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.

Gray-level transformations for interactive image enhancement. M.S. Thesis. Final Technical Report

NASA Technical Reports Server (NTRS)

Fittes, B. A.

1975-01-01

A gray-level transformation method suitable for interactive image enhancement was presented. It is shown that the well-known histogram equalization approach is a special case of this method. A technique for improving the uniformity of a histogram is also developed. Experimental results which illustrate the capabilities of both algorithms are described. Two proposals for implementing gray-level transformations in a real-time interactive image enhancement system are also presented.

Histogram analysis for smartphone-based rapid hematocrit determination

PubMed Central

Jalal, Uddin M.; Kim, Sang C.; Shim, Joon S.

2017-01-01

A novel and rapid analysis technique using histogram has been proposed for the colorimetric quantification of blood hematocrits. A smartphone-based “Histogram” app for the detection of hematocrits has been developed integrating the smartphone embedded camera with a microfluidic chip via a custom-made optical platform. The developed histogram analysis shows its effectiveness in the automatic detection of sample channel including auto-calibration and can analyze the single-channel as well as multi-channel images. Furthermore, the analyzing method is advantageous to the quantification of blood-hematocrit both in the equal and varying optical conditions. The rapid determination of blood hematocrits carries enormous information regarding physiological disorders, and the use of such reproducible, cost-effective, and standard techniques may effectively help with the diagnosis and prevention of a number of human diseases. PMID:28717569

Reducing charging effects in scanning electron microscope images by Rayleigh contrast stretching method (RCS).

PubMed

Wan Ismail, W Z; Sim, K S; Tso, C P; Ting, H Y

2011-01-01

To reduce undesirable charging effects in scanning electron microscope images, Rayleigh contrast stretching is developed and employed. First, re-scaling is performed on the input image histograms with Rayleigh algorithm. Then, contrast stretching or contrast adjustment is implemented to improve the images while reducing the contrast charging artifacts. This technique has been compared to some existing histogram equalization (HE) extension techniques: recursive sub-image HE, contrast stretching dynamic HE, multipeak HE and recursive mean separate HE. Other post processing methods, such as wavelet approach, spatial filtering, and exponential contrast stretching, are compared as well. Overall, the proposed method produces better image compensation in reducing charging artifacts. Copyright © 2011 Wiley Periodicals, Inc.

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

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.

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.

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

PubMed

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

2015-01-01

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

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.

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

NASA Astrophysics Data System (ADS)

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

2013-05-01

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

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

PubMed Central

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

2015-01-01

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

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.

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.

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.

Chest CT window settings with multiscale adaptive histogram equalization: pilot study.

PubMed

Fayad, Laura M; Jin, Yinpeng; Laine, Andrew F; Berkmen, Yahya M; Pearson, Gregory D; Freedman, Benjamin; Van Heertum, Ronald

2002-06-01

Multiscale adaptive histogram equalization (MAHE), a wavelet-based algorithm, was investigated as a method of automatic simultaneous display of the full dynamic contrast range of a computed tomographic image. Interpretation times were significantly lower for MAHE-enhanced images compared with those for conventionally displayed images. Diagnostic accuracy, however, was insufficient in this pilot study to allow recommendation of MAHE as a replacement for conventional window display.

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

Moderated histogram equalization, an automatic means of enhancing the contrast in digital light micrographs reversibly.

PubMed

Entwistle, A

2004-06-01

A means for improving the contrast in the images produced from digital light micrographs is described that requires no intervention by the experimenter: zero-order, scaling, tonally independent, moderated histogram equalization. It is based upon histogram equalization, which often results in digital light micrographs that contain regions that appear to be saturated, negatively biased or very grainy. Here a non-decreasing monotonic function is introduced into the process, which moderates the changes in contrast that are generated. This method is highly effective for all three of the main types of contrast found in digital light micrography: bright objects viewed against a dark background, e.g. fluorescence and dark-ground or dark-field image data sets; bright and dark objects sets against a grey background, e.g. image data sets collected with phase or Nomarski differential interference contrast optics; and darker objects set against a light background, e.g. views of absorbing specimens. Moreover, it is demonstrated that there is a single fixed moderating function, whose actions are independent of the number of elements of image data, which works well with all types of digital light micrographs, including multimodal or multidimensional image data sets. The use of this fixed function is very robust as the appearance of the final image is not altered discernibly when it is applied repeatedly to an image data set. Consequently, moderated histogram equalization can be applied to digital light micrographs as a push-button solution, thereby eliminating biases that those undertaking the processing might have introduced during manual processing. Finally, moderated histogram equalization yields a mapping function and so, through the use of look-up tables, indexes or palettes, the information present in the original data file can be preserved while an image with the improved contrast is displayed on the monitor screen.

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.

Improving the convergence rate in affine registration of PET and SPECT brain images using histogram equalization.

PubMed

Salas-Gonzalez, D; Górriz, J M; Ramírez, J; Padilla, P; Illán, I A

2013-01-01

A procedure to improve the convergence rate for affine registration methods of medical brain images when the images differ greatly from the template is presented. The methodology is based on a histogram matching of the source images with respect to the reference brain template before proceeding with the affine registration. The preprocessed source brain images are spatially normalized to a template using a general affine model with 12 parameters. A sum of squared differences between the source images and the template is considered as objective function, and a Gauss-Newton optimization algorithm is used to find the minimum of the cost function. Using histogram equalization as a preprocessing step improves the convergence rate in the affine registration algorithm of brain images as we show in this work using SPECT and PET brain images.

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.

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.

Analysis of the hand vein pattern for people recognition

NASA Astrophysics Data System (ADS)

Castro-Ortega, R.; Toxqui-Quitl, C.; Cristóbal, G.; Marcos, J. Victor; Padilla-Vivanco, A.; Hurtado Pérez, R.

2015-09-01

The shape of the hand vascular pattern contains useful and unique features that can be used for identifying and authenticating people, with applications in access control, medicine and financial services. In this work, an optical system for the image acquisition of the hand vascular pattern is implemented. It consists of a CCD camera with sensitivity in the IR and a light source with emission in the 880 nm. The IR radiation interacts with the desoxyhemoglobin, hemoglobin and water present in the blood of the veins, making possible to see the vein pattern underneath skin. The segmentation of the Region Of Interest (ROI) is achieved using geometrical moments locating the centroid of an image. For enhancement of the vein pattern we use the technique of Histogram Equalization and Contrast Limited Adaptive Histogram Equalization (CLAHE). In order to remove unnecessary information such as body hair and skinfolds, a low pass filter is implemented. A method based on geometric moments is used to obtain the invariant descriptors of the input images. The classification task is achieved using Artificial Neural Networks (ANN) and K-Nearest Neighbors (K-nn) algorithms. Experimental results using our database show a percentage of correct classification, higher of 86.36% with ANN for 912 images of 38 people with 12 versions each one.

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.

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.

An evaluation of the effectiveness of adaptive histogram equalization for contrast enhancement.

PubMed

Zimmerman, J B; Pizer, S M; Staab, E V; Perry, J R; McCartney, W; Brenton, B C

1988-01-01

Adaptive histogram equalization (AHE) and intensity windowing have been compared using psychophysical observer studies. Experienced radiologists were shown clinical CT (computerized tomographic) images of the chest. Into some of the images, appropriate artificial lesions were introduced; the physicians were then shown the images processed with both AHE and intensity windowing. They were asked to assess the probability that a given image contained the artificial lesion, and their accuracy was measured. The results of these experiments show that for this particular diagnostic task, there was no significant difference in the ability of the two methods to depict luminance contrast; thus, further evaluation of AHE using controlled clinical trials is indicated.

A cost-effective line-based light-balancing technique using adaptive processing.

PubMed

Hsia, Shih-Chang; Chen, Ming-Huei; Chen, Yu-Min

2006-09-01

The camera imaging system has been widely used; however, the displaying image appears to have an unequal light distribution. This paper presents novel light-balancing techniques to compensate uneven illumination based on adaptive signal processing. For text image processing, first, we estimate the background level and then process each pixel with nonuniform gain. This algorithm can balance the light distribution while keeping a high contrast in the image. For graph image processing, the adaptive section control using piecewise nonlinear gain is proposed to equalize the histogram. Simulations show that the performance of light balance is better than the other methods. Moreover, we employ line-based processing to efficiently reduce the memory requirement and the computational cost to make it applicable in real-time systems.

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

Uniform enhancement of optical micro-angiography images using Rayleigh contrast-limited adaptive histogram equalization.

PubMed

Yousefi, Siavash; Qin, Jia; Zhi, Zhongwei; Wang, Ruikang K

2013-02-01

Optical microangiography is an imaging technology that is capable of providing detailed functional blood flow maps within microcirculatory tissue beds in vivo. Some practical issues however exist when displaying and quantifying the microcirculation that perfuses the scanned tissue volume. These issues include: (I) Probing light is subject to specular reflection when it shines onto sample. The unevenness of the tissue surface makes the light energy entering the tissue not uniform over the entire scanned tissue volume. (II) The biological tissue is heterogeneous in nature, meaning the scattering and absorption properties of tissue would attenuate the probe beam. These physical limitations can result in local contrast degradation and non-uniform micro-angiogram images. In this paper, we propose a post-processing method that uses Rayleigh contrast-limited adaptive histogram equalization to increase the contrast and improve the overall appearance and uniformity of optical micro-angiograms without saturating the vessel intensity and changing the physical meaning of the micro-angiograms. The qualitative and quantitative performance of the proposed method is compared with those of common histogram equalization and contrast enhancement methods. We demonstrate that the proposed method outperforms other existing approaches. The proposed method is not limited to optical microangiography and can be used in other image modalities such as photo-acoustic tomography and scanning laser confocal microscopy.

Local dynamic range compensation for scanning electron microscope imaging system by sub-blocking multiple peak HE with convolution.

PubMed

Sim, K S; Teh, V; Tey, Y C; Kho, T K

2016-11-01

This paper introduces new development technique to improve the Scanning Electron Microscope (SEM) image quality and we name it as sub-blocking multiple peak histogram equalization (SUB-B-MPHE) with convolution operator. By using this new proposed technique, it shows that the new modified MPHE performs better than original MPHE. In addition, the sub-blocking method consists of convolution operator which can help to remove the blocking effect for SEM images after applying this new developed technique. Hence, by using the convolution operator, it effectively removes the blocking effect by properly distributing the suitable pixel value for the whole image. Overall, the SUB-B-MPHE with convolution outperforms the rest of methods. SCANNING 38:492-501, 2016. © 2015 Wiley Periodicals, Inc. © Wiley Periodicals, Inc.

[A fast iterative algorithm for adaptive histogram equalization].

PubMed

Cao, X; Liu, X; Deng, Z; Jiang, D; Zheng, C

1997-01-01

In this paper, we propose an iterative algorthm called FAHE., which is based on the relativity between the current local histogram and the one before the sliding window moving. Comparing with the basic AHE, the computing time of FAHE is decreased from 5 hours to 4 minutes on a 486dx/33 compatible computer, when using a 65 x 65 sliding window for a 512 x 512 with 8 bits gray-level range.

Preprocessing with image denoising and histogram equalization for endoscopy image analysis using texture analysis.

PubMed

Hiroyasu, Tomoyuki; Hayashinuma, Katsutoshi; Ichikawa, Hiroshi; Yagi, Nobuaki

2015-08-01

A preprocessing method for endoscopy image analysis using texture analysis is proposed. In a previous study, we proposed a feature value that combines a co-occurrence matrix and a run-length matrix to analyze the extent of early gastric cancer from images taken with narrow-band imaging endoscopy. However, the obtained feature value does not identify lesion zones correctly due to the influence of noise and halation. Therefore, we propose a new preprocessing method with a non-local means filter for de-noising and contrast limited adaptive histogram equalization. We have confirmed that the pattern of gastric mucosa in images can be improved by the proposed method. Furthermore, the lesion zone is shown more correctly by the obtained color map.

Perceptual Contrast Enhancement with Dynamic Range Adjustment

PubMed Central

Zhang, Hong; Li, Yuecheng; Chen, Hao; Yuan, Ding; Sun, Mingui

2013-01-01

Recent years, although great efforts have been made to improve its performance, few Histogram equalization (HE) methods take human visual perception (HVP) into account explicitly. The human visual system (HVS) is more sensitive to edges than brightness. This paper proposes to take use of this nature intuitively and develops a perceptual contrast enhancement approach with dynamic range adjustment through histogram modification. The use of perceptual contrast connects the image enhancement problem with the HVS. To pre-condition the input image before the HE procedure is implemented, a perceptual contrast map (PCM) is constructed based on the modified Difference of Gaussian (DOG) algorithm. As a result, the contrast of the image is sharpened and high frequency noise is suppressed. A modified Clipped Histogram Equalization (CHE) is also developed which improves visual quality by automatically detecting the dynamic range of the image with improved perceptual contrast. Experimental results show that the new HE algorithm outperforms several state-of-the-art algorithms in improving perceptual contrast and enhancing details. In addition, the new algorithm is simple to implement, making it suitable for real-time applications. PMID:24339452

An adaptive enhancement algorithm for infrared video based on modified k-means clustering

NASA Astrophysics Data System (ADS)

Zhang, Linze; Wang, Jingqi; Wu, Wen

2016-09-01

In this paper, we have proposed a video enhancement algorithm to improve the output video of the infrared camera. Sometimes the video obtained by infrared camera is very dark since there is no clear target. In this case, infrared video should be divided into frame images by frame extraction, in order to carry out the image enhancement. For the first frame image, which can be divided into k sub images by using K-means clustering according to the gray interval it occupies before k sub images' histogram equalization according to the amount of information per sub image, we used a method to solve a problem that final cluster centers close to each other in some cases; and for the other frame images, their initial cluster centers can be determined by the final clustering centers of the previous ones, and the histogram equalization of each sub image will be carried out after image segmentation based on K-means clustering. The histogram equalization can make the gray value of the image to the whole gray level, and the gray level of each sub image is determined by the ratio of pixels to a frame image. Experimental results show that this algorithm can improve the contrast of infrared video where night target is not obvious which lead to a dim scene, and reduce the negative effect given by the overexposed pixels adaptively in a certain range.

Uniform enhancement of optical micro-angiography images using Rayleigh contrast-limited adaptive histogram equalization

PubMed Central

Yousefi, Siavash; Qin, Jia; Zhi, Zhongwei

2013-01-01

Optical microangiography is an imaging technology that is capable of providing detailed functional blood flow maps within microcirculatory tissue beds in vivo. Some practical issues however exist when displaying and quantifying the microcirculation that perfuses the scanned tissue volume. These issues include: (I) Probing light is subject to specular reflection when it shines onto sample. The unevenness of the tissue surface makes the light energy entering the tissue not uniform over the entire scanned tissue volume. (II) The biological tissue is heterogeneous in nature, meaning the scattering and absorption properties of tissue would attenuate the probe beam. These physical limitations can result in local contrast degradation and non-uniform micro-angiogram images. In this paper, we propose a post-processing method that uses Rayleigh contrast-limited adaptive histogram equalization to increase the contrast and improve the overall appearance and uniformity of optical micro-angiograms without saturating the vessel intensity and changing the physical meaning of the micro-angiograms. The qualitative and quantitative performance of the proposed method is compared with those of common histogram equalization and contrast enhancement methods. We demonstrate that the proposed method outperforms other existing approaches. The proposed method is not limited to optical microangiography and can be used in other image modalities such as photo-acoustic tomography and scanning laser confocal microscopy. PMID:23482880

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.

Hue-preserving and saturation-improved color histogram equalization algorithm.

PubMed

Song, Ki Sun; Kang, Hee; Kang, Moon Gi

2016-06-01

In this paper, an algorithm is proposed to improve contrast and saturation without color degradation. The local histogram equalization (HE) method offers better performance than the global HE method, whereas the local HE method sometimes produces undesirable results due to the block-based processing. The proposed contrast-enhancement (CE) algorithm reflects the characteristics of the global HE method in the local HE method to avoid the artifacts, while global and local contrasts are enhanced. There are two ways to apply the proposed CE algorithm to color images. One is luminance processing methods, and the other one is each channel processing methods. However, these ways incur excessive or reduced saturation and color degradation problems. The proposed algorithm solves these problems by using channel adaptive equalization and similarity of ratios between the channels. Experimental results show that the proposed algorithm enhances contrast and saturation while preserving the hue and producing better performance than existing methods in terms of objective evaluation metrics.

Novel Variants of a Histogram Shift-Based Reversible Watermarking Technique for Medical Images to Improve Hiding Capacity

PubMed Central

Tuckley, Kushal

2017-01-01

In telemedicine systems, critical medical data is shared on a public communication channel. This increases the risk of unauthorised access to patient's information. This underlines the importance of secrecy and authentication for the medical data. This paper presents two innovative variations of classical histogram shift methods to increase the hiding capacity. The first technique divides the image into nonoverlapping blocks and embeds the watermark individually using the histogram method. The second method separates the region of interest and embeds the watermark only in the region of noninterest. This approach preserves the medical information intact. This method finds its use in critical medical cases. The high PSNR (above 45 dB) obtained for both techniques indicates imperceptibility of the approaches. Experimental results illustrate superiority of the proposed approaches when compared with other methods based on histogram shifting techniques. These techniques improve embedding capacity by 5–15% depending on the image type, without affecting the quality of the watermarked image. Both techniques also enable lossless reconstruction of the watermark and the host medical image. A higher embedding capacity makes the proposed approaches attractive for medical image watermarking applications without compromising the quality of the image. PMID:29104744

Lower-upper-threshold correlation for underwater range-gated imaging self-adaptive enhancement.

PubMed

Sun, Liang; Wang, Xinwei; Liu, Xiaoquan; Ren, Pengdao; Lei, Pingshun; He, Jun; Fan, Songtao; Zhou, Yan; Liu, Yuliang

2016-10-10

In underwater range-gated imaging (URGI), enhancement of low-brightness and low-contrast images is critical for human observation. Traditional histogram equalizations over-enhance images, with the result of details being lost. To compress over-enhancement, a lower-upper-threshold correlation method is proposed for underwater range-gated imaging self-adaptive enhancement based on double-plateau histogram equalization. The lower threshold determines image details and compresses over-enhancement. It is correlated with the upper threshold. First, the upper threshold is updated by searching for the local maximum in real time, and then the lower threshold is calculated by the upper threshold and the number of nonzero units selected from a filtered histogram. With this method, the backgrounds of underwater images are constrained with enhanced details. Finally, the proof experiments are performed. Peak signal-to-noise-ratio, variance, contrast, and human visual properties are used to evaluate the objective quality of the global and regions of interest images. The evaluation results demonstrate that the proposed method adaptively selects the proper upper and lower thresholds under different conditions. The proposed method contributes to URGI with effective image enhancement for human eyes.

Post-Modeling Histogram Matching of Maps Produced Using Regression Trees

Treesearch

Andrew J. Lister; Tonya W. Lister

2006-01-01

Spatial predictive models often use statistical techniques that in some way rely on averaging of values. Estimates from linear modeling are known to be susceptible to truncation of variance when the independent (predictor) variables are measured with error. A straightforward post-processing technique (histogram matching) for attempting to mitigate this effect is...

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.

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.

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).

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.

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.

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.

Recovery of Background Structures in Nanoscale Helium Ion Microscope Imaging.

PubMed

Carasso, Alfred S; Vladár, András E

2014-01-01

This paper discusses a two step enhancement technique applicable to noisy Helium Ion Microscope images in which background structures are not easily discernible due to a weak signal. The method is based on a preliminary adaptive histogram equalization, followed by 'slow motion' low-exponent Lévy fractional diffusion smoothing. This combined approach is unexpectedly effective, resulting in a companion enhanced image in which background structures are rendered much more visible, and noise is significantly reduced, all with minimal loss of image sharpness. The method also provides useful enhancements of scanning charged-particle microscopy images obtained by composing multiple drift-corrected 'fast scan' frames. The paper includes software routines, written in Interactive Data Language (IDL),(1) that can perform the above image processing tasks.

ERS-2 SAR and IRS-1C LISS III data fusion: A PCA approach to improve remote sensing based geological interpretation

NASA Astrophysics Data System (ADS)

Pal, S. K.; Majumdar, T. J.; Bhattacharya, Amit K.

Fusion of optical and synthetic aperture radar data has been attempted in the present study for mapping of various lithologic units over a part of the Singhbhum Shear Zone (SSZ) and its surroundings. ERS-2 SAR data over the study area has been enhanced using Fast Fourier Transformation (FFT) based filtering approach, and also using Frost filtering technique. Both the enhanced SAR imagery have been then separately fused with histogram equalized IRS-1C LISS III image using Principal Component Analysis (PCA) technique. Later, Feature-oriented Principal Components Selection (FPCS) technique has been applied to generate False Color Composite (FCC) images, from which corresponding geological maps have been prepared. Finally, GIS techniques have been successfully used for change detection analysis in the lithological interpretation between the published geological map and the fusion based geological maps. In general, there is good agreement between these maps over a large portion of the study area. Based on the change detection studies, few areas could be identified which need attention for further detailed ground-based geological studies.

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.

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.

Novel medical image enhancement algorithms

NASA Astrophysics Data System (ADS)

Agaian, Sos; McClendon, Stephen A.

2010-01-01

In this paper, we present two novel medical image enhancement algorithms. The first, a global image enhancement algorithm, utilizes an alpha-trimmed mean filter as its backbone to sharpen images. The second algorithm uses a cascaded unsharp masking technique to separate the high frequency components of an image in order for them to be enhanced using a modified adaptive contrast enhancement algorithm. Experimental results from enhancing electron microscopy, radiological, CT scan and MRI scan images, using the MATLAB environment, are then compared to the original images as well as other enhancement methods, such as histogram equalization and two forms of adaptive contrast enhancement. An image processing scheme for electron microscopy images of Purkinje cells will also be implemented and utilized as a comparison tool to evaluate the performance of our algorithm.

Recovery of Background Structures in Nanoscale Helium Ion Microscope Imaging

PubMed Central

Carasso, Alfred S; Vladár, András E

2014-01-01

This paper discusses a two step enhancement technique applicable to noisy Helium Ion Microscope images in which background structures are not easily discernible due to a weak signal. The method is based on a preliminary adaptive histogram equalization, followed by ‘slow motion’ low-exponent Lévy fractional diffusion smoothing. This combined approach is unexpectedly effective, resulting in a companion enhanced image in which background structures are rendered much more visible, and noise is significantly reduced, all with minimal loss of image sharpness. The method also provides useful enhancements of scanning charged-particle microscopy images obtained by composing multiple drift-corrected ‘fast scan’ frames. The paper includes software routines, written in Interactive Data Language (IDL),1 that can perform the above image processing tasks. PMID:26601050

Novel Histogram Based Unsupervised Classification Technique to Determine Natural Classes From Biophysically Relevant Fit Parameters to Hyperspectral Data

DOE PAGES

McCann, Cooper; Repasky, Kevin S.; Morin, Mikindra; ...

