Mean time to local failure was 15 ± 7.1 months. Physician-contoured gross tumor volumes (GTV) on the planning CT images were processed and 3D gray-level co-occurrence matrix (GLCM) based texture and histogram features were calculated in Matlab. Data were exported tomore » R and a multiple linear regression model was used to examine the relationship between texture features and time-to-local-failure. Results: Multiple linear regression revealed that entropy (p=0.0233, multiple R2=0.60) from GLCM-based texture analysis and the standard deviation (p=0.0194, multiple R2=0.60) from the histogram-based features were statistically significantly correlated with the time-to-local-failure. Conclusion: Image-based texture analysis can be used to predict certain aspects of treatment outcomes of NSCLC patients treated with SBRT. We found entropy and standard deviation calculated for the GTV on the CT images displayed a statistically significant correlation with and time-to-local-failure in lung SBRT patients.« less

Evaluation of thresholding techniques for segmenting scaffold images in tissue engineering

NASA Astrophysics Data System (ADS)

Rajagopalan, Srinivasan; Yaszemski, Michael J.; Robb, Richard A.

2004-05-01

Tissue engineering attempts to address the ever widening gap between the demand and supply of organ and tissue transplants using natural and biomimetic scaffolds. The regeneration of specific tissues aided by synthetic materials is dependent on the structural and morphometric properties of the scaffold. These properties can be derived non-destructively using quantitative analysis of high resolution microCT scans of scaffolds. Thresholding of the scanned images into polymeric and porous phase is central to the outcome of the subsequent structural and morphometric analysis. Visual thresholding of scaffolds produced using stochastic processes is inaccurate. Depending on the algorithmic assumptions made, automatic thresholding might also be inaccurate. Hence there is a need to analyze the performance of different techniques and propose alternate ones, if needed. This paper provides a quantitative comparison of different thresholding techniques for segmenting scaffold images. The thresholding algorithms examined include those that exploit spatial information, locally adaptive characteristics, histogram entropy information, histogram shape information, and clustering of gray-level information. The performance of different techniques was evaluated using established criteria, including misclassification error, edge mismatch, relative foreground error, and region non-uniformity. Algorithms that exploit local image characteristics seem to perform much better than those using global information.

Wood texture classification by fuzzy neural networks

NASA Astrophysics Data System (ADS)

Gonzaga, Adilson; de Franca, Celso A.; Frere, Annie F.

1999-03-01

The majority of scientific papers focusing on wood classification for pencil manufacturing take into account defects and visual appearance. Traditional methodologies are base don texture analysis by co-occurrence matrix, by image modeling, or by tonal measures over the plate surface. In this work, we propose to classify plates of wood without biological defects like insect holes, nodes, and cracks, by analyzing their texture. By this methodology we divide the plate image in several rectangular windows or local areas and reduce the number of gray levels. From each local area, we compute the histogram of difference sand extract texture features, given them as input to a Local Neuro-Fuzzy Network. Those features are from the histogram of differences instead of the image pixels due to their better performance and illumination independence. Among several features like media, contrast, second moment, entropy, and IDN, the last three ones have showed better results for network training. Each LNN output is taken as input to a Partial Neuro-Fuzzy Network (PNFN) classifying a pencil region on the plate. At last, the outputs from the PNFN are taken as input to a Global Fuzzy Logic doing the plate classification. Each pencil classification within the plate is done taking into account each quality index.

Classification of fresh and frozen-thawed pork muscles using visible and near infrared hyperspectral imaging and textural analysis.

PubMed

Pu, Hongbin; Sun, Da-Wen; Ma, Ji; Cheng, Jun-Hu

2015-01-01

The potential of visible and near infrared hyperspectral imaging was investigated as a rapid and nondestructive technique for classifying fresh and frozen-thawed meats by integrating critical spectral and image features extracted from hyperspectral images in the region of 400-1000 nm. Six feature wavelengths (400, 446, 477, 516, 592 and 686 nm) were identified using uninformative variable elimination and successive projections algorithm. Image textural features of the principal component images from hyperspectral images were obtained using histogram statistics (HS), gray level co-occurrence matrix (GLCM) and gray level-gradient co-occurrence matrix (GLGCM). By these spectral and textural features, probabilistic neural network (PNN) models for classification of fresh and frozen-thawed pork meats were established. Compared with the models using the optimum wavelengths only, optimum wavelengths with HS image features, and optimum wavelengths with GLCM image features, the model integrating optimum wavelengths with GLGCM gave the highest classification rate of 93.14% and 90.91% for calibration and validation sets, respectively. Results indicated that the classification accuracy can be improved by combining spectral features with textural features and the fusion of critical spectral and textural features had better potential than single spectral extraction in classifying fresh and frozen-thawed pork meat. Copyright © 2014 Elsevier Ltd. All rights reserved.

Comparative study of standard space and real space analysis of quantitative MR brain data.

PubMed

Aribisala, Benjamin S; He, Jiabao; Blamire, Andrew M

2011-06-01

To compare the robustness of region of interest (ROI) analysis of magnetic resonance imaging (MRI) brain data in real space with analysis in standard space and to test the hypothesis that standard space image analysis introduces more partial volume effect errors compared to analysis of the same dataset in real space. Twenty healthy adults with no history or evidence of neurological diseases were recruited; high-resolution T(1)-weighted, quantitative T(1), and B(0) field-map measurements were collected. Algorithms were implemented to perform analysis in real and standard space and used to apply a simple standard ROI template to quantitative T(1) datasets. Regional relaxation values and histograms for both gray and white matter tissues classes were then extracted and compared. Regional mean T(1) values for both gray and white matter were significantly lower using real space compared to standard space analysis. Additionally, regional T(1) histograms were more compact in real space, with smaller right-sided tails indicating lower partial volume errors compared to standard space analysis. Standard space analysis of quantitative MRI brain data introduces more partial volume effect errors biasing the analysis of quantitative data compared to analysis of the same dataset in real space. Copyright © 2011 Wiley-Liss, Inc.

Contrast Enhancement Algorithm Based on Gap Adjustment for Histogram Equalization

PubMed Central

Chiu, Chung-Cheng; Ting, Chih-Chung

2016-01-01

Image enhancement methods have been widely used to improve the visual effects of images. Owing to its simplicity and effectiveness histogram equalization (HE) is one of the methods used for enhancing image contrast. However, HE may result in over-enhancement and feature loss problems that lead to unnatural look and loss of details in the processed images. Researchers have proposed various HE-based methods to solve the over-enhancement problem; however, they have largely ignored the feature loss problem. Therefore, a contrast enhancement algorithm based on gap adjustment for histogram equalization (CegaHE) is proposed. It refers to a visual contrast enhancement algorithm based on histogram equalization (VCEA), which generates visually pleasing enhanced images, and improves the enhancement effects of VCEA. CegaHE adjusts the gaps between two gray values based on the adjustment equation, which takes the properties of human visual perception into consideration, to solve the over-enhancement problem. Besides, it also alleviates the feature loss problem and further enhances the textures in the dark regions of the images to improve the quality of the processed images for human visual perception. Experimental results demonstrate that CegaHE is a reliable method for contrast enhancement and that it significantly outperforms VCEA and other methods. PMID:27338412

Variability in CT lung-nodule quantification: Effects of dose reduction and reconstruction methods on density and texture based features.

PubMed

Lo, P; Young, S; Kim, H J; Brown, M S; McNitt-Gray, M F

2016-08-01

To investigate the effects of dose level and reconstruction method on density and texture based features computed from CT lung nodules. This study had two major components. In the first component, a uniform water phantom was scanned at three dose levels and images were reconstructed using four conventional filtered backprojection (FBP) and four iterative reconstruction (IR) methods for a total of 24 different combinations of acquisition and reconstruction conditions. In the second component, raw projection (sinogram) data were obtained for 33 lung nodules from patients scanned as a part of their clinical practice, where low dose acquisitions were simulated by adding noise to sinograms acquired at clinical dose levels (a total of four dose levels) and reconstructed using one FBP kernel and two IR kernels for a total of 12 conditions. For the water phantom, spherical regions of interest (ROIs) were created at multiple locations within the water phantom on one reference image obtained at a reference condition. For the lung nodule cases, the ROI of each nodule was contoured semiautomatically (with manual editing) from images obtained at a reference condition. All ROIs were applied to their corresponding images reconstructed at different conditions. For 17 of the nodule cases, repeat contours were performed to assess repeatability. Histogram (eight features) and gray level co-occurrence matrix (GLCM) based texture features (34 features) were computed for all ROIs. For the lung nodule cases, the reference condition was selected to be 100% of clinical dose with FBP reconstruction using the B45f kernel; feature values calculated from other conditions were compared to this reference condition. A measure was introduced, which the authors refer to as Q, to assess the stability of features across different conditions, which is defined as the ratio of reproducibility (across conditions) to repeatability (across repeat contours) of each feature. The water phantom results demonstrated substantial variability among feature values calculated across conditions, with the exception of histogram mean. Features calculated from lung nodules demonstrated similar results with histogram mean as the most robust feature (Q ≤ 1), having a mean and standard deviation Q of 0.37 and 0.22, respectively. Surprisingly, histogram standard deviation and variance features were also quite robust. Some GLCM features were also quite robust across conditions, namely, diff. variance, sum variance, sum average, variance, and mean. Except for histogram mean, all features have a Q of larger than one in at least one of the 3% dose level conditions. As expected, the histogram mean is the most robust feature in their study. The effects of acquisition and reconstruction conditions on GLCM features vary widely, though trending toward features involving summation of product between intensities and probabilities being more robust, barring a few exceptions. Overall, care should be taken into account for variation in density and texture features if a variety of dose and reconstruction conditions are used for the quantification of lung nodules in CT, otherwise changes in quantification results may be more reflective of changes due to acquisition and reconstruction conditions than in the nodule itself.

Visual Contrast Enhancement Algorithm Based on Histogram Equalization

PubMed Central

Ting, Chih-Chung; Wu, Bing-Fei; Chung, Meng-Liang; Chiu, Chung-Cheng; Wu, Ya-Ching

2015-01-01

Image enhancement techniques primarily improve the contrast of an image to lend it a better appearance. One of the popular enhancement methods is histogram equalization (HE) because of its simplicity and effectiveness. However, it is rarely applied to consumer electronics products because it can cause excessive contrast enhancement and feature loss problems. These problems make the images processed by HE look unnatural and introduce unwanted artifacts in them. In this study, a visual contrast enhancement algorithm (VCEA) based on HE is proposed. VCEA considers the requirements of the human visual perception in order to address the drawbacks of HE. It effectively solves the excessive contrast enhancement problem by adjusting the spaces between two adjacent gray values of the HE histogram. In addition, VCEA reduces the effects of the feature loss problem by using the obtained spaces. Furthermore, VCEA enhances the detailed textures of an image to generate an enhanced image with better visual quality. Experimental results show that images obtained by applying VCEA have higher contrast and are more suited to human visual perception than those processed by HE and other HE-based methods. PMID:26184219

a New Color Correction Method for Underwater Imaging

NASA Astrophysics Data System (ADS)

Bianco, G.; Muzzupappa, M.; Bruno, F.; Garcia, R.; Neumann, L.

2015-04-01

Recovering correct or at least realistic colors of underwater scenes is a very challenging issue for imaging techniques, since illumination conditions in a refractive and turbid medium as the sea are seriously altered. The need to correct colors of underwater images or videos is an important task required in all image-based applications like 3D imaging, navigation, documentation, etc. Many imaging enhancement methods have been proposed in literature for these purposes. The advantage of these methods is that they do not require the knowledge of the medium physical parameters while some image adjustments can be performed manually (as histogram stretching) or automatically by algorithms based on some criteria as suggested from computational color constancy methods. One of the most popular criterion is based on gray-world hypothesis, which assumes that the average of the captured image should be gray. An interesting application of this assumption is performed in the Ruderman opponent color space lαβ, used in a previous work for hue correction of images captured under colored light sources, which allows to separate the luminance component of the scene from its chromatic components. In this work, we present the first proposal for color correction of underwater images by using lαβ color space. In particular, the chromatic components are changed moving their distributions around the white point (white balancing) and histogram cutoff and stretching of the luminance component is performed to improve image contrast. The experimental results demonstrate the effectiveness of this method under gray-world assumption and supposing uniform illumination of the scene. Moreover, due to its low computational cost it is suitable for real-time implementation.

Parenchymal texture analysis in digital mammography: robust texture feature identification and equivalence across devices.

PubMed

Keller, Brad M; Oustimov, Andrew; Wang, Yan; Chen, Jinbo; Acciavatti, Raymond J; Zheng, Yuanjie; Ray, Shonket; Gee, James C; Maidment, Andrew D A; Kontos, Despina

2015-04-01

An analytical framework is presented for evaluating the equivalence of parenchymal texture features across different full-field digital mammography (FFDM) systems using a physical breast phantom. Phantom images (FOR PROCESSING) are acquired from three FFDM systems using their automated exposure control setting. A panel of texture features, including gray-level histogram, co-occurrence, run length, and structural descriptors, are extracted. To identify features that are robust across imaging systems, a series of equivalence tests are performed on the feature distributions, in which the extent of their intersystem variation is compared to their intrasystem variation via the Hodges-Lehmann test statistic. Overall, histogram and structural features tend to be most robust across all systems, and certain features, such as edge enhancement, tend to be more robust to intergenerational differences between detectors of a single vendor than to intervendor differences. Texture features extracted from larger regions of interest (i.e., [Formula: see text]) and with a larger offset length (i.e., [Formula: see text]), when applicable, also appear to be more robust across imaging systems. This framework and observations from our experiments may benefit applications utilizing mammographic texture analysis on images acquired in multivendor settings, such as in multicenter studies of computer-aided detection and breast cancer risk assessment.

Robust image region descriptor using local derivative ordinal binary pattern

NASA Astrophysics Data System (ADS)

Shang, Jun; Chen, Chuanbo; Pei, Xiaobing; Liang, Hu; Tang, He; Sarem, Mudar

2015-05-01

Binary image descriptors have received a lot of attention in recent years, since they provide numerous advantages, such as low memory footprint and efficient matching strategy. However, they utilize intermediate representations and are generally less discriminative than floating-point descriptors. We propose an image region descriptor, namely local derivative ordinal binary pattern, for object recognition and image categorization. In order to preserve more local contrast and edge information, we quantize the intensity differences between the central pixels and their neighbors of the detected local affine covariant regions in an adaptive way. These differences are then sorted and mapped into binary codes and histogrammed with a weight of the sum of the absolute value of the differences. Furthermore, the gray level of the central pixel is quantized to further improve the discriminative ability. Finally, we combine them to form a joint histogram to represent the features of the image. We observe that our descriptor preserves more local brightness and edge information than traditional binary descriptors. Also, our descriptor is robust to rotation, illumination variations, and other geometric transformations. We conduct extensive experiments on the standard ETHZ and Kentucky datasets for object recognition and PASCAL for image classification. The experimental results show that our descriptor outperforms existing state-of-the-art methods.

Design of a Borescope for Extravehicular Non-Destructive Applications

NASA Technical Reports Server (NTRS)

Bachnak, Rafic

2003-01-01

Anomalies such as corrosion, structural damage, misalignment, cracking, stress fiactures, pitting, or wear can be detected and monitored by the aid of a borescope. A borescope requires a source of light for proper operation. Today s current lighting technology market consists of incandescent lamps, fluorescent lamps and other types of electric arc and electric discharge vapor lamp. Recent advances in LED technology have made LEDs viable for a number of applications, including vehicle stoplights, traffic lights, machine-vision-inspection, illumination, and street signs. LEDs promise significant reduction in power consumption compared to other sources of light. This project focused on comparing images taken by the Olympus IPLEX, using two different light sources. One of the sources is the 50-W internal metal halide lamp and the other is a 1 W LED placed at the tip of the insertion tube. Images acquired using these two light sources were quantitatively compared using their histogram, intensity profile along a line segment, and edge detection. Also, images were qualitatively compared using image registration and transformation [l]. The gray-level histogram, edge detection, image profile and image registration do not offer conclusive results. The LED light source, however, produces good images for visual inspection by an operator. Analysis using pattern recognition using Eigenfaces and Gaussian Pyramid in face recognition may be more useful.

A contrast enhancement method for improving the segmentation of breast lesions on ultrasonography.

PubMed

Flores, Wilfrido Gómez; Pereira, Wagner Coelho de Albuquerque

2017-01-01

This paper presents an adaptive contrast enhancement method based on sigmoidal mapping function (SACE) used for improving the computerized segmentation of breast lesions on ultrasound. First, from the original ultrasound image an intensity variation map is obtained, which is used to generate local sigmoidal mapping functions related to distinct contextual regions. Then, a bilinear interpolation scheme is used to transform every original pixel to a new gray level value. Also, four contrast enhancement techniques widely used in breast ultrasound enhancement are implemented: histogram equalization (HEQ), contrast limited adaptive histogram equalization (CLAHE), fuzzy enhancement (FEN), and sigmoid based enhancement (SEN). In addition, these contrast enhancement techniques are considered in a computerized lesion segmentation scheme based on watershed transformation. The performance comparison among techniques is assessed in terms of both the quality of contrast enhancement and the segmentation accuracy. The former is quantified by the measure, where the greater the value, the better the contrast enhancement, whereas the latter is calculated by the Jaccard index, which should tend towards unity to indicate adequate segmentation. The experiments consider a data set with 500 breast ultrasound images. The results show that SACE outperforms its counterparts, where the median values for the measure are: SACE: 139.4, SEN: 68.2, HEQ: 64.1, CLAHE: 62.8, and FEN: 7.9. Considering the segmentation performance results, the SACE method presents the largest accuracy, where the median values for the Jaccard index are: SACE: 0.81, FEN: 0.80, CLAHE: 0.79, HEQ: 77, and SEN: 0.63. The SACE method performs well due to the combination of three elements: (1) the intensity variation map reduces intensity variations that could distort the real response of the mapping function, (2) the sigmoidal mapping function enhances the gray level range where the transition between lesion and background is found, and (3) the adaptive enhancing scheme for coping with local contrasts. Hence, the SACE approach is appropriate for enhancing contrast before computerized lesion segmentation. Copyright © 2016 Elsevier Ltd. All rights reserved.

A multiresolution processing method for contrast enhancement in portal imaging.

PubMed

Gonzalez-Lopez, Antonio

2018-06-18

Portal images have a unique feature among the imaging modalities used in radiotherapy: they provide direct visualization of the irradiated volumes. However, contrast and spatial resolution are strongly limited due to the high energy of the radiation sources. Because of this, imaging modalities using x-ray energy beams have gained importance in the verification of patient positioning, replacing portal imaging. The purpose of this work was to develop a method for the enhancement of local contrast in portal images. The method operates in the subbands of a wavelet decomposition of the image, re-scaling them in such a way that coefficients in the high and medium resolution subbands are amplified, an approach totally different of those operating on the image histogram, widely used nowadays. Portal images of an anthropomorphic phantom were acquired in an electronic portal imaging device (EPID). Then, different re-scaling strategies were investigated, studying the effects of the scaling parameters on the enhanced images. Also, the effect of using different types of transforms was studied. Finally, the implemented methods were combined with histogram equalization methods like the contrast limited adaptive histogram equalization (CLAHE), and these combinations were compared. Uniform amplification of the detail subbands shows the best results in contrast enhancement. On the other hand, linear re-escalation of the high resolution subbands increases the visibility of fine detail of the images, at the expense of an increase in noise levels. Also, since processing is applied only to detail subbands, not to the approximation, the mean gray level of the image is minimally modified and no further display adjustments are required. It is shown that re-escalation of the detail subbands of portal images can be used as an efficient method for the enhancement of both, the local contrast and the resolution of these images. © 2018 Institute of Physics and Engineering in Medicine.

Method for multimodal analysis of independent source differences in schizophrenia: combining gray matter structural and auditory oddball functional data.

PubMed

Calhoun, V D; Adali, T; Giuliani, N R; Pekar, J J; Kiehl, K A; Pearlson, G D

2006-01-01

The acquisition of both structural MRI (sMRI) and functional MRI (fMRI) data for a given study is a very common practice. However, these data are typically examined in separate analyses, rather than in a combined model. We propose a novel methodology to perform independent component analysis across image modalities, specifically, gray matter images and fMRI activation images as well as a joint histogram visualization technique. Joint independent component analysis (jICA) is used to decompose a matrix with a given row consisting of an fMRI activation image resulting from auditory oddball target stimuli and an sMRI gray matter segmentation image, collected from the same individual. We analyzed data collected on a group of schizophrenia patients and healthy controls using the jICA approach. Spatially independent joint-components are estimated and resulting components were further analyzed only if they showed a significant difference between patients and controls. The main finding was that group differences in bilateral parietal and frontal as well as posterior temporal regions in gray matter were associated with bilateral temporal regions activated by the auditory oddball target stimuli. A finding of less patient gray matter and less hemodynamic activity for target detection in these bilateral anterior temporal lobe regions was consistent with previous work. An unexpected corollary to this finding was that, in the regions showing the largest group differences, gray matter concentrations were larger in patients vs. controls, suggesting that more gray matter may be related to less functional connectivity in the auditory oddball fMRI task. Hum Brain Mapp, 2005. (c) 2005 Wiley-Liss, Inc.

A Hybrid Soft-computing Method for Image Analysis of Digital Plantar Scanners.

PubMed

Razjouyan, Javad; Khayat, Omid; Siahi, Mehdi; Mansouri, Ali Alizadeh

2013-01-01

Digital foot scanners have been developed in recent years to yield anthropometrists digital image of insole with pressure distribution and anthropometric information. In this paper, a hybrid algorithm containing gray level spatial correlation (GLSC) histogram and Shanbag entropy is presented for analysis of scanned foot images. An evolutionary algorithm is also employed to find the optimum parameters of GLSC and transform function of the membership values. Resulting binary images as the thresholded images are undergone anthropometric measurements taking in to account the scale factor of pixel size to metric scale. The proposed method is finally applied to plantar images obtained through scanning feet of randomly selected subjects by a foot scanner system as our experimental setup described in the paper. Running computation time and the effects of GLSC parameters are investigated in the simulation results.

Metrics and textural features of MRI diffusion to improve classification of pediatric posterior fossa tumors.

PubMed

Rodriguez Gutierrez, D; Awwad, A; Meijer, L; Manita, M; Jaspan, T; Dineen, R A; Grundy, R G; Auer, D P

2014-05-01

Qualitative radiologic MR imaging review affords limited differentiation among types of pediatric posterior fossa brain tumors and cannot detect histologic or molecular subtypes, which could help to stratify treatment. This study aimed to improve current posterior fossa discrimination of histologic tumor type by using support vector machine classifiers on quantitative MR imaging features. This retrospective study included preoperative MRI in 40 children with posterior fossa tumors (17 medulloblastomas, 16 pilocytic astrocytomas, and 7 ependymomas). Shape, histogram, and textural features were computed from contrast-enhanced T2WI and T1WI and diffusivity (ADC) maps. Combinations of features were used to train tumor-type-specific classifiers for medulloblastoma, pilocytic astrocytoma, and ependymoma types in separation and as a joint posterior fossa classifier. A tumor-subtype classifier was also produced for classic medulloblastoma. The performance of different classifiers was assessed and compared by using randomly selected subsets of training and test data. ADC histogram features (25th and 75th percentiles and skewness) yielded the best classification of tumor type (on average >95.8% of medulloblastomas, >96.9% of pilocytic astrocytomas, and >94.3% of ependymomas by using 8 training samples). The resulting joint posterior fossa classifier correctly assigned >91.4% of the posterior fossa tumors. For subtype classification, 89.4% of classic medulloblastomas were correctly classified on the basis of ADC texture features extracted from the Gray-Level Co-Occurence Matrix. Support vector machine-based classifiers using ADC histogram features yielded very good discrimination among pediatric posterior fossa tumor types, and ADC textural features show promise for further subtype discrimination. These findings suggest an added diagnostic value of quantitative feature analysis of diffusion MR imaging in pediatric neuro-oncology. © 2014 by American Journal of Neuroradiology.

Assessment of body fat based on potential function clustering segmentation of computed tomography images

NASA Astrophysics Data System (ADS)

Zhang, Lixin; Lin, Min; Wan, Baikun; Zhou, Yu; Wang, Yizhong

2005-01-01

In this paper, a new method of body fat and its distribution testing is proposed based on CT image processing. As it is more sensitive to slight differences in attenuation than standard radiography, CT depicts the soft tissues with better clarity. And body fat has a distinct grayness range compared with its neighboring tissues in a CT image. An effective multi-thresholds image segmentation method based on potential function clustering is used to deal with multiple peaks in the grayness histogram of a CT image. The CT images of abdomens of 14 volunteers with different fatness are processed with the proposed method. Not only can the result of total fat area be got, but also the differentiation of subcutaneous fat from intra-abdominal fat has been identified. The results show the adaptability and stability of the proposed method, which will be a useful tool for diagnosing obesity.

Cervical vertebral bone mineral density changes in adolescents during orthodontic treatment.

PubMed

Crawford, Bethany; Kim, Do-Gyoon; Moon, Eun-Sang; Johnson, Elizabeth; Fields, Henry W; Palomo, J Martin; Johnston, William M

2014-08-01

The cervical vertebral maturation (CVM) stages have been used to estimate facial growth status. In this study, we examined whether cone-beam computed tomography images can be used to detect changes of CVM-related parameters and bone mineral density distribution in adolescents during orthodontic treatment. Eighty-two cone-beam computed tomography images were obtained from 41 patients before (14.47 ± 1.42 years) and after (16.15 ± 1.38 years) orthodontic treatment. Two cervical vertebral bodies (C2 and C3) were digitally isolated from each image, and their volumes, means, and standard deviations of gray-level histograms were measured. The CVM stages and mandibular lengths were also estimated after converting the cone-beam computed tomography images. Significant changes for the examined variables were detected during the observation period (P ≤0.018) except for C3 vertebral body volume (P = 0.210). The changes of CVM stage had significant positive correlations with those of vertebral body volume (P ≤0.021). The change of the standard deviation of bone mineral density (variability) showed significant correlations with those of vertebral body volume and mandibular length for C2 (P ≤0.029). The means and variability of the gray levels account for bone mineral density and active remodeling, respectively. Our results indicate that bone mineral density distribution and the volume of the cervical vertebral body changed because of active bone remodeling during maturation. Copyright © 2014 American Association of Orthodontists. Published by Mosby, Inc. All rights reserved.

Diagnostic Performance of Mammographic Texture Analysis in the Differential Diagnosis of Benign and Malignant Breast Tumors.

PubMed

Li, Zhiming; Yu, Lan; Wang, Xin; Yu, Haiyang; Gao, Yuanxiang; Ren, Yande; Wang, Gang; Zhou, Xiaoming

2017-11-09

The purpose of this study was to investigate the diagnostic performance of mammographic texture analysis in the differential diagnosis of benign and malignant breast tumors. Digital mammography images were obtained from the Picture Archiving and Communication System at our institute. Texture features of mammographic images were calculated. Mann-Whitney U test was used to identify differences between the benign and malignant group. The receiver operating characteristic (ROC) curve analysis was used to assess the diagnostic performance of texture features. Significant differences of texture features of histogram, gray-level co-occurrence matrix (GLCM) and run length matrix (RLM) were found between the benign and malignant breast group (P < .05). The area under the ROC (AUROC) of histogram, GLCM, and RLM were 0.800, 0.787, and 0.761, with no differences between them (P > .05). The AUROCs of imaging-based diagnosis, texture analysis, and imaging-based diagnosis combined with texture analysis were 0.873, 0.863, and 0.961, respectively. When imaging-based diagnosis was combined with texture analysis, the AUROC was higher than that of imaging-based diagnosis or texture analysis (P < .05). Mammographic texture analysis is a reliable technique for differential diagnosis of benign and malignant breast tumors. Furthermore, the combination of imaging-based diagnosis and texture analysis can significantly improve diagnostic performance. Copyright © 2017 Elsevier Inc. All rights reserved.

Technique for bone volume measurement from human femur head samples by classification of micro-CT image histograms.

PubMed

Marinozzi, Franco; Bini, Fabiano; Marinozzi, Andrea; Zuppante, Francesca; De Paolis, Annalisa; Pecci, Raffaella; Bedini, Rossella

2013-01-01

Micro-CT analysis is a powerful technique for a non-invasive evaluation of the morphometric parameters of trabecular bone samples. This elaboration requires a previous binarization of the images. A problem which arises from the binarization process is the partial volume artifact. Voxels at the external surface of the sample can contain both bone and air so thresholding operates an incorrect estimation of volume occupied by the two materials. The aim of this study is the extraction of bone volumetric information directly from the image histograms, by fitting them with a suitable set of functions. Nineteen trabecular bone samples were extracted from femoral heads of eight patients subject to a hip arthroplasty surgery. Trabecular bone samples were acquired using micro-CT Scanner. Hystograms of the acquired images were computed and fitted by Gaussian-like functions accounting for: a) gray levels produced by the bone x-ray absorption, b) the portions of the image occupied by air and c) voxels that contain a mixture of bone and air. This latter contribution can be considered such as an estimation of the partial volume effect. The comparison of the proposed technique to the bone volumes measured by a reference instrument such as by a helium pycnometer show the method as a good way for an accurate bone volume calculation of trabecular bone samples.

A robust generalized fuzzy operator approach to film contrast correction in digital subtraction radiography.

PubMed

Leung, Chung-Chu

2006-03-01

Digital subtraction radiography requires close matching of the contrast in each pair of X-ray images to be subtracted. Previous studies have shown that nonparametric contrast/brightness correction methods using the cumulative density function (CDF) and its improvements, which are based on gray-level transformation associated with the pixel histogram, perform well in uniform contrast/brightness difference conditions. However, for radiographs with nonuniform contrast/ brightness, the CDF produces unsatisfactory results. In this paper, we propose a new approach in contrast correction based on the generalized fuzzy operator with least square method. The result shows that 50% of the contrast/brightness errors can be corrected using this approach when the contrast/brightness difference between a radiographic pair is 10 U. A comparison of our approach with that of CDF is presented, and this modified GFO method produces better contrast normalization results than the CDF approach.

Cloud and surface textural features in polar regions

NASA Technical Reports Server (NTRS)

Welch, Ronald M.; Kuo, Kwo-Sen; Sengupta, Sailes K.

1990-01-01

The study examines the textural signatures of clouds, ice-covered mountains, solid and broken sea ice and floes, and open water. The textural features are computed from sum and difference histogram and gray-level difference vector statistics defined at various pixel displacement distances derived from Landsat multispectral scanner data. Polar cloudiness, snow-covered mountainous regions, solid sea ice, glaciers, and open water have distinguishable texture features. This suggests that textural measures can be successfully applied to the detection of clouds over snow-covered mountains, an ability of considerable importance for the modeling of snow-melt runoff. However, broken stratocumulus cloud decks and thin cirrus over broken sea ice remain difficult to distinguish texturally. It is concluded that even with high spatial resolution imagery, it may not be possible to distinguish broken stratocumulus and thin clouds from sea ice in the marginal ice zone using the visible channel textural features alone.

A Heavy Tailed Expectation Maximization Hidden Markov Random Field Model with Applications to Segmentation of MRI

PubMed Central

Castillo-Barnes, Diego; Peis, Ignacio; Martínez-Murcia, Francisco J.; Segovia, Fermín; Illán, Ignacio A.; Górriz, Juan M.; Ramírez, Javier; Salas-Gonzalez, Diego

2017-01-01

A wide range of segmentation approaches assumes that intensity histograms extracted from magnetic resonance images (MRI) have a distribution for each brain tissue that can be modeled by a Gaussian distribution or a mixture of them. Nevertheless, intensity histograms of White Matter and Gray Matter are not symmetric and they exhibit heavy tails. In this work, we present a hidden Markov random field model with expectation maximization (EM-HMRF) modeling the components using the α-stable distribution. The proposed model is a generalization of the widely used EM-HMRF algorithm with Gaussian distributions. We test the α-stable EM-HMRF model in synthetic data and brain MRI data. The proposed methodology presents two main advantages: Firstly, it is more robust to outliers. Secondly, we obtain similar results than using Gaussian when the Gaussian assumption holds. This approach is able to model the spatial dependence between neighboring voxels in tomographic brain MRI. PMID:29209194

New method for predicting estrogen receptor status utilizing breast MRI texture kinetic analysis

NASA Astrophysics Data System (ADS)

Chaudhury, Baishali; Hall, Lawrence O.; Goldgof, Dmitry B.; Gatenby, Robert A.; Gillies, Robert; Drukteinis, Jennifer S.

2014-03-01

Magnetic Resonance Imaging (MRI) of breast cancer typically shows that tumors are heterogeneous with spatial variations in blood flow and cell density. Here, we examine the potential link between clinical tumor imaging and the underlying evolutionary dynamics behind heterogeneity in the cellular expression of estrogen receptors (ER) in breast cancer. We assume, in an evolutionary environment, that ER expression will only occur in the presence of significant concentrations of estrogen, which is delivered via the blood stream. Thus, we hypothesize, the expression of ER in breast cancer cells will correlate with blood flow on gadolinium enhanced breast MRI. To test this hypothesis, we performed quantitative analysis of blood flow on dynamic contrast enhanced MRI (DCE-MRI) and correlated it with the ER status of the tumor. Here we present our analytic methods, which utilize a novel algorithm to analyze 20 volumetric DCE-MRI breast cancer tumors. The algorithm generates post initial enhancement (PIE) maps from DCE-MRI and then performs texture features extraction from the PIE map, feature selection, and finally classification of tumors into ER positive and ER negative status. The combined gray level co-occurrence matrices, gray level run length matrices and local binary pattern histogram features allow quantification of breast tumor heterogeneity. The algorithm predicted ER expression with an accuracy of 85% using a Naive Bayes classifier in leave-one-out cross-validation. Hence, we conclude that our data supports the hypothesis that imaging characteristics can, through application of evolutionary principles, provide insights into the cellular and molecular properties of cancer cells.

The DataCube Server. Animate Agent Project Working Note 2, Version 1.0

DTIC Science & Technology

1993-11-01

before this can be called a histogram of all the needed levels must be made and their one band images must be made. Note if a levels backprojection...will not be used then the level does not need to be histogrammed. Any points outside the active region in a levels backprojection will be undefined...this can be called a histogram of all the needed levels must be made and their one band images must be made. Note if a levels backprojection will not

A similarity measure method combining location feature for mammogram retrieval.

PubMed

Wang, Zhiqiong; Xin, Junchang; Huang, Yukun; Li, Chen; Xu, Ling; Li, Yang; Zhang, Hao; Gu, Huizi; Qian, Wei

2018-05-28

Breast cancer, the most common malignancy among women, has a high mortality rate in clinical practice. Early detection, diagnosis and treatment can reduce the mortalities of breast cancer greatly. The method of mammogram retrieval can help doctors to find the early breast lesions effectively and determine a reasonable feature set for image similarity measure. This will improve the accuracy effectively for mammogram retrieval. This paper proposes a similarity measure method combining location feature for mammogram retrieval. Firstly, the images are pre-processed, the regions of interest are detected and the lesions are segmented in order to get the center point and radius of the lesions. Then, the method, namely Coherent Point Drift, is used for image registration with the pre-defined standard image. The center point and radius of the lesions after registration are obtained and the standard location feature of the image is constructed. This standard location feature can help figure out the location similarity between the image pair from the query image to each dataset image in the database. Next, the content feature of the image is extracted, including the Histogram of Oriented Gradients, the Edge Direction Histogram, the Local Binary Pattern and the Gray Level Histogram, and the image pair content similarity can be calculated using the Earth Mover's Distance. Finally, the location similarity and content similarity are fused to form the image fusion similarity, and the specified number of the most similar images can be returned according to it. In the experiment, 440 mammograms, which are from Chinese women in Northeast China, are used as the database. When fusing 40% lesion location feature similarity and 60% content feature similarity, the results have obvious advantages. At this time, precision is 0.83, recall is 0.76, comprehensive indicator is 0.79, satisfaction is 96.0%, mean is 4.2 and variance is 17.7. The results show that the precision and recall of this method have obvious advantage, compared with the content-based image retrieval.

Using Anisotropic 3D Minkowski Functionals for Trabecular Bone Characterization and Biomechanical Strength Prediction in Proximal Femur Specimens

PubMed Central

Nagarajan, Mahesh B.; De, Titas; Lochmüller, Eva-Maria; Eckstein, Felix; Wismüller, Axel

2017-01-01

The ability of Anisotropic Minkowski Functionals (AMFs) to capture local anisotropy while evaluating topological properties of the underlying gray-level structures has been previously demonstrated. We evaluate the ability of this approach to characterize local structure properties of trabecular bone micro-architecture in ex vivo proximal femur specimens, as visualized on multi-detector CT, for purposes of biomechanical bone strength prediction. To this end, volumetric AMFs were computed locally for each voxel of volumes of interest (VOI) extracted from the femoral head of 146 specimens. The local anisotropy captured by such AMFs was quantified using a fractional anisotropy measure; the magnitude and direction of anisotropy at every pixel was stored in histograms that served as a feature vectors that characterized the VOIs. A linear multi-regression analysis algorithm was used to predict the failure load (FL) from the feature sets; the predicted FL was compared to the true FL determined through biomechanical testing. The prediction performance was measured by the root mean square error (RMSE) for each feature set. The best prediction performance was obtained from the fractional anisotropy histogram of AMF Euler Characteristic (RMSE = 1.01 ± 0.13), which was significantly better than MDCT-derived mean BMD (RMSE = 1.12 ± 0.16, p<0.05). We conclude that such anisotropic Minkowski Functionals can capture valuable information regarding regional trabecular bone quality and contribute to improved bone strength prediction, which is important for improving the clinical assessment of osteoporotic fracture risk. PMID:29170581

Comparison between a new computer program and the reference software for gray-scale median analysis of atherosclerotic carotid plaques.

PubMed

Casella, Ivan Benaduce; Fukushima, Rodrigo Bono; Marques, Anita Battistini de Azevedo; Cury, Marcus Vinícius Martins; Presti, Calógero

2015-03-01

To compare a new dedicated software program and Adobe Photoshop for gray-scale median (GSM) analysis of B-mode images of carotid plaques. A series of 42 carotid plaques generating ≥50% diameter stenosis was evaluated by a single observer. The best segment for visualization of internal carotid artery plaque was identified on a single longitudinal view and images were recorded in JPEG format. Plaque analysis was performed by both programs. After normalization of image intensity (blood = 0, adventitial layer = 190), histograms were obtained after manual delineation of plaque. Results were compared with nonparametric Wilcoxon signed rank test and Kendall tau-b correlation analysis. GSM ranged from 00 to 100 with Adobe Photoshop and from 00 to 96 with IMTPC, with a high grade of similarity between image pairs, and a highly significant correlation (R = 0.94, p < .0001). IMTPC software appears suitable for the GSM analysis of carotid plaques. © 2014 Wiley Periodicals, Inc.

A New Quantum Gray-Scale Image Encoding Scheme

NASA Astrophysics Data System (ADS)

Naseri, Mosayeb; Abdolmaleky, Mona; Parandin, Fariborz; Fatahi, Negin; Farouk, Ahmed; Nazari, Reza

2018-02-01

In this paper, a new quantum images encoding scheme is proposed. The proposed scheme mainly consists of four different encoding algorithms. The idea behind of the scheme is a binary key generated randomly for each pixel of the original image. Afterwards, the employed encoding algorithm is selected corresponding to the qubit pair of the generated randomized binary key. The security analysis of the proposed scheme proved its enhancement through both randomization of the generated binary image key and altering the gray-scale value of the image pixels using the qubits of randomized binary key. The simulation of the proposed scheme assures that the final encoded image could not be recognized visually. Moreover, the histogram diagram of encoded image is flatter than the original one. The Shannon entropies of the final encoded images are significantly higher than the original one, which indicates that the attacker can not gain any information about the encoded images. Supported by Kermanshah Branch, Islamic Azad University, Kermanshah, IRAN

SU-F-R-35: Repeatability of Texture Features in T1- and T2-Weighted MR Images

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

Mahon, R; Weiss, E; Karki, K

Purpose: To evaluate repeatability of lung tumor texture features from inspiration/expiration MR image pairs for potential use in patient specific care models and applications. Repeatability is a desirable and necessary characteristic of features included in such models. Methods: T1-weighted Volumetric Interpolation Breath-Hold Examination (VIBE) and/or T2-weighted MRI scans were acquired for 15 patients with non-small cell lung cancer before and during radiotherapy for a total of 32 and 34 same session inspiration-expiration breath-hold image pairs respectively. Bias correction was applied to the VIBE (VIBE-BC) and T2-weighted (T2-BC) images. Fifty-nine texture features at five wavelet decomposition ratios were extracted from themore » delineated primary tumor including: histogram(HIST), gray level co-occurrence matrix(GLCM), gray level run length matrix(GLRLM), gray level size zone matrix(GLSZM), and neighborhood gray tone different matrix (NGTDM) based features. Repeatability of the texture features for VIBE, VIBE-BC, T2-weighted, and T2-BC image pairs was evaluated by the concordance correlation coefficient (CCC) between corresponding image pairs, with a value greater than 0.90 indicating repeatability. Results: For the VIBE image pairs, the percentage of repeatable texture features by wavelet ratio was between 20% and 24% of the 59 extracted features; the T2-weighted image pairs exhibited repeatability in the range of 44–49%. The percentage dropped to 10–20% for the VIBE-BC images, and 12–14% for the T2-BC images. In addition, five texture features were found to be repeatable in all four image sets including two GLRLM, two GLZSM, and one NGTDN features. No single texture feature category was repeatable among all three image types; however, certain categories performed more consistently on a per image type basis. Conclusion: We identified repeatable texture features on T1- and T2-weighted MRI scans. These texture features should be further investigated for use in specific applications such as tissue classification and changes during radiation therapy utilizing a standard imaging protocol. Authors have the following disclosures: a research agreement with Philips Medical systems (Hugo, Weiss), a license agreement with Varian Medical Systems (Hugo, Weiss), research grants from the National Institute of Health (Hugo, Weiss), UpToDate royalties (Weiss), and none(Mahon, Ford, Karki). Authors have no potential conflicts of interest to disclose.« less

Standardized volume-rendering of contrast-enhanced renal magnetic resonance angiography.

PubMed

Smedby, O; Oberg, R; Asberg, B; Stenström, H; Eriksson, P

2005-08-01

To propose a technique for standardizing volume-rendering technique (VRT) protocols and to compare this with maximum intensity projection (MIP) in regard to image quality and diagnostic confidence in stenosis diagnosis with magnetic resonance angiography (MRA). Twenty patients were examined with MRA under suspicion of renal artery stenosis. Using the histogram function in the volume-rendering software, the 95th and 99th percentiles of the 3D data set were identified and used to define the VRT transfer function. Two radiologists assessed the stenosis pathology and image quality from rotational sequences of MIP and VRT images. Good overall agreement (mean kappa=0.72) was found between MIP and VRT diagnoses. The agreement between MIP and VRT was considerably better than that between observers (mean kappa=0.43). One of the observers judged VRT images as having higher image quality than MIP images. Presenting renal MRA images with VRT gave results in good agreement with MIP. With VRT protocols defined from the histogram of the image, the lack of an absolute gray scale in MRI need not be a major problem.

SU-D-207B-07: Development of a CT-Radiomics Based Early Response Prediction Model During Delivery of Chemoradiation Therapy for Pancreatic Cancer

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

Klawikowski, S; Christian, J; Schott, D

Purpose: Pilot study developing a CT-texture based model for early assessment of treatment response during the delivery of chemoradiation therapy (CRT) for pancreatic cancer. Methods: Daily CT data acquired for 24 pancreatic head cancer patients using CT-on-rails, during the routine CT-guided CRT delivery with a radiation dose of 50.4 Gy in 28 fractions, were analyzed. The pancreas head was contoured on each daily CT. Texture analysis was performed within the pancreas head contour using a research tool (IBEX). Over 1300 texture metrics including: grey level co-occurrence, run-length, histogram, neighborhood intensity difference, and geometrical shape features were calculated for each dailymore » CT. Metric-trend information was established by finding the best fit of either a linear, quadratic, or exponential function for each metric value verses accumulated dose. Thus all the daily CT texture information was consolidated into a best-fit trend type for a given patient and texture metric. Linear correlation was performed between the patient histological response vector (good, medium, poor) and all combinations of 23 patient subgroups (statistical jackknife) determining which metrics were most correlated to response and repeatedly reliable across most patients. Control correlations against CT scanner, reconstruction kernel, and gated/nongated CT images were also calculated. Euclidean distance measure was used to group/sort patient vectors based on the data of these trend-response metrics. Results: We found four specific trend-metrics (Gray Level Coocurence Matrix311-1InverseDiffMomentNorm, Gray Level Coocurence Matrix311-1InverseDiffNorm, Gray Level Coocurence Matrix311-1 Homogeneity2, and Intensity Direct Local StdMean) that were highly correlated with patient response and repeatedly reliable. Our four trend-metric model successfully ordered our pilot response dataset (p=0.00070). We found no significant correlation to our control parameters: gating (p=0.7717), scanner (p=0.9741), and kernel (p=0.8586). Conclusion: We have successfully created a CT-texture based early treatment response prediction model using the CTs acquired during the delivery of chemoradiation therapy for pancreatic cancer. Future testing is required to validate the model with more patient data.« less

New algorithm for detecting smaller retinal blood vessels in fundus images

NASA Astrophysics Data System (ADS)

LeAnder, Robert; Bidari, Praveen I.; Mohammed, Tauseef A.; Das, Moumita; Umbaugh, Scott E.

2010-03-01

About 4.1 million Americans suffer from diabetic retinopathy. To help automatically diagnose various stages of the disease, a new blood-vessel-segmentation algorithm based on spatial high-pass filtering was developed to automatically segment blood vessels, including the smaller ones, with low noise. Methods: Image database: Forty, 584 x 565-pixel images were collected from the DRIVE image database. Preprocessing: Green-band extraction was used to obtain better contrast, which facilitated better visualization of retinal blood vessels. A spatial highpass filter of mask-size 11 was applied. A histogram stretch was performed to enhance contrast. A median filter was applied to mitigate noise. At this point, the gray-scale image was converted to a binary image using a binary thresholding operation. Then, a NOT operation was performed by gray-level value inversion between 0 and 255. Postprocessing: The resulting image was AND-ed with its corresponding ring mask to remove the outer-ring (lens-edge) artifact. At this point, the above algorithm steps had extracted most of the major and minor vessels, with some intersections and bifurcations missing. Vessel segments were reintegrated using the Hough transform. Results: After applying the Hough transform, both the average peak SNR and the RMS error improved by 10%. Pratt's Figure of Merit (PFM) was decreased by 6%. Those averages were better than [1] by 10-30%. Conclusions: The new algorithm successfully preserved the details of smaller blood vessels and should prove successful as a segmentation step for automatically identifying diseases that affect retinal blood vessels.

Characterization of coronary plaque regions in intravascular ultrasound images using a hybrid ensemble classifier.

PubMed

Hwang, Yoo Na; Lee, Ju Hwan; Kim, Ga Young; Shin, Eun Seok; Kim, Sung Min

2018-01-01

The purpose of this study was to propose a hybrid ensemble classifier to characterize coronary plaque regions in intravascular ultrasound (IVUS) images. Pixels were allocated to one of four tissues (fibrous tissue (FT), fibro-fatty tissue (FFT), necrotic core (NC), and dense calcium (DC)) through processes of border segmentation, feature extraction, feature selection, and classification. Grayscale IVUS images and their corresponding virtual histology images were acquired from 11 patients with known or suspected coronary artery disease using 20 MHz catheter. A total of 102 hybrid textural features including first order statistics (FOS), gray level co-occurrence matrix (GLCM), extended gray level run-length matrix (GLRLM), Laws, local binary pattern (LBP), intensity, and discrete wavelet features (DWF) were extracted from IVUS images. To select optimal feature sets, genetic algorithm was implemented. A hybrid ensemble classifier based on histogram and texture information was then used for plaque characterization in this study. The optimal feature set was used as input of this ensemble classifier. After tissue characterization, parameters including sensitivity, specificity, and accuracy were calculated to validate the proposed approach. A ten-fold cross validation approach was used to determine the statistical significance of the proposed method. Our experimental results showed that the proposed method had reliable performance for tissue characterization in IVUS images. The hybrid ensemble classification method outperformed other existing methods by achieving characterization accuracy of 81% for FFT and 75% for NC. In addition, this study showed that Laws features (SSV and SAV) were key indicators for coronary tissue characterization. The proposed method had high clinical applicability for image-based tissue characterization. Copyright © 2017 Elsevier B.V. All rights reserved.

Can we trust the calculation of texture indices of CT images? A phantom study.

PubMed

Caramella, Caroline; Allorant, Adrien; Orlhac, Fanny; Bidault, Francois; Asselain, Bernard; Ammari, Samy; Jaranowski, Patricia; Moussier, Aurelie; Balleyguier, Corinne; Lassau, Nathalie; Pitre-Champagnat, Stephanie

2018-04-01

Texture analysis is an emerging tool in the field of medical imaging analysis. However, many issues have been raised in terms of its use in assessing patient images and it is crucial to harmonize and standardize this new imaging measurement tool. This study was designed to evaluate the reliability of texture indices of CT images on a phantom including a reproducibility study, to assess the discriminatory capacity of indices potentially relevant in CT medical images and to determine their redundancy. For the reproducibility and discriminatory analysis, eight identical CT acquisitions were performed on a phantom including one homogeneous insert and two close heterogeneous inserts. Texture indices were selected for their high reproducibility and capability of discriminating different textures. For the redundancy analysis, 39 acquisitions of the same phantom were performed using varying acquisition parameters and a correlation matrix was used to explore the 2 × 2 relationships. LIFEx software was used to explore 34 different parameters including first order and texture indices. Only eight indices of 34 exhibited high reproducibility and discriminated textures from each other. Skewness and kurtosis from histogram were independent from the six other indices but were intercorrelated, the other six indices correlated in diverse degrees (entropy, dissimilarity, and contrast of the co-occurrence matrix, contrast of the Neighborhood Gray Level difference matrix, SZE, ZLNU of the Gray-Level Size Zone Matrix). Care should be taken when using texture analysis as a tool to characterize CT images because changes in quantitation may be primarily due to internal variability rather than from real physio-pathological effects. Some textural indices appear to be sufficiently reliable and capable to discriminate close textures on CT images. © 2018 American Association of Physicists in Medicine.

Computed gray levels in multislice and cone-beam computed tomography.

PubMed

Azeredo, Fabiane; de Menezes, Luciane Macedo; Enciso, Reyes; Weissheimer, Andre; de Oliveira, Rogério Belle

2013-07-01

Gray level is the range of shades of gray in the pixels, representing the x-ray attenuation coefficient that allows for tissue density assessments in computed tomography (CT). An in-vitro study was performed to investigate the relationship between computed gray levels in 3 cone-beam CT (CBCT) scanners and 1 multislice spiral CT device using 5 software programs. Six materials (air, water, wax, acrylic, plaster, and gutta-percha) were scanned with the CBCT and CT scanners, and the computed gray levels for each material at predetermined points were measured with OsiriX Medical Imaging software (Geneva, Switzerland), OnDemand3D (CyberMed International, Seoul, Korea), E-Film (Merge Healthcare, Milwaukee, Wis), Dolphin Imaging (Dolphin Imaging & Management Solutions, Chatsworth, Calif), and InVivo Dental Software (Anatomage, San Jose, Calif). The repeatability of these measurements was calculated with intraclass correlation coefficients, and the gray levels were averaged to represent each material. Repeated analysis of variance tests were used to assess the differences in gray levels among scanners and materials. There were no differences in mean gray levels with the different software programs. There were significant differences in gray levels between scanners for each material evaluated (P <0.001). The software programs were reliable and had no influence on the CT and CBCT gray level measurements. However, the gray levels might have discrepancies when different CT and CBCT scanners are used. Therefore, caution is essential when interpreting or evaluating CBCT images because of the significant differences in gray levels between different CBCT scanners, and between CBCT and CT values. Copyright © 2013 American Association of Orthodontists. Published by Mosby, Inc. All rights reserved.

Textural features of pretreatment 18F-FDG PET/CT images: prognostic significance in patients with advanced T-stage oropharyngeal squamous cell carcinoma.

PubMed

Cheng, Nai-Ming; Fang, Yu-Hua Dean; Chang, Joseph Tung-Chieh; Huang, Chung-Guei; Tsan, Din-Li; Ng, Shu-Hang; Wang, Hung-Ming; Lin, Chien-Yu; Liao, Chun-Ta; Yen, Tzu-Chen

2013-10-01

Previous studies have shown that total lesion glycolysis (TLG) may serve as a prognostic indicator in oropharyngeal squamous cell carcinoma (OPSCC). We sought to investigate whether the textural features of pretreatment (18)F-FDG PET/CT images can provide any additional prognostic information over TLG and clinical staging in patients with advanced T-stage OPSCC. We retrospectively analyzed the pretreatment (18)F-FDG PET/CT images of 70 patients with advanced T-stage OPSCC who had completed concurrent chemoradiotherapy, bioradiotherapy, or radiotherapy with curative intent. All of the patients had data on human papillomavirus (HPV) infection and were followed up for at least 24 mo or until death. A standardized uptake value (SUV) of 2.5 was taken as a cutoff for tumor boundary. The textural features of pretreatment (18)F-FDG PET/CT images were extracted from histogram analysis (SUV variance and SUV entropy), normalized gray-level cooccurrence matrix (uniformity, entropy, dissimilarity, contrast, homogeneity, inverse different moment, and correlation), and neighborhood gray-tone difference matrix (coarseness, contrast, busyness, complexity, and strength). Receiver-operating-characteristic curves were used to identify the optimal cutoff values for the textural features and TLG. Thirteen patients were HPV-positive. Multivariate Cox regression analysis showed that age, tumor TLG, and uniformity were independently associated with progression-free survival (PFS) and disease-specific survival (DSS). TLG, uniformity, and HPV positivity were significantly associated with overall survival (OS). A prognostic scoring system based on TLG and uniformity was derived. Patients who presented with TLG > 121.9 g and uniformity ≤ 0.138 experienced significantly worse PFS, DSS, and OS rates than those without (P < 0.001, < 0.001, and 0.002, respectively). Patients with TLG > 121.9 g or uniformity ≤ 0.138 were further divided according to age, and different PFS and DSS were observed. Uniformity extracted from the normalized gray-level cooccurrence matrix represents an independent prognostic predictor in patients with advanced T-stage OPSCC. A scoring system was developed and may serve as a risk-stratification strategy for guiding therapy.

Clinical Utility of Blood Cell Histogram Interpretation

PubMed Central

Bhagya, S.; Majeed, Abdul

2017-01-01

An automated haematology analyser provides blood cell histograms by plotting the sizes of different blood cells on X-axis and their relative number on Y-axis. Histogram interpretation needs careful analysis of Red Blood Cell (RBC), White Blood Cell (WBC) and platelet distribution curves. Histogram analysis is often a neglected part of the automated haemogram which if interpreted well, has significant potential to provide diagnostically relevant information even before higher level investigations are ordered. PMID:29207767

Clinical Utility of Blood Cell Histogram Interpretation.

PubMed

Thomas, E T Arun; Bhagya, S; Majeed, Abdul

2017-09-01

An automated haematology analyser provides blood cell histograms by plotting the sizes of different blood cells on X-axis and their relative number on Y-axis. Histogram interpretation needs careful analysis of Red Blood Cell (RBC), White Blood Cell (WBC) and platelet distribution curves. Histogram analysis is often a neglected part of the automated haemogram which if interpreted well, has significant potential to provide diagnostically relevant information even before higher level investigations are ordered.

Detection algorithm for glass bottle mouth defect by continuous wavelet transform based on machine vision

NASA Astrophysics Data System (ADS)

Qian, Jinfang; Zhang, Changjiang

2014-11-01

An efficient algorithm based on continuous wavelet transform combining with pre-knowledge, which can be used to detect the defect of glass bottle mouth, is proposed. Firstly, under the condition of ball integral light source, a perfect glass bottle mouth image is obtained by Japanese Computar camera through the interface of IEEE-1394b. A single threshold method based on gray level histogram is used to obtain the binary image of the glass bottle mouth. In order to efficiently suppress noise, moving average filter is employed to smooth the histogram of original glass bottle mouth image. And then continuous wavelet transform is done to accurately determine the segmentation threshold. Mathematical morphology operations are used to get normal binary bottle mouth mask. A glass bottle to be detected is moving to the detection zone by conveyor belt. Both bottle mouth image and binary image are obtained by above method. The binary image is multiplied with normal bottle mask and a region of interest is got. Four parameters (number of connected regions, coordinate of centroid position, diameter of inner cycle, and area of annular region) can be computed based on the region of interest. Glass bottle mouth detection rules are designed by above four parameters so as to accurately detect and identify the defect conditions of glass bottle. Finally, the glass bottles of Coca-Cola Company are used to verify the proposed algorithm. The experimental results show that the proposed algorithm can accurately detect the defect conditions of the glass bottles and have 98% detecting accuracy.

Quantitative analysis of brain magnetic resonance imaging for hepatic encephalopathy

NASA Astrophysics Data System (ADS)

Syh, Hon-Wei; Chu, Wei-Kom; Ong, Chin-Sing

1992-06-01

High intensity lesions around ventricles have recently been observed in T1-weighted brain magnetic resonance images for patients suffering hepatic encephalopathy. The exact etiology that causes magnetic resonance imaging (MRI) gray scale changes has not been totally understood. The objective of our study was to investigate, through quantitative means, (1) the amount of changes to brain white matter due to the disease process, and (2) the extent and distribution of these high intensity lesions, since it is believed that the abnormality may not be entirely limited to the white matter only. Eleven patients with proven haptic encephalopathy and three normal persons without any evidence of liver abnormality constituted our current data base. Trans-axial, sagittal, and coronal brain MRI were obtained on a 1.5 Tesla scanner. All processing was carried out on a microcomputer-based image analysis system in an off-line manner. Histograms were decomposed into regular brain tissues and lesions. Gray scale ranges coded as lesion were then brought back to original images to identify distribution of abnormality. Our results indicated the disease process involved pallidus, mesencephalon, and subthalamic regions.

Automatic crack detection and classification method for subway tunnel safety monitoring.

PubMed

Zhang, Wenyu; Zhang, Zhenjiang; Qi, Dapeng; Liu, Yun

2014-10-16

Cracks are an important indicator reflecting the safety status of infrastructures. This paper presents an automatic crack detection and classification methodology for subway tunnel safety monitoring. With the application of high-speed complementary metal-oxide-semiconductor (CMOS) industrial cameras, the tunnel surface can be captured and stored in digital images. In a next step, the local dark regions with potential crack defects are segmented from the original gray-scale images by utilizing morphological image processing techniques and thresholding operations. In the feature extraction process, we present a distance histogram based shape descriptor that effectively describes the spatial shape difference between cracks and other irrelevant objects. Along with other features, the classification results successfully remove over 90% misidentified objects. Also, compared with the original gray-scale images, over 90% of the crack length is preserved in the last output binary images. The proposed approach was tested on the safety monitoring for Beijing Subway Line 1. The experimental results revealed the rules of parameter settings and also proved that the proposed approach is effective and efficient for automatic crack detection and classification.

Automatic Crack Detection and Classification Method for Subway Tunnel Safety Monitoring

PubMed Central

Zhang, Wenyu; Zhang, Zhenjiang; Qi, Dapeng; Liu, Yun

2014-01-01

Cracks are an important indicator reflecting the safety status of infrastructures. This paper presents an automatic crack detection and classification methodology for subway tunnel safety monitoring. With the application of high-speed complementary metal-oxide-semiconductor (CMOS) industrial cameras, the tunnel surface can be captured and stored in digital images. In a next step, the local dark regions with potential crack defects are segmented from the original gray-scale images by utilizing morphological image processing techniques and thresholding operations. In the feature extraction process, we present a distance histogram based shape descriptor that effectively describes the spatial shape difference between cracks and other irrelevant objects. Along with other features, the classification results successfully remove over 90% misidentified objects. Also, compared with the original gray-scale images, over 90% of the crack length is preserved in the last output binary images. The proposed approach was tested on the safety monitoring for Beijing Subway Line 1. The experimental results revealed the rules of parameter settings and also proved that the proposed approach is effective and efficient for automatic crack detection and classification. PMID:25325337

Using the Bootstrap Method for a Statistical Significance Test of Differences between Summary Histograms

NASA Technical Reports Server (NTRS)

Xu, Kuan-Man

2006-01-01

A new method is proposed to compare statistical differences between summary histograms, which are the histograms summed over a large ensemble of individual histograms. It consists of choosing a distance statistic for measuring the difference between summary histograms and using a bootstrap procedure to calculate the statistical significance level. Bootstrapping is an approach to statistical inference that makes few assumptions about the underlying probability distribution that describes the data. Three distance statistics are compared in this study. They are the Euclidean distance, the Jeffries-Matusita distance and the Kuiper distance. The data used in testing the bootstrap method are satellite measurements of cloud systems called cloud objects. Each cloud object is defined as a contiguous region/patch composed of individual footprints or fields of view. A histogram of measured values over footprints is generated for each parameter of each cloud object and then summary histograms are accumulated over all individual histograms in a given cloud-object size category. The results of statistical hypothesis tests using all three distances as test statistics are generally similar, indicating the validity of the proposed method. The Euclidean distance is determined to be most suitable after comparing the statistical tests of several parameters with distinct probability distributions among three cloud-object size categories. Impacts on the statistical significance levels resulting from differences in the total lengths of satellite footprint data between two size categories are also discussed.

Comparison of Histograms for Use in Cloud Observation and Modeling

NASA Technical Reports Server (NTRS)

Green, Lisa; Xu, Kuan-Man

2005-01-01

Cloud observation and cloud modeling data can be presented in histograms for each characteristic to be measured. Combining information from single-cloud histograms yields a summary histogram. Summary histograms can be compared to each other to reach conclusions about the behavior of an ensemble of clouds in different places at different times or about the accuracy of a particular cloud model. As in any scientific comparison, it is necessary to decide whether any apparent differences are statistically significant. The usual methods of deciding statistical significance when comparing histograms do not apply in this case because they assume independent data. Thus, a new method is necessary. The proposed method uses the Euclidean distance metric and bootstrapping to calculate the significance level.

Symbol recognition via statistical integration of pixel-level constraint histograms: a new descriptor.

PubMed

Yang, Su

2005-02-01

A new descriptor for symbol recognition is proposed. 1) A histogram is constructed for every pixel to figure out the distribution of the constraints among the other pixels. 2) All the histograms are statistically integrated to form a feature vector with fixed dimension. The robustness and invariance were experimentally confirmed.

Theoretical foundations of spatially-variant mathematical morphology part ii: gray-level images.

PubMed

Bouaynaya, Nidhal; Schonfeld, Dan

2008-05-01

In this paper, we develop a spatially-variant (SV) mathematical morphology theory for gray-level signals and images in the Euclidean space. The proposed theory preserves the geometrical concept of the structuring function, which provides the foundation of classical morphology and is essential in signal and image processing applications. We define the basic SV gray-level morphological operators (i.e., SV gray-level erosion, dilation, opening, and closing) and investigate their properties. We demonstrate the ubiquity of SV gray-level morphological systems by deriving a kernel representation for a large class of systems, called V-systems, in terms of the basic SV graylevel morphological operators. A V-system is defined to be a gray-level operator, which is invariant under gray-level (vertical) translations. Particular attention is focused on the class of SV flat gray-level operators. The kernel representation for increasing V-systems is a generalization of Maragos' kernel representation for increasing and translation-invariant function-processing systems. A representation of V-systems in terms of their kernel elements is established for increasing and upper-semi-continuous V-systems. This representation unifies a large class of spatially-variant linear and non-linear systems under the same mathematical framework. Finally, simulation results show the potential power of the general theory of gray-level spatially-variant mathematical morphology in several image analysis and computer vision applications.

Design and testing of artifact-suppressed adaptive histogram equalization: a contrast-enhancement technique for display of digital chest radiographs.

PubMed

Rehm, K; Seeley, G W; Dallas, W J; Ovitt, T W; Seeger, J F

1990-01-01

One of the goals of our research in the field of digital radiography has been to develop contrast-enhancement algorithms for eventual use in the display of chest images on video devices with the aim of preserving the diagnostic information presently available with film, some of which would normally be lost because of the smaller dynamic range of video monitors. The ASAHE algorithm discussed in this article has been tested by investigating observer performance in a difficult detection task involving phantoms and simulated lung nodules, using film as the output medium. The results of the experiment showed that the algorithm is successful in providing contrast-enhanced, natural-looking chest images while maintaining diagnostic information. The algorithm did not effect an increase in nodule detectability, but this was not unexpected because film is a medium capable of displaying a wide range of gray levels. It is sufficient at this stage to show that there is no degradation in observer performance. Future tests will evaluate the performance of the ASAHE algorithm in preparing chest images for video display.

In Vivo Imaging of Tau Pathology Using Magnetic Resonance Imaging Textural Analysis

PubMed Central

Colgan, Niall; Ganeshan, Balaji; Harrison, Ian F.; Ismail, Ozama; Holmes, Holly E.; Wells, Jack A.; Powell, Nick M.; O'Callaghan, James M.; O'Neill, Michael J.; Murray, Tracey K.; Ahmed, Zeshan; Collins, Emily C.; Johnson, Ross A.; Groves, Ashley; Lythgoe, Mark F.

2017-01-01

Background: Non-invasive characterization of the pathological features of Alzheimer's disease (AD) could enhance patient management and the development of therapeutic strategies. Magnetic resonance imaging texture analysis (MRTA) has been used previously to extract texture descriptors from structural clinical scans in AD to determine cerebral tissue heterogeneity. In this study, we examined the potential of MRTA to specifically identify tau pathology in an AD mouse model and compared the MRTA metrics to histological measures of tau burden. Methods: MRTA was applied to T2 weighted high-resolution MR images of nine 8.5-month-old rTg4510 tau pathology (TG) mice and 16 litter matched wild-type (WT) mice. MRTA comprised of the filtration-histogram technique, where the filtration step extracted and enhanced features of different sizes (fine, medium, and coarse texture scales), followed by quantification of texture using histogram analysis (mean gray level intensity, mean intensity, entropy, uniformity, skewness, standard-deviation, and kurtosis). MRTA was applied to manually segmented regions of interest (ROI) drawn within the cortex, hippocampus, and thalamus regions and the level of tau burden was assessed in equivalent regions using histology. Results: Texture parameters were markedly different between WT and TG in the cortex (E, p < 0.01, K, p < 0.01), the hippocampus (K, p < 0.05) and in the thalamus (K, p < 0.01). In addition, we observed significant correlations between histological measurements of tau burden and kurtosis in the cortex, hippocampus and thalamus. Conclusions: MRTA successfully differentiated WT and TG in brain regions with varying degrees of tau pathology (cortex, hippocampus, and thalamus) based on T2 weighted MR images. Furthermore, the kurtosis measurement correlated with histological measures of tau burden. This initial study indicates that MRTA may have a role in the early diagnosis of AD and the assessment of tau pathology using routinely acquired structural MR images. PMID:29163005

A Set of Image Processing Algorithms for Computer-Aided Diagnosis in Nuclear Medicine Whole Body Bone Scan Images

NASA Astrophysics Data System (ADS)

Huang, Jia-Yann; Kao, Pan-Fu; Chen, Yung-Sheng

2007-06-01

Adjustment of brightness and contrast in nuclear medicine whole body bone scan images may confuse nuclear medicine physicians when identifying small bone lesions as well as making the identification of subtle bone lesion changes in sequential studies difficult. In this study, we developed a computer-aided diagnosis system, based on the fuzzy sets histogram thresholding method and anatomical knowledge-based image segmentation method that was able to analyze and quantify raw image data and identify the possible location of a lesion. To locate anatomical reference points, the fuzzy sets histogram thresholding method was adopted as a first processing stage to suppress the soft tissue in the bone images. Anatomical knowledge-based image segmentation method was then applied to segment the skeletal frame into different regions of homogeneous bones. For the different segmented bone regions, the lesion thresholds were set at different cut-offs. To obtain lesion thresholds in different segmented regions, the ranges and standard deviations of the image's gray-level distribution were obtained from 100 normal patients' whole body bone images and then, another 62 patients' images were used for testing. The two groups of images were independent. The sensitivity and the mean number of false lesions detected were used as performance indices to evaluate the proposed system. The overall sensitivity of the system is 92.1% (222 of 241) and 7.58 false detections per patient scan image. With a high sensitivity and an acceptable false lesions detection rate, this computer-aided automatic lesion detection system is demonstrated as useful and will probably in the future be able to help nuclear medicine physicians to identify possible bone lesions.

Veterinary software application for comparison of thermograms for pathology evaluation

NASA Astrophysics Data System (ADS)

Pant, Gita; Umbaugh, Scott E.; Dahal, Rohini; Lama, Norsang; Marino, Dominic J.; Sackman, Joseph

2017-09-01

The bilateral symmetry property in mammals allows for the detection of pathology by comparison of opposing sides. For any pathological disorder, thermal patterns differ compared to the normal body part. A software application for veterinary clinics has been under development to input two thermograms of body parts on both sides, one normal and the other unknown, and the application compares them based on extracted features and appropriate similarity and difference measures and outputs the likelihood of pathology. Here thermographic image data from 19° C to 40° C was linearly remapped to create images with 256 gray level values. Features were extracted from these images, including histogram, texture and spectral features. The comparison metrics used are the vector inner product, Tanimoto, Euclidean, city block, Minkowski and maximum value metric. Previous research with the anterior cruciate ligament (ACL) pathology in dogs suggested any thermogram variation below a threshold of 40% of Euclidean distance is normal and above 40% is abnormal. Here the 40% threshold was applied to a new ACL image set and achieved a sensitivity of 75%, an improvement from the 55% sensitivity of the previous work. With the new data set it was determined that using a threshold of 20% provided a much improved 92% sensitivity metric. However, this will require further research to determine the corresponding specificity success rate. Additionally, it was found that the anterior view provided better results than the lateral view. It was also determined that better results were obtained with all three feature sets than with just the histogram and texture sets. Further experiments are ongoing with larger image datasets, and pathologies, new features and comparison metric evaluation for determination of more accurate threshold values to separate normal and abnormal images.

Osteoarthritis classification using self organizing map based on gabor kernel and contrast-limited adaptive histogram equalization.

PubMed

Anifah, Lilik; Purnama, I Ketut Eddy; Hariadi, Mochamad; Purnomo, Mauridhi Hery

2013-01-01

Localization is the first step in osteoarthritis (OA) classification. Manual classification, however, is time-consuming, tedious, and expensive. The proposed system is designed as decision support system for medical doctors to classify the severity of knee OA. A method has been proposed here to localize a joint space area for OA and then classify it in 4 steps to classify OA into KL-Grade 0, KL-Grade 1, KL-Grade 2, KL-Grade 3 and KL-Grade 4, which are preprocessing, segmentation, feature extraction, and classification. In this proposed system, right and left knee detection was performed by employing the Contrast-Limited Adaptive Histogram Equalization (CLAHE) and the template matching. The Gabor kernel, row sum graph and moment methods were used to localize the junction space area of knee. CLAHE is used for preprocessing step, i.e.to normalize the varied intensities. The segmentation process was conducted using the Gabor kernel, template matching, row sum graph and gray level center of mass method. Here GLCM (contrast, correlation, energy, and homogeinity) features were employed as training data. Overall, 50 data were evaluated for training and 258 data for testing. Experimental results showed the best performance by using gabor kernel with parameters α=8, θ=0, Ψ=[0 π/2], γ=0,8, N=4 and with number of iterations being 5000, momentum value 0.5 and α0=0.6 for the classification process. The run gave classification accuracy rate of 93.8% for KL-Grade 0, 70% for KL-Grade 1, 4% for KL-Grade 2, 10% for KL-Grade 3 and 88.9% for KL-Grade 4.

Osteoarthritis Classification Using Self Organizing Map Based on Gabor Kernel and Contrast-Limited Adaptive Histogram Equalization

PubMed Central

Anifah, Lilik; Purnama, I Ketut Eddy; Hariadi, Mochamad; Purnomo, Mauridhi Hery

2013-01-01

Localization is the first step in osteoarthritis (OA) classification. Manual classification, however, is time-consuming, tedious, and expensive. The proposed system is designed as decision support system for medical doctors to classify the severity of knee OA. A method has been proposed here to localize a joint space area for OA and then classify it in 4 steps to classify OA into KL-Grade 0, KL-Grade 1, KL-Grade 2, KL-Grade 3 and KL-Grade 4, which are preprocessing, segmentation, feature extraction, and classification. In this proposed system, right and left knee detection was performed by employing the Contrast-Limited Adaptive Histogram Equalization (CLAHE) and the template matching. The Gabor kernel, row sum graph and moment methods were used to localize the junction space area of knee. CLAHE is used for preprocessing step, i.e.to normalize the varied intensities. The segmentation process was conducted using the Gabor kernel, template matching, row sum graph and gray level center of mass method. Here GLCM (contrast, correlation, energy, and homogeinity) features were employed as training data. Overall, 50 data were evaluated for training and 258 data for testing. Experimental results showed the best performance by using gabor kernel with parameters α=8, θ=0, Ψ=[0 π/2], γ=0,8, N=4 and with number of iterations being 5000, momentum value 0.5 and α0=0.6 for the classification process. The run gave classification accuracy rate of 93.8% for KL-Grade 0, 70% for KL-Grade 1, 4% for KL-Grade 2, 10% for KL-Grade 3 and 88.9% for KL-Grade 4. PMID:23525188

Effect of resin infiltration on white spot lesions after debonding orthodontic brackets.