2017-05-23

Hyperspectral image analysis has benefited from an array of methods that take advantage of the increased spectral depth compared to multispectral sensors; however, the focus of these developments has been on supervised classification methods. Lack of a priori knowledge regarding land cover characteristics can make unsupervised classification methods preferable under certain circumstances. An unsupervised classification technique is presented in this paper that utilizes physically relevant basis functions to model the reflectance spectra. These fit parameters used to generate the basis functions allow clustering based on spectral characteristics rather than spectral channels and provide both noise and data reduction. Histogram splittingmore » of the fit parameters is then used as a means of producing an unsupervised classification. Unlike current unsupervised classification techniques that rely primarily on Euclidian distance measures to determine similarity, the unsupervised classification technique uses the natural splitting of the fit parameters associated with the basis functions creating clusters that are similar in terms of physical parameters. The data set used in this work utilizes the publicly available data collected at Indian Pines, Indiana. This data set provides reference data allowing for comparisons of the efficacy of different unsupervised data analysis. The unsupervised histogram splitting technique presented in this paper is shown to be better than the standard unsupervised ISODATA clustering technique with an overall accuracy of 34.3/19.0% before merging and 40.9/39.2% after merging. Finally, this improvement is also seen as an improvement of kappa before/after merging of 24.8/30.5 for the histogram splitting technique compared to 15.8/28.5 for ISODATA.« less

Novel Histogram Based Unsupervised Classification Technique to Determine Natural Classes From Biophysically Relevant Fit Parameters to Hyperspectral Data

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

McCann, Cooper; Repasky, Kevin S.; Morin, Mikindra

Hyperspectral image analysis has benefited from an array of methods that take advantage of the increased spectral depth compared to multispectral sensors; however, the focus of these developments has been on supervised classification methods. Lack of a priori knowledge regarding land cover characteristics can make unsupervised classification methods preferable under certain circumstances. An unsupervised classification technique is presented in this paper that utilizes physically relevant basis functions to model the reflectance spectra. These fit parameters used to generate the basis functions allow clustering based on spectral characteristics rather than spectral channels and provide both noise and data reduction. Histogram splittingmore » of the fit parameters is then used as a means of producing an unsupervised classification. Unlike current unsupervised classification techniques that rely primarily on Euclidian distance measures to determine similarity, the unsupervised classification technique uses the natural splitting of the fit parameters associated with the basis functions creating clusters that are similar in terms of physical parameters. The data set used in this work utilizes the publicly available data collected at Indian Pines, Indiana. This data set provides reference data allowing for comparisons of the efficacy of different unsupervised data analysis. The unsupervised histogram splitting technique presented in this paper is shown to be better than the standard unsupervised ISODATA clustering technique with an overall accuracy of 34.3/19.0% before merging and 40.9/39.2% after merging. Finally, this improvement is also seen as an improvement of kappa before/after merging of 24.8/30.5 for the histogram splitting technique compared to 15.8/28.5 for ISODATA.« less

Cost-effective forensic image enhancement

NASA Astrophysics Data System (ADS)

Dalrymple, Brian E.

1998-12-01

In 1977, a paper was presented at the SPIE conference in Reston, Virginia, detailing the computer enhancement of the Zapruder film. The forensic value of this examination in a major homicide investigation was apparent to the viewer. Equally clear was the potential for extracting evidence which is beyond the reach of conventional detection techniques. The cost of this technology in 1976, however, was prohibitive, and well beyond the means of most police agencies. Twenty-two years later, a highly efficient means of image enhancement is easily within the grasp of most police agencies, not only for homicides but for any case application. A PC workstation combined with an enhancement software package allows a forensic investigator to fully exploit digital technology. The goal of this approach is the optimization of the signal to noise ratio in images. Obstructive backgrounds may be diminished or eliminated while weak signals are optimized by the use of algorithms including Fast Fourier Transform, Histogram Equalization and Image Subtraction. An added benefit is the speed with which these processes are completed and the results known. The efficacy of forensic image enhancement is illustrated through case applications.

Angular relational signature-based chest radiograph image view classification.

PubMed

Santosh, K C; Wendling, Laurent

2018-01-22

In a computer-aided diagnosis (CAD) system, especially for chest radiograph or chest X-ray (CXR) screening, CXR image view information is required. Automatically separating CXR image view, frontal and lateral can ease subsequent CXR screening process, since the techniques may not equally work for both views. We present a novel technique to classify frontal and lateral CXR images, where we introduce angular relational signature through force histogram to extract features and apply three different state-of-the-art classifiers: multi-layer perceptron, random forest, and support vector machine to make a decision. We validated our fully automatic technique on a set of 8100 images hosted by the U.S. National Library of Medicine (NLM), National Institutes of Health (NIH), and achieved an accuracy close to 100%. Our method outperforms the state-of-the-art methods in terms of processing time (less than or close to 2 s for the whole test data) while the accuracies can be compared, and therefore, it justifies its practicality. Graphical Abstract Interpreting chest X-ray (CXR) through the angular relational signature.

Infrared and visible image fusion using discrete cosine transform and swarm intelligence for surveillance applications

NASA Astrophysics Data System (ADS)

Paramanandham, Nirmala; Rajendiran, Kishore

2018-01-01

A novel image fusion technique is presented for integrating infrared and visible images. Integration of images from the same or various sensing modalities can deliver the required information that cannot be delivered by viewing the sensor outputs individually and consecutively. In this paper, a swarm intelligence based image fusion technique using discrete cosine transform (DCT) domain is proposed for surveillance application which integrates the infrared image with the visible image for generating a single informative fused image. Particle swarm optimization (PSO) is used in the fusion process for obtaining the optimized weighting factor. These optimized weighting factors are used for fusing the DCT coefficients of visible and infrared images. Inverse DCT is applied for obtaining the initial fused image. An enhanced fused image is obtained through adaptive histogram equalization for a better visual understanding and target detection. The proposed framework is evaluated using quantitative metrics such as standard deviation, spatial frequency, entropy and mean gradient. The experimental results demonstrate the outperformance of the proposed algorithm over many other state- of- the- art techniques reported in literature.

Joint OSNR monitoring and modulation format identification in digital coherent receivers using deep neural networks.

PubMed

Khan, Faisal Nadeem; Zhong, Kangping; Zhou, Xian; Al-Arashi, Waled Hussein; Yu, Changyuan; Lu, Chao; Lau, Alan Pak Tao

2017-07-24

We experimentally demonstrate the use of deep neural networks (DNNs) in combination with signals' amplitude histograms (AHs) for simultaneous optical signal-to-noise ratio (OSNR) monitoring and modulation format identification (MFI) in digital coherent receivers. The proposed technique automatically extracts OSNR and modulation format dependent features of AHs, obtained after constant modulus algorithm (CMA) equalization, and exploits them for the joint estimation of these parameters. Experimental results for 112 Gbps polarization-multiplexed (PM) quadrature phase-shift keying (QPSK), 112 Gbps PM 16 quadrature amplitude modulation (16-QAM), and 240 Gbps PM 64-QAM signals demonstrate OSNR monitoring with mean estimation errors of 1.2 dB, 0.4 dB, and 1 dB, respectively. Similarly, the results for MFI show 100% identification accuracy for all three modulation formats. The proposed technique applies deep machine learning algorithms inside standard digital coherent receiver and does not require any additional hardware. Therefore, it is attractive for cost-effective multi-parameter estimation in next-generation elastic optical networks (EONs).

Complex adaptation-based LDR image rendering for 3D image reconstruction

NASA Astrophysics Data System (ADS)

Lee, Sung-Hak; Kwon, Hyuk-Ju; Sohng, Kyu-Ik

2014-07-01

A low-dynamic tone-compression technique is developed for realistic image rendering that can make three-dimensional (3D) images similar to realistic scenes by overcoming brightness dimming in the 3D display mode. The 3D surround provides varying conditions for image quality, illuminant adaptation, contrast, gamma, color, sharpness, and so on. In general, gain/offset adjustment, gamma compensation, and histogram equalization have performed well in contrast compression; however, as a result of signal saturation and clipping effects, image details are removed and information is lost on bright and dark areas. Thus, an enhanced image mapping technique is proposed based on space-varying image compression. The performance of contrast compression is enhanced with complex adaptation in a 3D viewing surround combining global and local adaptation. Evaluating local image rendering in view of tone and color expression, noise reduction, and edge compensation confirms that the proposed 3D image-mapping model can compensate for the loss of image quality in the 3D mode.

Motor Oil Classification using Color Histograms and Pattern Recognition Techniques.

PubMed

Ahmadi, Shiva; Mani-Varnosfaderani, Ahmad; Habibi, Biuck

2018-04-20

Motor oil classification is important for quality control and the identification of oil adulteration. In thiswork, we propose a simple, rapid, inexpensive and nondestructive approach based on image analysis and pattern recognition techniques for the classification of nine different types of motor oils according to their corresponding color histograms. For this, we applied color histogram in different color spaces such as red green blue (RGB), grayscale, and hue saturation intensity (HSI) in order to extract features that can help with the classification procedure. These color histograms and their combinations were used as input for model development and then were statistically evaluated by using linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and support vector machine (SVM) techniques. Here, two common solutions for solving a multiclass classification problem were applied: (1) transformation to binary classification problem using a one-against-all (OAA) approach and (2) extension from binary classifiers to a single globally optimized multilabel classification model. In the OAA strategy, LDA, QDA, and SVM reached up to 97% in terms of accuracy, sensitivity, and specificity for both the training and test sets. In extension from binary case, despite good performances by the SVM classification model, QDA and LDA provided better results up to 92% for RGB-grayscale-HSI color histograms and up to 93% for the HSI color map, respectively. In order to reduce the numbers of independent variables for modeling, a principle component analysis algorithm was used. Our results suggest that the proposed method is promising for the identification and classification of different types of motor oils.

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.

Application of Markov Models for Analysis of Development of Psychological Characteristics

ERIC Educational Resources Information Center

Kuravsky, Lev S.; Malykh, Sergey B.

2004-01-01

A technique to study combined influence of environmental and genetic factors on the base of changes in phenotype distributions is presented. Histograms are exploited as base analyzed characteristics. A continuous time, discrete state Markov process with piece-wise constant interstate transition rates is associated with evolution of each histogram.…

A method for normalizing pathology images to improve feature extraction for quantitative pathology.

PubMed

Tam, Allison; Barker, Jocelyn; Rubin, Daniel

2016-01-01

With the advent of digital slide scanning technologies and the potential proliferation of large repositories of digital pathology images, many research studies can leverage these data for biomedical discovery and to develop clinical applications. However, quantitative analysis of digital pathology images is impeded by batch effects generated by varied staining protocols and staining conditions of pathological slides. To overcome this problem, this paper proposes a novel, fully automated stain normalization method to reduce batch effects and thus aid research in digital pathology applications. Their method, intensity centering and histogram equalization (ICHE), normalizes a diverse set of pathology images by first scaling the centroids of the intensity histograms to a common point and then applying a modified version of contrast-limited adaptive histogram equalization. Normalization was performed on two datasets of digitized hematoxylin and eosin (H&E) slides of different tissue slices from the same lung tumor, and one immunohistochemistry dataset of digitized slides created by restaining one of the H&E datasets. The ICHE method was evaluated based on image intensity values, quantitative features, and the effect on downstream applications, such as a computer aided diagnosis. For comparison, three methods from the literature were reimplemented and evaluated using the same criteria. The authors found that ICHE not only improved performance compared with un-normalized images, but in most cases showed improvement compared with previous methods for correcting batch effects in the literature. ICHE may be a useful preprocessing step a digital pathology image processing pipeline.

Contact-free palm-vein recognition based on local invariant features.

PubMed

Kang, Wenxiong; Liu, Yang; Wu, Qiuxia; Yue, Xishun

2014-01-01

Contact-free palm-vein recognition is one of the most challenging and promising areas in hand biometrics. In view of the existing problems in contact-free palm-vein imaging, including projection transformation, uneven illumination and difficulty in extracting exact ROIs, this paper presents a novel recognition approach for contact-free palm-vein recognition that performs feature extraction and matching on all vein textures distributed over the palm surface, including finger veins and palm veins, to minimize the loss of feature information. First, a hierarchical enhancement algorithm, which combines a DOG filter and histogram equalization, is adopted to alleviate uneven illumination and to highlight vein textures. Second, RootSIFT, a more stable local invariant feature extraction method in comparison to SIFT, is adopted to overcome the projection transformation in contact-free mode. Subsequently, a novel hierarchical mismatching removal algorithm based on neighborhood searching and LBP histograms is adopted to improve the accuracy of feature matching. Finally, we rigorously evaluated the proposed approach using two different databases and obtained 0.996% and 3.112% Equal Error Rates (EERs), respectively, which demonstrate the effectiveness of the proposed approach.

Bas-relief generation using adaptive histogram equalization.

PubMed

Sun, Xianfang; Rosin, Paul L; Martin, Ralph R; Langbein, Frank C

2009-01-01

An algorithm is presented to automatically generate bas-reliefs based on adaptive histogram equalization (AHE), starting from an input height field. A mesh model may alternatively be provided, in which case a height field is first created via orthogonal or perspective projection. The height field is regularly gridded and treated as an image, enabling a modified AHE method to be used to generate a bas-relief with a user-chosen height range. We modify the original image-contrast-enhancement AHE method to use gradient weights also to enhance the shape features of the bas-relief. To effectively compress the height field, we limit the height-dependent scaling factors used to compute relative height variations in the output from height variations in the input; this prevents any height differences from having too great effect. Results of AHE over different neighborhood sizes are averaged to preserve information at different scales in the resulting bas-relief. Compared to previous approaches, the proposed algorithm is simple and yet largely preserves original shape features. Experiments show that our results are, in general, comparable to and in some cases better than the best previously published methods.

A Framework for Reproducible Latent Fingerprint Enhancements.

PubMed

Carasso, Alfred S

2014-01-01

Photoshop processing of latent fingerprints is the preferred methodology among law enforcement forensic experts, but that appproach is not fully reproducible and may lead to questionable enhancements. Alternative, independent, fully reproducible enhancements, using IDL Histogram Equalization and IDL Adaptive Histogram Equalization, can produce better-defined ridge structures, along with considerable background information. Applying a systematic slow motion smoothing procedure to such IDL enhancements, based on the rapid FFT solution of a Lévy stable fractional diffusion equation, can attenuate background detail while preserving ridge information. The resulting smoothed latent print enhancements are comparable to, but distinct from, forensic Photoshop images suitable for input into automated fingerprint identification systems, (AFIS). In addition, this progressive smoothing procedure can be reexamined by displaying the suite of progressively smoother IDL images. That suite can be stored, providing an audit trail that allows monitoring for possible loss of useful information, in transit to the user-selected optimal image. Such independent and fully reproducible enhancements provide a valuable frame of reference that may be helpful in informing, complementing, and possibly validating the forensic Photoshop methodology.

Contact-Free Palm-Vein Recognition Based on Local Invariant Features

PubMed Central

Kang, Wenxiong; Liu, Yang; Wu, Qiuxia; Yue, Xishun

2014-01-01

Contact-free palm-vein recognition is one of the most challenging and promising areas in hand biometrics. In view of the existing problems in contact-free palm-vein imaging, including projection transformation, uneven illumination and difficulty in extracting exact ROIs, this paper presents a novel recognition approach for contact-free palm-vein recognition that performs feature extraction and matching on all vein textures distributed over the palm surface, including finger veins and palm veins, to minimize the loss of feature information. First, a hierarchical enhancement algorithm, which combines a DOG filter and histogram equalization, is adopted to alleviate uneven illumination and to highlight vein textures. Second, RootSIFT, a more stable local invariant feature extraction method in comparison to SIFT, is adopted to overcome the projection transformation in contact-free mode. Subsequently, a novel hierarchical mismatching removal algorithm based on neighborhood searching and LBP histograms is adopted to improve the accuracy of feature matching. Finally, we rigorously evaluated the proposed approach using two different databases and obtained 0.996% and 3.112% Equal Error Rates (EERs), respectively, which demonstrate the effectiveness of the proposed approach. PMID:24866176

A Framework for Reproducible Latent Fingerprint Enhancements

PubMed Central

Carasso, Alfred S.

2014-01-01

Photoshop processing1 of latent fingerprints is the preferred methodology among law enforcement forensic experts, but that appproach is not fully reproducible and may lead to questionable enhancements. Alternative, independent, fully reproducible enhancements, using IDL Histogram Equalization and IDL Adaptive Histogram Equalization, can produce better-defined ridge structures, along with considerable background information. Applying a systematic slow motion smoothing procedure to such IDL enhancements, based on the rapid FFT solution of a Lévy stable fractional diffusion equation, can attenuate background detail while preserving ridge information. The resulting smoothed latent print enhancements are comparable to, but distinct from, forensic Photoshop images suitable for input into automated fingerprint identification systems, (AFIS). In addition, this progressive smoothing procedure can be reexamined by displaying the suite of progressively smoother IDL images. That suite can be stored, providing an audit trail that allows monitoring for possible loss of useful information, in transit to the user-selected optimal image. Such independent and fully reproducible enhancements provide a valuable frame of reference that may be helpful in informing, complementing, and possibly validating the forensic Photoshop methodology. PMID:26601028

On algorithmic optimization of histogramming functions for GEM systems

NASA Astrophysics Data System (ADS)

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

2015-09-01

This article concerns optimization methods for data analysis for the X-ray GEM detector system. The offline analysis of collected samples was optimized for MATLAB computations. Compiled functions in C language were used with MEX library. Significant speedup was received for both ordering-preprocessing and for histogramming of samples. Utilized techniques with obtained results are presented.

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-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.

Blind identification of image manipulation type using mixed statistical moments

NASA Astrophysics Data System (ADS)

Jeong, Bo Gyu; Moon, Yong Ho; Eom, Il Kyu

2015-01-01

We present a blind identification of image manipulation types such as blurring, scaling, sharpening, and histogram equalization. Motivated by the fact that image manipulations can change the frequency characteristics of an image, we introduce three types of feature vectors composed of statistical moments. The proposed statistical moments are generated from separated wavelet histograms, the characteristic functions of the wavelet variance, and the characteristic functions of the spatial image. Our method can solve the n-class classification problem. Through experimental simulations, we demonstrate that our proposed method can achieve high performance in manipulation type detection. The average rate of the correctly identified manipulation types is as high as 99.22%, using 10,800 test images and six manipulation types including the authentic image.

A method for normalizing pathology images to improve feature extraction for quantitative pathology

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

Tam, Allison; Barker, Jocelyn; Rubin, Daniel

Purpose: With the advent of digital slide scanning technologies and the potential proliferation of large repositories of digital pathology images, many research studies can leverage these data for biomedical discovery and to develop clinical applications. However, quantitative analysis of digital pathology images is impeded by batch effects generated by varied staining protocols and staining conditions of pathological slides. Methods: To overcome this problem, this paper proposes a novel, fully automated stain normalization method to reduce batch effects and thus aid research in digital pathology applications. Their method, intensity centering and histogram equalization (ICHE), normalizes a diverse set of pathology imagesmore » by first scaling the centroids of the intensity histograms to a common point and then applying a modified version of contrast-limited adaptive histogram equalization. Normalization was performed on two datasets of digitized hematoxylin and eosin (H&E) slides of different tissue slices from the same lung tumor, and one immunohistochemistry dataset of digitized slides created by restaining one of the H&E datasets. Results: The ICHE method was evaluated based on image intensity values, quantitative features, and the effect on downstream applications, such as a computer aided diagnosis. For comparison, three methods from the literature were reimplemented and evaluated using the same criteria. The authors found that ICHE not only improved performance compared with un-normalized images, but in most cases showed improvement compared with previous methods for correcting batch effects in the literature. Conclusions: ICHE may be a useful preprocessing step a digital pathology image processing pipeline.« less

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.

A novel pre-processing technique for improving image quality in digital breast tomosynthesis.

PubMed

Kim, Hyeongseok; Lee, Taewon; Hong, Joonpyo; Sabir, Sohail; Lee, Jung-Ryun; Choi, Young Wook; Kim, Hak Hee; Chae, Eun Young; Cho, Seungryong

2017-02-01

Nonlinear pre-reconstruction processing of the projection data in computed tomography (CT) where accurate recovery of the CT numbers is important for diagnosis is usually discouraged, for such a processing would violate the physics of image formation in CT. However, one can devise a pre-processing step to enhance detectability of lesions in digital breast tomosynthesis (DBT) where accurate recovery of the CT numbers is fundamentally impossible due to the incompleteness of the scanned data. Since the detection of lesions such as micro-calcifications and mass in breasts is the purpose of using DBT, it is justified that a technique producing higher detectability of lesions is a virtue. A histogram modification technique was developed in the projection data domain. Histogram of raw projection data was first divided into two parts: One for the breast projection data and the other for background. Background pixel values were set to a single value that represents the boundary between breast and background. After that, both histogram parts were shifted by an appropriate amount of offset and the histogram-modified projection data were log-transformed. Filtered-backprojection (FBP) algorithm was used for image reconstruction of DBT. To evaluate performance of the proposed method, we computed the detectability index for the reconstructed images from clinically acquired data. Typical breast border enhancement artifacts were greatly suppressed and the detectability of calcifications and masses was increased by use of the proposed method. Compared to a global threshold-based post-reconstruction processing technique, the proposed method produced images of higher contrast without invoking additional image artifacts. In this work, we report a novel pre-processing technique that improves detectability of lesions in DBT and has potential advantages over the global threshold-based post-reconstruction processing technique. The proposed method not only increased the lesion detectability but also reduced typical image artifacts pronounced in conventional FBP-based DBT. © 2016 American Association of Physicists in Medicine.

Experimental Visualizations of a Generic Launch Vehicle Flow Field: Time-Resolved Shadowgraph and Infrared Imaging

NASA Technical Reports Server (NTRS)

Garbeff, Theodore J., II; Panda, Jayanta; Ross, James C.

2017-01-01

Time-Resolved shadowgraph and infrared (IR) imaging were performed to investigate off-body and on-body flow features of a generic, 'hammer-head' launch vehicle geometry previously tested by Coe and Nute (1962). The measurements discussed here were one part of a large range of wind tunnel test techniques that included steady-state pressure sensitive paint (PSP), dynamic PSP, unsteady surface pressures, and unsteady force measurements. Image data was captured over a Mach number range of 0.6 less than or equal to M less than or equal to 1.2 at a Reynolds number of 3 million per foot. Both shadowgraph and IR imagery were captured in conjunction with unsteady pressures and forces and correlated with IRIG-B timing. High-speed shadowgraph imagery was used to identify wake structure and reattachment behind the payload fairing of the vehicle. Various data processing strategies were employed and ultimately these results correlated well with the location and magnitude of unsteady surface pressure measurements. Two research grade IR cameras were positioned to image boundary layer transition at the vehicle nose and flow reattachment behind the payload fairing. The poor emissivity of the model surface treatment (fast PSP) proved to be challenging for the infrared measurement. Reference image subtraction and contrast limited adaptive histogram equalization (CLAHE) were used to analyze this dataset. Ultimately turbulent boundary layer transition was observed and located forward of the trip dot line at the model sphere-cone junction. Flow reattachment location was identified behind the payload fairing in both steady and unsteady thermal data. As demonstrated in this effort, recent advances in high-speed and thermal imaging technology have modernized classical techniques providing a new viewpoint for the modern researcher

The spectral archive of cosmic X-ray sources observed by the Einstein Observatory Focal Plane Crystal Spectrometer

NASA Technical Reports Server (NTRS)

Lum, Kenneth S. K.; Canizares, Claude R.; Clark, George W.; Coyne, Joan M.; Markert, Thomas H.; Saez, Pablo J.; Schattenburg, Mark L.; Winkler, P. F.

1992-01-01

The Einstein Observatory Focal Plane Crystal Spectrometer (FPCS) used the technique of Bragg spectroscopy to study cosmic X-ray sources in the 0.2-3 keV energy range. The high spectral resolving power (E/Delta-E is approximately equal to 100-1000) of this instrument allowed it to resolve closely spaced lines and study the structure of individual features in the spectra of 41 cosmic X-ray sources. An archival summary of the results is presented as a concise record the FPCS observations and a source of information for future analysis by the general astrophysics community. For each observation, the instrument configuration, background rate, X-ray flux or upper limit within the energy band observed, and spectral histograms are given. Examples of the contributions the FPCS observations have made to the understanding of the objects observed are discussed.

An Unsupervised Approach for Extraction of Blood Vessels from Fundus Images.

PubMed

Dash, Jyotiprava; Bhoi, Nilamani

2018-04-26

Pathological disorders may happen due to small changes in retinal blood vessels which may later turn into blindness. Hence, the accurate segmentation of blood vessels is becoming a challenging task for pathological analysis. This paper offers an unsupervised recursive method for extraction of blood vessels from ophthalmoscope images. First, a vessel-enhanced image is generated with the help of gamma correction and contrast-limited adaptive histogram equalization (CLAHE). Next, the vessels are extracted iteratively by applying an adaptive thresholding technique. At last, a final vessel segmented image is produced by applying a morphological cleaning operation. Evaluations are accompanied on the publicly available digital retinal images for vessel extraction (DRIVE) and Child Heart And Health Study in England (CHASE_DB1) databases using nine different measurements. The proposed method achieves average accuracies of 0.957 and 0.952 on DRIVE and CHASE_DB1 databases respectively.

A novel method for segmentation of Infrared Scanning Laser Ophthalmoscope (IR-SLO) images of retina.

PubMed

Ajaz, Aqsa; Aliahmad, Behzad; Kumar, Dinesh K

2017-07-01

Retinal vessel segmentation forms an essential element of automatic retinal disease screening systems. The development of multimodal imaging system with IR-SLO and OCT could help in studying the early stages of retinal disease. The advantages of IR-SLO to examine the alterations in the structure of retina and direct correlation with OCT can be useful for assessment of various diseases. This paper presents an automatic method for segmentation of IR-SLO fundus images based on the combination of morphological filters and image enhancement techniques. As a first step, the retinal vessels are contrasted using morphological filters followed by background exclusion using Contrast Limited Adaptive Histogram Equalization (CLAHE) and Bilateral filtering. The final segmentation is obtained by using Isodata technique. Our approach was tested on a set of 26 IR-SLO images and results were compared to two set of gold standard images. The performance of the proposed method was evaluated in terms of sensitivity, specificity and accuracy. The system has an average accuracy of 0.90 for both the sets.

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.

Labeling Defects in CT Images of Hardwood Logs with Species-Dependent and Species-Independent Classifiers

Treesearch

Pei Li; Jing He; A. Lynn Abbott; Daniel L. Schmoldt

1996-01-01

This paper analyses computed tomography (CT) images of hardwood logs, with the goal of locating internal defects. The ability to detect and identify defects automatically is a critical component of efficiency improvements for future sawmills and veneer mills. This paper describes an approach in which 1) histogram equalization is used during preprocessing to normalize...

Is there a preference for linearity when viewing natural images?

NASA Astrophysics Data System (ADS)

Kane, David; Bertamío, Marcelo

2015-01-01

The system gamma of the imaging pipeline, defined as the product of the encoding and decoding gammas, is typically greater than one and is stronger for images viewed with a dark background (e.g. cinema) than those viewed in lighter conditions (e.g. office displays).1-3 However, for high dynamic range (HDR) images reproduced on a low dynamic range (LDR) monitor, subjects often prefer a system gamma of less than one,4 presumably reflecting the greater need for histogram equalization in HDR images. In this study we ask subjects to rate the perceived quality of images presented on a LDR monitor using various levels of system gamma. We reveal that the optimal system gamma is below one for images with a HDR and approaches or exceeds one for images with a LDR. Additionally, the highest quality scores occur for images where a system gamma of one is optimal, suggesting a preference for linearity (where possible). We find that subjective image quality scores can be predicted by computing the degree of histogram equalization of the lightness distribution. Accordingly, an optimal, image dependent system gamma can be computed that maximizes perceived image quality.

Study of quality perception in medical images based on comparison of contrast enhancement techniques in mammographic images

NASA Astrophysics Data System (ADS)

Matheus, B.; Verçosa, L. B.; Barufaldi, B.; Schiabel, H.

2014-03-01

With the absolute prevalence of digital images in mammography several new tools became available for radiologist; such as CAD schemes, digital zoom and contrast alteration. This work focuses in contrast variation and how the radiologist reacts to these changes when asked to evaluated image quality. Three contrast enhancing techniques were used in this study: conventional equalization, CCB Correction [1] - a digitization correction - and value subtraction. A set of 100 images was used in tests from some available online mammographic databases. The tests consisted of the presentation of all four versions of an image (original plus the three contrast enhanced images) to the specialist, requested to rank each one from the best up to worst quality for diagnosis. Analysis of results has demonstrated that CCB Correction [1] produced better images in almost all cases. Equalization, which mathematically produces a better contrast, was considered the worst for mammography image quality enhancement in the majority of cases (69.7%). The value subtraction procedure produced images considered better than the original in 84% of cases. Tests indicate that, for the radiologist's perception, it seems more important to guaranty full visualization of nuances than a high contrast image. Another result observed is that the "ideal" scanner curve does not yield the best result for a mammographic image. The important contrast range is the middle of the histogram, where nodules and masses need to be seen and clearly distinguished.

Quality Improvement of Liver Ultrasound Images Using Fuzzy Techniques.

PubMed

Bayani, Azadeh; Langarizadeh, Mostafa; Radmard, Amir Reza; Nejad, Ahmadreza Farzaneh

2016-12-01

Liver ultrasound images are so common and are applied so often to diagnose diffuse liver diseases like fatty liver. However, the low quality of such images makes it difficult to analyze them and diagnose diseases. The purpose of this study, therefore, is to improve the contrast and quality of liver ultrasound images. In this study, a number of image contrast enhancement algorithms which are based on fuzzy logic were applied to liver ultrasound images - in which the view of kidney is observable - using Matlab2013b to improve the image contrast and quality which has a fuzzy definition; just like image contrast improvement algorithms using a fuzzy intensification operator, contrast improvement algorithms applying fuzzy image histogram hyperbolization, and contrast improvement algorithms by fuzzy IF-THEN rules. With the measurement of Mean Squared Error and Peak Signal to Noise Ratio obtained from different images, fuzzy methods provided better results, and their implementation - compared with histogram equalization method - led both to the improvement of contrast and visual quality of images and to the improvement of liver segmentation algorithms results in images. Comparison of the four algorithms revealed the power of fuzzy logic in improving image contrast compared with traditional image processing algorithms. Moreover, contrast improvement algorithm based on a fuzzy intensification operator was selected as the strongest algorithm considering the measured indicators. This method can also be used in future studies on other ultrasound images for quality improvement and other image processing and analysis applications.

Quality Improvement of Liver Ultrasound Images Using Fuzzy Techniques

PubMed Central

Bayani, Azadeh; Langarizadeh, Mostafa; Radmard, Amir Reza; Nejad, Ahmadreza Farzaneh

2016-01-01

Background: Liver ultrasound images are so common and are applied so often to diagnose diffuse liver diseases like fatty liver. However, the low quality of such images makes it difficult to analyze them and diagnose diseases. The purpose of this study, therefore, is to improve the contrast and quality of liver ultrasound images. Methods: In this study, a number of image contrast enhancement algorithms which are based on fuzzy logic were applied to liver ultrasound images - in which the view of kidney is observable - using Matlab2013b to improve the image contrast and quality which has a fuzzy definition; just like image contrast improvement algorithms using a fuzzy intensification operator, contrast improvement algorithms applying fuzzy image histogram hyperbolization, and contrast improvement algorithms by fuzzy IF-THEN rules. Results: With the measurement of Mean Squared Error and Peak Signal to Noise Ratio obtained from different images, fuzzy methods provided better results, and their implementation - compared with histogram equalization method - led both to the improvement of contrast and visual quality of images and to the improvement of liver segmentation algorithms results in images. Conclusion: Comparison of the four algorithms revealed the power of fuzzy logic in improving image contrast compared with traditional image processing algorithms. Moreover, contrast improvement algorithm based on a fuzzy intensification operator was selected as the strongest algorithm considering the measured indicators. This method can also be used in future studies on other ultrasound images for quality improvement and other image processing and analysis applications. PMID:28077898

Postmortem validation of breast density using dual-energy mammography

PubMed Central

Molloi, Sabee; Ducote, Justin L.; Ding, Huanjun; Feig, Stephen A.

2014-01-01

Purpose: Mammographic density has been shown to be an indicator of breast cancer risk and also reduces the sensitivity of screening mammography. Currently, there is no accepted standard for measuring breast density. Dual energy mammography has been proposed as a technique for accurate measurement of breast density. The purpose of this study is to validate its accuracy in postmortem breasts and compare it with other existing techniques. Methods: Forty postmortem breasts were imaged using a dual energy mammography system. Glandular and adipose equivalent phantoms of uniform thickness were used to calibrate a dual energy basis decomposition algorithm. Dual energy decomposition was applied after scatter correction to calculate breast density. Breast density was also estimated using radiologist reader assessment, standard histogram thresholding and a fuzzy C-mean algorithm. Chemical analysis was used as the reference standard to assess the accuracy of different techniques to measure breast composition. Results: Breast density measurements using radiologist reader assessment, standard histogram thresholding, fuzzy C-mean algorithm, and dual energy were in good agreement with the measured fibroglandular volume fraction using chemical analysis. The standard error estimates using radiologist reader assessment, standard histogram thresholding, fuzzy C-mean, and dual energy were 9.9%, 8.6%, 7.2%, and 4.7%, respectively. Conclusions: The results indicate that dual energy mammography can be used to accurately measure breast density. The variability in breast density estimation using dual energy mammography was lower than reader assessment rankings, standard histogram thresholding, and fuzzy C-mean algorithm. Improved quantification of breast density is expected to further enhance its utility as a risk factor for breast cancer. PMID:25086548

Postmortem validation of breast density using dual-energy mammography

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

Molloi, Sabee, E-mail: symolloi@uci.edu; Ducote, Justin L.; Ding, Huanjun

2014-08-15

Purpose: Mammographic density has been shown to be an indicator of breast cancer risk and also reduces the sensitivity of screening mammography. Currently, there is no accepted standard for measuring breast density. Dual energy mammography has been proposed as a technique for accurate measurement of breast density. The purpose of this study is to validate its accuracy in postmortem breasts and compare it with other existing techniques. Methods: Forty postmortem breasts were imaged using a dual energy mammography system. Glandular and adipose equivalent phantoms of uniform thickness were used to calibrate a dual energy basis decomposition algorithm. Dual energy decompositionmore » was applied after scatter correction to calculate breast density. Breast density was also estimated using radiologist reader assessment, standard histogram thresholding and a fuzzy C-mean algorithm. Chemical analysis was used as the reference standard to assess the accuracy of different techniques to measure breast composition. Results: Breast density measurements using radiologist reader assessment, standard histogram thresholding, fuzzy C-mean algorithm, and dual energy were in good agreement with the measured fibroglandular volume fraction using chemical analysis. The standard error estimates using radiologist reader assessment, standard histogram thresholding, fuzzy C-mean, and dual energy were 9.9%, 8.6%, 7.2%, and 4.7%, respectively. Conclusions: The results indicate that dual energy mammography can be used to accurately measure breast density. The variability in breast density estimation using dual energy mammography was lower than reader assessment rankings, standard histogram thresholding, and fuzzy C-mean algorithm. Improved quantification of breast density is expected to further enhance its utility as a risk factor for breast cancer.« less

Breast density quantification with cone-beam CT: A post-mortem study

PubMed Central

Johnson, Travis; Ding, Huanjun; Le, Huy Q.; Ducote, Justin L.; Molloi, Sabee

2014-01-01

Forty post-mortem breasts were imaged with a flat-panel based cone-beam x-ray CT system at 50 kVp. The feasibility of breast density quantification has been investigated using standard histogram thresholding and an automatic segmentation method based on the fuzzy c-means algorithm (FCM). The breasts were chemically decomposed into water, lipid, and protein immediately after image acquisition was completed. The percent fibroglandular volume (%FGV) from chemical analysis was used as the gold standard for breast density comparison. Both image-based segmentation techniques showed good precision in breast density quantification with high linear coefficients between the right and left breast of each pair. When comparing with the gold standard using %FGV from chemical analysis, Pearson’s r-values were estimated to be 0.983 and 0.968 for the FCM clustering and the histogram thresholding techniques, respectively. The standard error of the estimate (SEE) was also reduced from 3.92% to 2.45% by applying the automatic clustering technique. The results of the postmortem study suggested that breast tissue can be characterized in terms of water, lipid and protein contents with high accuracy by using chemical analysis, which offers a gold standard for breast density studies comparing different techniques. In the investigated image segmentation techniques, the FCM algorithm had high precision and accuracy in breast density quantification. In comparison to conventional histogram thresholding, it was more efficient and reduced inter-observer variation. PMID:24254317

A novel method for the evaluation of uncertainty in dose-volume histogram computation.

PubMed

Henríquez, Francisco Cutanda; Castrillón, Silvia Vargas

2008-03-15

Dose-volume histograms (DVHs) are a useful tool in state-of-the-art radiotherapy treatment planning, and it is essential to recognize their limitations. Even after a specific dose-calculation model is optimized, dose distributions computed by using treatment-planning systems are affected by several sources of uncertainty, such as algorithm limitations, measurement uncertainty in the data used to model the beam, and residual differences between measured and computed dose. This report presents a novel method to take them into account. To take into account the effect of associated uncertainties, a probabilistic approach using a new kind of histogram, a dose-expected volume histogram, is introduced. The expected value of the volume in the region of interest receiving an absorbed dose equal to or greater than a certain value is found by using the probability distribution of the dose at each point. A rectangular probability distribution is assumed for this point dose, and a formulation that accounts for uncertainties associated with point dose is presented for practical computations. This method is applied to a set of DVHs for different regions of interest, including 6 brain patients, 8 lung patients, 8 pelvis patients, and 6 prostate patients planned for intensity-modulated radiation therapy. Results show a greater effect on planning target volume coverage than in organs at risk. In cases of steep DVH gradients, such as planning target volumes, this new method shows the largest differences with the corresponding DVH; thus, the effect of the uncertainty is larger.

Breast density evaluation using spectral mammography, radiologist reader assessment and segmentation techniques: a retrospective study based on left and right breast comparison

PubMed Central

Molloi, Sabee; Ding, Huanjun; Feig, Stephen

2015-01-01

Purpose The purpose of this study was to compare the precision of mammographic breast density measurement using radiologist reader assessment, histogram threshold segmentation, fuzzy C-mean segmentation and spectral material decomposition. Materials and Methods Spectral mammography images from a total of 92 consecutive asymptomatic women (50–69 years old) who presented for annual screening mammography were retrospectively analyzed for this study. Breast density was estimated using 10 radiologist reader assessment, standard histogram thresholding, fuzzy C-mean algorithm and spectral material decomposition. The breast density correlation between left and right breasts was used to assess the precision of these techniques to measure breast composition relative to dual-energy material decomposition. Results In comparison to the other techniques, the results of breast density measurements using dual-energy material decomposition showed the highest correlation. The relative standard error of estimate for breast density measurements from left and right breasts using radiologist reader assessment, standard histogram thresholding, fuzzy C-mean algorithm and dual-energy material decomposition was calculated to be 1.95, 2.87, 2.07 and 1.00, respectively. Conclusion The results indicate that the precision of dual-energy material decomposition was approximately factor of two higher than the other techniques with regard to better correlation of breast density measurements from right and left breasts. PMID:26031229

Differential diagnosis of normal pressure hydrocephalus by MRI mean diffusivity histogram analysis.

PubMed

Ivkovic, M; Liu, B; Ahmed, F; Moore, D; Huang, C; Raj, A; Kovanlikaya, I; Heier, L; Relkin, N

2013-01-01

Accurate diagnosis of normal pressure hydrocephalus is challenging because the clinical symptoms and radiographic appearance of NPH often overlap those of other conditions, including age-related neurodegenerative disorders such as Alzheimer and Parkinson diseases. We hypothesized that radiologic differences between NPH and AD/PD can be characterized by a robust and objective MR imaging DTI technique that does not require intersubject image registration or operator-defined regions of interest, thus avoiding many pitfalls common in DTI methods. We collected 3T DTI data from 15 patients with probable NPH and 25 controls with AD, PD, or dementia with Lewy bodies. We developed a parametric model for the shape of intracranial mean diffusivity histograms that separates brain and ventricular components from a third component composed mostly of partial volume voxels. To accurately fit the shape of the third component, we constructed a parametric function named the generalized Voss-Dyke function. We then examined the use of the fitting parameters for the differential diagnosis of NPH from AD, PD, and DLB. Using parameters for the MD histogram shape, we distinguished clinically probable NPH from the 3 other disorders with 86% sensitivity and 96% specificity. The technique yielded 86% sensitivity and 88% specificity when differentiating NPH from AD only. An adequate parametric model for the shape of intracranial MD histograms can distinguish NPH from AD, PD, or DLB with high sensitivity and specificity.

Efficient visibility-driven medical image visualisation via adaptive binned visibility histogram.

PubMed

Jung, Younhyun; Kim, Jinman; Kumar, Ashnil; Feng, David Dagan; Fulham, Michael

2016-07-01

'Visibility' is a fundamental optical property that represents the observable, by users, proportion of the voxels in a volume during interactive volume rendering. The manipulation of this 'visibility' improves the volume rendering processes; for instance by ensuring the visibility of regions of interest (ROIs) or by guiding the identification of an optimal rendering view-point. The construction of visibility histograms (VHs), which represent the distribution of all the visibility of all voxels in the rendered volume, enables users to explore the volume with real-time feedback about occlusion patterns among spatially related structures during volume rendering manipulations. Volume rendered medical images have been a primary beneficiary of VH given the need to ensure that specific ROIs are visible relative to the surrounding structures, e.g. the visualisation of tumours that may otherwise be occluded by neighbouring structures. VH construction and its subsequent manipulations, however, are computationally expensive due to the histogram binning of the visibilities. This limits the real-time application of VH to medical images that have large intensity ranges and volume dimensions and require a large number of histogram bins. In this study, we introduce an efficient adaptive binned visibility histogram (AB-VH) in which a smaller number of histogram bins are used to represent the visibility distribution of the full VH. We adaptively bin medical images by using a cluster analysis algorithm that groups the voxels according to their intensity similarities into a smaller subset of bins while preserving the distribution of the intensity range of the original images. We increase efficiency by exploiting the parallel computation and multiple render targets (MRT) extension of the modern graphical processing units (GPUs) and this enables efficient computation of the histogram. We show the application of our method to single-modality computed tomography (CT), magnetic resonance (MR) imaging and multi-modality positron emission tomography-CT (PET-CT). In our experiments, the AB-VH markedly improved the computational efficiency for the VH construction and thus improved the subsequent VH-driven volume manipulations. This efficiency was achieved without major degradation in the VH visually and numerical differences between the AB-VH and its full-bin counterpart. We applied several variants of the K-means clustering algorithm with varying Ks (the number of clusters) and found that higher values of K resulted in better performance at a lower computational gain. The AB-VH also had an improved performance when compared to the conventional method of down-sampling of the histogram bins (equal binning) for volume rendering visualisation. Copyright © 2016 Elsevier Ltd. All rights reserved.

The effects of cloud inhomogeneities upon radiative fluxes, and the supply of a cloud truth validation dataset

NASA Technical Reports Server (NTRS)

Welch, Ronald M.

1996-01-01

The ASTER polar cloud mask algorithm is currently under development. Several classification techniques have been developed and implemented. The merits and accuracy of each are being examined. The classification techniques under investigation include fuzzy logic, hierarchical neural network, and a pairwise histogram comparison scheme based on sample histograms called the Paired Histogram Method. Scene adaptive methods also are being investigated as a means to improve classifier performance. The feature, arctan of Band 4 and Band 5, and the Band 2 vs. Band 4 feature space are key to separating frozen water (e.g., ice/snow, slush/wet ice, etc.) from cloud over frozen water, and land from cloud over land, respectively. A total of 82 Landsat TM circumpolar scenes are being used as a basis for algorithm development and testing. Numerous spectral features are being tested and include the 7 basic Landsat TM bands, in addition to ratios, differences, arctans, and normalized differences of each combination of bands. A technique for deriving cloud base and top height is developed. It uses 2-D cross correlation between a cloud edge and its corresponding shadow to determine the displacement of the cloud from its shadow. The height is then determined from this displacement, the solar zenith angle, and the sensor viewing angle.

Radiologists' preferences for digital mammographic display. The International Digital Mammography Development Group.

PubMed

Pisano, E D; Cole, E B; Major, S; Zong, S; Hemminger, B M; Muller, K E; Johnston, R E; Walsh, R; Conant, E; Fajardo, L L; Feig, S A; Nishikawa, R M; Yaffe, M J; Williams, M B; Aylward, S R

2000-09-01

To determine the preferences of radiologists among eight different image processing algorithms applied to digital mammograms obtained for screening and diagnostic imaging tasks. Twenty-eight images representing histologically proved masses or calcifications were obtained by using three clinically available digital mammographic units. Images were processed and printed on film by using manual intensity windowing, histogram-based intensity windowing, mixture model intensity windowing, peripheral equalization, multiscale image contrast amplification (MUSICA), contrast-limited adaptive histogram equalization, Trex processing, and unsharp masking. Twelve radiologists compared the processed digital images with screen-film mammograms obtained in the same patient for breast cancer screening and breast lesion diagnosis. For the screening task, screen-film mammograms were preferred to all digital presentations, but the acceptability of images processed with Trex and MUSICA algorithms were not significantly different. All printed digital images were preferred to screen-film radiographs in the diagnosis of masses; mammograms processed with unsharp masking were significantly preferred. For the diagnosis of calcifications, no processed digital mammogram was preferred to screen-film mammograms. When digital mammograms were preferred to screen-film mammograms, radiologists selected different digital processing algorithms for each of three mammographic reading tasks and for different lesion types. Soft-copy display will eventually allow radiologists to select among these options more easily.