PubMed

Hammad, Shaza M; El Banna, Mai; El Zayat, Inas; Mohsen, Mohamed Abdel

2012-02-01

To evaluate the effect of application of a resin infiltration material on masking the white spot lesions (WSLs) after bracket removal. 18 patients participated in this study and were divided into two groups of nine patients each; by a visual score based on the extent of demineralization, according to the classification of the WSLs. Group 1: Visible WSLs without surface disruption and Group 2: WSLs showed a roughened surface but not requiring restoration. Three successive photographs were taken for every patient; immediately after bracket removal, 1 week after oral hygiene measures and after Icon material application. The JPEG images were imported into image analysis software (Image J version 1.33u for Windows XP, US National Institutes of Health) which presented the images into histograms of gray scale from (0 to 255). Initial and final images were compared for percentage of WSLs masking area. For both groups, a statistically significant difference at P<0.05 was obtained as follows; for WSLs in Group 1, the means at gray scale for the initial and the final photographs were 126.091 +/- 13.452 and 221.268 +/- 9.350 respectively and they revealed significance by Wilcoxon's signed rank test = 0.038, P<0.05. For WSLs in Group 2, the means at gray scale for the initial and the final photographs were 95.585 +/- 20.973 and 155.612 +/- 31.203 respectively and they revealed significance by Wilcoxon's signed rank test = 0.029, P<0.05.

Infrared small target enhancement: grey level mapping based on improved sigmoid transformation and saliency histogram

NASA Astrophysics Data System (ADS)

Wan, Minjie; Gu, Guohua; Qian, Weixian; Ren, Kan; Chen, Qian

2018-06-01

Infrared (IR) small target enhancement plays a significant role in modern infrared search and track (IRST) systems and is the basic technique of target detection and tracking. In this paper, a coarse-to-fine grey level mapping method using improved sigmoid transformation and saliency histogram is designed to enhance IR small targets under different backgrounds. For the stage of rough enhancement, the intensity histogram is modified via an improved sigmoid function so as to narrow the regular intensity range of background as much as possible. For the part of further enhancement, a linear transformation is accomplished based on a saliency histogram constructed by averaging the cumulative saliency values provided by a saliency map. Compared with other typical methods, the presented method can achieve both better visual performances and quantitative evaluations.

Bin recycling strategy for improving the histogram precision on GPU

NASA Astrophysics Data System (ADS)

Cárdenas-Montes, Miguel; Rodríguez-Vázquez, Juan José; Vega-Rodríguez, Miguel A.

2016-07-01

Histogram is an easily comprehensible way to present data and analyses. In the current scientific context with access to large volumes of data, the processing time for building histogram has dramatically increased. For this reason, parallel construction is necessary to alleviate the impact of the processing time in the analysis activities. In this scenario, GPU computing is becoming widely used for reducing until affordable levels the processing time of histogram construction. Associated to the increment of the processing time, the implementations are stressed on the bin-count accuracy. Accuracy aspects due to the particularities of the implementations are not usually taken into consideration when building histogram with very large data sets. In this work, a bin recycling strategy to create an accuracy-aware implementation for building histogram on GPU is presented. In order to evaluate the approach, this strategy was applied to the computation of the three-point angular correlation function, which is a relevant function in Cosmology for the study of the Large Scale Structure of Universe. As a consequence of the study a high-accuracy implementation for histogram construction on GPU is proposed.

Mammogram segmentation using maximal cell strength updation in cellular automata.

PubMed

Anitha, J; Peter, J Dinesh

2015-08-01

Breast cancer is the most frequently diagnosed type of cancer among women. Mammogram is one of the most effective tools for early detection of the breast cancer. Various computer-aided systems have been introduced to detect the breast cancer from mammogram images. In a computer-aided diagnosis system, detection and segmentation of breast masses from the background tissues is an important issue. In this paper, an automatic segmentation method is proposed to identify and segment the suspicious mass regions of mammogram using a modified transition rule named maximal cell strength updation in cellular automata (CA). In coarse-level segmentation, the proposed method performs an adaptive global thresholding based on the histogram peak analysis to obtain the rough region of interest. An automatic seed point selection is proposed using gray-level co-occurrence matrix-based sum average feature in the coarse segmented image. Finally, the method utilizes CA with the identified initial seed point and the modified transition rule to segment the mass region. The proposed approach is evaluated over the dataset of 70 mammograms with mass from mini-MIAS database. Experimental results show that the proposed approach yields promising results to segment the mass region in the mammograms with the sensitivity of 92.25% and accuracy of 93.48%.

Tumor segmentation of multi-echo MR T2-weighted images with morphological operators

NASA Astrophysics Data System (ADS)

Torres, W.; Martín-Landrove, M.; Paluszny, M.; Figueroa, G.; Padilla, G.

2009-02-01

In the present work an automatic brain tumor segmentation procedure based on mathematical morphology is proposed. The approach considers sequences of eight multi-echo MR T2-weighted images. The relaxation time T2 characterizes the relaxation of water protons in the brain tissue: white matter, gray matter, cerebrospinal fluid (CSF) or pathological tissue. Image data is initially regularized by the application of a log-convex filter in order to adjust its geometrical properties to those of noiseless data, which exhibits monotonously decreasing convex behavior. Finally the regularized data is analyzed by means of an 8-dimensional morphological eccentricity filter. In a first stage, the filter was used for the spatial homogenization of the tissues in the image, replacing each pixel by the most representative pixel within its structuring element, i.e. the one which exhibits the minimum total distance to all members in the structuring element. On the filtered images, the relaxation time T2 is estimated by means of least square regression algorithm and the histogram of T2 is determined. The T2 histogram was partitioned using the watershed morphological operator; relaxation time classes were established and used for tissue classification and segmentation of the image. The method was validated on 15 sets of MRI data with excellent results.

Investigation of varying gray scale levels for remote manipulation

NASA Technical Reports Server (NTRS)

Bierschwale, John M.; Stuart, Mark A.; Sampaio, Carlos E.

1991-01-01

A study was conducted to investigate the effects of variant monitor gray scale levels and workplace illumination levels on operators' ability to discriminate between different colors on a monochrome monitor. It was determined that 8-gray scale viewing resulted in significantly worse discrimination performance compared to 16- and 32-gray scale viewing and that there was only a negligible difference found between 16 and 32 shades of gray. Therefore, it is recommended that monitors used while performing remote manipulation tasks have 16 or above shades of gray since this evaluation has found levels lower than this to be unacceptable for color discrimination task. There was no significant performance difference found between a high and a low workplace illumination condition. Further analysis was conducted to determine which specific combinations of colors can be used in conjunction with each other to ensure errorfree color coding/brightness discrimination performance while viewing a monochrome monitor. It was found that 92 three-color combination and 9 four-color combinations could be used with 100 percent accuracy. The results can help to determine which gray scale levels should be provided on monochrome monitors as well as which colors to use to ensure the maximal performance of remotely-viewed color discrimination/coding tasks.

Identification of cortex in magnetic resonance images

NASA Astrophysics Data System (ADS)

VanMeter, John W.; Sandon, Peter A.

1992-06-01

The overall goal of the work described here is to make available to the neurosurgeon in the operating room an on-line, three-dimensional, anatomically labeled model of the patient brain, based on pre-operative magnetic resonance (MR) images. A stereotactic operating microscope is currently in experimental use, which allows structures that have been manually identified in MR images to be made available on-line. We have been working to enhance this system by combining image processing techniques applied to the MR data with an anatomically labeled 3-D brain model developed from the Talairach and Tournoux atlas. Here we describe the process of identifying cerebral cortex in the patient MR images. MR images of brain tissue are reasonably well described by material mixture models, which identify each pixel as corresponding to one of a small number of materials, or as being a composite of two materials. Our classification algorithm consists of three steps. First, we apply hierarchical, adaptive grayscale adjustments to correct for nonlinearities in the MR sensor. The goal of this preprocessing step, based on the material mixture model, is to make the grayscale distribution of each tissue type constant across the entire image. Next, we perform an initial classification of all tissue types according to gray level. We have used a sum of Gaussian's approximation of the histogram to perform this classification. Finally, we identify pixels corresponding to cortex, by taking into account the spatial patterns characteristic of this tissue. For this purpose, we use a set of matched filters to identify image locations having the appropriate configuration of gray matter (cortex), cerebrospinal fluid and white matter, as determined by the previous classification step.

Macronuclear chromatin structure dynamics in Colpoda inflata (Protista, Ciliophora) resting encystment.

PubMed

Tiano, L; Chessa, M G; Carrara, S; Tagliafierro, G; Delmonte Corrado, M U

1999-01-01

The chromatin structure dynamics of the Colpoda inflata macronucleus have been investigated in relation to its functional condition, concerning chromatin body extrusion regulating activity. Samples of 2- and 25-day-old resting cysts derived from a standard culture, and of 1-year-old resting cysts derived from a senescent culture, were examined by means of histogram analysis performed on acquired optical microscopy images. Three groups of histograms were detected in each sample. Histogram classification, clustering and matching were assessed in order to obtain the mean histogram of each group. Comparative analysis of the mean histogram showed a similarity in the grey level range of 25-day- and 1-year-old cysts, unlike the wider grey level range found in 2-day-old cysts. Moreover, the respective mean histograms of the three cyst samples appeared rather similar in shape. All this implies that macronuclear chromatin structural features of 1-year-old cysts are common to both cyst standard cultures. The evaluation of the acquired images and their respective histograms evidenced a dynamic state of the macronuclear chromatin, appearing differently condensed in relation to the chromatin body extrusion regulating activity of the macronucleus. The coexistence of a chromatin-decondensed macronucleus with a pycnotic extrusion body suggests that chromatin unable to decondense, thus inactive, is extruded. This finding, along with the presence of chromatin structural features common to standard and senescent cyst populations, supports the occurrence of 'rejuvenated' cell lines from 1-year-old encysted senescent cells, a phenomenon which could be a result of accomplished macronuclear renewal.

Fuzzy Reasoning to More Accurately Determine Void Areas on Optical Micrographs of Composite Structures

NASA Technical Reports Server (NTRS)

Dominquez, Jesus A.; Tate, Lanetra C.; Wright, M. Clara; Caraccio, Anne

2013-01-01

Accomplishing the best-performing composite matrix (resin) requires that not only the processing method but also the cure cycle generate low-void-content structures. If voids are present, the performance of the composite matrix will be significantly reduced. This is usually noticed by significant reductions in matrix-dominated properties, such as compression and shear strength. Voids in composite materials are areas that are absent of the composite components: matrix and fibers. The characteristics of the voids and their accurate estimation are critical to determine for high performance composite structures. One widely used method of performing void analysis on a composite structure sample is acquiring optical micrographs or Scanning Electron Microscope (SEM) images of lateral sides of the sample and retrieving the void areas within the micrographs/images using an image analysis technique. Segmentation for the retrieval and subsequent computation of void areas within the micrographs/images is challenging as the gray-scaled values of the void areas are close to the gray-scaled values of the matrix leading to the need of manually performing the segmentation based on the histogram of the micrographs/images to retrieve the void areas. The use of an algorithm developed by NASA and based on Fuzzy Reasoning (FR) proved to overcome the difficulty of suitably differentiate void and matrix image areas with similar gray-scaled values leading not only to a more accurate estimation of void areas on composite matrix micrographs but also to a faster void analysis process as the algorithm is fully autonomous.

Application of gray level mapping in computed tomographic colonography: a pilot study to compare with traditional surface rendering method for identification and differentiation of endoluminal lesions

PubMed Central

Chen, Lih-Shyang; Hsu, Ta-Wen; Chang, Shu-Han; Lin, Chih-Wen; Chen, Yu-Ruei; Hsieh, Chin-Chiang; Han, Shu-Chen; Chang, Ku-Yaw; Hou, Chun-Ju

2017-01-01

Objective: In traditional surface rendering (SR) computed tomographic endoscopy, only the shape of endoluminal lesion is depicted without gray-level information unless the volume rendering technique is used. However, volume rendering technique is relatively slow and complex in terms of computation time and parameter setting. We use computed tomographic colonography (CTC) images as examples and report a new visualization technique by three-dimensional gray level mapping (GM) to better identify and differentiate endoluminal lesions. Methods: There are 33 various endoluminal cases from 30 patients evaluated in this clinical study. These cases were segmented using gray-level threshold. The marching cube algorithm was used to detect isosurfaces in volumetric data sets. GM is applied using the surface gray level of CTC. Radiologists conducted the clinical evaluation of the SR and GM images. The Wilcoxon signed-rank test was used for data analysis. Results: Clinical evaluation confirms GM is significantly superior to SR in terms of gray-level pattern and spatial shape presentation of endoluminal cases (p < 0.01) and improves the confidence of identification and clinical classification of endoluminal lesions significantly (p < 0.01). The specificity and diagnostic accuracy of GM is significantly better than those of SR in diagnostic performance evaluation (p < 0.01). Conclusion: GM can reduce confusion in three-dimensional CTC and well correlate CTC with sectional images by the location as well as gray-level value. Hence, GM increases identification and differentiation of endoluminal lesions, and facilitates diagnostic process. Advances in knowledge: GM significantly improves the traditional SR method by providing reliable gray-level information for the surface points and is helpful in identification and differentiation of endoluminal lesions according to their shape and density. PMID:27925483

Pixel-based image fusion with false color mapping

NASA Astrophysics Data System (ADS)

Zhao, Wei; Mao, Shiyi

2003-06-01

In this paper, we propose a pixel-based image fusion algorithm that combines the gray-level image fusion method with the false color mapping. This algorithm integrates two gray-level images presenting different sensor modalities or at different frequencies and produces a fused false-color image. The resulting image has higher information content than each of the original images. The objects in the fused color image are easy to be recognized. This algorithm has three steps: first, obtaining the fused gray-level image of two original images; second, giving the generalized high-boost filtering images between fused gray-level image and two source images respectively; third, generating the fused false-color image. We use the hybrid averaging and selection fusion method to obtain the fused gray-level image. The fused gray-level image will provide better details than two original images and reduce noise at the same time. But the fused gray-level image can't contain all detail information in two source images. At the same time, the details in gray-level image cannot be discerned as easy as in a color image. So a color fused image is necessary. In order to create color variation and enhance details in the final fusion image, we produce three generalized high-boost filtering images. These three images are displayed through red, green and blue channel respectively. A fused color image is produced finally. This method is used to fuse two SAR images acquired on the San Francisco area (California, USA). The result shows that fused false-color image enhances the visibility of certain details. The resolution of the final false-color image is the same as the resolution of the input images.

New segmentation-based tone mapping algorithm for high dynamic range image

NASA Astrophysics Data System (ADS)

Duan, Weiwei; Guo, Huinan; Zhou, Zuofeng; Huang, Huimin; Cao, Jianzhong

2017-07-01

The traditional tone mapping algorithm for the display of high dynamic range (HDR) image has the drawback of losing the impression of brightness, contrast and color information. To overcome this phenomenon, we propose a new tone mapping algorithm based on dividing the image into different exposure regions in this paper. Firstly, the over-exposure region is determined using the Local Binary Pattern information of HDR image. Then, based on the peak and average gray of the histogram, the under-exposure and normal-exposure region of HDR image are selected separately. Finally, the different exposure regions are mapped by differentiated tone mapping methods to get the final result. The experiment results show that the proposed algorithm achieve the better performance both in visual quality and objective contrast criterion than other algorithms.

Histogram based analysis of lung perfusion of children after congenital diaphragmatic hernia repair.

PubMed

Kassner, Nora; Weis, Meike; Zahn, Katrin; Schaible, Thomas; Schoenberg, Stefan O; Schad, Lothar R; Zöllner, Frank G

2018-05-01

To investigate a histogram based approach to characterize the distribution of perfusion in the whole left and right lung by descriptive statistics and to show how histograms could be used to visually explore perfusion defects in two year old children after Congenital Diaphragmatic Hernia (CDH) repair. 28 children (age of 24.2±1.7months; all left sided hernia; 9 after extracorporeal membrane oxygenation therapy) underwent quantitative DCE-MRI of the lung. Segmentations of left and right lung were manually drawn to mask the calculated pulmonary blood flow maps and then to derive histograms for each lung side. Individual and group wise analysis of histograms of left and right lung was performed. Ipsilateral and contralateral lung show significant difference in shape and descriptive statistics derived from the histogram (Wilcoxon signed-rank test, p<0.05) on group wise and individual level. Subgroup analysis (patients with vs without ECMO therapy) showed no significant differences using histogram derived parameters. Histogram analysis can be a valuable tool to characterize and visualize whole lung perfusion of children after CDH repair. It allows for several possibilities to analyze the data, either describing the perfusion differences between the right and left lung but also to explore and visualize localized perfusion patterns in the 3D lung volume. Subgroup analysis will be possible given sufficient sample sizes. Copyright © 2017 Elsevier Inc. All rights reserved.

Recognition of skin melanoma through dermoscopic image analysis

NASA Astrophysics Data System (ADS)

Gómez, Catalina; Herrera, Diana Sofia

2017-11-01

Melanoma skin cancer diagnosis can be challenging due to the similarities of the early stage symptoms with regular moles. Standardized visual parameters can be determined and characterized to suspect a melanoma cancer type. The automation of this diagnosis could have an impact in the medical field by providing a tool to support the specialists with high accuracy. The objective of this study is to develop an algorithm trained to distinguish a highly probable melanoma from a non-dangerous mole by the segmentation and classification of dermoscopic mole images. We evaluate our approach on the dataset provided by the International Skin Imaging Collaboration used in the International Challenge Skin Lesion Analysis Towards Melanoma Detection. For the segmentation task, we apply a preprocessing algorithm and use Otsu's thresholding in the best performing color space; the average Jaccard Index in the test dataset is 70.05%. For the subsequent classification stage, we use joint histograms in the YCbCr color space, a RBF Gaussian SVM trained with five features concerning circularity and irregularity of the segmented lesion, and the Gray Level Co-occurrence matrix features for texture analysis. These features are combined to obtain an Average Classification Accuracy of 63.3% in the test dataset.

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

Object-oriented approach to the automatic segmentation of bones from pediatric hand radiographs

NASA Astrophysics Data System (ADS)

Shim, Hyeonjoon; Liu, Brent J.; Taira, Ricky K.; Hall, Theodore R.

1997-04-01

The purpose of this paper is to develop a robust and accurate method that automatically segments phalangeal and epiphyseal bones from digital pediatric hand radiographs exhibiting various stages of growth. The development of this system draws principles from object-oriented design, model- guided analysis, and feedback control. A system architecture called 'the object segmentation machine' was implemented incorporating these design philosophies. The system is aided by a knowledge base where all model contours and other information such as age, race, and sex, are stored. These models include object structure models, shape models, 1-D wrist profiles, and gray level histogram models. Shape analysis is performed first by using an arc-length orientation transform to break down a given contour into elementary segments and curves. Then an interpretation tree is used as an inference engine to map known model contour segments to data contour segments obtained from the transform. Spatial and anatomical relationships among contour segments work as constraints from shape model. These constraints aid in generating a list of candidate matches. The candidate match with the highest confidence is chosen to be the current intermediate result. Verification of intermediate results are perform by a feedback control loop.

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

[Glossary of terms used by radiologists in image processing].

PubMed

Rolland, Y; Collorec, R; Bruno, A; Ramée, A; Morcet, N; Haigron, P

1995-01-01

We give the definition of 166 words used in image processing. Adaptivity, aliazing, analog-digital converter, analysis, approximation, arc, artifact, artificial intelligence, attribute, autocorrelation, bandwidth, boundary, brightness, calibration, class, classification, classify, centre, cluster, coding, color, compression, contrast, connectivity, convolution, correlation, data base, decision, decomposition, deconvolution, deduction, descriptor, detection, digitization, dilation, discontinuity, discretization, discrimination, disparity, display, distance, distorsion, distribution dynamic, edge, energy, enhancement, entropy, erosion, estimation, event, extrapolation, feature, file, filter, filter floaters, fitting, Fourier transform, frequency, fusion, fuzzy, Gaussian, gradient, graph, gray level, group, growing, histogram, Hough transform, Houndsfield, image, impulse response, inertia, intensity, interpolation, interpretation, invariance, isotropy, iterative, JPEG, knowledge base, label, laplacian, learning, least squares, likelihood, matching, Markov field, mask, matching, mathematical morphology, merge (to), MIP, median, minimization, model, moiré, moment, MPEG, neural network, neuron, node, noise, norm, normal, operator, optical system, optimization, orthogonal, parametric, pattern recognition, periodicity, photometry, pixel, polygon, polynomial, prediction, pulsation, pyramidal, quantization, raster, reconstruction, recursive, region, rendering, representation space, resolution, restoration, robustness, ROC, thinning, transform, sampling, saturation, scene analysis, segmentation, separable function, sequential, smoothing, spline, split (to), shape, threshold, tree, signal, speckle, spectrum, spline, stationarity, statistical, stochastic, structuring element, support, syntaxic, synthesis, texture, truncation, variance, vision, voxel, windowing.

Random walk numerical simulation for hopping transport at finite carrier concentrations: diffusion coefficient and transport energy concept.

PubMed

Gonzalez-Vazquez, J P; Anta, Juan A; Bisquert, Juan

2009-11-28

The random walk numerical simulation (RWNS) method is used to compute diffusion coefficients for hopping transport in a fully disordered medium at finite carrier concentrations. We use Miller-Abrahams jumping rates and an exponential distribution of energies to compute the hopping times in the random walk simulation. The computed diffusion coefficient shows an exponential dependence with respect to Fermi-level and Arrhenius behavior with respect to temperature. This result indicates that there is a well-defined transport level implicit to the system dynamics. To establish the origin of this transport level we construct histograms to monitor the energies of the most visited sites. In addition, we construct "corrected" histograms where backward moves are removed. Since these moves do not contribute to transport, these histograms provide a better estimation of the effective transport level energy. The analysis of this concept in connection with the Fermi-level dependence of the diffusion coefficient and the regime of interest for the functioning of dye-sensitised solar cells is thoroughly discussed.

Optimal scan strategy for mega-pixel and kilo-gray-level OLED-on-silicon microdisplay.

PubMed

Ji, Yuan; Ran, Feng; Ji, Weigui; Xu, Meihua; Chen, Zhangjing; Jiang, Yuxi; Shen, Weixin

2012-06-10

The digital pixel driving scheme makes the organic light-emitting diode (OLED) microdisplays more immune to the pixel luminance variations and simplifies the circuit architecture and design flow compared to the analog pixel driving scheme. Additionally, it is easily applied in full digital systems. However, the data bottleneck becomes a notable problem as the number of pixels and gray levels grow dramatically. This paper will discuss the digital driving ability to achieve kilogray-levels for megapixel displays. The optimal scan strategy is proposed for creating ultra high gray levels and increasing light efficiency and contrast ratio. Two correction schemes are discussed to improve the gray level linearity. A 1280×1024×3 OLED-on-silicon microdisplay, with 4096 gray levels, is designed based on the optimal scan strategy. The circuit driver is integrated in the silicon backplane chip in the 0.35 μm 3.3 V-6 V dual voltage one polysilicon layer, four metal layers (1P4M) complementary metal-oxide semiconductor (CMOS) process with custom top metal. The design aspects of the optimal scan controller are also discussed. The test results show the gray level linearity of the correction schemes for the optimal scan strategy is acceptable by the human eye.

Gray matter alterations and correlation of nutritional intake with the gray matter volume in prediabetes

PubMed Central

Hou, Yi-Cheng; Lai, Chien-Han; Wu, Yu-Te; Yang, Shwu-Huey

2016-01-01

Abstract The neurophysiology of prediabetes plays an important role in preventive medicine. The dysregulation of glucose metabolism is likely linked to changes in neuron-related gray matter. Therefore, we designed this study to investigate gray matter alterations in medication-naive prediabetic patients. We expected to find alterations in the gray matter of prediabetic patients. A total of 64 prediabetic patients and 54 controls were enrolled. All subjects received T1 scans using a 3-T magnetic resonance imaging machine. Subjects also completed nutritional intake records at the 24-hour and 3-day time points to determine their carbohydrate, protein, fat, and total calorie intake. We utilized optimized voxel-based morphometry to estimate the gray matter differences between the patients and controls. In addition, the preprandial serum glucose level and the carbohydrate, protein, fat, and total calorie intake levels were tested to determine whether these parameters were correlated with the gray matter volume. Prediabetic patients had lower gray matter volumes than controls in the right anterior cingulate gyrus, right posterior cingulate gyrus, left insula, left super temporal gyrus, and left middle temporal gyrus (corrected P < 0.05; voxel threshold: 33). Gray matter volume in the right anterior cingulate was also negatively correlated with the preprandial serum glucose level gyrus in a voxel-dependent manner (r = –0.501; 2-tailed P = 0.001). The cingulo-temporal and insula gray matter alterations may be associated with the glucose dysregulation in prediabetic patients. PMID:27336893

[Clinical application of MRI histogram in evaluation of muscle fatty infiltration].

PubMed

Zheng, Y M; Du, J; Li, W Z; Wang, Z X; Zhang, W; Xiao, J X; Yuan, Y

2016-10-18

To describe a method based on analysis of the histogram of intensity values produced from the magnetic resonance imaging (MRI) for quantifying the degree of fatty infiltration. The study included 25 patients with dystrophinopathy. All the subjects underwent muscle MRI test at thigh level. The histogram M values of 250 muscles adjusted for subcutaneous fat, representing the degree of fatty infiltration, were compared with the expert visual reading using the modified Mercuri scale. There was a significant positive correlation between the histogram M values and the scores of visual reading (r=0.854, P<0.001). The distinct pattern of muscle involvement detected in the patients with dystrophinopathy in our study of histogram M values was similar to that of visual reading and results in literature. The histogram M values had stronger correlations with the clinical data than the scores of visual reading as follows: the correlations with age (r=0.730, P<0.001) and (r=0.753, P<0.001); with strength of knee extensor (r=-0.468, P=0.024) and (r=-0.460, P=0.027) respectively. Meanwhile, the histogram M values analysis had better repeatability than visual reading with the interclass correlation coefficient was 0.998 (95% CI: 0.997-0.998, P<0.001) and 0.958 (95% CI: 0.946-0.967, P<0.001) respectively. Histogram M values analysis of MRI with the advantages of repeatability and objectivity can be used to evaluate the degree of muscle fatty infiltration.

Hierarchical brain tissue segmentation and its application in multiple sclerosis and Alzheimer's disease

NASA Astrophysics Data System (ADS)

Lei, Tianhu; Udupa, Jayaram K.; Moonis, Gul; Schwartz, Eric; Balcer, Laura

2005-04-01

Based on Fuzzy Connectedness (FC) object delineation principles and algorithms, a hierarchical brain tissue segmentation technique has been developed for MR images. After MR image background intensity inhomogeneity correction and intensity standardization, three FC objects for cerebrospinal fluid (CSF), gray matter (GM), and white matter (WM) are generated via FC object delineation, and an intracranial (IC) mask is created via morphological operations. Then, the IC mask is decomposed into parenchymal (BP) and CSF masks, while the BP mask is separated into WM and GM masks. WM mask is further divided into pure and dirty white matter masks (PWM and DWM). In Multiple Sclerosis studies, a severe white matter lesion (LS) mask is defined from DWM mask. Based on the segmented brain tissue images, a histogram-based method has been developed to find disease-specific, image-based quantitative markers for characterizing the macromolecular manifestation of the two diseases. These same procedures have been applied to 65 MS (46 patients and 19 normal subjects) and 25 AD (15 patients and 10 normal subjects) data sets, each of which consists of FSE PD- and T2-weighted MR images. Histograms representing standardized PD and T2 intensity distributions and their numerical parameters provide an effective means for characterizing the two diseases. The procedures are systematic, nearly automated, robust, and the results are reproducible.

Image Enhancement via Subimage Histogram Equalization Based on Mean and Variance

PubMed Central

2017-01-01

This paper puts forward a novel image enhancement method via Mean and Variance based Subimage Histogram Equalization (MVSIHE), which effectively increases the contrast of the input image with brightness and details well preserved compared with some other methods based on histogram equalization (HE). Firstly, the histogram of input image is divided into four segments based on the mean and variance of luminance component, and the histogram bins of each segment are modified and equalized, respectively. Secondly, the result is obtained via the concatenation of the processed subhistograms. Lastly, the normalization method is deployed on intensity levels, and the integration of the processed image with the input image is performed. 100 benchmark images from a public image database named CVG-UGR-Database are used for comparison with other state-of-the-art methods. The experiment results show that the algorithm can not only enhance image information effectively but also well preserve brightness and details of the original image. PMID:29403529

Quantitative Image Quality and Histogram-Based Evaluations of an Iterative Reconstruction Algorithm at Low-to-Ultralow Radiation Dose Levels: A Phantom Study in Chest CT

PubMed Central

Lee, Ki Baek

2018-01-01

Objective To describe the quantitative image quality and histogram-based evaluation of an iterative reconstruction (IR) algorithm in chest computed tomography (CT) scans at low-to-ultralow CT radiation dose levels. Materials and Methods In an adult anthropomorphic phantom, chest CT scans were performed with 128-section dual-source CT at 70, 80, 100, 120, and 140 kVp, and the reference (3.4 mGy in volume CT Dose Index [CTDIvol]), 30%-, 60%-, and 90%-reduced radiation dose levels (2.4, 1.4, and 0.3 mGy). The CT images were reconstructed by using filtered back projection (FBP) algorithms and IR algorithm with strengths 1, 3, and 5. Image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were statistically compared between different dose levels, tube voltages, and reconstruction algorithms. Moreover, histograms of subtraction images before and after standardization in x- and y-axes were visually compared. Results Compared with FBP images, IR images with strengths 1, 3, and 5 demonstrated image noise reduction up to 49.1%, SNR increase up to 100.7%, and CNR increase up to 67.3%. Noteworthy image quality degradations on IR images including a 184.9% increase in image noise, 63.0% decrease in SNR, and 51.3% decrease in CNR, and were shown between 60% and 90% reduced levels of radiation dose (p < 0.0001). Subtraction histograms between FBP and IR images showed progressively increased dispersion with increased IR strength and increased dose reduction. After standardization, the histograms appeared deviated and ragged between FBP images and IR images with strength 3 or 5, but almost normally-distributed between FBP images and IR images with strength 1. Conclusion The IR algorithm may be used to save radiation doses without substantial image quality degradation in chest CT scanning of the adult anthropomorphic phantom, down to approximately 1.4 mGy in CTDIvol (60% reduced dose). PMID:29354008

Gray matter network disruptions and amyloid beta in cognitively normal adults.

PubMed

Tijms, Betty M; Kate, Mara Ten; Wink, Alle Meije; Visser, Pieter Jelle; Ecay, Mirian; Clerigue, Montserrat; Estanga, Ainara; Garcia Sebastian, Maite; Izagirre, Andrea; Villanua, Jorge; Martinez Lage, Pablo; van der Flier, Wiesje M; Scheltens, Philip; Sanz Arigita, Ernesto; Barkhof, Frederik

2016-01-01

Gray matter networks are disrupted in Alzheimer's disease (AD). It is unclear when these disruptions start during the development of AD. Amyloid beta 1-42 (Aβ42) is among the earliest changes in AD. We studied, in cognitively healthy adults, the relationship between Aβ42 levels in cerebrospinal fluid (CSF) and single-subject cortical gray matter network measures. Single-subject gray matter networks were extracted from structural magnetic resonance imaging scans in a sample of cognitively healthy adults (N = 185; age range 39-79, mini-mental state examination >25, N = 12 showed abnormal Aβ42 < 550 pg/mL). Degree, clustering coefficient, and path length were computed at whole brain level and for 90 anatomical areas. Associations between continuous Aβ42 CSF levels and single-subject cortical gray matter network measures were tested. Smoothing splines were used to determine whether a linear or nonlinear relationship gave a better fit to the data. Lower Aβ42 CSF levels were linearly associated at whole brain level with lower connectivity density, and nonlinearly with lower clustering values and higher path length values, which is indicative of a less-efficient network organization. These relationships were specific to medial temporal areas, precuneus, and the middle frontal gyrus (all p < 0.05). These results suggest that mostly within the normal spectrum of amyloid, lower Aβ42 levels can be related to gray matter networks disruptions. Copyright © 2016 Elsevier Inc. All rights reserved.

Image barcodes

NASA Astrophysics Data System (ADS)

Damera-Venkata, Niranjan; Yen, Jonathan

2003-01-01

A Visually significant two-dimensional barcode (VSB) developed by Shaked et. al. is a method used to design an information carrying two-dimensional barcode, which has the appearance of a given graphical entity such as a company logo. The encoding and decoding of information using the VSB, uses a base image with very few graylevels (typically only two). This typically requires the image histogram to be bi-modal. For continuous-tone images such as digital photographs of individuals, the representation of tone or "shades of gray" is not only important to obtain a pleasing rendition of the face, but in most cases, the VSB renders these images unrecognizable due to its inability to represent true gray-tone variations. This paper extends the concept of a VSB to an image bar code (IBC). We enable the encoding and subsequent decoding of information embedded in the hardcopy version of continuous-tone base-images such as those acquired with a digital camera. The encoding-decoding process is modeled by robust data transmission through a noisy print-scan channel that is explicitly modeled. The IBC supports a high information capacity that differentiates it from common hardcopy watermarks. The reason for the improved image quality over the VSB is a joint encoding/halftoning strategy based on a modified version of block error diffusion. Encoder stability, image quality vs. information capacity tradeoffs and decoding issues with and without explicit knowledge of the base-image are discussed.

LSAH: a fast and efficient local surface feature for point cloud registration

NASA Astrophysics Data System (ADS)

Lu, Rongrong; Zhu, Feng; Wu, Qingxiao; Kong, Yanzi

2018-04-01

Point cloud registration is a fundamental task in high level three dimensional applications. Noise, uneven point density and varying point cloud resolutions are the three main challenges for point cloud registration. In this paper, we design a robust and compact local surface descriptor called Local Surface Angles Histogram (LSAH) and propose an effectively coarse to fine algorithm for point cloud registration. The LSAH descriptor is formed by concatenating five normalized sub-histograms into one histogram. The five sub-histograms are created by accumulating a different type of angle from a local surface patch respectively. The experimental results show that our LSAH is more robust to uneven point density and point cloud resolutions than four state-of-the-art local descriptors in terms of feature matching. Moreover, we tested our LSAH based coarse to fine algorithm for point cloud registration. The experimental results demonstrate that our algorithm is robust and efficient as well.

An effective method for cirrhosis recognition based on multi-feature fusion

NASA Astrophysics Data System (ADS)

Chen, Yameng; Sun, Gengxin; Lei, Yiming; Zhang, Jinpeng

2018-04-01

Liver disease is one of the main causes of human healthy problem. Cirrhosis, of course, is the critical phase during the development of liver lesion, especially the hepatoma. Many clinical cases are still influenced by the subjectivity of physicians in some degree, and some objective factors such as illumination, scale, edge blurring will affect the judgment of clinicians. Then the subjectivity will affect the accuracy of diagnosis and the treatment of patients. In order to solve the difficulty above and improve the recognition rate of liver cirrhosis, we propose a method of multi-feature fusion to obtain more robust representations of texture in ultrasound liver images, the texture features we extract include local binary pattern(LBP), gray level co-occurrence matrix(GLCM) and histogram of oriented gradient(HOG). In this paper, we firstly make a fusion of multi-feature to recognize cirrhosis and normal liver based on parallel combination concept, and the experimental results shows that the classifier is effective for cirrhosis recognition which is evaluated by the satisfying classification rate, sensitivity and specificity of receiver operating characteristic(ROC), and cost time. Through the method we proposed, it will be helpful to improve the accuracy of diagnosis of cirrhosis and prevent the development of liver lesion towards hepatoma.

An Adaptive Image Enhancement Technique by Combining Cuckoo Search and Particle Swarm Optimization Algorithm

PubMed Central

Ye, Zhiwei; Wang, Mingwei; Hu, Zhengbing; Liu, Wei

2015-01-01

Image enhancement is an important procedure of image processing and analysis. This paper presents a new technique using a modified measure and blending of cuckoo search and particle swarm optimization (CS-PSO) for low contrast images to enhance image adaptively. In this way, contrast enhancement is obtained by global transformation of the input intensities; it employs incomplete Beta function as the transformation function and a novel criterion for measuring image quality considering three factors which are threshold, entropy value, and gray-level probability density of the image. The enhancement process is a nonlinear optimization problem with several constraints. CS-PSO is utilized to maximize the objective fitness criterion in order to enhance the contrast and detail in an image by adapting the parameters of a novel extension to a local enhancement technique. The performance of the proposed method has been compared with other existing techniques such as linear contrast stretching, histogram equalization, and evolutionary computing based image enhancement methods like backtracking search algorithm, differential search algorithm, genetic algorithm, and particle swarm optimization in terms of processing time and image quality. Experimental results demonstrate that the proposed method is robust and adaptive and exhibits the better performance than other methods involved in the paper. PMID:25784928

An adaptive image enhancement technique by combining cuckoo search and particle swarm optimization algorithm.

PubMed

Ye, Zhiwei; Wang, Mingwei; Hu, Zhengbing; Liu, Wei

2015-01-01

Image enhancement is an important procedure of image processing and analysis. This paper presents a new technique using a modified measure and blending of cuckoo search and particle swarm optimization (CS-PSO) for low contrast images to enhance image adaptively. In this way, contrast enhancement is obtained by global transformation of the input intensities; it employs incomplete Beta function as the transformation function and a novel criterion for measuring image quality considering three factors which are threshold, entropy value, and gray-level probability density of the image. The enhancement process is a nonlinear optimization problem with several constraints. CS-PSO is utilized to maximize the objective fitness criterion in order to enhance the contrast and detail in an image by adapting the parameters of a novel extension to a local enhancement technique. The performance of the proposed method has been compared with other existing techniques such as linear contrast stretching, histogram equalization, and evolutionary computing based image enhancement methods like backtracking search algorithm, differential search algorithm, genetic algorithm, and particle swarm optimization in terms of processing time and image quality. Experimental results demonstrate that the proposed method is robust and adaptive and exhibits the better performance than other methods involved in the paper.

Anniversary Paper: Image processing and manipulation through the pages of Medical Physics

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

Armato, Samuel G. III; Ginneken, Bram van; Image Sciences Institute, University Medical Center Utrecht, Heidelberglaan 100, Room Q0S.459, 3584 CX Utrecht

The language of radiology has gradually evolved from ''the film'' (the foundation of radiology since Wilhelm Roentgen's 1895 discovery of x-rays) to ''the image,'' an electronic manifestation of a radiologic examination that exists within the bits and bytes of a computer. Rather than simply storing and displaying radiologic images in a static manner, the computational power of the computer may be used to enhance a radiologist's ability to visually extract information from the image through image processing and image manipulation algorithms. Image processing tools provide a broad spectrum of opportunities for image enhancement. Gray-level manipulations such as histogram equalization, spatialmore » alterations such as geometric distortion correction, preprocessing operations such as edge enhancement, and enhanced radiography techniques such as temporal subtraction provide powerful methods to improve the diagnostic quality of an image or to enhance structures of interest within an image. Furthermore, these image processing algorithms provide the building blocks of more advanced computer vision methods. The prominent role of medical physicists and the AAPM in the advancement of medical image processing methods, and in the establishment of the ''image'' as the fundamental entity in radiology and radiation oncology, has been captured in 35 volumes of Medical Physics.« less

Quantitative radiomics: impact of stochastic effects on textural feature analysis implies the need for standards

PubMed Central

Nyflot, Matthew J.; Yang, Fei; Byrd, Darrin; Bowen, Stephen R.; Sandison, George A.; Kinahan, Paul E.

2015-01-01

Abstract. Image heterogeneity metrics such as textural features are an active area of research for evaluating clinical outcomes with positron emission tomography (PET) imaging and other modalities. However, the effects of stochastic image acquisition noise on these metrics are poorly understood. We performed a simulation study by generating 50 statistically independent PET images of the NEMA IQ phantom with realistic noise and resolution properties. Heterogeneity metrics based on gray-level intensity histograms, co-occurrence matrices, neighborhood difference matrices, and zone size matrices were evaluated within regions of interest surrounding the lesions. The impact of stochastic variability was evaluated with percent difference from the mean of the 50 realizations, coefficient of variation and estimated sample size for clinical trials. Additionally, sensitivity studies were performed to simulate the effects of patient size and image reconstruction method on the quantitative performance of these metrics. Complex trends in variability were revealed as a function of textural feature, lesion size, patient size, and reconstruction parameters. In conclusion, the sensitivity of PET textural features to normal stochastic image variation and imaging parameters can be large and is feature-dependent. Standards are needed to ensure that prospective studies that incorporate textural features are properly designed to measure true effects that may impact clinical outcomes. PMID:26251842

Quantitative radiomics: impact of stochastic effects on textural feature analysis implies the need for standards.

PubMed

Nyflot, Matthew J; Yang, Fei; Byrd, Darrin; Bowen, Stephen R; Sandison, George A; Kinahan, Paul E

2015-10-01

Image heterogeneity metrics such as textural features are an active area of research for evaluating clinical outcomes with positron emission tomography (PET) imaging and other modalities. However, the effects of stochastic image acquisition noise on these metrics are poorly understood. We performed a simulation study by generating 50 statistically independent PET images of the NEMA IQ phantom with realistic noise and resolution properties. Heterogeneity metrics based on gray-level intensity histograms, co-occurrence matrices, neighborhood difference matrices, and zone size matrices were evaluated within regions of interest surrounding the lesions. The impact of stochastic variability was evaluated with percent difference from the mean of the 50 realizations, coefficient of variation and estimated sample size for clinical trials. Additionally, sensitivity studies were performed to simulate the effects of patient size and image reconstruction method on the quantitative performance of these metrics. Complex trends in variability were revealed as a function of textural feature, lesion size, patient size, and reconstruction parameters. In conclusion, the sensitivity of PET textural features to normal stochastic image variation and imaging parameters can be large and is feature-dependent. Standards are needed to ensure that prospective studies that incorporate textural features are properly designed to measure true effects that may impact clinical outcomes.

A statistical-textural-features based approach for classification of solid drugs using surface microscopic images.

PubMed

Tahir, Fahima; Fahiem, Muhammad Abuzar

2014-01-01

The quality of pharmaceutical products plays an important role in pharmaceutical industry as well as in our lives. Usage of defective tablets can be harmful for patients. In this research we proposed a nondestructive method to identify defective and nondefective tablets using their surface morphology. Three different environmental factors temperature, humidity and moisture are analyzed to evaluate the performance of the proposed method. Multiple textural features are extracted from the surface of the defective and nondefective tablets. These textural features are gray level cooccurrence matrix, run length matrix, histogram, autoregressive model and HAAR wavelet. Total textural features extracted from images are 281. We performed an analysis on all those 281, top 15, and top 2 features. Top 15 features are extracted using three different feature reduction techniques: chi-square, gain ratio and relief-F. In this research we have used three different classifiers: support vector machine, K-nearest neighbors and naïve Bayes to calculate the accuracies against proposed method using two experiments, that is, leave-one-out cross-validation technique and train test models. We tested each classifier against all selected features and then performed the comparison of their results. The experimental work resulted in that in most of the cases SVM performed better than the other two classifiers.

Sea-land segmentation for infrared remote sensing images based on superpixels and multi-scale features

NASA Astrophysics Data System (ADS)

Lei, Sen; Zou, Zhengxia; Liu, Dunge; Xia, Zhenghuan; Shi, Zhenwei

2018-06-01

Sea-land segmentation is a key step for the information processing of ocean remote sensing images. Traditional sea-land segmentation algorithms ignore the local similarity prior of sea and land, and thus fail in complex scenarios. In this paper, we propose a new sea-land segmentation method for infrared remote sensing images to tackle the problem based on superpixels and multi-scale features. Considering the connectivity and local similarity of sea or land, we interpret the sea-land segmentation task in view of superpixels rather than pixels, where similar pixels are clustered and the local similarity are explored. Moreover, the multi-scale features are elaborately designed, comprising of gray histogram and multi-scale total variation. Experimental results on infrared bands of Landsat-8 satellite images demonstrate that the proposed method can obtain more accurate and more robust sea-land segmentation results than the traditional algorithms.

[Detecting the information of cucumber in greenhouse for picking based on NIR image].

PubMed

Yuan, Ting; Xu, Chen-Guang; Ren, Yong-Xin; Feng, Qing-Chun; Tan, Yu-Zhi; Li, Wei

2009-08-01

For the cucumber harvesting robot, the identification of target information is one of important tasks in the automation of fruit-picking. In order to implement spatial fruit localization and quality discrimination in greenhouse, this paper presented a machine vision algorithm for the recognition and detection of cucumber fruits based on near-infrared spectral imaging. By comparing the spectral reflectance of cucumber plant (fruit, leaf and stem) from visible to infrared region (325-1 075 nm) measured by ASD FieldSpec Pro VNIR spectrometer, a monospectral near-infrared image at the 850 nm sensitive wavelength was captured to cope with the similar-color segmentation problem in complex environment. Then, a method of fruit extraction was developed on the basis of the following steps. Firstly, from the gray level histogram it was observed that the pixels of fruit distributed on the right are lesser than that of background, so "P parameter threshold method" was used to image segmentation. Subsequently, divided local image was partitioned into several sub-blocks by the application of adaptive template mining, which was feasible for processing the fruit with long-column feature. Finally, noises including parts of stem and leaf were eliminated using estimation condition of barycentre position and area size, proved by relative experiment In addition, the region for robotic grasping was established by gray variation between fruit-handle and fruit pedicel, as the quality feature was extracted with morphological characteristics of the centre-line length and the fruit flexure degree. A detecting experiment was carried out on 30 images with cucumber fruits and 10 images with no fruits, which were taken in a changing greenhouse environment. The results indicate that the accuracy rate of the recognition was 83.3% and 100%, while the success rate of effectively acquiring the grasping region was 83.3%, which can meet the demand of robotic fruit-harvesting.

Automated Robust Image Segmentation: Level Set Method Using Nonnegative Matrix Factorization with Application to Brain MRI.

PubMed

Dera, Dimah; Bouaynaya, Nidhal; Fathallah-Shaykh, Hassan M

2016-07-01

We address the problem of fully automated region discovery and robust image segmentation by devising a new deformable model based on the level set method (LSM) and the probabilistic nonnegative matrix factorization (NMF). We describe the use of NMF to calculate the number of distinct regions in the image and to derive the local distribution of the regions, which is incorporated into the energy functional of the LSM. The results demonstrate that our NMF-LSM method is superior to other approaches when applied to synthetic binary and gray-scale images and to clinical magnetic resonance images (MRI) of the human brain with and without a malignant brain tumor, glioblastoma multiforme. In particular, the NMF-LSM method is fully automated, highly accurate, less sensitive to the initial selection of the contour(s) or initial conditions, more robust to noise and model parameters, and able to detect as small distinct regions as desired. These advantages stem from the fact that the proposed method relies on histogram information instead of intensity values and does not introduce nuisance model parameters. These properties provide a general approach for automated robust region discovery and segmentation in heterogeneous images. Compared with the retrospective radiological diagnoses of two patients with non-enhancing grade 2 and 3 oligodendroglioma, the NMF-LSM detects earlier progression times and appears suitable for monitoring tumor response. The NMF-LSM method fills an important need of automated segmentation of clinical MRI.

Higher homocysteine associated with thinner cortical gray matter in 803 ADNI subjects

PubMed Central

Madsen, Sarah K.; Rajagopalan, Priya; Joshi, Shantanu H.; Toga, Arthur W.; Thompson, Paul M.

2014-01-01

A significant portion of our risk for dementia in old age is associated with lifestyle factors (diet, exercise, and cardiovascular health) that are modifiable, at least in principle. One such risk factor – high homocysteine levels in the blood – is known to increase risk for Alzheimer’s disease and vascular disorders. Here we set out to understand how homocysteine levels relate to 3D surface-based maps of cortical gray matter distribution (thickness, volume, surface area) computed from brain MRI in 803 elderly subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. Individuals with higher plasma levels of homocysteine had lower gray matter thickness in bilateral frontal, parietal, occipital and right temporal regions; and lower gray matter volumes in left frontal, parietal, temporal, and occipital regions, after controlling for diagnosis, age, and sex, and after correcting for multiple comparisons. No significant within-group associations were found in cognitively healthy people, mild cognitive impairment, or Alzheimer’s disease. These regional differences in gray matter structure may be useful biomarkers to assess the effectiveness of interventions, such as vitamin B supplements, that aim to prevent homocysteine-related brain atrophy by normalizing homocysteine levels. PMID:25444607

Universal and adapted vocabularies for generic visual categorization.

PubMed

Perronnin, Florent

2008-07-01

Generic Visual Categorization (GVC) is the pattern classification problem which consists in assigning labels to an image based on its semantic content. This is a challenging task as one has to deal with inherent object/scene variations as well as changes in viewpoint, lighting and occlusion. Several state-of-the-art GVC systems use a vocabulary of visual terms to characterize images with a histogram of visual word counts. We propose a novel practical approach to GVC based on a universal vocabulary, which describes the content of all the considered classes of images, and class vocabularies obtained through the adaptation of the universal vocabulary using class-specific data. The main novelty is that an image is characterized by a set of histograms - one per class - where each histogram describes whether the image content is best modeled by the universal vocabulary or the corresponding class vocabulary. This framework is applied to two types of local image features: low-level descriptors such as the popular SIFT and high-level histograms of word co-occurrences in a spatial neighborhood. It is shown experimentally on two challenging datasets (an in-house database of 19 categories and the PASCAL VOC 2006 dataset) that the proposed approach exhibits state-of-the-art performance at a modest computational cost.

Pesticide contamination of endangered gray bats and their food base in Boone County, Missouri, 1982

USGS Publications Warehouse

Clawson, R.L.; Clark, D.R.

1989-01-01

Gray bat guano from Devil's Icebox and Hunters Caves contained dieldrin at levels previously associated with gray bat mortality. Two of four gray bats found dead in Holton Cave had lethal brain concentrations of dieldrin. Twenty-five of 28 (86%) insect samples from bat foraging areas contained measurable dieldrin, heptachlor epoxide or both. Beetle samples were most heavily contaminated containing up to 2.2 ppm and 1.1 ppm heptachlor epoxide. The addition of Holton Cave brings to five the number of Missouri caves where gray bats have died of food chain pesticide poisoning.

Gray water recycle: Effect of pretreatment technologies on low pressure reverse osmosis treatment

USDA-ARS?s Scientific Manuscript database

Gray water can be a valuable source of water when properly treated to reduce the risks associated with chemical and microbial contamination to acceptable levels for the intended reuse application. In this study, the treatment of gray water using low pressure reverse osmosis (RO) filtration after pre...

Histogram-based normalization technique on human brain magnetic resonance images from different acquisitions.

PubMed

Sun, Xiaofei; Shi, Lin; Luo, Yishan; Yang, Wei; Li, Hongpeng; Liang, Peipeng; Li, Kuncheng; Mok, Vincent C T; Chu, Winnie C W; Wang, Defeng

2015-07-28

Intensity normalization is an important preprocessing step in brain magnetic resonance image (MRI) analysis. During MR image acquisition, different scanners or parameters would be used for scanning different subjects or the same subject at a different time, which may result in large intensity variations. This intensity variation will greatly undermine the performance of subsequent MRI processing and population analysis, such as image registration, segmentation, and tissue volume measurement. In this work, we proposed a new histogram normalization method to reduce the intensity variation between MRIs obtained from different acquisitions. In our experiment, we scanned each subject twice on two different scanners using different imaging parameters. With noise estimation, the image with lower noise level was determined and treated as the high-quality reference image. Then the histogram of the low-quality image was normalized to the histogram of the high-quality image. The normalization algorithm includes two main steps: (1) intensity scaling (IS), where, for the high-quality reference image, the intensities of the image are first rescaled to a range between the low intensity region (LIR) value and the high intensity region (HIR) value; and (2) histogram normalization (HN),where the histogram of low-quality image as input image is stretched to match the histogram of the reference image, so that the intensity range in the normalized image will also lie between LIR and HIR. We performed three sets of experiments to evaluate the proposed method, i.e., image registration, segmentation, and tissue volume measurement, and compared this with the existing intensity normalization method. It is then possible to validate that our histogram normalization framework can achieve better results in all the experiments. It is also demonstrated that the brain template with normalization preprocessing is of higher quality than the template with no normalization processing. We have proposed a histogram-based MRI intensity normalization method. The method can normalize scans which were acquired on different MRI units. We have validated that the method can greatly improve the image analysis performance. Furthermore, it is demonstrated that with the help of our normalization method, we can create a higher quality Chinese brain template.

True progression versus pseudoprogression in the treatment of glioblastomas: a comparison study of normalized cerebral blood volume and apparent diffusion coefficient by histogram analysis.

PubMed

Song, Yong Sub; Choi, Seung Hong; Park, Chul-Kee; Yi, Kyung Sik; Lee, Woong Jae; Yun, Tae Jin; Kim, Tae Min; Lee, Se-Hoon; Kim, Ji-Hoon; Sohn, Chul-Ho; Park, Sung-Hye; Kim, Il Han; Jahng, Geon-Ho; Chang, Kee-Hyun

2013-01-01

The purpose of this study was to differentiate true progression from pseudoprogression of glioblastomas treated with concurrent chemoradiotherapy (CCRT) with temozolomide (TMZ) by using histogram analysis of apparent diffusion coefficient (ADC) and normalized cerebral blood volume (nCBV) maps. Twenty patients with histopathologically proven glioblastoma who had received CCRT with TMZ underwent perfusion-weighted imaging and diffusion-weighted imaging (b = 0, 1000 sec/mm(2)). The corresponding nCBV and ADC maps for the newly visible, entirely enhancing lesions were calculated after the completion of CCRT with TMZ. Two observers independently measured the histogram parameters of the nCBV and ADC maps. The histogram parameters between the true progression group (n = 10) and the pseudoprogression group (n = 10) were compared by use of an unpaired Student's t test and subsequent multivariable stepwise logistic regression analysis to determine the best predictors for the differential diagnosis between the two groups. Receiver operating characteristic analysis was employed to determine the best cutoff values for the histogram parameters that proved to be significant predictors for differentiating true progression from pseudoprogression. Intraclass correlation coefficient was used to determine the level of inter-observer reliability for the histogram parameters. The 5th percentile value (C5) of the cumulative ADC histograms was a significant predictor for the differential diagnosis between true progression and pseudoprogression (p = 0.044 for observer 1; p = 0.011 for observer 2). Optimal cutoff values of 892 × 10(-6) mm(2)/sec for observer 1 and 907 × 10(-6) mm(2)/sec for observer 2 could help differentiate between the two groups with a sensitivity of 90% and 80%, respectively, a specificity of 90% and 80%, respectively, and an area under the curve of 0.880 and 0.840, respectively. There was no other significant differentiating parameter on the nCBV histograms. Inter-observer reliability was excellent or good for all histogram parameters (intraclass correlation coefficient range: 0.70-0.99). The C5 of the cumulative ADC histogram can be a promising parameter for the differentiation of true progression from pseudoprogression of newly visible, entirely enhancing lesions after CCRT with TMZ for glioblastomas.

True Progression versus Pseudoprogression in the Treatment of Glioblastomas: A Comparison Study of Normalized Cerebral Blood Volume and Apparent Diffusion Coefficient by Histogram Analysis

PubMed Central

Song, Yong Sub; Park, Chul-Kee; Yi, Kyung Sik; Lee, Woong Jae; Yun, Tae Jin; Kim, Tae Min; Lee, Se-Hoon; Kim, Ji-Hoon; Sohn, Chul-Ho; Park, Sung-Hye; Kim, Il Han; Jahng, Geon-Ho; Chang, Kee-Hyun

2013-01-01

Objective The purpose of this study was to differentiate true progression from pseudoprogression of glioblastomas treated with concurrent chemoradiotherapy (CCRT) with temozolomide (TMZ) by using histogram analysis of apparent diffusion coefficient (ADC) and normalized cerebral blood volume (nCBV) maps. Materials and Methods Twenty patients with histopathologically proven glioblastoma who had received CCRT with TMZ underwent perfusion-weighted imaging and diffusion-weighted imaging (b = 0, 1000 sec/mm2). The corresponding nCBV and ADC maps for the newly visible, entirely enhancing lesions were calculated after the completion of CCRT with TMZ. Two observers independently measured the histogram parameters of the nCBV and ADC maps. The histogram parameters between the true progression group (n = 10) and the pseudoprogression group (n = 10) were compared by use of an unpaired Student's t test and subsequent multivariable stepwise logistic regression analysis to determine the best predictors for the differential diagnosis between the two groups. Receiver operating characteristic analysis was employed to determine the best cutoff values for the histogram parameters that proved to be significant predictors for differentiating true progression from pseudoprogression. Intraclass correlation coefficient was used to determine the level of inter-observer reliability for the histogram parameters. Results The 5th percentile value (C5) of the cumulative ADC histograms was a significant predictor for the differential diagnosis between true progression and pseudoprogression (p = 0.044 for observer 1; p = 0.011 for observer 2). Optimal cutoff values of 892 × 10-6 mm2/sec for observer 1 and 907 × 10-6 mm2/sec for observer 2 could help differentiate between the two groups with a sensitivity of 90% and 80%, respectively, a specificity of 90% and 80%, respectively, and an area under the curve of 0.880 and 0.840, respectively. There was no other significant differentiating parameter on the nCBV histograms. Inter-observer reliability was excellent or good for all histogram parameters (intraclass correlation coefficient range: 0.70-0.99). Conclusion The C5 of the cumulative ADC histogram can be a promising parameter for the differentiation of true progression from pseudoprogression of newly visible, entirely enhancing lesions after CCRT with TMZ for glioblastomas. PMID:23901325

Physical activity and inflammation: effects on gray-matter volume and cognitive decline in aging.

PubMed

Papenberg, Goran; Ferencz, Beata; Mangialasche, Francesca; Mecocci, Patrizia; Cecchetti, Roberta; Kalpouzos, Grégoria; Fratiglioni, Laura; Bäckman, Lars

2016-10-01

Physical activity has been positively associated with gray-matter integrity. In contrast, pro-inflammatory cytokines seem to have negative effects on the aging brain and have been related to dementia. It was investigated whether an inactive lifestyle and high levels of inflammation resulted in smaller gray-matter volumes and predicted cognitive decline across 6 years in a population-based study of older adults (n = 414). Self-reported physical activity (fitness-enhancing, health-enhancing, inadequate) was linked to gray-matter volume, such that individuals with inadequate physical activity had the least gray matter. There were no overall associations between different pro-and anti-inflammatory markers (IL-1β, IL-6, IL-10, IL-12p40, IL-12p70, G-CSF, and TNF-α) and gray-matter integrity. However, persons with inadequate activity and high levels of the pro-inflammatory marker IL-12p40 had smaller volumes of lateral prefrontal cortex and hippocampus and declined more on the Mini-Mental State Examination test over 6 years compared with physically inactive individuals with low levels of IL-12p40 and to more physically active persons, irrespective of their levels of IL-12p40. These patterns of data suggested that inflammation was particularly detrimental in inactive older adults and may exacerbate the negative effects of physical inactivity on brain and cognition in old age. Hum Brain Mapp 37:3462-3473, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Skeletonization of gray-scale images by gray weighted distance transform

NASA Astrophysics Data System (ADS)

Qian, Kai; Cao, Siqi; Bhattacharya, Prabir

1997-07-01

In pattern recognition, thinning algorithms are often a useful tool to represent a digital pattern by means of a skeletonized image, consisting of a set of one-pixel-width lines that highlight the significant features interest in applying thinning directly to gray-scale images, motivated by the desire of processing images characterized by meaningful information distributed over different levels of gray intensity. In this paper, a new algorithm is presented which can skeletonize both black-white and gray pictures. This algorithm is based on the gray distance transformation and can be used to process any non-well uniformly distributed gray-scale picture and can preserve the topology of original picture. This process includes a preliminary phase of investigation in the 'hollows' in the gray-scale image; these hollows are considered not as topological constrains for the skeleton structure depending on their statistically significant depth. This algorithm can also be executed on a parallel machine as all the operations are executed in local. Some examples are discussed to illustrate the algorithm.

Assessment of placental volume and vascularization at 11-14 weeks of gestation in a Taiwanese population using three-dimensional power Doppler ultrasound.

PubMed

Wang, Hsing-I; Yang, Ming-Jie; Wang, Peng-Hui; Wu, Yi-Cheng; Chen, Chih-Yao

2014-12-01

The placental volume and vascular indices are crucial in helping doctors to evaluate early fetal growth and development. Inadequate placental volume or vascularity might indicate poor fetal growth or gestational complications. This study aimed to evaluate the placental volume and vascular indices during the period of 11-14 weeks of gestation in a Taiwanese population. From June 2006 to September 2009, three-dimensional power Doppler ultrasound was performed in 222 normal pregnancies from 11-14 weeks of gestation. Power Doppler ultrasound was applied to the placenta and the placental volume was obtained by a rotational technique (VOCAL). The three-dimensional power histogram was used to assess the placental vascular indices, including the mean gray value, the vascularization index, the flow index, and the vascularization flow index. The placental vascular indices were then plotted against gestational age (GA) and placental volume. Our results showed that the linear regression equation for placental volume using gestational week as the independent variable was placental volume = 18.852 × GA - 180.89 (r = 0.481, p < 0.05). All the placental vascular indices showed a constant distribution throughout the period 11-14 weeks of gestation. A tendency for a reduction in the placental mean gray value with gestational week was observed, but without statistical significance. All the placental vascular indices estimated by three-dimensional power Doppler ultrasonography showed a constant distribution throughout gestation. Copyright © 2014. Published by Elsevier Taiwan.

The ISI distribution of the stochastic Hodgkin-Huxley neuron.

PubMed

Rowat, Peter F; Greenwood, Priscilla E

2014-01-01

The simulation of ion-channel noise has an important role in computational neuroscience. In recent years several approximate methods of carrying out this simulation have been published, based on stochastic differential equations, and all giving slightly different results. The obvious, and essential, question is: which method is the most accurate and which is most computationally efficient? Here we make a contribution to the answer. We compare interspike interval histograms from simulated data using four different approximate stochastic differential equation (SDE) models of the stochastic Hodgkin-Huxley neuron, as well as the exact Markov chain model simulated by the Gillespie algorithm. One of the recent SDE models is the same as the Kurtz approximation first published in 1978. All the models considered give similar ISI histograms over a wide range of deterministic and stochastic input. Three features of these histograms are an initial peak, followed by one or more bumps, and then an exponential tail. We explore how these features depend on deterministic input and on level of channel noise, and explain the results using the stochastic dynamics of the model. We conclude with a rough ranking of the four SDE models with respect to the similarity of their ISI histograms to the histogram of the exact Markov chain model.

Technical Note: Gray tracking in medical color displays-A report of Task Group 196.

PubMed

Badano, Aldo; Wang, Joel; Boynton, Paul; Le Callet, Patrick; Cheng, Wei-Chung; Deroo, Danny; Flynn, Michael J; Matsui, Takashi; Penczek, John; Revie, Craig; Samei, Ehsan; Steven, Peter M; Swiderski, Stan; Van Hoey, Gert; Yamaguchi, Matsuhiro; Hasegawa, Mikio; Nagy, Balázs Vince

2016-07-01

The authors discuss measurement methods and instrumentation useful for the characterization of the gray tracking performance of medical color monitors for diagnostic applications. The authors define gray tracking as the variability in the chromaticity of the gray levels in a color monitor. The authors present data regarding the capability of color measurement instruments with respect to their abilities to measure a target white point corresponding to the CIE Standard Illuminant D65 at different luminance values within the grayscale palette of a medical display. The authors then discuss evidence of significant differences in performance among color measurement instruments currently available for medical physicists to perform calibrations and image quality checks for the consistent representation of color in medical displays. In addition, the authors introduce two metrics for quantifying grayscale chromaticity consistency of gray tracking. The authors' findings show that there is an order of magnitude difference in the accuracy of field and reference instruments. The gray tracking metrics quantify how close the grayscale chromaticity is to the chromaticity of the full white point (equal amounts of red, green, and blue at maximum level) or to consecutive levels (equal values for red, green, and blue), with a lower value representing an improved grayscale tracking performance. An illustrative example of how to calculate and report the gray tracking performance according to the Task Group definitions is provided. The authors' proposed methodology for characterizing the grayscale degradation in chromaticity for color monitors that can be used to establish standards and procedures aiding in the quality control testing of color displays and color measurement instrumentation.

[Comparison of film-screen combinations in contrast-detail diagram and with interactive image analysis. 3: Trimodal histograms of gray scale distribution in bar groups of lead pattern images].

PubMed

Hagemann, G; Eichbaum, G; Stamm, G

1998-05-01

The following four screen film combinations were compared: a) a combination of anticrossover film and UV-light emitting screens, b) a combination of blue-light emitting screens and film and c) two conventional green fluorescing screen film combinations. Radiographs of a specially designed plexiglass phantom (0.2 x 0.2 x 0.12 m3) with bar patterns of lead and plaster and of air, respectively were obtained using the following parameters: 12 pulse generator, 0.6 mm focus size, 4.7 mm aluminum prefilter, a grid with 40 lines/cm (12:1) and a focus-detector distance of 1.15 m. Image analysis was performed using an Ibas system and a Zeiss Kontron computer. Display conditions were the following: display distance 0.12 m, a vario film objective 35/70 (Zeiss), a video camera tube with a PbO photocathode, 625 lines (Siemens Heimann), an Ibas image matrix of 512 x 512 pixels with a spatial resolution of ca. 7 cycles/mm, the projected matrix area was 5000 micron 2. Maxima in the histograms of a grouped bar pattern were estimated as mean values from the bar and gap regions ("mean value method"). They were used to calculate signal contrast, standard deviations of the means and scatter fraction. Comparing the histograms with respect to spatial resolution and kV setting a clear advantage of the UVR system becomes obvious. The quantitative analysis yielded a maximum spatial resolution of approx. 3 cycles/mm for the UVR system at 60 kV which decreased to half of this value at 117 kV caused by the increasing influence of scattered radiation. A ranking of screen-film systems with respect to image quality and dose requirement is presented. For its evaluation an interactive image analysis using the mean value method was found to be superior to signal/noise ratio measurements and visual analysis in respect to diagnostic relevance and saving of time.

Measuring kinetics of complex single ion channel data using mean-variance histograms.

PubMed

Patlak, J B

1993-07-01

The measurement of single ion channel kinetics is difficult when those channels exhibit subconductance events. When the kinetics are fast, and when the current magnitudes are small, as is the case for Na+, Ca2+, and some K+ channels, these difficulties can lead to serious errors in the estimation of channel kinetics. I present here a method, based on the construction and analysis of mean-variance histograms, that can overcome these problems. A mean-variance histogram is constructed by calculating the mean current and the current variance within a brief "window" (a set of N consecutive data samples) superimposed on the digitized raw channel data. Systematic movement of this window over the data produces large numbers of mean-variance pairs which can be assembled into a two-dimensional histogram. Defined current levels (open, closed, or sublevel) appear in such plots as low variance regions. The total number of events in such low variance regions is estimated by curve fitting and plotted as a function of window width. This function decreases with the same time constants as the original dwell time probability distribution for each of the regions. The method can therefore be used: 1) to present a qualitative summary of the single channel data from which the signal-to-noise ratio, open channel noise, steadiness of the baseline, and number of conductance levels can be quickly determined; 2) to quantify the dwell time distribution in each of the levels exhibited. In this paper I present the analysis of a Na+ channel recording that had a number of complexities. The signal-to-noise ratio was only about 8 for the main open state, open channel noise, and fast flickers to other states were present, as were a substantial number of subconductance states. "Standard" half-amplitude threshold analysis of these data produce open and closed time histograms that were well fitted by the sum of two exponentials, but with apparently erroneous time constants, whereas the mean-variance histogram technique provided a more credible analysis of the open, closed, and subconductance times for the patch. I also show that the method produces accurate results on simulated data in a wide variety of conditions, whereas the half-amplitude method, when applied to complex simulated data shows the same errors as were apparent in the real data. The utility and the limitations of this new method are discussed.

Ultrasonographic evaluation of equine tendons: a quantitative in vitro study of the effects of amplifier gain level, transducer-tilt, and transducer-displacement.

PubMed

van Schie, J T; Bakker, E M; van Weeren, P R

1999-01-01

The objective of the in vitro experiments described in this paper was to quantify the effects of some instrumental variables on the quantitative evaluation, by means of first-order gray-level statistics, of ultrasonographic images of equine tendons. The experiments were done on three isolated equine superficial digital flexor tendons that were mounted in a frame and submerged in a waterbath. Sections with either normal tendon tissue, an acute lesion, or a chronic scar, were selected. In these sections, the following experiments were done: 1) a gradual increase of total amplifier gain output subdivided in 12 equal steps; 2) a transducer tilt plus or minus 3 degrees from perpendicular, with steps of 1 degree; and 3) a transducer displacement along, and perpendicular to, the tendon long axis, with 16 steps of 0.25 mm each. Transverse ultrasonographic images were collected, and in the regions of interest (ROI) first-order gray-level statistics were calculated to quantify the effects of each experiment. Some important observations were: 1) the total amplifier gain output has a substantial influence on the ultrasonographic image; for example, in the case of an acute lesion, a low gain setting results in an almost completely black image; whereas, with higher gain settings, a marked "filling in" effect on the lesion can be observed; 2) the relative effects of the tilting of the transducer are substantial in normal tendon tissue (18%) and chronic scar (12%); whereas, in the event of an acute lesion, the effects on the mean gray level are dramatic (40%); and 3) the relative effects of displacement of the transducer are small in normal tendon tissue, but on the other hand, the mean gray-level changes 7% in chronic scar, and even 20% in an acute lesion. In general, slight variations in scanner settings and transducer handling can have considerable effects on the gray levels of the ultrasonographic image. Furthermore, there is a strong indication that this quantitative method, as far as based exclusively on the first-order gray-level statistics, may be not discriminative enough to accurately assess the integrity of the tendon. Therefore, the value of a quantitative evaluation of the first-order gray-level statistics for the assessment of the integrity of the equine tendon is questionable.

Content based Image Retrieval based on Different Global and Local Color Histogram Methods: A Survey

NASA Astrophysics Data System (ADS)

Suhasini, Pallikonda Sarah; Sri Rama Krishna, K.; Murali Krishna, I. V.

2017-02-01

Different global and local color histogram methods for content based image retrieval (CBIR) are investigated in this paper. Color histogram is a widely used descriptor for CBIR. Conventional method of extracting color histogram is global, which misses the spatial content, is less invariant to deformation and viewpoint changes, and results in a very large three dimensional histogram corresponding to the color space used. To address the above deficiencies, different global and local histogram methods are proposed in recent research. Different ways of extracting local histograms to have spatial correspondence, invariant colour histogram to add deformation and viewpoint invariance and fuzzy linking method to reduce the size of the histogram are found in recent papers. The color space and the distance metric used are vital in obtaining color histogram. In this paper the performance of CBIR based on different global and local color histograms in three different color spaces, namely, RGB, HSV, L*a*b* and also with three distance measures Euclidean, Quadratic and Histogram intersection are surveyed, to choose appropriate method for future research.

Naturalness preservation image contrast enhancement via histogram modification

NASA Astrophysics Data System (ADS)

Tian, Qi-Chong; Cohen, Laurent D.

2018-04-01

Contrast enhancement is a technique for enhancing image contrast to obtain better visual quality. Since many existing contrast enhancement algorithms usually produce over-enhanced results, the naturalness preservation is needed to be considered in the framework of image contrast enhancement. This paper proposes a naturalness preservation contrast enhancement method, which adopts the histogram matching to improve the contrast and uses the image quality assessment to automatically select the optimal target histogram. The contrast improvement and the naturalness preservation are both considered in the target histogram, so this method can avoid the over-enhancement problem. In the proposed method, the optimal target histogram is a weighted sum of the original histogram, the uniform histogram, and the Gaussian-shaped histogram. Then the structural metric and the statistical naturalness metric are used to determine the weights of corresponding histograms. At last, the contrast-enhanced image is obtained via matching the optimal target histogram. The experiments demonstrate the proposed method outperforms the compared histogram-based contrast enhancement algorithms.

Thermography based diagnosis of ruptured anterior cruciate ligament (ACL) in canines

NASA Astrophysics Data System (ADS)

Lama, Norsang; Umbaugh, Scott E.; Mishra, Deependra; Dahal, Rohini; Marino, Dominic J.; Sackman, Joseph

2016-09-01

Anterior cruciate ligament (ACL) rupture in canines is a common orthopedic injury in veterinary medicine. Veterinarians use both imaging and non-imaging methods to diagnose the disease. Common imaging methods such as radiography, computed tomography (CT scan) and magnetic resonance imaging (MRI) have some disadvantages: expensive setup, high dose of radiation, and time-consuming. In this paper, we present an alternative diagnostic method based on feature extraction and pattern classification (FEPC) to diagnose abnormal patterns in ACL thermograms. The proposed method was experimented with a total of 30 thermograms for each camera view (anterior, lateral and posterior) including 14 disease and 16 non-disease cases provided from Long Island Veterinary Specialists. The normal and abnormal patterns in thermograms are analyzed in two steps: feature extraction and pattern classification. Texture features based on gray level co-occurrence matrices (GLCM), histogram features and spectral features are extracted from the color normalized thermograms and the computed feature vectors are applied to Nearest Neighbor (NN) classifier, K-Nearest Neighbor (KNN) classifier and Support Vector Machine (SVM) classifier with leave-one-out validation method. The algorithm gives the best classification success rate of 86.67% with a sensitivity of 85.71% and a specificity of 87.5% in ACL rupture detection using NN classifier for the lateral view and Norm-RGB-Lum color normalization method. Our results show that the proposed method has the potential to detect ACL rupture in canines.

What Happened to Gray Whales during the Pleistocene? The Ecological Impact of Sea-Level Change on Benthic Feeding Areas in the North Pacific Ocean

PubMed Central

Pyenson, Nicholas D.; Lindberg, David R.