The Utility of Using a Near-Infrared (NIR) Camera to Measure Beach Surface Moisture

NASA Astrophysics Data System (ADS)

Nelson, S.; Schmutz, P. P.

2017-12-01

Surface moisture content is an important factor that must be considered when studying aeolian sediment transport in a beach environment. A few different instruments and procedures are available for measuring surface moisture content (i.e. moisture probes, LiDAR, and gravimetric moisture data from surface scrapings); however, these methods can be inaccurate, costly, and inapplicable, particularly in the field. Near-infrared (NIR) spectral band imagery is another technique used to obtain moisture data. NIR imagery has been predominately used through remote sensing and has yet to be used for ground-based measurements. Dry sand reflects infrared radiation given off by the sun and wet sand absorbs IR radiation. All things considered, this study assesses the utility of measuring surface moisture content of beach sand with a modified NIR camera. A traditional point and shoot digital camera was internally modified with the placement of a visible light-blocking filter. Images were taken of three different types of beach sand at controlled moisture content values, with sunlight as the source of infrared radiation. A technique was established through trial and error by comparing resultant histogram values using Adobe Photoshop with the various moisture conditions. The resultant IR absorption histogram values were calibrated to actual gravimetric moisture content from surface scrapings of the samples. Overall, the results illustrate that the NIR spectrum modified camera does not provide the ability to adequately measure beach surface moisture content. However, there were noted differences in IR absorption histogram values among the different sediment types. Sediment with darker quartz mineralogy provided larger variations in histogram values, but the technique is not sensitive enough to accurately represent low moisture percentages, which are of most importance when studying aeolian sediment transport.

Texton-based analysis of paintings

NASA Astrophysics Data System (ADS)

van der Maaten, Laurens J. P.; Postma, Eric O.

2010-08-01

The visual examination of paintings is traditionally performed by skilled art historians using their eyes. Recent advances in intelligent systems may support art historians in determining the authenticity or date of creation of paintings. In this paper, we propose a technique for the examination of brushstroke structure that views the wildly overlapping brushstrokes as texture. The analysis of the painting texture is performed with the help of a texton codebook, i.e., a codebook of small prototypical textural patches. The texton codebook can be learned from a collection of paintings. Our textural analysis technique represents paintings in terms of histograms that measure the frequency by which the textons in the codebook occur in the painting (so-called texton histograms). We present experiments that show the validity and effectiveness of our technique for textural analysis on a collection of digitized high-resolution reproductions of paintings by Van Gogh and his contemporaries. As texton histograms cannot be easily be interpreted by art experts, the paper proposes to approaches to visualize the results on the textural analysis. The first approach visualizes the similarities between the histogram representations of paintings by employing a recently proposed dimensionality reduction technique, called t-SNE. We show that t-SNE reveals a clear separation of paintings created by Van Gogh and those created by other painters. In addition, the period of creation is faithfully reflected in the t-SNE visualizations. The second approach visualizes the similarities and differences between paintings by highlighting regions in a painting in which the textural structure of the painting is unusual. We illustrate the validity of this approach by means of an experiment in which we highlight regions in a painting by Monet that are not very "Van Gogh-like". Taken together, we believe the tools developed in this study are well capable of assisting for art historians in support of their study of paintings.

Performance analysis and enhancement for visible light communication using CMOS sensors

NASA Astrophysics Data System (ADS)

Guan, Weipeng; Wu, Yuxiang; Xie, Canyu; Fang, Liangtao; Liu, Xiaowei; Chen, Yingcong

2018-03-01

Complementary Metal-Oxide-Semiconductor (CMOS) sensors are widely used in mobile-phone and cameras. Hence, it is attractive if these camera can be used as the receivers of visible light communication (VLC). Using the rolling shutter mechanism can increase the data rate of VLC based on CMOS camera, and different techniques have been proposed to improve the demodulation of the rolling shutter mechanism. However, these techniques are too complexity. In this work, we demonstrate and analyze the performance of the VLC link using CMOS camera for different LED luminaires for the first time in our knowledge. Experimental evaluation to compare their bit-error-rate (BER) performances and demodulation are also performed, and it can be summarized that just need to change the LED luminaire with more uniformity light output, the blooming effect would not exist; which not only can reduce the complexity of the demodulation but also enhance the communication quality. In addition, we propose and demonstrate to use contrast limited adaptive histogram equalization to extend the transmission distance and mitigate the influence of the background noise. And the experimental results show that the BER can be decreased by an order of magnitude by using the proposed method.

A Morphological Hessian Based Approach for Retinal Blood Vessels Segmentation and Denoising Using Region Based Otsu Thresholding

PubMed Central

BahadarKhan, Khan; A Khaliq, Amir; Shahid, Muhammad

2016-01-01

Diabetic Retinopathy (DR) harm retinal blood vessels in the eye causing visual deficiency. The appearance and structure of blood vessels in retinal images play an essential part in the diagnoses of an eye sicknesses. We proposed a less computational unsupervised automated technique with promising results for detection of retinal vasculature by using morphological hessian based approach and region based Otsu thresholding. Contrast Limited Adaptive Histogram Equalization (CLAHE) and morphological filters have been used for enhancement and to remove low frequency noise or geometrical objects, respectively. The hessian matrix and eigenvalues approach used has been in a modified form at two different scales to extract wide and thin vessel enhanced images separately. Otsu thresholding has been further applied in a novel way to classify vessel and non-vessel pixels from both enhanced images. Finally, postprocessing steps has been used to eliminate the unwanted region/segment, non-vessel pixels, disease abnormalities and noise, to obtain a final segmented image. The proposed technique has been analyzed on the openly accessible DRIVE (Digital Retinal Images for Vessel Extraction) and STARE (STructured Analysis of the REtina) databases along with the ground truth data that has been precisely marked by the experts. PMID:27441646

Classification of high-resolution multi-swath hyperspectral data using Landsat 8 surface reflectance data as a calibration target and a novel histogram based unsupervised classification technique to determine natural classes from biophysically relevant fit parameters

NASA Astrophysics Data System (ADS)

McCann, C.; Repasky, K. S.; Morin, M.; Lawrence, R. L.; Powell, S. L.

2016-12-01

Compact, cost-effective, flight-based hyperspectral imaging systems can provide scientifically relevant data over large areas for a variety of applications such as ecosystem studies, precision agriculture, and land management. To fully realize this capability, unsupervised classification techniques based on radiometrically-calibrated data that cluster based on biophysical similarity rather than simply spectral similarity are needed. An automated technique to produce high-resolution, large-area, radiometrically-calibrated hyperspectral data sets based on the Landsat surface reflectance data product as a calibration target was developed and applied to three subsequent years of data covering approximately 1850 hectares. The radiometrically-calibrated data allows inter-comparison of the temporal series. Advantages of the radiometric calibration technique include the need for minimal site access, no ancillary instrumentation, and automated processing. Fitting the reflectance spectra of each pixel using a set of biophysically relevant basis functions reduces the data from 80 spectral bands to 9 parameters providing noise reduction and data compression. Examination of histograms of these parameters allows for determination of natural splitting into biophysical similar clusters. This method creates clusters that are similar in terms of biophysical parameters, not simply spectral proximity. Furthermore, this method can be applied to other data sets, such as urban scenes, by developing other physically meaningful basis functions. The ability to use hyperspectral imaging for a variety of important applications requires the development of data processing techniques that can be automated. The radiometric-calibration combined with the histogram based unsupervised classification technique presented here provide one potential avenue for managing big-data associated with hyperspectral imaging.

A tone mapping operator based on neural and psychophysical models of visual perception

NASA Astrophysics Data System (ADS)

Cyriac, Praveen; Bertalmio, Marcelo; Kane, David; Vazquez-Corral, Javier

2015-03-01

High dynamic range imaging techniques involve capturing and storing real world radiance values that span many orders of magnitude. However, common display devices can usually reproduce intensity ranges only up to two to three orders of magnitude. Therefore, in order to display a high dynamic range image on a low dynamic range screen, the dynamic range of the image needs to be compressed without losing details or introducing artefacts, and this process is called tone mapping. A good tone mapping operator must be able to produce a low dynamic range image that matches as much as possible the perception of the real world scene. We propose a two stage tone mapping approach, in which the first stage is a global method for range compression based on a gamma curve that equalizes the lightness histogram the best, and the second stage performs local contrast enhancement and color induction using neural activity models for the visual cortex.

Multivariate statistical model for 3D image segmentation with application to medical images.

PubMed

John, Nigel M; Kabuka, Mansur R; Ibrahim, Mohamed O

2003-12-01

In this article we describe a statistical model that was developed to segment brain magnetic resonance images. The statistical segmentation algorithm was applied after a pre-processing stage involving the use of a 3D anisotropic filter along with histogram equalization techniques. The segmentation algorithm makes use of prior knowledge and a probability-based multivariate model designed to semi-automate the process of segmentation. The algorithm was applied to images obtained from the Center for Morphometric Analysis at Massachusetts General Hospital as part of the Internet Brain Segmentation Repository (IBSR). The developed algorithm showed improved accuracy over the k-means, adaptive Maximum Apriori Probability (MAP), biased MAP, and other algorithms. Experimental results showing the segmentation and the results of comparisons with other algorithms are provided. Results are based on an overlap criterion against expertly segmented images from the IBSR. The algorithm produced average results of approximately 80% overlap with the expertly segmented images (compared with 85% for manual segmentation and 55% for other algorithms).

Discrete Walsh Hadamard transform based visible watermarking technique for digital color images

NASA Astrophysics Data System (ADS)

Santhi, V.; Thangavelu, Arunkumar

2011-10-01

As the size of the Internet is growing enormously the illegal manipulation of digital multimedia data become very easy with the advancement in technology tools. In order to protect those multimedia data from unauthorized access the digital watermarking system is used. In this paper a new Discrete walsh Hadamard Transform based visible watermarking system is proposed. As the watermark is embedded in transform domain, the system is robust to many signal processing attacks. Moreover in this proposed method the watermark is embedded in tiling manner in all the range of frequencies to make it robust to compression and cropping attack. The robustness of the algorithm is tested against noise addition, cropping, compression, Histogram equalization and resizing attacks. The experimental results show that the algorithm is robust to common signal processing attacks and the observed peak signal to noise ratio (PSNR) of watermarked image is varying from 20 to 30 db depends on the size of the watermark.

Robust Face Detection from Still Images

DTIC Science & Technology

2014-01-01

significant change in false acceptance rates. Keywords— face detection; illumination; skin color variation; Haar-like features; OpenCV I. INTRODUCTION... OpenCV and an algorithm which used histogram equalization. The test is performed against 17 subjects under 576 viewing conditions from the extended Yale...original OpenCV algorithm proved the least accurate, having a hit rate of only 75.6%. It also had the lowest FAR but only by a slight margin at 25.2

Statistical normalization techniques for magnetic resonance imaging.

PubMed

Shinohara, Russell T; Sweeney, Elizabeth M; Goldsmith, Jeff; Shiee, Navid; Mateen, Farrah J; Calabresi, Peter A; Jarso, Samson; Pham, Dzung L; Reich, Daniel S; Crainiceanu, Ciprian M

2014-01-01

While computed tomography and other imaging techniques are measured in absolute units with physical meaning, magnetic resonance images are expressed in arbitrary units that are difficult to interpret and differ between study visits and subjects. Much work in the image processing literature on intensity normalization has focused on histogram matching and other histogram mapping techniques, with little emphasis on normalizing images to have biologically interpretable units. Furthermore, there are no formalized principles or goals for the crucial comparability of image intensities within and across subjects. To address this, we propose a set of criteria necessary for the normalization of images. We further propose simple and robust biologically motivated normalization techniques for multisequence brain imaging that have the same interpretation across acquisitions and satisfy the proposed criteria. We compare the performance of different normalization methods in thousands of images of patients with Alzheimer's disease, hundreds of patients with multiple sclerosis, and hundreds of healthy subjects obtained in several different studies at dozens of imaging centers.

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.

Evaluation of pulmonary function using single-breath-hold dual-energy computed tomography with xenon

PubMed Central

Kyoyama, Hiroyuki; Hirata, Yusuke; Kikuchi, Satoshi; Sakai, Kosuke; Saito, Yuriko; Mikami, Shintaro; Moriyama, Gaku; Yanagita, Hisami; Watanabe, Wataru; Otani, Katharina; Honda, Norinari; Uematsu, Kazutsugu

2017-01-01

Abstract Xenon-enhanced dual-energy computed tomography (xenon-enhanced CT) can provide lung ventilation maps that may be useful for assessing structural and functional abnormalities of the lung. Xenon-enhanced CT has been performed using a multiple-breath-hold technique during xenon washout. We recently developed xenon-enhanced CT using a single-breath-hold technique to assess ventilation. We sought to evaluate whether xenon-enhanced CT using a single-breath-hold technique correlates with pulmonary function testing (PFT) results. Twenty-six patients, including 11 chronic obstructive pulmonary disease (COPD) patients, underwent xenon-enhanced CT and PFT. Three of the COPD patients underwent xenon-enhanced CT before and after bronchodilator treatment. Images from xenon-CT were obtained by dual-source CT during a breath-hold after a single vital-capacity inspiration of a xenon–oxygen gas mixture. Image postprocessing by 3-material decomposition generated conventional CT and xenon-enhanced images. Low-attenuation areas on xenon images matched low-attenuation areas on conventional CT in 21 cases but matched normal-attenuation areas in 5 cases. Volumes of Hounsfield unit (HU) histograms of xenon images correlated moderately and highly with vital capacity (VC) and total lung capacity (TLC), respectively (r = 0.68 and 0.85). Means and modes of histograms weakly correlated with VC (r = 0.39 and 0.38), moderately with forced expiratory volume in 1 second (FEV1) (r = 0.59 and 0.56), weakly with the ratio of FEV1 to FVC (r = 0.46 and 0.42), and moderately with the ratio of FEV1 to its predicted value (r = 0.64 and 0.60). Mode and volume of histograms increased in 2 COPD patients after the improvement of FEV1 with bronchodilators. Inhalation of xenon gas caused no adverse effects. Xenon-enhanced CT using a single-breath-hold technique depicted functional abnormalities not detectable on thin-slice CT. Mode, mean, and volume of HU histograms of xenon images reflected pulmonary function. Xenon images obtained with xenon-enhanced CT using a single-breath-hold technique can qualitatively depict pulmonary ventilation. A larger study comprising only COPD patients should be conducted, as xenon-enhanced CT is expected to be a promising technique for the management of COPD. PMID:28099359

Evaluation of pulmonary function using single-breath-hold dual-energy computed tomography with xenon: Results of a preliminary study.

PubMed

Kyoyama, Hiroyuki; Hirata, Yusuke; Kikuchi, Satoshi; Sakai, Kosuke; Saito, Yuriko; Mikami, Shintaro; Moriyama, Gaku; Yanagita, Hisami; Watanabe, Wataru; Otani, Katharina; Honda, Norinari; Uematsu, Kazutsugu

2017-01-01

Xenon-enhanced dual-energy computed tomography (xenon-enhanced CT) can provide lung ventilation maps that may be useful for assessing structural and functional abnormalities of the lung. Xenon-enhanced CT has been performed using a multiple-breath-hold technique during xenon washout. We recently developed xenon-enhanced CT using a single-breath-hold technique to assess ventilation. We sought to evaluate whether xenon-enhanced CT using a single-breath-hold technique correlates with pulmonary function testing (PFT) results.Twenty-six patients, including 11 chronic obstructive pulmonary disease (COPD) patients, underwent xenon-enhanced CT and PFT. Three of the COPD patients underwent xenon-enhanced CT before and after bronchodilator treatment. Images from xenon-CT were obtained by dual-source CT during a breath-hold after a single vital-capacity inspiration of a xenon-oxygen gas mixture. Image postprocessing by 3-material decomposition generated conventional CT and xenon-enhanced images.Low-attenuation areas on xenon images matched low-attenuation areas on conventional CT in 21 cases but matched normal-attenuation areas in 5 cases. Volumes of Hounsfield unit (HU) histograms of xenon images correlated moderately and highly with vital capacity (VC) and total lung capacity (TLC), respectively (r = 0.68 and 0.85). Means and modes of histograms weakly correlated with VC (r = 0.39 and 0.38), moderately with forced expiratory volume in 1 second (FEV1) (r = 0.59 and 0.56), weakly with the ratio of FEV1 to FVC (r = 0.46 and 0.42), and moderately with the ratio of FEV1 to its predicted value (r = 0.64 and 0.60). Mode and volume of histograms increased in 2 COPD patients after the improvement of FEV1 with bronchodilators. Inhalation of xenon gas caused no adverse effects.Xenon-enhanced CT using a single-breath-hold technique depicted functional abnormalities not detectable on thin-slice CT. Mode, mean, and volume of HU histograms of xenon images reflected pulmonary function. Xenon images obtained with xenon-enhanced CT using a single-breath-hold technique can qualitatively depict pulmonary ventilation. A larger study comprising only COPD patients should be conducted, as xenon-enhanced CT is expected to be a promising technique for the management of COPD.

A Bio Medical Waste Identification and Classification Algorithm Using Mltrp and Rvm.

PubMed

Achuthan, Aravindan; Ayyallu Madangopal, Vasumathi

2016-10-01

We aimed to extract the histogram features for text analysis and, to classify the types of Bio Medical Waste (BMW) for garbage disposal and management. The given BMW was preprocessed by using the median filtering technique that efficiently reduced the noise in the image. After that, the histogram features of the filtered image were extracted with the help of proposed Modified Local Tetra Pattern (MLTrP) technique. Finally, the Relevance Vector Machine (RVM) was used to classify the BMW into human body parts, plastics, cotton and liquids. The BMW image was collected from the garbage image dataset for analysis. The performance of the proposed BMW identification and classification system was evaluated in terms of sensitivity, specificity, classification rate and accuracy with the help of MATLAB. When compared to the existing techniques, the proposed techniques provided the better results. This work proposes a new texture analysis and classification technique for BMW management and disposal. It can be used in many real time applications such as hospital and healthcare management systems for proper BMW disposal.

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.

Histogram contrast analysis and the visual segregation of IID textures.

PubMed

Chubb, C; Econopouly, J; Landy, M S

1994-09-01

A new psychophysical methodology is introduced, histogram contrast analysis, that allows one to measure stimulus transformations, f, used by the visual system to draw distinctions between different image regions. The method involves the discrimination of images constructed by selecting texture micropatterns randomly and independently (across locations) on the basis of a given micropattern histogram. Different components of f are measured by use of different component functions to modulate the micropattern histogram until the resulting textures are discriminable. When no discrimination threshold can be obtained for a given modulating component function, a second titration technique may be used to measure the contribution of that component to f. The method includes several strong tests of its own assumptions. An example is given of the method applied to visual textures composed of small, uniform squares with randomly chosen gray levels. In particular, for a fixed mean gray level mu and a fixed gray-level variance sigma 2, histogram contrast analysis is used to establish that the class S of all textures composed of small squares with jointly independent, identically distributed gray levels with mean mu and variance sigma 2 is perceptually elementary in the following sense: there exists a single, real-valued function f S of gray level, such that two textures I and J in S are discriminable only if the average value of f S applied to the gray levels in I is significantly different from the average value of f S applied to the gray levels in J. Finally, histogram contrast analysis is used to obtain a seventh-order polynomial approximation of f S.

Asking the Right Questions: Techniques for Collaboration and School Change. 2nd Edition.

ERIC Educational Resources Information Center

Holcomb, Edie L.

This work provides school change leaders with tools, techniques, tips, examples, illustrations, and stories about promoting school change. Tools provided include histograms, surveys, run charts, weighted voting, force-field analysis, decision matrices, and many others. Chapter 1, "Introduction," applies a matrix for asking questions…

Sliding window adaptive histogram equalization of intraoral radiographs: effect on image quality.

PubMed

Sund, T; Møystad, A

2006-05-01

To investigate whether contrast enhancement by non-interactive, sliding window adaptive histogram equalization (SWAHE) can enhance the image quality of intraoral radiographs in the dental clinic. Three dentists read 22 periapical and 12 bitewing storage phosphor (SP) radiographs. For the periapical readings they graded the quality of the examination with regard to visually locating the root apex. For the bitewing readings they registered all occurrences of approximal caries on a confidence scale. Each reading was first done on an unprocessed radiograph ("single-view"), and then re-done with the image processed with SWAHE displayed beside the unprocessed version ("twin-view"). The processing parameters for SWAHE were the same for all the images. For the periapical examinations, twin-view was judged to raise the image quality for 52% of those cases where the single-view quality was below the maximum. For the bitewing radiographs, there was a change of caries classification (both positive and negative) with twin-view in 19% of the cases, but with only a 3% net increase in the total number of caries registrations. For both examinations interobserver variance was unaffected. Non-interactive SWAHE applied to dental SP radiographs produces a supplemental contrast enhanced image which in twin-view reading improves the image quality of periapical examinations. SWAHE also affects caries diagnosis of bitewing images, and further study using a gold standard is warranted.

High frequency measurements of shot noise suppression in atomic-scale metal contacts

NASA Astrophysics Data System (ADS)

Wheeler, Patrick J.; Evans, Kenneth; Russom, Jeffrey; King, Nicholas; Natelson, Douglas

2009-03-01

Shot noise provides a means of assessing the number and transmission coefficients of transmitting channels in atomic- and molecular-scale junctions. Previous experiments at low temperatures in metal and semiconductor point contacts have demonstrated the expected suppression of shot noise when junction conductance is near an integer multiple of the conductance quantum, G0≡2e^2/h. Using high frequency techniques, we demonstrate the high speed acquisition of such data at room temperature in mechanical break junctions. In clean Au contacts conductance histograms with clear peaks at G0, 2G0, and 3G0 are acquired within hours, and histograms of simultaneous measurements of the shot noise show clear suppression at those conductance values. We describe the dependence of the noise on bias voltage and analyze the noise vs. conductance histograms in terms of a model that averages over transmission coefficients.

SU-F-I-45: An Automated Technique to Measure Image Contrast in Clinical CT Images

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

Sanders, J; Abadi, E; Meng, B

Purpose: To develop and validate an automated technique for measuring image contrast in chest computed tomography (CT) exams. Methods: An automated computer algorithm was developed to measure the distribution of Hounsfield units (HUs) inside four major organs: the lungs, liver, aorta, and bones. These organs were first segmented or identified using computer vision and image processing techniques. Regions of interest (ROIs) were automatically placed inside the lungs, liver, and aorta and histograms of the HUs inside the ROIs were constructed. The mean and standard deviation of each histogram were computed for each CT dataset. Comparison of the mean and standardmore » deviation of the HUs in the different organs provides different contrast values. The ROI for the bones is simply the segmentation mask of the bones. Since the histogram for bones does not follow a Gaussian distribution, the 25th and 75th percentile were computed instead of the mean. The sensitivity and accuracy of the algorithm was investigated by comparing the automated measurements with manual measurements. Fifteen contrast enhanced and fifteen non-contrast enhanced chest CT clinical datasets were examined in the validation procedure. Results: The algorithm successfully measured the histograms of the four organs in both contrast and non-contrast enhanced chest CT exams. The automated measurements were in agreement with manual measurements. The algorithm has sufficient sensitivity as indicated by the near unity slope of the automated versus manual measurement plots. Furthermore, the algorithm has sufficient accuracy as indicated by the high coefficient of determination, R2, values ranging from 0.879 to 0.998. Conclusion: Patient-specific image contrast can be measured from clinical datasets. The algorithm can be run on both contrast enhanced and non-enhanced clinical datasets. The method can be applied to automatically assess the contrast characteristics of clinical chest CT images and quantify dependencies that may not be captured in phantom data.« less

Temporal analysis of regional wall motion from cine cardiac MRI

NASA Astrophysics Data System (ADS)

Ratib, Osman M.; Didier, Dominique; Chretien, Anne; Rosset, Antoine; Magnin, Isabelle E.; Ligier, Yves

1996-04-01

The purpose of this work is to develop and to evaluate an automatic analysis technique for quantitative assessment of cardiac function from cine MRI and to identify regional alterations in synchronicity based on Fourier analysis of ventricular wall motion (WM). A temporal analysis technique of left ventricular wall displacement was developed for quantitative analysis of temporal delays in wall motion and applied to gated cine 'dark blood' cardiac MRI. This imaging technique allows the user to saturate the blood both above and below the imaging slice simultaneously by using a specially designed rf presaturation pulse. The acquisition parameters are: TR equals 25 - 60 msec, TE equals 5 - 7 msec, 0 equals 25 degrees, slice thickness equals 10 mm, 16 to 32 frames/cycle. Automatic edge detection was used to outline the ventricular cavities on all frames of a cardiac cycle. Two different segmentation techniques were applied to all studies and lead to similar results. Further improvement in edge detection accuracy was achieved by temporal interpolation of individual contours on each image of the cardiac cycle. Radial analysis of the ventricular wall motion was then performed along 64 radii drawn from the center of the ventricular cavity. The first harmonic of the Fourier transform of each radial motion curve is calculated. The phase of the fundamental Fourier component is used as an index of synchrony (delay) of regional wall motion. Results are displayed in color-coded maps of regional alterations in the amplitude and synchrony of wall motion. The temporal delays measured from individual segments are evaluated through a histogram of phase distribution, where the width of the main peak is used as an index of overall synchrony of wall motion. The variability of this technique was validated in 10 normal volunteers and was used to identify regions with asynchronous WM in 15 patients with documented CAD. The standard deviation (SD) of phase distribution measured in short axis views was calculated and used to identify regions with asynchronous wall motion in patients with coronary artery disease. Results suggest that this technique is more sensitive than global functional parameters such as ejection fraction for the detection of ventricular dysfunction. Color coded parametric display offers a more convenient way for the identification and localization of regional wall motion asynchrony. Data obtained from endocardial wall motion analysis were not significantly different from wall thickening measurements. The innovative approach of evaluating the temporal behavior of regional wall motion anomalies is expected to provide clinically relevant data about subtle alteration that cannot be detected through simple analysis of the extent (amplitude) of wall motion or myocardial thickening. Temporal analysis of regional WM abnormality from cine MRI offers an innovative and promising means for objective quantitative evaluation of subtle regional abnormalities. Color coded parametric maps allowed a better identification and localization of regional WM asynchrony.

Scaling images using their background ratio. An application in statistical comparisons of images.

PubMed

Kalemis, A; Binnie, D; Bailey, D L; Flower, M A; Ott, R J

2003-06-07

Comparison of two medical images often requires image scaling as a pre-processing step. This is usually done with the scaling-to-the-mean or scaling-to-the-maximum techniques which, under certain circumstances, in quantitative applications may contribute a significant amount of bias. In this paper, we present a simple scaling method which assumes only that the most predominant values in the corresponding images belong to their background structure. The ratio of the two images to be compared is calculated and its frequency histogram is plotted. The scaling factor is given by the position of the peak in this histogram which belongs to the background structure. The method was tested against the traditional scaling-to-the-mean technique on simulated planar gamma-camera images which were compared using pixelwise statistical parametric tests. Both sensitivity and specificity for each condition were measured over a range of different contrasts and sizes of inhomogeneity for the two scaling techniques. The new method was found to preserve sensitivity in all cases while the traditional technique resulted in significant degradation of sensitivity in certain cases.

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.

Histogram of gradient and binarized statistical image features of wavelet subband-based palmprint features extraction

NASA Astrophysics Data System (ADS)

Attallah, Bilal; Serir, Amina; Chahir, Youssef; Boudjelal, Abdelwahhab

2017-11-01

Palmprint recognition systems are dependent on feature extraction. A method of feature extraction using higher discrimination information was developed to characterize palmprint images. In this method, two individual feature extraction techniques are applied to a discrete wavelet transform of a palmprint image, and their outputs are fused. The two techniques used in the fusion are the histogram of gradient and the binarized statistical image features. They are then evaluated using an extreme learning machine classifier before selecting a feature based on principal component analysis. Three palmprint databases, the Hong Kong Polytechnic University (PolyU) Multispectral Palmprint Database, Hong Kong PolyU Palmprint Database II, and the Delhi Touchless (IIDT) Palmprint Database, are used in this study. The study shows that our method effectively identifies and verifies palmprints and outperforms other methods based on feature extraction.

Single-photon technique for the detection of periodic extraterrestrial laser pulses.

PubMed

Leeb, W R; Poppe, A; Hammel, E; Alves, J; Brunner, M; Meingast, S

2013-06-01

To draw humankind's attention to its existence, an extraterrestrial civilization could well direct periodic laser pulses toward Earth. We developed a technique capable of detecting a quasi-periodic light signal with an average of less than one photon per pulse within a measurement time of a few tens of milliseconds in the presence of the radiation emitted by an exoplanet's host star. Each of the electronic events produced by one or more single-photon avalanche detectors is tagged with precise time-of-arrival information and stored. From this we compute a histogram displaying the frequency of event-time differences in classes with bin widths on the order of a nanosecond. The existence of periodic laser pulses manifests itself in histogram peaks regularly spaced at multiples of the-a priori unknown-pulse repetition frequency. With laser sources simulating both the pulse source and the background radiation, we tested a detection system in the laboratory at a wavelength of 850 nm. We present histograms obtained from various recorded data sequences with the number of photons per pulse, the background photons per pulse period, and the recording time as main parameters. We then simulated a periodic signal hypothetically generated on a planet orbiting a G2V-type star (distance to Earth 500 light-years) and show that the technique is capable of detecting the signal even if the received pulses carry as little as one photon on average on top of the star's background light.

Fast and efficient search for MPEG-4 video using adjacent pixel intensity difference quantization histogram feature

NASA Astrophysics Data System (ADS)

Lee, Feifei; Kotani, Koji; Chen, Qiu; Ohmi, Tadahiro

2010-02-01

In this paper, a fast search algorithm for MPEG-4 video clips from video database is proposed. An adjacent pixel intensity difference quantization (APIDQ) histogram is utilized as the feature vector of VOP (video object plane), which had been reliably applied to human face recognition previously. Instead of fully decompressed video sequence, partially decoded data, namely DC sequence of the video object are extracted from the video sequence. Combined with active search, a temporal pruning algorithm, fast and robust video search can be realized. The proposed search algorithm has been evaluated by total 15 hours of video contained of TV programs such as drama, talk, news, etc. to search for given 200 MPEG-4 video clips which each length is 15 seconds. Experimental results show the proposed algorithm can detect the similar video clip in merely 80ms, and Equal Error Rate (ERR) of 2 % in drama and news categories are achieved, which are more accurately and robust than conventional fast video search algorithm.

A New Vegetation Segmentation Approach for Cropped Fields Based on Threshold Detection from Hue Histograms

PubMed Central

Hassanein, Mohamed; El-Sheimy, Naser

2018-01-01

Over the last decade, the use of unmanned aerial vehicle (UAV) technology has evolved significantly in different applications as it provides a special platform capable of combining the benefits of terrestrial and aerial remote sensing. Therefore, such technology has been established as an important source of data collection for different precision agriculture (PA) applications such as crop health monitoring and weed management. Generally, these PA applications depend on performing a vegetation segmentation process as an initial step, which aims to detect the vegetation objects in collected agriculture fields’ images. The main result of the vegetation segmentation process is a binary image, where vegetations are presented in white color and the remaining objects are presented in black. Such process could easily be performed using different vegetation indexes derived from multispectral imagery. Recently, to expand the use of UAV imagery systems for PA applications, it was important to reduce the cost of such systems through using low-cost RGB cameras Thus, developing vegetation segmentation techniques for RGB images is a challenging problem. The proposed paper introduces a new vegetation segmentation methodology for low-cost UAV RGB images, which depends on using Hue color channel. The proposed methodology follows the assumption that the colors in any agriculture field image can be distributed into vegetation and non-vegetations colors. Therefore, four main steps are developed to detect five different threshold values using the hue histogram of the RGB image, these thresholds are capable to discriminate the dominant color, either vegetation or non-vegetation, within the agriculture field image. The achieved results for implementing the proposed methodology showed its ability to generate accurate and stable vegetation segmentation performance with mean accuracy equal to 87.29% and standard deviation as 12.5%. PMID:29670055

A histogram-based technique for rapid vector extraction from PIV photographs

NASA Technical Reports Server (NTRS)

Humphreys, William M., Jr.

1991-01-01

A new analysis technique, performed totally in the image plane, is proposed which rapidly extracts all available vectors from individual interrogation regions on PIV photographs. The technique avoids the need for using Fourier transforms with the associated computational burden. The data acquisition and analysis procedure is described, and results of a preliminary simulation study to evaluate the accuracy of the technique are presented. Recently obtained PIV photographs are analyzed.

Numerically accurate computational techniques for optimal estimator analyses of multi-parameter models

NASA Astrophysics Data System (ADS)

Berger, Lukas; Kleinheinz, Konstantin; Attili, Antonio; Bisetti, Fabrizio; Pitsch, Heinz; Mueller, Michael E.

2018-05-01

Modelling unclosed terms in partial differential equations typically involves two steps: First, a set of known quantities needs to be specified as input parameters for a model, and second, a specific functional form needs to be defined to model the unclosed terms by the input parameters. Both steps involve a certain modelling error, with the former known as the irreducible error and the latter referred to as the functional error. Typically, only the total modelling error, which is the sum of functional and irreducible error, is assessed, but the concept of the optimal estimator enables the separate analysis of the total and the irreducible errors, yielding a systematic modelling error decomposition. In this work, attention is paid to the techniques themselves required for the practical computation of irreducible errors. Typically, histograms are used for optimal estimator analyses, but this technique is found to add a non-negligible spurious contribution to the irreducible error if models with multiple input parameters are assessed. Thus, the error decomposition of an optimal estimator analysis becomes inaccurate, and misleading conclusions concerning modelling errors may be drawn. In this work, numerically accurate techniques for optimal estimator analyses are identified and a suitable evaluation of irreducible errors is presented. Four different computational techniques are considered: a histogram technique, artificial neural networks, multivariate adaptive regression splines, and an additive model based on a kernel method. For multiple input parameter models, only artificial neural networks and multivariate adaptive regression splines are found to yield satisfactorily accurate results. Beyond a certain number of input parameters, the assessment of models in an optimal estimator analysis even becomes practically infeasible if histograms are used. The optimal estimator analysis in this paper is applied to modelling the filtered soot intermittency in large eddy simulations using a dataset of a direct numerical simulation of a non-premixed sooting turbulent flame.

Whole brain myelin mapping using T1- and T2-weighted MR imaging data

PubMed Central

Ganzetti, Marco; Wenderoth, Nicole; Mantini, Dante

2014-01-01

Despite recent advancements in MR imaging, non-invasive mapping of myelin in the brain still remains an open issue. Here we attempted to provide a potential solution. Specifically, we developed a processing workflow based on T1-w and T2-w MR data to generate an optimized myelin enhanced contrast image. The workflow allows whole brain mapping using the T1-w/T2-w technique, which was originally introduced as a non-invasive method for assessing cortical myelin content. The hallmark of our approach is a retrospective calibration algorithm, applied to bias-corrected T1-w and T2-w images, that relies on image intensities outside the brain. This permits standardizing the intensity histogram of the ratio image, thereby allowing for across-subject statistical analyses. Quantitative comparisons of image histograms within and across different datasets confirmed the effectiveness of our normalization procedure. Not only did the calibrated T1-w/T2-w images exhibit a comparable intensity range, but also the shape of the intensity histograms was largely corresponding. We also assessed the reliability and specificity of the ratio image compared to other MR-based techniques, such as magnetization transfer ratio (MTR), fractional anisotropy (FA), and fluid-attenuated inversion recovery (FLAIR). With respect to these other techniques, T1-w/T2-w had consistently high values, as well as low inter-subject variability, in brain structures where myelin is most abundant. Overall, our results suggested that the T1-w/T2-w technique may be a valid tool supporting the non-invasive mapping of myelin in the brain. Therefore, it might find important applications in the study of brain development, aging and disease. PMID:25228871

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.

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.

Development of digital interactive processing system for NOAA satellites AVHRR data

NASA Astrophysics Data System (ADS)

Gupta, R. K.; Murthy, N. N.

The paper discusses the digital image processing system for NOAA/AVHRR data including Land applications - configured around VAX 11/750 host computer supported with FPS 100 Array Processor, Comtal graphic display and HP Plotting devices; wherein the system software for relational Data Base together with query and editing facilities, Man-Machine Interface using form, menu and prompt inputs including validation of user entries for data type and range; preprocessing software for data calibration, Sun-angle correction, Geometric Corrections for Earth curvature effect and Earth rotation offsets and Earth location of AVHRR image have been accomplished. The implemented image enhancement techniques such as grey level stretching, histogram equalization and convolution are discussed. The software implementation details for the computation of vegetative index and normalized vegetative index using NOAA/AVHRR channels 1 and 2 data together with output are presented; scientific background for such computations and obtainability of similar indices from Landsat/MSS data are also included. The paper concludes by specifying the further software developments planned and the progress envisaged in the field of vegetation index studies.

Adaptive sigmoid function bihistogram equalization for image contrast enhancement

NASA Astrophysics Data System (ADS)

Arriaga-Garcia, Edgar F.; Sanchez-Yanez, Raul E.; Ruiz-Pinales, Jose; Garcia-Hernandez, Ma. de Guadalupe

2015-09-01

Contrast enhancement plays a key role in a wide range of applications including consumer electronic applications, such as video surveillance, digital cameras, and televisions. The main goal of contrast enhancement is to increase the quality of images. However, most state-of-the-art methods induce different types of distortion such as intensity shift, wash-out, noise, intensity burn-out, and intensity saturation. In addition, in consumer electronics, simple and fast methods are required in order to be implemented in real time. A bihistogram equalization method based on adaptive sigmoid functions is proposed. It consists of splitting the image histogram into two parts that are equalized independently by using adaptive sigmoid functions. In order to preserve the mean brightness of the input image, the parameter of the sigmoid functions is chosen to minimize the absolute mean brightness metric. Experiments on the Berkeley database have shown that the proposed method improves the quality of images and preserves their mean brightness. An application to improve the colorfulness of images is also presented.

Spatial detection of tv channel logos as outliers from the content

NASA Astrophysics Data System (ADS)

Ekin, Ahmet; Braspenning, Ralph

2006-01-01

This paper proposes a purely image-based TV channel logo detection algorithm that can detect logos independently from their motion and transparency features. The proposed algorithm can robustly detect any type of logos, such as transparent and animated, without requiring any temporal constraints whereas known methods have to wait for the occurrence of large motion in the scene and assume stationary logos. The algorithm models logo pixels as outliers from the actual scene content that is represented by multiple 3-D histograms in the YC BC R space. We use four scene histograms corresponding to each of the four corners because the content characteristics change from one image corner to another. A further novelty of the proposed algorithm is that we define image corners and the areas where we compute the scene histograms by a cinematic technique called Golden Section Rule that is used by professionals. The robustness of the proposed algorithm is demonstrated over a dataset of representative TV content.

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.

Histogram-based ionogram displays and their application to autoscaling

NASA Astrophysics Data System (ADS)

Lynn, Kenneth J. W.

2018-03-01

A simple method is described for displaying and auto scaling the basic ionogram parameters foF2 and h'F2 as well as some additional layer parameters from digital ionograms. The technique employed is based on forming frequency and height histograms in each ionogram. This technique has now been applied specifically to ionograms produced by the IPS5D ionosonde developed and operated by the Australian Space Weather Service (SWS). The SWS ionograms are archived in a cleaned format and readily available from the SWS internet site. However, the method is applicable to any ionosonde which produces ionograms in a digital format at a useful signal-to-noise level. The most novel feature of the technique for autoscaling is its simplicity and the avoidance of the mathematical imaging and line fitting techniques often used. The program arose from the necessity to display many days of ionogram output to allow the location of specific types of ionospheric event such as ionospheric storms, travelling ionospheric disturbances and repetitive ionospheric height changes for further investigation and measurement. Examples and applications of the method are given including the removal of sporadic E and spread F.

Parallel Geospatial Data Management for Multi-Scale Environmental Data Analysis on GPUs

NASA Astrophysics Data System (ADS)

Wang, D.; Zhang, J.; Wei, Y.

2013-12-01

As the spatial and temporal resolutions of Earth observatory data and Earth system simulation outputs are getting higher, in-situ and/or post- processing such large amount of geospatial data increasingly becomes a bottleneck in scientific inquires of Earth systems and their human impacts. Existing geospatial techniques that are based on outdated computing models (e.g., serial algorithms and disk-resident systems), as have been implemented in many commercial and open source packages, are incapable of processing large-scale geospatial data and achieve desired level of performance. In this study, we have developed a set of parallel data structures and algorithms that are capable of utilizing massively data parallel computing power available on commodity Graphics Processing Units (GPUs) for a popular geospatial technique called Zonal Statistics. Given two input datasets with one representing measurements (e.g., temperature or precipitation) and the other one represent polygonal zones (e.g., ecological or administrative zones), Zonal Statistics computes major statistics (or complete distribution histograms) of the measurements in all regions. Our technique has four steps and each step can be mapped to GPU hardware by identifying its inherent data parallelisms. First, a raster is divided into blocks and per-block histograms are derived. Second, the Minimum Bounding Boxes (MBRs) of polygons are computed and are spatially matched with raster blocks; matched polygon-block pairs are tested and blocks that are either inside or intersect with polygons are identified. Third, per-block histograms are aggregated to polygons for blocks that are completely within polygons. Finally, for blocks that intersect with polygon boundaries, all the raster cells within the blocks are examined using point-in-polygon-test and cells that are within polygons are used to update corresponding histograms. As the task becomes I/O bound after applying spatial indexing and GPU hardware acceleration, we have developed a GPU-based data compression technique by reusing our previous work on Bitplane Quadtree (or BPQ-Tree) based indexing of binary bitmaps. Results have shown that our GPU-based parallel Zonal Statistic technique on 3000+ US counties over 20+ billion NASA SRTM 30 meter resolution Digital Elevation (DEM) raster cells has achieved impressive end-to-end runtimes: 101 seconds and 46 seconds a low-end workstation equipped with a Nvidia GTX Titan GPU using cold and hot cache, respectively; and, 60-70 seconds using a single OLCF TITAN computing node and 10-15 seconds using 8 nodes. Our experiment results clearly show the potentials of using high-end computing facilities for large-scale geospatial processing.

An Approach to Improve the Quality of Infrared Images of Vein-Patterns

PubMed Central

Lin, Chih-Lung

2011-01-01

This study develops an approach to improve the quality of infrared (IR) images of vein-patterns, which usually have noise, low contrast, low brightness and small objects of interest, thus requiring preprocessing to improve their quality. The main characteristics of the proposed approach are that no prior knowledge about the IR image is necessary and no parameters must be preset. Two main goals are sought: impulse noise reduction and adaptive contrast enhancement technologies. In our study, a fast median-based filter (FMBF) is developed as a noise reduction method. It is based on an IR imaging mechanism to detect the noisy pixels and on a modified median-based filter to remove the noisy pixels in IR images. FMBF has the advantage of a low computation load. In addition, FMBF can retain reasonably good edges and texture information when the size of the filter window increases. The most important advantage is that the peak signal-to-noise ratio (PSNR) caused by FMBF is higher than the PSNR caused by the median filter. A hybrid cumulative histogram equalization (HCHE) is proposed for adaptive contrast enhancement. HCHE can automatically generate a hybrid cumulative histogram (HCH) based on two different pieces of information about the image histogram. HCHE can improve the enhancement effect on hot objects rather than background. The experimental results are addressed and demonstrate that the proposed approach is feasible for use as an effective and adaptive process for enhancing the quality of IR vein-pattern images. PMID:22247674

An approach to improve the quality of infrared images of vein-patterns.

PubMed

Lin, Chih-Lung

2011-01-01

This study develops an approach to improve the quality of infrared (IR) images of vein-patterns, which usually have noise, low contrast, low brightness and small objects of interest, thus requiring preprocessing to improve their quality. The main characteristics of the proposed approach are that no prior knowledge about the IR image is necessary and no parameters must be preset. Two main goals are sought: impulse noise reduction and adaptive contrast enhancement technologies. In our study, a fast median-based filter (FMBF) is developed as a noise reduction method. It is based on an IR imaging mechanism to detect the noisy pixels and on a modified median-based filter to remove the noisy pixels in IR images. FMBF has the advantage of a low computation load. In addition, FMBF can retain reasonably good edges and texture information when the size of the filter window increases. The most important advantage is that the peak signal-to-noise ratio (PSNR) caused by FMBF is higher than the PSNR caused by the median filter. A hybrid cumulative histogram equalization (HCHE) is proposed for adaptive contrast enhancement. HCHE can automatically generate a hybrid cumulative histogram (HCH) based on two different pieces of information about the image histogram. HCHE can improve the enhancement effect on hot objects rather than background. The experimental results are addressed and demonstrate that the proposed approach is feasible for use as an effective and adaptive process for enhancing the quality of IR vein-pattern images.

Design of interpolation functions for subpixel-accuracy stereo-vision systems.