2011-01-01

Background Gray whales (Eschrichtius robustus) undertake long migrations, from Baja California to Alaska, to feed on seasonally productive benthos of the Bering and Chukchi seas. The invertebrates that form their primary prey are restricted to shallow water environments, but global sea-level changes during the Pleistocene eliminated or reduced this critical habitat multiple times. Because the fossil record of gray whales is coincident with the onset of Northern Hemisphere glaciation, gray whales survived these massive changes to their feeding habitat, but it is unclear how. Methodology/Principal Findings We reconstructed gray whale carrying capacity fluctuations during the past 120,000 years by quantifying gray whale feeding habitat availability using bathymetric data for the North Pacific Ocean, constrained by their maximum diving depth. We calculated carrying capacity based on modern estimates of metabolic demand, prey availability, and feeding duration; we also constrained our estimates to reflect current population size and account for glaciated and non-glaciated areas in the North Pacific. Our results show that key feeding areas eliminated by sea-level lowstands were not replaced by commensurate areas. Our reconstructions show that such reductions affected carrying capacity, and harmonic means of these fluctuations do not differ dramatically from genetic estimates of carrying capacity. Conclusions/Significance Assuming current carrying capacity estimates, Pleistocene glacial maxima may have created multiple, weak genetic bottlenecks, although the current temporal resolution of genetic datasets does not test for such signals. Our results do not, however, falsify molecular estimates of pre-whaling population size because those abundances would have been sufficient to survive the loss of major benthic feeding areas (i.e., the majority of the Bering Shelf) during glacial maxima. We propose that gray whales survived the disappearance of their primary feeding ground by employing generalist filter-feeding modes, similar to the resident gray whales found between northern Washington State and Vancouver Island. PMID:21754984

Phase-only modulation of a twisted nematic liquid crystal TV by use of the eigenpolarization states

NASA Astrophysics Data System (ADS)

Pezzanaiti, J. L.; Chipman, R. A.

1993-09-01

Measured eigenpolarization states of an InFocus TVT-6000 liquid crystal television (LCTV) for 0-255 range gray levels are reported. It is shown that the eigenpolarization states remain nearly constant with dependence on gray level for several bias voltage settings. The LCTV eigenpolarization states were computed from Mueller matrix.

A Modified Mean Gray Wolf Optimization Approach for Benchmark and Biomedical Problems.

PubMed

Singh, Narinder; Singh, S B

2017-01-01

A modified variant of gray wolf optimization algorithm, namely, mean gray wolf optimization algorithm has been developed by modifying the position update (encircling behavior) equations of gray wolf optimization algorithm. The proposed variant has been tested on 23 standard benchmark well-known test functions (unimodal, multimodal, and fixed-dimension multimodal), and the performance of modified variant has been compared with particle swarm optimization and gray wolf optimization. Proposed algorithm has also been applied to the classification of 5 data sets to check feasibility of the modified variant. The results obtained are compared with many other meta-heuristic approaches, ie, gray wolf optimization, particle swarm optimization, population-based incremental learning, ant colony optimization, etc. The results show that the performance of modified variant is able to find best solutions in terms of high level of accuracy in classification and improved local optima avoidance.

A novel parallel architecture for local histogram equalization

NASA Astrophysics Data System (ADS)

Ohannessian, Mesrob I.; Choueiter, Ghinwa F.; Diab, Hassan

2005-07-01

Local histogram equalization is an image enhancement algorithm that has found wide application in the pre-processing stage of areas such as computer vision, pattern recognition and medical imaging. The computationally intensive nature of the procedure, however, is a main limitation when real time interactive applications are in question. This work explores the possibility of performing parallel local histogram equalization, using an array of special purpose elementary processors, through an HDL implementation that targets FPGA or ASIC platforms. A novel parallelization scheme is presented and the corresponding architecture is derived. The algorithm is reduced to pixel-level operations. Processing elements are assigned image blocks, to maintain a reasonable performance-cost ratio. To further simplify both processor and memory organizations, a bit-serial access scheme is used. A brief performance assessment is provided to illustrate and quantify the merit of the approach.

Digital analysis of changes by Plasmodium vivax malaria in erythrocytes.

PubMed

Edison, Maombi; Jeeva, J B; Singh, Megha

2011-01-01

Blood samples of malaria patients (n = 30), selected based on the severity of parasitemia, were divided into low (LP), medium (MP) and high (HP) parasitemia, which represent increasing levels of the disease severity. Healthy subjects (n = 10) without any history of disease were selected as a control group. By processing of erythrocytes images their contours were obtained and from these the shape parameters area, perimeter and form factor were obtained. The gray level intensity was determined by scanning of erythrocyte along its largest diameter. A comparison of these with that of normal cells showed a significant change in shape parameters. The gray level intensity decreases with the increase of severity of the disease. The changes in shape parameters directly and gray level intensity variation inversely are correlated with the increase in parasite density due to the disease.

Gray matter regions statistically mediating the cross-sectional association of eotaxin and set-shifting among older adults with major depressive disorder.

PubMed

Smagula, Stephen F; Karim, Helmet T; Lenze, Eric J; Butters, Meryl A; Wu, Gregory F; Mulsant, Benoit H; Reynolds, Charles F; Aizenstein, Howard J

2017-12-01

Eotaxin is a chemokine that exerts negative effects on neurogenesis. We recently showed that peripheral eotaxin levels correlate with both lower gray matter volume and poorer executive performance in older adults with major depressive disorder. These findings suggest that the relationship between eotaxin and set-shifting may be accounted for by lower gray matter volume in specific regions. Prior studies have identified specific gray matter regions that correlate with set-shifting performance, but have not examined whether these specific gray matter regions mediate the cross-sectional association between eotaxin and set-shifting. In 27 older adults (mean age: 68 ± 5.2 years) with major depressive disorder, we performed a whole brain (voxel-wise) analysis testing whether/where gray matter density statistically mediates the cross-sectional association of eotaxin and set-shifting performance. We found the association between eotaxin and set-shifting performance was fully statistically mediated by lower gray matter density in left middle cingulate, right pre-/post-central, lingual, inferior/superior frontal, cuneus, and middle temporal regions. The regions identified above may be both susceptible to a potential neurodegenerative effect of eotaxin, and critical to preserving set-shifting function. Longitudinal and intervention studies are needed to further evaluate whether targeting eotaxin levels will prevent neurodegeneration and executive impairment in older adults with depression. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Histogram Analysis of Diffusion Tensor Imaging Parameters in Pediatric Cerebellar Tumors.

PubMed

Wagner, Matthias W; Narayan, Anand K; Bosemani, Thangamadhan; Huisman, Thierry A G M; Poretti, Andrea

2016-05-01

Apparent diffusion coefficient (ADC) values have been shown to assist in differentiating cerebellar pilocytic astrocytomas and medulloblastomas. Previous studies have applied only ADC measurements and calculated the mean/median values. Here we investigated the value of diffusion tensor imaging (DTI) histogram characteristics of the entire tumor for differentiation of cerebellar pilocytic astrocytomas and medulloblastomas. Presurgical DTI data were analyzed with a region of interest (ROI) approach to include the entire tumor. For each tumor, histogram-derived metrics including the 25th percentile, 75th percentile, and skewness were calculated for fractional anisotropy (FA) and mean (MD), axial (AD), and radial (RD) diffusivity. The histogram metrics were used as primary predictors of interest in a logistic regression model. Statistical significance levels were set at p < .01. The study population included 17 children with pilocytic astrocytoma and 16 with medulloblastoma (mean age, 9.21 ± 5.18 years and 7.66 ± 4.97 years, respectively). Compared to children with medulloblastoma, children with pilocytic astrocytoma showed higher MD (P = .003 and P = .008), AD (P = .004 and P = .007), and RD (P = .003 and P = .009) values for the 25th and 75th percentile. In addition, histogram skewness showed statistically significant differences for MD between low- and high-grade tumors (P = .008). The 25th percentile for MD yields the best results for the presurgical differentiation between pediatric cerebellar pilocytic astrocytomas and medulloblastomas. The analysis of other DTI metrics does not provide additional diagnostic value. Our study confirms the diagnostic value of the quantitative histogram analysis of DTI data in pediatric neuro-oncology. Copyright © 2015 by the American Society of Neuroimaging.

Optimal Binarization of Gray-Scaled Digital Images via Fuzzy Reasoning

NASA Technical Reports Server (NTRS)

Dominguez, Jesus A. (Inventor); Klinko, Steven J. (Inventor)

2007-01-01

A technique for finding an optimal threshold for binarization of a gray scale image employs fuzzy reasoning. A triangular membership function is employed which is dependent on the degree to which the pixels in the image belong to either the foreground class or the background class. Use of a simplified linear fuzzy entropy factor function facilitates short execution times and use of membership values between 0.0 and 1.0 for improved accuracy. To improve accuracy further, the membership function employs lower and upper bound gray level limits that can vary from image to image and are selected to be equal to the minimum and the maximum gray levels, respectively, that are present in the image to be converted. To identify the optimal binarization threshold, an iterative process is employed in which different possible thresholds are tested and the one providing the minimum fuzzy entropy measure is selected.

Face-iris multimodal biometric scheme based on feature level fusion

NASA Astrophysics Data System (ADS)

Huo, Guang; Liu, Yuanning; Zhu, Xiaodong; Dong, Hongxing; He, Fei

2015-11-01

Unlike score level fusion, feature level fusion demands all the features extracted from unimodal traits with high distinguishability, as well as homogeneity and compatibility, which is difficult to achieve. Therefore, most multimodal biometric research focuses on score level fusion, whereas few investigate feature level fusion. We propose a face-iris recognition method based on feature level fusion. We build a special two-dimensional-Gabor filter bank to extract local texture features from face and iris images, and then transform them by histogram statistics into an energy-orientation variance histogram feature with lower dimensions and higher distinguishability. Finally, through a fusion-recognition strategy based on principal components analysis and support vector machine (FRSPS), feature level fusion and one-to-n identification are accomplished. The experimental results demonstrate that this method can not only effectively extract face and iris features but also provide higher recognition accuracy. Compared with some state-of-the-art fusion methods, the proposed method has a significant performance advantage.

Measuring kinetics of complex single ion channel data using mean-variance histograms.

PubMed Central

Patlak, J B

1993-01-01

The measurement of single ion channel kinetics is difficult when those channels exhibit subconductance events. When the kinetics are fast, and when the current magnitudes are small, as is the case for Na+, Ca2+, and some K+ channels, these difficulties can lead to serious errors in the estimation of channel kinetics. I present here a method, based on the construction and analysis of mean-variance histograms, that can overcome these problems. A mean-variance histogram is constructed by calculating the mean current and the current variance within a brief "window" (a set of N consecutive data samples) superimposed on the digitized raw channel data. Systematic movement of this window over the data produces large numbers of mean-variance pairs which can be assembled into a two-dimensional histogram. Defined current levels (open, closed, or sublevel) appear in such plots as low variance regions. The total number of events in such low variance regions is estimated by curve fitting and plotted as a function of window width. This function decreases with the same time constants as the original dwell time probability distribution for each of the regions. The method can therefore be used: 1) to present a qualitative summary of the single channel data from which the signal-to-noise ratio, open channel noise, steadiness of the baseline, and number of conductance levels can be quickly determined; 2) to quantify the dwell time distribution in each of the levels exhibited. In this paper I present the analysis of a Na+ channel recording that had a number of complexities. The signal-to-noise ratio was only about 8 for the main open state, open channel noise, and fast flickers to other states were present, as were a substantial number of subconductance states. "Standard" half-amplitude threshold analysis of these data produce open and closed time histograms that were well fitted by the sum of two exponentials, but with apparently erroneous time constants, whereas the mean-variance histogram technique provided a more credible analysis of the open, closed, and subconductance times for the patch. I also show that the method produces accurate results on simulated data in a wide variety of conditions, whereas the half-amplitude method, when applied to complex simulated data shows the same errors as were apparent in the real data. The utility and the limitations of this new method are discussed. Images FIGURE 2 FIGURE 4 FIGURE 8 FIGURE 9 PMID:7690261

Image segmentation using local shape and gray-level appearance models

NASA Astrophysics Data System (ADS)

Seghers, Dieter; Loeckx, Dirk; Maes, Frederik; Suetens, Paul

2006-03-01

A new generic model-based segmentation scheme is presented, which can be trained from examples akin to the Active Shape Model (ASM) approach in order to acquire knowledge about the shape to be segmented and about the gray-level appearance of the object in the image. Because in the ASM approach the intensity and shape models are typically applied alternately during optimizing as first an optimal target location is selected for each landmark separately based on local gray-level appearance information only to which the shape model is fitted subsequently, the ASM may be misled in case of wrongly selected landmark locations. Instead, the proposed approach optimizes for shape and intensity characteristics simultaneously. Local gray-level appearance information at the landmark points extracted from feature images is used to automatically detect a number of plausible candidate locations for each landmark. The shape information is described by multiple landmark-specific statistical models that capture local dependencies between adjacent landmarks on the shape. The shape and intensity models are combined in a single cost function that is optimized non-iteratively using dynamic programming which allows to find the optimal landmark positions using combined shape and intensity information, without the need for initialization.

Brain early infarct detection using gamma correction extreme-level eliminating with weighting distribution.

PubMed

Teh, V; Sim, K S; Wong, E K

2016-11-01

According to the statistic from World Health Organization (WHO), stroke is one of the major causes of death globally. Computed tomography (CT) scan is one of the main medical diagnosis system used for diagnosis of ischemic stroke. CT scan provides brain images in Digital Imaging and Communication in Medicine (DICOM) format. The presentation of CT brain images is mainly relied on the window setting (window center and window width), which converts an image from DICOM format into normal grayscale format. Nevertheless, the ordinary window parameter could not deliver a proper contrast on CT brain images for ischemic stroke detection. In this paper, a new proposed method namely gamma correction extreme-level eliminating with weighting distribution (GCELEWD) is implemented to improve the contrast on CT brain images. GCELEWD is capable of highlighting the hypodense region for diagnosis of ischemic stroke. The performance of this new proposed technique, GCELEWD, is compared with four of the existing contrast enhancement technique such as brightness preserving bi-histogram equalization (BBHE), dualistic sub-image histogram equalization (DSIHE), extreme-level eliminating histogram equalization (ELEHE), and adaptive gamma correction with weighting distribution (AGCWD). GCELEWD shows better visualization for ischemic stroke detection and higher values with image quality assessment (IQA) module. SCANNING 38:842-856, 2016. © 2016 Wiley Periodicals, Inc. © Wiley Periodicals, Inc.

A new method of inshore ship detection in high-resolution optical remote sensing images

NASA Astrophysics Data System (ADS)

Hu, Qifeng; Du, Yaling; Jiang, Yunqiu; Ming, Delie

2015-10-01

Ship as an important military target and water transportation, of which the detection has great significance. In the military field, the automatic detection of ships can be used to monitor ship dynamic in the harbor and maritime of enemy, and then analyze the enemy naval power. In civilian field, the automatic detection of ships can be used in monitoring transportation of harbor and illegal behaviors such as illegal fishing, smuggling and pirates, etc. In recent years, research of ship detection is mainly concentrated in three categories: forward-looking infrared images, downward-looking SAR image, and optical remote sensing images with sea background. Little research has been done into ship detection of optical remote sensing images with harbor background, as the gray-scale and texture features of ships are similar to the coast in high-resolution optical remote sensing images. In this paper, we put forward an effective harbor ship target detection method. First of all, in order to overcome the shortage of the traditional difference method in obtaining histogram valley as the segmentation threshold, we propose an iterative histogram valley segmentation method which separates the harbor and ships from the water quite well. Secondly, as landing ships in optical remote sensing images usually lead to discontinuous harbor edges, we use Hough Transform method to extract harbor edges. First, lines are detected by Hough Transform. Then, lines that have similar slope are connected into a new line, thus we access continuous harbor edges. Secondary segmentation on the result of the land-and-sea separation, we eventually get the ships. At last, we calculate the aspect ratio of the ROIs, thereby remove those targets which are not ship. The experiment results show that our method has good robustness and can tolerate a certain degree of noise and occlusion.

Adaptive image contrast enhancement using generalizations of histogram equalization.

PubMed

Stark, J A

2000-01-01

This paper proposes a scheme for adaptive image-contrast enhancement based on a generalization of histogram equalization (HE). HE is a useful technique for improving image contrast, but its effect is too severe for many purposes. However, dramatically different results can be obtained with relatively minor modifications. A concise description of adaptive HE is set out, and this framework is used in a discussion of past suggestions for variations on HE. A key feature of this formalism is a "cumulation function," which is used to generate a grey level mapping from the local histogram. By choosing alternative forms of cumulation function one can achieve a wide variety of effects. A specific form is proposed. Through the variation of one or two parameters, the resulting process can produce a range of degrees of contrast enhancement, at one extreme leaving the image unchanged, at another yielding full adaptive equalization.

Association of white matter hyperintensities and gray matter volume with cognition in older individuals without cognitive impairment.

PubMed

Arvanitakis, Zoe; Fleischman, Debra A; Arfanakis, Konstantinos; Leurgans, Sue E; Barnes, Lisa L; Bennett, David A

2016-05-01

Both presence of white matter hyperintensities (WMH) and smaller total gray matter volume on brain magnetic resonance imaging (MRI) are common findings in old age, and contribute to impaired cognition. We tested whether total WMH volume and gray matter volume had independent associations with cognition in community-dwelling individuals without dementia or mild cognitive impairment (MCI). We used data from participants of the Rush Memory and Aging Project. Brain MRI was available in 209 subjects without dementia or MCI (mean age 80; education = 15 years; 74 % women). WMH and gray matter were automatically segmented, and the total WMH and gray matter volumes were measured. Both MRI-derived measures were normalized by the intracranial volume. Cognitive data included composite measures of five different cognitive domains, based on 19 individual tests. Linear regression analyses, adjusted for age, sex, and education, were used to examine the relationship of logarithmically-transformed total WMH volume and of total gray matter volume to cognition. Larger total WMH volumes were associated with lower levels of perceptual speed (p < 0.001), but not with episodic memory, semantic memory, working memory, or visuospatial abilities (all p > 0.10). Smaller total gray matter volumes were associated with lower levels of perceptual speed (p = 0.013) and episodic memory (p = 0.001), but not with the other three cognitive domains (all p > 0.14). Larger total WMH volume was correlated with smaller total gray matter volume (p < 0.001). In a model with both MRI-derived measures included, the relation of WMH to perceptual speed remained significant (p < 0.001), while gray matter volumes were no longer related (p = 0.14). This study of older community-dwelling individuals without overt cognitive impairment suggests that the association of larger total WMH volume with lower perceptual speed is independent of total gray matter volume. These results help elucidate the pathological processes leading to lower cognitive function in aging.

MRI histogram analysis enables objective and continuous classification of intervertebral disc degeneration.

PubMed

Waldenberg, Christian; Hebelka, Hanna; Brisby, Helena; Lagerstrand, Kerstin Magdalena

2018-05-01

Magnetic resonance imaging (MRI) is the best diagnostic imaging method for low back pain. However, the technique is currently not utilized in its full capacity, often failing to depict painful intervertebral discs (IVDs), potentially due to the rough degeneration classification system used clinically today. MR image histograms, which reflect the IVD heterogeneity, may offer sensitive imaging biomarkers for IVD degeneration classification. This study investigates the feasibility of using histogram analysis as means of objective and continuous grading of IVD degeneration. Forty-nine IVDs in ten low back pain patients (six males, 25-69 years) were examined with MRI (T2-weighted images and T2-maps). Each IVD was semi-automatically segmented on three mid-sagittal slices. Histogram features of the IVD were extracted from the defined regions of interest and correlated to Pfirrmann grade. Both T2-weighted images and T2-maps displayed similar histogram features. Histograms of well-hydrated IVDs displayed two separate peaks, representing annulus fibrosus and nucleus pulposus. Degenerated IVDs displayed decreased peak separation, where the separation was shown to correlate strongly with Pfirrmann grade (P < 0.05). In addition, some degenerated IVDs within the same Pfirrmann grade displayed diametrically different histogram appearances. Histogram features correlated well with IVD degeneration, suggesting that IVD histogram analysis is a suitable tool for objective and continuous IVD degeneration classification. As histogram analysis revealed IVD heterogeneity, it may be a clinical tool for characterization of regional IVD degeneration effects. To elucidate the usefulness of histogram analysis in patient management, IVD histogram features between asymptomatic and symptomatic individuals needs to be compared.

Antecedents of Gray Divorce: A Life Course Perspective.

PubMed

Lin, I-Fen; Brown, Susan L; Wright, Matthew R; Hammersmith, Anna M

2016-12-16

Increasingly, older adults are experiencing divorce, yet little is known about the risk factors associated with divorce after age 50 (termed "gray divorce"). Guided by a life course perspective, our study examined whether key later life turning points are related to gray divorce. We used data from the 1998-2012 Health and Retirement Study to conduct a prospective, couple-level discrete-time event history analysis of the antecedents of gray divorce. Our models incorporated key turning points (empty nest, retirement, and poor health) as well as demographic characteristics and economic resources. Contrary to our expectations, the onset of an empty nest, the wife's or husband's retirement, and the wife's or husband's chronic conditions were unrelated to the likelihood of gray divorce. Rather, factors traditionally associated with divorce among younger adults were also salient for older adults. Marital duration, marital quality, home ownership, and wealth were negatively related to the risk of gray divorce. Gray divorce is especially likely to occur among couples who are socially and economically disadvantaged, raising new questions about the consequences of gray divorce for individual health and well-being. © The Author 2016. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Local gray level S-curve transformation - A generalized contrast enhancement technique for medical images.

PubMed

Gandhamal, Akash; Talbar, Sanjay; Gajre, Suhas; Hani, Ahmad Fadzil M; Kumar, Dileep

2017-04-01

Most medical images suffer from inadequate contrast and brightness, which leads to blurred or weak edges (low contrast) between adjacent tissues resulting in poor segmentation and errors in classification of tissues. Thus, contrast enhancement to improve visual information is extremely important in the development of computational approaches for obtaining quantitative measurements from medical images. In this research, a contrast enhancement algorithm that applies gray-level S-curve transformation technique locally in medical images obtained from various modalities is investigated. The S-curve transformation is an extended gray level transformation technique that results into a curve similar to a sigmoid function through a pixel to pixel transformation. This curve essentially increases the difference between minimum and maximum gray values and the image gradient, locally thereby, strengthening edges between adjacent tissues. The performance of the proposed technique is determined by measuring several parameters namely, edge content (improvement in image gradient), enhancement measure (degree of contrast enhancement), absolute mean brightness error (luminance distortion caused by the enhancement), and feature similarity index measure (preservation of the original image features). Based on medical image datasets comprising 1937 images from various modalities such as ultrasound, mammograms, fluorescent images, fundus, X-ray radiographs and MR images, it is found that the local gray-level S-curve transformation outperforms existing techniques in terms of improved contrast and brightness, resulting in clear and strong edges between adjacent tissues. The proposed technique can be used as a preprocessing tool for effective segmentation and classification of tissue structures in medical images. Copyright © 2017 Elsevier Ltd. All rights reserved.

Dynamic-thresholding level set: a novel computer-aided volumetry method for liver tumors in hepatic CT images

NASA Astrophysics Data System (ADS)

Cai, Wenli; Yoshida, Hiroyuki; Harris, Gordon J.

2007-03-01

Measurement of the volume of focal liver tumors, called liver tumor volumetry, is indispensable for assessing the growth of tumors and for monitoring the response of tumors to oncology treatments. Traditional edge models, such as the maximum gradient and zero-crossing methods, often fail to detect the accurate boundary of a fuzzy object such as a liver tumor. As a result, the computerized volumetry based on these edge models tends to differ from manual segmentation results performed by physicians. In this study, we developed a novel computerized volumetry method for fuzzy objects, called dynamic-thresholding level set (DT level set). An optimal threshold value computed from a histogram tends to shift, relative to the theoretical threshold value obtained from a normal distribution model, toward a smaller region in the histogram. We thus designed a mobile shell structure, called a propagating shell, which is a thick region encompassing the level set front. The optimal threshold calculated from the histogram of the shell drives the level set front toward the boundary of a liver tumor. When the volume ratio between the object and the background in the shell approaches one, the optimal threshold value best fits the theoretical threshold value and the shell stops propagating. Application of the DT level set to 26 hepatic CT cases with 63 biopsy-confirmed hepatocellular carcinomas (HCCs) and metastases showed that the computer measured volumes were highly correlated with those of tumors measured manually by physicians. Our preliminary results showed that DT level set was effective and accurate in estimating the volumes of liver tumors detected in hepatic CT images.

Calibration Methods for a 3D Triangulation Based Camera

NASA Astrophysics Data System (ADS)

Schulz, Ulrike; Böhnke, Kay

A sensor in a camera takes a gray level image (1536 x 512 pixels), which is reflected by a reference body. The reference body is illuminated by a linear laser line. This gray level image can be used for a 3D calibration. The following paper describes how a calibration program calculates the calibration factors. The calibration factors serve to determine the size of an unknown reference body.

Bin Ratio-Based Histogram Distances and Their Application to Image Classification.

PubMed

Hu, Weiming; Xie, Nianhua; Hu, Ruiguang; Ling, Haibin; Chen, Qiang; Yan, Shuicheng; Maybank, Stephen

2014-12-01

Large variations in image background may cause partial matching and normalization problems for histogram-based representations, i.e., the histograms of the same category may have bins which are significantly different, and normalization may produce large changes in the differences between corresponding bins. In this paper, we deal with this problem by using the ratios between bin values of histograms, rather than bin values' differences which are used in the traditional histogram distances. We propose a bin ratio-based histogram distance (BRD), which is an intra-cross-bin distance, in contrast with previous bin-to-bin distances and cross-bin distances. The BRD is robust to partial matching and histogram normalization, and captures correlations between bins with only a linear computational complexity. We combine the BRD with the ℓ1 histogram distance and the χ(2) histogram distance to generate the ℓ1 BRD and the χ(2) BRD, respectively. These combinations exploit and benefit from the robustness of the BRD under partial matching and the robustness of the ℓ1 and χ(2) distances to small noise. We propose a method for assessing the robustness of histogram distances to partial matching. The BRDs and logistic regression-based histogram fusion are applied to image classification. The experimental results on synthetic data sets show the robustness of the BRDs to partial matching, and the experiments on seven benchmark data sets demonstrate promising results of the BRDs for image classification.

Optimal Non-Invasive Fault Classification Model for Packaged Ceramic Tile Quality Monitoring Using MMW Imaging

NASA Astrophysics Data System (ADS)

Agarwal, Smriti; Singh, Dharmendra

2016-04-01

Millimeter wave (MMW) frequency has emerged as an efficient tool for different stand-off imaging applications. In this paper, we have dealt with a novel MMW imaging application, i.e., non-invasive packaged goods quality estimation for industrial quality monitoring applications. An active MMW imaging radar operating at 60 GHz has been ingeniously designed for concealed fault estimation. Ceramic tiles covered with commonly used packaging cardboard were used as concealed targets for undercover fault classification. A comparison of computer vision-based state-of-the-art feature extraction techniques, viz, discrete Fourier transform (DFT), wavelet transform (WT), principal component analysis (PCA), gray level co-occurrence texture (GLCM), and histogram of oriented gradient (HOG) has been done with respect to their efficient and differentiable feature vector generation capability for undercover target fault classification. An extensive number of experiments were performed with different ceramic tile fault configurations, viz., vertical crack, horizontal crack, random crack, diagonal crack along with the non-faulty tiles. Further, an independent algorithm validation was done demonstrating classification accuracy: 80, 86.67, 73.33, and 93.33 % for DFT, WT, PCA, GLCM, and HOG feature-based artificial neural network (ANN) classifier models, respectively. Classification results show good capability for HOG feature extraction technique towards non-destructive quality inspection with appreciably low false alarm as compared to other techniques. Thereby, a robust and optimal image feature-based neural network classification model has been proposed for non-invasive, automatic fault monitoring for a financially and commercially competent industrial growth.

A novel fully automatic multilevel thresholding technique based on optimized intuitionistic fuzzy sets and tsallis entropy for MR brain tumor image segmentation.

PubMed

Kaur, Taranjit; Saini, Barjinder Singh; Gupta, Savita

2018-03-01

In the present paper, a hybrid multilevel thresholding technique that combines intuitionistic fuzzy sets and tsallis entropy has been proposed for the automatic delineation of the tumor from magnetic resonance images having vague boundaries and poor contrast. This novel technique takes into account both the image histogram and the uncertainty information for the computation of multiple thresholds. The benefit of the methodology is that it provides fast and improved segmentation for the complex tumorous images with imprecise gray levels. To further boost the computational speed, the mutation based particle swarm optimization is used that selects the most optimal threshold combination. The accuracy of the proposed segmentation approach has been validated on simulated, real low-grade glioma tumor volumes taken from MICCAI brain tumor segmentation (BRATS) challenge 2012 dataset and the clinical tumor images, so as to corroborate its generality and novelty. The designed technique achieves an average Dice overlap equal to 0.82010, 0.78610 and 0.94170 for three datasets. Further, a comparative analysis has also been made between the eight existing multilevel thresholding implementations so as to show the superiority of the designed technique. In comparison, the results indicate a mean improvement in Dice by an amount equal to 4.00% (p < 0.005), 9.60% (p < 0.005) and 3.58% (p < 0.005), respectively in contrast to the fuzzy tsallis approach.

Bag-of-features approach for improvement of lung tissue classification in diffuse lung disease

NASA Astrophysics Data System (ADS)

Kato, Noriji; Fukui, Motofumi; Isozaki, Takashi

2009-02-01

Many automated techniques have been proposed to classify diffuse lung disease patterns. Most of the techniques utilize texture analysis approaches with second and higher order statistics, and show successful classification result among various lung tissue patterns. However, the approaches do not work well for the patterns with inhomogeneous texture distribution within a region of interest (ROI), such as reticular and honeycombing patterns, because the statistics can only capture averaged feature over the ROI. In this work, we have introduced the bag-of-features approach to overcome this difficulty. In the approach, texture images are represented as histograms or distributions of a few basic primitives, which are obtained by clustering local image features. The intensity descriptor and the Scale Invariant Feature Transformation (SIFT) descriptor are utilized to extract the local features, which have significant discriminatory power due to their specificity to a particular image class. In contrast, the drawback of the local features is lack of invariance under translation and rotation. We improved the invariance by sampling many local regions so that the distribution of the local features is unchanged. We evaluated the performance of our system in the classification task with 5 image classes (ground glass, reticular, honeycombing, emphysema, and normal) using 1109 ROIs from 211 patients. Our system achieved high classification accuracy of 92.8%, which is superior to that of the conventional system with the gray level co-occurrence matrix (GLCM) feature especially for inhomogeneous texture patterns.

Case control study to assess the possibility of decrease the risk of osteoradionecrosis in relation to the dose of radiation absorbed by the jaw

PubMed Central

Carini, Fabrizio; Bucalo, Concetta; Saggese, Vito; Monai, Dario; Porcaro, Gianluca

2012-01-01

Summary Aims the assessment of the limit dose for the organs at risk in external radiotherapy is a fundamental step to guarantee an optimal risk-benefit ratio. The aim of this study was to assess, through contouring the single dental cavities, the absorbed radiation dose on irradiated alveolar bones during the treatment of cervico-facial tumours, so as to test the correlation between the absorbed dose of radiation at alveolar level and the level of individual surgical risk for osteonecrosis. Materials and methods we selected 45 out of 89 patients on the basis of different exclusion criteria. Nine of these patients showed evidence of osteoradionecrosis. The patients were treated either with 3D conformational radiation therapy (3D-CRT) or with intensity-modulated radiation therapy (IMRT), there after alveolar bones were contoured using computed axial tomography (CAT scans) carried out following oncological and dental treatment. The dose-volume histograms (DVH) were obtained on the basis of such data, which included those relating to the dental cavities in addition to those inherent to the tumours and the organs at risk. Results all patients, irrespective of type of treatment, received an average of 60 to 70 grays in 30/35 sittings. The patients treated with IMRT showed higher variation in absorbed radiation dose than those treated with 3D-CRT. The alveolar encirclement allowed the assessment of the absorbed radiation dose, and consequently it also allowed to assess the individual surgical risk for osteonecrosis in patients with head and neck tumours who underwent radiography treatment. Conclusions the study of DVH allows the assessment of limit dose and the detection of the areas at greater risk for osteoradionecrosis before dental surgery. PMID:23285316

Effectiveness of Global Features for Automatic Medical Image Classification and Retrieval – the experiences of OHSU at ImageCLEFmed

PubMed Central

Kalpathy-Cramer, Jayashree; Hersh, William

2008-01-01

In 2006 and 2007, Oregon Health & Science University (OHSU) participated in the automatic image annotation task for medical images at ImageCLEF, an annual international benchmarking event that is part of the Cross Language Evaluation Forum (CLEF). The goal of the automatic annotation task was to classify 1000 test images based on the Image Retrieval in Medical Applications (IRMA) code, given a set of 10,000 training images. There were 116 distinct classes in 2006 and 2007. We evaluated the efficacy of a variety of primarily global features for this classification task. These included features based on histograms, gray level correlation matrices and the gist technique. A multitude of classifiers including k-nearest neighbors, two-level neural networks, support vector machines, and maximum likelihood classifiers were evaluated. Our official error rates for the 1000 test images were 26% in 2006 using the flat classification structure. The error count in 2007 was 67.8 using the hierarchical classification error computation based on the IRMA code in 2007. Confusion matrices as well as clustering experiments were used to identify visually similar classes. The use of the IRMA code did not help us in the classification task as the semantic hierarchy of the IRMA classes did not correspond well with the hierarchy based on clustering of image features that we used. Our most frequent misclassification errors were along the view axis. Subsequent experiments based on a two-stage classification system decreased our error rate to 19.8% for the 2006 dataset and our error count to 55.4 for the 2007 data. PMID:19884953

Seed viability detection using computerized false-color radiographic image enhancement

NASA Technical Reports Server (NTRS)

Vozzo, J. A.; Marko, Michael

1994-01-01

Seed radiographs are divided into density zones which are related to seed germination. The seeds which germinate have densities relating to false-color red. In turn, a seed sorter may be designed which rejects those seeds not having sufficient red to activate a gate along a moving belt containing the seed source. This results in separating only seeds with the preselected densities representing biological viability lending to germination. These selected seeds demand a higher market value. Actual false-coloring isn't required for a computer to distinguish the significant gray-zone range. This range can be predetermined and screened without the necessity of red imaging. Applying false-color enhancement is a means of emphasizing differences in densities of gray within any subject from photographic, radiographic, or video imaging. Within the 0-255 range of gray levels, colors can be assigned to any single level or group of gray levels. Densitometric values then become easily recognized colors which relate to the image density. Choosing a color to identify any given density allows separation by morphology or composition (form or function). Additionally, relative areas of each color are readily available for determining distribution of that density by comparison with other densities within the image.

Gray bats and pollution in Missouri and northern Alabama

USGS Publications Warehouse

Clark, D.R.; Bunck, C.M.; Cromartie, E.; LaVal, R.K.; Tuttle, M.D.

1981-01-01

Gray bats died with lethal brain concentrations of dieldrin and rising levels of heptachlor epoxide in 1976, 1977, and 1978 at Bat Caves No. 2-3, Franklin County, Missouri. The colony disappeared in 1979. Dieldrin was banned in 1974 and 1981 was the last year for heptachlor use in Missouri. The State is recommendiing three organophosphates (chlorpyrifos or Dursban, dyfonate or Fonophos, and ethoprop or Mocap) as substitutes for heptachlor. All three compounds have excellent records in the environment. Analyses of insects collected where bats of this colony fed showed beetles, particularly rove beetles (Staphylinidae), to be the most heavily contaminated part of the bat's diet. Lactation concentrated these residues so that levels in milk were approximately 30 times those in the insect diet. Gray bats found dead in caves in northern Alabama showed DDD (a DDT derivative) contamination. Bats from the colony at Cave Springs Cave on the Wheeler National Wildlife Refuge contained up to 29 ppm DDD in their brains, but this is probably less than one-half the lethal level. Bats from other colonies contained less. The DDD contamination enters the Terinessee River just above the Wheeler Refuge and is seen in gray bat colonies as far as 60 miles downriver.

Correlation of 18F-FDG PET and MRI Apparent Diffusion Coefficient Histogram Metrics with Survival in Diffuse Intrinsic Pontine Glioma: A Report from the Pediatric Brain Tumor Consortium.

PubMed

Zukotynski, Katherine A; Vajapeyam, Sridhar; Fahey, Frederic H; Kocak, Mehmet; Brown, Douglas; Ricci, Kelsey I; Onar-Thomas, Arzu; Fouladi, Maryam; Poussaint, Tina Young

2017-08-01

The purpose of this study was to describe baseline 18 F-FDG PET voxel characteristics in pediatric diffuse intrinsic pontine glioma (DIPG) and to correlate these metrics with baseline MRI apparent diffusion coefficient (ADC) histogram metrics, progression-free survival (PFS), and overall survival. Methods: Baseline brain 18 F-FDG PET and MRI scans were obtained in 33 children from Pediatric Brain Tumor Consortium clinical DIPG trials. 18 F-FDG PET images, postgadolinium MR images, and ADC MR images were registered to baseline fluid attenuation inversion recovery MR images. Three-dimensional regions of interest on fluid attenuation inversion recovery MR images and postgadolinium MR images and 18 F-FDG PET and MR ADC histograms were generated. Metrics evaluated included peak number, skewness, and kurtosis. Correlation between PET and MR ADC histogram metrics was evaluated. PET pixel values within the region of interest for each tumor were plotted against MR ADC values. The association of these imaging markers with survival was described. Results: PET histograms were almost always unimodal (94%, vs. 6% bimodal). None of the PET histogram parameters (skewness or kurtosis) had a significant association with PFS, although a higher PET postgadolinium skewness tended toward a less favorable PFS (hazard ratio, 3.48; 95% confidence interval [CI], 0.75-16.28 [ P = 0.11]). There was a significant association between higher MR ADC postgadolinium skewness and shorter PFS (hazard ratio, 2.56; 95% CI, 1.11-5.91 [ P = 0.028]), and there was the suggestion that this also led to shorter overall survival (hazard ratio, 2.18; 95% CI, 0.95-5.04 [ P = 0.067]). Higher MR ADC postgadolinium kurtosis tended toward shorter PFS (hazard ratio, 1.30; 95% CI, 0.98-1.74 [ P = 0.073]). PET and MR ADC pixel values were negatively correlated using the Pearson correlation coefficient. Further, the level of PET and MR ADC correlation was significantly positively associated with PFS; tumors with higher values of ADC-PET correlation had more favorable PFS (hazard ratio, 0.17; 95% CI, 0.03-0.89 [ P = 0.036]), suggesting that a higher level of negative ADC-PET correlation leads to less favorable PFS. A more significant negative correlation may indicate higher-grade elements within the tumor leading to poorer outcomes. Conclusion: 18 F-FDG PET and MR ADC histogram metrics in pediatric DIPG demonstrate different characteristics with often a negative correlation between PET and MR ADC pixel values. A higher negative correlation is associated with a worse PFS, which may indicate higher-grade elements within the tumor. © 2017 by the Society of Nuclear Medicine and Molecular Imaging.

Theory and Application of DNA Histogram Analysis.

ERIC Educational Resources Information Center

Bagwell, Charles Bruce

The underlying principles and assumptions associated with DNA histograms are discussed along with the characteristics of fluorescent probes. Information theory was described and used to calculate the information content of a DNA histogram. Two major types of DNA histogram analyses are proposed: parametric and nonparametric analysis. Three levels…

Adaptive Electronic Camouflage Using Texture Synthesis

DTIC Science & Technology

2012-04-01

algorithm begins by computing the GLCMs, GIN and GOUT , of the input image (e.g., image of local environment) and output image (randomly generated...respectively. The algorithm randomly selects a pixel from the output image and cycles its gray-level through all values. For each value, GOUT is updated...The value of the selected pixel is permanently changed to the gray-level value that minimizes the error between GIN and GOUT . Without selecting a

Non-negative matrix factorization in texture feature for classification of dementia with MRI data

NASA Astrophysics Data System (ADS)

Sarwinda, D.; Bustamam, A.; Ardaneswari, G.

2017-07-01

This paper investigates applications of non-negative matrix factorization as feature selection method to select the features from gray level co-occurrence matrix. The proposed approach is used to classify dementia using MRI data. In this study, texture analysis using gray level co-occurrence matrix is done to feature extraction. In the feature extraction process of MRI data, we found seven features from gray level co-occurrence matrix. Non-negative matrix factorization selected three features that influence of all features produced by feature extractions. A Naïve Bayes classifier is adapted to classify dementia, i.e. Alzheimer's disease, Mild Cognitive Impairment (MCI) and normal control. The experimental results show that non-negative factorization as feature selection method able to achieve an accuracy of 96.4% for classification of Alzheimer's and normal control. The proposed method also compared with other features selection methods i.e. Principal Component Analysis (PCA).

A Nonlinear Diffusion Equation-Based Model for Ultrasound Speckle Noise Removal

NASA Astrophysics Data System (ADS)

Zhou, Zhenyu; Guo, Zhichang; Zhang, Dazhi; Wu, Boying

2018-04-01

Ultrasound images are contaminated by speckle noise, which brings difficulties in further image analysis and clinical diagnosis. In this paper, we address this problem in the view of nonlinear diffusion equation theories. We develop a nonlinear diffusion equation-based model by taking into account not only the gradient information of the image, but also the information of the gray levels of the image. By utilizing the region indicator as the variable exponent, we can adaptively control the diffusion type which alternates between the Perona-Malik diffusion and the Charbonnier diffusion according to the image gray levels. Furthermore, we analyze the proposed model with respect to the theoretical and numerical properties. Experiments show that the proposed method achieves much better speckle suppression and edge preservation when compared with the traditional despeckling methods, especially in the low gray level and low-contrast regions.

Feature extraction using gray-level co-occurrence matrix of wavelet coefficients and texture matching for batik motif recognition

NASA Astrophysics Data System (ADS)

Suciati, Nanik; Herumurti, Darlis; Wijaya, Arya Yudhi

2017-02-01

Batik is one of Indonesian's traditional cloth. Motif or pattern drawn on a piece of batik fabric has a specific name and philosopy. Although batik cloths are widely used in everyday life, but only few people understand its motif and philosophy. This research is intended to develop a batik motif recognition system which can be used to identify motif of Batik image automatically. First, a batik image is decomposed into sub-images using wavelet transform. Six texture descriptors, i.e. max probability, correlation, contrast, uniformity, homogenity and entropy, are extracted from gray-level co-occurrence matrix of each sub-image. The texture features are then matched to the template features using canberra distance. The experiment is performed on Batik Dataset consisting of 1088 batik images grouped into seven motifs. The best recognition rate, that is 92,1%, is achieved using feature extraction process with 5 level wavelet decomposition and 4 directional gray-level co-occurrence matrix.

The Role of Color and Morphologic Characteristics in Dermoscopic Diagnosis.

PubMed

Bajaj, Shirin; Marchetti, Michael A; Navarrete-Dechent, Cristian; Dusza, Stephen W; Kose, Kivanc; Marghoob, Ashfaq A

2016-06-01

Both colors and structures are considered important in the dermoscopic evaluation of skin lesions but their relative significance is unknown. To determine if diagnostic accuracy for common skin lesions differs between gray-scale and color dermoscopic images. A convenience sample of 40 skin lesions (8 nevi, 8 seborrheic keratoses, 7 basal cell carcinomas, 7 melanomas, 4 hemangiomas, 4 dermatofibromas, 2 squamous cell carcinomas [SCCs]) was selected and shown to attendees of a dermoscopy course (2014 Memorial Sloan Kettering Cancer Center dermoscopy course). Twenty lesions were shown only once, either in gray-scale (n = 10) or color (n = 10) (nonpaired). Twenty lesions were shown twice, once in gray-scale (n = 20) and once in color (n = 20) (paired). Participants provided their diagnosis and confidence level for each of the 60 images. Of the 261 attendees, 158 participated (60.5%) in the study. Most were attending physicians (n = 76 [48.1%]). Most participants were practicing or training in dermatology (n = 144 [91.1%]). The median (interquartile range) experience evaluating skin lesions and using dermoscopy of participants was 6 (13.5) and 2 (4.0) years, respectively. Diagnostic accuracy and confidence level of participants evaluating gray-scale and color images. Two separate analyses were performed: (1) an unpaired evaluation comparing gray-scale and color images shown either once or for the first time, and (2) a paired evaluation comparing pairs of gray-scale and color images of the same lesion. In univariate analysis of unpaired images, color images were less likely to be diagnosed correctly compared with gray-scale images (odds ratio [OR], 0.8; P < .001). Using gray-scale images as the reference, multivariate analyses of both unpaired and paired images found no association between correct lesion diagnosis and use of color images (OR, 1.0; P = .99, and OR, 1.2; P = .82, respectively). Stratified analysis of paired images using a color by diagnosis interaction term showed that participants were more likely to make a correct diagnosis of SCC and hemangioma in color (P < .001 for both comparisons) and dermatofibroma in gray-scale (P < .001). Morphologic characteristics (ie, structures and patterns), not color, provide the primary diagnostic clue in dermoscopy. Use of gray-scale images may improve teaching of dermoscopy to novices by emphasizing the evaluation of morphology.

Scanning Probe Platform | Materials Science | NREL

Science.gov Websites

level; this image obtained using a scanning tunneling microscope shows gray and white clusters of produce high-resolution color images or maps like this one obtained using scanning tunneling luminescence gray clusters. Gold substrate: (Left) STM image reveals the terraces of the H2 flamed substrate. (Right

Significance levels for studies with correlated test statistics.

PubMed

Shi, Jianxin; Levinson, Douglas F; Whittemore, Alice S

2008-07-01

When testing large numbers of null hypotheses, one needs to assess the evidence against the global null hypothesis that none of the hypotheses is false. Such evidence typically is based on the test statistic of the largest magnitude, whose statistical significance is evaluated by permuting the sample units to simulate its null distribution. Efron (2007) has noted that correlation among the test statistics can induce substantial interstudy variation in the shapes of their histograms, which may cause misleading tail counts. Here, we show that permutation-based estimates of the overall significance level also can be misleading when the test statistics are correlated. We propose that such estimates be conditioned on a simple measure of the spread of the observed histogram, and we provide a method for obtaining conditional significance levels. We justify this conditioning using the conditionality principle described by Cox and Hinkley (1974). Application of the method to gene expression data illustrates the circumstances when conditional significance levels are needed.

Histogram deconvolution - An aid to automated classifiers

NASA Technical Reports Server (NTRS)

Lorre, J. J.

1983-01-01

It is shown that N-dimensional histograms are convolved by the addition of noise in the picture domain. Three methods are described which provide the ability to deconvolve such noise-affected histograms. The purpose of the deconvolution is to provide automated classifiers with a higher quality N-dimensional histogram from which to obtain classification statistics.

Parameterization of the Age-Dependent Whole Brain Apparent Diffusion Coefficient Histogram

PubMed Central

Batra, Marion; Nägele, Thomas

2015-01-01

Purpose. The distribution of apparent diffusion coefficient (ADC) values in the brain can be used to characterize age effects and pathological changes of the brain tissue. The aim of this study was the parameterization of the whole brain ADC histogram by an advanced model with influence of age considered. Methods. Whole brain ADC histograms were calculated for all data and for seven age groups between 10 and 80 years. Modeling of the histograms was performed for two parts of the histogram separately: the brain tissue part was modeled by two Gaussian curves, while the remaining part was fitted by the sum of a Gaussian curve, a biexponential decay, and a straight line. Results. A consistent fitting of the histograms of all age groups was possible with the proposed model. Conclusions. This study confirms the strong dependence of the whole brain ADC histograms on the age of the examined subjects. The proposed model can be used to characterize changes of the whole brain ADC histogram in certain diseases under consideration of age effects. PMID:26609526

RATIO_TOOL - SOFTWARE FOR COMPUTING IMAGE RATIOS

NASA Technical Reports Server (NTRS)

Yates, G. L.

1994-01-01

Geological studies analyze spectral data in order to gain information on surface materials. RATIO_TOOL is an interactive program for viewing and analyzing large multispectral image data sets that have been created by an imaging spectrometer. While the standard approach to classification of multispectral data is to match the spectrum for each input pixel against a library of known mineral spectra, RATIO_TOOL uses ratios of spectral bands in order to spot significant areas of interest within a multispectral image. Each image band can be viewed iteratively, or a selected image band of the data set can be requested and displayed. When the image ratios are computed, the result is displayed as a gray scale image. At this point a histogram option helps in viewing the distribution of values. A thresholding option can then be used to segment the ratio image result into two to four classes. The segmented image is then color coded to indicate threshold classes and displayed alongside the gray scale image. RATIO_TOOL is written in C language for Sun series computers running SunOS 4.0 and later. It requires the XView toolkit and the OpenWindows window manager (version 2.0 or 3.0). The XView toolkit is distributed with Open Windows. A color monitor is also required. The standard distribution medium for RATIO_TOOL is a .25 inch streaming magnetic tape cartridge in UNIX tar format. An electronic copy of the documentation is included on the program media. RATIO_TOOL was developed in 1992 and is a copyrighted work with all copyright vested in NASA. Sun, SunOS, and OpenWindows are trademarks of Sun Microsystems, Inc. UNIX is a registered trademark of AT&T Bell Laboratories.

An improved contrast enhancement algorithm for infrared images based on adaptive double plateaus histogram equalization

NASA Astrophysics Data System (ADS)

Li, Shuo; Jin, Weiqi; Li, Li; Li, Yiyang

2018-05-01

Infrared thermal images can reflect the thermal-radiation distribution of a particular scene. However, the contrast of the infrared images is usually low. Hence, it is generally necessary to enhance the contrast of infrared images in advance to facilitate subsequent recognition and analysis. Based on the adaptive double plateaus histogram equalization, this paper presents an improved contrast enhancement algorithm for infrared thermal images. In the proposed algorithm, the normalized coefficient of variation of the histogram, which characterizes the level of contrast enhancement, is introduced as feedback information to adjust the upper and lower plateau thresholds. The experiments on actual infrared images show that compared to the three typical contrast-enhancement algorithms, the proposed algorithm has better scene adaptability and yields better contrast-enhancement results for infrared images with more dark areas or a higher dynamic range. Hence, it has high application value in contrast enhancement, dynamic range compression, and digital detail enhancement for infrared thermal images.

A-law/Mu-law Dynamic Range Compression Deconvolution (Preprint)

DTIC Science & Technology

2008-02-04

noise filtering via the spectrum proportionality filter, and second the signal deblurring via the inverse filter. In this process for regions when...is the joint image of motion impulse response and the noisy blurred image with signal to noise ratio 5, 6(A’) is the gray level recovered image...joint image of motion impulse response and the noisy blurred image with signal to noise ratio 5, (A’) the gray level recovered image using the A-law

A Chemical and Dynamical Link Between Red Centaur Objects and the Cold Classical Kuiper Belt

NASA Astrophysics Data System (ADS)

Tegler, Stephen C.; Romanishin, William; Consolmagno, Guy

2015-11-01

We present new B-V, V-R, and B-R colors for 32 Centaurs objects using the 4.3-meter Discovery Channel Telescope (DCT) near Happy Jack, AZ and the 1.8-meter Vatican Advanced Technology Telescope on Mt. Graham, AZ. Combining these new colors with our previously reported colors, we now have optical broad-band colors for 58 Centaur objects.Application of the non-parametric Dip Test to our previous sample of only 26 objects showed Centaurs split into gray and red groups at the 99.5% confidence level, and application of the Wilcoxon Rank Sum Test to the same sample showed that red Centaurs have a higher median albedo than gray Centaurs at the 99% confidence level (Tegler et al., 2008, Solar System Beyond Neptune, U Arizona Press, pp. 105-114).Here we report application of the Wilcoxon Rank Sum Test to our sample of 58 Centaurs. We confirm red Centaurs have a higher median albedo than gray Centaurs at the 99.7% level. In addition, we find that red Centaurs have a lower median inclination angle than gray Centaurs at the 99.5% confidence level. Because of their red colors and lower inclination angles, we suggest red Centaurs originate in the cold classical Kuiper belt. We thank the NASA Solar System Observations Program for its support.

A western gray whale mitigation and monitoring program for a 3-D seismic survey, Sakhalin Island, Russia.

PubMed

Johnson, S R; Richardson, W J; Yazvenko, S B; Blokhin, S A; Gailey, G; Jenkerson, M R; Meier, S K; Melton, H R; Newcomer, M W; Perlov, A S; Rutenko, S A; Würsig, B; Martin, C R; Egging, D E

2007-11-01

The introduction of anthropogenic sounds into the marine environment can impact some marine mammals. Impacts can be greatly reduced if appropriate mitigation measures and monitoring are implemented. This paper concerns such measures undertaken by Exxon Neftegas Limited, as operator of the Sakhalin-1 Consortium, during the Odoptu 3-D seismic survey conducted during 17 August-9 September 2001. The key environmental issue was protection of the critically endangered western gray whale (Eschrichtius robustus), which feeds in summer and fall primarily in the Piltun feeding area off northeast Sakhalin Island. Existing mitigation and monitoring practices for seismic surveys in other jurisdictions were evaluated to identify best practices for reducing impacts on feeding activity by western gray whales. Two buffer zones were established to protect whales from physical injury or undue disturbance during feeding. A 1 km buffer protected all whales from exposure to levels of sound energy potentially capable of producing physical injury. A 4-5 km buffer was established to avoid displacing western gray whales from feeding areas. Trained Marine Mammal Observers (MMOs) on the seismic ship Nordic Explorer had the authority to shut down the air guns if whales were sighted within these buffers. Additional mitigation measures were also incorporated: Temporal mitigation was provided by rescheduling the program from June-August to August-September to avoid interference with spring arrival of migrating gray whales. The survey area was reduced by 19% to avoid certain waters <20 m deep where feeding whales concentrated and where seismic acquisition was a lower priority. The number of air guns and total volume of the air guns were reduced by about half (from 28 to 14 air guns and from 3,390 in(3) to 1,640 in(3)) relative to initial plans. "Ramp-up" (="soft-start") procedures were implemented. Monitoring activities were conducted as needed to implement some mitigation measures, and to assess residual impacts. Aerial and vessel-based surveys determined the distribution of whales before, during and after the seismic survey. Daily aerial reconnaissance helped verify whale-free areas and select the sequence of seismic lines to be surveyed. A scout vessel with MMOs aboard was positioned 4 km shoreward of the active seismic vessel to provide better visual coverage of the 4-5 km buffer and to help define the inshore edge of the 4-5 km buffer. A second scout vessel remained near the seismic vessel. Shore-based observers determined whale numbers, distribution, and behavior during and after the seismic survey. Acoustic monitoring documented received sound levels near and in the main whale feeding area. Statistical analyses of aerial survey data indicated that about 5-10 gray whales moved away from waters near (inshore of) the seismic survey during seismic operations. They shifted into the core gray whale feeding area farther south, and the proportion of gray whales observed feeding did not change over the study period. Five shutdowns of the air guns were invoked for gray whales seen within or near the buffer. A previously unknown gray whale feeding area (the Offshore feeding area) was discovered south and offshore from the nearshore Piltun feeding area. The Offshore area has subsequently been shown to be used by feeding gray whales during several years when no anthropogenic activity occurred near the Piltun feeding area.Shore-based counts indicated that whales continued to feed inshore of the Odoptu block throughout the seismic survey, with no significant correlation between gray whale abundance and seismic activity. Average values of most behavioral parameters were similar to those without seismic surveys. Univariate analysis showed no correlation between seismic sound levels and any behavioral parameter. Multiple regression analyses indicated that, after allowance for environmental covariates, 5 of 11 behavioral parameters were statistically correlated with estimated seismic survey-related variables; 6 of 11 behavioral parameters were not statistically correlated with seismic survey-related variables. Behavioral parameters that were correlated with seismic variables were transient and within the range of variation attributable to environmental effects. Acoustic monitoring determined that the 4-5 km buffer zone, in conjunction with reduction of the air gun array to 14 guns and 1,640 in(3), was effective in limiting sound exposure. Within the Piltun feeding area, these mitigation measures were designed to insure that western gray whales were not exposed to received levels exceeding the 163 dB re 1 microPa (rms) threshold. This was among the most complex and intensive mitigation programs ever conducted for any marine mammal. It provided valuable new information about underwater sounds and gray whale responses during a nearshore seismic program that will be useful in planning future work. Overall, the efforts in 2001 were successful in reducing impacts to levels tolerable by western gray whales. Research in 2002-2005 suggested no biologically significant or population-level impacts of the 2001 seismic survey.

Introducing parallelism to histogramming functions for GEM systems

NASA Astrophysics Data System (ADS)

Krawczyk, Rafał D.; Czarski, Tomasz; Kolasinski, Piotr; Pozniak, Krzysztof T.; Linczuk, Maciej; Byszuk, Adrian; Chernyshova, Maryna; Juszczyk, Bartlomiej; Kasprowicz, Grzegorz; Wojenski, Andrzej; Zabolotny, Wojciech

2015-09-01

This article is an assessment of potential parallelization of histogramming algorithms in GEM detector system. Histogramming and preprocessing algorithms in MATLAB were analyzed with regard to adding parallelism. Preliminary implementation of parallel strip histogramming resulted in speedup. Analysis of algorithms parallelizability is presented. Overview of potential hardware and software support to implement parallel algorithm is discussed.

Substance use and regional gray matter volume in individuals at high risk of psychosis.

PubMed

Stone, James M; Bhattacharyya, Sagnik; Barker, Gareth J; McGuire, Philip K

2012-02-01

Individuals with an at risk mental state (ARMS) are at greatly increased risk of developing a psychotic illness. Risk of transition to psychosis is associated with regionally reduced cortical gray matter volume. There has been considerable interest in the interaction between psychosis risk and substance use. In this study we investigate the relationship between alcohol, cannabis and nicotine use with gray matter volume in ARMS subjects and healthy volunteers. Twenty seven ARMS subjects and 27 healthy volunteers took part in the study. All subjects underwent volumetric MRI imaging. The relationship between regional gray matter volume and cannabis use, smoking, and alcohol use in controls and ARMS subjects was analysed using voxel-based morphometry. In any region where a significant relationship with drug was present, data were analysed to determine if there was any group difference in this relationship. Alcohol intake was inversely correlated with gray matter volume in cerebellum, cannabis intake was use was inversely correlated with gray matter volume in prefrontal cortex and tobacco intake was inversely correlated with gray matter volume in left temporal cortex. There were no significant interactions by group in any region. There is no evidence to support the hypothesis of increased susceptibility to harmful effects of drugs and alcohol on regional gray matter in ARMS subjects. However, alcohol, tobacco and cannabis at low to moderate intake may be associated with lower gray matter in both ARMS subjects and healthy volunteers-possibly representing low-level cortical damage or change in neural plasticity. Copyright © 2011 Elsevier B.V. All rights reserved.

Signal recognition by green treefrogs (Hyla cinerea) and Cope's gray treefrogs (Hyla chrysoscelis) in naturally fluctuating noise.

PubMed

Vélez, Alejandro; Bee, Mark A

2013-05-01

This study tested three hypotheses about the ability of female frogs to exploit temporal fluctuations in the level of background noise to overcome the problem of recognizing male advertisement calls in noisy breeding choruses. Phonotaxis tests with green treefrogs (Hyla cinerea) and Cope's gray treefrogs (Hyla chrysoscelis) were used to measure thresholds for recognizing calls in the presence of noise maskers with (a) no level fluctuations, (b) random fluctuations, or level fluctuations characteristic of (c) conspecific choruses and (d) heterospecific choruses. The dip-listening hypothesis predicted lower signal recognition thresholds in the presence of fluctuating maskers compared with nonfluctuating maskers. Support for the dip-listening hypothesis was weak; only Cope's gray treefrogs experienced dip listening and only in the presence of randomly fluctuating maskers. The natural soundscapes advantage hypothesis predicted lower recognition thresholds when level fluctuations resembled those of natural soundscapes compared with artificial fluctuations. This hypothesis was rejected. In noise backgrounds with natural fluctuations, the species-specific advantage hypothesis predicted lower recognition thresholds when fluctuations resembled species-specific patterns of conspecific soundscapes. No evidence was found to support this hypothesis. These results corroborate previous findings showing that Cope's gray treefrogs, but not green treefrogs, experience dip listening under some noise conditions. Together, the results suggest level fluctuations in the soundscape of natural breeding choruses may present few dip-listening opportunities. The findings of this study provide little support for the hypothesis that receivers are adapted to exploit level fluctuations of natural soundscapes in recognizing communication signals.

Signal Recognition by Green Treefrogs (Hyla cinerea) and Cope’s Gray Treefrogs (Hyla chrysoscelis) in Naturally Fluctuating Noise

PubMed Central

Vélez, Alejandro; Bee, Mark A.

2013-01-01

This study tested three hypotheses about the ability of female frogs to exploit temporal fluctuations in the level of background noise to overcome the problem of recognizing male advertisement calls in noisy breeding choruses. Phonotaxis tests with green treefrogs (Hyla cinerea) and Cope’s gray treefrogs (Hyla chrysoscelis) were used to measure thresholds for recognizing calls in the presence of noise maskers with (i) no level fluctuations, (ii) random fluctuations, or level fluctuations characteristic of (iii) conspecific choruses and (iv) heterospecific choruses. The dip-listening hypothesis predicted lower signal recognition thresholds in the presence of fluctuating maskers compared with non-fluctuating maskers. Support for the dip listening hypothesis was weak; only Cope’s gray treefrogs experienced dip listening and only in the presence of randomly fluctuating maskers. The natural soundscapes advantage hypothesis predicted lower recognition thresholds when level fluctuations resembled those of natural soundscapes compared with artificial fluctuations. This hypothesis was rejected. In noise backgrounds with natural fluctuations, the species-specific advantage hypothesis predicted lower recognition thresholds when fluctuations resembled species-specific patterns of conspecific soundscapes. No evidence was found to support this hypothesis. These results corroborate previous findings showing that Cope’s gray treefrogs, but not green treefrogs, experience dip listening under some noise conditions. Together, the results suggest level fluctuations in the soundscape of natural breeding choruses may present few dip-listening opportunities. The findings of this study provide little support for the hypothesis that receivers are adapted to exploit level fluctuations of natural soundscapes in recognizing communication signals. PMID:23106802

Waves, Hydrodynamics and Sediment Transport Modeling at Grays Harbor, WA

DTIC Science & Technology

2010-12-01

Grays Harbor Federal navigation project. At the same time, offshore wind and wave data were available from NDBC Buoy 46029 and CDIP Buoy 036 / NDBC...is forced by the regional ADCIRC water levels and currents, surface wind field, and offshore waves based on the CDIP Buoy 036 (NDBC 46211). Figures

Gray matter responsiveness to adaptive working memory training: a surface-based morphometry study

PubMed Central

Román, Francisco J.; Lewis, Lindsay B.; Chen, Chi-Hua; Karama, Sherif; Burgaleta, Miguel; Martínez, Kenia; Lepage, Claude; Jaeggi, Susanne M.; Evans, Alan C.; Kremen, William S.

2016-01-01

Here we analyze gray matter indices before and after completing a challenging adaptive cognitive training program based on the n-back task. The considered gray matter indices were cortical thickness (CT) and cortical surface area (CSA). Twenty-eight young women (age range 17–22 years) completed 24 training sessions over the course of 3 months (12 weeks, 24 sessions), showing expected performance improvements. CT and CSA values for the training group were compared with those of a matched control group. Statistical analyses were computed using a ROI framework defined by brain areas distinguished by their genetic underpinning. The interaction between group and time was analyzed. Middle temporal, ventral frontal, inferior parietal cortices, and pars opercularis were the regions where the training group showed conservation of gray matter with respect to the control group. These regions support working memory, resistance to interference, and inhibition. Furthermore, an interaction with baseline intelligence differences showed that the expected decreasing trend at the biological level for individuals showing relatively low intelligence levels at baseline was attenuated by the completed training. PMID:26701168

Computer-aided diagnosis for classifying benign versus malignant thyroid nodules based on ultrasound images: A comparison with radiologist-based assessments.

PubMed

Chang, Yongjun; Paul, Anjan Kumar; Kim, Namkug; Baek, Jung Hwan; Choi, Young Jun; Ha, Eun Ju; Lee, Kang Dae; Lee, Hyoung Shin; Shin, DaeSeock; Kim, Nakyoung

2016-01-01

To develop a semiautomated computer-aided diagnosis (cad) system for thyroid cancer using two-dimensional ultrasound images that can be used to yield a second opinion in the clinic to differentiate malignant and benign lesions. A total of 118 ultrasound images that included axial and longitudinal images from patients with biopsy-confirmed malignant (n = 30) and benign (n = 29) nodules were collected. Thyroid cad software was developed to extract quantitative features from these images based on thyroid nodule segmentation in which adaptive diffusion flow for active contours was used. Various features, including histogram, intensity differences, elliptical fit, gray-level co-occurrence matrixes, and gray-level run-length matrixes, were evaluated for each region imaged. Based on these imaging features, a support vector machine (SVM) classifier was used to differentiate benign and malignant nodules. Leave-one-out cross-validation with sequential forward feature selection was performed to evaluate the overall accuracy of this method. Additionally, analyses with contingency tables and receiver operating characteristic (ROC) curves were performed to compare the performance of cad with visual inspection by expert radiologists based on established gold standards. Most univariate features for this proposed cad system attained accuracies that ranged from 78.0% to 83.1%. When optimal SVM parameters that were established using a grid search method with features that radiologists use for visual inspection were employed, the authors could attain rates of accuracy that ranged from 72.9% to 84.7%. Using leave-one-out cross-validation results in a multivariate analysis of various features, the highest accuracy achieved using the proposed cad system was 98.3%, whereas visual inspection by radiologists reached 94.9% accuracy. To obtain the highest accuracies, "axial ratio" and "max probability" in axial images were most frequently included in the optimal feature sets for the authors' proposed cad system, while "shape" and "calcification" in longitudinal images were most frequently included in the optimal feature sets for visual inspection by radiologists. The computed areas under curves in the ROC analysis were 0.986 and 0.979 for the proposed cad system and visual inspection by radiologists, respectively; no significant difference was detected between these groups. The use of thyroid cad to differentiate malignant from benign lesions shows accuracy similar to that obtained via visual inspection by radiologists. Thyroid cad might be considered a viable way to generate a second opinion for radiologists in clinical practice.

Computer-aided diagnosis for classifying benign versus malignant thyroid nodules based on ultrasound images: A comparison with radiologist-based assessments

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

Chang, Yongjun; Paul, Anjan Kumar; Kim, Namkug, E-mail: namkugkim@gmail.com

Purpose: To develop a semiautomated computer-aided diagnosis (CAD) system for thyroid cancer using two-dimensional ultrasound images that can be used to yield a second opinion in the clinic to differentiate malignant and benign lesions. Methods: A total of 118 ultrasound images that included axial and longitudinal images from patients with biopsy-confirmed malignant (n = 30) and benign (n = 29) nodules were collected. Thyroid CAD software was developed to extract quantitative features from these images based on thyroid nodule segmentation in which adaptive diffusion flow for active contours was used. Various features, including histogram, intensity differences, elliptical fit, gray-level co-occurrencemore » matrixes, and gray-level run-length matrixes, were evaluated for each region imaged. Based on these imaging features, a support vector machine (SVM) classifier was used to differentiate benign and malignant nodules. Leave-one-out cross-validation with sequential forward feature selection was performed to evaluate the overall accuracy of this method. Additionally, analyses with contingency tables and receiver operating characteristic (ROC) curves were performed to compare the performance of CAD with visual inspection by expert radiologists based on established gold standards. Results: Most univariate features for this proposed CAD system attained accuracies that ranged from 78.0% to 83.1%. When optimal SVM parameters that were established using a grid search method with features that radiologists use for visual inspection were employed, the authors could attain rates of accuracy that ranged from 72.9% to 84.7%. Using leave-one-out cross-validation results in a multivariate analysis of various features, the highest accuracy achieved using the proposed CAD system was 98.3%, whereas visual inspection by radiologists reached 94.9% accuracy. To obtain the highest accuracies, “axial ratio” and “max probability” in axial images were most frequently included in the optimal feature sets for the authors’ proposed CAD system, while “shape” and “calcification” in longitudinal images were most frequently included in the optimal feature sets for visual inspection by radiologists. The computed areas under curves in the ROC analysis were 0.986 and 0.979 for the proposed CAD system and visual inspection by radiologists, respectively; no significant difference was detected between these groups. Conclusions: The use of thyroid CAD to differentiate malignant from benign lesions shows accuracy similar to that obtained via visual inspection by radiologists. Thyroid CAD might be considered a viable way to generate a second opinion for radiologists in clinical practice.« less

Gray-scale transform and evaluation for digital x-ray chest images on CRT monitor

NASA Astrophysics Data System (ADS)

Furukawa, Isao; Suzuki, Junji; Ono, Sadayasu; Kitamura, Masayuki; Ando, Yutaka

1997-04-01

In this paper, an experimental evaluation of a super high definition (SHD) imaging system for digital x-ray chest images is presented. The SHD imaging system is proposed as a platform for integrating conventional image media. We are involved in the use of SHD images in the total digitizing of medical records that include chest x-rays and pathological microscopic images, both which demand the highest level of quality among the various types of medical images. SHD images use progressive scanning and have a spatial resolution of 2000 by 2000 pixels or more and a temporal resolution (frame rate) of 60 frames/sec or more. For displaying medical x-ray images on a CRT, we derived gray scale transform characteristics based on radiologists' comments during the experiment, and elucidated the relationship between that gray scale transform and the linearization transform for maintaining the linear relationship with the luminance of film on a light box (luminance linear transform). We then carried out viewing experiments based on a five-stage evaluation. Nine radiologists participated in our experiment, and the ten cases evaluated included pulmonary fibrosis, lung cancer, and pneumonia. The experimental results indicated that conventional film images and those on super high definition CRT monitors have nearly the same quality. They also show that the gray scale transform for CRT images decided according to radiologists' comments agrees with the luminance linear transform in the high luminance region. And in the low luminance region, it was found that the gray scale transform had the characteristics of level expansion to increase the number of levels that can be expressed.

To compute lightness, illumination is not estimated, it is held constant.

PubMed

Gilchrist, Alan L

2018-05-03

The light reaching the eye from a surface does not indicate the black-gray-white shade of a surface (called lightness) because the effects of illumination level are confounded with the reflectance of the surface. Rotating a gray paper relative to a light source alters its luminance (intensity of light reaching the eye) but the lightness of the paper remains relatively constant. Recent publications have argued, as had Helmholtz (1866/1924), that the visual system unconsciously estimates the direction and intensity of the light source. We report experiments in which this theory was pitted against an alternative theory according to which illumination level and surface reflectance are disentangled by comparing only those surfaces that are equally illuminated, in other words, by holding illumination level constant. A 3-dimensional scene was created within which the rotation of a target surface would be expected to become darker gray according to the lighting estimation theory, but lighter gray according to the equi-illumination comparison theory, with results clearly favoring the latter. In a further experiment cues held to indicate light source direction (cast shadows, attached shadows, and glossy highlights) were completely eliminated and yet this had no effect on the results. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Atherogenic lipid phenotype in a general group of subjects.

PubMed

Van, Joanne; Pan, Jianqiu; Charles, M Arthur; Krauss, Ronald; Wong, Nathan; Wu, Xiaoshan

2007-11-01

The atherogenic lipid phenotype is a major cardiovascular risk factor, but normal values do not exist derived from 1 analysis in a general study group. To determine normal values of all of the atherogenic lipid phenotype parameters using subjects from a general study group. One hundred two general subjects were used to determine their atherogenic lipid phenotype using polyacrylamide gradient gels. Low-density lipoprotein (LDL) size revealed 24% of subjects express LDL phenotype B, defined as average LDL peak particle size 258 A or less; however, among the Chinese subjects, the expression of the B phenotype was higher at 44% (P = .02). For the total group, mean LDL size was 265 +/- 11 A (1 SD); however, histograms were bimodal in both men and women. After excluding subjects expressing LDL phenotype B, because they are at increased cardiovascular risk and thus are not completely healthy, LDL histograms were unimodal and the mean LDL size was 270 +/- 7 A. A small, dense LDL concentration histogram (total group) revealed skewing; thus, phenotype B subjects were excluded, for the rationale described previously, and the mean value was 13 +/- 9 mg/dL (0.33 +/- 0.23 mmol/L). High-density lipoprotein (HDL) cholesterol histograms were bimodal in both sexes. After removing subjects as described previously or if HDL cholesterol levels were less than 45 mg/dL, histograms were unimodal and revealed a mean HDL cholesterol value of 61 +/- 12 mg/dL (1.56 +/- 0.31 mmol/L). HDL 2, HDL 2a, and HDL 2b were similarly evaluated. Approximate normal values for the atherogenic lipid phenotype, similar to those derived from cardiovascular endpoint trials, can be determined if those high proportions of subjects with dyslipidemic cardiovascular risk are excluded.

Physical activity, fitness, and gray matter volume

PubMed Central

Erickson, Kirk I.; Leckie, Regina L.; Weinstein, Andrea M.

2014-01-01

In this review we explore the association between physical activity, cardiorespiratory fitness, and exercise on gray matter volume in older adults. We conclude that higher cardiorespiratory fitness levels are routinely associated with greater gray matter volume in the prefrontal cortex and hippocampus, and less consistently in other regions. We also conclude that physical activity is associated with greater gray matter volume in the same regions that are associated with cardiorespiratory fitness including the prefrontal cortex and hippocampus. Some heterogeneity in the literature may be explained by effect moderation by age, stress, or other factors. Finally, we report promising results from randomized exercise interventions that suggest that the volume of the hippocampus and prefrontal cortex remain pliable and responsive to moderate intensity exercise for 6-months to 1-year. Physical activity appears to be a propitious method for influencing gray matter volume in late adulthood, but additional well-controlled studies are necessary to inform public policies about the potential protective or therapeutic effects of exercise on brain volume. PMID:24952993

Climatological Study to Determine the Impact of Icing on the Low Level Windshear Alert System. Volume II. Statistics.

DOT National Transportation Integrated Search

1989-01-01

The climatological study was performed to determine the impact of icing on the performance of the Low Level Winshear Alert System (LLWAS). : This report Presents the Icing Statisctical profile in the form of data tables and histograms of 106 LLWAS si...

Climatological Study to Determine the Impact of Icing on the Low Level Windshear Alert System. Volume I. Analysis.

DOT National Transportation Integrated Search

1989-09-01

The climatological study was performed to determine the impact of icing on the performance of Low Level Windshear Alert System (LLWAS). : This report presents the icing statistical profile in the form of data tables and histograms of 106 LLWAS sites....

Image Edge Extraction via Fuzzy Reasoning

NASA Technical Reports Server (NTRS)

Dominquez, Jesus A. (Inventor); Klinko, Steve (Inventor)

2008-01-01

A computer-based technique for detecting edges in gray level digital images employs fuzzy reasoning to analyze whether each pixel in an image is likely on an edge. The image is analyzed on a pixel-by-pixel basis by analyzing gradient levels of pixels in a square window surrounding the pixel being analyzed. An edge path passing through the pixel having the greatest intensity gradient is used as input to a fuzzy membership function, which employs fuzzy singletons and inference rules to assigns a new gray level value to the pixel that is related to the pixel's edginess degree.

Exploring the role of testosterone in the cerebellum link to neuroticism: From adolescence to early adulthood.

PubMed

Schutter, Dennis J L G; Meuwese, Rosa; Bos, Marieke G N; Crone, Eveline A; Peper, Jiska S

2017-04-01

Previous research has found an association between a smaller cerebellar volume and higher levels of neuroticism. The steroid hormone testosterone reduces stress responses and the susceptibility to negative mood. Together with in vitro studies showing a positive effect of testosterone on cerebellar gray matter volumes, we set out to explore the role of testosterone in the relation between cerebellar gray matter and neuroticism. Structural magnetic resonance imaging scans were acquired, and indices of neurotic personality traits were assessed by administering the depression and anxiety scale of the revised NEO personality inventory and Gray's behavioural avoidance in one hundred and forty-nine healthy volunteers between 12 and 27 years of age. Results demonstrated an inverse relation between total brain corrected cerebellar volumes and neurotic personality traits in adolescents and young adults. In males, higher endogenous testosterone levels were associated with lower scores on neurotic personality traits and larger cerebellar gray matter volumes. No such relations were observed in the female participants. Analyses showed that testosterone significantly mediated the relation between male cerebellar gray matter and measures of neuroticism. Our findings on the interrelations between endogenous testosterone, neuroticism and cerebellar morphology provide a cerebellum-oriented framework for the susceptibility to experience negative emotions and mood in adolescence and early adulthood. Copyright © 2017 Elsevier Ltd. All rights reserved.

A Study of Feature Extraction Using Divergence Analysis of Texture Features

NASA Technical Reports Server (NTRS)

Hallada, W. A.; Bly, B. G.; Boyd, R. K.; Cox, S.

1982-01-01

An empirical study of texture analysis for feature extraction and classification of high spatial resolution remotely sensed imagery (10 meters) is presented in terms of specific land cover types. The principal method examined is the use of spatial gray tone dependence (SGTD). The SGTD method reduces the gray levels within a moving window into a two-dimensional spatial gray tone dependence matrix which can be interpreted as a probability matrix of gray tone pairs. Haralick et al (1973) used a number of information theory measures to extract texture features from these matrices, including angular second moment (inertia), correlation, entropy, homogeneity, and energy. The derivation of the SGTD matrix is a function of: (1) the number of gray tones in an image; (2) the angle along which the frequency of SGTD is calculated; (3) the size of the moving window; and (4) the distance between gray tone pairs. The first three parameters were varied and tested on a 10 meter resolution panchromatic image of Maryville, Tennessee using the five SGTD measures. A transformed divergence measure was used to determine the statistical separability between four land cover categories forest, new residential, old residential, and industrial for each variation in texture parameters.

Reduced Brain Gray Matter Concentration in Patients With Obstructive Sleep Apnea Syndrome

PubMed Central

Joo, Eun Yeon; Tae, Woo Suk; Lee, Min Joo; Kang, Jung Woo; Park, Hwan Seok; Lee, Jun Young; Suh, Minah; Hong, Seung Bong

2010-01-01

Study Objectives: To investigate differences in brain gray matter concentrations or volumes in patients with obstructive sleep apnea syndrome (OSA) and healthy volunteers. Designs: Optimized voxel-based morphometry, an automated processing technique for MRI, was used to characterize structural differences in gray matter in newly diagnosed male patients. Setting: University hospital Patients and Participants: The study consisted of 36 male OSA and 31 non-apneic male healthy volunteers matched for age (mean age, 44.8 years). Interventions: Using the t-test, gray matter differences were identified. The statistical significance level was set to a false discovery rate P < 0.05 with an extent threshold of kE > 200 voxels. Measurements and Results: The mean apnea-hypopnea index (AHI) of patients was 52.5/ h. On visual inspection of MRI, no structural abnormalities were observed. Compared to healthy volunteers, the gray matter concentrations of OSA patients were significantly decreased in the left gyrus rectus, bilateral superior frontal gyri, left precentral gyrus, bilateral frontomarginal gyri, bilateral anterior cingulate gyri, right insular gyrus, bilateral caudate nuclei, bilateral thalami, bilateral amygdalo-hippocampi, bilateral inferior temporal gyri, and bilateral quadrangular and biventer lobules in the cerebellum (false discovery rate P < 0.05). Gray matter volume was not different between OSA patients and healthy volunteers. Conclusions: The brain gray matter deficits may suggest that memory impairment, affective and cardiovascular disturbances, executive dysfunctions, and dysregulation of autonomic and respiratory control frequently found in OSA patients might be related to morphological differences in the brain gray matter areas. Citation: Joo EY; Tae WS; Lee MJ; Kang JW; Park HS; Lee JY; Suh M; Hong SB. Reduced brain gray matter concentration in patients with obstructive sleep apnea syndrome. SLEEP 2010;33(2):235-241. PMID:20175407

Migratory behavior of eastern North Pacific gray whales tracked using a hydrophone array

PubMed Central

Helble, Tyler A.; D’Spain, Gerald L.; Weller, David W.; Wiggins, Sean M.; Hildebrand, John A.

2017-01-01

Eastern North Pacific gray whales make one of the longest annual migrations of any mammal, traveling from their summer feeding areas in the Bering and Chukchi Seas to their wintering areas in the lagoons of Baja California, Mexico. Although a significant body of knowledge on gray whale biology and behavior exists, little is known about their vocal behavior while migrating. In this study, we used a sparse hydrophone array deployed offshore of central California to investigate how gray whales behave and use sound while migrating. We detected, localized, and tracked whales for one full migration season, a first for gray whales. We verified and localized 10,644 gray whale M3 calls and grouped them into 280 tracks. Results confirm that gray whales are acoustically active while migrating and their swimming and acoustic behavior changes on daily and seasonal time scales. The seasonal timing of the calls verifies the gray whale migration timing determined using other methods such as counts conducted by visual observers. The total number of calls and the percentage of calls that were part of a track changed significantly over both seasonal and daily time scales. An average calling rate of 5.7 calls/whale/day was observed, which is significantly greater than previously reported migration calling rates. We measured a mean speed of 1.6 m/s and quantified heading, direction, and water depth where tracks were located. Mean speed and water depth remained constant between night and day, but these quantities had greater variation at night. Gray whales produce M3 calls with a root mean square source level of 156.9 dB re 1 μPa at 1 m. Quantities describing call characteristics were variable and dependent on site-specific propagation characteristics. PMID:29084266

Accelerated Gray and White Matter Deterioration With Age in Schizophrenia.

PubMed

Cropley, Vanessa L; Klauser, Paul; Lenroot, Rhoshel K; Bruggemann, Jason; Sundram, Suresh; Bousman, Chad; Pereira, Avril; Di Biase, Maria A; Weickert, Thomas W; Weickert, Cynthia Shannon; Pantelis, Christos; Zalesky, Andrew

2017-03-01

Although brain changes in schizophrenia have been proposed to mirror those found with advancing age, the trajectory of gray matter and white matter changes during the disease course remains unclear. The authors sought to measure whether these changes in individuals with schizophrenia remain stable, are accelerated, or are diminished with age. Gray matter volume and fractional anisotropy were mapped in 326 individuals diagnosed with schizophrenia or schizoaffective disorder and in 197 healthy comparison subjects aged 20-65 years. Polynomial regression was used to model the influence of age on gray matter volume and fractional anisotropy at a whole-brain and voxel level. Between-group differences in gray matter volume and fractional anisotropy were regionally localized across the lifespan using permutation testing and cluster-based inference. Significant loss of gray matter volume was evident in schizophrenia, progressively worsening with age to a maximal loss of 8% in the seventh decade of life. The inferred rate of gray matter volume loss was significantly accelerated in schizophrenia up to middle age and plateaued thereafter. In contrast, significant reductions in fractional anisotropy emerged in schizophrenia only after age 35, and the rate of fractional anisotropy deterioration with age was constant and best modeled with a straight line. The slope of this line was 60% steeper in schizophrenia relative to comparison subjects, indicating a significantly faster rate of white matter deterioration with age. The rates of reduction of gray matter volume and fractional anisotropy were significantly faster in males than in females, but an interaction between sex and diagnosis was not evident. The findings suggest that schizophrenia is characterized by an initial, rapid rate of gray matter loss that slows in middle life, followed by the emergence of a deficit in white matter that progressively worsens with age at a constant rate.

Migratory behavior of eastern North Pacific gray whales tracked using a hydrophone array.

PubMed

Guazzo, Regina A; Helble, Tyler A; D'Spain, Gerald L; Weller, David W; Wiggins, Sean M; Hildebrand, John A

2017-01-01

Eastern North Pacific gray whales make one of the longest annual migrations of any mammal, traveling from their summer feeding areas in the Bering and Chukchi Seas to their wintering areas in the lagoons of Baja California, Mexico. Although a significant body of knowledge on gray whale biology and behavior exists, little is known about their vocal behavior while migrating. In this study, we used a sparse hydrophone array deployed offshore of central California to investigate how gray whales behave and use sound while migrating. We detected, localized, and tracked whales for one full migration season, a first for gray whales. We verified and localized 10,644 gray whale M3 calls and grouped them into 280 tracks. Results confirm that gray whales are acoustically active while migrating and their swimming and acoustic behavior changes on daily and seasonal time scales. The seasonal timing of the calls verifies the gray whale migration timing determined using other methods such as counts conducted by visual observers. The total number of calls and the percentage of calls that were part of a track changed significantly over both seasonal and daily time scales. An average calling rate of 5.7 calls/whale/day was observed, which is significantly greater than previously reported migration calling rates. We measured a mean speed of 1.6 m/s and quantified heading, direction, and water depth where tracks were located. Mean speed and water depth remained constant between night and day, but these quantities had greater variation at night. Gray whales produce M3 calls with a root mean square source level of 156.9 dB re 1 μPa at 1 m. Quantities describing call characteristics were variable and dependent on site-specific propagation characteristics.

Sensitivity to Punishment and Explanatory Style as Predictors of Public Speaking State Anxiety. Brief Reports

ERIC Educational Resources Information Center

Kopecky, Courtney; Sawyer, Chris; Behnke, Ralph

2004-01-01

Recent biological theories of state anxiety have focused on temperament and neurophysiology as factors that predispose some people to be particularly at risk of debilitating levels of performance anxiety. The present study extends Gray's (1982; Gray & McNaughton, 2000) reinforcement sensitivity theory by proposing a linkage between sensitivity to…

Identification of alleles conferring resistance to gray leaf spot in maize derived from its wild progenitor species teosinte (Zea mays ssp. parviglumis)

USDA-ARS?s Scientific Manuscript database

Gray Leaf Spot [(GLS), causal agent Cercospora zeae-maydis and Cercospora zeina] is an important maize disease in the United States. Current control methods for GLS include using resistant cultivars, crop rotation, chemical applications, and conventional tillage to reduce inoculum levels. Teosinte ...

Experiments in encoding multilevel images as quadtrees

NASA Technical Reports Server (NTRS)

Lansing, Donald L.

1987-01-01

Image storage requirements for several encoding methods are investigated and the use of quadtrees with multigray level or multicolor images are explored. The results of encoding a variety of images having up to 256 gray levels using three schemes (full raster, runlength and quadtree) are presented. Although there is considerable literature on the use of quadtrees to store and manipulate binary images, their application to multilevel images is relatively undeveloped. The potential advantage of quadtree encoding is that an entire area with a uniform gray level may be encoded as a unit. A pointerless quadtree encoding scheme is described. Data are presented on the size of the quadtree required to encode selected images and on the relative storage requirements of the three encoding schemes. A segmentation scheme based on the statistical variation of gray levels within a quadtree quadrant is described. This parametric scheme may be used to control the storage required by an encoded image and to preprocess a scene for feature identification. Several sets of black and white and pseudocolor images obtained by varying the segmentation parameter are shown.

Predicting the Valence of a Scene from Observers’ Eye Movements

PubMed Central

R.-Tavakoli, Hamed; Atyabi, Adham; Rantanen, Antti; Laukka, Seppo J.; Nefti-Meziani, Samia; Heikkilä, Janne

2015-01-01

Multimedia analysis benefits from understanding the emotional content of a scene in a variety of tasks such as video genre classification and content-based image retrieval. Recently, there has been an increasing interest in applying human bio-signals, particularly eye movements, to recognize the emotional gist of a scene such as its valence. In order to determine the emotional category of images using eye movements, the existing methods often learn a classifier using several features that are extracted from eye movements. Although it has been shown that eye movement is potentially useful for recognition of scene valence, the contribution of each feature is not well-studied. To address the issue, we study the contribution of features extracted from eye movements in the classification of images into pleasant, neutral, and unpleasant categories. We assess ten features and their fusion. The features are histogram of saccade orientation, histogram of saccade slope, histogram of saccade length, histogram of saccade duration, histogram of saccade velocity, histogram of fixation duration, fixation histogram, top-ten salient coordinates, and saliency map. We utilize machine learning approach to analyze the performance of features by learning a support vector machine and exploiting various feature fusion schemes. The experiments reveal that ‘saliency map’, ‘fixation histogram’, ‘histogram of fixation duration’, and ‘histogram of saccade slope’ are the most contributing features. The selected features signify the influence of fixation information and angular behavior of eye movements in the recognition of the valence of images. PMID:26407322

Histogram analysis of T2*-based pharmacokinetic imaging in cerebral glioma grading.

PubMed

Liu, Hua-Shan; Chiang, Shih-Wei; Chung, Hsiao-Wen; Tsai, Ping-Huei; Hsu, Fei-Ting; Cho, Nai-Yu; Wang, Chao-Ying; Chou, Ming-Chung; Chen, Cheng-Yu

2018-03-01

To investigate the feasibility of histogram analysis of the T2*-based permeability parameter volume transfer constant (K trans ) for glioma grading and to explore the diagnostic performance of the histogram analysis of K trans and blood plasma volume (v p ). We recruited 31 and 11 patients with high- and low-grade gliomas, respectively. The histogram parameters of K trans and v p , derived from the first-pass pharmacokinetic modeling based on the T2* dynamic susceptibility-weighted contrast-enhanced perfusion-weighted magnetic resonance imaging (T2* DSC-PW-MRI) from the entire tumor volume, were evaluated for differentiating glioma grades. Histogram parameters of K trans and v p showed significant differences between high- and low-grade gliomas and exhibited significant correlations with tumor grades. The mean K trans derived from the T2* DSC-PW-MRI had the highest sensitivity and specificity for differentiating high-grade gliomas from low-grade gliomas compared with other histogram parameters of K trans and v p . Histogram analysis of T2*-based pharmacokinetic imaging is useful for cerebral glioma grading. The histogram parameters of the entire tumor K trans measurement can provide increased accuracy with additional information regarding microvascular permeability changes for identifying high-grade brain tumors. Copyright © 2017 Elsevier B.V. All rights reserved.

3D shape recovery from image focus using gray level co-occurrence matrix

NASA Astrophysics Data System (ADS)

Mahmood, Fahad; Munir, Umair; Mehmood, Fahad; Iqbal, Javaid

2018-04-01

Recovering a precise and accurate 3-D shape of the target object utilizing robust 3-D shape recovery algorithm is an ultimate objective of computer vision community. Focus measure algorithm plays an important role in this architecture which convert the color values of each pixel of the acquired 2-D image dataset into corresponding focus values. After convolving the focus measure filter with the input 2-D image dataset, a 3-D shape recovery approach is applied which will recover the depth map. In this document, we are concerned with proposing Gray Level Co-occurrence Matrix along with its statistical features for computing the focus information of the image dataset. The Gray Level Co-occurrence Matrix quantifies the texture present in the image using statistical features and then applies joint probability distributive function of the gray level pairs of the input image. Finally, we quantify the focus value of the input image using Gaussian Mixture Model. Due to its little computational complexity, sharp focus measure curve, robust to random noise sources and accuracy, it is considered as superior alternative to most of recently proposed 3-D shape recovery approaches. This algorithm is deeply investigated on real image sequences and synthetic image dataset. The efficiency of the proposed scheme is also compared with the state of art 3-D shape recovery approaches. Finally, by means of two global statistical measures, root mean square error and correlation, we claim that this approach -in spite of simplicity generates accurate results.

Information granules in image histogram analysis.

PubMed

Wieclawek, Wojciech

2018-04-01

A concept of granular computing employed in intensity-based image enhancement is discussed. First, a weighted granular computing idea is introduced. Then, the implementation of this term in the image processing area is presented. Finally, multidimensional granular histogram analysis is introduced. The proposed approach is dedicated to digital images, especially to medical images acquired by Computed Tomography (CT). As the histogram equalization approach, this method is based on image histogram analysis. Yet, unlike the histogram equalization technique, it works on a selected range of the pixel intensity and is controlled by two parameters. Performance is tested on anonymous clinical CT series. Copyright © 2017 Elsevier Ltd. All rights reserved.

Stochastic HKMDHE: A multi-objective contrast enhancement algorithm

NASA Astrophysics Data System (ADS)

Pratiher, Sawon; Mukhopadhyay, Sabyasachi; Maity, Srideep; Pradhan, Asima; Ghosh, Nirmalya; Panigrahi, Prasanta K.

2018-02-01

This contribution proposes a novel extension of the existing `Hyper Kurtosis based Modified Duo-Histogram Equalization' (HKMDHE) algorithm, for multi-objective contrast enhancement of biomedical images. A novel modified objective function has been formulated by joint optimization of the individual histogram equalization objectives. The optimal adequacy of the proposed methodology with respect to image quality metrics such as brightness preserving abilities, peak signal-to-noise ratio (PSNR), Structural Similarity Index (SSIM) and universal image quality metric has been experimentally validated. The performance analysis of the proposed Stochastic HKMDHE with existing histogram equalization methodologies like Global Histogram Equalization (GHE) and Contrast Limited Adaptive Histogram Equalization (CLAHE) has been given for comparative evaluation.

The Amazing Histogram.

ERIC Educational Resources Information Center

Vandermeulen, H.; DeWreede, R. E.

1983-01-01

Presents a histogram drawing program which sorts real numbers in up to 30 categories. Entered data are sorted and saved in a text file which is then used to generate the histogram. Complete Applesoft program listings are included. (JN)

Performance assessment of automated tissue characterization for prostate H and E stained histopathology

NASA Astrophysics Data System (ADS)

DiFranco, Matthew D.; Reynolds, Hayley M.; Mitchell, Catherine; Williams, Scott; Allan, Prue; Haworth, Annette

2015-03-01

Reliable automated prostate tumor detection and characterization in whole-mount histology images is sought in many applications, including post-resection tumor staging and as ground-truth data for multi-parametric MRI interpretation. In this study, an ensemble-based supervised classification algorithm for high-resolution histology images was trained on tile-based image features including histogram and gray-level co-occurrence statistics. The algorithm was assessed using different combinations of H and E prostate slides from two separate medical centers and at two different magnifications (400x and 200x), with the aim of applying tumor classification models to new data. Slides from both datasets were annotated by expert pathologists in order to identify homogeneous cancerous and non-cancerous tissue regions of interest, which were then categorized as (1) low-grade tumor (LG-PCa), including Gleason 3 and high-grade prostatic intraepithelial neoplasia (HG-PIN), (2) high-grade tumor (HG-PCa), including various Gleason 4 and 5 patterns, or (3) non-cancerous, including benign stroma and benign prostatic hyperplasia (BPH). Classification models for both LG-PCa and HG-PCa were separately trained using a support vector machine (SVM) approach, and per-tile tumor prediction maps were generated from the resulting ensembles. Results showed high sensitivity for predicting HG-PCa with an AUC up to 0.822 using training data from both medical centres, while LG-PCa showed a lower sensitivity of 0.763 with the same training data. Visual inspection of cancer probability heatmaps from 9 patients showed that 17/19 tumors were detected, and HG-PCa generally reported less false positives than LG-PCa.

A novel approach for fire recognition using hybrid features and manifold learning-based classifier

NASA Astrophysics Data System (ADS)

Zhu, Rong; Hu, Xueying; Tang, Jiajun; Hu, Sheng

2018-03-01

Although image/video based fire recognition has received growing attention, an efficient and robust fire detection strategy is rarely explored. In this paper, we propose a novel approach to automatically identify the flame or smoke regions in an image. It is composed to three stages: (1) a block processing is applied to divide an image into several nonoverlapping image blocks, and these image blocks are identified as suspicious fire regions or not by using two color models and a color histogram-based similarity matching method in the HSV color space, (2) considering that compared to other information, the flame and smoke regions have significant visual characteristics, so that two kinds of image features are extracted for fire recognition, where local features are obtained based on the Scale Invariant Feature Transform (SIFT) descriptor and the Bags of Keypoints (BOK) technique, and texture features are extracted based on the Gray Level Co-occurrence Matrices (GLCM) and the Wavelet-based Analysis (WA) methods, and (3) a manifold learning-based classifier is constructed based on two image manifolds, which is designed via an improve Globular Neighborhood Locally Linear Embedding (GNLLE) algorithm, and the extracted hybrid features are used as input feature vectors to train the classifier, which is used to make decision for fire images or non fire images. Experiments and comparative analyses with four approaches are conducted on the collected image sets. The results show that the proposed approach is superior to the other ones in detecting fire and achieving a high recognition accuracy and a low error rate.

Intramuscular adipose tissue determined by T1-weighted MRI at 3T primarily reflects extramyocellular lipids.

PubMed

Akima, Hiroshi; Hioki, Maya; Yoshiko, Akito; Koike, Teruhiko; Sakakibara, Hisataka; Takahashi, Hideyuki; Oshida, Yoshiharu

2016-05-01

The purpose of this study was to assess relationships between intramuscular adipose tissue (IntraMAT) content determined by MRI and intramyocellular lipids (IMCL) and extramyocellular lipids (EMCL) determined by (1)H magnetic resonance spectroscopy ((1)H MRS) or echo intensity determined by B-mode ultrasonography of human skeletal muscles. Thirty young and elderly men and women were included. T1-weighted MRI was taken from the right mid-thigh to measure IntraMAT content of the vastus lateralis (VL) and biceps femoris (BF) using a histogram shape-based thresholding technique. IMCL and EMCL were measured from the VL and BF at the right mid-thigh using (1)H MRS. Ultrasonographic images were taken from the VL and BF of the right mid-thigh to measure echo intensity based on gray-scale level for quantitative analysis. There was a significant correlation between IntraMAT content by MRI and EMCL of the VL and BF (VL, r=0.506, P<0.01; BF, r=0.591, P<0.001) and between echo intensity and EMCL of the VL and BF (VL, r=0.485, P<0.05; BF, r=0.648, P<0.01). IntraMAT content was also significantly correlated with echo intensity of the VL and BF (VL, r=0.404, P<0.05; BF, r=0.493, P<0.01). Our study suggests that IntraMAT content determined by T1-weighted MRI at 3T primarily reflects extramyocellular lipids, not intramyocellular lipids, in human skeletal muscles. Copyright © 2016 Elsevier Inc. All rights reserved.

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 simple objective method to assess the radiopacity of urinary calculi and its use to predict extracorporeal shock wave lithotripsy outcomes.

PubMed

el-Gamal, Osama; el-Badry, Amr

2009-07-01

We describe an objective method to evaluate kidney stone radiopacity for use in selection of cases suitable for ESWL. We recruited 76 adult patients with a solitary 1 to 2 cm renal pelvic stone. All patients underwent routine plain x-ray of the urinary tract but an aluminum step wedge (Gammex) was adapted to the cassette before x-ray exposure. This x-ray was then digitized and analyzed by histogram to calculate the gray level of the stone and of each step of the aluminum step wedge. This allowed radiographic stone density to be expressed in mm aluminum equivalent. All patients also underwent abdominopelvic computerized tomography and then ESWL was started. Stone density on plain x-ray was 1.83 to 5.93 mm aluminum equivalent. There was a positive correlation between these values and stone attenuation values on computerized tomography (r(2) 0.83, p <0.005). The 12 patients in whom ESWL failed were found to have stones of significantly higher density than stones in patients with complete stone fragmentation (mean +/- SD 4.8 +/- 0.74 vs 3.35 +/- 0.88 mm aluminum equivalent, p <0.005). There was also a positive correlation between stone radiopacity in mm aluminum equivalent and the total number of shock waves required to achieve complete fragmentation (r(2) 0.66, p <0.005). The aluminum step wedge with plain x-ray of the urinary tract provides a good reference for objectively assessing the radiopacity of renal calculi.

Survey of contemporary trends in color image segmentation

NASA Astrophysics Data System (ADS)

Vantaram, Sreenath Rao; Saber, Eli

2012-10-01

In recent years, the acquisition of image and video information for processing, analysis, understanding, and exploitation of the underlying content in various applications, ranging from remote sensing to biomedical imaging, has grown at an unprecedented rate. Analysis by human observers is quite laborious, tiresome, and time consuming, if not infeasible, given the large and continuously rising volume of data. Hence the need for systems capable of automatically and effectively analyzing the aforementioned imagery for a variety of uses that span the spectrum from homeland security to elderly care. In order to achieve the above, tools such as image segmentation provide the appropriate foundation for expediting and improving the effectiveness of subsequent high-level tasks by providing a condensed and pertinent representation of image information. We provide a comprehensive survey of color image segmentation strategies adopted over the last decade, though notable contributions in the gray scale domain will also be discussed. Our taxonomy of segmentation techniques is sampled from a wide spectrum of spatially blind (or feature-based) approaches such as clustering and histogram thresholding as well as spatially guided (or spatial domain-based) methods such as region growing/splitting/merging, energy-driven parametric/geometric active contours, supervised/unsupervised graph cuts, and watersheds, to name a few. In addition, qualitative and quantitative results of prominent algorithms on several images from the Berkeley segmentation dataset are shown in order to furnish a fair indication of the current quality of the state of the art. Finally, we provide a brief discussion on our current perspective of the field as well as its associated future trends.

Scene segmentation of natural images using texture measures and back-propagation

NASA Technical Reports Server (NTRS)

Sridhar, Banavar; Phatak, Anil; Chatterji, Gano

1993-01-01

Knowledge of the three-dimensional world is essential for many guidance and navigation applications. A sequence of images from an electro-optical sensor can be processed using optical flow algorithms to provide a sparse set of ranges as a function of azimuth and elevation. A natural way to enhance the range map is by interpolation. However, this should be undertaken with care since interpolation assumes continuity of range. The range is continuous in certain parts of the image and can jump at object boundaries. In such situations, the ability to detect homogeneous object regions by scene segmentation can be used to determine regions in the range map that can be enhanced by interpolation. The use of scalar features derived from the spatial gray-level dependence matrix for texture segmentation is explored. Thresholding of histograms of scalar texture features is done for several images to select scalar features which result in a meaningful segmentation of the images. Next, the selected scalar features are used with a neural net to automate the segmentation procedure. Back-propagation is used to train the feed forward neural network. The generalization of the network approach to subsequent images in the sequence is examined. It is shown that the use of multiple scalar features as input to the neural network result in a superior segmentation when compared with a single scalar feature. It is also shown that the scalar features, which are not useful individually, result in a good segmentation when used together. The methodology is applied to both indoor and outdoor images.

Improved characterization of molecular phenotypes in breast lesions using 18F-FDG PET image homogeneity

NASA Astrophysics Data System (ADS)

Cao, Kunlin; Bhagalia, Roshni; Sood, Anup; Brogi, Edi; Mellinghoff, Ingo K.; Larson, Steven M.

2015-03-01

Positron emission tomography (PET) using uorodeoxyglucose (18F-FDG) is commonly used in the assessment of breast lesions by computing voxel-wise standardized uptake value (SUV) maps. Simple metrics derived from ensemble properties of SUVs within each identified breast lesion are routinely used for disease diagnosis. The maximum SUV within the lesion (SUVmax) is the most popular of these metrics. However these simple metrics are known to be error-prone and are susceptible to image noise. Finding reliable SUV map-based features that correlate to established molecular phenotypes of breast cancer (viz. estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2) expression) will enable non-invasive disease management. This study investigated 36 SUV features based on first and second order statistics, local histograms and texture of segmented lesions to predict ER and PR expression in 51 breast cancer patients. True ER and PR expression was obtained via immunohistochemistry (IHC) of tissue samples from each lesion. A supervised learning, adaptive boosting-support vector machine (AdaBoost-SVM), framework was used to select a subset of features to classify breast lesions into distinct phenotypes. Performance of the trained multi-feature classifier was compared against the baseline single-feature SUVmax classifier using receiver operating characteristic (ROC) curves. Results show that texture features encoding local lesion homogeneity extracted from gray-level co-occurrence matrices are the strongest discriminator of lesion ER expression. In particular, classifiers including these features increased prediction accuracy from 0.75 (baseline) to 0.82 and the area under the ROC curve from 0.64 (baseline) to 0.75.

AHIMSA - Ad hoc histogram information measure sensing algorithm for feature selection in the context of histogram inspired clustering techniques

NASA Technical Reports Server (NTRS)

Dasarathy, B. V.

1976-01-01

An algorithm is proposed for dimensionality reduction in the context of clustering techniques based on histogram analysis. The approach is based on an evaluation of the hills and valleys in the unidimensional histograms along the different features and provides an economical means of assessing the significance of the features in a nonparametric unsupervised data environment. The method has relevance to remote sensing applications.

18F-FDG PET radiomics approaches: comparing and clustering features in cervical cancer.

PubMed

Tsujikawa, Tetsuya; Rahman, Tasmiah; Yamamoto, Makoto; Yamada, Shizuka; Tsuyoshi, Hideaki; Kiyono, Yasushi; Kimura, Hirohiko; Yoshida, Yoshio; Okazawa, Hidehiko

2017-11-01

The aims of our study were to find the textural features on 18 F-FDG PET/CT which reflect the different histological architectures between cervical cancer subtypes and to make a visual assessment of the association between 18 F-FDG PET textural features in cervical cancer. Eighty-three cervical cancer patients [62 squamous cell carcinomas (SCCs) and 21 non-SCCs (NSCCs)] who had undergone pretreatment 18 F-FDG PET/CT were enrolled. A texture analysis was performed on PET/CT images, from which 18 PET radiomics features were extracted including first-order features such as standardized uptake value (SUV), metabolic tumor volume (MTV) and total lesion glycolysis (TLG), second- and high-order textural features using SUV histogram, normalized gray-level co-occurrence matrix (NGLCM), and neighborhood gray-tone difference matrix, respectively. These features were compared between SCC and NSCC using a Bonferroni adjusted P value threshold of 0.0028 (0.05/18). To assess the association between PET features, a heat map analysis with hierarchical clustering, one of the radiomics approaches, was performed. Among 18 PET features, correlation, a second-order textural feature derived from NGLCM, was a stable parameter and it was the only feature which showed a robust trend toward significant difference between SCC and NSCC. Cervical SCC showed a higher correlation (0.70 ± 0.07) than NSCC (0.64 ± 0.07, P = 0.0030). The other PET features did not show any significant differences between SCC and NSCC. A higher correlation in SCC might reflect higher structural integrity and stronger spatial/linear relationship of cancer cells compared with NSCC. A heat map with a PET feature dendrogram clearly showed 5 distinct clusters, where correlation belonged to a cluster including MTV and TLG. However, the association between correlation and MTV/TLG was not strong. Correlation was a relatively independent PET feature in cervical cancer. 18 F-FDG PET textural features might reflect the differences in histological architecture between cervical cancer subtypes. PET radiomics approaches reveal the association between PET features and will be useful for finding a single feature or a combination of features leading to precise diagnoses, potential prognostic models, and effective therapeutic strategies.

Potential of MR histogram analyses for prediction of response to chemotherapy in patients with colorectal hepatic metastases.

PubMed

Liang, He-Yue; Huang, Ya-Qin; Yang, Zhao-Xia; Ying-Ding; Zeng, Meng-Su; Rao, Sheng-Xiang

2016-07-01

To determine if magnetic resonance imaging (MRI) histogram analyses can help predict response to chemotherapy in patients with colorectal hepatic metastases by using response evaluation criteria in solid tumours (RECIST1.1) as the reference standard. Standard MRI including diffusion-weighted imaging (b=0, 500 s/mm(2)) was performed before chemotherapy in 53 patients with colorectal hepatic metastases. Histograms were performed for apparent diffusion coefficient (ADC) maps, arterial, and portal venous phase images; thereafter, mean, percentiles (1st, 10th, 50th, 90th, 99th), skewness, kurtosis, and variance were generated. Quantitative histogram parameters were compared between responders (partial and complete response, n=15) and non-responders (progressive and stable disease, n=38). Receiver operator characteristics (ROC) analyses were further analyzed for the significant parameters. The mean, 1st percentile, 10th percentile, 50th percentile, 90th percentile, 99th percentile of the ADC maps were significantly lower in responding group than that in non-responding group (p=0.000-0.002) with area under the ROC curve (AUCs) of 0.76-0.82. The histogram parameters of arterial and portal venous phase showed no significant difference (p>0.05) between the two groups. Histogram-derived parameters for ADC maps seem to be a promising tool for predicting response to chemotherapy in patients with colorectal hepatic metastases. • ADC histogram analyses can potentially predict chemotherapy response in colorectal liver metastases. • Lower histogram-derived parameters (mean, percentiles) for ADC tend to have good response. • MR enhancement histogram analyses are not reliable to predict response.

Video rate morphological processor based on a redundant number representation

NASA Astrophysics Data System (ADS)

Kuczborski, Wojciech; Attikiouzel, Yianni; Crebbin, Gregory A.

1992-03-01

This paper presents a video rate morphological processor for automated visual inspection of printed circuit boards, integrated circuit masks, and other complex objects. Inspection algorithms are based on gray-scale mathematical morphology. Hardware complexity of the known methods of real-time implementation of gray-scale morphology--the umbra transform and the threshold decomposition--has prompted us to propose a novel technique which applied an arithmetic system without carrying propagation. After considering several arithmetic systems, a redundant number representation has been selected for implementation. Two options are analyzed here. The first is a pure signed digit number representation (SDNR) with the base of 4. The second option is a combination of the base-2 SDNR (to represent gray levels of images) and the conventional twos complement code (to represent gray levels of structuring elements). Operation principle of the morphological processor is based on the concept of the digit level systolic array. Individual processing units and small memory elements create a pipeline. The memory elements store current image windows (kernels). All operation primitives of processing units apply a unified direction of digit processing: most significant digit first (MSDF). The implementation technology is based on the field programmable gate arrays by Xilinx. This paper justified the rationality of a new approach to logic design, which is the decomposition of Boolean functions instead of Boolean minimization.

Neurofilament light protein in blood predicts regional atrophy in Huntington disease

PubMed Central

Johnson, Eileanoir B.; Byrne, Lauren M.; Gregory, Sarah; Rodrigues, Filipe B.; Blennow, Kaj; Durr, Alexandra; Leavitt, Blair R.; Roos, Raymund A.; Zetterberg, Henrik; Tabrizi, Sarah J.; Scahill, Rachael I.

2018-01-01

Objective Neurofilament light (NfL) protein in blood plasma has been proposed as a prognostic biomarker of neurodegeneration in a number of conditions, including Huntington disease (HD). This study investigates the regional distribution of NfL-associated neural pathology in HD gene expansion carriers. Methods We examined associations between NfL measured in plasma and regionally specific atrophy in cross-sectional (n = 198) and longitudinal (n = 177) data in HD gene expansion carriers from the international multisite TRACK-HD study. Using voxel-based morphometry, we measured associations between baseline NfL levels and both baseline gray matter and white matter volume; and longitudinal change in gray matter and white matter over the subsequent 3 years in HD gene expansion carriers. Results After controlling for demographics, associations between increased NfL levels and reduced brain volume were seen in cortical and subcortical gray matter and within the white matter. After also controlling for known predictors of disease progression (age and CAG repeat length), associations were limited to the caudate and putamen. Longitudinally, NfL predicted subsequent occipital gray matter atrophy and widespread white matter reduction, both before and after correction for other predictors of disease progression. Conclusions These findings highlight the value of NfL as a dynamic marker of brain atrophy and, more generally, provide further evidence of the strong association between plasma NfL level, a candidate blood biomarker, and pathologic neuronal change. PMID:29367444

Modeling Disjunct Gray Wolf Populations in Semi-Wild Landscapes

Treesearch

Robert G. Haight; David J. Mladenoff; Adrian P. Wydeven

1998-01-01

Gray wolves (Canis lupus) in parts of the United States and Europe live in networks of disjunct populations, many of which are close to human settlement. Because wolf management goals include sustaining disjunct populations, it is important to ask what types of areas and protections are needed for population survival. To predict the effects of different levels of human...

Multipurpose contrast enhancement on epiphyseal plates and ossification centers for bone age assessment

PubMed Central

2013-01-01

Background The high variations of background luminance, low contrast and excessively enhanced contrast of hand bone radiograph often impede the bone age assessment rating system in evaluating the degree of epiphyseal plates and ossification centers development. The Global Histogram equalization (GHE) has been the most frequently adopted image contrast enhancement technique but the performance is not satisfying. A brightness and detail preserving histogram equalization method with good contrast enhancement effect has been a goal of much recent research in histogram equalization. Nevertheless, producing a well-balanced histogram equalized radiograph in terms of its brightness preservation, detail preservation and contrast enhancement is deemed to be a daunting task. Method In this paper, we propose a novel framework of histogram equalization with the aim of taking several desirable properties into account, namely the Multipurpose Beta Optimized Bi-Histogram Equalization (MBOBHE). This method performs the histogram optimization separately in both sub-histograms after the segmentation of histogram using an optimized separating point determined based on the regularization function constituted by three components. The result is then assessed by the qualitative and quantitative analysis to evaluate the essential aspects of histogram equalized image using a total of 160 hand radiographs that are implemented in testing and analyses which are acquired from hand bone online database. Result From the qualitative analysis, we found that basic bi-histogram equalizations are not capable of displaying the small features in image due to incorrect selection of separating point by focusing on only certain metric without considering the contrast enhancement and detail preservation. From the quantitative analysis, we found that MBOBHE correlates well with human visual perception, and this improvement shortens the evaluation time taken by inspector in assessing the bone age. Conclusions The proposed MBOBHE outperforms other existing methods regarding comprehensive performance of histogram equalization. All the features which are pertinent to bone age assessment are more protruding relative to other methods; this has shorten the required evaluation time in manual bone age assessment using TW method. While the accuracy remains unaffected or slightly better than using unprocessed original image. The holistic properties in terms of brightness preservation, detail preservation and contrast enhancement are simultaneous taken into consideration and thus the visual effect is contributive to manual inspection. PMID:23565999

Using histograms to introduce randomization in the generation of ensembles of decision trees

DOEpatents

Kamath, Chandrika; Cantu-Paz, Erick; Littau, David

2005-02-22

A system for decision tree ensembles that includes a module to read the data, a module to create a histogram, a module to evaluate a potential split according to some criterion using the histogram, a module to select a split point randomly in an interval around the best split, a module to split the data, and a module to combine multiple decision trees in ensembles. The decision tree method includes the steps of reading the data; creating a histogram; evaluating a potential split according to some criterion using the histogram, selecting a split point randomly in an interval around the best split, splitting the data, and combining multiple decision trees in ensembles.

Color Histogram Diffusion for Image Enhancement

NASA Technical Reports Server (NTRS)

Kim, Taemin

2011-01-01

Various color histogram equalization (CHE) methods have been proposed to extend grayscale histogram equalization (GHE) for color images. In this paper a new method called histogram diffusion that extends the GHE method to arbitrary dimensions is proposed. Ranges in a histogram are specified as overlapping bars of uniform heights and variable widths which are proportional to their frequencies. This diagram is called the vistogram. As an alternative approach to GHE, the squared error of the vistogram from the uniform distribution is minimized. Each bar in the vistogram is approximated by a Gaussian function. Gaussian particles in the vistoram diffuse as a nonlinear autonomous system of ordinary differential equations. CHE results of color images showed that the approach is effective.

PIV measurements in near-wake turbulent regions

NASA Astrophysics Data System (ADS)

Chen, Wei-Cheng; Chang, Keh-Chin

2018-05-01

Particle image velocimetry (PIV) is a non-intrusive optical diagnostic and must be made of the seedings (foreign particles) instead of the fluid itself. However, reliable PIV measurement of turbulence requires sufficient numbers of seeding falling in each interrogation window of image. A gray-level criterion is developed in this work to check the attainment of statistically stationary status of turbulent flow properties. It is suggested that the gray level of no less than 0.66 is used as the threshold for reliable PIV measurements in the present near-wake turbulent regions.

Carotid Plaque Morphology in Asymptomatic Patients with and without Metabolic Syndrome.

PubMed

Cury, Marcus Vinícius Martins; Presti, Calógero; Bonadiman, Suellen Stevam Timotheo; Casella, Ivan Benaduce; Benabou, Joseph Elias; da Silva, Erasmo Simão; de Luccia, Nelson; Puech-Leão, Pedro

2017-02-01

The aim of this study was to determine the impact of metabolic syndrome (MetS) on the morphology of carotid plaques, as evaluated using duplex ultrasound (DUS) with computer-assisted analysis. In this cross-sectional observational study, we analyzed 148 carotid artery plaques in asymptomatic patients. Data were obtained via clinical and laboratory examinations, and DUS was performed by a single operator. All plaques were scanned in a longitudinal fashion, and the best segment was selected, recorded, and evaluated using dedicated software. The main software-based analyses included gray-scale median (GSM) measurements and carotid plaque morphology histograms. MetS was identified in 51.8% of patients. Comparisons of patients with MetS and patients without MetS indicated that the former patients used more classes of antihypertensive drugs (2.49 vs. 1.93; P = 0.004) and were treated with statins for a longer period (71.08 vs. 49.17 months; P = 0.003). Most patients of both types exhibited moderate carotid artery stenosis ranging from 50% to 69% (n = 62; 37.3%), and MetS was not associated with an increased prevalence of severe carotid artery stenosis. The mean GSM was greater in the MetS group than in the non-MetS group (74.18 vs. 61.63; P = 0.012). The histogram analysis revealed that there were lower quantities of blood and fat (2.91 vs. 3.88; P = 0.006; 10.21 vs. 15.08; P = 0.004, respectively) and more fibrous tissue (19.93 vs. 14.55; P = 0.015) in the carotid plaques of patients with MetS than in the carotid plaques of patients without MetS. The present study demonstrated that MetS did not affect the stenosis grade or did it lead to unstable carotid plaques. Copyright © 2016 Elsevier Inc. All rights reserved.

Brain-water diffusion coefficients reflect the severity of inherited prion disease

PubMed Central

Hyare, H.; Wroe, S.; Siddique, D.; Webb, T.; Fox, N. C.; Stevens, J.; Collinge, J.; Yousry, T.; Thornton, J. S.

2010-01-01

Objective: Inherited prion diseases are progressive neurodegenerative conditions, characterized by cerebral spongiosis, gliosis, and neuronal loss, caused by mutations within the prion protein (PRNP) gene. We wished to assess the potential of diffusion-weighted MRI as a biomarker of disease severity in inherited prion diseases. Methods: Twenty-five subjects (mean age 45.2 years) with a known PRNP mutation including 19 symptomatic patients, 6 gene-positive asymptomatic subjects, and 7 controls (mean age 54.1 years) underwent conventional and diffusion-weighted MRI. An index of normalized brain volume (NBV) and region of interest (ROI) mean apparent diffusion coefficient (ADC) for the head of caudate, putamen, and pulvinar nuclei were recorded. ADC histograms were computed for whole brain (WB) and gray matter (GM) tissue fractions. Clinical assessment utilized standardized clinical scores. Mann-Whitney U test and regression analyses were performed. Results: Symptomatic patients exhibited an increased WB mean ADC (p = 0.006) and GM mean ADC (p = 0.024) compared to controls. Decreased NBV and increased mean ADC measures significantly correlated with clinical measures of disease severity. Using a stepwise multivariate regression procedure, GM mean ADC was an independent predictor of Clinician's Dementia Rating score (p = 0.001), Barthel Index of activities of daily living (p = 0.001), and Rankin disability score (p = 0.019). Conclusions: Brain volume loss in inherited prion diseases is accompanied by increased cerebral apparent diffusion coefficient (ADC), correlating with increased disease severity. The association between gray matter ADC and clinical neurologic status suggests this measure may prove a useful biomarker of disease activity in inherited prion diseases. GLOSSARY ADAS-Cog = Alzheimer's Disease Assessment Scale–Cognitive subscale; ADC = apparent diffusion coefficient; ADL = Barthel Activities of Daily Living scale; BET = brain extraction tool; BPRS = Brief Psychiatric Rating Scale; BSE = bovine spongiform encephalopathy; CDR = Clinician's Dementia Rating Scale; CGIS = Clinician's Global Impression of Disease; CI = confidence interval; DWI = diffusion-weighted imaging; FLAIR = fluid-attenuated inversion recovery; FOV = field of view; GM = gray matter; LC = left head of caudate; LP = left putamen; LPu = left pulvinar; MMSE = Mini-Mental State Examination; NBV = normalized brain volume; PH = peak height; PL = peak location; RC = right head of caudate; RP = right putamen; RPu = right pulvinar; ROI = region of interest; sCJD = sporadic Creutzfeldt-Jakob disease; TE = echo time; TI = inversion time; TR = repetition time; vCJD = variant Creutzfeldt-Jakob disease; WB = whole brain; WM = white matter. PMID:20177119

FPGA based charge fast histogramming for GEM detector

NASA Astrophysics Data System (ADS)

Poźniak, Krzysztof T.; Byszuk, A.; Chernyshova, M.; Cieszewski, R.; Czarski, T.; Dominik, W.; Jakubowska, K.; Kasprowicz, G.; Rzadkiewicz, J.; Scholz, M.; Zabolotny, W.

2013-10-01

This article presents a fast charge histogramming method for the position sensitive X-ray GEM detector. The energy resolved measurements are carried out simultaneously for 256 channels of the GEM detector. The whole process of histogramming is performed in 21 FPGA chips (Spartan-6 series from Xilinx) . The results of the histogramming process are stored in an external DDR3 memory. The structure of an electronic measuring equipment and a firmware functionality implemented in the FPGAs is described. Examples of test measurements are presented.

Local dynamic range compensation for scanning electron microscope imaging system.

PubMed

Sim, K S; Huang, Y H

2015-01-01

This is the extended project by introducing the modified dynamic range histogram modification (MDRHM) and is presented in this paper. This technique is used to enhance the scanning electron microscope (SEM) imaging system. By comparing with the conventional histogram modification compensators, this technique utilizes histogram profiling by extending the dynamic range of each tile of an image to the limit of 0-255 range while retains its histogram shape. The proposed technique yields better image compensation compared to conventional methods. © Wiley Periodicals, Inc.

Effect of scaling and root planing on alveolar bone as measured by subtraction radiography.

PubMed

Hwang, You-Jeong; Fien, Matthew Jonas; Lee, Sam-Sun; Kim, Tae-Il; Seol, Yang-Jo; Lee, Yong-Moo; Ku, Young; Rhyu, In-Chul; Chung, Chong-Pyoung; Han, Soo-Boo

2008-09-01

Scaling and root planing of diseased periodontal pockets is fundamental to the treatment of periodontal disease. Although various clinical parameters have been used to assess the efficacy of this therapy, radiographic analysis of changes in bone density following scaling and root planing has not been extensively researched. In this study, digital subtraction radiography was used to analyze changes that occurred in the periodontal hard tissues following scaling and root planing. Thirteen subjects with a total of 39 sites that presented with >3 mm of vertical bone loss were included in this study. Clinical examinations were performed and radiographs were taken prior to treatment and were repeated 6 months following scaling and root planing. Radiographic analysis was performed with computer-assisted radiographic evaluation software. Three regions of interest (ROI) were defined as the most coronal, middle, and apical portions of each defect. A fourth ROI was used for each site as a control region and was placed at a distant, untreated area. Statistical analysis was carried out to evaluate changes in the mean gray level at the coronal, middle, and apical region of each treated defect. Digital subtraction radiography revealed an increase in radiographic density in 101 of the 117 test regions (83.3%). A 256 gray level was used, and a value >128 was assumed to represent a density gain in the ROI. The average gray level increase was 18.65. Although the coronal, middle, and apical regions displayed increases in bone density throughout this study, the bone density of the apical ROI (gray level = 151.27 +/- 20.62) increased significantly more than the bone density of the coronal ROI (gray level = 139.19 +/- 21.78). A significant increase in bone density was seen in probing depths >5 mm compared to those <5 mm in depth. No significant difference was found with regard to bone-density changes surrounding single- versus multiple-rooted teeth. Scaling and root planing of diseased periodontal pockets can significantly increase radiographic alveolar bone density as demonstrated through the use of digital subtraction radiography.

Whole-lesion apparent diffusion coefficient histogram analysis: significance in T and N staging of gastric cancers.

PubMed

Liu, Song; Zhang, Yujuan; Chen, Ling; Guan, Wenxian; Guan, Yue; Ge, Yun; He, Jian; Zhou, Zhengyang

2017-10-02

Whole-lesion apparent diffusion coefficient (ADC) histogram analysis has been introduced and proved effective in assessment of multiple tumors. However, the application of whole-volume ADC histogram analysis in gastrointestinal tumors has just started and never been reported in T and N staging of gastric cancers. Eighty patients with pathologically confirmed gastric carcinomas underwent diffusion weighted (DW) magnetic resonance imaging before surgery prospectively. Whole-lesion ADC histogram analysis was performed by two radiologists independently. The differences of ADC histogram parameters among different T and N stages were compared with independent-samples Kruskal-Wallis test. Receiver operating characteristic (ROC) analysis was performed to evaluate the performance of ADC histogram parameters in differentiating particular T or N stages of gastric cancers. There were significant differences of all the ADC histogram parameters for gastric cancers at different T (except ADC min and ADC max ) and N (except ADC max ) stages. Most ADC histogram parameters differed significantly between T1 vs T3, T1 vs T4, T2 vs T4, N0 vs N1, N0 vs N3, and some parameters (ADC 5% , ADC 10% , ADC min ) differed significantly between N0 vs N2, N2 vs N3 (all P < 0.05). Most parameters except ADC max performed well in differentiating different T and N stages of gastric cancers. Especially for identifying patients with and without lymph node metastasis, the ADC 10% yielded the largest area under the ROC curve of 0.794 (95% confidence interval, 0.677-0.911). All the parameters except ADC max showed excellent inter-observer agreement with intra-class correlation coefficients higher than 0.800. Whole-volume ADC histogram parameters held great potential in differentiating different T and N stages of gastric cancers preoperatively.

Histogram Profiling of Postcontrast T1-Weighted MRI Gives Valuable Insights into Tumor Biology and Enables Prediction of Growth Kinetics and Prognosis in Meningiomas.

PubMed

Gihr, Georg Alexander; Horvath-Rizea, Diana; Kohlhof-Meinecke, Patricia; Ganslandt, Oliver; Henkes, Hans; Richter, Cindy; Hoffmann, Karl-Titus; Surov, Alexey; Schob, Stefan

2018-06-14

Meningiomas are the most frequently diagnosed intracranial masses, oftentimes requiring surgery. Especially procedure-related morbidity can be substantial, particularly in elderly patients. Hence, reliable imaging modalities enabling pretherapeutic prediction of tumor grade, growth kinetic, realistic prognosis, and-as a consequence-necessity of surgery are of great value. In this context, a promising diagnostic approach is advanced analysis of magnetic resonance imaging data. Therefore, our study investigated whether histogram profiling of routinely acquired postcontrast T1-weighted images is capable of separating low-grade from high-grade lesions and whether histogram parameters reflect Ki-67 expression in meningiomas. Pretreatment T1-weighted postcontrast volumes of 44 meningioma patients were used for signal intensity histogram profiling. WHO grade, tumor volume, and Ki-67 expression were evaluated. Comparative and correlative statistics investigating the association between histogram profile parameters and neuropathology were performed. None of the investigated histogram parameters revealed significant differences between low-grade and high-grade meningiomas. However, significant correlations were identified between Ki-67 and the histogram parameters skewness and entropy as well as between entropy and tumor volume. Contrary to previously reported findings, pretherapeutic postcontrast T1-weighted images can be used to predict growth kinetics in meningiomas if whole tumor histogram analysis is employed. However, no differences between distinct WHO grades were identifiable in out cohort. As a consequence, histogram analysis of postcontrast T1-weighted images is a promising approach to obtain quantitative in vivo biomarkers reflecting the proliferative potential in meningiomas. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

Comparison of image enhancement methods for the effective diagnosis in successive whole-body bone scans.

PubMed

Jeong, Chang Bu; Kim, Kwang Gi; Kim, Tae Sung; Kim, Seok Ki

2011-06-01

Whole-body bone scan is one of the most frequent diagnostic procedures in nuclear medicine. Especially, it plays a significant role in important procedures such as the diagnosis of osseous metastasis and evaluation of osseous tumor response to chemotherapy and radiation therapy. It can also be used to monitor the possibility of any recurrence of the tumor. However, it is a very time-consuming effort for radiologists to quantify subtle interval changes between successive whole-body bone scans because of many variations such as intensity, geometry, and morphology. In this paper, we present the most effective method of image enhancement based on histograms, which may assist radiologists in interpreting successive whole-body bone scans effectively. Forty-eight successive whole-body bone scans from 10 patients were obtained and evaluated using six methods of image enhancement based on histograms: histogram equalization, brightness-preserving bi-histogram equalization, contrast-limited adaptive histogram equalization, end-in search, histogram matching, and exact histogram matching (EHM). Comparison of the results of the different methods was made using three similarity measures peak signal-to-noise ratio, histogram intersection, and structural similarity. Image enhancement of successive bone scans using EHM showed the best results out of the six methods measured for all similarity measures. EHM is the best method of image enhancement based on histograms for diagnosing successive whole-body bone scans. The method for successive whole-body bone scans has the potential to greatly assist radiologists quantify interval changes more accurately and quickly by compensating for the variable nature of intensity information. Consequently, it can improve radiologists' diagnostic accuracy as well as reduce reading time for detecting interval changes.

Dose-volume histogram prediction using density estimation.

PubMed

Skarpman Munter, Johanna; Sjölund, Jens

2015-09-07

Knowledge of what dose-volume histograms can be expected for a previously unseen patient could increase consistency and quality in radiotherapy treatment planning. We propose a machine learning method that uses previous treatment plans to predict such dose-volume histograms. The key to the approach is the framing of dose-volume histograms in a probabilistic setting.The training consists of estimating, from the patients in the training set, the joint probability distribution of some predictive features and the dose. The joint distribution immediately provides an estimate of the conditional probability of the dose given the values of the predictive features. The prediction consists of estimating, from the new patient, the distribution of the predictive features and marginalizing the conditional probability from the training over this. Integrating the resulting probability distribution for the dose yields an estimate of the dose-volume histogram.To illustrate how the proposed method relates to previously proposed methods, we use the signed distance to the target boundary as a single predictive feature. As a proof-of-concept, we predicted dose-volume histograms for the brainstems of 22 acoustic schwannoma patients treated with stereotactic radiosurgery, and for the lungs of 9 lung cancer patients treated with stereotactic body radiation therapy. Comparing with two previous attempts at dose-volume histogram prediction we find that, given the same input data, the predictions are similar.In summary, we propose a method for dose-volume histogram prediction that exploits the intrinsic probabilistic properties of dose-volume histograms. We argue that the proposed method makes up for some deficiencies in previously proposed methods, thereby potentially increasing ease of use, flexibility and ability to perform well with small amounts of training data.

Face recognition algorithm using extended vector quantization histogram features.

PubMed

Yan, Yan; Lee, Feifei; Wu, Xueqian; Chen, Qiu

2018-01-01

In this paper, we propose a face recognition algorithm based on a combination of vector quantization (VQ) and Markov stationary features (MSF). The VQ algorithm has been shown to be an effective method for generating features; it extracts a codevector histogram as a facial feature representation for face recognition. Still, the VQ histogram features are unable to convey spatial structural information, which to some extent limits their usefulness in discrimination. To alleviate this limitation of VQ histograms, we utilize Markov stationary features (MSF) to extend the VQ histogram-based features so as to add spatial structural information. We demonstrate the effectiveness of our proposed algorithm by achieving recognition results superior to those of several state-of-the-art methods on publicly available face databases.

Ultrasonic histogram assessment of early response to concurrent chemo-radiotherapy in patients with locally advanced cervical cancer: a feasibility study.

PubMed

Xu, Yan; Ru, Tong; Zhu, Lijing; Liu, Baorui; Wang, Huanhuan; Zhu, Li; He, Jian; Liu, Song; Zhou, Zhengyang; Yang, Xiaofeng

To monitor early response for locally advanced cervical cancers undergoing concurrent chemo-radiotherapy (CCRT) by ultrasonic histogram. B-mode ultrasound examinations were performed at 4 time points in thirty-four patients during CCRT. Six ultrasonic histogram parameters were used to assess the echogenicity, homogeneity and heterogeneity of tumors. I peak increased rapidly since the first week after therapy initiation, whereas W low , W high and A high changed significantly at the second week. The average ultrasonic histogram progressively moved toward the right and converted into more symmetrical shape. Ultrasonic histogram could be served as a potential marker to monitor early response during CCRT. Copyright © 2018 Elsevier Inc. All rights reserved.

Face verification system for Android mobile devices using histogram based features

NASA Astrophysics Data System (ADS)

Sato, Sho; Kobayashi, Kazuhiro; Chen, Qiu

2016-07-01

This paper proposes a face verification system that runs on Android mobile devices. In this system, facial image is captured by a built-in camera on the Android device firstly, and then face detection is implemented using Haar-like features and AdaBoost learning algorithm. The proposed system verify the detected face using histogram based features, which are generated by binary Vector Quantization (VQ) histogram using DCT coefficients in low frequency domains, as well as Improved Local Binary Pattern (Improved LBP) histogram in spatial domain. Verification results with different type of histogram based features are first obtained separately and then combined by weighted averaging. We evaluate our proposed algorithm by using publicly available ORL database and facial images captured by an Android tablet.

Reduced gray matter volume in the anterior cingulate, orbitofrontal cortex and thalamus as a function of mild depressive symptoms: a voxel-based morphometric analysis.

PubMed

Webb, C A; Weber, M; Mundy, E A; Killgore, W D S

2014-10-01

Studies investigating structural brain abnormalities in depression have typically employed a categorical rather than dimensional approach to depression [i.e., comparing subjects with Diagnostic and Statistical Manual of Mental Disorders (DSM)-defined major depressive disorder (MDD) v. healthy controls]. The National Institute of Mental Health, through their Research Domain Criteria initiative, has encouraged a dimensional approach to the study of psychopathology as opposed to an over-reliance on categorical (e.g., DSM-based) diagnostic approaches. Moreover, subthreshold levels of depressive symptoms (i.e., severity levels below DSM criteria) have been found to be associated with a range of negative outcomes, yet have been relatively neglected in neuroimaging research. To examine the extent to which depressive symptoms--even at subclinical levels--are linearly related to gray matter volume reductions in theoretically important brain regions, we employed whole-brain voxel-based morphometry in a sample of 54 participants. The severity of mild depressive symptoms, even in a subclinical population, was associated with reduced gray matter volume in the orbitofrontal cortex, anterior cingulate, thalamus, superior temporal gyrus/temporal pole and superior frontal gyrus. A conjunction analysis revealed concordance across two separate measures of depression. Reduced gray matter volume in theoretically important brain regions can be observed even in a sample that does not meet DSM criteria for MDD, but who nevertheless report relatively elevated levels of depressive symptoms. Overall, these findings highlight the need for additional research using dimensional conceptual and analytic approaches, as well as further investigation of subclinical populations.

Female adolescents with severe substance and conduct problems have substantially less brain gray matter volume.

PubMed

Dalwani, Manish S; McMahon, Mary Agnes; Mikulich-Gilbertson, Susan K; Young, Susan E; Regner, Michael F; Raymond, Kristen M; McWilliams, Shannon K; Banich, Marie T; Tanabe, Jody L; Crowley, Thomas J; Sakai, Joseph T

2015-01-01

Structural neuroimaging studies have demonstrated lower regional gray matter volume in adolescents with severe substance and conduct problems. These research studies, including ours, have generally focused on male-only or mixed-sex samples of adolescents with conduct and/or substance problems. Here we compare gray matter volume between female adolescents with severe substance and conduct problems and female healthy controls of similar ages. Female adolescents with severe substance and conduct problems will show significantly less gray matter volume in frontal regions critical to inhibition (i.e. dorsolateral prefrontal cortex and ventrolateral prefrontal cortex), conflict processing (i.e., anterior cingulate), valuation of expected outcomes (i.e., medial orbitofrontal cortex) and the dopamine reward system (i.e. striatum). We conducted whole-brain voxel-based morphometric comparison of structural MR images of 22 patients (14-18 years) with severe substance and conduct problems and 21 controls of similar age using statistical parametric mapping (SPM) and voxel-based morphometric (VBM8) toolbox. We tested group differences in regional gray matter volume with analyses of covariance, adjusting for age and IQ at p<0.05, corrected for multiple comparisons at whole-brain cluster-level threshold. Female adolescents with severe substance and conduct problems compared to controls showed significantly less gray matter volume in right dorsolateral prefrontal cortex, left ventrolateral prefrontal cortex, medial orbitofrontal cortex, anterior cingulate, bilateral somatosensory cortex, left supramarginal gyrus, and bilateral angular gyrus. Considering the entire brain, patients had 9.5% less overall gray matter volume compared to controls. Female adolescents with severe substance and conduct problems in comparison to similarly aged female healthy controls showed substantially lower gray matter volume in brain regions involved in inhibition, conflict processing, valuation of outcomes, decision-making, reward, risk-taking, and rule-breaking antisocial behavior.

A cross-sectional and follow-up voxel-based morphometric MRI study in adolescent anorexia nervosa.

PubMed

Castro-Fornieles, Josefina; Bargalló, Nuria; Lázaro, Luisa; Andrés, Susana; Falcon, Carles; Plana, Maria Teresa; Junqué, Carme

2009-01-01

The objective was to examine whether cerebral volumes are reduced, and in what regions, in adolescents with anorexia nervosa and to study changes after nutritional recovery. Twelve anorexia nervosa (DSM-IV) patients aged 11-17 consecutively admitted to an Eating Disorders Unit were assessed by means of psychopathological scales, neuropsychological battery and voxel-based morphometric (VBM) magnetic resonance imaging at admission and after 7 months' follow-up. Nine control subjects of similar age, gender and estimated intelligence level were also studied. The two groups showed differences in gray matter (F=22.2; p<0.001) and cerebrospinal fluid (CSF) (F=21.2; p<0.001) but not in white matter volumes. In anorexic patients, gray matter volume correlated negatively with the copy time from the Rey Complex Figure Test. In the regional VBM study several temporal and parietal gray matter regions were reduced. During follow-up there was a greater global increase in gray matter (F=10.7; p=0.004) and decrease in CSF (F=22.1; p=0.001) in anorexic patients. The increase in gray matter correlated with a decrease in cortisol (Spearman correlation=-0.73; p=0.017). At follow-up there were no differences in global gray matter (F=2.1; p=0.165), white matter (F=0.02, p=0.965) or CSF (F=1.8; p=0.113) volumes between both groups. There were still some smaller areas, in the right temporal and both supplementary motor area, showing differences between them in the regional VBM study. In conclusion, in adolescent anorexic patients gray matter is more affected than white matter and mainly involves the posterior regions of the brain. Overall gray matter alterations are reversible after nutritional recovery.

Combining Vector Quantization and Histogram Equalization.

ERIC Educational Resources Information Center

Cosman, Pamela C.; And Others

1992-01-01

Discussion of contrast enhancement techniques focuses on the use of histogram equalization with a data compression technique, i.e., tree-structured vector quantization. The enhancement technique of intensity windowing is described, and the use of enhancement techniques for medical images is explained, including adaptive histogram equalization.…

Histogram Curve Matching Approaches for Object-based Image Classification of Land Cover and Land Use

PubMed Central

Toure, Sory I.; Stow, Douglas A.; Weeks, John R.; Kumar, Sunil

2013-01-01

The classification of image-objects is usually done using parametric statistical measures of central tendency and/or dispersion (e.g., mean or standard deviation). The objectives of this study were to analyze digital number histograms of image objects and evaluate classifications measures exploiting characteristic signatures of such histograms. Two histograms matching classifiers were evaluated and compared to the standard nearest neighbor to mean classifier. An ADS40 airborne multispectral image of San Diego, California was used for assessing the utility of curve matching classifiers in a geographic object-based image analysis (GEOBIA) approach. The classifications were performed with data sets having 0.5 m, 2.5 m, and 5 m spatial resolutions. Results show that histograms are reliable features for characterizing classes. Also, both histogram matching classifiers consistently performed better than the one based on the standard nearest neighbor to mean rule. The highest classification accuracies were produced with images having 2.5 m spatial resolution. PMID:24403648

Whole-Lesion Histogram Analysis of Apparent Diffusion Coefficient for the Assessment of Cervical Cancer.

PubMed

Guan, Yue; Shi, Hua; Chen, Ying; Liu, Song; Li, Weifeng; Jiang, Zhuoran; Wang, Huanhuan; He, Jian; Zhou, Zhengyang; Ge, Yun

2016-01-01

The aim of this study was to explore the application of whole-lesion histogram analysis of apparent diffusion coefficient (ADC) values of cervical cancer. A total of 54 women (mean age, 53 years) with cervical cancers underwent 3-T diffusion-weighted imaging with b values of 0 and 800 s/mm prospectively. Whole-lesion histogram analysis of ADC values was performed. Paired sample t test was used to compare differences in ADC histogram parameters between cervical cancers and normal cervical tissues. Receiver operating characteristic curves were constructed to identify the optimal threshold of each parameter. All histogram parameters in this study including ADCmean, ADCmin, ADC10%-ADC90%, mode, skewness, and kurtosis of cervical cancers were significantly lower than those of normal cervical tissues (all P < 0.0001). ADC90% had the largest area under receiver operating characteristic curve of 0.996. Whole-lesion histogram analysis of ADC maps is useful in the assessment of cervical cancer.

Time-cumulated visible and infrared radiance histograms used as descriptors of surface and cloud variations

NASA Technical Reports Server (NTRS)

Seze, Genevieve; Rossow, William B.

1991-01-01

The spatial and temporal stability of the distributions of satellite-measured visible and infrared radiances, caused by variations in clouds and surfaces, are investigated using bidimensional and monodimensional histograms and time-composite images. Similar analysis of the histograms of the original and time-composite images provides separation of the contributions of the space and time variations to the total variations. The variability of both the surfaces and clouds is found to be larger at scales much larger than the minimum resolved by satellite imagery. This study shows that the shapes of these histograms are distinctive characteristics of the different climate regimes and that particular attributes of these histograms can be related to several general, though not universal, properties of clouds and surface variations at regional and synoptic scales. There are also significant exceptions to these relationships in particular climate regimes. The characteristics of these radiance histograms provide a stable well defined descriptor of the cloud and surface properties.

Particle swarm optimization-based local entropy weighted histogram equalization for infrared image enhancement

NASA Astrophysics Data System (ADS)

Wan, Minjie; Gu, Guohua; Qian, Weixian; Ren, Kan; Chen, Qian; Maldague, Xavier

2018-06-01

Infrared image enhancement plays a significant role in intelligent urban surveillance systems for smart city applications. Unlike existing methods only exaggerating the global contrast, we propose a particle swam optimization-based local entropy weighted histogram equalization which involves the enhancement of both local details and fore-and background contrast. First of all, a novel local entropy weighted histogram depicting the distribution of detail information is calculated based on a modified hyperbolic tangent function. Then, the histogram is divided into two parts via a threshold maximizing the inter-class variance in order to improve the contrasts of foreground and background, respectively. To avoid over-enhancement and noise amplification, double plateau thresholds of the presented histogram are formulated by means of particle swarm optimization algorithm. Lastly, each sub-image is equalized independently according to the constrained sub-local entropy weighted histogram. Comparative experiments implemented on real infrared images prove that our algorithm outperforms other state-of-the-art methods in terms of both visual and quantized evaluations.

Steady State Load Characterization Fact Sheet: 2012 Chevy Volt

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

Scoffield, Don

2015-03-01

This fact sheet characterizes the steady state charging behavior of a 2012 Chevy Volt. Both level 1 charging (120 volt) and level 2 charging (208 volts) is investigated. This fact sheet contains plots of efficiency, power factor, and current harmonics as vehicle charging is curtailed. Prominent current harmonics are also displayed in a histogram for various charge rates.

Apparent diffusion coefficient histogram shape analysis for monitoring early response in patients with advanced cervical cancers undergoing concurrent chemo-radiotherapy.

PubMed

Meng, Jie; Zhu, Lijing; Zhu, Li; Wang, Huanhuan; Liu, Song; Yan, Jing; Liu, Baorui; Guan, Yue; Ge, Yun; He, Jian; Zhou, Zhengyang; Yang, Xiaofeng

2016-10-22

To explore the role of apparent diffusion coefficient (ADC) histogram shape related parameters in early assessment of treatment response during the concurrent chemo-radiotherapy (CCRT) course of advanced cervical cancers. This prospective study was approved by the local ethics committee and informed consent was obtained from all patients. Thirty-two patients with advanced cervical squamous cell carcinomas underwent diffusion weighted magnetic resonance imaging (b values, 0 and 800 s/mm 2 ) before CCRT, at the end of 2nd and 4th week during CCRT and immediately after CCRT completion. Whole lesion ADC histogram analysis generated several histogram shape related parameters including skewness, kurtosis, s-sD av , width, standard deviation, as well as first-order entropy and second-order entropies. The averaged ADC histograms of 32 patients were generated to visually observe dynamic changes of the histogram shape following CCRT. All parameters except width and standard deviation showed significant changes during CCRT (all P < 0.05), and their variation trends fell into four different patterns. Skewness and kurtosis both showed high early decline rate (43.10 %, 48.29 %) at the end of 2nd week of CCRT. All entropies kept decreasing significantly since 2 weeks after CCRT initiated. The shape of averaged ADC histogram also changed obviously following CCRT. ADC histogram shape analysis held the potential in monitoring early tumor response in patients with advanced cervical cancers undergoing CCRT.

Dissimilarity representations in lung parenchyma classification

NASA Astrophysics Data System (ADS)

Sørensen, Lauge; de Bruijne, Marleen

2009-02-01

A good problem representation is important for a pattern recognition system to be successful. The traditional approach to statistical pattern recognition is feature representation. More specifically, objects are represented by a number of features in a feature vector space, and classifiers are built in this representation. This is also the general trend in lung parenchyma classification in computed tomography (CT) images, where the features often are measures on feature histograms. Instead, we propose to build normal density based classifiers in dissimilarity representations for lung parenchyma classification. This allows for the classifiers to work on dissimilarities between objects, which might be a more natural way of representing lung parenchyma. In this context, dissimilarity is defined between CT regions of interest (ROI)s. ROIs are represented by their CT attenuation histogram and ROI dissimilarity is defined as a histogram dissimilarity measure between the attenuation histograms. In this setting, the full histograms are utilized according to the chosen histogram dissimilarity measure. We apply this idea to classification of different emphysema patterns as well as normal, healthy tissue. Two dissimilarity representation approaches as well as different histogram dissimilarity measures are considered. The approaches are evaluated on a set of 168 CT ROIs using normal density based classifiers all showing good performance. Compared to using histogram dissimilarity directly as distance in a emph{k} nearest neighbor classifier, which achieves a classification accuracy of 92.9%, the best dissimilarity representation based classifier is significantly better with a classification accuracy of 97.0% (text{emph{p" border="0" class="imgtopleft"> = 0.046).

Researches on Position Detection for Vacuum Switch Electrode

NASA Astrophysics Data System (ADS)

Dong, Huajun; Guo, Yingjie; Li, Jie; Kong, Yihan

2018-03-01

Form and transformation character of vacuum arc is important influencing factor on the vacuum switch performance, and the dynamic separations of electrode is the chief effecting factor on the transformation of vacuum arcs forms. Consequently, how to detect the position of electrode to calculate the separations in the arcs image is of great significance. However, gray level distribution of vacuum arcs image isn’t even, the gray level of burning arcs is high, but the gray level of electrode is low, meanwhile, the forms of vacuum arcs changes sharply, the problems above restrict electrode position detection precisely. In this paper, algorithm of detecting electrode position base on vacuum arcs image was proposed. The digital image processing technology was used in vacuum switch arcs image analysis, the upper edge and lower edge were detected respectively, then linear fitting was done using the result of edge detection, the fitting result was the position of electrode, thus, accurate position detection of electrode was realized. From the experimental results, we can see that: algorithm described in this paper detected upper and lower edge of arcs successfully and the position of electrode was obtained through calculation.

A systematic review and meta-analysis of structural magnetic resonance imaging studies investigating cognitive and social activity levels in older adults.

PubMed

Anatürk, M; Demnitz, N; Ebmeier, K P; Sexton, C E

2018-06-22

Population aging has prompted considerable interest in identifying modifiable factors that may help protect the brain and its functions. Collectively, epidemiological studies show that leisure activities with high mental and social demands are linked with better cognition in old age. The extent to which socio-intellectual activities relate to the brain's structure is, however, not yet fully understood. This systematic review and meta-analysis summarizes magnetic resonance imaging studies that have investigated whether cognitive and social activities correlate with measures of gray and white matter volume, white matter microstructure and white matter lesions. Across eighteen included studies (total n = 8429), activity levels were associated with whole-brain white matter volume, white matter lesions and regional gray matter volume, although effect sizes were small. No associations were found for global gray matter volume and the evidence concerning white matter microstructure was inconclusive. While the causality of the reviewed associations needs to be established, our findings implicate socio-intellectual activity levels as promising targets for interventions aimed at promoting healthy brain aging. Copyright © 2018. Published by Elsevier Ltd.