PubMed

Haller, Istvan; Nedevschi, Sergiu

2012-02-01

Traditionally, subpixel interpolation in stereo-vision systems was designed for the block-matching algorithm. During the evaluation of different interpolation strategies, a strong correlation was observed between the type of the stereo algorithm and the subpixel accuracy of the different solutions. Subpixel interpolation should be adapted to each stereo algorithm to achieve maximum accuracy. In consequence, it is more important to propose methodologies for interpolation function generation than specific function shapes. We propose two such methodologies based on data generated by the stereo algorithms. The first proposal uses a histogram to model the environment and applies histogram equalization to an existing solution adapting it to the data. The second proposal employs synthetic images of a known environment and applies function fitting to the resulted data. The resulting function matches the algorithm and the data as best as possible. An extensive evaluation set is used to validate the findings. Both real and synthetic test cases were employed in different scenarios. The test results are consistent and show significant improvements compared with traditional solutions. © 2011 IEEE

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.

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.

Histogram-based adaptive gray level scaling for texture feature classification of colorectal polyps

NASA Astrophysics Data System (ADS)

Pomeroy, Marc; Lu, Hongbing; Pickhardt, Perry J.; Liang, Zhengrong

2018-02-01

Texture features have played an ever increasing role in computer aided detection (CADe) and diagnosis (CADx) methods since their inception. Texture features are often used as a method of false positive reduction for CADe packages, especially for detecting colorectal polyps and distinguishing them from falsely tagged residual stool and healthy colon wall folds. While texture features have shown great success there, the performance of texture features for CADx have lagged behind primarily because of the more similar features among different polyps types. In this paper, we present an adaptive gray level scaling and compare it to the conventional equal-spacing of gray level bins. We use a dataset taken from computed tomography colonography patients, with 392 polyp regions of interest (ROIs) identified and have a confirmed diagnosis through pathology. Using the histogram information from the entire ROI dataset, we generate the gray level bins such that each bin contains roughly the same number of voxels Each image ROI is the scaled down to two different numbers of gray levels, using both an equal spacing of Hounsfield units for each bin, and our adaptive method. We compute a set of texture features from the scaled images including 30 gray level co-occurrence matrix (GLCM) features and 11 gray level run length matrix (GLRLM) features. Using a random forest classifier to distinguish between hyperplastic polyps and all others (adenomas and adenocarcinomas), we find that the adaptive gray level scaling can improve performance based on the area under the receiver operating characteristic curve by up to 4.6%.

Three-Class Mammogram Classification Based on Descriptive CNN Features

PubMed Central

Zhang, Qianni; Jadoon, Adeel

2017-01-01

In this paper, a novel classification technique for large data set of mammograms using a deep learning method is proposed. The proposed model targets a three-class classification study (normal, malignant, and benign cases). In our model we have presented two methods, namely, convolutional neural network-discrete wavelet (CNN-DW) and convolutional neural network-curvelet transform (CNN-CT). An augmented data set is generated by using mammogram patches. To enhance the contrast of mammogram images, the data set is filtered by contrast limited adaptive histogram equalization (CLAHE). In the CNN-DW method, enhanced mammogram images are decomposed as its four subbands by means of two-dimensional discrete wavelet transform (2D-DWT), while in the second method discrete curvelet transform (DCT) is used. In both methods, dense scale invariant feature (DSIFT) for all subbands is extracted. Input data matrix containing these subband features of all the mammogram patches is created that is processed as input to convolutional neural network (CNN). Softmax layer and support vector machine (SVM) layer are used to train CNN for classification. Proposed methods have been compared with existing methods in terms of accuracy rate, error rate, and various validation assessment measures. CNN-DW and CNN-CT have achieved accuracy rate of 81.83% and 83.74%, respectively. Simulation results clearly validate the significance and impact of our proposed model as compared to other well-known existing techniques. PMID:28191461

Three-Class Mammogram Classification Based on Descriptive CNN Features.

PubMed

Jadoon, M Mohsin; Zhang, Qianni; Haq, Ihsan Ul; Butt, Sharjeel; Jadoon, Adeel

2017-01-01

In this paper, a novel classification technique for large data set of mammograms using a deep learning method is proposed. The proposed model targets a three-class classification study (normal, malignant, and benign cases). In our model we have presented two methods, namely, convolutional neural network-discrete wavelet (CNN-DW) and convolutional neural network-curvelet transform (CNN-CT). An augmented data set is generated by using mammogram patches. To enhance the contrast of mammogram images, the data set is filtered by contrast limited adaptive histogram equalization (CLAHE). In the CNN-DW method, enhanced mammogram images are decomposed as its four subbands by means of two-dimensional discrete wavelet transform (2D-DWT), while in the second method discrete curvelet transform (DCT) is used. In both methods, dense scale invariant feature (DSIFT) for all subbands is extracted. Input data matrix containing these subband features of all the mammogram patches is created that is processed as input to convolutional neural network (CNN). Softmax layer and support vector machine (SVM) layer are used to train CNN for classification. Proposed methods have been compared with existing methods in terms of accuracy rate, error rate, and various validation assessment measures. CNN-DW and CNN-CT have achieved accuracy rate of 81.83% and 83.74%, respectively. Simulation results clearly validate the significance and impact of our proposed model as compared to other well-known existing techniques.

A robust technique based on VLM and Frangi filter for retinal vessel extraction and denoising.

PubMed

Khan, Khan Bahadar; Khaliq, Amir A; Jalil, Abdul; Shahid, Muhammad

2018-01-01

The exploration of retinal vessel structure is colossally important on account of numerous diseases including stroke, Diabetic Retinopathy (DR) and coronary heart diseases, which can damage the retinal vessel structure. The retinal vascular network is very hard to be extracted due to its spreading and diminishing geometry and contrast variation in an image. The proposed technique consists of unique parallel processes for denoising and extraction of blood vessels in retinal images. In the preprocessing section, an adaptive histogram equalization enhances dissimilarity between the vessels and the background and morphological top-hat filters are employed to eliminate macula and optic disc, etc. To remove local noise, the difference of images is computed from the top-hat filtered image and the high-boost filtered image. Frangi filter is applied at multi scale for the enhancement of vessels possessing diverse widths. Segmentation is performed by using improved Otsu thresholding on the high-boost filtered image and Frangi's enhanced image, separately. In the postprocessing steps, a Vessel Location Map (VLM) is extracted by using raster to vector transformation. Postprocessing steps are employed in a novel way to reject misclassified vessel pixels. The final segmented image is obtained by using pixel-by-pixel AND operation between VLM and Frangi output image. The method has been rigorously analyzed on the STARE, DRIVE and HRF datasets.

Bright Retinal Lesions Detection using Colour Fundus Images Containing Reflective Features

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

Giancardo, Luca; Karnowski, Thomas Paul; Chaum, Edward

2009-01-01

In the last years the research community has developed many techniques to detect and diagnose diabetic retinopathy with retinal fundus images. This is a necessary step for the implementation of a large scale screening effort in rural areas where ophthalmologists are not available. In the United States of America, the incidence of diabetes is worryingly increasing among the young population. Retina fundus images of patients younger than 20 years old present a high amount of reflection due to the Nerve Fibre Layer (NFL), the younger the patient the more these reflections are visible. To our knowledge we are not awaremore » of algorithms able to explicitly deal with this type of reflection artefact. This paper presents a technique to detect bright lesions also in patients with a high degree of reflective NFL. First, the candidate bright lesions are detected using image equalization and relatively simple histogram analysis. Then, a classifier is trained using texture descriptor (Multi-scale Local Binary Patterns) and other features in order to remove the false positives in the lesion detection. Finally, the area of the lesions is used to diagnose diabetic retinopathy. Our database consists of 33 images from a telemedicine network currently developed. When determining moderate to high diabetic retinopathy using the bright lesions detected the algorithm achieves a sensitivity of 100% at a specificity of 100% using hold-one-out testing.« less

Touch HDR: photograph enhancement by user controlled wide dynamic range adaptation

NASA Astrophysics Data System (ADS)

Verrall, Steve; Siddiqui, Hasib; Atanassov, Kalin; Goma, Sergio; Ramachandra, Vikas

2013-03-01

High Dynamic Range (HDR) technology enables photographers to capture a greater range of tonal detail. HDR is typically used to bring out detail in a dark foreground object set against a bright background. HDR technologies include multi-frame HDR and single-frame HDR. Multi-frame HDR requires the combination of a sequence of images taken at different exposures. Single-frame HDR requires histogram equalization post-processing of a single image, a technique referred to as local tone mapping (LTM). Images generated using HDR technology can look less natural than their non- HDR counterparts. Sometimes it is only desired to enhance small regions of an original image. For example, it may be desired to enhance the tonal detail of one subject's face while preserving the original background. The Touch HDR technique described in this paper achieves these goals by enabling selective blending of HDR and non-HDR versions of the same image to create a hybrid image. The HDR version of the image can be generated by either multi-frame or single-frame HDR. Selective blending can be performed as a post-processing step, for example, as a feature of a photo editor application, at any time after the image has been captured. HDR and non-HDR blending is controlled by a weighting surface, which is configured by the user through a sequence of touches on a touchscreen.

A comparison of methods using optical coherence tomography to detect demineralized regions in teeth

PubMed Central

Sowa, Michael G.; Popescu, Dan P.; Friesen, Jeri R.; Hewko, Mark D.; Choo-Smith, Lin-P’ing

2013-01-01

Optical coherence tomography (OCT) is a three- dimensional optical imaging technique that can be used to identify areas of early caries formation in dental enamel. The OCT signal at 850 nm back-reflected from sound enamel is attenuated stronger than the signal back-reflected from demineralized regions. To quantify this observation, the OCT signal as a function of depth into the enamel (also known as the A-scan intensity), the histogram of the A-scan intensities and three summary parameters derived from the A-scan are defined and their diagnostic potential compared. A total of 754 OCT A-scans were analyzed. The three summary parameters derived from the A-scans, the OCT attenuation coefficient as well as the mean and standard deviation of the lognormal fit to the histogram of the A-scan ensemble show statistically significant differences (p < 0.01) when comparing parameters from sound enamel and caries. Furthermore, these parameters only show a modest correlation. Based on the area under the curve (AUC) of the receiver operating characteristics (ROC) plot, the OCT attenuation coefficient shows higher discriminatory capacity (AUC=0.98) compared to the parameters derived from the lognormal fit to the histogram of the A-scan. However, direct analysis of the A-scans or the histogram of A-scan intensities using linear support vector machine classification shows diagnostic discrimination (AUC = 0.96) comparable to that achieved using the attenuation coefficient. These findings suggest that either direct analysis of the A-scan, its intensity histogram or the attenuation coefficient derived from the descending slope of the OCT A-scan have high capacity to discriminate between regions of caries and sound enamel. PMID:22052833

Comparative study of pulsed-continuous arterial spin labeling and dynamic susceptibility contrast imaging by histogram analysis in evaluation of glial tumors.

PubMed

Arisawa, Atsuko; Watanabe, Yoshiyuki; Tanaka, Hisashi; Takahashi, Hiroto; Matsuo, Chisato; Fujiwara, Takuya; Fujiwara, Masahiro; Fujimoto, Yasunori; Tomiyama, Noriyuki

2018-06-01

Arterial spin labeling (ASL) is a non-invasive perfusion technique that may be an alternative to dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) for assessment of brain tumors. To our knowledge, there have been no reports on histogram analysis of ASL. The purpose of this study was to determine whether ASL is comparable with DSC-MRI in terms of differentiating high-grade and low-grade gliomas by evaluating the histogram analysis of cerebral blood flow (CBF) in the entire tumor. Thirty-four patients with pathologically proven glioma underwent ASL and DSC-MRI. High-signal areas on contrast-enhanced T 1 -weighted images or high-intensity areas on fluid-attenuated inversion recovery images were designated as the volumes of interest (VOIs). ASL-CBF, DSC-CBF, and DSC-cerebral blood volume maps were constructed and co-registered to the VOI. Perfusion histogram analyses of the whole VOI and statistical analyses were performed to compare the ASL and DSC images. There was no significant difference in the mean values for any of the histogram metrics in both of the low-grade gliomas (n = 15) and the high-grade gliomas (n = 19). Strong correlations were seen in the 75th percentile, mean, median, and standard deviation values between the ASL and DSC images. The area under the curve values tended to be greater for the DSC images than for the ASL images. DSC-MRI is superior to ASL for distinguishing high-grade from low-grade glioma. ASL could be an alternative evaluation method when DSC-MRI cannot be used, e.g., in patients with renal failure, those in whom repeated examination is required, and in children.

Blind technique using blocking artifacts and entropy of histograms for image tampering detection

NASA Astrophysics Data System (ADS)

Manu, V. T.; Mehtre, B. M.

2017-06-01

The tremendous technological advancements in recent times has enabled people to create, edit and circulate images easily than ever before. As a result of this, ensuring the integrity and authenticity of the images has become challenging. Malicious editing of images to deceive the viewer is referred to as image tampering. A widely used image tampering technique is image splicing or compositing, in which regions from different images are copied and pasted. In this paper, we propose a tamper detection method utilizing the blocking and blur artifacts which are the footprints of splicing. The classification of images as tampered or not, is done based on the standard deviations of the entropy histograms and block discrete cosine transformations. We can detect the exact boundaries of the tampered area in the image, if the image is classified as tampered. Experimental results on publicly available image tampering datasets show that the proposed method outperforms the existing methods in terms of accuracy.

Adaptive local thresholding for robust nucleus segmentation utilizing shape priors

NASA Astrophysics Data System (ADS)

Wang, Xiuzhong; Srinivas, Chukka

2016-03-01

This paper describes a novel local thresholding method for foreground detection. First, a Canny edge detection method is used for initial edge detection. Then, tensor voting is applied on the initial edge pixels, using a nonsymmetric tensor field tailored to encode prior information about nucleus size, shape, and intensity spatial distribution. Tensor analysis is then performed to generate the saliency image and, based on that, the refined edge. Next, the image domain is divided into blocks. In each block, at least one foreground and one background pixel are sampled for each refined edge pixel. The saliency weighted foreground histogram and background histogram are then created. These two histograms are used to calculate a threshold by minimizing the background and foreground pixel classification error. The block-wise thresholds are then used to generate the threshold for each pixel via interpolation. Finally, the foreground is obtained by comparing the original image with the threshold image. The effective use of prior information, combined with robust techniques, results in far more reliable foreground detection, which leads to robust nucleus segmentation.

Finger vein recognition based on finger crease location

NASA Astrophysics Data System (ADS)

Lu, Zhiying; Ding, Shumeng; Yin, Jing

2016-07-01

Finger vein recognition technology has significant advantages over other methods in terms of accuracy, uniqueness, and stability, and it has wide promising applications in the field of biometric recognition. We propose using finger creases to locate and extract an object region. Then we use linear fitting to overcome the problem of finger rotation in the plane. The method of modular adaptive histogram equalization (MAHE) is presented to enhance image contrast and reduce computational cost. To extract the finger vein features, we use a fusion method, which can obtain clear and distinguishable vein patterns under different conditions. We used the Hausdorff average distance algorithm to examine the recognition performance of the system. The experimental results demonstrate that MAHE can better balance the recognition accuracy and the expenditure of time compared with three other methods. Our resulting equal error rate throughout the total procedure was 3.268% in a database of 153 finger vein images.

Fusion-based multi-target tracking and localization for intelligent surveillance systems

NASA Astrophysics Data System (ADS)

Rababaah, Haroun; Shirkhodaie, Amir

2008-04-01

In this paper, we have presented two approaches addressing visual target tracking and localization in complex urban environment. The two techniques presented in this paper are: fusion-based multi-target visual tracking, and multi-target localization via camera calibration. For multi-target tracking, the data fusion concepts of hypothesis generation/evaluation/selection, target-to-target registration, and association are employed. An association matrix is implemented using RGB histograms for associated tracking of multi-targets of interests. Motion segmentation of targets of interest (TOI) from the background was achieved by a Gaussian Mixture Model. Foreground segmentation, on other hand, was achieved by the Connected Components Analysis (CCA) technique. The tracking of individual targets was estimated by fusing two sources of information, the centroid with the spatial gating, and the RGB histogram association matrix. The localization problem is addressed through an effective camera calibration technique using edge modeling for grid mapping (EMGM). A two-stage image pixel to world coordinates mapping technique is introduced that performs coarse and fine location estimation of moving TOIs. In coarse estimation, an approximate neighborhood of the target position is estimated based on nearest 4-neighbor method, and in fine estimation, we use Euclidean interpolation to localize the position within the estimated four neighbors. Both techniques were tested and shown reliable results for tracking and localization of Targets of interests in complex urban environment.

Freezing Transition Studies Through Constrained Cell Model Simulation

NASA Astrophysics Data System (ADS)

Nayhouse, Michael; Kwon, Joseph Sang-Il; Heng, Vincent R.; Amlani, Ankur M.; Orkoulas, G.

2014-10-01

In the present work, a simulation method based on cell models is used to deduce the fluid-solid transition of a system of particles that interact via a pair potential, , which is of the form with . The simulations are implemented under constant-pressure conditions on a generalized version of the constrained cell model. The constrained cell model is constructed by dividing the volume into Wigner-Seitz cells and confining each particle in a single cell. This model is a special case of a more general cell model which is formed by introducing an additional field variable that controls the number of particles per cell and, thus, the relative stability of the solid against the fluid phase. High field values force configurations with one particle per cell and thus favor the solid phase. Fluid-solid coexistence on the isotherm that corresponds to a reduced temperature of 2 is determined from constant-pressure simulations of the generalized cell model using tempering and histogram reweighting techniques. The entire fluid-solid phase boundary is determined through a thermodynamic integration technique based on histogram reweighting, using the previous coexistence point as a reference point. The vapor-liquid phase diagram is obtained from constant-pressure simulations of the unconstrained system using tempering and histogram reweighting. The phase diagram of the system is found to contain a stable critical point and a triple point. The phase diagram of the corresponding constrained cell model is also found to contain both a stable critical point and a triple point.

Color Swapping to Enhance Breast Cancer Digital Images Qualities Using Stain Normalization

NASA Astrophysics Data System (ADS)

Muhimmah, Izzati; Puspasari Wijaya, Dhina; Indrayanti

2017-03-01

Histopathology is the disease diagnosis by means of the visual examination of tissues under the microscope. The virtually transparent tissue sections were prepared using a number of colored histochemical stains bound selectively to the cellular components. A variation of colors comes to be a problem in histopathology based upon the microscope lighting for the range of factors. This research aimed to investigate an image enhancement by applying a nonlinear mapping approach to stain normalization and histogram equalization for contrast enhancement. Validation was carried out in 59 datasets with 96.6% accordance and expert justification.

Mesoscale, Radiometrically Referenced, Multi-Temporal Hyperspectral Data for Co2 Leak Detection by Locating Spatial Variation of Biophysically Relevant Parameters

NASA Astrophysics Data System (ADS)

McCann, Cooper Patrick

Low-cost flight-based hyperspectral imaging systems have the potential to provide valuable information for ecosystem and environmental studies as well as aide in land management and land health monitoring. This thesis describes (1) a bootstrap method of producing mesoscale, radiometrically-referenced hyperspectral data using the Landsat surface reflectance (LaSRC) data product as a reference target, (2) biophysically relevant basis functions to model the reflectance spectra, (3) an unsupervised classification technique based on natural histogram splitting of these biophysically relevant parameters, and (4) local and multi-temporal anomaly detection. The bootstrap method extends standard processing techniques to remove uneven illumination conditions between flight passes, allowing the creation of radiometrically self-consistent data. Through selective spectral and spatial resampling, LaSRC data is used as a radiometric reference target. Advantages of the bootstrap method include the need for minimal site access, no ancillary instrumentation, and automated data processing. Data from a flight on 06/02/2016 is compared with concurrently collected ground based reflectance spectra as a means of validation achieving an average error of 2.74%. Fitting reflectance spectra using basis functions, based on biophysically relevant spectral features, allows both noise and data reductions while shifting information from spectral bands to biophysical features. Histogram splitting is used to determine a clustering based on natural splittings of these fit parameters. The Indian Pines reference data enabled comparisons of the efficacy of this technique to established techniques. The splitting technique is shown to be an improvement over the ISODATA clustering technique with an overall accuracy of 34.3/19.0% before merging and 40.9/39.2% after merging. This improvement is also seen as an improvement of kappa before/after merging of 24.8/30.5 for the histogram splitting technique compared to 15.8/28.5 for ISODATA. Three hyperspectral flights over the Kevin Dome area, covering 1843 ha, acquired 06/21/2014, 06/24/2015 and 06/26/2016 are examined with different methods of anomaly detection. Detection of anomalies within a single data set is examined to determine, on a local scale, areas that are significantly different from the surrounding area. Additionally, the detection and identification of persistent anomalies and non-persistent anomalies was investigated across multiple data sets.

Differentially Private Synthesization of Multi-Dimensional Data using Copula Functions

PubMed Central

Li, Haoran; Xiong, Li; Jiang, Xiaoqian

2014-01-01

Differential privacy has recently emerged in private statistical data release as one of the strongest privacy guarantees. Most of the existing techniques that generate differentially private histograms or synthetic data only work well for single dimensional or low-dimensional histograms. They become problematic for high dimensional and large domain data due to increased perturbation error and computation complexity. In this paper, we propose DPCopula, a differentially private data synthesization technique using Copula functions for multi-dimensional data. The core of our method is to compute a differentially private copula function from which we can sample synthetic data. Copula functions are used to describe the dependence between multivariate random vectors and allow us to build the multivariate joint distribution using one-dimensional marginal distributions. We present two methods for estimating the parameters of the copula functions with differential privacy: maximum likelihood estimation and Kendall’s τ estimation. We present formal proofs for the privacy guarantee as well as the convergence property of our methods. Extensive experiments using both real datasets and synthetic datasets demonstrate that DPCopula generates highly accurate synthetic multi-dimensional data with significantly better utility than state-of-the-art techniques. PMID:25405241

Conductance fluctuation of edge-disordered graphene nanoribbons: Crossover from diffusive transport to Anderson localization

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

Takashima, Kengo; Yamamoto, Takahiro, E-mail: takahiro@rs.tus.ac.jp; Department of Liberal Arts

Conductance fluctuation of edge-disordered graphene nanoribbons (ED-GNRs) is examined using the non-equilibrium Green's function technique combined with the extended Hückel approximation. The mean free path λ and the localization length ξ of the ED-GNRs are determined to classify the quantum transport regimes. In the diffusive regime where the length L{sub c} of the ED-GNRs is much longer than λ and much shorter than ξ, the conductance histogram is given by a Gaussian distribution function with universal conductance fluctuation. In the localization regime where L{sub c}≫ξ, the histogram is no longer the universal Gaussian distribution but a lognormal distribution that characterizesmore » Anderson localization.« less

Automated segmentation of geographic atrophy using deep convolutional neural networks

NASA Astrophysics Data System (ADS)

Hu, Zhihong; Wang, Ziyuan; Sadda, SriniVas R.