A Study for Texture Feature Extraction of High-Resolution Satellite Images Based on a Direction Measure and Gray Level Co-Occurrence Matrix Fusion Algorithm

PubMed Central

Zhang, Xin; Cui, Jintian; Wang, Weisheng; Lin, Chao

2017-01-01

To address the problem of image texture feature extraction, a direction measure statistic that is based on the directionality of image texture is constructed, and a new method of texture feature extraction, which is based on the direction measure and a gray level co-occurrence matrix (GLCM) fusion algorithm, is proposed in this paper. This method applies the GLCM to extract the texture feature value of an image and integrates the weight factor that is introduced by the direction measure to obtain the final texture feature of an image. A set of classification experiments for the high-resolution remote sensing images were performed by using support vector machine (SVM) classifier with the direction measure and gray level co-occurrence matrix fusion algorithm. Both qualitative and quantitative approaches were applied to assess the classification results. The experimental results demonstrated that texture feature extraction based on the fusion algorithm achieved a better image recognition, and the accuracy of classification based on this method has been significantly improved. PMID:28640181

The implementation of thermal image visualization by HDL based on pseudo-color

NASA Astrophysics Data System (ADS)

Zhu, Yong; Zhang, JiangLing

2004-11-01

The pseudo-color method which maps the sampled data to intuitive perception colors is a kind of powerful visualization way. And the all-around system of pseudo-color visualization, which includes the primary principle, model and HDL (Hardware Description Language) implementation for the thermal images, is expatiated on in the paper. The thermal images whose signal is modulated as video reflect the temperature distribution of measured object, so they have the speciality of mass and real-time. The solution to the intractable problem is as follows: First, the reasonable system, i.e. the combining of global pseudo-color visualization and local special area accurate measure, muse be adopted. Then, the HDL pseudo-color algorithms in SoC (System on Chip) carry out the system to ensure the real-time. Finally, the key HDL algorithms for direct gray levels connection coding, proportional gray levels map coding and enhanced gray levels map coding are presented, and its simulation results are showed. The pseudo-color visualization of thermal images implemented by HDL in the paper has effective application in the aspect of electric power equipment test and medical health diagnosis.

Stochastic resonance in attention control

NASA Astrophysics Data System (ADS)

Kitajo, K.; Yamanaka, K.; Ward, L. M.; Yamamoto, Y.

2006-12-01

We investigated the beneficial role of noise in a human higher brain function, namely visual attention control. We asked subjects to detect a weak gray-level target inside a marker box either in the left or the right visual field. Signal detection performance was optimized by presenting a low level of randomly flickering gray-level noise between and outside the two possible target locations. Further, we found that an increase in eye movement (saccade) rate helped to compensate for the usual deterioration in detection performance at higher noise levels. To our knowledge, this is the first experimental evidence that noise can optimize a higher brain function which involves distinct brain regions above the level of primary sensory systems -- switching behavior between multi-stable attention states -- via the mechanism of stochastic resonance.

The Histogram-Area Connection

ERIC Educational Resources Information Center

Gratzer, William; Carpenter, James E.

2008-01-01

This article demonstrates an alternative approach to the construction of histograms--one based on the notion of using area to represent relative density in intervals of unequal length. The resulting histograms illustrate the connection between the area of the rectangles associated with particular outcomes and the relative frequency (probability)…

Investigating Student Understanding of Histograms

ERIC Educational Resources Information Center

Kaplan, Jennifer J.; Gabrosek, John G.; Curtiss, Phyllis; Malone, Chris

2014-01-01

Histograms are adept at revealing the distribution of data values, especially the shape of the distribution and any outlier values. They are included in introductory statistics texts, research methods texts, and in the popular press, yet students often have difficulty interpreting the information conveyed by a histogram. This research identifies…

Gaussian mixture models for detection of autism spectrum disorders (ASD) in magnetic resonance imaging

NASA Astrophysics Data System (ADS)

Almeida, Javier; Velasco, Nelson; Alvarez, Charlens; Romero, Eduardo

2017-11-01

Autism Spectrum Disorder (ASD) is a complex neurological condition characterized by a triad of signs: stereotyped behaviors, verbal and non-verbal communication problems. The scientific community has been interested on quantifying anatomical brain alterations of this disorder. Several studies have focused on measuring brain cortical and sub-cortical volumes. This article presents a fully automatic method which finds out differences among patients diagnosed with autism and control patients. After the usual pre-processing, a template (MNI152) is registered to an evaluated brain which becomes then a set of regions. Each of these regions is the represented by the normalized histogram of intensities which is approximated by mixture of Gaussian (GMM). The gray and white matter are separated to calculate the mean and standard deviation of each Gaussian. These features are then used to train, region per region, a binary SVM classifier. The method was evaluated in an adult population aged from 18 to 35 years, from the public database Autism Brain Imaging Data Exchange (ABIDE). Highest discrimination values were found for the Right Middle Temporal Gyrus, with an Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) the curve of 0.72.

Thresholding histogram equalization.

PubMed

Chuang, K S; Chen, S; Hwang, I M

2001-12-01

The drawbacks of adaptive histogram equalization techniques are the loss of definition on the edges of the object and overenhancement of noise in the images. These drawbacks can be avoided if the noise is excluded in the equalization transformation function computation. A method has been developed to separate the histogram into zones, each with its own equalization transformation. This method can be used to suppress the nonanatomic noise and enhance only certain parts of the object. This method can be combined with other adaptive histogram equalization techniques. Preliminary results indicate that this method can produce images with superior contrast.

Variations of attractors and wavelet spectra of the immunofluorescence distributions for women in the pregnant period

NASA Astrophysics Data System (ADS)

Galich, Nikolay E.

2008-07-01

Communication contains the description of the immunology data treatment. New nonlinear methods of immunofluorescence statistical analysis of peripheral blood neutrophils have been developed. We used technology of respiratory burst reaction of DNA fluorescence in the neutrophils cells nuclei due to oxidative activity. The histograms of photon count statistics the radiant neutrophils populations' in flow cytometry experiments are considered. Distributions of the fluorescence flashes frequency as functions of the fluorescence intensity are analyzed. Statistic peculiarities of histograms set for women in the pregnant period allow dividing all histograms on the three classes. The classification is based on three different types of smoothing and long-range scale averaged immunofluorescence distributions, their bifurcation and wavelet spectra. Heterogeneity peculiarities of long-range scale immunofluorescence distributions and peculiarities of wavelet spectra allow dividing all histograms on three groups. First histograms group belongs to healthy donors. Two other groups belong to donors with autoimmune and inflammatory diseases. Some of the illnesses are not diagnosed by standards biochemical methods. Medical standards and statistical data of the immunofluorescence histograms for identifications of health and illnesses are interconnected. Peculiarities of immunofluorescence for women in pregnant period are classified. Health or illness criteria are connected with statistics features of immunofluorescence histograms. Neutrophils populations' fluorescence presents the sensitive clear indicator of health status.

Complexity of possibly gapped histogram and analysis of histogram.

PubMed

Fushing, Hsieh; Roy, Tania

2018-02-01

We demonstrate that gaps and distributional patterns embedded within real-valued measurements are inseparable biological and mechanistic information contents of the system. Such patterns are discovered through data-driven possibly gapped histogram, which further leads to the geometry-based analysis of histogram (ANOHT). Constructing a possibly gapped histogram is a complex problem of statistical mechanics due to the ensemble of candidate histograms being captured by a two-layer Ising model. This construction is also a distinctive problem of Information Theory from the perspective of data compression via uniformity. By defining a Hamiltonian (or energy) as a sum of total coding lengths of boundaries and total decoding errors within bins, this issue of computing the minimum energy macroscopic states is surprisingly resolved by applying the hierarchical clustering algorithm. Thus, a possibly gapped histogram corresponds to a macro-state. And then the first phase of ANOHT is developed for simultaneous comparison of multiple treatments, while the second phase of ANOHT is developed based on classical empirical process theory for a tree-geometry that can check the authenticity of branches of the treatment tree. The well-known Iris data are used to illustrate our technical developments. Also, a large baseball pitching dataset and a heavily right-censored divorce data are analysed to showcase the existential gaps and utilities of ANOHT.

Histogram-based quantitative evaluation of endobronchial ultrasonography images of peripheral pulmonary lesion.

PubMed

Morikawa, Kei; Kurimoto, Noriaki; Inoue, Takeo; Mineshita, Masamichi; Miyazawa, Teruomi

2015-01-01

Endobronchial ultrasonography using a guide sheath (EBUS-GS) is an increasingly common bronchoscopic technique, but currently, no methods have been established to quantitatively evaluate EBUS images of peripheral pulmonary lesions. The purpose of this study was to evaluate whether histogram data collected from EBUS-GS images can contribute to the diagnosis of lung cancer. Histogram-based analyses focusing on the brightness of EBUS images were retrospectively conducted: 60 patients (38 lung cancer; 22 inflammatory diseases), with clear EBUS images were included. For each patient, a 400-pixel region of interest was selected, typically located at a 3- to 5-mm radius from the probe, from recorded EBUS images during bronchoscopy. Histogram height, width, height/width ratio, standard deviation, kurtosis and skewness were investigated as diagnostic indicators. Median histogram height, width, height/width ratio and standard deviation were significantly different between lung cancer and benign lesions (all p < 0.01). With a cutoff value for standard deviation of 10.5, lung cancer could be diagnosed with an accuracy of 81.7%. Other characteristics investigated were inferior when compared to histogram standard deviation. Histogram standard deviation appears to be the most useful characteristic for diagnosing lung cancer using EBUS images. © 2015 S. Karger AG, Basel.

Complexity of possibly gapped histogram and analysis of histogram

PubMed Central

Roy, Tania

2018-01-01

We demonstrate that gaps and distributional patterns embedded within real-valued measurements are inseparable biological and mechanistic information contents of the system. Such patterns are discovered through data-driven possibly gapped histogram, which further leads to the geometry-based analysis of histogram (ANOHT). Constructing a possibly gapped histogram is a complex problem of statistical mechanics due to the ensemble of candidate histograms being captured by a two-layer Ising model. This construction is also a distinctive problem of Information Theory from the perspective of data compression via uniformity. By defining a Hamiltonian (or energy) as a sum of total coding lengths of boundaries and total decoding errors within bins, this issue of computing the minimum energy macroscopic states is surprisingly resolved by applying the hierarchical clustering algorithm. Thus, a possibly gapped histogram corresponds to a macro-state. And then the first phase of ANOHT is developed for simultaneous comparison of multiple treatments, while the second phase of ANOHT is developed based on classical empirical process theory for a tree-geometry that can check the authenticity of branches of the treatment tree. The well-known Iris data are used to illustrate our technical developments. Also, a large baseball pitching dataset and a heavily right-censored divorce data are analysed to showcase the existential gaps and utilities of ANOHT. PMID:29515829

Complexity of possibly gapped histogram and analysis of histogram

NASA Astrophysics Data System (ADS)

Fushing, Hsieh; Roy, Tania

2018-02-01

We demonstrate that gaps and distributional patterns embedded within real-valued measurements are inseparable biological and mechanistic information contents of the system. Such patterns are discovered through data-driven possibly gapped histogram, which further leads to the geometry-based analysis of histogram (ANOHT). Constructing a possibly gapped histogram is a complex problem of statistical mechanics due to the ensemble of candidate histograms being captured by a two-layer Ising model. This construction is also a distinctive problem of Information Theory from the perspective of data compression via uniformity. By defining a Hamiltonian (or energy) as a sum of total coding lengths of boundaries and total decoding errors within bins, this issue of computing the minimum energy macroscopic states is surprisingly resolved by applying the hierarchical clustering algorithm. Thus, a possibly gapped histogram corresponds to a macro-state. And then the first phase of ANOHT is developed for simultaneous comparison of multiple treatments, while the second phase of ANOHT is developed based on classical empirical process theory for a tree-geometry that can check the authenticity of branches of the treatment tree. The well-known Iris data are used to illustrate our technical developments. Also, a large baseball pitching dataset and a heavily right-censored divorce data are analysed to showcase the existential gaps and utilities of ANOHT.

Neurofilament light protein in blood predicts regional atrophy in Huntington disease.

PubMed

Johnson, Eileanoir B; Byrne, Lauren M; Gregory, Sarah; Rodrigues, Filipe B; Blennow, Kaj; Durr, Alexandra; Leavitt, Blair R; Roos, Raymund A; Zetterberg, Henrik; Tabrizi, Sarah J; Scahill, Rachael I; Wild, Edward J

2018-02-20

Neurofilament light (NfL) protein in blood plasma has been proposed as a prognostic biomarker of neurodegeneration in a number of conditions, including Huntington disease (HD). This study investigates the regional distribution of NfL-associated neural pathology in HD gene expansion carriers. We examined associations between NfL measured in plasma and regionally specific atrophy in cross-sectional (n = 198) and longitudinal (n = 177) data in HD gene expansion carriers from the international multisite TRACK-HD study. Using voxel-based morphometry, we measured associations between baseline NfL levels and both baseline gray matter and white matter volume; and longitudinal change in gray matter and white matter over the subsequent 3 years in HD gene expansion carriers. After controlling for demographics, associations between increased NfL levels and reduced brain volume were seen in cortical and subcortical gray matter and within the white matter. After also controlling for known predictors of disease progression (age and CAG repeat length), associations were limited to the caudate and putamen. Longitudinally, NfL predicted subsequent occipital gray matter atrophy and widespread white matter reduction, both before and after correction for other predictors of disease progression. These findings highlight the value of NfL as a dynamic marker of brain atrophy and, more generally, provide further evidence of the strong association between plasma NfL level, a candidate blood biomarker, and pathologic neuronal change. © 2018 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Academy of Neurology.

CERESVis: A QC Tool for CERES that Leverages Browser Technology for Data Validation

NASA Astrophysics Data System (ADS)

Chu, C.; Sun-Mack, S.; Heckert, E.; Chen, Y.; Doelling, D.

2015-12-01

In this poster, we are going to present three user interfaces that CERES team uses to validate pixel-level data. Besides our home grown tools, we will aslo present the browser technology that we use to provide interactive interfaces, such as jquery, HighCharts and Google Earth. We pass data to the users' browsers and use the browsers to do some simple computations. The three user interfaces are: Thumbnails -- it displays hundrends images to allow users to browse 24-hour data files in few seconds. Multiple-synchronized cursors -- it allows users to compare multiple images side by side. Bounding Boxes and Histograms -- it allows users to draw multiple bounding boxes on an image and the browser computes/display the histograms.

White and gray pumice in pyroclastic deposits. (Invited)

NASA Astrophysics Data System (ADS)

Wright, H. M.; Cashman, K. V.

2013-12-01

Many primary pyroclastic deposits contain at least two different colors of pumice, including volumetrically dominant white and subordinate gray. White pumice is vesicular, microlite-free, and in most cases represents direct samples of the principal magma reservoir. In contrast, subordinate gray pumice with lower vesicularity and/or more abundant microlites may sample either deep recharge OR shallow vanguard magma, where both may record information on eruption triggers. Pumice may appear gray for several reasons: 1. Gray pumice has a less-evolved bulk composition than white pumice. Presence of less-evolved (generally deep-derived) magma provides information about possible recharge magma and/or pre-eruptive compositional variation in the magma storage region. A well-known example of this difference is the 1912 eruption of Novarupta [Hildreth & Fierstein, 2012], which includes white (rhyolite) and gray (andesite and dacite) pumice. 2. Gray pumice contains elevated microlite number densities and/or microlite crystallinities and is compositionally similar to white pumice. a. Gray pumice contains abundant broken crystal fragments and lithic fragments. Broken crystals and incorporated white pumice indicate passage through the primary magma reservoir. Incorporated lithic fragments indicate breakage of wall rock and creation of new transport pathways. Microlites and breadcrusted surfaces indicate slow and/or episodic ascent at shallow levels. This textural association indicates that proto-gray pumice magma played an active role in creating a conduit to the surface. In some cases, small differences in chemistry may further indicate differences in magma batches (recharge pulses). This textural variation is found in the products of high-crystallinity large-volume (Plinian or boil-over style) eruptions, as in the Cerro Galan Ignimbrite, Argentina [Wright et al., 2011]. b. Gray pumice contains abundant microlites due to differences in decompression and/or cooling history. In this case, microlites indicate shallow degassing-induced crystallization, where proto-gray pumice forms vanguard magma or a shallow conduit plug. Gray pumice originating in a shallow conduit plug is common in Vulcanian and subPlinian silicic eruptions and is seen in the 1980 Plinian and subPlinian eruptions of Mount St. Helens [Klug & Cashman, 1994; Cashman & McConnell, 2005]. c. Gray pumice may record syn-eruptive changes in the magmatic system, often manifested as crystallization caused by either decompression or cooling [cf., Gurioli et al., 2005; Andrews & Gardner, 2010]. In summary, the compositional and textural complexities of gray pumice provide detail on pre- and syn-eruptive magmatic processes that may be impossible to obtain from (dominant) white pumice alone. Subtle compositional variations may characterize melts available to recharge and destabilize the upper magma reservoir, whereas crystal textures and compositions can be compared with experimental data to infer shallow magma ascent associated with conduit formation prior to climatic activity. Thus, analysis of gray pumice in pyroclastic deposits can yield new insight into the dynamics of eruptive processes.

Changes in lactate dehydrogenase are associated with central gray matter lesions in newborns with hypoxic-ischemic encephalopathy.

PubMed

Yum, Sook Kyung; Moon, Cheong-Jun; Youn, Young-Ah; Sung, In Kyung

2017-05-01

Biomarkers may predict neurological prognosis in infants with hypoxic-ischemic encephalopathy (HIE). We evaluated the relationship between serum lactate dehydrogenase (LDH) and brain magnetic resonance imaging (MRI), which predicts neurodevelopmental outcomes, in order to assess whether LDH levels are similarly predictive. Medical records were reviewed for infants with HIE and LDH levels were assessed on the first (LDH 1 ) and third (LDH 3 ) days following birth. Receiver operating characteristic curves were obtained in relation to central gray matter hypoxic-ischemic lesions. Of 92 patients, 52 (56.5%) had hypoxic-ischemic lesions on brain MRI, and 21 of these infants (40.4%) had central gray matter lesions. LDH 1 and LDH 3 did not differ; however, the percentage change (ΔLDH%) was significantly higher in infants with central gray matter lesions (36.9% versus 6.6%, p = 0.006). With cutoffs of 187 (IU/L, ΔLDH) and 19.4 (%, ΔLDH%), the sensitivity, specificity, positive predictive value and negative predictive value were 71.4, 69.0, 40.5 and 89.1%, respectively. The relative risk was 5.57 (p = 0.001). Changes in serum LDH may be a useful biomarker for predicting future neurodevelopmental prognosis in infants with HIE.

Gray Matter Hypertrophy and Thickening with Obstructive Sleep Apnea in Middle-aged and Older Adults.

PubMed

Baril, Andrée-Ann; Gagnon, Katia; Brayet, Pauline; Montplaisir, Jacques; De Beaumont, Louis; Carrier, Julie; Lafond, Chantal; L'Heureux, Francis; Gagnon, Jean-François; Gosselin, Nadia

2017-06-01

Obstructive sleep apnea causes intermittent hypoxemia, hemodynamic fluctuations, and sleep fragmentation, all of which could damage cerebral gray matter that can be indirectly assessed by neuroimaging. To investigate whether markers of obstructive sleep apnea severity are associated with gray matter changes among middle-aged and older individuals. Seventy-one subjects (ages, 55-76 yr; apnea-hypopnea index, 0.2-96.6 events/h) were evaluated by magnetic resonance imaging. Two techniques were used: (1) voxel-based morphometry, which measures gray matter volume and concentration; and (2) FreeSurfer (an open source software suite) automated segmentation, which estimates the volume of predefined cortical/subcortical regions and cortical thickness. Regression analyses were performed between gray matter characteristics and markers of obstructive sleep apnea severity (hypoxemia, respiratory disturbances, and sleep fragmentation). Subjects had few symptoms, that is, sleepiness, depression, anxiety, and cognitive deficits. Although no association was found with voxel-based morphometry, FreeSurfer revealed increased gray matter with obstructive sleep apnea. Higher levels of hypoxemia correlated with increased volume and thickness of the left lateral prefrontal cortex as well as increased thickness of the right frontal pole, the right lateral parietal lobules, and the left posterior cingulate cortex. Respiratory disturbances positively correlated with right amygdala volume, and more severe sleep fragmentation was associated with increased thickness of the right inferior frontal gyrus. Gray matter hypertrophy and thickening were associated with hypoxemia, respiratory disturbances, and sleep fragmentation. These structural changes in a group of middle-aged and older individuals may represent adaptive/reactive brain mechanisms attributed to a presymptomatic stage of obstructive sleep apnea.

An Experimental Comparison of Similarity Assessment Measures for 3D Models on Constrained Surface Deformation

NASA Astrophysics Data System (ADS)

Quan, Lulin; Yang, Zhixin

2010-05-01

To address the issues in the area of design customization, this paper expressed the specification and application of the constrained surface deformation, and reported the experimental performance comparison of three prevail effective similarity assessment algorithms on constrained surface deformation domain. Constrained surface deformation becomes a promising method that supports for various downstream applications of customized design. Similarity assessment is regarded as the key technology for inspecting the success of new design via measuring the difference level between the deformed new design and the initial sample model, and indicating whether the difference level is within the limitation. According to our theoretical analysis and pre-experiments, three similarity assessment algorithms are suitable for this domain, including shape histogram based method, skeleton based method, and U system moment based method. We analyze their basic functions and implementation methodologies in detail, and do a series of experiments on various situations to test their accuracy and efficiency using precision-recall diagram. Shoe model is chosen as an industrial example for the experiments. It shows that shape histogram based method gained an optimal performance in comparison. Based on the result, we proposed a novel approach that integrating surface constrains and shape histogram description with adaptive weighting method, which emphasize the role of constrains during the assessment. The limited initial experimental result demonstrated that our algorithm outperforms other three algorithms. A clear direction for future development is also drawn at the end of the paper.

Detection of simulated microcalcifications in fixed mammary tissue: An ROC study of the effect of local versus global histogram equalization.

PubMed

Sund, T; Olsen, J B

2006-09-01

To investigate whether sliding window adaptive histogram equalization (SWAHE) of digital mammograms improves the detection of simulated calcifications, as compared to images normalized by global histogram equalization (GHE). Direct digital mammograms were obtained from mammary tissue phantoms superimposed with different frames. Each frame was divided into forty squares by a wire mesh, and contained granular calcifications randomly positioned in about 50% of the squares. Three radiologists read the mammograms on a display monitor. They classified their confidence in the presence of microcalcifications in each square on a scale of 1 to 5. Images processed with GHE were first read and used as a reference. In a later session, the same images processed with SWAHE were read. The results were compared using ROC methodology. When the total areas AZ were compared, the results were completely equivocal. When comparing the high-specificity partial ROC area AZ,0.2 below false-positive fraction (FPF) 0.20, two of the three observers performed best with the images processed with SWAHE. The difference was not statistically significant. When the reader's confidence threshold in malignancy is set at a high level, increasing the contrast of mammograms with SWAHE may enhance the visibility of microcalcifications without adversely affecting the false-positive rate. When the reader's confidence threshold is set at a low level, the effect of SWAHE is an increase of false positives. Further investigation is needed to confirm the validity of the conclusions.

Construction and Evaluation of Histograms in Teacher Training

ERIC Educational Resources Information Center

Bruno, A.; Espinel, M. C.

2009-01-01

This article details the results of a written test designed to reveal how education majors construct and evaluate histograms and frequency polygons. Included is a description of the mistakes made by the students which shows how they tend to confuse histograms with bar diagrams, incorrectly assign data along the Cartesian axes and experience…

Empirical Histograms in Item Response Theory with Ordinal Data

ERIC Educational Resources Information Center

Woods, Carol M.

2007-01-01

The purpose of this research is to describe, test, and illustrate a new implementation of the empirical histogram (EH) method for ordinal items. The EH method involves the estimation of item response model parameters simultaneously with the approximation of the distribution of the random latent variable (theta) as a histogram. Software for the EH…

Airborne gamma-ray spectrometer and magnetometer survey, Durango D, Colorado. Final report Volume II A. Detail area

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

Not Available

1983-01-01

This volume contains geology of the Durango D detail area, radioactive mineral occurrences in Colorado, and geophysical data interpretation. Eight appendices provide: stacked profiles, geologic histograms, geochemical histograms, speed and altitude histograms, geologic statistical tables, geochemical statistical tables, magnetic and ancillary profiles, and test line data.

Airborne gamma-ray spectrometer and magnetometer survey, Durango C, Colorado. Final report Volume II A. Detail area

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

Not Available

1983-01-01

Geology of Durango C detail area, radioactive mineral occurrences in Colorado, and geophysical data interpretation are included in this report. Eight appendices provide: stacked profiles, geologic histograms, geochemical histograms, speed and altitude histograms, geologic statistical tables, magnetic and ancillary profiles, and test line data.

Assessing the disturbance potential of small unoccupied aircraft systems (UAS) on gray seals (Halichoerus grypus) at breeding colonies in Nova Scotia, Canada

PubMed Central

Arona, Lauren; Dale, Julian; Heaslip, Susan G.; Hammill, Michael O.

2018-01-01

The use of small unoccupied aircraft systems (UAS) for ecological studies and wildlife population assessments is increasing. These methods can provide significant benefits in terms of costs and reductions in human risk, but little is known if UAS-based approaches cause disturbance of animals during operations. To address this knowledge gap, we conducted a series of UAS flights at gray seal breeding colonies on Hay and Saddle Islands in Nova Scotia, Canada. Using a small fixed-wing UAS, we assessed both immediate and short-term effects of surveys using sequential image analysis and between-flight seal counts in ten, 50 m2 random quadrats at each colony. Counts of adult gray seals and young-of-the-year animals between first and second flights revealed no changes in abundance in quadrats (matched pair t-test p > 0.69) and slopes approaching 1 for linear regression comparisons (r2 > 0.80). Sequential image analysis revealed no changes in orientation or posture of imaged animals. We also assessed the acoustic properties of the small UAS in relation to low ambient noise conditions using sound equivalent level (Leq) measurements with a calibrated U-MIK 1 and a 1/3 octave band soundscape approach. The results of Leq measurements indicate that small fixed-wing UAS are quiet, with most energy above 160 Hz, and that levels across 1/3 octave bands do not greatly exceed ambient acoustic measurements in a quiet field during operations at standard survey altitudes. As such, this platform is unlikely to acoustically disturb gray seals at breeding colonies during population surveys. The results of the present study indicate that the effects of small fixed-wing UAS on gray seals at breeding colonies are negligible, and that fixed-wing UAS-based approaches should be considered amongst best practices for assessing gray seal colonies. PMID:29576950

Action recognition via cumulative histogram of multiple features

NASA Astrophysics Data System (ADS)

Yan, Xunshi; Luo, Yupin

2011-01-01

Spatial-temporal interest points (STIPs) are popular in human action recognition. However, they suffer from difficulties in determining size of codebook and losing much information during forming histograms. In this paper, spatial-temporal interest regions (STIRs) are proposed, which are based on STIPs and are capable of marking the locations of the most ``shining'' human body parts. In order to represent human actions, the proposed approach takes great advantages of multiple features, including STIRs, pyramid histogram of oriented gradients and pyramid histogram of oriented optical flows. To achieve this, cumulative histogram is used to integrate dynamic information in sequences and to form feature vectors. Furthermore, the widely used nearest neighbor and AdaBoost methods are employed as classification algorithms. Experiments on public datasets KTH, Weizmann and UCF sports show that the proposed approach achieves effective and robust results.

Variability of textural features in FDG PET images due to different acquisition modes and reconstruction parameters.

PubMed

Galavis, Paulina E; Hollensen, Christian; Jallow, Ngoneh; Paliwal, Bhudatt; Jeraj, Robert

2010-10-01

Characterization of textural features (spatial distributions of image intensity levels) has been considered as a tool for automatic tumor segmentation. The purpose of this work is to study the variability of the textural features in PET images due to different acquisition modes and reconstruction parameters. Twenty patients with solid tumors underwent PET/CT scans on a GE Discovery VCT scanner, 45-60 minutes post-injection of 10 mCi of [(18)F]FDG. Scans were acquired in both 2D and 3D modes. For each acquisition the raw PET data was reconstructed using five different reconstruction parameters. Lesions were segmented on a default image using the threshold of 40% of maximum SUV. Fifty different texture features were calculated inside the tumors. The range of variations of the features were calculated with respect to the average value. Fifty textural features were classified based on the range of variation in three categories: small, intermediate and large variability. Features with small variability (range ≤ 5%) were entropy-first order, energy, maximal correlation coefficient (second order feature) and low-gray level run emphasis (high-order feature). The features with intermediate variability (10% ≤ range ≤ 25%) were entropy-GLCM, sum entropy, high gray level run emphsis, gray level non-uniformity, small number emphasis, and entropy-NGL. Forty remaining features presented large variations (range > 30%). Textural features such as entropy-first order, energy, maximal correlation coefficient, and low-gray level run emphasis exhibited small variations due to different acquisition modes and reconstruction parameters. Features with low level of variations are better candidates for reproducible tumor segmentation. Even though features such as contrast-NGTD, coarseness, homogeneity, and busyness have been previously used, our data indicated that these features presented large variations, therefore they could not be considered as a good candidates for tumor segmentation.

Variability of textural features in FDG PET images due to different acquisition modes and reconstruction parameters

PubMed Central

GALAVIS, PAULINA E.; HOLLENSEN, CHRISTIAN; JALLOW, NGONEH; PALIWAL, BHUDATT; JERAJ, ROBERT

2014-01-01

Background Characterization of textural features (spatial distributions of image intensity levels) has been considered as a tool for automatic tumor segmentation. The purpose of this work is to study the variability of the textural features in PET images due to different acquisition modes and reconstruction parameters. Material and methods Twenty patients with solid tumors underwent PET/CT scans on a GE Discovery VCT scanner, 45–60 minutes post-injection of 10 mCi of [18F]FDG. Scans were acquired in both 2D and 3D modes. For each acquisition the raw PET data was reconstructed using five different reconstruction parameters. Lesions were segmented on a default image using the threshold of 40% of maximum SUV. Fifty different texture features were calculated inside the tumors. The range of variations of the features were calculated with respect to the average value. Results Fifty textural features were classified based on the range of variation in three categories: small, intermediate and large variability. Features with small variability (range ≤ 5%) were entropy-first order, energy, maximal correlation coefficient (second order feature) and low-gray level run emphasis (high-order feature). The features with intermediate variability (10% ≤ range ≤ 25%) were entropy-GLCM, sum entropy, high gray level run emphsis, gray level non-uniformity, small number emphasis, and entropy-NGL. Forty remaining features presented large variations (range > 30%). Conclusion Textural features such as entropy-first order, energy, maximal correlation coefficient, and low-gray level run emphasis exhibited small variations due to different acquisition modes and reconstruction parameters. Features with low level of variations are better candidates for reproducible tumor segmentation. Even though features such as contrast-NGTD, coarseness, homogeneity, and busyness have been previously used, our data indicated that these features presented large variations, therefore they could not be considered as a good candidates for tumor segmentation. PMID:20831489

Automated Counting of Particles To Quantify Cleanliness

NASA Technical Reports Server (NTRS)

Rhode, James

2005-01-01

A machine vision system, similar to systems used in microbiological laboratories to count cultured microbes, has been proposed for quantifying the cleanliness of nominally precisely cleaned hardware by counting residual contaminant particles. The system would include a microscope equipped with an electronic camera and circuitry to digitize the camera output, a personal computer programmed with machine-vision and interface software, and digital storage media. A filter pad, through which had been aspirated solvent from rinsing the hardware in question, would be placed on the microscope stage. A high-resolution image of the filter pad would be recorded. The computer would analyze the image and present a histogram of sizes of particles on the filter. On the basis of the histogram and a measure of the desired level of cleanliness, the hardware would be accepted or rejected. If the hardware were accepted, the image would be saved, along with other information, as a quality record. If the hardware were rejected, the histogram and ancillary information would be recorded for analysis of trends. The software would perceive particles that are too large or too numerous to meet a specified particle-distribution profile. Anomalous particles or fibrous material would be flagged for inspection.

Multi-scale Morphological Image Enhancement of Chest Radiographs by a Hybrid Scheme.

PubMed

Alavijeh, Fatemeh Shahsavari; Mahdavi-Nasab, Homayoun

2015-01-01

Chest radiography is a common diagnostic imaging test, which contains an enormous amount of information about a patient. However, its interpretation is highly challenging. The accuracy of the diagnostic process is greatly influenced by image processing algorithms; hence enhancement of the images is indispensable in order to improve visibility of the details. This paper aims at improving radiograph parameters such as contrast, sharpness, noise level, and brightness to enhance chest radiographs, making use of a triangulation method. Here, contrast limited adaptive histogram equalization technique and noise suppression are simultaneously performed in wavelet domain in a new scheme, followed by morphological top-hat and bottom-hat filtering. A unique implementation of morphological filters allows for adjustment of the image brightness and significant enhancement of the contrast. The proposed method is tested on chest radiographs from Japanese Society of Radiological Technology database. The results are compared with conventional enhancement techniques such as histogram equalization, contrast limited adaptive histogram equalization, Retinex, and some recently proposed methods to show its strengths. The experimental results reveal that the proposed method can remarkably improve the image contrast while keeping the sensitive chest tissue information so that radiologists might have a more precise interpretation.

Multi-scale Morphological Image Enhancement of Chest Radiographs by a Hybrid Scheme

PubMed Central

Alavijeh, Fatemeh Shahsavari; Mahdavi-Nasab, Homayoun

2015-01-01

Chest radiography is a common diagnostic imaging test, which contains an enormous amount of information about a patient. However, its interpretation is highly challenging. The accuracy of the diagnostic process is greatly influenced by image processing algorithms; hence enhancement of the images is indispensable in order to improve visibility of the details. This paper aims at improving radiograph parameters such as contrast, sharpness, noise level, and brightness to enhance chest radiographs, making use of a triangulation method. Here, contrast limited adaptive histogram equalization technique and noise suppression are simultaneously performed in wavelet domain in a new scheme, followed by morphological top-hat and bottom-hat filtering. A unique implementation of morphological filters allows for adjustment of the image brightness and significant enhancement of the contrast. The proposed method is tested on chest radiographs from Japanese Society of Radiological Technology database. The results are compared with conventional enhancement techniques such as histogram equalization, contrast limited adaptive histogram equalization, Retinex, and some recently proposed methods to show its strengths. The experimental results reveal that the proposed method can remarkably improve the image contrast while keeping the sensitive chest tissue information so that radiologists might have a more precise interpretation. PMID:25709942

Electrocardiographic consequences of a peripatetic lifestyle in gray wolves (Canis lupus)

USGS Publications Warehouse

Constable, Peter; Hinchcliff, Ken; Demma, Nick; Callahan, Margaret; Dale, Bruce W.; Fox, Kevin; Adams, Layne G.; Wack, Ray; Kramer, Lynn

1998-01-01

Cardiac chamber enlargement and hypertrophy are normal physiologic responses to repetitive endurance exercise activity in human beings and domestic dogs. Whether similar changes occur in wild animals as a consequence of increased activity is unknown. We found that free-ranging gray wolves (Canis lupus, n=11), the archetypical endurance athlete, have electrocardiographic evidence of cardiac chamber enlargement and hypertrophy relative to sedentary captive gray wolves (n=20), as demonstrated by significant increases in QRS duration, QT interval, and QT interval corrected for heart rate, a tendency towards increased Q, R, and S wave voltages in all leads, and a significant decrease in heart rate. We conclude that exercise activity level and therefore lifestyle affects physiologic variables in wild animals. An immediate consequence of this finding is that physiologic measurements obtained from a captive wild-animal population with reduced exercise activity level may not accurately reflect the normal physiologic state for free-ranging members of the same species.

In vivo magnetic resonance spectroscopy measurement of gray-matter and white-matter gamma-aminobutyric acid concentration in sensorimotor cortex using a motion-controlled MEGA point-resolved spectroscopy sequence.

PubMed

Bhattacharyya, Pallab K; Phillips, Micheal D; Stone, Lael A; Lowe, Mark J

2011-04-01

Gamma-aminobutyric acid (GABA) is a major inhibitory neurotransmitter in the brain. Understanding the GABA concentration, in vivo, is important to understand normal brain function. Using MEGA point-resolved spectroscopy sequence with interleaved water scans to detect subject motion, GABA level of sensorimotor cortex was measured using a voxel identified from a functional magnetic resonance imaging scan. The GABA level in a 20×20×20-mm(3) voxel consisting of 37%±7% gray matter, 52%±12% white matter and 11%±8% cerebrospinal fluid in the sensorimotor region was measured to be 1.43±0.48 mM. In addition, using linear regression analysis, GABA concentrations within gray and white matter were calculated to be 2.87±0.61 and 0.33±0.11 mM, respectively. Copyright © 2011 Elsevier Inc. All rights reserved.

A completely automated CAD system for mass detection in a large mammographic database.

PubMed

Bellotti, R; De Carlo, F; Tangaro, S; Gargano, G; Maggipinto, G; Castellano, M; Massafra, R; Cascio, D; Fauci, F; Magro, R; Raso, G; Lauria, A; Forni, G; Bagnasco, S; Cerello, P; Zanon, E; Cheran, S C; Lopez Torres, E; Bottigli, U; Masala, G L; Oliva, P; Retico, A; Fantacci, M E; Cataldo, R; De Mitri, I; De Nunzio, G

2006-08-01

Mass localization plays a crucial role in computer-aided detection (CAD) systems for the classification of suspicious regions in mammograms. In this article we present a completely automated classification system for the detection of masses in digitized mammographic images. The tool system we discuss consists in three processing levels: (a) Image segmentation for the localization of regions of interest (ROIs). This step relies on an iterative dynamical threshold algorithm able to select iso-intensity closed contours around gray level maxima of the mammogram. (b) ROI characterization by means of textural features computed from the gray tone spatial dependence matrix (GTSDM), containing second-order spatial statistics information on the pixel gray level intensity. As the images under study were recorded in different centers and with different machine settings, eight GTSDM features were selected so as to be invariant under monotonic transformation. In this way, the images do not need to be normalized, as the adopted features depend on the texture only, rather than on the gray tone levels, too. (c) ROI classification by means of a neural network, with supervision provided by the radiologist's diagnosis. The CAD system was evaluated on a large database of 3369 mammographic images [2307 negative, 1062 pathological (or positive), containing at least one confirmed mass, as diagnosed by an expert radiologist]. To assess the performance of the system, receiver operating characteristic (ROC) and free-response ROC analysis were employed. The area under the ROC curve was found to be Az = 0.783 +/- 0.008 for the ROI-based classification. When evaluating the accuracy of the CAD against the radiologist-drawn boundaries, 4.23 false positives per image are found at 80% of mass sensitivity.

Parameter optimization of parenchymal texture analysis for prediction of false-positive recalls from screening mammography

NASA Astrophysics Data System (ADS)

Ray, Shonket; Keller, Brad M.; Chen, Jinbo; Conant, Emily F.; Kontos, Despina

2016-03-01

This work details a methodology to obtain optimal parameter values for a locally-adaptive texture analysis algorithm that extracts mammographic texture features representative of breast parenchymal complexity for predicting falsepositive (FP) recalls from breast cancer screening with digital mammography. The algorithm has two components: (1) adaptive selection of localized regions of interest (ROIs) and (2) Haralick texture feature extraction via Gray- Level Co-Occurrence Matrices (GLCM). The following parameters were systematically varied: mammographic views used, upper limit of the ROI window size used for adaptive ROI selection, GLCM distance offsets, and gray levels (binning) used for feature extraction. Each iteration per parameter set had logistic regression with stepwise feature selection performed on a clinical screening cohort of 474 non-recalled women and 68 FP recalled women; FP recall prediction was evaluated using area under the curve (AUC) of the receiver operating characteristic (ROC) and associations between the extracted features and FP recall were assessed via odds ratios (OR). A default instance of mediolateral (MLO) view, upper ROI size limit of 143.36 mm (2048 pixels2), GLCM distance offset combination range of 0.07 to 0.84 mm (1 to 12 pixels) and 16 GLCM gray levels was set. The highest ROC performance value of AUC=0.77 [95% confidence intervals: 0.71-0.83] was obtained at three specific instances: the default instance, upper ROI window equal to 17.92 mm (256 pixels2), and gray levels set to 128. The texture feature of sum average was chosen as a statistically significant (p<0.05) predictor and associated with higher odds of FP recall for 12 out of 14 total instances.

Distributions-per-level: a means of testing level detectors and models of patch-clamp data.

PubMed

Schröder, I; Huth, T; Suitchmezian, V; Jarosik, J; Schnell, S; Hansen, U P

2004-01-01

Level or jump detectors generate the reconstructed time series from a noisy record of patch-clamp current. The reconstructed time series is used to create dwell-time histograms for the kinetic analysis of the Markov model of the investigated ion channel. It is shown here that some additional lines in the software of such a detector can provide a powerful new means of patch-clamp analysis. For each current level that can be recognized by the detector, an array is declared. The new software assigns every data point of the original time series to the array that belongs to the actual state of the detector. From the data sets in these arrays distributions-per-level are generated. Simulated and experimental time series analyzed by Hinkley detectors are used to demonstrate the benefits of these distributions-per-level. First, they can serve as a test of the reliability of jump and level detectors. Second, they can reveal beta distributions as resulting from fast gating that would usually be hidden in the overall amplitude histogram. Probably the most valuable feature is that the malfunctions of the Hinkley detectors turn out to depend on the Markov model of the ion channel. Thus, the errors revealed by the distributions-per-level can be used to distinguish between different putative Markov models of the measured time series.

Temporary hearing loss influences post-stimulus time histogram and single neuron action potential estimates from human compound action potentials

PubMed Central

Lichtenhan, Jeffery T.; Chertoff, Mark E.

2008-01-01

An analytic compound action potential (CAP) obtained by convolving functional representations of the post-stimulus time histogram summed across auditory nerve neurons [P(t)] and a single neuron action potential [U(t)] was fit to human CAPs. The analytic CAP fit to pre- and postnoise-induced temporary hearing threshold shift (TTS) estimated in vivoP(t) and U(t) and the number of neurons contributing to the CAPs (N). The width of P(t) decreased with increasing signal level and was wider at the lowest signal level following noise exposure. P(t) latency decreased with increasing signal level and was shorter at all signal levels following noise exposure. The damping and oscillatory frequency of U(t) increased with signal level. For subjects with large amounts of TTS, U(t) had greater damping than before noise exposure particularly at low signal levels. Additionally, U(t) oscillation was lower in frequency at all click intensities following noise exposure. N increased with signal level and was smaller after noise exposure at the lowest signal level. Collectively these findings indicate that neurons contributing to the CAP during TTS are fewer in number, shorter in latency, and poorer in synchrony than before noise exposure. Moreover, estimates of single neuron action potentials may decay more rapidly and have a lower oscillatory frequency during TTS. PMID:18397026

Histogram analysis of apparent diffusion coefficient for monitoring early response in patients with advanced cervical cancers undergoing concurrent chemo-radiotherapy.

PubMed

Meng, Jie; Zhu, Lijing; Zhu, Li; Ge, Yun; He, Jian; Zhou, Zhengyang; Yang, Xiaofeng

2017-11-01

Background Apparent diffusion coefficient (ADC) histogram analysis has been widely used in determining tumor prognosis. Purpose To investigate the dynamic changes of ADC histogram parameters during concurrent chemo-radiotherapy (CCRT) in patients with advanced cervical cancers. Material and Methods This prospective study enrolled 32 patients with advanced cervical cancers undergoing CCRT who received diffusion-weighted (DW) magnetic resonance imaging (MRI) before CCRT, at the end of the second and fourth week during CCRT and one month after CCRT completion. The ADC histogram for the entire tumor volume was generated, and a series of histogram parameters was obtained. Dynamic changes of those parameters in cervical cancers were investigated as early biomarkers for treatment response. Results All histogram parameters except AUC low showed significant changes during CCRT (all P < 0.05). There were three variable trends involving different parameters. The mode, 5th, 10th, and 25th percentiles showed similar early increase rates (33.33%, 33.99%, 34.12%, and 30.49%, respectively) at the end of the second week of CCRT. The pre-CCRT 5th and 25th percentiles of the complete response (CR) group were significantly lower than those of the partial response (PR) group. Conclusion A series of ADC histogram parameters of cervical cancers changed significantly at the early stage of CCRT, indicating their potential in monitoring early tumor response to therapy.

Whole Tumor Histogram-profiling of Diffusion-Weighted Magnetic Resonance Images Reflects Tumorbiological Features of Primary Central Nervous System Lymphoma.

PubMed

Schob, Stefan; Münch, Benno; Dieckow, Julia; Quäschling, Ulf; Hoffmann, Karl-Titus; Richter, Cindy; Garnov, Nikita; Frydrychowicz, Clara; Krause, Matthias; Meyer, Hans-Jonas; Surov, Alexey

2018-04-01

Diffusion weighted imaging (DWI) quantifies motion of hydrogen nuclei in biological tissues and hereby has been used to assess the underlying tissue microarchitecture. Histogram-profiling of DWI provides more detailed information on diffusion characteristics of a lesion than the standardly calculated values of the apparent diffusion coefficient (ADC)-minimum, mean and maximum. Hence, the aim of our study was to investigate, which parameters of histogram-profiling of DWI in primary central nervous system lymphoma can be used to specifically predict features like cellular density, chromatin content and proliferative activity. Pre-treatment ADC maps of 21 PCNSL patients (8 female, 13 male, 28-89 years) from a 1.5T system were used for Matlab-based histogram profiling. Results of histopathology (H&E staining) and immunohistochemistry (Ki-67 expression) were quantified. Correlations between histogram-profiling parameters and neuropathologic examination were calculated using SPSS 23.0. The lower percentiles (p10 and p25) showed significant correlations with structural parameters of the neuropathologic examination (cellular density, chromatin content). The highest percentile, p90, correlated significantly with Ki-67 expression, resembling proliferative activity. Kurtosis of the ADC histogram correlated significantly with cellular density. Histogram-profiling of DWI in PCNSL provides a comprehensible set of parameters, which reflect distinct tumor-architectural and tumor-biological features, and hence, are promising biomarkers for treatment response and prognosis. Copyright © 2018. Published by Elsevier Inc.

ADC histogram analysis of muscle lymphoma - Correlation with histopathology in a rare entity.

PubMed

Meyer, Hans-Jonas; Pazaitis, Nikolaos; Surov, Alexey

2018-06-21

Diffusion weighted imaging (DWI) is able to reflect histopathology architecture. A novel imaging approach, namely histogram analysis, is used to further characterize lesion on MRI. The purpose of this study is to correlate histogram parameters derived from apparent diffusion coefficient- (ADC) maps with histopathology parameters in muscle lymphoma. Eight patients (mean age 64.8 years, range 45-72 years) with histopathologically confirmed muscle lymphoma were retrospectively identified. Cell count, total nucleic and average nucleic areas were estimated using ImageJ. Additionally, Ki67-index was calculated. DWI was obtained on a 1.5T scanner by using the b values of 0 and 1000 s/mm2. Histogram analysis was performed as a whole lesion measurement by using a custom-made Matlabbased application. The correlation analysis revealed statistically significant correlation between cell count and ADCmean (p=-0.76, P=0.03) as well with ADCp75 (p=-0.79, P=0.02). Kurtosis and entropy correlated with average nucleic area (p=-0.81, P=0.02, p=0.88, P=0.007, respectively). None of the analyzed ADC parameters correlated with total nucleic area and with Ki67-index. This study identified significant correlations between cellularity and histogram parameters derived from ADC maps in muscle lymphoma. Thus, histogram analysis parameters reflect histopathology in muscle tumors. Advances in knowledge: Whole lesion ADC histogram analysis is able to reflect histopathology parameters in muscle lymphomas.

Delay, change and bifurcation of the immunofluorescence distribution attractors in health statuses diagnostics and in medical treatment

NASA Astrophysics Data System (ADS)

Galich, Nikolay E.; Filatov, Michael V.

2008-07-01

Communication contains the description of the immunology experiments and the experimental data treatment. New nonlinear methods of immunofluorescence statistical analysis of peripheral blood neutrophils have been developed. We used technology of respiratory burst reaction of DNA fluorescence in the neutrophils cells nuclei due to oxidative activity. The histograms of photon count statistics the radiant neutrophils populations' in flow cytometry experiments are considered. Distributions of the fluorescence flashes frequency as functions of the fluorescence intensity are analyzed. Statistic peculiarities of histograms set for healthy and unhealthy donors allow dividing all histograms on the three classes. The classification is based on three different types of smoothing and long-range scale averaged immunofluorescence distributions and their bifurcation. Heterogeneity peculiarities of long-range scale immunofluorescence distributions allow dividing all histograms on three groups. First histograms group belongs to healthy donors. Two other groups belong to donors with autoimmune and inflammatory diseases. Some of the illnesses are not diagnosed by standards biochemical methods. Medical standards and statistical data of the immunofluorescence histograms for identifications of health and illnesses are interconnected. Possibilities and alterations of immunofluorescence statistics in registration, diagnostics and monitoring of different diseases in various medical treatments have been demonstrated. Health or illness criteria are connected with statistics features of immunofluorescence histograms. Neutrophils populations' fluorescence presents the sensitive clear indicator of health status.

Age-related differences in GABA levels are driven by bulk tissue changes.

PubMed

Maes, Celine; Hermans, Lize; Pauwels, Lisa; Chalavi, Sima; Leunissen, Inge; Levin, Oron; Cuypers, Koen; Peeters, Ronald; Sunaert, Stefan; Mantini, Dante; Puts, Nicolaas A J; Edden, Richard A E; Swinnen, Stephan P

2018-05-02

Levels of GABA, the main inhibitory neurotransmitter in the brain, can be regionally quantified using magnetic resonance spectroscopy (MRS). Although GABA is crucial for efficient neuronal functioning, little is known about age-related differences in GABA levels and their relationship with age-related changes in brain structure. Here, we investigated the effect of age on GABA levels within the left sensorimotor cortex and the occipital cortex in a sample of 85 young and 85 older adults using the MEGA-PRESS sequence. Because the distribution of GABA varies across different brain tissues, various correction methods are available to account for this variation. Considering that these correction methods are highly dependent on the tissue composition of the voxel of interest, we examined differences in voxel composition between age groups and the impact of these various correction methods on the identification of age-related differences in GABA levels. Results indicated that, within both voxels of interest, older (as compared to young adults) exhibited smaller gray matter fraction accompanied by larger fraction of cerebrospinal fluid. Whereas uncorrected GABA levels were significantly lower in older as compared to young adults, this age effect was absent when GABA levels were corrected for voxel composition. These results suggest that age-related differences in GABA levels are at least partly driven by the age-related gray matter loss. However, as alterations in GABA levels might be region-specific, further research should clarify to what extent gray matter changes may account for age-related differences in GABA levels within other brain regions. © 2018 Wiley Periodicals, Inc.

Image quality assessment metric for frame accumulated image

NASA Astrophysics Data System (ADS)

Yu, Jianping; Li, Gang; Wang, Shaohui; Lin, Ling

2018-01-01

The medical image quality determines the accuracy of diagnosis, and the gray-scale resolution is an important parameter to measure image quality. But current objective metrics are not very suitable for assessing medical images obtained by frame accumulation technology. Little attention was paid to the gray-scale resolution, basically based on spatial resolution and limited to the 256 level gray scale of the existing display device. Thus, this paper proposes a metric, "mean signal-to-noise ratio" (MSNR) based on signal-to-noise in order to be more reasonable to evaluate frame accumulated medical image quality. We demonstrate its potential application through a series of images under a constant illumination signal. Here, the mean image of enough images was regarded as the reference image. Several groups of images by different frame accumulation and their MSNR were calculated. The results of the experiment show that, compared with other quality assessment methods, the metric is simpler, more effective, and more suitable for assessing frame accumulated images that surpass the gray scale and precision of the original image.

Quantification technology study on flaws in steam-filled pipelines based on image processing

NASA Astrophysics Data System (ADS)

Sun, Lina; Yuan, Peixin

2009-07-01

Starting from exploiting the applied detection system of gas transmission pipeline, a set of X-ray image processing methods and pipeline flaw quantificational evaluation methods are proposed. Defective and non-defective strings and rows in gray image were extracted and oscillogram was obtained. We can distinguish defects in contrast with two gray images division. According to the gray value of defects with different thicknesses, the gray level depth curve is founded. Through exponential and polynomial fitting way to obtain the attenuation mathematical model which the beam penetrates pipeline, thus attain flaw deep dimension. This paper tests on the PPR pipe in the production of simulated holes flaw and cracks flaw, 135KV used the X-ray source on the testing. Test results show that X-ray image processing method, which meet the needs of high efficient flaw detection and provide quality safeguard for thick oil recovery, can be used successfully in detecting corrosion of insulated pipe.

Quantification technology study on flaws in steam-filled pipelines based on image processing

NASA Astrophysics Data System (ADS)

Yuan, Pei-xin; Cong, Jia-hui; Chen, Bo

2008-03-01

Starting from exploiting the applied detection system of gas transmission pipeline, a set of X-ray image processing methods and pipeline flaw quantificational evaluation methods are proposed. Defective and non-defective strings and rows in gray image were extracted and oscillogram was obtained. We can distinguish defects in contrast with two gray images division. According to the gray value of defects with different thicknesses, the gray level depth curve is founded. Through exponential and polynomial fitting way to obtain the attenuation mathematical model which the beam penetrates pipeline, thus attain flaw deep dimension. This paper tests on the PPR pipe in the production of simulated holes flaw and cracks flaw. The X-ray source tube voltage was selected as 130kv and valve current was 1.5mA.Test results show that X-ray image processing methods, which meet the needs of high efficient flaw detection and provide quality safeguard for thick oil recovery, can be used successfully in detecting corrosion of insulated pipe.

Generative complexity of Gray-Scott model

NASA Astrophysics Data System (ADS)

Adamatzky, Andrew

2018-03-01

In the Gray-Scott reaction-diffusion system one reactant is constantly fed in the system, another reactant is reproduced by consuming the supplied reactant and also converted to an inert product. The rate of feeding one reactant in the system and the rate of removing another reactant from the system determine configurations of concentration profiles: stripes, spots, waves. We calculate the generative complexity-a morphological complexity of concentration profiles grown from a point-wise perturbation of the medium-of the Gray-Scott system for a range of the feeding and removal rates. The morphological complexity is evaluated using Shannon entropy, Simpson diversity, approximation of Lempel-Ziv complexity, and expressivity (Shannon entropy divided by space-filling). We analyse behaviour of the systems with highest values of the generative morphological complexity and show that the Gray-Scott systems expressing highest levels of the complexity are composed of the wave-fragments (similar to wave-fragments in sub-excitable media) and travelling localisations (similar to quasi-dissipative solitons and gliders in Conway's Game of Life).

Activity Recognition in Egocentric video using SVM, kNN and Combined SVMkNN Classifiers

NASA Astrophysics Data System (ADS)

Sanal Kumar, K. P.; Bhavani, R., Dr.

2017-08-01

Egocentric vision is a unique perspective in computer vision which is human centric. The recognition of egocentric actions is a challenging task which helps in assisting elderly people, disabled patients and so on. In this work, life logging activity videos are taken as input. There are 2 categories, first one is the top level and second one is second level. Here, the recognition is done using the features like Histogram of Oriented Gradients (HOG), Motion Boundary Histogram (MBH) and Trajectory. The features are fused together and it acts as a single feature. The extracted features are reduced using Principal Component Analysis (PCA). The features that are reduced are provided as input to the classifiers like Support Vector Machine (SVM), k nearest neighbor (kNN) and combined Support Vector Machine (SVM) and k Nearest Neighbor (kNN) (combined SVMkNN). These classifiers are evaluated and the combined SVMkNN provided better results than other classifiers in the literature.

CPU Performance Counter-Based Problem Diagnosis for Software Systems

DTIC Science & Technology

2009-09-01

application servers and implementation techniques), this thesis only used the Enterprise Java Bean (EJB) SessionBean version of RUBiS. The PHP and Servlet ...collection statistics at the Java Virtual Machine (JVM) level can be reused for any Java application. Other examples of gray-box instrumentation include path...used gray-box approaches. For example, PinPoint [11, 14] and [29] use request tracing to diagnose Java exceptions, endless calls, and null calls in

GrayStar: Web-based pedagogical stellar modeling

NASA Astrophysics Data System (ADS)

Short, C. Ian

2017-01-01

GrayStar is a web-based pedagogical stellar model. It approximates stellar atmospheric and spectral line modeling in JavaScript with visualization in HTML. It is suitable for a wide range of education and public outreach levels depending on which optional plots and print-outs are turned on. All plots and renderings are pure basic HTML and the plotting module contains original HTML procedures for automatically scaling and graduating x- and y-axes.

Time-cumulated visible and infrared histograms used as descriptor of cloud cover

NASA Technical Reports Server (NTRS)

Seze, G.; Rossow, W.

1987-01-01

To study the statistical behavior of clouds for different climate regimes, the spatial and temporal stability of VIS-IR bidimensional histograms is tested. Also, the effect of data sampling and averaging on the histogram shapes is considered; in particular the sampling strategy used by the International Satellite Cloud Climatology Project is tested.

Interpreting Histograms. As Easy as It Seems?

ERIC Educational Resources Information Center

Lem, Stephanie; Onghena, Patrick; Verschaffel, Lieven; Van Dooren, Wim

2014-01-01

Histograms are widely used, but recent studies have shown that they are not as easy to interpret as it might seem. In this article, we report on three studies on the interpretation of histograms in which we investigated, namely, (1) whether the misinterpretation by university students can be considered to be the result of heuristic reasoning, (2)…

Improving Real World Performance of Vision Aided Navigation in a Flight Environment

DTIC Science & Technology

2016-09-15

Introduction . . . . . . . 63 4.2 Wide Area Search Extent . . . . . . . . . . . . . . . . . 64 4.3 Large-Scale Image Navigation Histogram Filter ...65 4.3.1 Location Model . . . . . . . . . . . . . . . . . . 66 4.3.2 Measurement Model . . . . . . . . . . . . . . . 66 4.3.3 Histogram Filter ...Iteration of Histogram Filter . . . . . . . . . . . 70 4.4 Implementation and Flight Test Campaign . . . . . . . . 71 4.4.1 Software Implementation

Airborne gamma-ray spectrometer and magnetometer survey, Durango A, Colorado. Final report Volume II A. Detail area

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

Not Available

1983-01-01

This volume contains geology of the Durango A detail area, radioactive mineral occurences in Colorado, and geophysical data interpretation. Eight appendices provide the following: stacked profiles, geologic histograms, geochemical histograms, speed and altitude histograms, geologic statistical tables, geochemical statistical tables, magnetic and ancillary profiles, and test line data.

Airborne gamma-ray spectrometer and magnetometer survey, Durango B, Colorado. Final report Volume II A. Detail area

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

Not Available

1983-01-01

The geology of the Durango B detail area, the radioactive mineral occurrences in Colorado and the geophysical data interpretation are included in this report. Seven appendices contain: stacked profiles, geologic histograms, geochemical histograms, speed and altitude histograms, geologic statistical tables, geochemical statistical tables, and test line data.

Students' Understanding of Bar Graphs and Histograms: Results from the LOCUS Assessments

ERIC Educational Resources Information Center

Whitaker, Douglas; Jacobbe, Tim

2017-01-01

Bar graphs and histograms are core statistical tools that are widely used in statistical practice and commonly taught in classrooms. Despite their importance and the instructional time devoted to them, many students demonstrate misunderstandings when asked to read and interpret bar graphs and histograms. Much of the research that has been…

Histogram analysis derived from apparent diffusion coefficient (ADC) is more sensitive to reflect serological parameters in myositis than conventional ADC analysis.

PubMed

Meyer, Hans Jonas; Emmer, Alexander; Kornhuber, Malte; Surov, Alexey

2018-05-01

Diffusion-weighted imaging (DWI) has the potential of being able to reflect histopathology architecture. A novel imaging approach, namely histogram analysis, is used to further characterize tissues on MRI. The aim of this study was to correlate histogram parameters derived from apparent diffusion coefficient (ADC) maps with serological parameters in myositis. 16 patients with autoimmune myositis were included in this retrospective study. DWI was obtained on a 1.5 T scanner by using the b-values of 0 and 1000 s mm - 2 . Histogram analysis was performed as a whole muscle measurement by using a custom-made Matlab-based application. The following ADC histogram parameters were estimated: ADCmean, ADCmax, ADCmin, ADCmedian, ADCmode, and the following percentiles ADCp10, ADCp25, ADCp75, ADCp90, as well histogram parameters kurtosis, skewness, and entropy. In all patients, the blood sample was acquired within 3 days to the MRI. The following serological parameters were estimated: alanine aminotransferase, aspartate aminotransferase, creatine kinase, lactate dehydrogenase, C-reactive protein (CRP) and myoglobin. All patients were screened for Jo1-autobodies. Kurtosis correlated inversely with CRP (p = -0.55 and 0.03). Furthermore, ADCp10 and ADCp90 values tended to correlate with creatine kinase (p = -0.43, 0.11, and p = -0.42, = 0.12 respectively). In addition, ADCmean, p10, p25, median, mode, and entropy were different between Jo1-positive and Jo1-negative patients. ADC histogram parameters are sensitive for detection of muscle alterations in myositis patients. Advances in knowledge: This study identified that kurtosis derived from ADC maps is associated with CRP in myositis patients. Furthermore, several ADC histogram parameters are statistically different between Jo1-positive and Jo1-negative patients.

Can histogram analysis of MR images predict aggressiveness in pancreatic neuroendocrine tumors?

PubMed

De Robertis, Riccardo; Maris, Bogdan; Cardobi, Nicolò; Tinazzi Martini, Paolo; Gobbo, Stefano; Capelli, Paola; Ortolani, Silvia; Cingarlini, Sara; Paiella, Salvatore; Landoni, Luca; Butturini, Giovanni; Regi, Paolo; Scarpa, Aldo; Tortora, Giampaolo; D'Onofrio, Mirko

2018-06-01

To evaluate MRI derived whole-tumour histogram analysis parameters in predicting pancreatic neuroendocrine neoplasm (panNEN) grade and aggressiveness. Pre-operative MR of 42 consecutive patients with panNEN >1 cm were retrospectively analysed. T1-/T2-weighted images and ADC maps were analysed. Histogram-derived parameters were compared to histopathological features using the Mann-Whitney U test. Diagnostic accuracy was assessed by ROC-AUC analysis; sensitivity and specificity were assessed for each histogram parameter. ADC entropy was significantly higher in G2-3 tumours with ROC-AUC 0.757; sensitivity and specificity were 83.3 % (95 % CI: 61.2-94.5) and 61.1 % (95 % CI: 36.1-81.7). ADC kurtosis was higher in panNENs with vascular involvement, nodal and hepatic metastases (p= .008, .021 and .008; ROC-AUC= 0.820, 0.709 and 0.820); sensitivity and specificity were: 85.7/74.3 % (95 % CI: 42-99.2 /56.4-86.9), 36.8/96.5 % (95 % CI: 17.2-61.4 /76-99.8) and 100/62.8 % (95 % CI: 56.1-100/44.9-78.1). No significant differences between groups were found for other histogram-derived parameters (p >.05). Whole-tumour histogram analysis of ADC maps may be helpful in predicting tumour grade, vascular involvement, nodal and liver metastases in panNENs. ADC entropy and ADC kurtosis are the most accurate parameters for identification of panNENs with malignant behaviour. • Whole-tumour ADC histogram analysis can predict aggressiveness in pancreatic neuroendocrine neoplasms. • ADC entropy and kurtosis are higher in aggressive tumours. • ADC histogram analysis can quantify tumour diffusion heterogeneity. • Non-invasive quantification of tumour heterogeneity can provide adjunctive information for prognostication.

Non-small cell lung cancer: Whole-lesion histogram analysis of the apparent diffusion coefficient for assessment of tumor grade, lymphovascular invasion and pleural invasion.

PubMed

Tsuchiya, Naoko; Doai, Mariko; Usuda, Katsuo; Uramoto, Hidetaka; Tonami, Hisao

2017-01-01

Investigating the diagnostic accuracy of histogram analyses of apparent diffusion coefficient (ADC) values for determining non-small cell lung cancer (NSCLC) tumor grades, lymphovascular invasion, and pleural invasion. We studied 60 surgically diagnosed NSCLC patients. Diffusion-weighted imaging (DWI) was performed in the axial plane using a navigator-triggered single-shot, echo-planar imaging sequence with prospective acquisition correction. The ADC maps were generated, and we placed a volume-of-interest on the tumor to construct the whole-lesion histogram. Using the histogram, we calculated the mean, 5th, 10th, 25th, 50th, 75th, 90th, and 95th percentiles of ADC, skewness, and kurtosis. Histogram parameters were correlated with tumor grade, lymphovascular invasion, and pleural invasion. We performed a receiver operating characteristics (ROC) analysis to assess the diagnostic performance of histogram parameters for distinguishing different pathologic features. The ADC mean, 10th, 25th, 50th, 75th, 90th, and 95th percentiles showed significant differences among the tumor grades. The ADC mean, 25th, 50th, 75th, 90th, and 95th percentiles were significant histogram parameters between high- and low-grade tumors. The ROC analysis between high- and low-grade tumors showed that the 95th percentile ADC achieved the highest area under curve (AUC) at 0.74. Lymphovascular invasion was associated with the ADC mean, 50th, 75th, 90th, and 95th percentiles, skewness, and kurtosis. Kurtosis achieved the highest AUC at 0.809. Pleural invasion was only associated with skewness, with the AUC of 0.648. ADC histogram analyses on the basis of the entire tumor volume are able to stratify NSCLCs' tumor grade, lymphovascular invasion and pleural invasion.

Haralick texture features from apparent diffusion coefficient (ADC) MRI images depend on imaging and pre-processing parameters.

PubMed

Brynolfsson, Patrik; Nilsson, David; Torheim, Turid; Asklund, Thomas; Karlsson, Camilla Thellenberg; Trygg, Johan; Nyholm, Tufve; Garpebring, Anders

2017-06-22

In recent years, texture analysis of medical images has become increasingly popular in studies investigating diagnosis, classification and treatment response assessment of cancerous disease. Despite numerous applications in oncology and medical imaging in general, there is no consensus regarding texture analysis workflow, or reporting of parameter settings crucial for replication of results. The aim of this study was to assess how sensitive Haralick texture features of apparent diffusion coefficient (ADC) MR images are to changes in five parameters related to image acquisition and pre-processing: noise, resolution, how the ADC map is constructed, the choice of quantization method, and the number of gray levels in the quantized image. We found that noise, resolution, choice of quantization method and the number of gray levels in the quantized images had a significant influence on most texture features, and that the effect size varied between different features. Different methods for constructing the ADC maps did not have an impact on any texture feature. Based on our results, we recommend using images with similar resolutions and noise levels, using one quantization method, and the same number of gray levels in all quantized images, to make meaningful comparisons of texture feature results between different subjects.

Classification of CT examinations for COPD visual severity analysis

NASA Astrophysics Data System (ADS)

Tan, Jun; Zheng, Bin; Wang, Xingwei; Pu, Jiantao; Gur, David; Sciurba, Frank C.; Leader, J. Ken

2012-03-01

In this study we present a computational method of CT examination classification into visual assessed emphysema severity. The visual severity categories ranged from 0 to 5 and were rated by an experienced radiologist. The six categories were none, trace, mild, moderate, severe and very severe. Lung segmentation was performed for every input image and all image features are extracted from the segmented lung only. We adopted a two-level feature representation method for the classification. Five gray level distribution statistics, six gray level co-occurrence matrix (GLCM), and eleven gray level run-length (GLRL) features were computed for each CT image depicted segment lung. Then we used wavelets decomposition to obtain the low- and high-frequency components of the input image, and again extract from the lung region six GLCM features and eleven GLRL features. Therefore our feature vector length is 56. The CT examinations were classified using the support vector machine (SVM) and k-nearest neighbors (KNN) and the traditional threshold (density mask) approach. The SVM classifier had the highest classification performance of all the methods with an overall sensitivity of 54.4% and a 69.6% sensitivity to discriminate "no" and "trace visually assessed emphysema. We believe this work may lead to an automated, objective method to categorically classify emphysema severity on CT exam.

Improved automatic adjustment of density and contrast in FCR system using neural network

NASA Astrophysics Data System (ADS)

Takeo, Hideya; Nakajima, Nobuyoshi; Ishida, Masamitsu; Kato, Hisatoyo

1994-05-01

FCR system has an automatic adjustment of image density and contrast by analyzing the histogram of image data in the radiation field. Advanced image recognition methods proposed in this paper can improve the automatic adjustment performance, in which neural network technology is used. There are two methods. Both methods are basically used 3-layer neural network with back propagation. The image data are directly input to the input-layer in one method and the histogram data is input in the other method. The former is effective to the imaging menu such as shoulder joint in which the position of interest region occupied on the histogram changes by difference of positioning and the latter is effective to the imaging menu such as chest-pediatrics in which the histogram shape changes by difference of positioning. We experimentally confirm the validity of these methods (about the automatic adjustment performance) as compared with the conventional histogram analysis methods.

Investigation on improved infrared image detail enhancement algorithm based on adaptive histogram statistical stretching and gradient filtering

NASA Astrophysics Data System (ADS)

Zeng, Bangze; Zhu, Youpan; Li, Zemin; Hu, Dechao; Luo, Lin; Zhao, Deli; Huang, Juan

2014-11-01

Duo to infrared image with low contrast, big noise and unclear visual effect, target is very difficult to observed and identified. This paper presents an improved infrared image detail enhancement algorithm based on adaptive histogram statistical stretching and gradient filtering (AHSS-GF). Based on the fact that the human eyes are very sensitive to the edges and lines, the author proposed to extract the details and textures by using the gradient filtering. New histogram could be acquired by calculating the sum of original histogram based on fixed window. With the minimum value for cut-off point, author carried on histogram statistical stretching. After the proper weights given to the details and background, the detail-enhanced results could be acquired finally. The results indicate image contrast could be improved and the details and textures could be enhanced effectively as well.

Behavioral responses of gray whales to industrial noise: feeding observations and predictive modeling

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

Malme, C.I.; Wuersig, B.; Bird, J.E.

1986-08-01

An investigation was made of the potential effects of underwater noise from petroleum-industry activities on feeding gray whales. The investigation consisted of two components, a field study and an acoustic model study. The field study was performed near Southeast Cape, St. Lawrence Island in August, 1985, using a 100 cu. in. air gun source and playback of drillship noise. Sound-source levels and acoustic-propagation losses were measured to permit estimation of sound exposure levels at whale-sighting positions. For the air-gun source there was a 0.5 probability that the whales would stop feeding and move away from the area when the averagemore » pulse levels reached 173 dB.« less

Geiger-mode APD camera system for single-photon 3D LADAR imaging

NASA Astrophysics Data System (ADS)

Entwistle, Mark; Itzler, Mark A.; Chen, Jim; Owens, Mark; Patel, Ketan; Jiang, Xudong; Slomkowski, Krystyna; Rangwala, Sabbir

2012-06-01

The unparalleled sensitivity of 3D LADAR imaging sensors based on single photon detection provides substantial benefits for imaging at long stand-off distances and minimizing laser pulse energy requirements. To obtain 3D LADAR images with single photon sensitivity, we have demonstrated focal plane arrays (FPAs) based on InGaAsP Geiger-mode avalanche photodiodes (GmAPDs) optimized for use at either 1.06 μm or 1.55 μm. These state-of-the-art FPAs exhibit excellent pixel-level performance and the capability for 100% pixel yield on a 32 x 32 format. To realize the full potential of these FPAs, we have recently developed an integrated camera system providing turnkey operation based on FPGA control. This system implementation enables the extremely high frame-rate capability of the GmAPD FPA, and frame rates in excess of 250 kHz (for 0.4 μs range gates) can be accommodated using an industry-standard CameraLink interface in full configuration. Real-time data streaming for continuous acquisition of 2 μs range gate point cloud data with 13-bit time-stamp resolution at 186 kHz frame rates has been established using multiple solid-state storage drives. Range gate durations spanning 4 ns to 10 μs provide broad operational flexibility. The camera also provides real-time signal processing in the form of multi-frame gray-scale contrast images and single-frame time-stamp histograms, and automated bias control has been implemented to maintain a constant photon detection efficiency in the presence of ambient temperature changes. A comprehensive graphical user interface has been developed to provide complete camera control using a simple serial command set, and this command set supports highly flexible end-user customization.

Automatic seed picking for brachytherapy postimplant validation with 3D CT images.

PubMed

Zhang, Guobin; Sun, Qiyuan; Jiang, Shan; Yang, Zhiyong; Ma, Xiaodong; Jiang, Haisong

2017-11-01

Postimplant validation is an indispensable part in the brachytherapy technique. It provides the necessary feedback to ensure the quality of operation. The ability to pick implanted seed relates directly to the accuracy of validation. To address it, an automatic approach is proposed for picking implanted brachytherapy seeds in 3D CT images. In order to pick seed configuration (location and orientation) efficiently, the approach starts with the segmentation of seed from CT images using a thresholding filter which based on gray-level histogram. Through the process of filtering and denoising, the touching seed and single seed are classified. The true novelty of this approach is found in the application of the canny edge detection and improved concave points matching algorithm to separate touching seeds. Through the computation of image moments, the seed configuration can be determined efficiently. Finally, two different experiments are designed to verify the performance of the proposed approach: (1) physical phantom with 60 model seeds, and (2) patient data with 16 cases. Through assessment of validated results by a medical physicist, the proposed method exhibited promising results. Experiment on phantom demonstrates that the error of seed location and orientation is within ([Formula: see text]) mm and ([Formula: see text])[Formula: see text], respectively. In addition, the most seed location and orientation error is controlled within 0.8 mm and 3.5[Formula: see text] in all cases, respectively. The average process time of seed picking is 8.7 s per 100 seeds. In this paper, an automatic, efficient and robust approach, performed on CT images, is proposed to determine the implanted seed location as well as orientation in a 3D workspace. Through the experiments with phantom and patient data, this approach also successfully exhibits good performance.