2018-02-01

Geographic atrophy (GA) is an end-stage manifestation of the advanced age-related macular degeneration (AMD), the leading cause of blindness and visual impairment in developed nations. Techniques to rapidly and precisely detect and quantify GA would appear to be of critical importance in advancing the understanding of its pathogenesis. In this study, we develop an automated supervised classification system using deep convolutional neural networks (CNNs) for segmenting GA in fundus autofluorescene (FAF) images. More specifically, to enhance the contrast of GA relative to the background, we apply the contrast limited adaptive histogram equalization. Blood vessels may cause GA segmentation errors due to similar intensity level to GA. A tensor-voting technique is performed to identify the blood vessels and a vessel inpainting technique is applied to suppress the GA segmentation errors due to the blood vessels. To handle the large variation of GA lesion sizes, three deep CNNs with three varying sized input image patches are applied. Fifty randomly chosen FAF images are obtained from fifty subjects with GA. The algorithm-defined GA regions are compared with manual delineation by a certified grader. A two-fold cross-validation is applied to evaluate the algorithm performance. The mean segmentation accuracy, true positive rate (i.e. sensitivity), true negative rate (i.e. specificity), positive predictive value, false discovery rate, and overlap ratio, between the algorithm- and manually-defined GA regions are 0.97 +/- 0.02, 0.89 +/- 0.08, 0.98 +/- 0.02, 0.87 +/- 0.12, 0.13 +/- 0.12, and 0.79 +/- 0.12 respectively, demonstrating a high level of agreement.

Energy conservation using face detection

NASA Astrophysics Data System (ADS)

Deotale, Nilesh T.; Kalbande, Dhananjay R.; Mishra, Akassh A.

2011-10-01

Computerized Face Detection, is concerned with the difficult task of converting a video signal of a person to written text. It has several applications like face recognition, simultaneous multiple face processing, biometrics, security, video surveillance, human computer interface, image database management, digital cameras use face detection for autofocus, selecting regions of interest in photo slideshows that use a pan-and-scale and The Present Paper deals with energy conservation using face detection. Automating the process to a computer requires the use of various image processing techniques. There are various methods that can be used for Face Detection such as Contour tracking methods, Template matching, Controlled background, Model based, Motion based and color based. Basically, the video of the subject are converted into images are further selected manually for processing. However, several factors like poor illumination, movement of face, viewpoint-dependent Physical appearance, Acquisition geometry, Imaging conditions, Compression artifacts makes Face detection difficult. This paper reports an algorithm for conservation of energy using face detection for various devices. The present paper suggests Energy Conservation can be done by Detecting the Face and reducing the brightness of complete image and then adjusting the brightness of the particular area of an image where the face is located using histogram equalization.

Chromaticity based smoke removal in endoscopic images

NASA Astrophysics Data System (ADS)

Tchaka, Kevin; Pawar, Vijay M.; Stoyanov, Danail

2017-02-01

In minimally invasive surgery, image quality is a critical pre-requisite to ensure a surgeons ability to perform a procedure. In endoscopic procedures, image quality can deteriorate for a number of reasons such as fogging due to the temperature gradient after intra-corporeal insertion, lack of focus and due to smoke generated when using electro-cautery to dissect tissues without bleeding. In this paper we investigate the use of vision processing techniques to remove surgical smoke and improve the clarity of the image. We model the image formation process by introducing a haze medium to account for the degradation of visibility. For simplicity and computational efficiency we use an adapted dark-channel prior method combined with histogram equalization to remove smoke artifacts to recover the radiance image and enhance the contrast and brightness of the final result. Our initial results on images from robotic assisted procedures are promising and show that the proposed approach may be used to enhance image quality during surgery without additional suction devices. In addition, the processing pipeline may be used as an important part of a robust surgical vision pipeline that can continue working in the presence of smoke.

Using Statistical Process Control to Make Data-Based Clinical Decisions.

ERIC Educational Resources Information Center

Pfadt, Al; Wheeler, Donald J.

1995-01-01

Statistical process control (SPC), which employs simple statistical tools and problem-solving techniques such as histograms, control charts, flow charts, and Pareto charts to implement continual product improvement procedures, can be incorporated into human service organizations. Examples illustrate use of SPC procedures to analyze behavioral data…

Benchmarking the Degree of Implementation of Learner-Centered Approaches

ERIC Educational Resources Information Center

Blumberg, Phyllis; Pontiggia, Laura

2011-01-01

We describe an objective way to measure whether curricula, educational programs, and institutions are learner-centered. This technique for benchmarking learner-centeredness uses rubrics to measure courses on 29 components within Weimer's five dimensions. We converted the scores on the rubrics to four-point indices and constructed histograms that…

Breast lesion characterization using whole-lesion histogram analysis with stretched-exponential diffusion model.

PubMed

Liu, Chunling; Wang, Kun; Li, Xiaodan; Zhang, Jine; Ding, Jie; Spuhler, Karl; Duong, Timothy; Liang, Changhong; Huang, Chuan

2018-06-01

Diffusion-weighted imaging (DWI) has been studied in breast imaging and can provide more information about diffusion, perfusion and other physiological interests than standard pulse sequences. The stretched-exponential model has previously been shown to be more reliable than conventional DWI techniques, but different diagnostic sensitivities were found from study to study. This work investigated the characteristics of whole-lesion histogram parameters derived from the stretched-exponential diffusion model for benign and malignant breast lesions, compared them with conventional apparent diffusion coefficient (ADC), and further determined which histogram metrics can be best used to differentiate malignant from benign lesions. This was a prospective study. Seventy females were included in the study. Multi-b value DWI was performed on a 1.5T scanner. Histogram parameters of whole lesions for distributed diffusion coefficient (DDC), heterogeneity index (α), and ADC were calculated by two radiologists and compared among benign lesions, ductal carcinoma in situ (DCIS), and invasive carcinoma confirmed by pathology. Nonparametric tests were performed for comparisons among invasive carcinoma, DCIS, and benign lesions. Comparisons of receiver operating characteristic (ROC) curves were performed to show the ability to discriminate malignant from benign lesions. The majority of histogram parameters (mean/min/max, skewness/kurtosis, 10-90 th percentile values) from DDC, α, and ADC were significantly different among invasive carcinoma, DCIS, and benign lesions. DDC 10% (area under curve [AUC] = 0.931), ADC 10% (AUC = 0.893), and α mean (AUC = 0.787) were found to be the best metrics in differentiating benign from malignant tumors among all histogram parameters derived from ADC and α, respectively. The combination of DDC 10% and α mean , using logistic regression, yielded the highest sensitivity (90.2%) and specificity (95.5%). DDC 10% and α mean derived from the stretched-exponential model provides more information and better diagnostic performance in differentiating malignancy from benign lesions than ADC parameters derived from a monoexponential model. 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:1701-1710. © 2017 International Society for Magnetic Resonance in Medicine.

Semi-automatic breast ultrasound image segmentation based on mean shift and graph cuts.

PubMed

Zhou, Zhuhuang; Wu, Weiwei; Wu, Shuicai; Tsui, Po-Hsiang; Lin, Chung-Chih; Zhang, Ling; Wang, Tianfu

2014-10-01

Computerized tumor segmentation on breast ultrasound (BUS) images remains a challenging task. In this paper, we proposed a new method for semi-automatic tumor segmentation on BUS images using Gaussian filtering, histogram equalization, mean shift, and graph cuts. The only interaction required was to select two diagonal points to determine a region of interest (ROI) on an input image. The ROI image was shrunken by a factor of 2 using bicubic interpolation to reduce computation time. The shrunken image was smoothed by a Gaussian filter and then contrast-enhanced by histogram equalization. Next, the enhanced image was filtered by pyramid mean shift to improve homogeneity. The object and background seeds for graph cuts were automatically generated on the filtered image. Using these seeds, the filtered image was then segmented by graph cuts into a binary image containing the object and background. Finally, the binary image was expanded by a factor of 2 using bicubic interpolation, and the expanded image was processed by morphological opening and closing to refine the tumor contour. The method was implemented with OpenCV 2.4.3 and Visual Studio 2010 and tested for 38 BUS images with benign tumors and 31 BUS images with malignant tumors from different ultrasound scanners. Experimental results showed that our method had a true positive rate (TP) of 91.7%, a false positive (FP) rate of 11.9%, and a similarity (SI) rate of 85.6%. The mean run time on Intel Core 2.66 GHz CPU and 4 GB RAM was 0.49 ± 0.36 s. The experimental results indicate that the proposed method may be useful in BUS image segmentation. © The Author(s) 2014.

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

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

Font, Joan; Beckman, John E.; Fathi, Kambiz

In this Letter, we introduce a technique for finding resonance radii in a disk galaxy. We use a two-dimensional velocity field in H{alpha} emission obtained with Fabry-Perot interferometry, derive the classical rotation curve, and subtract it off, leaving a residual velocity map. As the streaming motions should reverse sign at corotation, we detect these reversals and plot them in a histogram against galactocentric radius, excluding points where the amplitude of the reversal is smaller than the measurement uncertainty. The histograms show well-defined peaks which we assume to occur at resonance radii, identifying corotations as the most prominent peaks corresponding tomore » the relevant morphological features of the galaxy (notably bars and spiral arm systems). We compare our results with published measurements on the same galaxies using other methods and different types of data.« less

Analysis of memory use for improved design and compile-time allocation of local memory

NASA Technical Reports Server (NTRS)

Mcniven, Geoffrey D.; Davidson, Edward S.

1986-01-01

Trace analysis techniques are used to study memory referencing behavior for the purpose of designing local memories and determining how to allocate them for data and instructions. In an attempt to assess the inherent behavior of the source code, the trace analysis system described here reduced the effects of the compiler and host architecture on the trace by using a technical called flattening. The variables in the trace, their associated single-assignment values, and references are histogrammed on the basis of various parameters describing memory referencing behavior. Bounds are developed specifying the amount of memory space required to store all live values in a particular histogram class. The reduction achieved in main memory traffic by allocating local memory is specified for each class.

Improved image retrieval based on fuzzy colour feature vector

NASA Astrophysics Data System (ADS)

Ben-Ahmeida, Ahlam M.; Ben Sasi, Ahmed Y.

2013-03-01

One of Image indexing techniques is the Content-Based Image Retrieval which is an efficient way for retrieving images from the image database automatically based on their visual contents such as colour, texture, and shape. In this paper will be discuss how using content-based image retrieval (CBIR) method by colour feature extraction and similarity checking. By dividing the query image and all images in the database into pieces and extract the features of each part separately and comparing the corresponding portions in order to increase the accuracy in the retrieval. The proposed approach is based on the use of fuzzy sets, to overcome the problem of curse of dimensionality. The contribution of colour of each pixel is associated to all the bins in the histogram using fuzzy-set membership functions. As a result, the Fuzzy Colour Histogram (FCH), outperformed the Conventional Colour Histogram (CCH) in image retrieving, due to its speedy results, where were images represented as signatures that took less size of memory, depending on the number of divisions. The results also showed that FCH is less sensitive and more robust to brightness changes than the CCH with better retrieval recall values.

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.

Deforestation due to Urbanization: a Case Study for Trabzon, Turkey

NASA Astrophysics Data System (ADS)

Telkenaroglu, C.; Dikmen, M.

2017-11-01

This paper inspects the deforestation of Trabzon in Turkey, due to urbanization, between 2006 and 2016. For this purpose, Landsat 7 ETM+ (Enhanced Thematic Mapper Plus) images are obtained from United States Geographical Survey (USGS) archive (USGS, 2017a) and their VNIR bands related to this study are utilized. For both years, and for each band, histograms are equalized. Finally, Normalized Difference Vegetation Index (NDVI) values are calculated as images. Resulting vegetation indexes are assessed in comparison to the binary ground truth images. A visual inspection is also done with respect to Google's Timelapse images for each year to validate and support the results.

METEOSAT studies of clouds and radiation budget

NASA Technical Reports Server (NTRS)

Saunders, R. W.

1982-01-01

Radiation budget studies of the atmosphere/surface system from Meteosat, cloud parameter determination from space, and sea surface temperature measurements from TIROS N data are all described. This work was carried out on the interactive planetary image processing system (IPIPS), which allows interactive manipulationion of the image data in addition to the conventional computational tasks. The current hardware configuration of IPIPS is shown. The I(2)S is the principal interactive display allowing interaction via a trackball, four buttons under program control, or a touch tablet. Simple image processing operations such as contrast enhancing, pseudocoloring, histogram equalization, and multispectral combinations, can all be executed at the push of a button.

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.

Muscle categorization using PDF estimation and Naive Bayes classification.

PubMed

Adel, Tameem M; Smith, Benn E; Stashuk, Daniel W

2012-01-01

The structure of motor unit potentials (MUPs) and their times of occurrence provide information about the motor units (MUs) that created them. As such, electromyographic (EMG) data can be used to categorize muscles as normal or suffering from a neuromuscular disease. Using pattern discovery (PD) allows clinicians to understand the rationale underlying a certain muscle characterization; i.e. it is transparent. Discretization is required in PD, which leads to some loss in accuracy. In this work, characterization techniques that are based on estimating probability density functions (PDFs) for each muscle category are implemented. Characterization probabilities of each motor unit potential train (MUPT) are obtained from these PDFs and then Bayes rule is used to aggregate the MUPT characterization probabilities to calculate muscle level probabilities. Even though this technique is not as transparent as PD, its accuracy is higher than the discrete PD. Ultimately, the goal is to use a technique that is based on both PDFs and PD and make it as transparent and as efficient as possible, but first it was necessary to thoroughly assess how accurate a fully continuous approach can be. Using gaussian PDF estimation achieved improvements in muscle categorization accuracy over PD and further improvements resulted from using feature value histograms to choose more representative PDFs; for instance, using log-normal distribution to represent skewed histograms.

Automatic lesion boundary detection in dermoscopy images using gradient vector flow snakes

PubMed Central

Erkol, Bulent; Moss, Randy H.; Stanley, R. Joe; Stoecker, William V.; Hvatum, Erik

2011-01-01

Background Malignant melanoma has a good prognosis if treated early. Dermoscopy images of pigmented lesions are most commonly taken at × 10 magnification under lighting at a low angle of incidence while the skin is immersed in oil under a glass plate. Accurate skin lesion segmentation from the background skin is important because some of the features anticipated to be used for diagnosis deal with shape of the lesion and others deal with the color of the lesion compared with the color of the surrounding skin. Methods In this research, gradient vector flow (GVF) snakes are investigated to find the border of skin lesions in dermoscopy images. An automatic initialization method is introduced to make the skin lesion border determination process fully automated. Results Skin lesion segmentation results are presented for 70 benign and 30 melanoma skin lesion images for the GVF-based method and a color histogram analysis technique. The average errors obtained by the GVF-based method are lower for both the benign and melanoma image sets than for the color histogram analysis technique based on comparison with manually segmented lesions determined by a dermatologist. Conclusions The experimental results for the GVF-based method demonstrate promise as an automated technique for skin lesion segmentation in dermoscopy images. PMID:15691255

Performance Evaluation of EnKF-based Hydrogeological Site Characterization using Color Coherent Vectors

NASA Astrophysics Data System (ADS)

Moslehi, M.; de Barros, F.

2017-12-01

Complexity of hydrogeological systems arises from the multi-scale heterogeneity and insufficient measurements of their underlying parameters such as hydraulic conductivity and porosity. An inadequate characterization of hydrogeological properties can significantly decrease the trustworthiness of numerical models that predict groundwater flow and solute transport. Therefore, a variety of data assimilation methods have been proposed in order to estimate hydrogeological parameters from spatially scarce data by incorporating the governing physical models. In this work, we propose a novel framework for evaluating the performance of these estimation methods. We focus on the Ensemble Kalman Filter (EnKF) approach that is a widely used data assimilation technique. It reconciles multiple sources of measurements to sequentially estimate model parameters such as the hydraulic conductivity. Several methods have been used in the literature to quantify the accuracy of the estimations obtained by EnKF, including Rank Histograms, RMSE and Ensemble Spread. However, these commonly used methods do not regard the spatial information and variability of geological formations. This can cause hydraulic conductivity fields with very different spatial structures to have similar histograms or RMSE. We propose a vision-based approach that can quantify the accuracy of estimations by considering the spatial structure embedded in the estimated fields. Our new approach consists of adapting a new metric, Color Coherent Vectors (CCV), to evaluate the accuracy of estimated fields achieved by EnKF. CCV is a histogram-based technique for comparing images that incorporate spatial information. We represent estimated fields as digital three-channel images and use CCV to compare and quantify the accuracy of estimations. The sensitivity of CCV to spatial information makes it a suitable metric for assessing the performance of spatial data assimilation techniques. Under various factors of data assimilation methods such as number, layout, and type of measurements, we compare the performance of CCV with other metrics such as RMSE. By simulating hydrogeological processes using estimated and true fields, we observe that CCV outperforms other existing evaluation metrics.

Application of whole-lesion histogram analysis of pharmacokinetic parameters in dynamic contrast-enhanced MRI of breast lesions with the CAIPIRINHA-Dixon-TWIST-VIBE technique.

PubMed

Li, Zhiwei; Ai, Tao; Hu, Yiqi; Yan, Xu; Nickel, Marcel Dominik; Xu, Xiao; Xia, Liming

2018-01-01

To investigate the application of whole-lesion histogram analysis of pharmacokinetic parameters for differentiating malignant from benign breast lesions on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). In all, 92 women with 97 breast lesions (26 benign and 71 malignant lesions) were enrolled in this study. Patients underwent dynamic breast MRI at 3T using a prototypical CAIPIRINHA-Dixon-TWIST-VIBE (CDT-VIBE) sequence and a subsequent surgery or biopsy. Inflow rate of the agent between plasma and interstitium (K trans ), outflow rate of agent between interstitium and plasma (K ep ), extravascular space volume per unit volume of tissue (v e ) including mean value, 25th/50th/75th/90th percentiles, skewness, and kurtosis were then calculated based on the whole lesion. A single-sample Kolmogorov-Smirnov test, paired t-test, and receiver operating characteristic curve (ROC) analysis were used for statistical analysis. Malignant breast lesions had significantly higher K trans , K ep , and lower v e in mean values, 25th/50th/75th/90th percentiles, and significantly higher skewness of v e than benign breast lesions (all P < 0.05). There was no significant difference in kurtosis values between malignant and benign breast lesions (all P > 0.05). The 90th percentile of K trans , the 90th percentile of K ep , and the 50th percentile of v e showed the greatest areas under the ROC curve (AUC) for each pharmacokinetic parameter derived from DCE-MRI. The 90th percentile of K ep achieved the highest AUC value (0.927) among all histogram-derived values. The whole-lesion histogram analysis of pharmacokinetic parameters can improve the diagnostic accuracy of breast DCE-MRI with the CDT-VIBE technique. The 90th percentile of K ep may be the best indicator in differentiation between malignant and benign breast lesions. 4 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2018;47:91-96. © 2017 International Society for Magnetic Resonance in Medicine.

The value of a kurtosis metric in estimating the hazard to hearing of complex industrial noise exposures.

PubMed

Qiu, Wei; Hamernik, Roger P; Davis, Robert I

2013-05-01

A series of Gaussian and non-Gaussian equal energy noise exposures were designed with the objective of establishing the extent to which the kurtosis statistic could be used to grade the severity of noise trauma produced by the exposures. Here, 225 chinchillas distributed in 29 groups, with 6 to 8 animals per group, were exposed at 97 dB SPL. The equal energy exposures were presented either continuously for 5 d or on an interrupted schedule for 19 d. The non-Gaussian noises all differed in the level of the kurtosis statistic or in the temporal structure of the noise, where the latter was defined by different peak, interval, and duration histograms of the impact noise transients embedded in the noise signal. Noise-induced trauma was estimated from auditory evoked potential hearing thresholds and surface preparation histology that quantified sensory cell loss. Results indicated that the equal energy hypothesis is a valid unifying principle for estimating the consequences of an exposure if and only if the equivalent energy exposures had the same kurtosis. Furthermore, for the same level of kurtosis the detailed temporal structure of an exposure does not have a strong effect on trauma.

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…

A general method for the definition of margin recipes depending on the treatment technique applied in helical tomotherapy prostate plans.

PubMed

Sevillano, David; Mínguez, Cristina; Sánchez, Alicia; Sánchez-Reyes, Alberto

2016-01-01

To obtain specific margin recipes that take into account the dosimetric characteristics of the treatment plans used in a single institution. We obtained dose-population histograms (DPHs) of 20 helical tomotherapy treatment plans for prostate cancer by simulating the effects of different systematic errors (Σ) and random errors (σ) on these plans. We obtained dosimetric margins and margin reductions due to random errors (random margins) by fitting the theoretical results of coverages for Gaussian distributions with coverages of the planned D99% obtained from the DPHs. The dosimetric margins obtained for helical tomotherapy prostate treatments were 3.3 mm, 3 mm, and 1 mm in the lateral (Lat), anterior-posterior (AP), and superior-inferior (SI) directions. Random margins showed parabolic dependencies, yielding expressions of 0.16σ(2), 0.13σ(2), and 0.15σ(2) for the Lat, AP, and SI directions, respectively. When focusing on values up to σ = 5 mm, random margins could be fitted considering Gaussian penumbras with standard deviations (σp) equal to 4.5 mm Lat, 6 mm AP, and 5.5 mm SI. Despite complex dose distributions in helical tomotherapy treatment plans, we were able to simplify the behaviour of our plans against treatment errors to single values of dosimetric and random margins for each direction. These margins allowed us to develop specific margin recipes for the respective treatment technique. The method is general and could be used for any treatment technique provided that DPHs can be obtained. Copyright © 2015 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

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.

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

IN VITRO QUANTIFICATION OF THE SIZE DISTRIBUTION OF INTRASACCULAR VOIDS LEFT AFTER ENDOVASCULAR COILING OF CEREBRAL ANEURYSMS.

PubMed

Sadasivan, Chander; Brownstein, Jeremy; Patel, Bhumika; Dholakia, Ronak; Santore, Joseph; Al-Mufti, Fawaz; Puig, Enrique; Rakian, Audrey; Fernandez-Prada, Kenneth D; Elhammady, Mohamed S; Farhat, Hamad; Fiorella, David J; Woo, Henry H; Aziz-Sultan, Mohammad A; Lieber, Baruch B

2013-03-01

Endovascular coiling of cerebral aneurysms remains limited by coil compaction and associated recanalization. Recent coil designs which effect higher packing densities may be far from optimal because hemodynamic forces causing compaction are not well understood since detailed data regarding the location and distribution of coil masses are unavailable. We present an in vitro methodology to characterize coil masses deployed within aneurysms by quantifying intra-aneurysmal void spaces. Eight identical aneurysms were packed with coils by both balloon- and stent-assist techniques. The samples were embedded, sequentially sectioned and imaged. Empty spaces between the coils were numerically filled with circles (2D) in the planar images and with spheres (3D) in the three-dimensional composite images. The 2D and 3D void size histograms were analyzed for local variations and by fitting theoretical probability distribution functions. Balloon-assist packing densities (31±2%) were lower ( p =0.04) than the stent-assist group (40±7%). The maximum and average 2D and 3D void sizes were higher ( p =0.03 to 0.05) in the balloon-assist group as compared to the stent-assist group. None of the void size histograms were normally distributed; theoretical probability distribution fits suggest that the histograms are most probably exponentially distributed with decay constants of 6-10 mm. Significant ( p <=0.001 to p =0.03) spatial trends were noted with the void sizes but correlation coefficients were generally low (absolute r <=0.35). The methodology we present can provide valuable input data for numerical calculations of hemodynamic forces impinging on intra-aneurysmal coil masses and be used to compare and optimize coil configurations as well as coiling techniques.