Military personnel recognition system using texture, colour, and SURF features

NASA Astrophysics Data System (ADS)

Irhebhude, Martins E.; Edirisinghe, Eran A.

2014-06-01

This paper presents an automatic, machine vision based, military personnel identification and classification system. Classification is done using a Support Vector Machine (SVM) on sets of Army, Air Force and Navy camouflage uniform personnel datasets. In the proposed system, the arm of service of personnel is recognised by the camouflage of a persons uniform, type of cap and the type of badge/logo. The detailed analysis done include; camouflage cap and plain cap differentiation using gray level co-occurrence matrix (GLCM) texture feature; classification on Army, Air Force and Navy camouflaged uniforms using GLCM texture and colour histogram bin features; plain cap badge classification into Army, Air Force and Navy using Speed Up Robust Feature (SURF). The proposed method recognised camouflage personnel arm of service on sets of data retrieved from google images and selected military websites. Correlation-based Feature Selection (CFS) was used to improve recognition and reduce dimensionality, thereby speeding the classification process. With this method success rates recorded during the analysis include 93.8% for camouflage appearance category, 100%, 90% and 100% rates of plain cap and camouflage cap categories for Army, Air Force and Navy categories, respectively. Accurate recognition was recorded using SURF for the plain cap badge category. Substantial analysis has been carried out and results prove that the proposed method can correctly classify military personnel into various arms of service. We show that the proposed method can be integrated into a face recognition system, which will recognise personnel in addition to determining the arm of service which the personnel belong. Such a system can be used to enhance the security of a military base or facility.

Measurement and statistical analysis of single-molecule current-voltage characteristics, transition voltage spectroscopy, and tunneling barrier height.

PubMed

Guo, Shaoyin; Hihath, Joshua; Díez-Pérez, Ismael; Tao, Nongjian

2011-11-30

We report on the measurement and statistical study of thousands of current-voltage characteristics and transition voltage spectra (TVS) of single-molecule junctions with different contact geometries that are rapidly acquired using a new break junction method at room temperature. This capability allows one to obtain current-voltage, conductance voltage, and transition voltage histograms, thus adding a new dimension to the previous conductance histogram analysis at a fixed low-bias voltage for single molecules. This method confirms the low-bias conductance values of alkanedithiols and biphenyldithiol reported in literature. However, at high biases the current shows large nonlinearity and asymmetry, and TVS allows for the determination of a critically important parameter, the tunneling barrier height or energy level alignment between the molecule and the electrodes of single-molecule junctions. The energy level alignment is found to depend on the molecule and also on the contact geometry, revealing the role of contact geometry in both the contact resistance and energy level alignment of a molecular junction. Detailed statistical analysis further reveals that, despite the dependence of the energy level alignment on contact geometry, the variation in single-molecule conductance is primarily due to contact resistance rather than variations in the energy level alignment.

Grayscale photomask fabricated by laser direct writing in metallic nano-films.

PubMed

Guo, Chuan Fei; Cao, Sihai; Jiang, Peng; Fang, Ying; Zhang, Jianming; Fan, Yongtao; Wang, Yongsheng; Xu, Wendong; Zhao, Zhensheng; Liu, Qian

2009-10-26

The grayscale photomask plays a key role in grayscale lithography for creating 3D microstructures like micro-optical elements and MEMS structures, but how to fabricate grayscale masks in a cost-effective way is still a big challenge. Here we present novel low cost grayscale masks created in a two-step method by laser direct writing on Sn nano-films, which demonstrate continuous-tone gray levels depended on writing powers. The mechanism of the gray levels is due to the coexistence of the metal and the oxides formed in a laser-induced thermal process. The photomasks reveal good technical properties in fabricating 3D microstructures for practical applications.

Serum vitamin D and hippocampal gray matter volume in schizophrenia.

PubMed

Shivakumar, Venkataram; Kalmady, Sunil V; Amaresha, Anekal C; Jose, Dania; Narayanaswamy, Janardhanan C; Agarwal, Sri Mahavir; Joseph, Boban; Venkatasubramanian, Ganesan; Ravi, Vasanthapuram; Keshavan, Matcheri S; Gangadhar, Bangalore N

2015-08-30

Disparate lines of evidence including epidemiological and case-control studies have increasingly implicated vitamin D in the pathogenesis of schizophrenia. Vitamin D deficiency can lead to dysfunction of the hippocampus--a brain region hypothesized to be critically involved in schizophrenia. In this study, we examined for potential association between serum vitamin D level and hippocampal gray matter volume in antipsychotic-naïve or antipsychotic-free schizophrenia patients (n = 35). Serum vitamin D level was estimated using 25-OH vitamin D immunoassay. Optimized voxel-based morphometry was used to analyze 3-Tesla magnetic resonance imaging (MRI) (1-mm slice thickness). Ninety-seven percent of the schizophrenia patients (n = 34) had sub-optimal levels of serum vitamin D (83%, deficiency; 14%, insufficiency). A significant positive correlation was seen between vitamin D and regional gray matter volume in the right hippocampus after controlling for age, years of education and total intracranial volume (Montreal Neurological Institute (MNI) coordinates: x = 35, y = -18, z = -8; t = 4.34 pFWE(Corrected) = 0.018). These observations support a potential role of vitamin D deficiency in mediating hippocampal volume deficits, possibly through neurotrophic, neuroimmunomodulatory and glutamatergic effects. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Stroke-model-based character extraction from gray-level document images.

PubMed

Ye, X; Cheriet, M; Suen, C Y

2001-01-01

Global gray-level thresholding techniques such as Otsu's method, and local gray-level thresholding techniques such as edge-based segmentation or the adaptive thresholding method are powerful in extracting character objects from simple or slowly varying backgrounds. However, they are found to be insufficient when the backgrounds include sharply varying contours or fonts in different sizes. A stroke-model is proposed to depict the local features of character objects as double-edges in a predefined size. This model enables us to detect thin connected components selectively, while ignoring relatively large backgrounds that appear complex. Meanwhile, since the stroke width restriction is fully factored in, the proposed technique can be used to extract characters in predefined font sizes. To process large volumes of documents efficiently, a hybrid method is proposed for character extraction from various backgrounds. Using the measurement of class separability to differentiate images with simple backgrounds from those with complex backgrounds, the hybrid method can process documents with different backgrounds by applying the appropriate methods. Experiments on extracting handwriting from a check image, as well as machine-printed characters from scene images demonstrate the effectiveness of the proposed model.

Research of image retrieval technology based on color feature

NASA Astrophysics Data System (ADS)

Fu, Yanjun; Jiang, Guangyu; Chen, Fengying

2009-10-01

Recently, with the development of the communication and the computer technology and the improvement of the storage technology and the capability of the digital image equipment, more and more image resources are given to us than ever. And thus the solution of how to locate the proper image quickly and accurately is wanted.The early method is to set up a key word for searching in the database, but now the method has become very difficult when we search much more picture that we need. In order to overcome the limitation of the traditional searching method, content based image retrieval technology was aroused. Now, it is a hot research subject.Color image retrieval is the important part of it. Color is the most important feature for color image retrieval. Three key questions on how to make use of the color characteristic are discussed in the paper: the expression of color, the abstraction of color characteristic and the measurement of likeness based on color. On the basis, the extraction technology of the color histogram characteristic is especially discussed. Considering the advantages and disadvantages of the overall histogram and the partition histogram, a new method based the partition-overall histogram is proposed. The basic thought of it is to divide the image space according to a certain strategy, and then calculate color histogram of each block as the color feature of this block. Users choose the blocks that contain important space information, confirming the right value. The system calculates the distance between the corresponding blocks that users choosed. Other blocks merge into part overall histograms again, and the distance should be calculated. Then accumulate all the distance as the real distance between two pictures. The partition-overall histogram comprehensive utilizes advantages of two methods above, by choosing blocks makes the feature contain more spatial information which can improve performance; the distances between partition-overall histogram make rotating and translation does not change. The HSV color space is used to show color characteristic of image, which is suitable to the visual characteristic of human. Taking advance of human's feeling to color, it quantifies color sector with unequal interval, and get characteristic vector. Finally, it matches the similarity of image with the algorithm of the histogram intersection and the partition-overall histogram. Users can choose a demonstration image to show inquired vision require, and also can adjust several right value through the relevance-feedback method to obtain the best result of search.An image retrieval system based on these approaches is presented. The result of the experiments shows that the image retrieval based on partition-overall histogram can keep the space distribution information while abstracting color feature efficiently, and it is superior to the normal color histograms in precision rate while researching. The query precision rate is more than 95%. In addition, the efficient block expression will lower the complicate degree of the images to be searched, and thus the searching efficiency will be increased. The image retrieval algorithms based on the partition-overall histogram proposed in the paper is efficient and effective.

A pilot study using an infrared imaging system in prevention of post-endoscopic submucosal dissection ulcer bleeding.

PubMed

Yoshida, Yukinaga; Matsuda, Koji; Tamai, Naoto; Yoshizawa, Kai; Nikami, Toshiki; Ishiguro, Haruya; Tajiri, Hisao

2014-01-01

Endoscopic submucosal dissection (ESD) for superficial gastric neoplasm is a curative method. The aim of this study was to detect potential nonbleeding visible vessels (NBVVs) by using an infrared imaging (IRI) system. A total of 24 patients (25 lesions) were consecutively enrolled between March 2010 and December 2010. The day after ESD, endoscopist A (K.M.), who was blinded to the actual procedure of ESD, performed esophagogastroduodenoscopy (EGD) of the post-ESD ulcer base using the IRI system. Endoscopist A marked gray/blue points in the hard-copy images with the IRI system. After the first procedure, endoscopist B (Y.Y.), who was blinded to the results recorded by endoscopist A, performed a second EGD with white light endoscopy and administered water-jet pressure with the maximum level of an Olympus flushing pump onto the post-ESD ulcer base. This test can cause iatrogenic bleeding via application of pressure to NBVV in the post-ESD ulcer. The IRI system detected 58 gray points and 71 blue points. The post-ESD ulcer was divided into the central area and the peripheral area. There were 14 gray points (24 %) in the central area and 44 gray points (76 %) in the peripheral area. There were 19 blue points (27 %) in the central area and 52 blue points (73 %) in the peripheral area. There was no significant difference when comparing the distribution of gray points and blue points. Bleeding occurred with a water-jet pressure in 11 of 58 gray points and in none of the blue points (P = 0.000478). Among the gray points, bleeding in response to a water-jet pressure occurred in 2 points in the central area and in 9 points in the peripheral area. The IRI system detects visible vessels (VVs) that are in no need of coagulation as blue points, and VVs have a potential risk of bleeding as gray points.

Combining Segmented Grey and White Matter Images Improves Voxel-based Morphometry for the Case of Dilated Lateral Ventricles.

PubMed

Goto, Masami; Abe, Osamu; Aoki, Shigeki; Kamagata, Koji; Hori, Masaaki; Miyati, Tosiaki; Gomi, Tsutomu; Takeda, Tohoru

2018-01-18

To evaluate the error in segmented tissue images and to show the usefulness of the brain image in voxel-based morphometry (VBM) using Statistical Parametric Mapping (SPM) 12 software and 3D T 1 -weighted magnetic resonance images (3D-T 1 WIs) processed to simulate idiopathic normal pressure hydrocephalus (iNPH). VBM analysis was performed on sagittal 3D-T 1 WIs obtained in 22 healthy volunteers using a 1.5T MR scanner. Regions of interest for the lateral ventricles of all subjects were carefully outlined on the original 3D-T 1 WIs, and two types of simulated 3D-T 1 WI were also prepared (non-dilated 3D-T 1 WI as normal control and dilated 3D-T 1 WI to simulate iNPH). All simulated 3D-T 1 WIs were segmented into gray matter, white matter, and cerebrospinal fluid images, and normalized to standard space. A brain image was made by adding the gray and white matter images. After smoothing with a 6-mm isotropic Gaussian kernel, group comparisons (dilated vs non-dilated) were made for gray and white matter, cerebrospinal fluid, and brain images using a paired t-test. In evaluation of tissue volume, estimation error was larger using gray or white matter images than using the brain image, and estimation errors in gray and white matter volume change were found for the brain surface. To our knowledge, this is the first VBM study to show the possibility that VBM of gray and white matter volume on the brain surface may be more affected by individual differences in the level of dilation of the lateral ventricles than by individual differences in gray and white matter volumes. We recommend that VBM evaluation in patients with iNPH should be performed using the brain image rather than the gray and white matter images.

Spline smoothing of histograms by linear programming

NASA Technical Reports Server (NTRS)

Bennett, J. O.

1972-01-01

An algorithm for an approximating function to the frequency distribution is obtained from a sample of size n. To obtain the approximating function a histogram is made from the data. Next, Euclidean space approximations to the graph of the histogram using central B-splines as basis elements are obtained by linear programming. The approximating function has area one and is nonnegative.

Histogram analysis of greyscale sonograms to differentiate between the subtypes of follicular variant of papillary thyroid cancer.

PubMed

Kwon, M-R; Shin, J H; Hahn, S Y; Oh, Y L; Kwak, J Y; Lee, E; Lim, Y

2018-06-01

To evaluate the diagnostic value of histogram analysis using ultrasound (US) to differentiate between the subtypes of follicular variant of papillary thyroid carcinoma (FVPTC). The present study included 151 patients with surgically confirmed FVPTC diagnosed between January 2014 and May 2016. Their preoperative US features were reviewed retrospectively. Histogram parameters (mean, maximum, minimum, range, root mean square, skewness, kurtosis, energy, entropy, and correlation) were obtained for each nodule. The 152 nodules in 151 patients comprised 48 non-invasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTPs; 31.6%), 60 invasive encapsulated FVPTCs (EFVPTCs; 39.5%), and 44 infiltrative FVPTCs (28.9%). The US features differed significantly between the subtypes of FVPTC. Discrimination was achieved between NIFTPs and infiltrative FVPTC, and between invasive EFVPTC and infiltrative FVPTC using histogram parameters; however, the parameters were not significantly different between NIFTP and invasive EFVPTC. It is feasible to use greyscale histogram analysis to differentiate between NIFTP and infiltrative FVPTC, but not between NIFTP and invasive EFVPTC. Histograms can be used as a supplementary tool to differentiate the subtypes of FVPTC. Copyright © 2017 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

DSP+FPGA-based real-time histogram equalization system of infrared image

NASA Astrophysics Data System (ADS)

Gu, Dongsheng; Yang, Nansheng; Pi, Defu; Hua, Min; Shen, Xiaoyan; Zhang, Ruolan

2001-10-01

Histogram Modification is a simple but effective method to enhance an infrared image. There are several methods to equalize an infrared image's histogram due to the different characteristics of the different infrared images, such as the traditional HE (Histogram Equalization) method, and the improved HP (Histogram Projection) and PE (Plateau Equalization) method and so on. If to realize these methods in a single system, the system must have a mass of memory and extremely fast speed. In our system, we introduce a DSP + FPGA based real-time procession technology to do these things together. FPGA is used to realize the common part of these methods while DSP is to do the different part. The choice of methods and the parameter can be input by a keyboard or a computer. By this means, the function of the system is powerful while it is easy to operate and maintain. In this article, we give out the diagram of the system and the soft flow chart of the methods. And at the end of it, we give out the infrared image and its histogram before and after the process of HE method.

Detection of License Plate using Sliding Window, Histogram of Oriented Gradient, and Support Vector Machines Method

NASA Astrophysics Data System (ADS)

Astawa, INGA; Gusti Ngurah Bagus Caturbawa, I.; Made Sajayasa, I.; Dwi Suta Atmaja, I. Made Ari

2018-01-01

The license plate recognition usually used as part of system such as parking system. License plate detection considered as the most important step in the license plate recognition system. We propose methods that can be used to detect the vehicle plate on mobile phone. In this paper, we used Sliding Window, Histogram of Oriented Gradient (HOG), and Support Vector Machines (SVM) method to license plate detection so it will increase the detection level even though the image is not in a good quality. The image proceed by Sliding Window method in order to find plate position. Feature extraction in every window movement had been done by HOG and SVM method. Good result had shown in this research, which is 96% of accuracy.

Gray matter abnormalities in opioid-dependent patients: A neuroimaging meta-analysis.

PubMed

Wollman, Scott C; Alhassoon, Omar M; Hall, Matthew G; Stern, Mark J; Connors, Eric J; Kimmel, Christine L; Allen, Kenneth E; Stephan, Rick A; Radua, Joaquim

2017-09-01

Prior research utilizing whole-brain neuroimaging techniques has identified structural differences in gray matter in opioid-dependent individuals. However, the results have been inconsistent. The current study meta-analytically examines the neuroimaging findings of studies published before 2016 comparing opioid-dependent individuals to drug-naïve controls. Exhaustive search of five databases yielded 12 studies that met inclusion criteria. Anisotropic Effect-Size Seed-Based d Mapping (AES-SDM) was used to analyze the data extracted by three independent researchers. Voxel-based AES-SDM distinguishes increases and decreases in brain matter significant at the whole-brain level. AES-SDM identified the fronto-temporal region, bilaterally, as being the primary site of gray matter deficits associated with opioid use. Moderator analysis revealed that length of opioid use was negatively associated with gray matter in the left cerebellar vermis and the right Rolandic operculum, including the insula. Meta-regression revealed no remaining significant areas of gray matter reductions, except in the precuneus, following longer abstinence from opioids. Opioid-dependent individuals had significantly less gray matter in several regions that play a key role in cognitive and affective processing. The findings provide evidence that opioid dependence may result in the breakdown of two distinct yet highly overlapping structural and functional systems. These are the fronto-cerebellar system that might be more responsible for impulsivity, compulsive behaviors, and affective disturbances and the fronto-insular system that might account more for the cognitive and decision-making impairments.

Higher gamma-aminobutyric acid neuron density in the white matter of orbital frontal cortex in schizophrenia.

PubMed

Joshi, Dipesh; Fung, Samantha J; Rothwell, Alice; Weickert, Cynthia Shannon

2012-11-01

In the orbitofrontal cortex (OFC), reduced gray matter volume and reduced glutamic acid decarboxylase 67kDa isoform (GAD67) messenger (m)RNA are found in schizophrenia; however, how these alterations relate to developmental pathology of interneurons is unclear. The present study therefore aimed to determine if increased interstitial white matter neuron (IWMN) density exists in the OFC; whether gamma-aminobutyric acid (GABA)ergic neuron density in OFC white matter was altered; and how IWMN density may be related to an early-expressed inhibitory neuron marker, Dlx1, in OFC gray matter in schizophrenia. IWMN densities were determined (38 schizophrenia and 38 control subjects) for neuronal nuclear antigen (NeuN+) and 65/67 kDa isoform of glutamic acid decarboxylase immunopositive (GAD65/67+) neurons. In situ hybridization was performed to determine Dlx1 and GAD67 mRNA expression in the OFC gray matter. NeuN and GAD65/67 immunopositive cell density was significantly increased in the superficial white matter in schizophrenia. Gray matter Dlx1 and GAD67 mRNA expression were reduced in schizophrenia. Dlx1 mRNA levels were negatively correlated with GAD65/67 IWMN density. Our study provides evidence that pathology of IWMNs in schizophrenia includes GABAergic interneurons and that increased IWMN density may be related to GABAergic deficits in the overlying gray matter. These findings provide evidence at the cellular level that the OFC is a site of pathology in schizophrenia and support the hypothesis that inappropriate migration of cortical inhibitory interneurons occurs in schizophrenia. Copyright © 2012 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Histogram Analysis of Diffusion Weighted Imaging at 3T is Useful for Prediction of Lymphatic Metastatic Spread, Proliferative Activity, and Cellularity in Thyroid Cancer.

PubMed

Schob, Stefan; Meyer, Hans Jonas; Dieckow, Julia; Pervinder, Bhogal; Pazaitis, Nikolaos; Höhn, Anne Kathrin; Garnov, Nikita; Horvath-Rizea, Diana; Hoffmann, Karl-Titus; Surov, Alexey

2017-04-12

Pre-surgical diffusion weighted imaging (DWI) is increasingly important in the context of thyroid cancer for identification of the optimal treatment strategy. It has exemplarily been shown that DWI at 3T can distinguish undifferentiated from well-differentiated thyroid carcinoma, which has decisive implications for the magnitude of surgery. This study used DWI histogram analysis of whole tumor apparent diffusion coefficient (ADC) maps. The primary aim was to discriminate thyroid carcinomas which had already gained the capacity to metastasize lymphatically from those not yet being able to spread via the lymphatic system. The secondary aim was to reflect prognostically important tumor-biological features like cellularity and proliferative activity with ADC histogram analysis. Fifteen patients with follicular-cell derived thyroid cancer were enrolled. Lymph node status, extent of infiltration of surrounding tissue, and Ki-67 and p53 expression were assessed in these patients. DWI was obtained in a 3T system using b values of 0, 400, and 800 s/mm². Whole tumor ADC volumes were analyzed using a histogram-based approach. Several ADC parameters showed significant correlations with immunohistopathological parameters. Most importantly, ADC histogram skewness and ADC histogram kurtosis were able to differentiate between nodal negative and nodal positive thyroid carcinoma. histogram analysis of whole ADC tumor volumes has the potential to provide valuable information on tumor biology in thyroid carcinoma. However, further studies are warranted.

Histogram Analysis of Diffusion Weighted Imaging at 3T is Useful for Prediction of Lymphatic Metastatic Spread, Proliferative Activity, and Cellularity in Thyroid Cancer

PubMed Central

Schob, Stefan; Meyer, Hans Jonas; Dieckow, Julia; Pervinder, Bhogal; Pazaitis, Nikolaos; Höhn, Anne Kathrin; Garnov, Nikita; Horvath-Rizea, Diana; Hoffmann, Karl-Titus; Surov, Alexey

2017-01-01

Pre-surgical diffusion weighted imaging (DWI) is increasingly important in the context of thyroid cancer for identification of the optimal treatment strategy. It has exemplarily been shown that DWI at 3T can distinguish undifferentiated from well-differentiated thyroid carcinoma, which has decisive implications for the magnitude of surgery. This study used DWI histogram analysis of whole tumor apparent diffusion coefficient (ADC) maps. The primary aim was to discriminate thyroid carcinomas which had already gained the capacity to metastasize lymphatically from those not yet being able to spread via the lymphatic system. The secondary aim was to reflect prognostically important tumor-biological features like cellularity and proliferative activity with ADC histogram analysis. Fifteen patients with follicular-cell derived thyroid cancer were enrolled. Lymph node status, extent of infiltration of surrounding tissue, and Ki-67 and p53 expression were assessed in these patients. DWI was obtained in a 3T system using b values of 0, 400, and 800 s/mm2. Whole tumor ADC volumes were analyzed using a histogram-based approach. Several ADC parameters showed significant correlations with immunohistopathological parameters. Most importantly, ADC histogram skewness and ADC histogram kurtosis were able to differentiate between nodal negative and nodal positive thyroid carcinoma. Conclusions: histogram analysis of whole ADC tumor volumes has the potential to provide valuable information on tumor biology in thyroid carcinoma. However, further studies are warranted. PMID:28417929

Enhancing tumor apparent diffusion coefficient histogram skewness stratifies the postoperative survival in recurrent glioblastoma multiforme patients undergoing salvage surgery.

PubMed

Zolal, Amir; Juratli, Tareq A; Linn, Jennifer; Podlesek, Dino; Sitoci Ficici, Kerim Hakan; Kitzler, Hagen H; Schackert, Gabriele; Sobottka, Stephan B; Rieger, Bernhard; Krex, Dietmar

2016-05-01

Objective To determine the value of apparent diffusion coefficient (ADC) histogram parameters for the prediction of individual survival in patients undergoing surgery for recurrent glioblastoma (GBM) in a retrospective cohort study. Methods Thirty-one patients who underwent surgery for first recurrence of a known GBM between 2008 and 2012 were included. The following parameters were collected: age, sex, enhancing tumor size, mean ADC, median ADC, ADC skewness, ADC kurtosis and fifth percentile of the ADC histogram, initial progression free survival (PFS), extent of second resection and further adjuvant treatment. The association of these parameters with survival and PFS after second surgery was analyzed using log-rank test and Cox regression. Results Using log-rank test, ADC histogram skewness of the enhancing tumor was significantly associated with both survival (p = 0.001) and PFS after second surgery (p = 0.005). Further parameters associated with prolonged survival after second surgery were: gross total resection at second surgery (p = 0.026), tumor size (0.040) and third surgery (p = 0.003). In the multivariate Cox analysis, ADC histogram skewness was shown to be an independent prognostic factor for survival after second surgery. Conclusion ADC histogram skewness of the enhancing lesion, enhancing lesion size, third surgery, as well as gross total resection have been shown to be associated with survival following the second surgery. ADC histogram skewness was an independent prognostic factor for survival in the multivariate analysis.

Postcranial diversity and recent ecomorphic impoverishment of North American gray wolves.

PubMed

Tomiya, Susumu; Meachen, Julie A

2018-01-01

Recent advances in genomics and palaeontology have begun to unravel the complex evolutionary history of the gray wolf, Canis lupus Still, much of their phenotypic variation across time and space remains to be documented. We examined the limb morphology of the fossil and modern North American gray wolves from the late Quaternary (< ca 70 ka) to better understand their postcranial diversity through time. We found that the late-Pleistocene gray wolves were characterized by short-leggedness on both sides of the Cordilleran-Laurentide ice sheets, and that this trait survived well into the Holocene despite the collapse of Pleistocene megafauna and disappearance of the 'Beringian wolf' from Alaska. By contrast, extant populations in the Midwestern USA and northwestern North America are distinguished by their elongate limbs with long distal segments, which appear to have evolved during the Holocene possibly in response to a new level or type of prey depletion. One of the consequences of recent extirpation of the Plains ( Canis lupus nubilus ) and Mexican wolves ( C. l. baileyi ) from much of the USA is an unprecedented loss of postcranial diversity through removal of short-legged forms. Conservation of these wolves is thus critical to restoration of the ecophenotypic diversity and evolutionary potential of gray wolves in North America. © 2018 The Author(s).

Bilateral pallidal hemorrhage in toxoplasmosis update of acute symmetric lesions of deep nuclei.

PubMed

Finelli, Pasquale F; Wrubel, Gregory L

2015-08-01

As acute symmetric lesions of deep gray nuclei are often associated with an impaired level of consciousness and neuroimaging by itself cannot distinguish between etiologies, diagnosis may be problematic. Appreciation of the cause of the various neuroimaging patterns in conjunction with the history, examination and laboratory investigations allows for accurate diagnosis in the vast majority of cases. Given the metabolic vulnerability of deep gray nuclei, other than bi-thalamic infarction, it follows that toxic-metabolic and hypoxic-ischemic events account for the majority of cases. Nevertheless, the differential diagnosis is broad and diverse. We here describe two cases of bilateral pallidal hemorrhage in AIDS-associated toxoplasmosis, and review conditions recently described with acute symmetric deep gray nuclei lesions on neuroimaging. © The Author(s) 2015.

Alexithymia in Neurodegenerative Disease

PubMed Central

Sturm, Virginia E.; Levenson, Robert W.

2012-01-01

We investigated alexithymia, a deficit in the ability to identify and describe one’s emotions, in a sample that included patients with neurodegenerative disease and healthy controls. In addition, we investigated the relationship that alexithymia has with behavioral disturbance and with regional gray matter volumes. Alexithymia was examined with the Toronto Alexithymia Scale-20, behavioral disturbance was assessed with the Neuropsychiatric Inventory, and regional gray matter volumes were obtained from structural magnetic resonance images. Group analyses revealed higher levels of alexithymia in patients than controls. Alexithymia scores were positively correlated with behavioral disturbance (apathy and informant distress, in particular) and negatively correlated with the gray matter volume of the right pregenual anterior cingulate cortex, a region of the brain that is thought to play an important role in self and emotion processing. PMID:21432723

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.

Correlation Between Echinoidea Size and Threat Level

NASA Astrophysics Data System (ADS)

Bakshi, S.; Lee, A.; Heim, N.; Payne, J.

2017-12-01

Echinoidea (or sea urchins), are small, spiny, globular, animals that populate the seafloors of nearly the entire planet. Echinoidea have existed on Earth since the Ordovician period, and from their archaic origin there is much to be learned about the relationship between Echinoidea body size and how it affects the survivability of the individual. The goal of this project is to determine how Echinoidea dimensions such as body volume, area, and length compare across extinct and extant species by plotting Echinoidea data in R. We will use stratigraphic data as a source to find which species of sea urchin from our data is extinct. We will then create three sets of three histograms of the size data for each type of measurement. One set will include histograms for sea urchin length, area, and volume. The other set will include histograms for extinct sea urchin length, area, and volume. The last set will include histograms for extant sea urchin length, area, and volume. Our data showed that extant sea urchins had a larger size, and extinct sea urchins were smaller. Our length data showed that the average length of all sea urchins were 54.95791 mm, the average length of extinct sea urchins were 51.0337 mm, and the average length of extant sea urchins were 66.12774 mm. There is a generally increasing trend of size over time, except for a small outlier about 350 million years ago, where echinoderm extinction selected towards larger species and biovolume was abnormally high. Our data also showed that over the past 200 million years, echinoderm extinction selectivity drove slightly smaller sea urchins towards extinction, further supporting the idea that a larger size was and still is advantageous for echinoderms.

Pamela Gray-Hann | NREL

Science.gov Websites

Pamela Gray-Hann Photo of Pamela Gray-Hann Pamela Gray-Hann Project Support Specialist Pamela.Gray.hann@nrel.gov | 303-275-4626 Pamela Gray-Hann is a member of the Geospatial Data Science team within Pam Gray-Hann

Improved dose-volume histogram estimates for radiopharmaceutical therapy by optimizing quantitative SPECT reconstruction parameters

NASA Astrophysics Data System (ADS)

Cheng, Lishui; Hobbs, Robert F.; Segars, Paul W.; Sgouros, George; Frey, Eric C.

2013-06-01

In radiopharmaceutical therapy, an understanding of the dose distribution in normal and target tissues is important for optimizing treatment. Three-dimensional (3D) dosimetry takes into account patient anatomy and the nonuniform uptake of radiopharmaceuticals in tissues. Dose-volume histograms (DVHs) provide a useful summary representation of the 3D dose distribution and have been widely used for external beam treatment planning. Reliable 3D dosimetry requires an accurate 3D radioactivity distribution as the input. However, activity distribution estimates from SPECT are corrupted by noise and partial volume effects (PVEs). In this work, we systematically investigated OS-EM based quantitative SPECT (QSPECT) image reconstruction in terms of its effect on DVHs estimates. A modified 3D NURBS-based Cardiac-Torso (NCAT) phantom that incorporated a non-uniform kidney model and clinically realistic organ activities and biokinetics was used. Projections were generated using a Monte Carlo (MC) simulation; noise effects were studied using 50 noise realizations with clinical count levels. Activity images were reconstructed using QSPECT with compensation for attenuation, scatter and collimator-detector response (CDR). Dose rate distributions were estimated by convolution of the activity image with a voxel S kernel. Cumulative DVHs were calculated from the phantom and QSPECT images and compared both qualitatively and quantitatively. We found that noise, PVEs, and ringing artifacts due to CDR compensation all degraded histogram estimates. Low-pass filtering and early termination of the iterative process were needed to reduce the effects of noise and ringing artifacts on DVHs, but resulted in increased degradations due to PVEs. Large objects with few features, such as the liver, had more accurate histogram estimates and required fewer iterations and more smoothing for optimal results. Smaller objects with fine details, such as the kidneys, required more iterations and less smoothing at early time points post-radiopharmaceutical administration but more smoothing and fewer iterations at later time points when the total organ activity was lower. The results of this study demonstrate the importance of using optimal reconstruction and regularization parameters. Optimal results were obtained with different parameters at each time point, but using a single set of parameters for all time points produced near-optimal dose-volume histograms.

Lake Michigan Bluff Dewatering and Stabilization Study - Allegan County, Michigan

DTIC Science & Technology

2012-09-01

laminated to cross- bedded sand interbedded with reddish brown, often laminated clay; and reddish-brown to gray to blue-gray diamicton (till) containing...Till also is extremely variable in thickness and may be a thin gravel lens, or up to 44 ft of graded sand beds , planar and trough cross- beds , thin...lies lacustrine clay to below lake level. The in-place layers are nominally flat , behind the slumped bluff face. ERDC TR-12-11 12 Figure 8

The Role of ABC Proteins in Drug Resistant Breast Cancer Cells

DTIC Science & Technology

2008-04-01

are indicated. (B) Cartoon of the predicted PfMDR1 secondary structure indicating helices ( gray ), loops (lines), and Walker A (vertical stripes...GTC 6 22 2 18 11 GTG 11 20 5 2 13 GTT 41 41 31 13 5 6064 Biochemistry, Vol. 46, No. 20, 2007 Amoah et al. to vanadate (Figure 4E) but high sensitivity... gray vs black bars) as were inhibitory effects seen at high dose CQ (Figure 9C, solid bars). DISCUSSION Reproducible high level overexpression of

Inhibition of Androgen-Independent Growth of Prostate Cancer by siRNA- Mediated Androgen Receptor Gene Silencing

DTIC Science & Technology

2008-02-01

gcattgttcccatagagttcc-3V; ref. 38). 28S ribozyme RNA (forward 5V-gttcacccactaatagggaac gtg -3V; backward 5V-gatt- ctgacttagaggcgttcagt-3V) was used as an... gray columns ) at 10 nmol/L in the culture medium supplied with 2% charcoal- stripped FBS. Cell death rate [dying cells versus (dying plus living...and determining the level of free-form PSA helps distinguish the patients who fall into the “diagnostic gray zone” [7]. Table 1 summarized the

Image contrast enhancement using adjacent-blocks-based modification for local histogram equalization

NASA Astrophysics Data System (ADS)

Wang, Yang; Pan, Zhibin

2017-11-01

Infrared images usually have some non-ideal characteristics such as weak target-to-background contrast and strong noise. Because of these characteristics, it is necessary to apply the contrast enhancement algorithm to improve the visual quality of infrared images. Histogram equalization (HE) algorithm is a widely used contrast enhancement algorithm due to its effectiveness and simple implementation. But a drawback of HE algorithm is that the local contrast of an image cannot be equally enhanced. Local histogram equalization algorithms are proved to be the effective techniques for local image contrast enhancement. However, over-enhancement of noise and artifacts can be easily found in the local histogram equalization enhanced images. In this paper, a new contrast enhancement technique based on local histogram equalization algorithm is proposed to overcome the drawbacks mentioned above. The input images are segmented into three kinds of overlapped sub-blocks using the gradients of them. To overcome the over-enhancement effect, the histograms of these sub-blocks are then modified by adjacent sub-blocks. We pay more attention to improve the contrast of detail information while the brightness of the flat region in these sub-blocks is well preserved. It will be shown that the proposed algorithm outperforms other related algorithms by enhancing the local contrast without introducing over-enhancement effects and additional noise.

Value of MR histogram analyses for prediction of microvascular invasion of hepatocellular carcinoma.

PubMed

Huang, Ya-Qin; Liang, He-Yue; Yang, Zhao-Xia; Ding, Ying; Zeng, Meng-Su; Rao, Sheng-Xiang

2016-06-01

The objective is to explore the value of preoperative magnetic resonance (MR) histogram analyses in predicting microvascular invasion (MVI) of hepatocellular carcinoma (HCC).Fifty-one patients with histologically confirmed HCC who underwent diffusion-weighted and contrast-enhanced MR imaging were included. Histogram analyses were performed and mean, variance, skewness, kurtosis, 1th, 10th, 50th, 90th, and 99th percentiles were derived. Quantitative histogram parameters were compared between HCCs with and without MVI. Receiver operating characteristics (ROC) analyses were generated to compare the diagnostic performance of tumor size, histogram analyses of apparent diffusion coefficient (ADC) maps, and MR enhancement.The mean, 1th, 10th, and 50th percentiles of ADC maps, and the mean, variance. 1th, 10th, 50th, 90th, and 99th percentiles of the portal venous phase (PVP) images were significantly different between the groups with and without MVI (P <0.05), with area under the ROC curves (AUCs) of 0.66 to 0.74 for ADC and 0.76 to 0.88 for PVP. The largest AUC of PVP (1th percentile) showed significantly higher accuracy compared with that of arterial phase (AP) or tumor size (P <0.001).MR histogram analyses-in particular for 1th percentile for PVP images-held promise for prediction of MVI of HCC.

Histogram analysis of apparent diffusion coefficient maps for differentiating primary CNS lymphomas from tumefactive demyelinating lesions.

PubMed

Lu, Shan Shan; Kim, Sang Joon; Kim, Namkug; Kim, Ho Sung; Choi, Choong Gon; Lim, Young Min

2015-04-01

This study intended to investigate the usefulness of histogram analysis of apparent diffusion coefficient (ADC) maps for discriminating primary CNS lymphomas (PCNSLs), especially atypical PCNSLs, from tumefactive demyelinating lesions (TDLs). Forty-seven patients with PCNSLs and 18 with TDLs were enrolled in our study. Hyperintense lesions seen on T2-weighted images were defined as ROIs after ADC maps were registered to the corresponding T2-weighted image. ADC histograms were calculated from the ROIs containing the entire lesion on every section and on a voxel-by-voxel basis. The ADC histogram parameters were compared among all PCNSLs and TDLs as well as between the subgroup of atypical PCNSLs and TDLs. ROC curves were constructed to evaluate the diagnostic performance of the histogram parameters and to determine the optimum thresholds. The differences between the PCNSLs and TDLs were found in the minimum ADC values (ADCmin) and in the 5th and 10th percentiles (ADC5% and ADC10%) of the cumulative ADC histograms. However, no statistical significance was found in the mean ADC value or in the ADC value concerning the mode, kurtosis, and skewness. The ADCmin, ADC5%, and ADC10% were also lower in atypical PCNSLs than in TDLs. ADCmin was the best indicator for discriminating atypical PCNSLs from TDLs, with a threshold of 556×10(-6) mm2/s (sensitivity, 81.3 %; specificity, 88.9%). Histogram analysis of ADC maps may help to discriminate PCNSLs from TDLs and may be particularly useful in differentiating atypical PCNSLs from TDLs.

Assessment of histological differentiation in gastric cancers using whole-volume histogram analysis of apparent diffusion coefficient maps.

PubMed

Zhang, Yujuan; Chen, Jun; Liu, Song; Shi, Hua; Guan, Wenxian; Ji, Changfeng; Guo, Tingting; Zheng, Huanhuan; Guan, Yue; Ge, Yun; He, Jian; Zhou, Zhengyang; Yang, Xiaofeng; Liu, Tian

2017-02-01

To investigate the efficacy of histogram analysis of the entire tumor volume in apparent diffusion coefficient (ADC) maps for differentiating between histological grades in gastric cancer. Seventy-eight patients with gastric cancer were enrolled in a retrospective 3.0T magnetic resonance imaging (MRI) study. ADC maps were obtained at two different b values (0 and 1000 sec/mm 2 ) for each patient. Tumors were delineated on each slice of the ADC maps, and a histogram for the entire tumor volume was subsequently generated. A series of histogram parameters (eg, skew and kurtosis) were calculated and correlated with the histological grade of the surgical specimen. The diagnostic performance of each parameter for distinguishing poorly from moderately well-differentiated gastric cancers was assessed by using the area under the receiver operating characteristic curve (AUC). There were significant differences in the 5 th , 10 th , 25 th , and 50 th percentiles, skew, and kurtosis between poorly and well-differentiated gastric cancers (P < 0.05). There were correlations between the degrees of differentiation and histogram parameters, including the 10 th percentile, skew, kurtosis, and max frequency; the correlation coefficients were 0.273, -0.361, -0.339, and -0.370, respectively. Among all the histogram parameters, the max frequency had the largest AUC value, which was 0.675. Histogram analysis of the ADC maps on the basis of the entire tumor volume can be useful in differentiating between histological grades for gastric cancer. 4 J. Magn. Reson. Imaging 2017;45:440-449. © 2016 International Society for Magnetic Resonance in Medicine.

A neural network detection model of spilled oil based on the texture analysis of SAR image

NASA Astrophysics Data System (ADS)

An, Jubai; Zhu, Lisong

2006-01-01

A Radial Basis Function Neural Network (RBFNN) Model is investigated for the detection of spilled oil based on the texture analysis of SAR imagery. In this paper, to take the advantage of the abundant texture information of SAR imagery, the texture features are extracted by both wavelet transform and the Gray Level Co-occurrence matrix. The RBFNN Model is fed with a vector of these texture features. The RBFNN Model is trained and tested by the sample data set of the feature vectors. Finally, a SAR image is classified by this model. The classification results of a spilled oil SAR image show that the classification accuracy for oil spill is 86.2 by the RBFNN Model using both wavelet texture and gray texture, while the classification accuracy for oil spill is 78.0 by same RBFNN Model using only wavelet texture as the input of this RBFNN model. The model using both wavelet transform and the Gray Level Co-occurrence matrix is more effective than that only using wavelet texture. Furthermore, it keeps the complicated proximity and has a good performance of classification.

Non-small cell lung cancer: Whole-lesion histogram analysis of the apparent diffusion coefficient for assessment of tumor grade, lymphovascular invasion and pleural invasion

PubMed Central

Tsuchiya, Naoko; Doai, Mariko; Usuda, Katsuo; Uramoto, Hidetaka

2017-01-01

Purpose Investigating the diagnostic accuracy of histogram analyses of apparent diffusion coefficient (ADC) values for determining non-small cell lung cancer (NSCLC) tumor grades, lymphovascular invasion, and pleural invasion. Materials and methods We studied 60 surgically diagnosed NSCLC patients. Diffusion-weighted imaging (DWI) was performed in the axial plane using a navigator-triggered single-shot, echo-planar imaging sequence with prospective acquisition correction. The ADC maps were generated, and we placed a volume-of-interest on the tumor to construct the whole-lesion histogram. Using the histogram, we calculated the mean, 5th, 10th, 25th, 50th, 75th, 90th, and 95th percentiles of ADC, skewness, and kurtosis. Histogram parameters were correlated with tumor grade, lymphovascular invasion, and pleural invasion. We performed a receiver operating characteristics (ROC) analysis to assess the diagnostic performance of histogram parameters for distinguishing different pathologic features. Results The ADC mean, 10th, 25th, 50th, 75th, 90th, and 95th percentiles showed significant differences among the tumor grades. The ADC mean, 25th, 50th, 75th, 90th, and 95th percentiles were significant histogram parameters between high- and low-grade tumors. The ROC analysis between high- and low-grade tumors showed that the 95th percentile ADC achieved the highest area under curve (AUC) at 0.74. Lymphovascular invasion was associated with the ADC mean, 50th, 75th, 90th, and 95th percentiles, skewness, and kurtosis. Kurtosis achieved the highest AUC at 0.809. Pleural invasion was only associated with skewness, with the AUC of 0.648. Conclusions ADC histogram analyses on the basis of the entire tumor volume are able to stratify NSCLCs' tumor grade, lymphovascular invasion and pleural invasion. PMID:28207858

The “Gray Zone” of Police Work During Mental Health Encounters: Findings from an Observational Study in Chicago

PubMed Central

Wood, Jennifer D; Watson, Amy C; Fulambarker, Anjali J

2016-01-01

Although improving police responses to mental health crises has received significant policy attention, most encounters between police and persons with mental illnesses do not involve major crimes or violence, nor do they rise to the level of requiring emergency apprehension. Here, we report on field observations of police officers handling mental health-related encounters in Chicago. Findings confirm that these encounters often occur in the “gray zone”, where the problems at hand do not call for formal or legalistic interventions including arrest and emergency apprehension. In examining how police resolved such situations, we observed three core features of police work: (1) accepting temporary solutions to chronic vulnerability; (2) using local knowledge to guide decision-making; and (3) negotiating peace with complainants and call subjects. Study findings imply the need to advance field-based studies using systematic social observations of gray zone decision-making within and across distinct geographic and place-based contexts. Policy implications for supporting police interventions, including place-based enhancements of gray zone resources, are also discussed. PMID:28286406

GrayQb TM Single-Faced Version 2 (SF2) Hanford Plutonium Reclamation Facility (PRF) deployment report

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

Plummer, J. R.; Immel, D. M.; Serrato, M. G.

2015-11-18

The Savannah River National Laboratory (SRNL) in partnership with CH2M Plateau Remediation Company (CHPRC) deployed the GrayQb TM SF2 radiation imaging device at the Hanford Plutonium Reclamation Facility (PRF) to assist in the radiological characterization of the canyon. The deployment goal was to locate radiological contamination hot spots in the PRF canyon, where pencil tanks were removed and decontamination/debris removal operations are on-going, to support the CHPRC facility decontamination and decommissioning (D&D) effort. The PRF canyon D&D effort supports completion of the CHPRC Plutonium Finishing Plant Decommissioning Project. The GrayQb TM SF2 (Single Faced Version 2) is a non-destructive examinationmore » device developed by SRNL to generate radiation contour maps showing source locations and relative radiological levels present in the area under examination. The Hanford PRF GrayQbTM Deployment was sponsored by CH2M Plateau Remediation Company (CHPRC) through the DOE Richland Operations Office, Inter-Entity Work Order (IEWO), DOE-RL IEWO- M0SR900210.« less

Chemical contaminants in gray whales (eschichtius robustus) stranded in Alaska, Washington, and California, USA. Technical memo

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

Varanasi, U.; Stein, J.E.; Tilbury, K.L.

1993-08-01

The concentrations of chlorinated hydrocarbons (CHs) such as polychlorinated biphenyls (PCBs), 1,1,1-trichloro-2,2-bis(p-chlorophenyl) ethanes (DDTs), 1,1-dichloro-2,2-bis(p- chlorophenyl) ethenes (DDEs), and chlordanes, and essential (e.g., zinc, selenium, copper) and toxic (e.g., mercury, lead) elements were measured in tissues and stomach contents from 22 gray whales (Eschrichtius robustus) stranded between 1988 and 1991. The stranding sites ranged from the relatively pristine areas of Kodiak Island, Alaska, to more urbanized areas in Puget Sound, Washington, and San Francisco Bay, California, with the majority of the sites on the Washington outer coast and in Puget Sound. Similar to concentrations in tissues, no significant differences weremore » observed in concentrations of elements in stomach contents between whales stranded in Puget Sound and whales stranded at the more pristine sites. The lack of data from apparently healthy gray whales limits the assessment of whether the levels of anthropogenic contaminants found in tissues may have deleterious effects on the health of gray whales.« less

Gray Matter Pathology in MS: Neuroimaging and Clinical Correlations

PubMed Central

Honce, Justin Morris

2013-01-01

It is abundantly clear that there is extensive gray matter pathology occurring in multiple sclerosis. While attention to gray matter pathology was initially limited to studies of autopsy specimens and biopsies, the development of new MRI techniques has allowed assessment of gray matter pathology in vivo. Current MRI techniques allow the direct visualization of gray matter demyelinating lesions, the quantification of diffuse damage to normal appearing gray matter, and the direct measurement of gray matter atrophy. Gray matter demyelination (both focal and diffuse) and gray matter atrophy are found in the very earliest stages of multiple sclerosis and are progressive over time. Accumulation of gray matter damage has substantial impact on the lives of multiple sclerosis patients; a growing body of the literature demonstrates correlations between gray matter pathology and various measures of both clinical disability and cognitive impairment. The effect of disease modifying therapies on the rate accumulation of gray matter pathology in MS has been investigated. This review focuses on the neuroimaging of gray matter pathology in MS, the effect of the accumulation of gray matter pathology on clinical and cognitive disability, and the effect of disease-modifying agents on various measures of gray matter damage. PMID:23878736

Context-sensitive patch histograms for detecting rare events in histopathological data

NASA Astrophysics Data System (ADS)

Diaz, Kristians; Baust, Maximilian; Navab, Nassir

2017-03-01

Assessment of histopathological data is not only difficult due to its varying appearance, e.g. caused by staining artifacts, but also due to its sheer size: Common whole slice images feature a resolution of 6000x4000 pixels. Therefore, finding rare events in such data sets is a challenging and tedious task and developing sophisticated computerized tools is not easy, especially when no or little training data is available. In this work, we propose learning-free yet effective approach based on context sensitive patch-histograms in order to find extramedullary hematopoiesis events in Hematoxylin-Eosin-stained images. When combined with a simple nucleus detector, one can achieve performance levels in terms of sensitivity 0.7146, specificity 0.8476 and accuracy 0.8353 which are very well comparable to a recently published approach based on random forests.

Adaptive pixel-to-pixel projection intensity adjustment for measuring a shiny surface using orthogonal color fringe pattern projection

NASA Astrophysics Data System (ADS)

Chen, Chao; Gao, Nan; Wang, Xiangjun; Zhang, Zonghua

2018-05-01

Three-dimensional (3D) shape measurement based on fringe pattern projection techniques has been commonly used in various fields. One of the remaining challenges in fringe pattern projection is that camera sensor saturation may occur if there is a large range of reflectivity variation across the surface that causes measurement errors. To overcome this problem, a novel fringe pattern projection method is proposed to avoid image saturation and maintain high-intensity modulation for measuring shiny surfaces by adaptively adjusting the pixel-to-pixel projection intensity according to the surface reflectivity. First, three sets of orthogonal color fringe patterns and a sequence of uniform gray-level patterns with different gray levels are projected onto a measured surface by a projector. The patterns are deformed with respect to the object surface and captured by a camera from a different viewpoint. Subsequently, the optimal projection intensity at each pixel is determined by fusing different gray levels and transforming the camera pixel coordinate system into the projector pixel coordinate system. Finally, the adapted fringe patterns are created and used for 3D shape measurement. Experimental results on a flat checkerboard and shiny objects demonstrate that the proposed method can measure shiny surfaces with high accuracy.

Reduction of image-based ADI-to-AEI overlay inconsistency with improved algorithm

NASA Astrophysics Data System (ADS)

Chen, Yen-Liang; Lin, Shu-Hong; Chen, Kai-Hsiung; Ke, Chih-Ming; Gau, Tsai-Sheng

2013-04-01

In image-based overlay (IBO) measurement, the measurement quality of various measurement spectra can be judged by quality indicators and also the ADI-to-AEI similarity to determine the optimum light spectrum. However we found some IBO measured results showing erroneous indication of wafer expansion from the difference between the ADI and the AEI maps, even after their measurement spectra were optimized. To reduce this inconsistency, an improved image calculation algorithm is proposed in this paper. Different gray levels composed of inner- and outer-box contours are extracted to calculate their ADI overlay errors. The symmetry of intensity distribution at the thresholds dictated by a range of gray levels is used to determine the particular gray level that can minimize the ADI-to-AEI overlay inconsistency. After this improvement, the ADI is more similar to AEI with less expansion difference. The same wafer was also checked by the diffraction-based overlay (DBO) tool to verify that there is no physical wafer expansion. When there is actual wafer expansion induced by large internal stress, both the IBO and the DBO measurements indicate similar expansion results. The scanning white-light interference microscope was used to check the variation of wafer warpage during the ADI and AEI stages. It predicts a similar trend with the overlay difference map, confirming the internal stress.

Quantitative histogram analysis of images

NASA Astrophysics Data System (ADS)

Holub, Oliver; Ferreira, Sérgio T.

2006-11-01

A routine for histogram analysis of images has been written in the object-oriented, graphical development environment LabVIEW. The program converts an RGB bitmap image into an intensity-linear greyscale image according to selectable conversion coefficients. This greyscale image is subsequently analysed by plots of the intensity histogram and probability distribution of brightness, and by calculation of various parameters, including average brightness, standard deviation, variance, minimal and maximal brightness, mode, skewness and kurtosis of the histogram and the median of the probability distribution. The program allows interactive selection of specific regions of interest (ROI) in the image and definition of lower and upper threshold levels (e.g., to permit the removal of a constant background signal). The results of the analysis of multiple images can be conveniently saved and exported for plotting in other programs, which allows fast analysis of relatively large sets of image data. The program file accompanies this manuscript together with a detailed description of two application examples: The analysis of fluorescence microscopy images, specifically of tau-immunofluorescence in primary cultures of rat cortical and hippocampal neurons, and the quantification of protein bands by Western-blot. The possibilities and limitations of this kind of analysis are discussed. Program summaryTitle of program: HAWGC Catalogue identifier: ADXG_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/ADXG_v1_0 Program obtainable from: CPC Program Library, Queen's University of Belfast, N. Ireland Computers: Mobile Intel Pentium III, AMD Duron Installations: No installation necessary—Executable file together with necessary files for LabVIEW Run-time engine Operating systems or monitors under which the program has been tested: WindowsME/2000/XP Programming language used: LabVIEW 7.0 Memory required to execute with typical data:˜16MB for starting and ˜160MB used for loading of an image No. of bits in a word: 32 No. of processors used: 1 Has the code been vectorized or parallelized?: No No of lines in distributed program, including test data, etc.:138 946 No. of bytes in distributed program, including test data, etc.:15 166 675 Distribution format: tar.gz Nature of physical problem: Quantification of image data (e.g., for discrimination of molecular species in gels or fluorescent molecular probes in cell cultures) requires proprietary or complex software packages, which might not include the relevant statistical parameters or make the analysis of multiple images a tedious procedure for the general user. Method of solution: Tool for conversion of RGB bitmap image into luminance-linear image and extraction of luminance histogram, probability distribution, and statistical parameters (average brightness, standard deviation, variance, minimal and maximal brightness, mode, skewness and kurtosis of histogram and median of probability distribution) with possible selection of region of interest (ROI) and lower and upper threshold levels. Restrictions on the complexity of the problem: Does not incorporate application-specific functions (e.g., morphometric analysis) Typical running time: Seconds (depending on image size and processor speed) Unusual features of the program: None

Multiprotocol MR image segmentation in multiple sclerosis: experience with over 1000 studies

NASA Astrophysics Data System (ADS)

Udupa, Jayaram K.; Nyul, Laszlo G.; Ge, Yulin; Grossman, Robert I.

2000-06-01

Multiple Sclerosis (MS) is an acquired disease of the central nervous system. Subjective cognitive and ambulatory test scores on a scale called EDSS are currently utilized to assess the disease severity. Various MRI protocols are being investigated to study the disease based on how it manifests itself in the images. In an attempt to eventually replace EDSS by an objective measure to assess the natural course of the disease and its response to therapy, we have developed image segmentation methods based on fuzzy connectedness to quantify various objects in multiprotocol MRI. These include the macroscopic objects such as lesions, the gray matter (GM), white matter (WM), cerebrospinal fluid (CSF), and brain parenchyma as well as the microscopic aspects of the diseased WM. Over 1000 studies have been processed to date. By far the strongest correlations with the clinical measures were demonstrated by the Magnetization Transfer Ratio (MTR) histogram parameters obtained for the various segmented tissue regions emphasizing the importance of considering the microscopic/diffused nature of the disease in the individual tissue regions. Brain parenchymal volume also demonstrated a strong correlation with the clinical measures indicating that brain atrophy is an important indicator of the disease. Fuzzy connectedness is a viable segmentation method for studying MS.

Fast exposure time decision in multi-exposure HDR imaging

NASA Astrophysics Data System (ADS)

Piao, Yongjie; Jin, Guang

2012-10-01

Currently available imaging and display system exists the problem of insufficient dynamic range, and the system cannot restore all the information for an high dynamic range (HDR) scene. The number of low dynamic range(LDR) image samples and fastness of exposure time decision impacts the real-time performance of the system dramatically. In order to realize a real-time HDR video acquisition system, this paper proposed a fast and robust method for exposure time selection in under and over exposure area which is based on system response function. The method utilized the monotony of the imaging system. According to this characteristic the exposure time is adjusted to an initial value to make the median value of the image equals to the middle value of the system output range; then adjust the exposure time to make the pixel value on two sides of histogram be the middle value of the system output range. Thus three low dynamic range images are acquired. Experiments show that the proposed method for adjusting the initial exposure time can converge in two iterations which is more fast and stable than average gray control method. As to the exposure time adjusting in under and over exposed area, the proposed method can use the dynamic range of the system more efficiently than fixed exposure time method.

Military deployment correlates with smaller prefrontal gray matter volume and psychological symptoms in a subclinical population.

PubMed

Butler, O; Adolf, J; Gleich, T; Willmund, G; Zimmermann, P; Lindenberger, U; Gallinat, J; Kühn, S

2017-02-14

Research investigating the effects of trauma exposure on brain structure and function in adults has mainly focused on post-traumatic stress disorder (PTSD), whereas trauma-exposed individuals without a clinical diagnoses often serve as controls. However, this assumes a dichotomy between clinical and subclinical populations that may not be supported at the neural level. In the current study we investigate whether the effects of repeated or long-term stress exposure on brain structure in a subclinical sample are similar to previous PTSD neuroimaging findings. We assessed 27 combat trauma-exposed individuals by means of whole-brain voxel-based morphometry on 3 T magnetic resonance imaging scans and identified a negative association between duration of military deployment and gray matter volumes in ventromedial prefrontal cortex (vmPFC) and dorsal anterior cingulate cortex (ACC). We also found a negative relationship between deployment-related gray matter volumes and psychological symptoms, but not between military deployment and psychological symptoms. To our knowledge, this is the first whole-brain analysis showing that longer military deployment is associated with smaller regional brain volumes in combat-exposed individuals without PTSD. Notably, the observed gray matter associations resemble those previously identified in PTSD populations, and concern regions involved in emotional regulation and fear extinction. These findings question the current dichotomy between clinical and subclinical populations in PTSD neuroimaging research. Instead, neural correlates of both stress exposure and PTSD symptomatology may be more meaningfully investigated at a continuous level.

The characteristics of phosphorus adsorption and desorption in gray desert soil of Xinjiang, China

NASA Astrophysics Data System (ADS)

Wang, B.; Sun, J. S.; Liu, H.; Ma, Y. B.

2017-07-01

The characteristics of phosphorus (P) adsorption and desorption in Xinjiang gray desert soil (0 - 200 mm) of China in the long-term fertilization condition is affected by the level of soil P content which studied through an isothermal adsorption and desorption experiments of P. The results stated that within the experimental concentration range, with the increase of the amount of outer-source phosphorus, P adsorption, desorption and desorption rate increased and adsorption rate decreased gradually in different Olsen-P levels of gray desert soil in Xinjiang, China. Olsen-P content is significantly correlated with the P adsorption saturation (DPS) of gray desert soil. The maximum adsorption capacity (Xm ) of the treatments followed an extremely significant decreasing order of CK>NPK≈NPKM>PK≈NPKS. The maximum buffer capacity (MBC) and adsorption constant (K) of the NPK treatment was much higher than NPKM, NPKS, PK and CK treatments. And, MBC value of CK treatment was extremely higher than NPKS and PK, however, the differences between NPKM and CK, NPKS and PK were not significant. The comparison between NPKM, NPKS, PK and CK treatments showed no significant difference in K value, but these four showed significantly lower than NPK treatments. The value of soil easy desorption P (RDP) of NPKS and NPKM was significantly higher than NPK and PK, and the chemical fertilizer with organic fertilizer was a best way to release the phosphorus for Xinjiang agricultural production, China.

Military deployment correlates with smaller prefrontal gray matter volume and psychological symptoms in a subclinical population

PubMed Central

Butler, O; Adolf, J; Gleich, T; Willmund, G; Zimmermann, P; Lindenberger, U; Gallinat, J; Kühn, S

2017-01-01

Research investigating the effects of trauma exposure on brain structure and function in adults has mainly focused on post-traumatic stress disorder (PTSD), whereas trauma-exposed individuals without a clinical diagnoses often serve as controls. However, this assumes a dichotomy between clinical and subclinical populations that may not be supported at the neural level. In the current study we investigate whether the effects of repeated or long-term stress exposure on brain structure in a subclinical sample are similar to previous PTSD neuroimaging findings. We assessed 27 combat trauma-exposed individuals by means of whole-brain voxel-based morphometry on 3 T magnetic resonance imaging scans and identified a negative association between duration of military deployment and gray matter volumes in ventromedial prefrontal cortex (vmPFC) and dorsal anterior cingulate cortex (ACC). We also found a negative relationship between deployment-related gray matter volumes and psychological symptoms, but not between military deployment and psychological symptoms. To our knowledge, this is the first whole-brain analysis showing that longer military deployment is associated with smaller regional brain volumes in combat-exposed individuals without PTSD. Notably, the observed gray matter associations resemble those previously identified in PTSD populations, and concern regions involved in emotional regulation and fear extinction. These findings question the current dichotomy between clinical and subclinical populations in PTSD neuroimaging research. Instead, neural correlates of both stress exposure and PTSD symptomatology may be more meaningfully investigated at a continuous level. PMID:28195568

Expression profiling suggests a regulatory role of gallbladder in lipid homeostasis

PubMed Central

Yuan, Zuo-Biao; Han, Tian-Quan; Jiang, Zhao-Yan; Fei, Jian; Zhang, Yi; Qin, Jian; Tian, Zhi-Jie; Shang, Jun; Jiang, Zhi-Hong; Cai, Xing-Xing; Jiang, Yu; Zhang, Sheng-Dao; Jin, Gang

2005-01-01

AIM: To examine expression profile of gallbladder using microarray and to investigate the role of gallbladder in lipid homeostasis. METHODS: 33P-labelled cDNA derived from total RNA of gallbladder tissue was hybridized to a cDNA array representing 17000 cDNA clusters. Genes with intensities ≥2 and variation <0.33 between two samples were considered as positive signals with subtraction of background chosen from an area where no cDNA was spotted. The average gray level of two gallbladders was adopted to analyze its bioinformatics. Identified target genes were confirmed by touch-down polymerase chain reaction and sequencing. RESULTS: A total of 11 047 genes expressed in normal gallbladder, which was more than that predicted by another author, and the first 10 genes highly expressed (high gray level in hybridization image), e.g., ARPC5 (2225.88±90.46), LOC55972 (2220.32±446.51) and SLC20A2 (1865.21±98.02), were related to the function of smooth muscle contraction and material transport. Meanwhile, 149 lipid-related genes were expressed in the gallbladder, 89 of which were first identified (with gray level in hybridization image), e.g., FASN (11.42±2.62), APOD (92.61±8.90) and CYP21A2 (246.11±42.36), and they were involved in each step of lipid metabolism pathway. In addition, 19 of those 149 genes were gallstone candidate susceptibility genes (with gray level in hybridization image), e.g., HMGCR (10.98±0.31), NPC1 (34.88±12.12) and NR1H4 (16.8±0.65), which were previously thought to be expressed in the liver and/or intestine tissue only. CONCLUSION: Gallbladder expresses 11 047 genes and takes part in lipid homeostasis. PMID:15810076

Classifying brain metastases by their primary site of origin using a radiomics approach based on texture analysis: a feasibility study.

PubMed

Ortiz-Ramón, Rafael; Larroza, Andrés; Ruiz-España, Silvia; Arana, Estanislao; Moratal, David

2018-05-14

To examine the capability of MRI texture analysis to differentiate the primary site of origin of brain metastases following a radiomics approach. Sixty-seven untreated brain metastases (BM) were found in 3D T1-weighted MRI of 38 patients with cancer: 27 from lung cancer, 23 from melanoma and 17 from breast cancer. These lesions were segmented in 2D and 3D to compare the discriminative power of 2D and 3D texture features. The images were quantized using different number of gray-levels to test the influence of quantization. Forty-three rotation-invariant texture features were examined. Feature selection and random forest classification were implemented within a nested cross-validation structure. Classification was evaluated with the area under receiver operating characteristic curve (AUC) considering two strategies: multiclass and one-versus-one. In the multiclass approach, 3D texture features were more discriminative than 2D features. The best results were achieved for images quantized with 32 gray-levels (AUC = 0.873 ± 0.064) using the top four features provided by the feature selection method based on the p-value. In the one-versus-one approach, high accuracy was obtained when differentiating lung cancer BM from breast cancer BM (four features, AUC = 0.963 ± 0.054) and melanoma BM (eight features, AUC = 0.936 ± 0.070) using the optimal dataset (3D features, 32 gray-levels). Classification of breast cancer and melanoma BM was unsatisfactory (AUC = 0.607 ± 0.180). Volumetric MRI texture features can be useful to differentiate brain metastases from different primary cancers after quantizing the images with the proper number of gray-levels. • Texture analysis is a promising source of biomarkers for classifying brain neoplasms. • MRI texture features of brain metastases could help identifying the primary cancer. • Volumetric texture features are more discriminative than traditional 2D texture features.

DWI-associated entire-tumor histogram analysis for the differentiation of low-grade prostate cancer from intermediate-high-grade prostate cancer.

PubMed

Wu, Chen-Jiang; Wang, Qing; Li, Hai; Wang, Xiao-Ning; Liu, Xi-Sheng; Shi, Hai-Bin; Zhang, Yu-Dong

2015-10-01

To investigate diagnostic efficiency of DWI using entire-tumor histogram analysis in differentiating the low-grade (LG) prostate cancer (PCa) from intermediate-high-grade (HG) PCa in comparison with conventional ROI-based measurement. DW images (b of 0-1400 s/mm(2)) from 126 pathology-confirmed PCa (diameter >0.5 cm) in 110 patients were retrospectively collected and processed by mono-exponential model. The measurement of tumor apparent diffusion coefficients (ADCs) was performed with using histogram-based and ROI-based approach, respectively. The diagnostic ability of ADCs from two methods for differentiating LG-PCa (Gleason score, GS ≤ 6) from HG-PCa (GS > 6) was determined by ROC regression, and compared by McNemar's test. There were 49 LG-tumor and 77 HG-tumor at pathologic findings. Histogram-based ADCs (mean, median, 10th and 90th) and ROI-based ADCs (mean) showed dominant relationships with ordinal GS of Pca (ρ = -0.225 to -0.406, p < 0.05). All above imaging indices reflected significant difference between LG-PCa and HG-PCa (all p values <0.01). Histogram 10th ADCs had dominantly high Az (0.738), Youden index (0.415), and positive likelihood ratio (LR+, 2.45) in stratifying tumor GS against mean, median and 90th ADCs, and ROI-based ADCs. Histogram mean, median, and 10th ADCs showed higher specificity (65.3%-74.1% vs. 44.9%, p < 0.01), but lower sensitivity (57.1%-71.3% vs. 84.4%, p < 0.05) than ROI-based ADCs in differentiating LG-PCa from HG-PCa. DWI-associated histogram analysis had higher specificity, Az, Youden index, and LR+ for differentiation of PCa Gleason grade than ROI-based approach.

Dynamic contrast-enhanced MR imaging of the rectum: Correlations between single-section and whole-tumor histogram analyses.

PubMed

Choi, M H; Oh, S N; Park, G E; Yeo, D-M; Jung, S E

2018-05-10

To evaluate the interobserver and intermethod correlations of histogram metrics of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parameters acquired by multiple readers using the single-section and whole-tumor volume methods. Four DCE parameters (K trans , K ep , V e , V p ) were evaluated in 45 patients (31 men and 14 women; mean age, 61±11 years [range, 29-83 years]) with locally advanced rectal cancer using pre-chemoradiotherapy (CRT) MRI. Ten histogram metrics were extracted using two methods of lesion selection performed by three radiologists: the whole-tumor volume method for the whole tumor on axial section-by-section images and the single-section method for the entire area of the tumor on one axial image. The interobserver and intermethod correlations were evaluated using the intraclass correlation coefficients (ICCs). The ICCs showed excellent interobserver and intermethod correlations in most of histogram metrics of the DCE parameters. The ICCs among the three readers were > 0.7 (P<0.001) for all histogram metrics, except for the minimum and maximum. The intermethod correlations for most of the histogram metrics were excellent for each radiologist, regardless of the differences in the radiologists' experience. The interobserver and intermethod correlations for most of the histogram metrics of the DCE parameters are excellent in rectal cancer. Therefore, the single-section method may be a potential alternative to the whole-tumor volume method using pre-CRT MRI, despite the fact that the high agreement between the two methods cannot be extrapolated to post-CRT MRI. Copyright © 2018 Société française de radiologie. Published by Elsevier Masson SAS. All rights reserved.

Measuring the apparent diffusion coefficient in primary rectal tumors: is there a benefit in performing histogram analyses?

PubMed

van Heeswijk, Miriam M; Lambregts, Doenja M J; Maas, Monique; Lahaye, Max J; Ayas, Z; Slenter, Jos M G M; Beets, Geerard L; Bakers, Frans C H; Beets-Tan, Regina G H

2017-06-01

The apparent diffusion coefficient (ADC) is a potential prognostic imaging marker in rectal cancer. Typically, mean ADC values are used, derived from precise manual whole-volume tumor delineations by experts. The aim was first to explore whether non-precise circular delineation combined with histogram analysis can be a less cumbersome alternative to acquire similar ADC measurements and second to explore whether histogram analyses provide additional prognostic information. Thirty-seven patients who underwent a primary staging MRI including diffusion-weighted imaging (DWI; b0, 25, 50, 100, 500, 1000; 1.5 T) were included. Volumes-of-interest (VOIs) were drawn on b1000-DWI: (a) precise delineation, manually tracing tumor boundaries (2 expert readers), and (b) non-precise delineation, drawing circular VOIs with a wide margin around the tumor (2 non-experts). Mean ADC and histogram metrics (mean, min, max, median, SD, skewness, kurtosis, 5th-95th percentiles) were derived from the VOIs and delineation time was recorded. Measurements were compared between the two methods and correlated with prognostic outcome parameters. Median delineation time reduced from 47-165 s (precise) to 21-43 s (non-precise). The 45th percentile of the non-precise delineation showed the best correlation with the mean ADC from the precise delineation as the reference standard (ICC 0.71-0.75). None of the mean ADC or histogram parameters showed significant prognostic value; only the total tumor volume (VOI) was significantly larger in patients with positive clinical N stage and mesorectal fascia involvement. When performing non-precise tumor delineation, histogram analysis (in specific 45th ADC percentile) may be used as an alternative to obtain similar ADC values as with precise whole tumor delineation. Histogram analyses are not beneficial to obtain additional prognostic information.

The histogram analysis of diffusion-weighted intravoxel incoherent motion (IVIM) imaging for differentiating the gleason grade of prostate cancer.

PubMed

Zhang, Yu-Dong; Wang, Qing; Wu, Chen-Jiang; Wang, Xiao-Ning; Zhang, Jing; Liu, Hui; Liu, Xi-Sheng; Shi, Hai-Bin

2015-04-01

To evaluate histogram analysis of intravoxel incoherent motion (IVIM) for discriminating the Gleason grade of prostate cancer (PCa). A total of 48 patients pathologically confirmed as having clinically significant PCa (size > 0.5 cm) underwent preoperative DW-MRI (b of 0-900 s/mm(2)). Data was post-processed by monoexponential and IVIM model for quantitation of apparent diffusion coefficients (ADCs), perfusion fraction f, diffusivity D and pseudo-diffusivity D*. Histogram analysis was performed by outlining entire-tumour regions of interest (ROIs) from histological-radiological correlation. The ability of imaging indices to differentiate low-grade (LG, Gleason score (GS) ≤6) from intermediate/high-grade (HG, GS > 6) PCa was analysed by ROC regression. Eleven patients had LG tumours (18 foci) and 37 patients had HG tumours (42 foci) on pathology examination. HG tumours had significantly lower ADCs and D in terms of mean, median, 10th and 75th percentiles, combined with higher histogram kurtosis and skewness for ADCs, D and f, than LG PCa (p < 0.05). Histogram D showed relatively higher correlations (ñ = 0.641-0.668 vs. ADCs: 0.544-0.574) with ordinal GS of PCa; and its mean, median and 10th percentile performed better than ADCs did in distinguishing LG from HG PCa. It is feasible to stratify the pathological grade of PCa by IVIM with histogram metrics. D performed better in distinguishing LG from HG tumour than conventional ADCs. • GS had relatively higher correlation with tumour D than ADCs. • Difference of histogram D among two-grade tumours was statistically significant. • D yielded better individual features in demonstrating tumour grade than ADC. • D* and f failed to determine tumour grade of PCa.

Subtype Differentiation of Small (≤ 4 cm) Solid Renal Mass Using Volumetric Histogram Analysis of DWI at 3-T MRI.

PubMed

Li, Anqin; Xing, Wei; Li, Haojie; Hu, Yao; Hu, Daoyu; Li, Zhen; Kamel, Ihab R

2018-05-29

The purpose of this article is to evaluate the utility of volumetric histogram analysis of apparent diffusion coefficient (ADC) derived from reduced-FOV DWI for small (≤ 4 cm) solid renal mass subtypes at 3-T MRI. This retrospective study included 38 clear cell renal cell carcinomas (RCCs), 16 papillary RCCs, 18 chromophobe RCCs, 13 minimal fat angiomyolipomas (AMLs), and seven oncocytomas evaluated with preoperative MRI. Volumetric ADC maps were generated using all slices of the reduced-FOV DW images to obtain histogram parameters, including mean, median, 10th percentile, 25th percentile, 75th percentile, 90th percentile, and SD ADC values, as well as skewness, kurtosis, and entropy. Comparisons of these parameters were made by one-way ANOVA, t test, and ROC curves analysis. ADC histogram parameters differentiated eight of 10 pairs of renal tumors. Three subtype pairs (clear cell RCC vs papillary RCC, clear cell RCC vs chromophobe RCC, and clear cell RCC vs minimal fat AML) were differentiated by mean ADC. However, five other subtype pairs (clear cell RCC vs oncocytoma, papillary RCC vs minimal fat AML, papillary RCC vs oncocytoma, chromophobe RCC vs minimal fat AML, and chromophobe RCC vs oncocytoma) were differentiated by histogram distribution parameters exclusively (all p < 0.05). Mean ADC, median ADC, 75th and 90th percentile ADC, SD ADC, and entropy of malignant tumors were significantly higher than those of benign tumors (all p < 0.05). Combination of mean ADC with histogram parameters yielded the highest AUC (0.851; sensitivity, 80.0%; specificity, 86.1%). Quantitative volumetric ADC histogram analysis may help differentiate various subtypes of small solid renal tumors, including benign and malignant lesions.

Diffusion-weighted imaging: Apparent diffusion coefficient histogram analysis for detecting pathologic complete response to chemoradiotherapy in locally advanced rectal cancer.

PubMed

Choi, Moon Hyung; Oh, Soon Nam; Rha, Sung Eun; Choi, Joon-Il; Lee, Sung Hak; Jang, Hong Seok; Kim, Jun-Gi; Grimm, Robert; Son, Yohan

2016-07-01

To investigate the usefulness of apparent diffusion coefficient (ADC) values derived from histogram analysis of the whole rectal cancer as a quantitative parameter to evaluate pathologic complete response (pCR) on preoperative magnetic resonance imaging (MRI). We enrolled a total of 86 consecutive patients who had undergone surgery for rectal cancer after neoadjuvant chemoradiotherapy (CRT) at our institution between July 2012 and November 2014. Two radiologists who were blinded to the final pathological results reviewed post-CRT MRI to evaluate tumor stage. Quantitative image analysis was performed using T2 -weighted and diffusion-weighted images independently by two radiologists using dedicated software that performed histogram analysis to assess the distribution of ADC in the whole tumor. After surgery, 16 patients were confirmed to have achieved pCR (18.6%). All parameters from pre- and post-CRT ADC histogram showed good or excellent agreement between two readers. The minimum, 10th, 25th, 50th, and 75th percentile and mean ADC from post-CRT ADC histogram were significantly higher in the pCR group than in the non-pCR group for both readers. The 25th percentile value from ADC histogram in post-CRT MRI had the best diagnostic performance for detecting pCR, with an area under the receiver operating characteristic curve of 0.796. Low percentile values derived from the ADC histogram analysis of rectal cancer on MRI after CRT showed a significant difference between pCR and non-pCR groups, demonstrating the utility of the ADC value as a quantitative and objective marker to evaluate complete pathologic response to preoperative CRT in rectal cancer. J. Magn. Reson. Imaging 2016;44:212-220. © 2015 Wiley Periodicals, Inc.

Serial data acquisition for GEM-2D detector

NASA Astrophysics Data System (ADS)

Kolasinski, Piotr; Pozniak, Krzysztof T.; Czarski, Tomasz; Linczuk, Maciej; Byszuk, Adrian; Chernyshova, Maryna; Juszczyk, Bartlomiej; Kasprowicz, Grzegorz; Wojenski, Andrzej; Zabolotny, Wojciech; Zienkiewicz, Pawel; Mazon, Didier; Malard, Philippe; Herrmann, Albrecht; Vezinet, Didier

2014-11-01

This article debates about data fast acquisition and histogramming method for the X-ray GEM detector. The whole process of histogramming is performed by FPGA chips (Spartan-6 series from Xilinx). The results of the histogramming process are stored in an internal FPGA memory and then sent to PC. In PC data is merged and processed by MATLAB. The structure of firmware functionality implemented in the FPGAs is described. Examples of test measurements and results are presented.

Frequency distribution histograms for the rapid analysis of data

NASA Technical Reports Server (NTRS)

Burke, P. V.; Bullen, B. L.; Poff, K. L.

1988-01-01

The mean and standard error are good representations for the response of a population to an experimental parameter and are frequently used for this purpose. Frequency distribution histograms show, in addition, responses of individuals in the population. Both the statistics and a visual display of the distribution of the responses can be obtained easily using a microcomputer and available programs. The type of distribution shown by the histogram may suggest different mechanisms to be tested.

A Monte Carlo study of the impact of the choice of rectum volume definition on estimates of equivalent uniform doses and the volume parameter

NASA Astrophysics Data System (ADS)

Kvinnsland, Yngve; Muren, Ludvig Paul; Dahl, Olav

2004-08-01

Calculations of normal tissue complication probability (NTCP) values for the rectum are difficult because it is a hollow, non-rigid, organ. Finding the true cumulative dose distribution for a number of treatment fractions requires a CT scan before each treatment fraction. This is labour intensive, and several surrogate distributions have therefore been suggested, such as dose wall histograms, dose surface histograms and histograms for the solid rectum, with and without margins. In this study, a Monte Carlo method is used to investigate the relationships between the cumulative dose distributions based on all treatment fractions and the above-mentioned histograms that are based on one CT scan only, in terms of equivalent uniform dose. Furthermore, the effect of a specific choice of histogram on estimates of the volume parameter of the probit NTCP model was investigated. It was found that the solid rectum and the rectum wall histograms (without margins) gave equivalent uniform doses with an expected value close to the values calculated from the cumulative dose distributions in the rectum wall. With the number of patients available in this study the standard deviations of the estimates of the volume parameter were large, and it was not possible to decide which volume gave the best estimates of the volume parameter, but there were distinct differences in the mean values of the values obtained.

Detection of Local Tumor Recurrence After Definitive Treatment of Head and Neck Squamous Cell Carcinoma: Histogram Analysis of Dynamic Contrast-Enhanced T1-Weighted Perfusion MRI.

PubMed

Choi, Sang Hyun; Lee, Jeong Hyun; Choi, Young Jun; Park, Ji Eun; Sung, Yu Sub; Kim, Namkug; Baek, Jung Hwan

2017-01-01

This study aimed to explore the added value of histogram analysis of the ratio of initial to final 90-second time-signal intensity AUC (AUCR) for differentiating local tumor recurrence from contrast-enhancing scar on follow-up dynamic contrast-enhanced T1-weighted perfusion MRI of patients treated for head and neck squamous cell carcinoma (HNSCC). AUCR histogram parameters were assessed among tumor recurrence (n = 19) and contrast-enhancing scar (n = 27) at primary sites and compared using the t test. ROC analysis was used to determine the best differentiating parameters. The added value of AUCR histogram parameters was assessed when they were added to inconclusive conventional MRI results. Histogram analysis showed statistically significant differences in the 50th, 75th, and 90th percentiles of the AUCR values between the two groups (p < 0.05). The 90th percentile of the AUCR values (AUCR 90 ) was the best predictor of local tumor recurrence (AUC, 0.77; 95% CI, 0.64-0.91) with an estimated cutoff of 1.02. AUCR 90 increased sensitivity by 11.7% over that of conventional MRI alone when added to inconclusive results. Histogram analysis of AUCR can improve the diagnostic yield for local tumor recurrence during surveillance after treatment for HNSCC.

Value of MR histogram analyses for prediction of microvascular invasion of hepatocellular carcinoma

PubMed Central

Huang, Ya-Qin; Liang, He-Yue; Yang, Zhao-Xia; Ding, Ying; Zeng, Meng-Su; Rao, Sheng-Xiang

2016-01-01

Abstract The objective is to explore the value of preoperative magnetic resonance (MR) histogram analyses in predicting microvascular invasion (MVI) of hepatocellular carcinoma (HCC). Fifty-one patients with histologically confirmed HCC who underwent diffusion-weighted and contrast-enhanced MR imaging were included. Histogram analyses were performed and mean, variance, skewness, kurtosis, 1th, 10th, 50th, 90th, and 99th percentiles were derived. Quantitative histogram parameters were compared between HCCs with and without MVI. Receiver operating characteristics (ROC) analyses were generated to compare the diagnostic performance of tumor size, histogram analyses of apparent diffusion coefficient (ADC) maps, and MR enhancement. The mean, 1th, 10th, and 50th percentiles of ADC maps, and the mean, variance. 1th, 10th, 50th, 90th, and 99th percentiles of the portal venous phase (PVP) images were significantly different between the groups with and without MVI (P <0.05), with area under the ROC curves (AUCs) of 0.66 to 0.74 for ADC and 0.76 to 0.88 for PVP. The largest AUC of PVP (1th percentile) showed significantly higher accuracy compared with that of arterial phase (AP) or tumor size (P <0.001). MR histogram analyses—in particular for 1th percentile for PVP images—held promise for prediction of MVI of HCC. PMID:27368028

Effect of respiratory and cardiac gating on the major diffusion-imaging metrics

PubMed Central

Hamaguchi, Hiroyuki; Sugimori, Hiroyuki; Nakanishi, Mitsuhiro; Nakagawa, Shin; Fujiwara, Taro; Yoshida, Hirokazu; Takamori, Sayaka; Shirato, Hiroki

2016-01-01

The effect of respiratory gating on the major diffusion-imaging metrics and that of cardiac gating on mean kurtosis (MK) are not known. For evaluation of whether the major diffusion-imaging metrics—MK, fractional anisotropy (FA), and mean diffusivity (MD) of the brain—varied between gated and non-gated acquisitions, respiratory-gated, cardiac-gated, and non-gated diffusion-imaging of the brain were performed in 10 healthy volunteers. MK, FA, and MD maps were constructed for all acquisitions, and the histograms were constructed. The normalized peak height and location of the histograms were compared among the acquisitions by use of Friedman and post hoc Wilcoxon tests. The effect of the repetition time (TR) on the diffusion-imaging metrics was also tested, and we corrected for its variation among acquisitions, if necessary. The results showed a shift in the peak location of the MK and MD histograms to the right with an increase in TR (p ≤ 0.01). The corrected peak location of the MK histograms, the normalized peak height of the FA histograms, the normalized peak height and the corrected peak location of the MD histograms varied significantly between the gated and non-gated acquisitions (p < 0.05). These results imply an influence of respiration and cardiac pulsation on the major diffusion-imaging metrics. The gating conditions must be kept identical if reproducible results are to be achieved. PMID:27073115

Characterization of the Gray Whale Eschrichtius robustus Genome and a Genotyping Array Based on Single-Nucleotide Polymorphisms in Candidate Genes.

PubMed

DeWoody, J Andrew; Fernandez, Nadia B; Brüniche-Olsen, Anna; Antonides, Jennifer D; Doyle, Jacqueline M; San Miguel, Phillip; Westerman, Rick; Vertyankin, Vladimir V; Godard-Codding, Céline A J; Bickham, John W

2017-06-01

Genetic and genomic approaches have much to offer in terms of ecology, evolution, and conservation. To better understand the biology of the gray whale Eschrichtius robustus (Lilljeborg, 1861), we sequenced the genome and produced an assembly that contains ∼95% of the genes known to be highly conserved among eukaryotes. From this assembly, we annotated 22,711 genes and identified 2,057,254 single-nucleotide polymorphisms (SNPs). Using this assembly, we generated a curated list of candidate genes potentially subject to strong natural selection, including genes associated with osmoregulation, oxygen binding and delivery, and other aspects of marine life. From these candidate genes, we queried 92 autosomal protein-coding markers with a panel of 96 SNPs that also included 2 sexing and 2 mitochondrial markers. Genotyping error rates, calculated across loci and across 69 intentional replicate samples, were low (0.021%), and observed heterozygosity was 0.33 averaged over all autosomal markers. This level of variability provides substantial discriminatory power across loci (mean probability of identity of 1.6 × 10 -25 and mean probability of exclusion >0.999 with neither parent known), indicating that these markers provide a powerful means to assess parentage and relatedness in gray whales. We found 29 unique multilocus genotypes represented among our 36 biopsies (indicating that we inadvertently sampled 7 whales twice). In total, we compiled an individual data set of 28 western gray whales (WGSs) and 1 presumptive eastern gray whale (EGW). The lone EGW we sampled was no more or less related to the WGWs than expected by chance alone. The gray whale genomes reported here will enable comparative studies of natural selection in cetaceans, and the SNP markers should be highly informative for future studies of gray whale evolution, population structure, demography, and relatedness.

Regionally adaptive histogram equalization of the chest.

PubMed

Sherrier, R H; Johnson, G A

1987-01-01

Advances in the area of digital chest radiography have resulted in the acquisition of high-quality images of the human chest. With these advances, there arises a genuine need for image processing algorithms specific to the chest, in order to fully exploit this digital technology. We have implemented the well-known technique of histogram equalization, noting the problems encountered when it is adapted to chest images. These problems have been successfully solved with our regionally adaptive histogram equalization method. With this technique histograms are calculated locally and then modified according to both the mean pixel value of that region as well as certain characteristics of the cumulative distribution function. This process, which has allowed certain regions of the chest radiograph to be enhanced differentially, may also have broader implications for other image processing tasks.

Infrared face recognition based on LBP histogram and KW feature selection

NASA Astrophysics Data System (ADS)

Xie, Zhihua

2014-07-01

The conventional LBP-based feature as represented by the local binary pattern (LBP) histogram still has room for performance improvements. This paper focuses on the dimension reduction of LBP micro-patterns and proposes an improved infrared face recognition method based on LBP histogram representation. To extract the local robust features in infrared face images, LBP is chosen to get the composition of micro-patterns of sub-blocks. Based on statistical test theory, Kruskal-Wallis (KW) feature selection method is proposed to get the LBP patterns which are suitable for infrared face recognition. The experimental results show combination of LBP and KW features selection improves the performance of infrared face recognition, the proposed method outperforms the traditional methods based on LBP histogram, discrete cosine transform(DCT) or principal component analysis(PCA).

A color-coded vision scheme for robotics

NASA Technical Reports Server (NTRS)

Johnson, Kelley Tina

1991-01-01

Most vision systems for robotic applications rely entirely on the extraction of information from gray-level images. Humans, however, regularly depend on color to discriminate between objects. Therefore, the inclusion of color in a robot vision system seems a natural extension of the existing gray-level capabilities. A method for robot object recognition using a color-coding classification scheme is discussed. The scheme is based on an algebraic system in which a two-dimensional color image is represented as a polynomial of two variables. The system is then used to find the color contour of objects. In a controlled environment, such as that of the in-orbit space station, a particular class of objects can thus be quickly recognized by its color.

Multispectral histogram normalization contrast enhancement

NASA Technical Reports Server (NTRS)

Soha, J. M.; Schwartz, A. A.

1979-01-01

A multispectral histogram normalization or decorrelation enhancement which achieves effective color composites by removing interband correlation is described. The enhancement procedure employs either linear or nonlinear transformations to equalize principal component variances. An additional rotation to any set of orthogonal coordinates is thus possible, while full histogram utilization is maintained by avoiding the reintroduction of correlation. For the three-dimensional case, the enhancement procedure may be implemented with a lookup table. An application of the enhancement to Landsat multispectral scanning imagery is presented.

Remote logo detection using angle-distance histograms

NASA Astrophysics Data System (ADS)

Youn, Sungwook; Ok, Jiheon; Baek, Sangwook; Woo, Seongyoun; Lee, Chulhee

2016-05-01

Among all the various computer vision applications, automatic logo recognition has drawn great interest from industry as well as various academic institutions. In this paper, we propose an angle-distance map, which we used to develop a robust logo detection algorithm. The proposed angle-distance histogram is invariant against scale and rotation. The proposed method first used shape information and color characteristics to find the candidate regions and then applied the angle-distance histogram. Experiments show that the proposed method detected logos of various sizes and orientations.

Color image processing and vision system for an automated laser paint-stripping system

NASA Astrophysics Data System (ADS)

Hickey, John M., III; Hise, Lawson

1994-10-01

Color image processing in machine vision systems has not gained general acceptance. Most machine vision systems use images that are shades of gray. The Laser Automated Decoating System (LADS) required a vision system which could discriminate between substrates of various colors and textures and paints ranging from semi-gloss grays to high gloss red, white and blue (Air Force Thunderbirds). The changing lighting levels produced by the pulsed CO2 laser mandated a vision system that did not require a constant color temperature lighting for reliable image analysis.

Ecological risk assessment of aerial insectivores of the Clinch River/Poplar Creek system

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

Baron, L.A.; Sample, B.E.

Risks to aerial insectivores (species that consume flying insects; rough-winged swallows, little brown bats, and endangered gray bats) were assessed for the CERCLA remedial investigation of the Clinch River/Poplar Creek system. Adult mayflies and sediment were collected from four locations and analyzed for contaminants. Sediment-to-mayfly contaminant transfer factors were generated from these data and used to estimate contaminant concentrations in mayflies from thirteen additional locations. Contaminants of potential concern (COPCs) were identified by comparing exposure estimates, generated using point estimates of parameter values, to NOAELS. COPCs included mercury, arsenic, and PCBs. Exposure to COPCs was re-estimated using Monte Carlo simulations.more » Adverse population effects were assumed likely if > 20% of the estimated exposure distribution was greater than the LOAEL. Exposure of swallows to mercury was a significant risk at two locations. Exposure of bats to mercury was a significant risk at only one location. While consideration of movement and foraging territory did not reduce estimated risks to swallows, when exposures for gray and little brown bats were re-estimated, population-level risks from mercury were no longer considered likely. As an endangered species however, protection is extended to individual gray bats. While less than 20% of the mercury exposure distribution for gray bats was > LOAEL, > 99% of the distribution was >NOAEL. Therefore, adverse effects may occur among maximally exposed individual gray bats. Available data indicate that contaminants in Poplar Creek are likely to present a risk to the swallow population, do not present a risk to the little brown bat population, and may present a risk to individual gray bats.« less

Breeding biology of the blue-gray noddy

USGS Publications Warehouse

Rauson, M.J.; Harrison, C.S.; Clapp, R.B.

1984-01-01

Blue-gray Noddies, the smallest marine terns, are similar in many respects to all tropical terns in Hawaii: single-egg clutches are laid, growth and development take about 7 weeks, breeding is colonial. Its small size results in eggs that comprise over 27% of adult body weight, compared to 15-20% for most marine terns (Langham 1983). Blue-gray Noddies are widespread in the tropical Pacific, but populations are generally small. This may be the result of its inshore feeding habits and the fact that it is a resident species (Diamond 1978). However, populations in the Hawaiian Archipelago are probably limited by the availability of suitable nest sites in cliffs or rocky outcrops, not food supplies..... Food habits in Hawaii confirm the unique dependence of this species on sea-striders but consumption may be seasonal Blue-gray Noddies take the smallest prey of any seabird in Hawaii and may feed on a lower trophic level..... The Hawaiian population is apparently heavier and produces larger eggs than Blue-gray Noddies elsewhere in the Pacific. This conforms with the general proposition that Hawaiian seabirds are larger than those in the central Pacific (Harrison et al. 1983). The Hawaiian population also has a more predictable breeding season than those farther south.This may be due to a greater seasonality of food supply, but the factors that control the timing of breeding are not clear. There does not appear to be any competition for nest sites with other seabirds.....Our information on growth and development will enable future investigators to estimate the ages of chicks during brief visits to Blue-gray Noddy colonies. This will facilitate programs that are designed to monitor the basic health of seabird populations and to detect changes from baseline that may result from human activities or oceanographic conditions.

Voxel-Based Morphometry and fMRI Revealed Differences in Brain Gray Matter in Breastfed and Milk Formula-Fed Children.

PubMed

Ou, X; Andres, A; Pivik, R T; Cleves, M A; Snow, J H; Ding, Z; Badger, T M

2016-04-01

Infant diets may have significant impact on brain development in children. The aim of this study was to evaluate brain gray matter structure and function in 8-year-old children who were predominantly breastfed or fed cow's milk formula as infants. Forty-two healthy children (breastfed: n = 22, 10 boys and 12 girls; cow's milk formula: n = 20, 10 boys and 10 girls) were studied by using structural MR imaging (3D T1-weighted imaging) and blood oxygen level-dependent fMRI (while performing tasks involving visual perception and language functions). They were also administered standardized tests evaluating intelligence (Reynolds Intellectual Assessment Scales) and language skills (Clinical Evaluation of Language Fundamentals). Total brain gray matter volume did not differ between the breastfed and cow's milk formula groups. However, breastfed children had significantly higher (P < .05, corrected) regional gray matter volume measured by voxel-based morphometry in the left inferior temporal lobe and left superior parietal lobe compared with cow's milk formula-fed children. Breastfed children showed significantly more brain activation in the right frontal and left/right temporal lobes on fMRI when processing the perception task and in the left temporal/occipital lobe when processing the visual language task than cow's milk formula-fed children. The imaging findings were associated with significantly better performance for breastfed than cow's milk formula-fed children on both tasks. Our findings indicated greater regional gray matter development and better regional gray matter function in breastfed than cow's milk formula-fed children at 8 years of age and suggested that infant diets may have long-term influences on brain development in children. © 2016 by American Journal of Neuroradiology.

Gray Matter Volume Decrease Distinguishes Schizophrenia From Bipolar Offspring During Childhood and Adolescence.

PubMed

Sugranyes, Gisela; de la Serna, Elena; Romero, Soledad; Sanchez-Gistau, Vanessa; Calvo, Anna; Moreno, Dolores; Baeza, Inmaculada; Diaz-Caneja, Covadonga M; Sanchez-Gutierrez, Teresa; Janssen, Joost; Bargallo, Nuria; Castro-Fornieles, Josefina

2015-08-01

There is increasing support toward the notion that schizophrenia and bipolar disorder share neurodevelopmental underpinnings, although areas of divergence remain. We set out to examine gray matter volume characteristics of child and adolescent offspring of patients with schizophrenia or bipolar disorder comparatively. In this 2-center study, magnetic resonance structural neuroimaging data were acquired in 198 children and adolescents (aged 6-17 years): 38 offspring of patients with schizophrenia, 77 offspring of patients with bipolar disorder, and 83 offspring of community controls. Analyses of global brain volumes and voxel-based morphometry (using familywise error correction) were conducted. There was an effect of group on total cerebral gray matter volume (F = 3.26, p = .041), driven by a decrease in offspring of patients with schizophrenia relative to offspring of controls (p = .035). At a voxel-based level, we observed an effect of group in the left inferior frontal cortex/anterior insula (F = 14.7, p < .001), which was driven by gray matter volume reduction in offspring of patients with schizophrenia relative to both offspring of controls (p = .044) and of patients with bipolar disorder (p < .001). No differences were observed between offspring of patients with bipolar disorder and offspring of controls in either global or voxel-based gray matter volumes. This first comparative study between offspring of patients with schizophrenia and bipolar disorder suggests that gray matter volume reduction in childhood and adolescence may be specific to offspring of patients with schizophrenia; this may index a greater neurodevelopmental impact of risk for schizophrenia relative to bipolar disorder during youth. Copyright © 2015 American Academy of Child and Adolescent Psychiatry. Published by Elsevier Inc. All rights reserved.

Impact of the radiotherapy technique on the correlation between dose-volume histograms of the bladder wall defined on MRI imaging and dose-volume/surface histograms in prostate cancer patients

NASA Astrophysics Data System (ADS)

Maggio, Angelo; Carillo, Viviana; Cozzarini, Cesare; Perna, Lucia; Rancati, Tiziana; Valdagni, Riccardo; Gabriele, Pietro; Fiorino, Claudio

2013-04-01

The aim of this study was to evaluate the correlation between the ‘true’ absolute and relative dose-volume histograms (DVHs) of the bladder wall, dose-wall histogram (DWH) defined on MRI imaging and other surrogates of bladder dosimetry in prostate cancer patients, planned both with 3D-conformal and intensity-modulated radiation therapy (IMRT) techniques. For 17 prostate cancer patients, previously treated with radical intent, CT and MRI scans were acquired and matched. The contours of bladder walls were drawn by using MRI images. External bladder surfaces were then used to generate artificial bladder walls by performing automatic contractions of 5, 7 and 10 mm. For each patient a 3D conformal radiotherapy (3DCRT) and an IMRT treatment plan was generated with a prescription dose of 77.4 Gy (1.8 Gy/fr) and DVH of the whole bladder of the artificial walls (DVH-5/10) and dose-surface histograms (DSHs) were calculated and compared against the DWH in absolute and relative value, for both treatment planning techniques. A specific software (VODCA v. 4.4.0, MSS Inc.) was used for calculating the dose-volume/surface histogram. Correlation was quantified for selected dose-volume/surface parameters by the Spearman correlation coefficient. The agreement between %DWH and DVH5, DVH7 and DVH10 was found to be very good (maximum average deviations below 2%, SD < 5%): DVH5 showed the best agreement. The correlation was slightly better for absolute (R = 0.80-0.94) compared to relative (R = 0.66-0.92) histograms. The DSH was also found to be highly correlated with the DWH, although slightly higher deviations were generally found. The DVH was not a good surrogate of the DWH (R < 0.7 for most of parameters). When comparing the two treatment techniques, more pronounced differences between relative histograms were seen for IMRT with respect to 3DCRT (p < 0.0001).

Improved uniformity in high-performance organic photovoltaics enabled by (3-aminopropyl)triethoxysilane cathode functionalization.

PubMed

Luck, Kyle A; Shastry, Tejas A; Loser, Stephen; Ogien, Gabriel; Marks, Tobin J; Hersam, Mark C

2013-12-28

Organic photovoltaics have the potential to serve as lightweight, low-cost, mechanically flexible solar cells. However, losses in efficiency as laboratory cells are scaled up to the module level have to date impeded large scale deployment. Here, we report that a 3-aminopropyltriethoxysilane (APTES) cathode interfacial treatment significantly enhances performance reproducibility in inverted high-efficiency PTB7:PC71BM organic photovoltaic cells, as demonstrated by the fabrication of 100 APTES-treated devices versus 100 untreated controls. The APTES-treated devices achieve a power conversion efficiency of 8.08 ± 0.12% with histogram skewness of -0.291, whereas the untreated controls achieve 7.80 ± 0.26% with histogram skewness of -1.86. By substantially suppressing the interfacial origins of underperforming cells, the APTES treatment offers a pathway for fabricating large-area modules with high spatial performance uniformity.

Feature and contrast enhancement of mammographic image based on multiscale analysis and morphology.

PubMed

Wu, Shibin; Yu, Shaode; Yang, Yuhan; Xie, Yaoqin

2013-01-01

A new algorithm for feature and contrast enhancement of mammographic images is proposed in this paper. The approach bases on multiscale transform and mathematical morphology. First of all, the Laplacian Gaussian pyramid operator is applied to transform the mammography into different scale subband images. In addition, the detail or high frequency subimages are equalized by contrast limited adaptive histogram equalization (CLAHE) and low-pass subimages are processed by mathematical morphology. Finally, the enhanced image of feature and contrast is reconstructed from the Laplacian Gaussian pyramid coefficients modified at one or more levels by contrast limited adaptive histogram equalization and mathematical morphology, respectively. The enhanced image is processed by global nonlinear operator. The experimental results show that the presented algorithm is effective for feature and contrast enhancement of mammogram. The performance evaluation of the proposed algorithm is measured by contrast evaluation criterion for image, signal-noise-ratio (SNR), and contrast improvement index (CII).

Feature and Contrast Enhancement of Mammographic Image Based on Multiscale Analysis and Morphology

PubMed Central

Wu, Shibin; Xie, Yaoqin

2013-01-01

A new algorithm for feature and contrast enhancement of mammographic images is proposed in this paper. The approach bases on multiscale transform and mathematical morphology. First of all, the Laplacian Gaussian pyramid operator is applied to transform the mammography into different scale subband images. In addition, the detail or high frequency subimages are equalized by contrast limited adaptive histogram equalization (CLAHE) and low-pass subimages are processed by mathematical morphology. Finally, the enhanced image of feature and contrast is reconstructed from the Laplacian Gaussian pyramid coefficients modified at one or more levels by contrast limited adaptive histogram equalization and mathematical morphology, respectively. The enhanced image is processed by global nonlinear operator. The experimental results show that the presented algorithm is effective for feature and contrast enhancement of mammogram. The performance evaluation of the proposed algorithm is measured by contrast evaluation criterion for image, signal-noise-ratio (SNR), and contrast improvement index (CII). PMID:24416072

Face Liveness Detection Using Defocus

PubMed Central

Kim, Sooyeon; Ban, Yuseok; Lee, Sangyoun

2015-01-01

In order to develop security systems for identity authentication, face recognition (FR) technology has been applied. One of the main problems of applying FR technology is that the systems are especially vulnerable to attacks with spoofing faces (e.g., 2D pictures). To defend from these attacks and to enhance the reliability of FR systems, many anti-spoofing approaches have been recently developed. In this paper, we propose a method for face liveness detection using the effect of defocus. From two images sequentially taken at different focuses, three features, focus, power histogram and gradient location and orientation histogram (GLOH), are extracted. Afterwards, we detect forged faces through the feature-level fusion approach. For reliable performance verification, we develop two databases with a handheld digital camera and a webcam. The proposed method achieves a 3.29% half total error rate (HTER) at a given depth of field (DoF) and can be extended to camera-equipped devices, like smartphones. PMID:25594594

Development of a classification method for a crack on a pavement surface images using machine learning

NASA Astrophysics Data System (ADS)

Hizukuri, Akiyoshi; Nagata, Takeshi

2017-03-01

The purpose of this study is to develop a classification method for a crack on a pavement surface image using machine learning to reduce a maintenance fee. Our database consists of 3500 pavement surface images. This includes 800 crack and 2700 normal pavement surface images. The pavement surface images first are decomposed into several sub-images using a discrete wavelet transform (DWT) decomposition. We then calculate the wavelet sub-band histogram from each several sub-images at each level. The support vector machine (SVM) with computed wavelet sub-band histogram is employed for distinguishing between a crack and normal pavement surface images. The accuracies of the proposed classification method are 85.3% for crack and 84.4% for normal pavement images. The proposed classification method achieved high performance. Therefore, the proposed method would be useful in maintenance inspection.

Example-Based Image Colorization Using Locality Consistent Sparse Representation.

PubMed

Bo Li; Fuchen Zhao; Zhuo Su; Xiangguo Liang; Yu-Kun Lai; Rosin, Paul L

2017-11-01

Image colorization aims to produce a natural looking color image from a given gray-scale image, which remains a challenging problem. In this paper, we propose a novel example-based image colorization method exploiting a new locality consistent sparse representation. Given a single reference color image, our method automatically colorizes the target gray-scale image by sparse pursuit. For efficiency and robustness, our method operates at the superpixel level. We extract low-level intensity features, mid-level texture features, and high-level semantic features for each superpixel, which are then concatenated to form its descriptor. The collection of feature vectors for all the superpixels from the reference image composes the dictionary. We formulate colorization of target superpixels as a dictionary-based sparse reconstruction problem. Inspired by the observation that superpixels with similar spatial location and/or feature representation are likely to match spatially close regions from the reference image, we further introduce a locality promoting regularization term into the energy formulation, which substantially improves the matching consistency and subsequent colorization results. Target superpixels are colorized based on the chrominance information from the dominant reference superpixels. Finally, to further improve coherence while preserving sharpness, we develop a new edge-preserving filter for chrominance channels with the guidance from the target gray-scale image. To the best of our knowledge, this is the first work on sparse pursuit image colorization from single reference images. Experimental results demonstrate that our colorization method outperforms the state-of-the-art methods, both visually and quantitatively using a user study.

Gray and white matter correlates of the Big Five personality traits.

PubMed

Privado, Jesús; Román, Francisco J; Saénz-Urturi, Carlota; Burgaleta, Miguel; Colom, Roberto

2017-05-04

Personality neuroscience defines the scientific study of the neurobiological basis of personality. This field assumes that individual differences in personality traits are related with structural and functional variations of the human brain. Gray and white matters are structural properties considered separately in previous research. Available findings in this regard are largely disparate. Here we analyze the relationships between gray matter (cortical thickness (CT), cortical surface area (CSA), and cortical volume) and integrity scores obtained after several white matter tracts connecting different brain regions, with individual differences in the personality traits comprised by the Five-Factor Model (extraversion, agreeableness, conscientiousness, neuroticism, and openness to experience). These psychological and biological data were obtained from young healthy women. The main findings showed statistically significant associations between occipital CSA variations and extraversion, as well as between parietal CT variations and neuroticism. Regarding white matter integrity, openness showed positive correlations with tracts connecting posterior and anterior brain regions. Therefore, variations in discrete gray matter clusters were associated with temperamental traits (extraversion and neuroticism), whereas long-distance structural connections were related with the dimension of personality that has been associated with high-level cognitive processes (openness). Copyright © 2017 IBRO. Published by Elsevier Ltd. All rights reserved.

Gray matter network measures are associated with cognitive decline in mild cognitive impairment.

PubMed

Dicks, Ellen; Tijms, Betty M; Ten Kate, Mara; Gouw, Alida A; Benedictus, Marije R; Teunissen, Charlotte E; Barkhof, Frederik; Scheltens, Philip; van der Flier, Wiesje M

2018-01-01

Gray matter networks are disrupted in Alzheimer's disease and related to cognitive impairment. However, it is still unclear whether these disruptions are associated with cognitive decline over time. Here, we studied this question in a large sample of patients with mild cognitive impairment with extensive longitudinal neuropsychological assessments. Gray matter networks were extracted from baseline structural magnetic resonance imaging, and we tested associations of network measures and cognitive decline in Mini-Mental State Examination and 5 cognitive domains (i.e., memory, attention, executive function, visuospatial, and language). Disrupted network properties were cross-sectionally related to worse cognitive impairment. Longitudinally, lower small-world coefficient values were associated with a steeper decline in almost all domains. Lower betweenness centrality values correlated with a faster decline in Mini-Mental State Examination and memory, and at a regional level, these associations were specific for the precuneus, medial frontal, and temporal cortex. Furthermore, network measures showed additive value over established biomarkers in predicting cognitive decline. Our results suggest that gray matter network measures might have use in identifying patients who will show fast disease progression. Copyright © 2017 Elsevier Inc. All rights reserved.

Effects of Tithonia diversifolia (Hemsl.) A. Gray Extract on Adipocyte Differentiation of Human Mesenchymal Stem Cells

PubMed Central

Di Giacomo, Claudia; Vanella, Luca; Sorrenti, Valeria; Santangelo, Rosa; Barbagallo, Ignazio; Calabrese, Giovanna; Genovese, Carlo; Mastrojeni, Silvana; Ragusa, Salvatore; Acquaviva, Rosaria

2015-01-01

Tithonia diversifolia (Hemsl.) A. Gray (Asteraceae) is widely used in traditional medicine. There is increasing interest on the in vivo protective effects of natural compounds contained in plants against oxidative damage caused from reactive oxygen species. In the present study the total phenolic and flavonoid contents of aqueous, methanol and dichloromethane extracts of leaves of Tithonia diversifolia (Hemsl.) A. Gray were determined; furthermore, free radical scavenging capacity of each extract and the ability of these extracts to inhibit in vitro plasma lipid peroxidation were also evaluated. Since oxidative stress may be involved in trasformation of pre-adipocytes into adipocytes, to test the hypothesis that Tithonia extract may also affect adipocyte differentiation, human mesenchymal stem cell cultures were treated with Tithonia diversifolia aqueous extract and cell viability, free radical levels, Oil-Red O staining and western bolt analysis for heme oxygenase and 5'-adenosine monophoshate-activated protein kinase were carried out. Results obtained in the present study provide evidence that Tithonia diversifolia (Hemsl.) A. Gray exhibits interesting health promoting properties, resulting both from its free radical scavenger capacity and also by induction of protective cellular systems involved in cellular stress defenses and in adipogenesis of mesenchymal cells. PMID:25848759

Histograms and Raisin Bread

ERIC Educational Resources Information Center

Leyden, Michael B.

1975-01-01

Describes various elementary school activities using a loaf of raisin bread to promote inquiry skills. Activities include estimating the number of raisins in the loaf by constructing histograms of the number of raisins in a slice. (MLH)

A domain-knowledge-inspired mathematical framework for the description and classification of H&E stained histopathology images.

PubMed

Massar, Melody L; Bhagavatula, Ramamurthy; Ozolek, John A; Castro, Carlos A; Fickus, Matthew; Kovačević, Jelena

2011-10-19

We present the current state of our work on a mathematical framework for identification and delineation of histopathology images-local histograms and occlusion models. Local histograms are histograms computed over defined spatial neighborhoods whose purpose is to characterize an image locally. This unit of description is augmented by our occlusion models that describe a methodology for image formation. In the context of this image formation model, the power of local histograms with respect to appropriate families of images will be shown through various proved statements about expected performance. We conclude by presenting a preliminary study to demonstrate the power of the framework in the context of histopathology image classification tasks that, while differing greatly in application, both originate from what is considered an appropriate class of images for this framework.

Neutron camera employing row and column summations

DOEpatents

Clonts, Lloyd G.; Diawara, Yacouba; Donahue, Jr, Cornelius; Montcalm, Christopher A.; Riedel, Richard A.; Visscher, Theodore

2016-06-14

For each photomultiplier tube in an Anger camera, an R.times.S array of preamplifiers is provided to detect electrons generated within the photomultiplier tube. The outputs of the preamplifiers are digitized to measure the magnitude of the signals from each preamplifier. For each photomultiplier tube, a corresponding summation circuitry including R row summation circuits and S column summation circuits numerically add the magnitudes of the signals from preamplifiers for each row and for each column to generate histograms. For a P.times.Q array of photomultiplier tubes, P.times.Q summation circuitries generate P.times.Q row histograms including R entries and P.times.Q column histograms including S entries. The total set of histograms include P.times.Q.times.(R+S) entries, which can be analyzed by a position calculation circuit to determine the locations of events (detection of a neutron).

Evaluation of breast cancer using intravoxel incoherent motion (IVIM) histogram analysis: comparison with malignant status, histological subtype, and molecular prognostic factors.

PubMed

Cho, Gene Young; Moy, Linda; Kim, Sungheon G; Baete, Steven H; Moccaldi, Melanie; Babb, James S; Sodickson, Daniel K; Sigmund, Eric E

2016-08-01

To examine heterogeneous breast cancer through intravoxel incoherent motion (IVIM) histogram analysis. This HIPAA-compliant, IRB-approved retrospective study included 62 patients (age 48.44 ± 11.14 years, 50 malignant lesions and 12 benign) who underwent contrast-enhanced 3 T breast MRI and diffusion-weighted imaging. Apparent diffusion coefficient (ADC) and IVIM biomarkers of tissue diffusivity (Dt), perfusion fraction (fp), and pseudo-diffusivity (Dp) were calculated using voxel-based analysis for the whole lesion volume. Histogram analysis was performed to quantify tumour heterogeneity. Comparisons were made using Mann-Whitney tests between benign/malignant status, histological subtype, and molecular prognostic factor status while Spearman's rank correlation was used to characterize the association between imaging biomarkers and prognostic factor expression. The average values of the ADC and IVIM biomarkers, Dt and fp, showed significant differences between benign and malignant lesions. Additional significant differences were found in the histogram parameters among tumour subtypes and molecular prognostic factor status. IVIM histogram metrics, particularly fp and Dp, showed significant correlation with hormonal factor expression. Advanced diffusion imaging biomarkers show relationships with molecular prognostic factors and breast cancer malignancy. This analysis reveals novel diagnostic metrics that may explain some of the observed variability in treatment response among breast cancer patients. • Novel IVIM biomarkers characterize heterogeneous breast cancer. • Histogram analysis enables quantification of tumour heterogeneity. • IVIM biomarkers show relationships with breast cancer malignancy and molecular prognostic factors.

Whole-tumor histogram analysis of the cerebral blood volume map: tumor volume defined by 11C-methionine positron emission tomography image improves the diagnostic accuracy of cerebral glioma grading.

PubMed

Wu, Rongli; Watanabe, Yoshiyuki; Arisawa, Atsuko; Takahashi, Hiroto; Tanaka, Hisashi; Fujimoto, Yasunori; Watabe, Tadashi; Isohashi, Kayako; Hatazawa, Jun; Tomiyama, Noriyuki

2017-10-01

This study aimed to compare the tumor volume definition using conventional magnetic resonance (MR) and 11C-methionine positron emission tomography (MET/PET) images in the differentiation of the pre-operative glioma grade by using whole-tumor histogram analysis of normalized cerebral blood volume (nCBV) maps. Thirty-four patients with histopathologically proven primary brain low-grade gliomas (n = 15) and high-grade gliomas (n = 19) underwent pre-operative or pre-biopsy MET/PET, fluid-attenuated inversion recovery, dynamic susceptibility contrast perfusion-weighted magnetic resonance imaging, and contrast-enhanced T1-weighted at 3.0 T. The histogram distribution derived from the nCBV maps was obtained by co-registering the whole tumor volume delineated on conventional MR or MET/PET images, and eight histogram parameters were assessed. The mean nCBV value had the highest AUC value (0.906) based on MET/PET images. Diagnostic accuracy significantly improved when the tumor volume was measured from MET/PET images compared with conventional MR images for the parameters of mean, 50th, and 75th percentile nCBV value (p = 0.0246, 0.0223, and 0.0150, respectively). Whole-tumor histogram analysis of CBV map provides more valuable histogram parameters and increases diagnostic accuracy in the differentiation of pre-operative cerebral gliomas when the tumor volume is derived from MET/PET images.

Effect of respiratory and cardiac gating on the major diffusion-imaging metrics.

PubMed

Hamaguchi, Hiroyuki; Tha, Khin Khin; Sugimori, Hiroyuki; Nakanishi, Mitsuhiro; Nakagawa, Shin; Fujiwara, Taro; Yoshida, Hirokazu; Takamori, Sayaka; Shirato, Hiroki

2016-08-01

The effect of respiratory gating on the major diffusion-imaging metrics and that of cardiac gating on mean kurtosis (MK) are not known. For evaluation of whether the major diffusion-imaging metrics-MK, fractional anisotropy (FA), and mean diffusivity (MD) of the brain-varied between gated and non-gated acquisitions, respiratory-gated, cardiac-gated, and non-gated diffusion-imaging of the brain were performed in 10 healthy volunteers. MK, FA, and MD maps were constructed for all acquisitions, and the histograms were constructed. The normalized peak height and location of the histograms were compared among the acquisitions by use of Friedman and post hoc Wilcoxon tests. The effect of the repetition time (TR) on the diffusion-imaging metrics was also tested, and we corrected for its variation among acquisitions, if necessary. The results showed a shift in the peak location of the MK and MD histograms to the right with an increase in TR (p ≤ 0.01). The corrected peak location of the MK histograms, the normalized peak height of the FA histograms, the normalized peak height and the corrected peak location of the MD histograms varied significantly between the gated and non-gated acquisitions (p < 0.05). These results imply an influence of respiration and cardiac pulsation on the major diffusion-imaging metrics. The gating conditions must be kept identical if reproducible results are to be achieved. © The Author(s) 2016.

Effects of irradiation on fungi and fumonisin B(1) in corn, and of microwave-popping on fumonisins in popcorn.

PubMed

D'Ovidio, K L; Trucksess, M W; Devries, J W; Bean, G

2007-07-01

Fumonisins are metabolites produced in corn primarily by the fungus Fusarium verticillioides (F. moniliforme) and are toxic to humans and animals. Fumonisin B(1) (FB(1)) is the primary fumonisin produced and is found frequently in corn kernels, some of which may be used as food or food ingredients. A three-part study was conducted to determine the effects of gamma- and electron beam irradiation on the levels of fumonisins in naturally contaminated field corn, and the effects of microwave-popping on fumonisins in selected, naturally contaminated popcorn. To date, no effective means have been found to reduce consistently mycotoxin levels once foods are contaminated. Aqueous solutions of FB(1) at various concentrations, samples of whole corn, and samples of ground corn containing known levels of FB(1) were irradiated with various levels of cobalt and electron beam irradiation. Popcorn samples, taken from the reject streams of popcorn processing, were popped using normal microwave-popping conditions. FB(1) in aqueous solutions was reduced by 99.7% using a minimal level of irradiation (0.5 kGray). Gamma- and electron beam irradiation did not significantly reduce levels of FB(1) in whole and ground corn. Aspergillus sp., Penicillium sp. and Fusarium sp. fungi were totally eliminated at 30 kGray in ground corn and at 100 kGray in whole corn. The normal commercial cleaning processes for microwave popcorn before packaging reduced fumonisins to <0.03 microg g(-1) for the cleaned product stream. Microwave popping of popcorn from reject streams of the cleaning operation that contained fumonisins resulted in significant reduction of the mould toxin.

Entropy-Based Adaptive Nuclear Texture Features are Independent Prognostic Markers in a Total Population of Uterine Sarcomas

PubMed Central

Nielsen, Birgitte; Hveem, Tarjei Sveinsgjerd; Kildal, Wanja; Abeler, Vera M; Kristensen, Gunnar B; Albregtsen, Fritz; Danielsen, Håvard E; Rohde, Gustavo K

2015-01-01

Nuclear texture analysis measures the spatial arrangement of the pixel gray levels in a digitized microscopic nuclear image and is a promising quantitative tool for prognosis of cancer. The aim of this study was to evaluate the prognostic value of entropy-based adaptive nuclear texture features in a total population of 354 uterine sarcomas. Isolated nuclei (monolayers) were prepared from 50 µm tissue sections and stained with Feulgen-Schiff. Local gray level entropy was measured within small windows of each nuclear image and stored in gray level entropy matrices, and two superior adaptive texture features were calculated from each matrix. The 5-year crude survival was significantly higher (P < 0.001) for patients with high texture feature values (72%) than for patients with low feature values (36%). When combining DNA ploidy classification (diploid/nondiploid) and texture (high/low feature value), the patients could be stratified into three risk groups with 5-year crude survival of 77, 57, and 34% (Hazard Ratios (HR) of 1, 2.3, and 4.1, P < 0.001). Entropy-based adaptive nuclear texture was an independent prognostic marker for crude survival in multivariate analysis including relevant clinicopathological features (HR = 2.1, P = 0.001), and should therefore be considered as a potential prognostic marker in uterine sarcomas. © The Authors. Published 2014 International Society for Advancement of Cytometry PMID:25483227

Pattern-histogram-based temporal change detection using personal chest radiographs

NASA Astrophysics Data System (ADS)

Ugurlu, Yucel; Obi, Takashi; Hasegawa, Akira; Yamaguchi, Masahiro; Ohyama, Nagaaki

1999-05-01

An accurate and reliable detection of temporal changes from a pair of images has considerable interest in the medical science. Traditional registration and subtraction techniques can be applied to extract temporal differences when,the object is rigid or corresponding points are obvious. However, in radiological imaging, loss of the depth information, the elasticity of object, the absence of clearly defined landmarks and three-dimensional positioning differences constraint the performance of conventional registration techniques. In this paper, we propose a new method in order to detect interval changes accurately without using an image registration technique. The method is based on construction of so-called pattern histogram and comparison procedure. The pattern histogram is a graphic representation of the frequency counts of all allowable patterns in the multi-dimensional pattern vector space. K-means algorithm is employed to partition pattern vector space successively. Any differences in the pattern histograms imply that different patterns are involved in the scenes. In our experiment, a pair of chest radiographs of pneumoconiosis is employed and the changing histogram bins are visualized on both of the images. We found that the method can be used as an alternative way of temporal change detection, particularly when the precise image registration is not available.

A Concise Guide to Feature Histograms with Applications to LIDAR-Based Spacecraft Relative Navigation

NASA Astrophysics Data System (ADS)

Rhodes, Andrew P.; Christian, John A.; Evans, Thomas

2017-12-01

With the availability and popularity of 3D sensors, it is advantageous to re-examine the use of point cloud descriptors for the purpose of pose estimation and spacecraft relative navigation. One popular descriptor is the oriented unique repeatable clustered viewpoint feature histogram (OUR-CVFH), which is most often utilized in personal and industrial robotics to simultaneously recognize and navigate relative to an object. Recent research into using the OUR-CVFH descriptor for spacecraft navigation has produced favorable results. Since OUR-CVFH is the most recent innovation in a large family of feature histogram point cloud descriptors, discussions of parameter settings and insights into its functionality are spread among various publications and online resources. This paper organizes the history of feature histogram point cloud descriptors for a straightforward explanation of their evolution. This article compiles all the requisite information needed to implement OUR-CVFH into one location, as well as providing useful suggestions on how to tune the generation parameters. This work is beneficial for anyone interested in using this histogram descriptor for object recognition or navigation - may it be personal robotics or spacecraft navigation.

Hybrid Histogram Descriptor: A Fusion Feature Representation for Image Retrieval.

PubMed

Feng, Qinghe; Hao, Qiaohong; Chen, Yuqi; Yi, Yugen; Wei, Ying; Dai, Jiangyan

2018-06-15

Currently, visual sensors are becoming increasingly affordable and fashionable, acceleratingly the increasing number of image data. Image retrieval has attracted increasing interest due to space exploration, industrial, and biomedical applications. Nevertheless, designing effective feature representation is acknowledged as a hard yet fundamental issue. This paper presents a fusion feature representation called a hybrid histogram descriptor (HHD) for image retrieval. The proposed descriptor comprises two histograms jointly: a perceptually uniform histogram which is extracted by exploiting the color and edge orientation information in perceptually uniform regions; and a motif co-occurrence histogram which is acquired by calculating the probability of a pair of motif patterns. To evaluate the performance, we benchmarked the proposed descriptor on RSSCN7, AID, Outex-00013, Outex-00014 and ETHZ-53 datasets. Experimental results suggest that the proposed descriptor is more effective and robust than ten recent fusion-based descriptors under the content-based image retrieval framework. The computational complexity was also analyzed to give an in-depth evaluation. Furthermore, compared with the state-of-the-art convolutional neural network (CNN)-based descriptors, the proposed descriptor also achieves comparable performance, but does not require any training process.

Improved LSB matching steganography with histogram characters reserved

NASA Astrophysics Data System (ADS)

Chen, Zhihong; Liu, Wenyao

2008-03-01

This letter bases on the researches of LSB (least significant bit, i.e. the last bit of a binary pixel value) matching steganographic method and the steganalytic method which aims at histograms of cover images, and proposes a modification to LSB matching. In the LSB matching, if the LSB of the next cover pixel matches the next bit of secret data, do nothing; otherwise, choose to add or subtract one from the cover pixel value at random. In our improved method, a steganographic information table is defined and records the changes which embedded secrete bits introduce in. Through the table, the next LSB which has the same pixel value will be judged to add or subtract one dynamically in order to ensure the histogram's change of cover image is minimized. Therefore, the modified method allows embedding the same payload as the LSB matching but with improved steganographic security and less vulnerability to attacks compared with LSB matching. The experimental results of the new method show that the histograms maintain their attributes, such as peak values and alternative trends, in an acceptable degree and have better performance than LSB matching in the respects of histogram distortion and resistance against existing steganalysis.

Knowledge-based low-level image analysis for computer vision systems

NASA Technical Reports Server (NTRS)

Dhawan, Atam P.; Baxi, Himanshu; Ranganath, M. V.

1988-01-01

Two algorithms for entry-level image analysis and preliminary segmentation are proposed which are flexible enough to incorporate local properties of the image. The first algorithm involves pyramid-based multiresolution processing and a strategy to define and use interlevel and intralevel link strengths. The second algorithm, which is designed for selected window processing, extracts regions adaptively using local histograms. The preliminary segmentation and a set of features are employed as the input to an efficient rule-based low-level analysis system, resulting in suboptimal meaningful segmentation.

Histograms and Frequency Density.

ERIC Educational Resources Information Center

Micromath, 2003

2003-01-01

Introduces exercises on histograms and frequency density. Guides pupils to Discovering Important Statistical Concepts Using Spreadsheets (DISCUSS), created at the University of Coventry. Includes curriculum points, teaching tips, activities, and internet address (http://www.coventry.ac.uk/discuss/). (KHR)

50 CFR 32.34 - Iowa.

Code of Federal Regulations, 2014 CFR

2014-10-01

..., bobwhite quail, gray partridge, rabbit (cottontail and jack), squirrel (fox and gray), groundhog, raccoon... pheasant, gray partridge, rabbit (cottontail and jack), squirrel (fox and gray), groundhog, raccoon... rabbit, gray and fox squirrel, and fall wild turkey (2 weeks within the season) on designated areas of...

Contrast limited adaptive histogram equalization image processing to improve the detection of simulated spiculations in dense mammograms.

PubMed

Pisano, E D; Zong, S; Hemminger, B M; DeLuca, M; Johnston, R E; Muller, K; Braeuning, M P; Pizer, S M

1998-11-01

The purpose of this project was to determine whether Contrast Limited Adaptive Histogram Equalization (CLAHE) improves detection of simulated spiculations in dense mammograms. Lines simulating the appearance of spiculations, a common marker of malignancy when visualized with masses, were embedded in dense mammograms digitized at 50 micron pixels, 12 bits deep. Film images with no CLAHE applied were compared to film images with nine different combinations of clip levels and region sizes applied. A simulated spiculation was embedded in a background of dense breast tissue, with the orientation of the spiculation varied. The key variables involved in each trial included the orientation of the spiculation, contrast level of the spiculation and the CLAHE settings applied to the image. Combining the 10 CLAHE conditions, 4 contrast levels and 4 orientations gave 160 combinations. The trials were constructed by pairing 160 combinations of key variables with 40 backgrounds. Twenty student observers were asked to detect the orientation of the spiculation in the image. There was a statistically significant improvement in detection performance for spiculations with CLAHE over unenhanced images when the region size was set at 32 with a clip level of 2, and when the region size was set at 32 with a clip level of 4. The selected CLAHE settings should be tested in the clinic with digital mammograms to determine whether detection of spiculations associated with masses detected at mammography can be improved.

Diffusion Profiling via a Histogram Approach Distinguishes Low-grade from High-grade Meningiomas, Can Reflect the Respective Proliferative Potential and Progesterone Receptor Status.

PubMed

Gihr, Georg Alexander; Horvath-Rizea, Diana; Garnov, Nikita; Kohlhof-Meinecke, Patricia; Ganslandt, Oliver; Henkes, Hans; Meyer, Hans Jonas; Hoffmann, Karl-Titus; Surov, Alexey; Schob, Stefan

2018-02-01

Presurgical grading, estimation of growth kinetics, and other prognostic factors are becoming increasingly important for selecting the best therapeutic approach for meningioma patients. Diffusion-weighted imaging (DWI) provides microstructural information and reflects tumor biology. A novel DWI approach, histogram profiling of apparent diffusion coefficient (ADC) volumes, provides more distinct information than conventional DWI. Therefore, our study investigated whether ADC histogram profiling distinguishes low-grade from high-grade lesions and reflects Ki-67 expression and progesterone receptor status. Pretreatment ADC volumes of 37 meningioma patients (28 low-grade, 9 high-grade) were used for histogram profiling. WHO grade, Ki-67 expression, and progesterone receptor status were evaluated. Comparative and correlative statistics investigating the association between histogram profiling and neuropathology were performed. The entire ADC profile (p10, p25, p75, p90, mean, median) was significantly lower in high-grade versus low-grade meningiomas. The lower percentiles, mean, and modus showed significant correlations with Ki-67 expression. Skewness and entropy of the ADC volumes were significantly associated with progesterone receptor status and Ki-67 expression. ROC analysis revealed entropy to be the most accurate parameter distinguishing low-grade from high-grade meningiomas. ADC histogram profiling provides a distinct set of parameters, which help differentiate low-grade versus high-grade meningiomas. Also, histogram metrics correlate significantly with histological surrogates of the respective proliferative potential. More specifically, entropy revealed to be the most promising imaging biomarker for presurgical grading. Both, entropy and skewness were significantly associated with progesterone receptor status and Ki-67 expression and therefore should be investigated further as predictors for prognostically relevant tumor biological features. Since absolute ADC values vary between MRI scanners of different vendors and field strengths, their use is more limited in the presurgical setting.

Histogram Analysis of CT Perfusion of Hepatocellular Carcinoma for Predicting Response to Transarterial Radioembolization: Value of Tumor Heterogeneity Assessment.

PubMed

Reiner, Caecilia S; Gordic, Sonja; Puippe, Gilbert; Morsbach, Fabian; Wurnig, Moritz; Schaefer, Niklaus; Veit-Haibach, Patrick; Pfammatter, Thomas; Alkadhi, Hatem

2016-03-01

To evaluate in patients with hepatocellular carcinoma (HCC), whether assessment of tumor heterogeneity by histogram analysis of computed tomography (CT) perfusion helps predicting response to transarterial radioembolization (TARE). Sixteen patients (15 male; mean age 65 years; age range 47-80 years) with HCC underwent CT liver perfusion for treatment planning prior to TARE with Yttrium-90 microspheres. Arterial perfusion (AP) derived from CT perfusion was measured in the entire tumor volume, and heterogeneity was analyzed voxel-wise by histogram analysis. Response to TARE was evaluated on follow-up imaging (median follow-up, 129 days) based on modified Response Evaluation Criteria in Solid Tumors (mRECIST). Results of histogram analysis and mean AP values of the tumor were compared between responders and non-responders. Receiver operating characteristics were calculated to determine the parameters' ability to discriminate responders from non-responders. According to mRECIST, 8 patients (50%) were responders and 8 (50%) non-responders. Comparing responders and non-responders, the 50th and 75th percentile of AP derived from histogram analysis was significantly different [AP 43.8/54.3 vs. 27.6/34.3 mL min(-1) 100 mL(-1)); p < 0.05], while the mean AP of HCCs (43.5 vs. 27.9 mL min(-1) 100 mL(-1); p > 0.05) was not. Further heterogeneity parameters from histogram analysis (skewness, coefficient of variation, and 25th percentile) did not differ between responders and non-responders (p > 0.05). If the cut-off for the 75th percentile was set to an AP of 37.5 mL min(-1) 100 mL(-1), therapy response could be predicted with a sensitivity of 88% (7/8) and specificity of 75% (6/8). Voxel-wise histogram analysis of pretreatment CT perfusion indicating tumor heterogeneity of HCC improves the pretreatment prediction of response to TARE.

ADC histogram analysis for adrenal tumor histogram analysis of apparent diffusion coefficient in differentiating adrenal adenoma from pheochromocytoma.

PubMed

Umanodan, Tomokazu; Fukukura, Yoshihiko; Kumagae, Yuichi; Shindo, Toshikazu; Nakajo, Masatoyo; Takumi, Koji; Nakajo, Masanori; Hakamada, Hiroto; Umanodan, Aya; Yoshiura, Takashi

2017-04-01

To determine the diagnostic performance of apparent diffusion coefficient (ADC) histogram analysis in diffusion-weighted (DW) magnetic resonance imaging (MRI) for differentiating adrenal adenoma from pheochromocytoma. We retrospectively evaluated 52 adrenal tumors (39 adenomas and 13 pheochromocytomas) in 47 patients (21 men, 26 women; mean age, 59.3 years; range, 16-86 years) who underwent DW 3.0T MRI. Histogram parameters of ADC (b-values of 0 and 200 [ADC 200 ], 0 and 400 [ADC 400 ], and 0 and 800 s/mm 2 [ADC 800 ])-mean, variance, coefficient of variation (CV), kurtosis, skewness, and entropy-were compared between adrenal adenomas and pheochromocytomas, using the Mann-Whitney U-test. Receiver operating characteristic (ROC) curves for the histogram parameters were generated to differentiate adrenal adenomas from pheochromocytomas. Sensitivity and specificity were calculated by using a threshold criterion that would maximize the average of sensitivity and specificity. Variance and CV of ADC 800 were significantly higher in pheochromocytomas than in adrenal adenomas (P < 0.001 and P = 0.001, respectively). With all b-value combinations, the entropy of ADC was significantly higher in pheochromocytomas than in adrenal adenomas (all P ≤ 0.001), and showed the highest area under the ROC curve among the ADC histogram parameters for diagnosing adrenal adenomas (ADC 200 , 0.82; ADC 400 , 0.87; and ADC 800 , 0.92), with sensitivity of 84.6% and specificity of 84.6% (cutoff, ≤2.82) with ADC 200 ; sensitivity of 89.7% and specificity of 84.6% (cutoff, ≤2.77) with ADC 400 ; and sensitivity of 94.9% and specificity of 92.3% (cutoff, ≤2.67) with ADC 800 . ADC histogram analysis of DW MRI can help differentiate adrenal adenoma from pheochromocytoma. 3 J. Magn. Reson. Imaging 2017;45:1195-1203. © 2016 International Society for Magnetic Resonance in Medicine.

A Novel Machine Vision System for the Inspection of Micro-Spray Nozzle

PubMed Central

Huang, Kuo-Yi; Ye, Yu-Ting

2015-01-01

In this study, we present an application of neural network and image processing techniques for detecting the defects of an internal micro-spray nozzle. The defect regions were segmented by Canny edge detection, a randomized algorithm for detecting circles and a circle inspection (CI) algorithm. The gray level co-occurrence matrix (GLCM) was further used to evaluate the texture features of the segmented region. These texture features (contrast, entropy, energy), color features (mean and variance of gray level) and geometric features (distance variance, mean diameter and diameter ratio) were used in the classification procedures. A back-propagation neural network classifier was employed to detect the defects of micro-spray nozzles. The methodology presented herein effectively works for detecting micro-spray nozzle defects to an accuracy of 90.71%. PMID:26131678

A Novel Machine Vision System for the Inspection of Micro-Spray Nozzle.

PubMed

Huang, Kuo-Yi; Ye, Yu-Ting

2015-06-29

In this study, we present an application of neural network and image processing techniques for detecting the defects of an internal micro-spray nozzle. The defect regions were segmented by Canny edge detection, a randomized algorithm for detecting circles and a circle inspection (CI) algorithm. The gray level co-occurrence matrix (GLCM) was further used to evaluate the texture features of the segmented region. These texture features (contrast, entropy, energy), color features (mean and variance of gray level) and geometric features (distance variance, mean diameter and diameter ratio) were used in the classification procedures. A back-propagation neural network classifier was employed to detect the defects of micro-spray nozzles. The methodology presented herein effectively works for detecting micro-spray nozzle defects to an accuracy of 90.71%.

Robust Audio Watermarking by Using Low-Frequency Histogram

NASA Astrophysics Data System (ADS)

Xiang, Shijun

In continuation to earlier work where the problem of time-scale modification (TSM) has been studied [1] by modifying the shape of audio time domain histogram, here we consider the additional ingredient of resisting additive noise-like operations, such as Gaussian noise, lossy compression and low-pass filtering. In other words, we study the problem of the watermark against both TSM and additive noises. To this end, in this paper we extract the histogram from a Gaussian-filtered low-frequency component for audio watermarking. The watermark is inserted by shaping the histogram in a way that the use of two consecutive bins as a group is exploited for hiding a bit by reassigning their population. The watermarked signals are perceptibly similar to the original one. Comparing with the previous time-domain watermarking scheme [1], the proposed watermarking method is more robust against additive noise, MP3 compression, low-pass filtering, etc.

[Image Feature Extraction and Discriminant Analysis of Xinjiang Uygur Medicine Based on Color Histogram].

PubMed

Hamit, Murat; Yun, Weikang; Yan, Chuanbo; Kutluk, Abdugheni; Fang, Yang; Alip, Elzat

2015-06-01

Image feature extraction is an important part of image processing and it is an important field of research and application of image processing technology. Uygur medicine is one of Chinese traditional medicine and researchers pay more attention to it. But large amounts of Uygur medicine data have not been fully utilized. In this study, we extracted the image color histogram feature of herbal and zooid medicine of Xinjiang Uygur. First, we did preprocessing, including image color enhancement, size normalizition and color space transformation. Then we extracted color histogram feature and analyzed them with statistical method. And finally, we evaluated the classification ability of features by Bayes discriminant analysis. Experimental results showed that high accuracy for Uygur medicine image classification was obtained by using color histogram feature. This study would have a certain help for the content-based medical image retrieval for Xinjiang Uygur medicine.

Advanced concentration analysis of atom probe tomography data: Local proximity histograms and pseudo-2D concentration maps.

PubMed

Felfer, Peter; Cairney, Julie

2018-06-01

Analysing the distribution of selected chemical elements with respect to interfaces is one of the most common tasks in data mining in atom probe tomography. This can be represented by 1D concentration profiles, 2D concentration maps or proximity histograms, which represent concentration, density etc. of selected species as a function of the distance from a reference surface/interface. These are some of the most useful tools for the analysis of solute distributions in atom probe data. In this paper, we present extensions to the proximity histogram in the form of 'local' proximity histograms, calculated for selected parts of a surface, and pseudo-2D concentration maps, which are 2D concentration maps calculated on non-flat surfaces. This way, local concentration changes at interfaces or and other structures can be assessed more effectively. Copyright © 2018 Elsevier B.V. All rights reserved.

Interactions of allele E of the MC1R gene with FM and mutations in the MLPH gene cause the five-gray phenotype in the Anyi tile-like gray chicken.

PubMed

Xu, J G; Xie, M G; Zou, S Y; Liu, X F; Li, X H; Xie, J F; Zhang, X Q

2016-04-26

The Anyi tile-like gray chicken is a Chinese indigenous breed with a gray dilution phenotype, having gray feathers, comb, skin, shanks, and beak, which is valuable for genetic research on pigmentation. However, the genetic basis of the gray dilution phenotype remains unknown. The objective of this study was to investigate the genetic basis of the gray dilution phenotype in the Anyi tile-like gray chicken. We found that all Anyi tile-like gray chickens tested in this study carried at least one E allele, which is responsible for the appearance of black feathers, and some of them carried the FM allele, which is responsible for the black skin phenotype. A single nucleotide polymorphism (C.1909A>G) was identified within the melanophilin (MLPH) gene and was significantly associated with the gray dilution phenotype. Our findings suggest that the E and FM alleles act together to cause the development of the "five-black" phenotype (black feather, comb, skin, shank, and beak), whereas the MLPH mutation results in defective melanosome transport, leading to the development of the "five-gray" phenotype.

Insight on AV-45 binding in white and grey matter from histogram analysis: a study on early Alzheimer's disease patients and healthy subjects

PubMed Central

Nemmi, Federico; Saint-Aubert, Laure; Adel, Djilali; Salabert, Anne-Sophie; Pariente, Jérémie; Barbeau, Emmanuel; Payoux, Pierre; Péran, Patrice

2014-01-01

Purpose AV-45 amyloid biomarker is known to show uptake in white matter in patients with Alzheimer’s disease (AD) but also in healthy population. This binding; thought to be of a non-specific lipophilic nature has not yet been investigated. The aim of this study was to determine the differential pattern of AV-45 binding in healthy and pathological populations in white matter. Methods We recruited 24 patients presenting with AD at early stage and 17 matched, healthy subjects. We used an optimized PET-MRI registration method and an approach based on intensity histogram using several indexes. We compared the results of the intensity histogram analyses with a more canonical approach based on target-to-cerebellum Standard Uptake Value (SUVr) in white and grey matters using MANOVA and discriminant analyses. A cluster analysis on white and grey matter histograms was also performed. Results White matter histogram analysis revealed significant differences between AD and healthy subjects, which were not revealed by SUVr analysis. However, white matter histograms was not decisive to discriminate groups, and indexes based on grey matter only showed better discriminative power than SUVr. The cluster analysis divided our sample in two clusters, showing different uptakes in grey but also in white matter. Conclusion These results demonstrate that AV-45 binding in white matter conveys subtle information not detectable using SUVr approach. Although it is not better than standard SUVr to discriminate AD patients from healthy subjects, this information could reveal white matter modifications. PMID:24573658

Predicting pathologic tumor response to chemoradiotherapy with histogram distances characterizing longitudinal changes in 18F-FDG uptake patterns

PubMed Central

Tan, Shan; Zhang, Hao; Zhang, Yongxue; Chen, Wengen; D’Souza, Warren D.; Lu, Wei

2013-01-01

Purpose: A family of fluorine-18 (18F)-fluorodeoxyglucose (18F-FDG) positron-emission tomography (PET) features based on histogram distances is proposed for predicting pathologic tumor response to neoadjuvant chemoradiotherapy (CRT). These features describe the longitudinal change of FDG uptake distribution within a tumor. Methods: Twenty patients with esophageal cancer treated with CRT plus surgery were included in this study. All patients underwent PET/CT scans before (pre-) and after (post-) CRT. The two scans were first rigidly registered, and the original tumor sites were then manually delineated on the pre-PET/CT by an experienced nuclear medicine physician. Two histograms representing the FDG uptake distribution were extracted from the pre- and the registered post-PET images, respectively, both within the delineated tumor. Distances between the two histograms quantify longitudinal changes in FDG uptake distribution resulting from CRT, and thus are potential predictors of tumor response. A total of 19 histogram distances were examined and compared to both traditional PET response measures and Haralick texture features. Receiver operating characteristic analyses and Mann-Whitney U test were performed to assess their predictive ability. Results: Among all tested histogram distances, seven bin-to-bin and seven crossbin distances outperformed traditional PET response measures using maximum standardized uptake value (AUC = 0.70) or total lesion glycolysis (AUC = 0.80). The seven bin-to-bin distances were: L2 distance (AUC = 0.84), χ2 distance (AUC = 0.83), intersection distance (AUC = 0.82), cosine distance (AUC = 0.83), squared Euclidean distance (AUC = 0.83), L1 distance (AUC = 0.82), and Jeffrey distance (AUC = 0.82). The seven crossbin distances were: quadratic-chi distance (AUC = 0.89), earth mover distance (AUC = 0.86), fast earth mover distance (AUC = 0.86), diffusion distance (AUC = 0.88), Kolmogorov-Smirnov distance (AUC = 0.88), quadratic form distance (AUC = 0.87), and match distance (AUC = 0.84). These crossbin histogram distance features showed slightly higher prediction accuracy than texture features on post-PET images. Conclusions: The results suggest that longitudinal patterns in 18F-FDG uptake characterized using histogram distances provide useful information for predicting the pathologic response of esophageal cancer to CRT. PMID:24089897

[Characteristics of high resolution diffusion weighted imaging apparent diffusion coefficient histogram and its correlations with cancer stages in patients with nasopharyngeal carcinoma].

PubMed

Wang, G J; Wang, Y; Ye, Y; Chen, F; Lu, Y T; Li, S L

2017-11-07

Objective: To investigate the features of apparent diffusion coefficient (ADC) histogram parameters based on entire tumor volume data in high resolution diffusion weighted imaging of nasopharyngeal carcinoma (NPC) and to evaluate its correlations with cancer stages. Methods: This retrospective study included 154 cases of NPC patients[102 males and 52 females, mean age (48±11) years]who had received readout segmentation of long variable echo trains of MRI scan before radiation therapy. The area of tumor was delineated on each section of axial ADC maps to generate ADC histogram by using Image J. ADC histogram of entire tumor along with the histogram parameters-the tumor voxels, ADC(mean), ADC(25%), ADC(50%), ADC(75%), skewness and kurtosis were obtained by merging all sections with SPSS 22.0 software. Intra-observer repeatability was assessed by using intra-class correlation coefficients (ICC). The patients were subdivided into two groups according to cancer volume: small cancer group (<305 voxels, about 2 cm(3)) and large cancer group (≥2 cm(3)). The correlation between ADC histogram parameters and cancer stages was evaluated with Spearman test. Results: The ICC of measuring ADC histogram parameters of tumor voxels, ADC(mean), ADC(25%), ADC(50%), ADC(75%), skewness, kurtosis was 0.938, 0.861, 0.885, 0.838, 0.836, 0.358 and 0.456, respectively. The tumor voxels was positively correlated with T staging ( r =0.368, P <0.05). There were significant differences in tumor voxels among patients with different T stages ( K =22.306, P <0.05). There were significant differences in the ADC(mean), ADC(25%), ADC(50%) among patients with different T stages in the small cancer group( K =8.409, 8.187, 8.699, all P <0.05), and the up-mentioned three indices were positively correlated with T staging ( r =0.221, 0.209, 0.235, all P <0.05). Skewness and kurtosis differed significantly between the groups with different cancer volume( t =-2.987, Z =-3.770, both P <0.05). Conclusion: The tumor volume, tissue uniformity of NPC are important factors affecting ADC and cancer stages, parameters of ADC histogram (ADC(mean), ADC(25%), ADC(50%)) increases with T staging in NPC smaller than 2 cm(3).

Folic acid in diet

MedlinePlus

... carries genetic information Folate deficiency may cause: Diarrhea Gray hair Mouth ulcers Peptic ulcer Poor growth Swollen ... used at recommended levels. Folic acid dissolves in water. This means that it is regularly removed from ...

Investigation of attenuation correction in SPECT using textural features, Monte Carlo simulations, and computational anthropomorphic models.

PubMed

Spirou, Spiridon V; Papadimitroulas, Panagiotis; Liakou, Paraskevi; Georgoulias, Panagiotis; Loudos, George

2015-09-01

To present and evaluate a new methodology to investigate the effect of attenuation correction (AC) in single-photon emission computed tomography (SPECT) using textural features analysis, Monte Carlo techniques, and a computational anthropomorphic model. The GATE Monte Carlo toolkit was used to simulate SPECT experiments using the XCAT computational anthropomorphic model, filled with a realistic biodistribution of (99m)Tc-N-DBODC. The simulated gamma camera was the Siemens ECAM Dual-Head, equipped with a parallel hole lead collimator, with an image resolution of 3.54 × 3.54 mm(2). Thirty-six equispaced camera positions, spanning a full 360° arc, were simulated. Projections were calculated after applying a ± 20% energy window or after eliminating all scattered photons. The activity of the radioisotope was reconstructed using the MLEM algorithm. Photon attenuation was accounted for by calculating the radiological pathlength in a perpendicular line from the center of each voxel to the gamma camera. Twenty-two textural features were calculated on each slice, with and without AC, using 16 and 64 gray levels. A mask was used to identify only those pixels that belonged to each organ. Twelve of the 22 features showed almost no dependence on AC, irrespective of the organ involved. In both the heart and the liver, the mean and SD were the features most affected by AC. In the liver, six features were affected by AC only on some slices. Depending on the slice, skewness decreased by 22-34% with AC, kurtosis by 35-50%, long-run emphasis mean by 71-91%, and long-run emphasis range by 62-95%. In contrast, gray-level non-uniformity mean increased by 78-218% compared with the value without AC and run percentage mean by 51-159%. These results were not affected by the number of gray levels (16 vs. 64) or the data used for reconstruction: with the energy window or without scattered photons. The mean and SD were the main features affected by AC. In the heart, no other feature was affected. In the liver, other features were affected, but the effect was slice dependent. The number of gray levels did not affect the results.

Absence of Cerebrospinal Fluid Signs of Neuronal Injury Before and After Immediate Antiretroviral Therapy in Acute HIV Infection

PubMed Central

Peluso, Michael J.; Valcour, Victor; Ananworanich, Jintanat; Sithinamsuwan, Pasiri; Chalermchai, Thep; Fletcher, James L. K.; Lerdlum, Sukalya; Chomchey, Nitiya; Slike, Bonnie; Sailasuta, Napapon; Gisslén, Magnus; Zetterberg, Henrik; Spudich, Serena

2015-01-01

Background. It is unknown whether neuronal injury begins during acute human immunodeficiency virus (HIV) infection, and whether immediate initiation of combination antiretroviral therapy (cART) prevents neuronal injury. Methods. Cerebrospinal fluid (CSF) neurofilament light chain (NFL), a measure of axonal injury, was assessed before and after cART initiation in individuals starting treatment during acute or chronic HIV infection. Nonparametric statistics examined relationships between NFL and disease progression, neuroinflammation, and cognitive performance. Results. Before treatment, subjects with acute infection had lower CSF NFL levels, with elevations for their age in 1 of 32 subjects with acute infection (3.1%) and 10 of 32 with chronic infection (31%) (P = .006). This persisted after cART initiation, with 1 of 25 acute (4%) and 4 of 9 chronic subjects (44%) showing elevated NFL levels (P = .01). In acute infection, pre-cART NFL levels were inversely correlated with proton magnetic resonance spectroscopic findings of N-acetylaspartate/creatine in frontal gray matter (r = −0.40; P = .03), frontal white matter (r = −0.46; P = .01), and parietal gray matter (r = −0.47; P = .01); correlations persisted after treatment in the frontal white matter (r = −0.51; P = .02) and parietal gray matter (r = −0.46; P = .04). Conclusions. CSF NFL levels are not elevated in untreated acute HIV infection or after 6 months of immediately initiated cART but are abnormal in chronic HIV infection before and after treatment. In acute HIV infection, CSF NFL levels are inversely associated with neuroimaging markers of neuronal health. PMID:25995196

Absence of Cerebrospinal Fluid Signs of Neuronal Injury Before and After Immediate Antiretroviral Therapy in Acute HIV Infection.

PubMed

Peluso, Michael J; Valcour, Victor; Ananworanich, Jintanat; Sithinamsuwan, Pasiri; Chalermchai, Thep; Fletcher, James L K; Lerdlum, Sukalya; Chomchey, Nitiya; Slike, Bonnie; Sailasuta, Napapon; Gisslén, Magnus; Zetterberg, Henrik; Spudich, Serena

2015-12-01

It is unknown whether neuronal injury begins during acute human immunodeficiency virus (HIV) infection, and whether immediate initiation of combination antiretroviral therapy (cART) prevents neuronal injury. Cerebrospinal fluid (CSF) neurofilament light chain (NFL), a measure of axonal injury, was assessed before and after cART initiation in individuals starting treatment during acute or chronic HIV infection. Nonparametric statistics examined relationships between NFL and disease progression, neuroinflammation, and cognitive performance. Before treatment, subjects with acute infection had lower CSF NFL levels, with elevations for their age in 1 of 32 subjects with acute infection (3.1%) and 10 of 32 with chronic infection (31%) (P = .006). This persisted after cART initiation, with 1 of 25 acute (4%) and 4 of 9 chronic subjects (44%) showing elevated NFL levels (P = .01). In acute infection, pre-cART NFL levels were inversely correlated with proton magnetic resonance spectroscopic findings of N-acetylaspartate/creatine in frontal gray matter (r = -0.40; P = .03), frontal white matter (r = -0.46; P = .01), and parietal gray matter (r = -0.47; P = .01); correlations persisted after treatment in the frontal white matter (r = -0.51; P = .02) and parietal gray matter (r = -0.46; P = .04). CSF NFL levels are not elevated in untreated acute HIV infection or after 6 months of immediately initiated cART but are abnormal in chronic HIV infection before and after treatment. In acute HIV infection, CSF NFL levels are inversely associated with neuroimaging markers of neuronal health. © The Author 2015. Published by Oxford University Press on behalf of the Infectious Diseases Society of America. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Variations in Brain Volume and Growth in Young Children With Type 1 Diabetes.

PubMed

Mazaika, Paul K; Weinzimer, Stuart A; Mauras, Nelly; Buckingham, Bruce; White, Neil H; Tsalikian, Eva; Hershey, Tamara; Cato, Allison; Aye, Tandy; Fox, Larry; Wilson, Darrell M; Tansey, Michael J; Tamborlane, William; Peng, Daniel; Raman, Mira; Marzelli, Matthew; Reiss, Allan L

2016-02-01

Early-onset type 1 diabetes may affect the developing brain during a critical window of rapid brain maturation. Structural MRI was performed on 141 children with diabetes (4-10 years of age at study entry) and 69 age-matched control subjects at two time points spaced 18 months apart. For the children with diabetes, the mean (±SD) HbA1c level was 7.9 ± 0.9% (63 ± 9.8 mmol/mol) at both time points. Relative to control subjects, children with diabetes had significantly less growth of cortical gray matter volume and cortical surface area and significantly less growth of white matter volume throughout the cortex and cerebellum. For the population with diabetes, the change in the blood glucose level at the time of scan across longitudinal time points was negatively correlated with the change in gray and white matter volumes, suggesting that fluctuating glucose levels in children with diabetes may be associated with corresponding fluctuations in brain volume. In addition, measures of hyperglycemia and glycemic variation were significantly negatively correlated with the development of surface curvature. These results demonstrate that early-onset type 1 diabetes has widespread effects on the growth of gray and white matter in children whose blood glucose levels are well within the current treatment guidelines for the management of diabetes. © 2016 by the American Diabetes Association. Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered.

Frequency identification of vibration signals using video camera image data.

PubMed

Jeng, Yih-Nen; Wu, Chia-Hung

2012-10-16

This study showed that an image data acquisition system connecting a high-speed camera or webcam to a notebook or personal computer (PC) can precisely capture most dominant modes of vibration signal, but may involve the non-physical modes induced by the insufficient frame rates. Using a simple model, frequencies of these modes are properly predicted and excluded. Two experimental designs, which involve using an LED light source and a vibration exciter, are proposed to demonstrate the performance. First, the original gray-level resolution of a video camera from, for instance, 0 to 256 levels, was enhanced by summing gray-level data of all pixels in a small region around the point of interest. The image signal was further enhanced by attaching a white paper sheet marked with a black line on the surface of the vibration system in operation to increase the gray-level resolution. Experimental results showed that the Prosilica CV640C CMOS high-speed camera has the critical frequency of inducing the false mode at 60 Hz, whereas that of the webcam is 7.8 Hz. Several factors were proven to have the effect of partially suppressing the non-physical modes, but they cannot eliminate them completely. Two examples, the prominent vibration modes of which are less than the associated critical frequencies, are examined to demonstrate the performances of the proposed systems. In general, the experimental data show that the non-contact type image data acquisition systems are potential tools for collecting the low-frequency vibration signal of a system.

Frequency Identification of Vibration Signals Using Video Camera Image Data

PubMed Central

Jeng, Yih-Nen; Wu, Chia-Hung

2012-01-01

This study showed that an image data acquisition system connecting a high-speed camera or webcam to a notebook or personal computer (PC) can precisely capture most dominant modes of vibration signal, but may involve the non-physical modes induced by the insufficient frame rates. Using a simple model, frequencies of these modes are properly predicted and excluded. Two experimental designs, which involve using an LED light source and a vibration exciter, are proposed to demonstrate the performance. First, the original gray-level resolution of a video camera from, for instance, 0 to 256 levels, was enhanced by summing gray-level data of all pixels in a small region around the point of interest. The image signal was further enhanced by attaching a white paper sheet marked with a black line on the surface of the vibration system in operation to increase the gray-level resolution. Experimental results showed that the Prosilica CV640C CMOS high-speed camera has the critical frequency of inducing the false mode at 60 Hz, whereas that of the webcam is 7.8 Hz. Several factors were proven to have the effect of partially suppressing the non-physical modes, but they cannot eliminate them completely. Two examples, the prominent vibration modes of which are less than the associated critical frequencies, are examined to demonstrate the performances of the proposed systems. In general, the experimental data show that the non-contact type image data acquisition systems are potential tools for collecting the low-frequency vibration signal of a system. PMID:23202026

GRAY: a program to calculate gray-body radiation heat-transfer view factors from black-body view factors

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

Wong, R. L.

1976-06-14

Program GRAY is written to perform the matrix manipulations necessary to convert black-body radiation heat-transfer view factors to gray-body view factors as required by thermal analyzer codes. The black-body view factors contain only geometric relationships. Program GRAY allows the effects of multiple gray-body reflections to be included. The resulting effective gray-body factors can then be used with the corresponding fourth-power temperature differences to obtain the net radiative heat flux. The program is written to accept a matrix input or the card image output generated by the black-body view factor program CNVUFAC. The resulting card image output generated by GRAY ismore » in a form usable by the TRUMP thermal analyzer.« less

Responsiveness to Change of Functional Limitation Reporting: Cross-sectional Study Using the Intermountain ROMS Scale in Outpatient Rehabilitation.

PubMed

Brennan, Gerard P; Hunter, Stephen J; Snow, Greg; Minick, Kate I

2017-12-01

The Centers for Medicare and Medicaid Services (CMS) require physical therapists document patients' functional limitations. The process is not standardized. 
A systematic approach to determine a patient's functional limitations and responsiveness to change is needed. The purpose of this study is to compare patient-reported outcomes (PROs) responsiveness to change using 7-level severity/complexity modifier scale proposed by Medicare to a derived scale implemented by Intermountain Healthcare's Rehabilitation Outcomes Management System (ROMS). This was a retrospective, observational cohort design. 165,183 PROs prior to July 1, 2013, were compared to 46,334 records from July 1, 2013, to December 31, 2015. Histograms and ribbon plots illustrate distribution and change of patients' scores. ROMS raw score ranges were calculated and compared to CMS' severity/complexity levels based on score percentage. Distribution of the population was compared based on the 2 methods. Sensitivity and specificity were compared for responsiveness to change based on minimal clinically important difference (MCID). Histograms demonstrated few patient scores placed in CMS scale levels at the extremes, whereas the majority of scores placed in 2 middle levels (CJ, CK). ROMS distributed scores more evenly across levels. Ribbon plots illustrated advantage of ROMS' using narrower score ranges. Greater chance for patients to change levels was observed with ROMS when an MCID was achieved. ROMS narrower scale levels resulted in greater sensitivity and good specificity. Geographic representation for the United States was limited. Without patients' global rating of change, a reference standard to gauge validation of improvement could not be provided. ROMS provides a standard approach to identify accurately functional limitation modifier levels and to detect improvement more accurately than a straight across transposition using the CMS scale. © 2017 American Physical Therapy Association

Microbubble cloud characterization by nonlinear frequency mixing.

PubMed

Cavaro, M; Payan, C; Moysan, J; Baqué, F

2011-05-01

In the frame of the fourth generation forum, France decided to develop sodium fast nuclear reactors. French Safety Authority requests the associated monitoring of argon gas into sodium. This implies to estimate the void fraction, and a histogram indicating the bubble population. In this context, the present letter studies the possibility of achieving an accurate determination of the histogram with acoustic methods. A nonlinear, two-frequency mixing technique has been implemented, and a specific optical device has been developed in order to validate the experimental results. The acoustically reconstructed histograms are in excellent agreement with those obtained using optical methods.

Histogram equalization with Bayesian estimation for noise robust speech recognition.

PubMed

Suh, Youngjoo; Kim, Hoirin

2018-02-01

The histogram equalization approach is an efficient feature normalization technique for noise robust automatic speech recognition. However, it suffers from performance degradation when some fundamental conditions are not satisfied in the test environment. To remedy these limitations of the original histogram equalization methods, class-based histogram equalization approach has been proposed. Although this approach showed substantial performance improvement under noise environments, it still suffers from performance degradation due to the overfitting problem when test data are insufficient. To address this issue, the proposed histogram equalization technique employs the Bayesian estimation method in the test cumulative distribution function estimation. It was reported in a previous study conducted on the Aurora-4 task that the proposed approach provided substantial performance gains in speech recognition systems based on the acoustic modeling of the Gaussian mixture model-hidden Markov model. In this work, the proposed approach was examined in speech recognition systems with deep neural network-hidden Markov model (DNN-HMM), the current mainstream speech recognition approach where it also showed meaningful performance improvement over the conventional maximum likelihood estimation-based method. The fusion of the proposed features with the mel-frequency cepstral coefficients provided additional performance gains in DNN-HMM systems, which otherwise suffer from performance degradation in the clean test condition.

Dynamic Contrast-enhanced MR Imaging in Renal Cell Carcinoma: Reproducibility of Histogram Analysis on Pharmacokinetic Parameters

PubMed Central

Wang, Hai-yi; Su, Zi-hua; Xu, Xiao; Sun, Zhi-peng; Duan, Fei-xue; Song, Yuan-yuan; Li, Lu; Wang, Ying-wei; Ma, Xin; Guo, Ai-tao; Ma, Lin; Ye, Hui-yi

2016-01-01

Pharmacokinetic parameters derived from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) have been increasingly used to evaluate the permeability of tumor vessel. Histogram metrics are a recognized promising method of quantitative MR imaging that has been recently introduced in analysis of DCE-MRI pharmacokinetic parameters in oncology due to tumor heterogeneity. In this study, 21 patients with renal cell carcinoma (RCC) underwent paired DCE-MRI studies on a 3.0 T MR system. Extended Tofts model and population-based arterial input function were used to calculate kinetic parameters of RCC tumors. Mean value and histogram metrics (Mode, Skewness and Kurtosis) of each pharmacokinetic parameter were generated automatically using ImageJ software. Intra- and inter-observer reproducibility and scan–rescan reproducibility were evaluated using intra-class correlation coefficients (ICCs) and coefficient of variation (CoV). Our results demonstrated that the histogram method (Mode, Skewness and Kurtosis) was not superior to the conventional Mean value method in reproducibility evaluation on DCE-MRI pharmacokinetic parameters (K trans & Ve) in renal cell carcinoma, especially for Skewness and Kurtosis which showed lower intra-, inter-observer and scan-rescan reproducibility than Mean value. Our findings suggest that additional studies are necessary before wide incorporation of histogram metrics in quantitative analysis of DCE-MRI pharmacokinetic parameters. PMID:27380733

MQ-1C Gray Eagle Unmanned Aircraft System (MQ-1C Gray Eagle)

DTIC Science & Technology

2015-12-01

Selected Acquisition Report ( SAR ) RCS: DD-A&T(Q&A)823-420 MQ-1C Gray Eagle Unmanned Aircraft System (MQ-1C Gray Eagle) As of FY 2017 President’s...Budget Defense Acquisition Management Information Retrieval (DAMIR) March 21, 2016 17:33:19 UNCLASSIFIED MQ-1C Gray Eagle December 2015 SAR March 21...Gray Eagle December 2015 SAR March 21, 2016 17:33:19 UNCLASSIFIED 3 PB - President’s Budget PE - Program Element PEO - Program Executive Officer PM

Age-related interference from irrelevant distracters in visual feature search among heterogeneous distracters.

PubMed

Merrill, Edward C; Conners, Frances A

2013-08-01

We evaluated age-related variations in the influence of heterogeneous distracters during feature target. Participants in three age groups-6-year-old children, 9-year-old children, and young adults-completed three conditions of search. In a singleton search condition, participants searched for a circle among squares of the same color. In two feature mode search conditions, participants searched for a gray circle or a black circle among gray and black squares. Singleton search was performed at the same level of efficiency for all age groups. In contrast, the two feature mode search conditions yielded age-related performance differences in both conditions. Younger children exhibited a steeper slope than young adults when searching for a gray or black circle. Older children exhibited a steeper slope than young adults when searching for a gray circle but not when searching for a black circle. We concluded that these differences revealed age-related improvements in the relative abilities of adults and children to execute attentional control processes during visual search. In particular, it appears that children found it more difficult to maintain the goal of searching for a circle target than adults and were distracted by the presence of the irrelevant feature dimension of color. Published by Elsevier Inc.

Impaired Recognition and Regulation of Disgust Is Associated with Distinct but Partially Overlapping Patterns of Decreased Gray Matter Volume in the Ventroanterior Insula.

PubMed

Woolley, Josh D; Strobl, Eric V; Sturm, Virginia E; Shany-Ur, Tal; Poorzand, Pardis; Grossman, Scott; Nguyen, Lauren; Eckart, Janet A; Levenson, Robert W; Seeley, William W; Miller, Bruce L; Rankin, Katherine P

2015-10-01

The ventroanterior insula is implicated in the experience, expression, and recognition of disgust; however, whether this brain region is required for recognizing disgust or regulating disgusting behaviors remains unknown. We examined the brain correlates of the presence of disgusting behavior and impaired recognition of disgust using voxel-based morphometry in a sample of 305 patients with heterogeneous patterns of neurodegeneration. Permutation-based analyses were used to determine regions of decreased gray matter volume at a significance level p <= .05 corrected for family-wise error across the whole brain and within the insula. Patients with behavioral variant frontotemporal dementia and semantic variant primary progressive aphasia were most likely to exhibit disgusting behaviors and were, on average, the most impaired at recognizing disgust in others. Imaging analysis revealed that patients who exhibited disgusting behaviors had significantly less gray matter volume bilaterally in the ventral anterior insula. A region of interest analysis restricted to behavioral variant frontotemporal dementia and semantic variant primary progressive aphasia patients alone confirmed this result. Moreover, impaired recognition of disgust was associated with decreased gray matter volume in the bilateral ventroanterior and ventral middle regions of the insula. There was an area of overlap in the bilateral anterior insula where decreased gray matter volume was associated with both the presence of disgusting behavior and impairments in recognizing disgust. These findings suggest that regulating disgusting behaviors and recognizing disgust in others involve two partially overlapping neural systems within the insula. Moreover, the ventral anterior insula is required for both processes. Published by Elsevier Inc.

Increased gray matter density in the parietal cortex of mathematicians: a voxel-based morphometry study.

PubMed

Aydin, K; Ucar, A; Oguz, K K; Okur, O O; Agayev, A; Unal, Z; Yilmaz, S; Ozturk, C

2007-01-01

The training to acquire or practicing to perform a skill, which may lead to structural changes in the brain, is called experience-dependent structural plasticity. The main purpose of this cross-sectional study was to investigate the presence of experience-dependent structural plasticity in mathematicians' brains, which may develop after long-term practice of mathematic thinking. Twenty-six volunteer mathematicians, who have been working as academicians, were enrolled in the study. We applied an optimized method of voxel-based morphometry in the mathematicians and the age- and sex-matched control subjects. We assessed the gray and white matter density differences in mathematicians and the control subjects. Moreover, the correlation between the cortical density and the time spent as an academician was investigated. We found that cortical gray matter density in the left inferior frontal and bilateral inferior parietal lobules of the mathematicians were significantly increased compared with the control subjects. Furthermore, increase in gray matter density in the right inferior parietal lobule of the mathematicians was strongly correlated with the time spent as an academician (r = 0.84; P < .01). Left-inferior frontal and bilateral parietal regions are involved in arithmetic processing. Inferior parietal regions are also involved in high-level mathematic thinking, which requires visuospatial imagery, such as mental creation and manipulation of 3D objects. The voxel-based morphometric analysis of mathematicians' brains revealed increased gray matter density in the cortical regions related to mathematic thinking. The correlation between cortical density increase and the time spent as an academician suggests experience-dependent structural plasticity in mathematicians' brains.

Influence of parameter settings in voxel-based morphometry 8. Using DARTEL and region-of-interest on reproducibility in gray matter volumetry.

PubMed

Goto, M; Abe, O; Aoki, S; Hayashi, N; Miyati, T; Takao, H; Matsuda, H; Yamashita, F; Iwatsubo, T; Mori, H; Kunimatsu, A; Ino, K; Yano, K; Ohtomo, K

2015-01-01

To investigate whether reproducibility of gray matter volumetry is influenced by parameter settings for VBM 8 using Diffeomorphic Anatomical Registration Through Exponentiated Lie Algebra (DARTEL) with region-of-interest (ROI) analyses. We prepared three-dimensional T1-weighted magnetic resonance images (3D-T1WIs) of 21 healthy subjects. All subjects were imaged with each of five MRI systems. Voxel-based morphometry 8 (VBM 8) and WFU PickAtlas software were used for gray matter volumetry. The bilateral ROI labels used were those provided as default settings with the software: Frontal Lobe, Hippocampus, Occipital Lobe, Orbital Gyrus, Parietal Lobe, Putamen, and Temporal Lobe. All 3D-T1WIs were segmented to gray matter with six parameters of VBM 8, with each parameter having between three and eight selectable levels. Reproducibility was evaluated as the standard deviation (mm³) of measured values for the five MRI systems. Reproducibility was influenced by 'Bias regularization (BiasR)', 'Bias FWHM', and 'De-noising filter' settings, but not by 'MRF weighting', 'Sampling distance', or 'Warping regularization' settings. Reproducibility in BiasR was influenced by ROI. Superior reproducibility was observed in Frontal Lobe with the BiasR1 setting, and in Hippocampus, Parietal Lobe, and Putamen with the BiasR3*, BiasR1, and BiasR5 settings, respectively. Reproducibility of gray matter volumetry was influenced by parameter settings in VBM 8 using DARTEL and ROI. In multi-center studies, the use of appropriate settings in VBM 8 with DARTEL results in reduced scanner effect.

Reproducibility of sonographic measurement of thickness and echogenicity of the plantar fascia.

PubMed

Cheng, Ju-Wen; Tsai, Wen-Chung; Yu, Tung-Yang; Huang, Kuo-Yao

2012-01-01

To evaluate the intra- and interrater reliability of ultrasonographic measurements of the thickness and echogenicity of the plantar fascia. Eleven patients (20 feet), who complained of inferior heel pain, and 26 volunteers (52 feet) were enrolled. Two sonographers independently imaged the plantar fascia in both longitudinal and transverse planes. Volunteers were assessed twice to evaluate intrarater reliability. Quantitative evaluation of the echogenicity of the plantar fascia was performed by measuring the mean gray level of the region of interest using Digital Imaging and Communications in Medicine viewer software. Sonographic evaluation of the thickness of the plantar fascia showed high reliability. Sonographic evaluations of the presence or absence of hypoechoic change in the plantar fascia showed surprisingly low agreement. The reliability of gray-scale evaluations appears to be much better than subjective judgments in the evaluation of echogenicity. Transverse scanning did not show any advantage in sonographic evaluation of the plantar fascia. The reliability of sonographic examination of the thickness of the plantar fascia is high. Mean gray-level analysis of quantitative sonography can be used for the evaluation of echogenicity, which could reduce discrepancies in the interpretation of echogenicity by different sonographers. Longitudinal instead of transverse scanning is recommended for imaging the plantar fascia. Copyright © 2011 Wiley Periodicals, Inc.

Community violence exposure correlates with smaller gray matter volume and lower IQ in urban adolescents.

PubMed

Butler, Oisin; Yang, Xiao-Fei; Laube, Corinna; Kühn, Simone; Immordino-Yang, Mary Helen

2018-05-01

Adolescents' exposure to community violence is a significant public health issue in urban settings and has been associated with poorer cognitive performance and increased risk for psychiatric illnesses, including PTSD. However, no study to date has investigated the neural correlates of community violence exposure in adolescents. Sixty-five healthy adolescents (age = 14-18 years; 36 females, 29 males) from moderate- to high-crime neighborhoods in Los Angeles reported their violence exposure, parents' education level, and free/reduced school lunch status (socio-economic status, SES), and underwent structural neuroimaging and intelligence testing. Violence exposure negatively correlated with measures of SES, IQ, and gray matter volume. Above and beyond the effect of SES, violence exposure negatively correlated with IQ and with gray matter volume in the left inferior frontal gyrus and anterior cingulate cortex, regions involved in high-level cognitive functions and autonomic modulation, and previously shown to be reduced in PTSD and combat-exposed military populations. The current results provide first evidence that frontal brain regions involved in cognition and affect appear to be selectively affected by exposure to community violence, even in healthy nondelinquent adolescents who are not the direct victims or perpetrators of violence. © 2018 Wiley Periodicals, Inc.

Cortical fibers orientation mapping using in-vivo whole brain 7 T diffusion MRI.

PubMed

Gulban, Omer F; De Martino, Federico; Vu, An T; Yacoub, Essa; Uğurbil, Kamil; Lenglet, Christophe

2018-05-10

Diffusion MRI of the cortical gray matter is challenging because the micro-environment probed by water molecules is much more complex than within the white matter. High spatial and angular resolutions are therefore necessary to uncover anisotropic diffusion patterns and laminar structures, which provide complementary (e.g. to anatomical and functional MRI) microstructural information about the cortex architectonic. Several ex-vivo and in-vivo MRI studies have recently addressed this question, however predominantly with an emphasis on specific cortical areas. There is currently no whole brain in-vivo data leveraging multi-shell diffusion MRI acquisition at high spatial resolution, and depth dependent analysis, to characterize the complex organization of cortical fibers. Here, we present unique in-vivo human 7T diffusion MRI data, and a dedicated cortical depth dependent analysis pipeline. We leverage the high spatial (1.05 mm isotropic) and angular (198 diffusion gradient directions) resolution of this whole brain dataset to improve cortical fiber orientations mapping, and study neurites (axons and/or dendrites) trajectories across cortical depths. Tangential fibers in superficial cortical depths and crossing fiber configurations in deep cortical depths are identified. Fibers gradually inserting into the gyral walls are visualized, which contributes to mitigating the gyral bias effect. Quantitative radiality maps and histograms in individual subjects and cortex-based aligned datasets further support our results. Copyright © 2018 Elsevier Inc. All rights reserved.

Unsupervised semantic indoor scene classification for robot vision based on context of features using Gist and HSV-SIFT

NASA Astrophysics Data System (ADS)

Madokoro, H.; Yamanashi, A.; Sato, K.

2013-08-01

This paper presents an unsupervised scene classification method for actualizing semantic recognition of indoor scenes. Background and foreground features are respectively extracted using Gist and color scale-invariant feature transform (SIFT) as feature representations based on context. We used hue, saturation, and value SIFT (HSV-SIFT) because of its simple algorithm with low calculation costs. Our method creates bags of features for voting visual words created from both feature descriptors to a two-dimensional histogram. Moreover, our method generates labels as candidates of categories for time-series images while maintaining stability and plasticity together. Automatic labeling of category maps can be realized using labels created using adaptive resonance theory (ART) as teaching signals for counter propagation networks (CPNs). We evaluated our method for semantic scene classification using KTH's image database for robot localization (KTH-IDOL), which is popularly used for robot localization and navigation. The mean classification accuracies of Gist, gray SIFT, one class support vector machines (OC-SVM), position-invariant robust features (PIRF), and our method are, respectively, 39.7, 58.0, 56.0, 63.6, and 79.4%. The result of our method is 15.8% higher than that of PIRF. Moreover, we applied our method for fine classification using our original mobile robot. We obtained mean classification accuracy of 83.2% for six zones.

Least-squares model-based halftoning

NASA Astrophysics Data System (ADS)

Pappas, Thrasyvoulos N.; Neuhoff, David L.

1992-08-01

A least-squares model-based approach to digital halftoning is proposed. It exploits both a printer model and a model for visual perception. It attempts to produce an 'optimal' halftoned reproduction, by minimizing the squared error between the response of the cascade of the printer and visual models to the binary image and the response of the visual model to the original gray-scale image. Conventional methods, such as clustered ordered dither, use the properties of the eye only implicitly, and resist printer distortions at the expense of spatial and gray-scale resolution. In previous work we showed that our printer model can be used to modify error diffusion to account for printer distortions. The modified error diffusion algorithm has better spatial and gray-scale resolution than conventional techniques, but produces some well known artifacts and asymmetries because it does not make use of an explicit eye model. Least-squares model-based halftoning uses explicit eye models and relies on printer models that predict distortions and exploit them to increase, rather than decrease, both spatial and gray-scale resolution. We have shown that the one-dimensional least-squares problem, in which each row or column of the image is halftoned independently, can be implemented with the Viterbi's algorithm. Unfortunately, no closed form solution can be found in two dimensions. The two-dimensional least squares solution is obtained by iterative techniques. Experiments show that least-squares model-based halftoning produces more gray levels and better spatial resolution than conventional techniques. We also show that the least- squares approach eliminates the problems associated with error diffusion. Model-based halftoning can be especially useful in transmission of high quality documents using high fidelity gray-scale image encoders. As we have shown, in such cases halftoning can be performed at the receiver, just before printing. Apart from coding efficiency, this approach permits the halftoner to be tuned to the individual printer, whose characteristics may vary considerably from those of other printers, for example, write-black vs. write-white laser printers.

Gray level co-occurrence and random forest algorithm-based gender determination with maxillary tooth plaster images.

PubMed

Akkoç, Betül; Arslan, Ahmet; Kök, Hatice

2016-06-01

Gender is one of the intrinsic properties of identity, with performance enhancement reducing the cluster when a search is performed. Teeth have durable and resistant structure, and as such are important sources of identification in disasters (accident, fire, etc.). In this study, gender determination is accomplished by maxillary tooth plaster models of 40 people (20 males and 20 females). The images of tooth plaster models are taken with a lighting mechanism set-up. A gray level co-occurrence matrix of the image with segmentation is formed and classified via a Random Forest (RF) algorithm by extracting pertinent features of the matrix. Automatic gender determination has a 90% success rate, with an applicable system to determine gender from maxillary tooth plaster images. Copyright © 2016 Elsevier Ltd. All rights reserved.

Multiple cortical thickness sub-networks and cognitive impairments in first episode, drug naïve patients with late life depression: A graph theory analysis.

PubMed

Shin, Jeong-Hyeon; Um, Yu Hyun; Lee, Chang Uk; Lim, Hyun Kook; Seong, Joon-Kyung

2018-03-15

Coordinated and pattern-wise changes in large scale gray matter structural networks reflect neural circuitry dysfunction in late life depression (LLD), which in turn is associated with emotional dysregulation and cognitive impairments. However, due to methodological limitations, there have been few attempts made to identify individual-level structural network properties or sub-networks that are involved in important brain functions in LLD. In this study, we sought to construct individual-level gray matter structural networks using average cortical thicknesses of several brain areas to investigate the characteristics of the gray matter structural networks in normal controls and LLD patients. Additionally, we investigated the structural sub-networks correlated with several clinical measurements including cognitive impairment and depression severity. We observed that small worldness, clustering coefficients, global and local efficiency, and hub structures in the brains of LLD patients were significantly different from healthy controls. We further found that a sub-network including the anterior cingulate, dorsolateral prefrontal cortex and superior prefrontal cortex is significantly associated with attention control and executive function. The severity of depression was associated with the sub-networks comprising the salience network, including the anterior cingulate and insula. We investigated cortico-cortical connectivity, but omitted the subcortical structures such as the striatum and thalamus. We report differences in patterns between several clinical measurements and sub-networks from large-scale and individual-level cortical thickness networks in LLD. Copyright © 2018 Elsevier B.V. All rights reserved.

Can Laws Be a Potential PET Image Texture Analysis Approach for Evaluation of Tumor Heterogeneity and Histopathological Characteristics in NSCLC?

PubMed

Karacavus, Seyhan; Yılmaz, Bülent; Tasdemir, Arzu; Kayaaltı, Ömer; Kaya, Eser; İçer, Semra; Ayyıldız, Oguzhan

2018-04-01

We investigated the association between the textural features obtained from 18 F-FDG images, metabolic parameters (SUVmax , SUVmean, MTV, TLG), and tumor histopathological characteristics (stage and Ki-67 proliferation index) in non-small cell lung cancer (NSCLC). The FDG-PET images of 67 patients with NSCLC were evaluated. MATLAB technical computing language was employed in the extraction of 137 features by using first order statistics (FOS), gray-level co-occurrence matrix (GLCM), gray-level run length matrix (GLRLM), and Laws' texture filters. Textural features and metabolic parameters were statistically analyzed in terms of good discrimination power between tumor stages, and selected features/parameters were used in the automatic classification by k-nearest neighbors (k-NN) and support vector machines (SVM). We showed that one textural feature (gray-level nonuniformity, GLN) obtained using GLRLM approach and nine textural features using Laws' approach were successful in discriminating all tumor stages, unlike metabolic parameters. There were significant correlations between Ki-67 index and some of the textural features computed using Laws' method (r = 0.6, p = 0.013). In terms of automatic classification of tumor stage, the accuracy was approximately 84% with k-NN classifier (k = 3) and SVM, using selected five features. Texture analysis of FDG-PET images has a potential to be an objective tool to assess tumor histopathological characteristics. The textural features obtained using Laws' approach could be useful in the discrimination of tumor stage.

Apparent diffusion coefficient histogram analysis can evaluate radiation-induced parotid damage and predict late xerostomia degree in nasopharyngeal carcinoma

PubMed Central

Zhou, Nan; Guo, Tingting; Zheng, Huanhuan; Pan, Xia; Chu, Chen; Dou, Xin; Li, Ming; Liu, Song; Zhu, Lijing; Liu, Baorui; Chen, Weibo; He, Jian; Yan, Jing; Zhou, Zhengyang; Yang, Xiaofeng

2017-01-01

We investigated apparent diffusion coefficient (ADC) histogram analysis to evaluate radiation-induced parotid damage and predict xerostomia degrees in nasopharyngeal carcinoma (NPC) patients receiving radiotherapy. The imaging of bilateral parotid glands in NPC patients was conducted 2 weeks before radiotherapy (time point 1), one month after radiotherapy (time point 2), and four months after radiotherapy (time point 3). From time point 1 to 2, parotid volume, skewness, and kurtosis decreased (P < 0.001, = 0.001, and < 0.001, respectively), but all other ADC histogram parameters increased (all P < 0.001, except P = 0.006 for standard deviation [SD]). From time point 2 to 3, parotid volume continued to decrease (P = 0.022), and SD, 75th and 90th percentiles continued to increase (P = 0.024, 0.010, and 0.006, respectively). Early change rates of parotid ADCmean, ADCmin, kurtosis, and 25th, 50th, 75th, 90th percentiles (from time point 1 to 2) correlated with late parotid atrophy rate (from time point 1 to 3) (all P < 0.05). Multiple linear regression analysis revealed correlations among parotid volume, time point, and ADC histogram parameters. Early mean change rates for bilateral parotid SD and ADCmax could predict late xerostomia degrees at seven months after radiotherapy (three months after time point 3) with AUC of 0.781 and 0.818 (P = 0.014, 0.005, respectively). ADC histogram parameters were reproducible (intraclass correlation coefficient, 0.830 - 0.999). ADC histogram analysis could be used to evaluate radiation-induced parotid damage noninvasively, and predict late xerostomia degrees of NPC patients treated with radiotherapy. PMID:29050274

Correlation of histogram analysis of apparent diffusion coefficient with uterine cervical pathologic finding.

PubMed

Lin, Yuning; Li, Hui; Chen, Ziqian; Ni, Ping; Zhong, Qun; Huang, Huijuan; Sandrasegaran, Kumar

2015-05-01

The purpose of this study was to investigate the application of histogram analysis of apparent diffusion coefficient (ADC) in characterizing pathologic features of cervical cancer and benign cervical lesions. This prospective study was approved by the institutional review board, and written informed consent was obtained. Seventy-three patients with cervical cancer (33-69 years old; 35 patients with International Federation of Gynecology and Obstetrics stage IB cervical cancer) and 38 patients (38-61 years old) with normal cervix or cervical benign lesions (control group) were enrolled. All patients underwent 3-T diffusion-weighted imaging (DWI) with b values of 0 and 800 s/mm(2). ADC values of the entire tumor in the patient group and the whole cervix volume in the control group were assessed. Mean ADC, median ADC, 25th and 75th percentiles of ADC, skewness, and kurtosis were calculated. Histogram parameters were compared between different pathologic features, as well as between stage IB cervical cancer and control groups. Mean ADC, median ADC, and 25th percentile of ADC were significantly higher for adenocarcinoma (p = 0.021, 0.006, and 0.004, respectively), and skewness was significantly higher for squamous cell carcinoma (p = 0.011). Median ADC was statistically significantly higher for well or moderately differentiated tumors (p = 0.044), and skewness was statistically significantly higher for poorly differentiated tumors (p = 0.004). No statistically significant difference of ADC histogram was observed between lymphovascular space invasion subgroups. All histogram parameters differed significantly between stage IB cervical cancer and control groups (p < 0.05). Distribution of ADCs characterized by histogram analysis may help to distinguish early-stage cervical cancer from normal cervix or cervical benign lesions and may be useful for evaluating the different pathologic features of cervical cancer.

Differentiating between Glioblastoma and Primary CNS Lymphoma Using Combined Whole-tumor Histogram Analysis of the Normalized Cerebral Blood Volume and the Apparent Diffusion Coefficient.

PubMed

Bao, Shixing; Watanabe, Yoshiyuki; Takahashi, Hiroto; Tanaka, Hisashi; Arisawa, Atsuko; Matsuo, Chisato; Wu, Rongli; Fujimoto, Yasunori; Tomiyama, Noriyuki

2018-05-31

This study aimed to determine whether whole-tumor histogram analysis of normalized cerebral blood volume (nCBV) and apparent diffusion coefficient (ADC) for contrast-enhancing lesions can be used to differentiate between glioblastoma (GBM) and primary central nervous system lymphoma (PCNSL). From 20 patients, 9 with PCNSL and 11 with GBM without any hemorrhagic lesions, underwent MRI, including diffusion-weighted imaging and dynamic susceptibility contrast perfusion-weighted imaging before surgery. Histogram analysis of nCBV and ADC from whole-tumor voxels in contrast-enhancing lesions was performed. An unpaired t-test was used to compare the mean values for each type of tumor. A multivariate logistic regression model (LRM) was performed to classify GBM and PCNSL using the best parameters of ADC and nCBV. All nCBV histogram parameters of GBMs were larger than those of PCNSLs, but only average nCBV was statistically significant after Bonferroni correction. Meanwhile, ADC histogram parameters were also larger in GBM compared to those in PCNSL, but these differences were not statistically significant. According to receiver operating characteristic curve analysis, the nCBV average and ADC 25th percentile demonstrated the largest area under the curve with values of 0.869 and 0.838, respectively. The LRM combining these two parameters differentiated between GBM and PCNSL with a higher area under the curve value (Logit (P) = -21.12 + 10.00 × ADC 25th percentile (10 -3 mm 2 /s) + 5.420 × nCBV mean, P < 0.001). Our results suggest that whole-tumor histogram analysis of nCBV and ADC combined can be a valuable objective diagnostic method for differentiating between GBM and PCNSL.

Histogram analysis of diffusion kurtosis imaging estimates for in vivo assessment of 2016 WHO glioma grades: A cross-sectional observational study.

PubMed

Hempel, Johann-Martin; Schittenhelm, Jens; Brendle, Cornelia; Bender, Benjamin; Bier, Georg; Skardelly, Marco; Tabatabai, Ghazaleh; Castaneda Vega, Salvador; Ernemann, Ulrike; Klose, Uwe

2017-10-01

To assess the diagnostic performance of histogram analysis of diffusion kurtosis imaging (DKI) maps for in vivo assessment of the 2016 World Health Organization Classification of Tumors of the Central Nervous System (2016 CNS WHO) integrated glioma grades. Seventy-seven patients with histopathologically-confirmed glioma who provided written informed consent were retrospectively assessed between 01/2014 and 03/2017 from a prospective trial approved by the local institutional review board. Ten histogram parameters of mean kurtosis (MK) and mean diffusivity (MD) metrics from DKI were independently assessed by two blinded physicians from a volume of interest around the entire solid tumor. One-way ANOVA was used to compare MK and MD histogram parameter values between 2016 CNS WHO-based tumor grades. Receiver operating characteristic analysis was performed on MK and MD histogram parameters for significant results. The 25th, 50th, 75th, and 90th percentiles of MK and average MK showed significant differences between IDH1/2 wild-type gliomas, IDH1/2 mutated gliomas, and oligodendrogliomas with chromosome 1p/19q loss of heterozygosity and IDH1/2 mutation (p<0.001). The 50th, 75th, and 90th percentiles showed a slightly higher diagnostic performance (area under the curve (AUC) range; 0.868-0.991) than average MK (AUC range; 0.855-0.988) in classifying glioma according to the integrated approach of 2016 CNS WHO. Histogram analysis of DKI can stratify gliomas according to the integrated approach of 2016 CNS WHO. The 50th (median), 75th , and the 90th percentiles showed the highest diagnostic performance. However, the average MK is also robust and feasible in routine clinical practice. Copyright © 2017 Elsevier B.V. All rights reserved.

Apparent diffusion coefficient histogram analysis can evaluate radiation-induced parotid damage and predict late xerostomia degree in nasopharyngeal carcinoma.

PubMed

Zhou, Nan; Guo, Tingting; Zheng, Huanhuan; Pan, Xia; Chu, Chen; Dou, Xin; Li, Ming; Liu, Song; Zhu, Lijing; Liu, Baorui; Chen, Weibo; He, Jian; Yan, Jing; Zhou, Zhengyang; Yang, Xiaofeng

2017-09-19

We investigated apparent diffusion coefficient (ADC) histogram analysis to evaluate radiation-induced parotid damage and predict xerostomia degrees in nasopharyngeal carcinoma (NPC) patients receiving radiotherapy. The imaging of bilateral parotid glands in NPC patients was conducted 2 weeks before radiotherapy (time point 1), one month after radiotherapy (time point 2), and four months after radiotherapy (time point 3). From time point 1 to 2, parotid volume, skewness, and kurtosis decreased ( P < 0.001, = 0.001, and < 0.001, respectively), but all other ADC histogram parameters increased (all P < 0.001, except P = 0.006 for standard deviation [SD]). From time point 2 to 3, parotid volume continued to decrease ( P = 0.022), and SD, 75 th and 90 th percentiles continued to increase ( P = 0.024, 0.010, and 0.006, respectively). Early change rates of parotid ADC mean , ADC min , kurtosis, and 25 th , 50 th , 75 th , 90 th percentiles (from time point 1 to 2) correlated with late parotid atrophy rate (from time point 1 to 3) (all P < 0.05). Multiple linear regression analysis revealed correlations among parotid volume, time point, and ADC histogram parameters. Early mean change rates for bilateral parotid SD and ADC max could predict late xerostomia degrees at seven months after radiotherapy (three months after time point 3) with AUC of 0.781 and 0.818 ( P = 0.014, 0.005, respectively). ADC histogram parameters were reproducible (intraclass correlation coefficient, 0.830 - 0.999). ADC histogram analysis could be used to evaluate radiation-induced parotid damage noninvasively, and predict late xerostomia degrees of NPC patients treated with radiotherapy.

Comparison between types I and II epithelial ovarian cancer using histogram analysis of monoexponential, biexponential, and stretched-exponential diffusion models.

PubMed

Wang, Feng; Wang, Yuxiang; Zhou, Yan; Liu, Congrong; Xie, Lizhi; Zhou, Zhenyu; Liang, Dong; Shen, Yang; Yao, Zhihang; Liu, Jianyu

2017-12-01

To evaluate the utility of histogram analysis of monoexponential, biexponential, and stretched-exponential models to a dualistic model of epithelial ovarian cancer (EOC). Fifty-two patients with histopathologically proven EOC underwent preoperative magnetic resonance imaging (MRI) (including diffusion-weighted imaging [DWI] with 11 b-values) using a 3.0T system and were divided into two groups: types I and II. Apparent diffusion coefficient (ADC), true diffusion coefficient (D), pseudodiffusion coefficient (D*), perfusion fraction (f), distributed diffusion coefficient (DDC), and intravoxel water diffusion heterogeneity (α) histograms were obtained based on solid components of the entire tumor. The following metrics of each histogram were compared between two types: 1) mean; 2) median; 3) 10th percentile and 90th percentile. Conventional MRI morphological features were also recorded. Significant morphological features for predicting EOC type were maximum diameter (P = 0.007), texture of lesion (P = 0.001), and peritoneal implants (P = 0.001). For ADC, D, f, DDC, and α, all metrics were significantly lower in type II than type I (P < 0.05). Mean, median, 10th, and 90th percentile of D* were not significantly different (P = 0.336, 0.154, 0.779, and 0.203, respectively). Most histogram metrics of ADC, D, and DDC had significantly higher area under the receiver operating characteristic curve values than those of f and α (P < 0.05) CONCLUSION: It is feasible to grade EOC by morphological features and three models with histogram analysis. ADC, D, and DDC have better performance than f and α; f and α may provide additional information. 4 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2017;46:1797-1809. © 2017 International Society for Magnetic Resonance in Medicine.

Principal component analysis of the CT density histogram to generate parametric response maps of COPD

NASA Astrophysics Data System (ADS)

Zha, N.; Capaldi, D. P. I.; Pike, D.; McCormack, D. G.; Cunningham, I. A.; Parraga, G.

2015-03-01

Pulmonary x-ray computed tomography (CT) may be used to characterize emphysema and airways disease in patients with chronic obstructive pulmonary disease (COPD). One analysis approach - parametric response mapping (PMR) utilizes registered inspiratory and expiratory CT image volumes and CT-density-histogram thresholds, but there is no consensus regarding the threshold values used, or their clinical meaning. Principal-component-analysis (PCA) of the CT density histogram can be exploited to quantify emphysema using data-driven CT-density-histogram thresholds. Thus, the objective of this proof-of-concept demonstration was to develop a PRM approach using PCA-derived thresholds in COPD patients and ex-smokers without airflow limitation. Methods: Fifteen COPD ex-smokers and 5 normal ex-smokers were evaluated. Thoracic CT images were also acquired at full inspiration and full expiration and these images were non-rigidly co-registered. PCA was performed for the CT density histograms, from which the components with the highest eigenvalues greater than one were summed. Since the values of the principal component curve correlate directly with the variability in the sample, the maximum and minimum points on the curve were used as threshold values for the PCA-adjusted PRM technique. Results: A significant correlation was determined between conventional and PCA-adjusted PRM with 3He MRI apparent diffusion coefficient (p<0.001), with CT RA950 (p<0.0001), as well as with 3He MRI ventilation defect percent, a measurement of both small airways disease (p=0.049 and p=0.06, respectively) and emphysema (p=0.02). Conclusions: PRM generated using PCA thresholds of the CT density histogram showed significant correlations with CT and 3He MRI measurements of emphysema, but not airways disease.

External beam ionizing radiation for inhibition of myointimal hyperplasia after dilatation and anastomoses: experimental models and results.

PubMed

Ducasse, Eric; Cosset, Jean-Marc; Eschwege, François; Creusy, Colette; Chevalier, Jacques; Puppinck, Paul; Lartigau, Eric

2004-01-01

In recent years there has been intensive research on the use of ionizing radiation for inhibition of intimal hyperplasia (IH). Results have clearly established that beta ionizing radiation delivered from an endoluminal source after angioplasty inhibits intimal restenosis. This effect has been confirmed by recent multicenter clinical trials in patients undergoing coronary dilatation. The purpose of this study was to determine if gamma radiation therapy delivered superficially from an external source also reduced smooth muscle cell proliferation in two animals models-the first involving experimentally induced restenosis and the second involving anastomosis between a prosthesis and artery. Ultimately we hope to develop a therapeutic application for patients undergoing peripheral anastomoses, especially in the lower extremities. Two different animal models were used in this two-stage study. The first-stage rabbit model (model 1) involved balloon injury of the aorta to validate the dose effect of external beam irradiation. The second-stage porcine model (model 2) involved aortic bypass followed by external beam irradiation of the distal anastomosis site. In model 1 a total of 56 rabbits were studied. They were divided into five groups including one control group in which external radiation was not applied after balloon injury and four test groups in which external radiation was applied in a single fraction on day 0 at four different doses: 10 grays, 15 grays, 20 grays, and 25 grays. In model 2, a total of 24 pigs underwent aortic bypass with a 6-mm PTFE graft followed by irradiation of the distal end-to-side anastomosis at a dose of 20 grays on day 0. In both models specimens were harvested after 6 weeks and studied histologically after staining with HES and orcein, histomorphometrically by measuring intimal hyperplasia, and immunohistochemically using actin and factor VIII/von Willebrand factor (F VIII/vWF). The zones of study on the anastomosis were separated into base of the artery to the tip and heel of the anastomosis and the edge of the arteriotomy. Measurements were compared using the Mann Whitney test. In the first-stage model designed to study IH in rabbits, mean intimal and medial thickness values and the intima-to-media ratio showed no difference between the control group and the groups irradiated at doses of 10 grays and 15 grays (p = 0.111, p = 0.405, and p = 0.14); (p = 0.301, p = 0.206, and p = 0.199). Conversely, there was a significant difference between the control group and the groups irradiated at 20 grays and 25 grays (p < 0.0001, p = 0.107 and p = 0.008; p = 0.008, p = 0.155, and p = 0.008). Histological examination demonstrated extensive changes in the wall with high-grade fibrosis after application of ionizing radiation. In the second-stage swine model, irradiation significantly inhibited development of IH at the level of anastomosis both at the base of the artery (p < 0.01) (tip 0.06 vs. 0.27 mm and heel 0.04 vs. 0.36) and at the level of the arteriotomy at the suture site (p < 0.001) (0.13 vs. 0.86 mm). Immunochemical analysis of the thickened zones showed a positive reaction of endothelial cells to smooth muscle actin and F VII/vWF. Like irradiation applied using an endoluminal source, superficial gamma ionizing radiation from an external source inhibits IH. Analysis of the dose effect showed that the overall dose must be between 15 and 20 grays. External radiation also reduces overall IH at the anastomosis between a prosthesis and artery. Although these experimental data are promising, further study will probably be necessary before attempting to undertake clinical trials using external beam radiation therapy for patients undergoing peripheral anastomoses.

Magnetization Transfer Ratio Relates to Cognitive Impairment in Normal Elderly

PubMed Central

Seiler, Stephan; Pirpamer, Lukas; Hofer, Edith; Duering, Marco; Jouvent, Eric; Fazekas, Franz; Mangin, Jean-Francois; Chabriat, Hugues; Dichgans, Martin; Ropele, Stefan; Schmidt, Reinhold

2014-01-01

Magnetization transfer imaging (MTI) can detect microstructural brain tissue changes and may be helpful in determining age-related cerebral damage. We investigated the association between the magnetization transfer ratio (MTR) in gray and white matter (WM) and cognitive functioning in 355 participants of the Austrian stroke prevention family study (ASPS-Fam) aged 38–86 years. MTR maps were generated for the neocortex, deep gray matter structures, WM hyperintensities, and normal appearing WM (NAWM). Adjusted mixed models determined whole brain and lobar cortical MTR to be directly and significantly related to performance on tests of memory, executive function, and motor skills. There existed an almost linear dose-effect relationship. MTR of deep gray matter structures and NAWM correlated to executive functioning. All associations were independent of demographics, vascular risk factors, focal brain lesions, and cortex volume. Further research is needed to understand the basis of this association at the tissue level, and to determine the role of MTR in predicting cognitive decline and dementia. PMID:25309438

Mercury in gray wolves (Canis lupus) in Alaska: increased exposure through consumption of marine prey.

PubMed

McGrew, Ashley K; Ballweber, Lora R; Moses, Sara K; Stricker, Craig A; Beckmen, Kimberlee B; Salman, Mo D; O'Hara, Todd M

2014-01-15

Mercury (Hg) bioaccumulates in the tissues of organisms and biomagnifies within food-webs. Gray wolves (Canis lupus) in Alaska primarily acquire Hg through diet; therefore, comparing the extent of Hg exposure in wolves, in conjunction with stable isotopes, from interior and coastal regions of Alaska offers important insight into their feeding ecology. Liver, kidney, and skeletal muscle samples from 162 gray wolves were analyzed for total mercury (THg) concentrations and stable isotopic signatures (δ(13)C, δ(15)N, and δ(34)S). Median hepatic THg concentrations were significantly higher in wolves with coastal access compared to wolves from interior Alaska. Stable isotope ratios, in conjunction with THg concentrations, provide strong evidence that coastal wolves are utilizing marine prey representing several trophic levels. The utilization of cross-ecosystem food resources by coastal wolves is clearly contributing to increased THg exposure, and may ultimately have negative health implications for these animals. © 2013 Elsevier B.V. All rights reserved.

Correlations among Brain Gray Matter Volumes, Age, Gender, and Hemisphere in Healthy Individuals

PubMed Central

Taki, Yasuyuki; Thyreau, Benjamin; Kinomura, Shigeo; Sato, Kazunori; Goto, Ryoi; Kawashima, Ryuta; Fukuda, Hiroshi

2011-01-01

To determine the relationship between age and gray matter structure and how interactions between gender and hemisphere impact this relationship, we examined correlations between global or regional gray matter volume and age, including interactions of gender and hemisphere, using a general linear model with voxel-based and region-of-interest analyses. Brain magnetic resonance images were collected from 1460 healthy individuals aged 20–69 years; the images were linearly normalized and segmented and restored to native space for analysis of global gray matter volume. Linearly normalized images were then non-linearly normalized and smoothed for analysis of regional gray matter volume. Analysis of global gray matter volume revealed a significant negative correlation between gray matter ratio (gray matter volume divided by intracranial volume) and age in both genders, and a significant interaction effect of age × gender on the gray matter ratio. In analyzing regional gray matter volume, the gray matter volume of all regions showed significant main effects of age, and most regions, with the exception of several including the inferior parietal lobule, showed a significant age × gender interaction. Additionally, the inferior temporal gyrus showed a significant age × gender × hemisphere interaction. No regional volumes showed significant age × hemisphere interactions. Our study may contribute to clarifying the mechanism(s) of normal brain aging in each brain region. PMID:21818377

Local intensity area descriptor for facial recognition in ideal and noise conditions

NASA Astrophysics Data System (ADS)

Tran, Chi-Kien; Tseng, Chin-Dar; Chao, Pei-Ju; Ting, Hui-Min; Chang, Liyun; Huang, Yu-Jie; Lee, Tsair-Fwu

2017-03-01

We propose a local texture descriptor, local intensity area descriptor (LIAD), which is applied for human facial recognition in ideal and noisy conditions. Each facial image is divided into small regions from which LIAD histograms are extracted and concatenated into a single feature vector to represent the facial image. The recognition is performed using a nearest neighbor classifier with histogram intersection and chi-square statistics as dissimilarity measures. Experiments were conducted with LIAD using the ORL database of faces (Olivetti Research Laboratory, Cambridge), the Face94 face database, the Georgia Tech face database, and the FERET database. The results demonstrated the improvement in accuracy of our proposed descriptor compared to conventional descriptors [local binary pattern (LBP), uniform LBP, local ternary pattern, histogram of oriented gradients, and local directional pattern]. Moreover, the proposed descriptor was less sensitive to noise and had low histogram dimensionality. Thus, it is expected to be a powerful texture descriptor that can be used for various computer vision problems.

Measurement of Device Parameters Using Image Recovery Techniques in Large-Scale IC Devices

NASA Technical Reports Server (NTRS)

Scheick, Leif; Edmonds, Larry

2004-01-01

Devices that respond to radiation on a cell level will produce histograms showing the relative frequency of cell damage as a function of damage. The measured distribution is the convolution of distributions from radiation responses, measurement noise, and manufacturing parameters. A method of extracting device characteristics and parameters from measured distributions via mathematical and image subtraction techniques is described.

A graph-based approach for the retrieval of multi-modality medical images.

PubMed

Kumar, Ashnil; Kim, Jinman; Wen, Lingfeng; Fulham, Michael; Feng, Dagan

2014-02-01

In this paper, we address the retrieval of multi-modality medical volumes, which consist of two different imaging modalities, acquired sequentially, from the same scanner. One such example, positron emission tomography and computed tomography (PET-CT), provides physicians with complementary functional and anatomical features as well as spatial relationships and has led to improved cancer diagnosis, localisation, and staging. The challenge of multi-modality volume retrieval for cancer patients lies in representing the complementary geometric and topologic attributes between tumours and organs. These attributes and relationships, which are used for tumour staging and classification, can be formulated as a graph. It has been demonstrated that graph-based methods have high accuracy for retrieval by spatial similarity. However, naïvely representing all relationships on a complete graph obscures the structure of the tumour-anatomy relationships. We propose a new graph structure derived from complete graphs that structurally constrains the edges connected to tumour vertices based upon the spatial proximity of tumours and organs. This enables retrieval on the basis of tumour localisation. We also present a similarity matching algorithm that accounts for different feature sets for graph elements from different imaging modalities. Our method emphasises the relationships between a tumour and related organs, while still modelling patient-specific anatomical variations. Constraining tumours to related anatomical structures improves the discrimination potential of graphs, making it easier to retrieve similar images based on tumour location. We evaluated our retrieval methodology on a dataset of clinical PET-CT volumes. Our results showed that our method enabled the retrieval of multi-modality images using spatial features. Our graph-based retrieval algorithm achieved a higher precision than several other retrieval techniques: gray-level histograms as well as state-of-the-art methods such as visual words using the scale- invariant feature transform (SIFT) and relational matrices representing the spatial arrangements of objects. Copyright © 2013 Elsevier B.V. All rights reserved.

Development of a Support Vector Machine - Based Image Analysis System for Focal Liver Lesions Classification in Magnetic Resonance Images

NASA Astrophysics Data System (ADS)

Gatos, I.; Tsantis, S.; Karamesini, M.; Skouroliakou, A.; Kagadis, G.

2015-09-01

Purpose: The design and implementation of a computer-based image analysis system employing the support vector machine (SVM) classifier system for the classification of Focal Liver Lesions (FLLs) on routine non-enhanced, T2-weighted Magnetic Resonance (MR) images. Materials and Methods: The study comprised 92 patients; each one of them has undergone MRI performed on a Magnetom Concerto (Siemens). Typical signs on dynamic contrast-enhanced MRI and biopsies were employed towards a three class categorization of the 92 cases: 40-benign FLLs, 25-Hepatocellular Carcinomas (HCC) within Cirrhotic liver parenchyma and 27-liver metastases from Non-Cirrhotic liver. Prior to FLLs classification an automated lesion segmentation algorithm based on Marcov Random Fields was employed in order to acquire each FLL Region of Interest. 42 texture features derived from the gray-level histogram, co-occurrence and run-length matrices and 12 morphological features were obtained from each lesion. Stepwise multi-linear regression analysis was utilized to avoid feature redundancy leading to a feature subset that fed the multiclass SVM classifier designed for lesion classification. SVM System evaluation was performed by means of leave-one-out method and ROC analysis. Results: Maximum accuracy for all three classes (90.0%) was obtained by means of the Radial Basis Kernel Function and three textural features (Inverse- Different-Moment, Sum-Variance and Long-Run-Emphasis) that describe lesion's contrast, variability and shape complexity. Sensitivity values for the three classes were 92.5%, 81.5% and 96.2% respectively, whereas specificity values were 94.2%, 95.3% and 95.5%. The AUC value achieved for the selected subset was 0.89 with 0.81 - 0.94 confidence interval. Conclusion: The proposed SVM system exhibit promising results that could be utilized as a second opinion tool to the radiologist in order to decrease the time/cost of diagnosis and the need for patients to undergo invasive examination.

Detection of latent fingerprints using high-resolution 3D confocal microscopy in non-planar acquisition scenarios

NASA Astrophysics Data System (ADS)

Kirst, Stefan; Vielhauer, Claus

2015-03-01

In digitized forensics the support of investigators in any manner is one of the main goals. Using conservative lifting methods, the detection of traces is done manually. For non-destructive contactless methods, the necessity for detecting traces is obvious for further biometric analysis. High resolutional 3D confocal laser scanning microscopy (CLSM) grants the possibility for a detection by segmentation approach with improved detection results. Optimal scan results with CLSM are achieved on surfaces orthogonal to the sensor, which is not always possible due to environmental circumstances or the surface's shape. This introduces additional noise, outliers and a lack of contrast, making a detection of traces even harder. Prior work showed the possibility of determining angle-independent classification models for the detection of latent fingerprints (LFP). Enhancing this approach, we introduce a larger feature space containing a variety of statistical-, roughness-, color-, edge-directivity-, histogram-, Gabor-, gradient- and Tamura features based on raw data and gray-level co-occurrence matrices (GLCM) using high resolutional data. Our test set consists of eight different surfaces for the detection of LFP in four different acquisition angles with a total of 1920 single scans. For each surface and angles in steps of 10, we capture samples from five donors to introduce variance by a variety of sweat compositions and application influences such as pressure or differences in ridge thickness. By analyzing the present test set with our approach, we intend to determine angle- and substrate-dependent classification models to determine optimal surface specific acquisition setups and also classification models for a general detection purpose for both, angles and substrates. The results on overall models with classification rates up to 75.15% (kappa 0.50) already show a positive tendency regarding the usability of the proposed methods for LFP detection on varying surfaces in non-planar scenarios.

Computer-assisted liver graft steatosis assessment via learning-based texture analysis.

PubMed

Moccia, Sara; Mattos, Leonardo S; Patrini, Ilaria; Ruperti, Michela; Poté, Nicolas; Dondero, Federica; Cauchy, François; Sepulveda, Ailton; Soubrane, Olivier; De Momi, Elena; Diaspro, Alberto; Cesaretti, Manuela

2018-05-23

Fast and accurate graft hepatic steatosis (HS) assessment is of primary importance for lowering liver dysfunction risks after transplantation. Histopathological analysis of biopsied liver is the gold standard for assessing HS, despite being invasive and time consuming. Due to the short time availability between liver procurement and transplantation, surgeons perform HS assessment through clinical evaluation (medical history, blood tests) and liver texture visual analysis. Despite visual analysis being recognized as challenging in the clinical literature, few efforts have been invested to develop computer-assisted solutions for HS assessment. The objective of this paper is to investigate the automatic analysis of liver texture with machine learning algorithms to automate the HS assessment process and offer support for the surgeon decision process. Forty RGB images of forty different donors were analyzed. The images were captured with an RGB smartphone camera in the operating room (OR). Twenty images refer to livers that were accepted and 20 to discarded livers. Fifteen randomly selected liver patches were extracted from each image. Patch size was [Formula: see text]. This way, a balanced dataset of 600 patches was obtained. Intensity-based features (INT), histogram of local binary pattern ([Formula: see text]), and gray-level co-occurrence matrix ([Formula: see text]) were investigated. Blood-sample features (Blo) were included in the analysis, too. Supervised and semisupervised learning approaches were investigated for feature classification. The leave-one-patient-out cross-validation was performed to estimate the classification performance. With the best-performing feature set ([Formula: see text]) and semisupervised learning, the achieved classification sensitivity, specificity, and accuracy were 95, 81, and 88%, respectively. This research represents the first attempt to use machine learning and automatic texture analysis of RGB images from ubiquitous smartphone cameras for the task of graft HS assessment. The results suggest that is a promising strategy to develop a fully automatic solution to assist surgeons in HS assessment inside the OR.

Texture descriptions of lunar surface derived from LOLA data: Kilometer-scale roughness and entropy maps

NASA Astrophysics Data System (ADS)

Li, Bo; Ling, Zongcheng; Zhang, Jiang; Chen, Jian; Wu, Zhongchen; Ni, Yuheng; Zhao, Haowei

2015-11-01

The lunar global texture maps of roughness and entropy are derived at kilometer scales from Digital Elevation Models (DEMs) data obtained by Lunar Orbiter Laser Altimeter (LOLA) aboard on Lunar Reconnaissance Orbiter (LRO) spacecraft. We use statistical moments of a gray-level histogram of elevations in a neighborhood to compute the roughness and entropy value. Our texture descriptors measurements are shown in global maps at multi-sized square neighborhoods, whose length of side is 3, 5, 10, 20, 40 and 80 pixels, respectively. We found that large-scale topographical changes can only be displayed in maps with longer side of neighborhood, but the small scale global texture maps are more disorderly and unsystematic because of more complicated textures' details. Then, the frequency curves of texture maps are made out, whose shapes and distributions are changing as the spatial scales increases. Entropy frequency curve with minimum 3-pixel scale has large fluctuations and six peaks. According to this entropy curve we can classify lunar surface into maria, highlands, different parts of craters preliminarily. The most obvious textures in the middle-scale roughness and entropy maps are the two typical morphological units, smooth maria and rough highlands. For the impact crater, its roughness and entropy value are characterized by a multiple-ring structure obviously, and its different parts have different texture results. In the last, we made a 2D scatter plot between the two texture results of typical lunar maria and highlands. There are two clusters with largest dot density which are corresponded to the lunar highlands and maria separately. In the lunar mare regions (cluster A), there is a high correlation between roughness and entropy, but in the highlands (Cluster B), the entropy shows little change. This could be subjected to different geological processes of maria and highlands forming different landforms.

A Bayesian Modeling Approach for Estimation of a Shape-Free Groundwater Age Distribution using Multiple Tracers

DOE PAGES

Massoudieh, Arash; Visser, Ate; Sharifi, Soroosh; ...

2013-10-15

The mixing of groundwaters with different ages in aquifers, groundwater age is more appropriately represented by a distribution rather than a scalar number. To infer a groundwater age distribution from environmental tracers, a mathematical form is often assumed for the shape of the distribution and the parameters of the mathematical distribution are estimated using deterministic or stochastic inverse methods. We found that the prescription of the mathematical form limits the exploration of the age distribution to the shapes that can be described by the selected distribution. In this paper, the use of freeform histograms as groundwater age distributions is evaluated.more » A Bayesian Markov Chain Monte Carlo approach is used to estimate the fraction of groundwater in each histogram bin. This method was able to capture the shape of a hypothetical gamma distribution from the concentrations of four age tracers. The number of bins that can be considered in this approach is limited based on the number of tracers available. The histogram method was also tested on tracer data sets from Holten (The Netherlands; 3H, 3He, 85Kr, 39Ar) and the La Selva Biological Station (Costa-Rica; SF 6, CFCs, 3H, 4He and 14C), and compared to a number of mathematical forms. According to standard Bayesian measures of model goodness, the best mathematical distribution performs better than the histogram distributions in terms of the ability to capture the observed tracer data relative to their complexity. Among the histogram distributions, the four bin histogram performs better in most of the cases. The Monte Carlo simulations showed strong correlations in the posterior estimates of bin contributions, indicating that these bins cannot be well constrained using the available age tracers. The fact that mathematical forms overall perform better than the freeform histogram does not undermine the benefit of the freeform approach, especially for the cases where a larger amount of observed data is available and when the real groundwater distribution is more complex than can be represented by simple mathematical forms.« less

A Bayesian Modeling Approach for Estimation of a Shape-Free Groundwater Age Distribution using Multiple Tracers

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

Massoudieh, Arash; Visser, Ate; Sharifi, Soroosh

The mixing of groundwaters with different ages in aquifers, groundwater age is more appropriately represented by a distribution rather than a scalar number. To infer a groundwater age distribution from environmental tracers, a mathematical form is often assumed for the shape of the distribution and the parameters of the mathematical distribution are estimated using deterministic or stochastic inverse methods. We found that the prescription of the mathematical form limits the exploration of the age distribution to the shapes that can be described by the selected distribution. In this paper, the use of freeform histograms as groundwater age distributions is evaluated.more » A Bayesian Markov Chain Monte Carlo approach is used to estimate the fraction of groundwater in each histogram bin. This method was able to capture the shape of a hypothetical gamma distribution from the concentrations of four age tracers. The number of bins that can be considered in this approach is limited based on the number of tracers available. The histogram method was also tested on tracer data sets from Holten (The Netherlands; 3H, 3He, 85Kr, 39Ar) and the La Selva Biological Station (Costa-Rica; SF 6, CFCs, 3H, 4He and 14C), and compared to a number of mathematical forms. According to standard Bayesian measures of model goodness, the best mathematical distribution performs better than the histogram distributions in terms of the ability to capture the observed tracer data relative to their complexity. Among the histogram distributions, the four bin histogram performs better in most of the cases. The Monte Carlo simulations showed strong correlations in the posterior estimates of bin contributions, indicating that these bins cannot be well constrained using the available age tracers. The fact that mathematical forms overall perform better than the freeform histogram does not undermine the benefit of the freeform approach, especially for the cases where a larger amount of observed data is available and when the real groundwater distribution is more complex than can be represented by simple mathematical forms.« less

Histogram Analysis of Apparent Diffusion Coefficients for Occult Tonsil Cancer in Patients with Cervical Nodal Metastasis from an Unknown Primary Site at Presentation.

PubMed

Choi, Young Jun; Lee, Jeong Hyun; Kim, Hye Ok; Kim, Dae Yoon; Yoon, Ra Gyoung; Cho, So Hyun; Koh, Myeong Ju; Kim, Namkug; Kim, Sang Yoon; Baek, Jung Hwan

2016-01-01

To explore the added value of histogram analysis of apparent diffusion coefficient (ADC) values over magnetic resonance (MR) imaging and fluorine 18 ((18)F) fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT) for the detection of occult palatine tonsil squamous cell carcinoma (SCC) in patients with cervical nodal metastasis from a cancer of an unknown primary site. The institutional review board approved this retrospective study, and the requirement for informed consent was waived. Differences in the bimodal histogram parameters of the ADC values were assessed among occult palatine tonsil SCC (n = 19), overt palatine tonsil SCC (n = 20), and normal palatine tonsils (n = 20). One-way analysis of variance was used to analyze differences among the three groups. Receiver operating characteristic curve analysis was used to determine the best differentiating parameters. The increased sensitivity of histogram analysis over MR imaging and (18)F-FDG PET/CT for the detection of occult palatine tonsil SCC was evaluated as added value. Histogram analysis showed statistically significant differences in the mean, standard deviation, and 50th and 90th percentile ADC values among the three groups (P < .0045). Occult palatine tonsil SCC had a significantly higher standard deviation for the overall curves, mean and standard deviation of the higher curves, and 90th percentile ADC value, compared with normal palatine tonsils (P < .0167). Receiver operating characteristic curve analysis showed that the standard deviation of the overall curve best delineated occult palatine tonsil SCC from normal palatine tonsils, with a sensitivity of 78.9% (15 of 19 patients) and a specificity of 60% (12 of 20 patients). The added value of ADC histogram analysis was 52.6% over MR imaging alone and 15.8% over combined conventional MR imaging and (18)F-FDG PET/CT. Adding ADC histogram analysis to conventional MR imaging can improve the detection sensitivity for occult palatine tonsil SCC in patients with a cervical nodal metastasis originating from a cancer of an unknown primary site. © RSNA, 2015.

Gliomas: Application of Cumulative Histogram Analysis of Normalized Cerebral Blood Volume on 3 T MRI to Tumor Grading

PubMed Central

Kim, Hyungjin; Choi, Seung Hong; Kim, Ji-Hoon; Ryoo, Inseon; Kim, Soo Chin; Yeom, Jeong A.; Shin, Hwaseon; Jung, Seung Chai; Lee, A. Leum; Yun, Tae Jin; Park, Chul-Kee; Sohn, Chul-Ho; Park, Sung-Hye

2013-01-01

Background Glioma grading assumes significant importance in that low- and high-grade gliomas display different prognoses and are treated with dissimilar therapeutic strategies. The objective of our study was to retrospectively assess the usefulness of a cumulative normalized cerebral blood volume (nCBV) histogram for glioma grading based on 3 T MRI. Methods From February 2010 to April 2012, 63 patients with astrocytic tumors underwent 3 T MRI with dynamic susceptibility contrast perfusion-weighted imaging. Regions of interest containing the entire tumor volume were drawn on every section of the co-registered relative CBV (rCBV) maps and T2-weighted images. The percentile values from the cumulative nCBV histograms and the other histogram parameters were correlated with tumor grades. Cochran’s Q test and the McNemar test were used to compare the diagnostic accuracies of the histogram parameters after the receiver operating characteristic curve analysis. Using the parameter offering the highest diagnostic accuracy, a validation process was performed with an independent test set of nine patients. Results The 99th percentile of the cumulative nCBV histogram (nCBV C99), mean and peak height differed significantly between low- and high-grade gliomas (P = <0.001, 0.014 and <0.001, respectively) and between grade III and IV gliomas (P = <0.001, 0.001 and <0.001, respectively). The diagnostic accuracy of nCBV C99 was significantly higher than that of the mean nCBV (P = 0.016) in distinguishing high- from low-grade gliomas and was comparable to that of the peak height (P = 1.000). Validation using the two cutoff values of nCBV C99 achieved a diagnostic accuracy of 66.7% (6/9) for the separation of all three glioma grades. Conclusion Cumulative histogram analysis of nCBV using 3 T MRI can be a useful method for preoperative glioma grading. The nCBV C99 value is helpful in distinguishing high- from low-grade gliomas and grade IV from III gliomas. PMID:23704910

Comparison of Utility of Histogram Apparent Diffusion Coefficient and R2* for Differentiation of Low-Grade From High-Grade Clear Cell Renal Cell Carcinoma.

PubMed

Zhang, Yu-Dong; Wu, Chen-Jiang; Wang, Qing; Zhang, Jing; Wang, Xiao-Ning; Liu, Xi-Sheng; Shi, Hai-Bin

2015-08-01

The purpose of this study was to compare histogram analysis of apparent diffusion coefficient (ADC) and R2* for differentiating low-grade from high-grade clear cell renal cell carcinoma (RCC). Forty-six patients with pathologically confirmed clear cell RCC underwent preoperative BOLD and DWI MRI of the kidneys. ADCs based on the entire tumor volume were calculated with b value combinations of 0 and 800 s/mm(2). ROI-based R2* was calculated with eight TE combinations of 6.7-22.8 milliseconds. Histogram analysis of tumor ADCs and R2* values was performed to obtain mean; median; width; and fifth, 10th, 90th, and 95th percentiles and histogram inhomogeneity, kurtosis, and skewness for all lesions. Thirty-three low-grade and 13 high-grade clear cell RCCs were found at pathologic examination. The TNM classification and tumor volume of clear cell RCC significantly correlated with histogram ADC and R2* (ρ = -0.317 to 0.506; p < 0.05). High-grade clear cell RCC had significantly lower mean, median, and 10th percentile ADCs but higher inhomogeneity and median R2* than low-grade clear cell RCC (all p < 0.05). Compared with other histogram ADC and R2* indexes, 10th percentile ADC had the highest accuracy (91.3%) in discriminating low- from high-grade clear cell RCC. R2* in discriminating hemorrhage was achieved with a threshold of 68.95 Hz. At this threshold, high-grade clear cell RCC had a significantly higher prevalence of intratumor hemorrhage (high-grade, 76.9%; low-grade, 45.4%; p < 0.05) and larger hemorrhagic area than low-grade clear cell RCC (high-grade, 34.9% ± 31.6%; low-grade, 8.9 ± 16.8%; p < 0.05). A close relation was found between MRI indexes and pathologic findings. Histogram analysis of ADC and R2* allows differentiation of low- from high-grade clear cell RCC with high accuracy.