Visual performance-based image enhancement methodology: an investigation of contrast enhancement algorithms

NASA Astrophysics Data System (ADS)

Neriani, Kelly E.; Herbranson, Travis J.; Reis, George A.; Pinkus, Alan R.; Goodyear, Charles D.

2006-05-01

While vast numbers of image enhancing algorithms have already been developed, the majority of these algorithms have not been assessed in terms of their visual performance-enhancing effects using militarily relevant scenarios. The goal of this research was to apply a visual performance-based assessment methodology to evaluate six algorithms that were specifically designed to enhance the contrast of digital images. The image enhancing algorithms used in this study included three different histogram equalization algorithms, the Autolevels function, the Recursive Rational Filter technique described in Marsi, Ramponi, and Carrato1 and the multiscale Retinex algorithm described in Rahman, Jobson and Woodell2. The methodology used in the assessment has been developed to acquire objective human visual performance data as a means of evaluating the contrast enhancement algorithms. Objective performance metrics, response time and error rate, were used to compare algorithm enhanced images versus two baseline conditions, original non-enhanced images and contrast-degraded images. Observers completed a visual search task using a spatial-forcedchoice paradigm. Observers searched images for a target (a military vehicle) hidden among foliage and then indicated in which quadrant of the screen the target was located. Response time and percent correct were measured for each observer. Results of the study and future directions are discussed.

A new method for fusion, denoising and enhancement of x-ray images retrieved from Talbot-Lau grating interferometry.

PubMed

Scholkmann, Felix; Revol, Vincent; Kaufmann, Rolf; Baronowski, Heidrun; Kottler, Christian

2014-03-21

This paper introduces a new image denoising, fusion and enhancement framework for combining and optimal visualization of x-ray attenuation contrast (AC), differential phase contrast (DPC) and dark-field contrast (DFC) images retrieved from x-ray Talbot-Lau grating interferometry. The new image fusion framework comprises three steps: (i) denoising each input image (AC, DPC and DFC) through adaptive Wiener filtering, (ii) performing a two-step image fusion process based on the shift-invariant wavelet transform, i.e. first fusing the AC with the DPC image and then fusing the resulting image with the DFC image, and finally (iii) enhancing the fused image to obtain a final image using adaptive histogram equalization, adaptive sharpening and contrast optimization. Application examples are presented for two biological objects (a human tooth and a cherry) and the proposed method is compared to two recently published AC/DPC/DFC image processing techniques. In conclusion, the new framework for the processing of AC, DPC and DFC allows the most relevant features of all three images to be combined in one image while reducing the noise and enhancing adaptively the relevant image features. The newly developed framework may be used in technical and medical applications.

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

Quality based approach for adaptive face recognition

NASA Astrophysics Data System (ADS)

Abboud, Ali J.; Sellahewa, Harin; Jassim, Sabah A.

2009-05-01

Recent advances in biometric technology have pushed towards more robust and reliable systems. We aim to build systems that have low recognition errors and are less affected by variation in recording conditions. Recognition errors are often attributed to the usage of low quality biometric samples. Hence, there is a need to develop new intelligent techniques and strategies to automatically measure/quantify the quality of biometric image samples and if necessary restore image quality according to the need of the intended application. In this paper, we present no-reference image quality measures in the spatial domain that have impact on face recognition. The first is called symmetrical adaptive local quality index (SALQI) and the second is called middle halve (MH). Also, an adaptive strategy has been developed to select the best way to restore the image quality, called symmetrical adaptive histogram equalization (SAHE). The main benefits of using quality measures for adaptive strategy are: (1) avoidance of excessive unnecessary enhancement procedures that may cause undesired artifacts, and (2) reduced computational complexity which is essential for real time applications. We test the success of the proposed measures and adaptive approach for a wavelet-based face recognition system that uses the nearest neighborhood classifier. We shall demonstrate noticeable improvements in the performance of adaptive face recognition system over the corresponding non-adaptive scheme.

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.

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.

Analytical techniques of pilot scanning behavior and their application

NASA Technical Reports Server (NTRS)

Harris, R. L., Sr.; Glover, B. J.; Spady, A. A., Jr.

1986-01-01

The state of the art of oculometric data analysis techniques and their applications in certain research areas such as pilot workload, information transfer provided by various display formats, crew role in automated systems, and pilot training are documented. These analytical techniques produce the following data: real-time viewing of the pilot's scanning behavior, average dwell times, dwell percentages, instrument transition paths, dwell histograms, and entropy rate measures. These types of data are discussed, and overviews of the experimental setup, data analysis techniques, and software are presented. A glossary of terms frequently used in pilot scanning behavior and a bibliography of reports on related research sponsored by NASA Langley Research Center are also presented.

Fast and efficient molecule detection in localization-based super-resolution microscopy by parallel adaptive histogram equalization.

PubMed

Li, Yiming; Ishitsuka, Yuji; Hedde, Per Niklas; Nienhaus, G Ulrich

2013-06-25

In localization-based super-resolution microscopy, individual fluorescent markers are stochastically photoactivated and subsequently localized within a series of camera frames, yielding a final image with a resolution far beyond the diffraction limit. Yet, before localization can be performed, the subregions within the frames where the individual molecules are present have to be identified-oftentimes in the presence of high background. In this work, we address the importance of reliable molecule identification for the quality of the final reconstructed super-resolution image. We present a fast and robust algorithm (a-livePALM) that vastly improves the molecule detection efficiency while minimizing false assignments that can lead to image artifacts.

Automated Age-related Macular Degeneration screening system using fundus images.

PubMed

Kunumpol, P; Umpaipant, W; Kanchanaranya, N; Charoenpong, T; Vongkittirux, S; Kupakanjana, T; Tantibundhit, C

2017-07-01

This work proposed an automated screening system for Age-related Macular Degeneration (AMD), and distinguishing between wet or dry types of AMD using fundus images to assist ophthalmologists in eye disease screening and management. The algorithm employs contrast-limited adaptive histogram equalization (CLAHE) in image enhancement. Subsequently, discrete wavelet transform (DWT) and locality sensitivity discrimination analysis (LSDA) were used to extract features for a neural network model to classify the results. The results showed that the proposed algorithm was able to distinguish between normal eyes, dry AMD, or wet AMD with 98.63% sensitivity, 99.15% specificity, and 98.94% accuracy, suggesting promising potential as a medical support system for faster eye disease screening at lower costs.

Free energy profiles from single-molecule pulling experiments.

PubMed

Hummer, Gerhard; Szabo, Attila

2010-12-14

Nonequilibrium pulling experiments provide detailed information about the thermodynamic and kinetic properties of molecules. We show that unperturbed free energy profiles as a function of molecular extension can be obtained rigorously from such experiments without using work-weighted position histograms. An inverse Weierstrass transform is used to relate the system free energy obtained from the Jarzynski equality directly to the underlying molecular free energy surface. An accurate approximation for the free energy surface is obtained by using the method of steepest descent to evaluate the inverse transform. The formalism is applied to simulated data obtained from a kinetic model of RNA folding, in which the dynamics consists of jumping between linker-dominated folded and unfolded free energy surfaces.

CHOBS: Color Histogram of Block Statistics for Automatic Bleeding Detection in Wireless Capsule Endoscopy Video.

PubMed

Ghosh, Tonmoy; Fattah, Shaikh Anowarul; Wahid, Khan A

2018-01-01

Wireless capsule endoscopy (WCE) is the most advanced technology to visualize whole gastrointestinal (GI) tract in a non-invasive way. But the major disadvantage here, it takes long reviewing time, which is very laborious as continuous manual intervention is necessary. In order to reduce the burden of the clinician, in this paper, an automatic bleeding detection method for WCE video is proposed based on the color histogram of block statistics, namely CHOBS. A single pixel in WCE image may be distorted due to the capsule motion in the GI tract. Instead of considering individual pixel values, a block surrounding to that individual pixel is chosen for extracting local statistical features. By combining local block features of three different color planes of RGB color space, an index value is defined. A color histogram, which is extracted from those index values, provides distinguishable color texture feature. A feature reduction technique utilizing color histogram pattern and principal component analysis is proposed, which can drastically reduce the feature dimension. For bleeding zone detection, blocks are classified using extracted local features that do not incorporate any computational burden for feature extraction. From extensive experimentation on several WCE videos and 2300 images, which are collected from a publicly available database, a very satisfactory bleeding frame and zone detection performance is achieved in comparison to that obtained by some of the existing methods. In the case of bleeding frame detection, the accuracy, sensitivity, and specificity obtained from proposed method are 97.85%, 99.47%, and 99.15%, respectively, and in the case of bleeding zone detection, 95.75% of precision is achieved. The proposed method offers not only low feature dimension but also highly satisfactory bleeding detection performance, which even can effectively detect bleeding frame and zone in a continuous WCE video data.

Nonlinear histogram binning for quantitative analysis of lung tissue fibrosis in high-resolution CT data

NASA Astrophysics Data System (ADS)

Zavaletta, Vanessa A.; Bartholmai, Brian J.; Robb, Richard A.

2007-03-01

Diffuse lung diseases, such as idiopathic pulmonary fibrosis (IPF), can be characterized and quantified by analysis of volumetric high resolution CT scans of the lungs. These data sets typically have dimensions of 512 x 512 x 400. It is too subjective and labor intensive for a radiologist to analyze each slice and quantify regional abnormalities manually. Thus, computer aided techniques are necessary, particularly texture analysis techniques which classify various lung tissue types. Second and higher order statistics which relate the spatial variation of the intensity values are good discriminatory features for various textures. The intensity values in lung CT scans range between [-1024, 1024]. Calculation of second order statistics on this range is too computationally intensive so the data is typically binned between 16 or 32 gray levels. There are more effective ways of binning the gray level range to improve classification. An optimal and very efficient way to nonlinearly bin the histogram is to use a dynamic programming algorithm. The objective of this paper is to show that nonlinear binning using dynamic programming is computationally efficient and improves the discriminatory power of the second and higher order statistics for more accurate quantification of diffuse lung disease.

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.

Towards human behavior recognition based on spatio temporal features and support vector machines

NASA Astrophysics Data System (ADS)

Ghabri, Sawsen; Ouarda, Wael; Alimi, Adel M.

2017-03-01

Security and surveillance are vital issues in today's world. The recent acts of terrorism have highlighted the urgent need for efficient surveillance. There is indeed a need for an automated system for video surveillance which can detect identity and activity of person. In this article, we propose a new paradigm to recognize an aggressive human behavior such as boxing action. Our proposed system for human activity detection includes the use of a fusion between Spatio Temporal Interest Point (STIP) and Histogram of Oriented Gradient (HoG) features. The novel feature called Spatio Temporal Histogram Oriented Gradient (STHOG). To evaluate the robustness of our proposed paradigm with a local application of HoG technique on STIP points, we made experiments on KTH human action dataset based on Multi Class Support Vector Machines classification. The proposed scheme outperforms basic descriptors like HoG and STIP to achieve 82.26% us an accuracy value of classification rate.

Liquid-liquid transition in the ST2 model of water

NASA Astrophysics Data System (ADS)

Debenedetti, Pablo

2013-03-01

We present clear evidence of the existence of a metastable liquid-liquid phase transition in the ST2 model of water. Using four different techniques (the weighted histogram analysis method with single-particle moves, well-tempered metadynamics with single-particle moves, weighted histograms with parallel tempering and collective particle moves, and conventional molecular dynamics), we calculate the free energy surface over a range of thermodynamic conditions, we perform a finite size scaling analysis for the free energy barrier between the coexisting liquid phases, we demonstrate the attainment of diffusive behavior, and we perform stringent thermodynamic consistency checks. The results provide conclusive evidence of a first-order liquid-liquid transition. We also show that structural equilibration in the sluggish low-density phase is attained over the time scale of our simulations, and that crystallization times are significantly longer than structural equilibration, even under deeply supercooled conditions. We place our results in the context of the theory of metastability.

Enhancement of low visibility aerial images using histogram truncation and an explicit Retinex representation for balancing contrast and color consistency

NASA Astrophysics Data System (ADS)

Liu, Changjiang; Cheng, Irene; Zhang, Yi; Basu, Anup

2017-06-01

This paper presents an improved multi-scale Retinex (MSR) based enhancement for ariel images under low visibility. For traditional multi-scale Retinex, three scales are commonly employed, which limits its application scenarios. We extend our research to a general purpose enhanced method, and design an MSR with more than three scales. Based on the mathematical analysis and deductions, an explicit multi-scale representation is proposed that balances image contrast and color consistency. In addition, a histogram truncation technique is introduced as a post-processing strategy to remap the multi-scale Retinex output to the dynamic range of the display. Analysis of experimental results and comparisons with existing algorithms demonstrate the effectiveness and generality of the proposed method. Results on image quality assessment proves the accuracy of the proposed method with respect to both objective and subjective criteria.

A new phase correction method in NMR imaging based on autocorrelation and histogram analysis.

PubMed

Ahn, C B; Cho, Z H

1987-01-01

A new statistical approach to phase correction in NMR imaging is proposed. The proposed scheme consists of first-and zero-order phase corrections each by the inverse multiplication of estimated phase error. The first-order error is estimated by the phase of autocorrelation calculated from the complex valued phase distorted image while the zero-order correction factor is extracted from the histogram of phase distribution of the first-order corrected image. Since all the correction procedures are performed on the spatial domain after completion of data acquisition, no prior adjustments or additional measurements are required. The algorithm can be applicable to most of the phase-involved NMR imaging techniques including inversion recovery imaging, quadrature modulated imaging, spectroscopic imaging, and flow imaging, etc. Some experimental results with inversion recovery imaging as well as quadrature spectroscopic imaging are shown to demonstrate the usefulness of the algorithm.

Comparison of algorithms for automatic border detection of melanoma in dermoscopy images

NASA Astrophysics Data System (ADS)

Srinivasa Raghavan, Sowmya; Kaur, Ravneet; LeAnder, Robert

2016-09-01

Melanoma is one of the most rapidly accelerating cancers in the world [1]. Early diagnosis is critical to an effective cure. We propose a new algorithm for more accurately detecting melanoma borders in dermoscopy images. Proper border detection requires eliminating occlusions like hair and bubbles by processing the original image. The preprocessing step involves transforming the RGB image to the CIE L*u*v* color space, in order to decouple brightness from color information, then increasing contrast, using contrast-limited adaptive histogram equalization (CLAHE), followed by artifacts removal using a Gaussian filter. After preprocessing, the Chen-Vese technique segments the preprocessed images to create a lesion mask which undergoes a morphological closing operation. Next, the largest central blob in the lesion is detected, after which, the blob is dilated to generate an image output mask. Finally, the automatically-generated mask is compared to the manual mask by calculating the XOR error [3]. Our border detection algorithm was developed using training and test sets of 30 and 20 images, respectively. This detection method was compared to the SRM method [4] by calculating the average XOR error for each of the two algorithms. Average error for test images was 0.10, using the new algorithm, and 0.99, using SRM method. In comparing the average error values produced by the two algorithms, it is evident that the average XOR error for our technique is lower than the SRM method, thereby implying that the new algorithm detects borders of melanomas more accurately than the SRM algorithm.

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.

Infrared image segmentation method based on spatial coherence histogram and maximum entropy

NASA Astrophysics Data System (ADS)

Liu, Songtao; Shen, Tongsheng; Dai, Yao

2014-11-01

In order to segment the target well and suppress background noises effectively, an infrared image segmentation method based on spatial coherence histogram and maximum entropy is proposed. First, spatial coherence histogram is presented by weighting the importance of the different position of these pixels with the same gray-level, which is obtained by computing their local density. Then, after enhancing the image by spatial coherence histogram, 1D maximum entropy method is used to segment the image. The novel method can not only get better segmentation results, but also have a faster computation time than traditional 2D histogram-based segmentation methods.

Automatic image equalization and contrast enhancement using Gaussian mixture modeling.

PubMed

Celik, Turgay; Tjahjadi, Tardi

2012-01-01

In this paper, we propose an adaptive image equalization algorithm that automatically enhances the contrast in an input image. The algorithm uses the Gaussian mixture model to model the image gray-level distribution, and the intersection points of the Gaussian components in the model are used to partition the dynamic range of the image into input gray-level intervals. The contrast equalized image is generated by transforming the pixels' gray levels in each input interval to the appropriate output gray-level interval according to the dominant Gaussian component and the cumulative distribution function of the input interval. To take account of the hypothesis that homogeneous regions in the image represent homogeneous silences (or set of Gaussian components) in the image histogram, the Gaussian components with small variances are weighted with smaller values than the Gaussian components with larger variances, and the gray-level distribution is also used to weight the components in the mapping of the input interval to the output interval. Experimental results show that the proposed algorithm produces better or comparable enhanced images than several state-of-the-art algorithms. Unlike the other algorithms, the proposed algorithm is free of parameter setting for a given dynamic range of the enhanced image and can be applied to a wide range of image types.

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)

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.

SU-E-J-16: Automatic Image Contrast Enhancement Based On Automatic Parameter Optimization for Radiation Therapy Setup Verification

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

Qiu, J; Washington University in St Louis, St Louis, MO; Li, H. Harlod

Purpose: In RT patient setup 2D images, tissues often cannot be seen well due to the lack of image contrast. Contrast enhancement features provided by image reviewing software, e.g. Mosaiq and ARIA, require manual selection of the image processing filters and parameters thus inefficient and cannot be automated. In this work, we developed a novel method to automatically enhance the 2D RT image contrast to allow automatic verification of patient daily setups as a prerequisite step of automatic patient safety assurance. Methods: The new method is based on contrast limited adaptive histogram equalization (CLAHE) and high-pass filtering algorithms. The mostmore » important innovation is to automatically select the optimal parameters by optimizing the image contrast. The image processing procedure includes the following steps: 1) background and noise removal, 2) hi-pass filtering by subtracting the Gaussian smoothed Result, and 3) histogram equalization using CLAHE algorithm. Three parameters were determined through an iterative optimization which was based on the interior-point constrained optimization algorithm: the Gaussian smoothing weighting factor, the CLAHE algorithm block size and clip limiting parameters. The goal of the optimization is to maximize the entropy of the processed Result. Results: A total 42 RT images were processed. The results were visually evaluated by RT physicians and physicists. About 48% of the images processed by the new method were ranked as excellent. In comparison, only 29% and 18% of the images processed by the basic CLAHE algorithm and by the basic window level adjustment process, were ranked as excellent. Conclusion: This new image contrast enhancement method is robust and automatic, and is able to significantly outperform the basic CLAHE algorithm and the manual window-level adjustment process that are currently used in clinical 2D image review software tools.« less

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.

Image Processing for Planetary Limb/Terminator Extraction

NASA Technical Reports Server (NTRS)

Udomkesmalee, S.; Zhu, D. Q.; Chu, C. -C.

1995-01-01

A novel image segmentation technique for extracting limb and terminator of planetary bodies is proposed. Conventional edge- based histogramming approaches are used to trace object boundaries. The limb and terminator bifurcation is achieved by locating the harmonized segment in the two equations representing the 2-D parameterized boundary curve. Real planetary images from Voyager 1 and 2 served as representative test cases to verify the proposed methodology.

Determination of the smoke-plume heights with scanning lidar using alternative functions for establishing the atmospheric heterogeneity locations

Treesearch

Vladimir A. Kovalev; Alexander Petkov; Cyle Wold; Wei Min Hao

2010-01-01

Data-processing techniques for the scanning lidar data are considered that allow determining the upper and lower boundaries of the smoke plume or smoke layering in the vicinity of wildfires. The task is fulfilled by utilizing the Atmospheric Heterogeneity Height Indicator (AHHI). The AHHI is a histogram, which shows a number of heterogeneity events defined by scanning...

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

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.

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.

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.

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.

Low-level image properties in facial expressions.

PubMed

Menzel, Claudia; Redies, Christoph; Hayn-Leichsenring, Gregor U

2018-06-04

We studied low-level image properties of face photographs and analyzed whether they change with different emotional expressions displayed by an individual. Differences in image properties were measured in three databases that depicted a total of 167 individuals. Face images were used either in their original form, cut to a standard format or superimposed with a mask. Image properties analyzed were: brightness, redness, yellowness, contrast, spectral slope, overall power and relative power in low, medium and high spatial frequencies. Results showed that image properties differed significantly between expressions within each individual image set. Further, specific facial expressions corresponded to patterns of image properties that were consistent across all three databases. In order to experimentally validate our findings, we equalized the luminance histograms and spectral slopes of three images from a given individual who showed two expressions. Participants were significantly slower in matching the expression in an equalized compared to an original image triad. Thus, existing differences in these image properties (i.e., spectral slope, brightness or contrast) facilitate emotion detection in particular sets of face images. Copyright © 2018. Published by Elsevier B.V.

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.

FEM: Feature-enhanced map

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

Afonine, Pavel V.; Moriarty, Nigel W.; Mustyakimov, Marat

A method is presented that modifies a 2 m F obs- D F modelσ A-weighted map such that the resulting map can strengthen a weak signal, if present, and can reduce model bias and noise. The method consists of first randomizing the starting map and filling in missing reflections using multiple methods. This is followed by restricting the map to regions with convincing density and the application of sharpening. The final map is then created by combining a series of histogram-equalized intermediate maps. In the test cases shown, the maps produced in this way are found to have increased interpretabilitymore » and decreased model bias compared with the starting 2 m F obs- D F modelσ A-weighted map.« less

FEM: feature-enhanced map

PubMed Central

Afonine, Pavel V.; Moriarty, Nigel W.; Mustyakimov, Marat; Sobolev, Oleg V.; Terwilliger, Thomas C.; Turk, Dusan; Urzhumtsev, Alexandre; Adams, Paul D.

2015-01-01

A method is presented that modifies a 2m F obs − D F model σA-weighted map such that the resulting map can strengthen a weak signal, if present, and can reduce model bias and noise. The method consists of first randomizing the starting map and filling in missing reflections using multiple methods. This is followed by restricting the map to regions with convincing density and the application of sharpening. The final map is then created by combining a series of histogram-equalized intermediate maps. In the test cases shown, the maps produced in this way are found to have increased interpretability and decreased model bias compared with the starting 2m F obs − D F model σA-weighted map. PMID:25760612

FEM: Feature-enhanced map

DOE PAGES

Afonine, Pavel V.; Moriarty, Nigel W.; Mustyakimov, Marat; ...

2015-02-26

A method is presented that modifies a 2 m F obs- D F modelσ A-weighted map such that the resulting map can strengthen a weak signal, if present, and can reduce model bias and noise. The method consists of first randomizing the starting map and filling in missing reflections using multiple methods. This is followed by restricting the map to regions with convincing density and the application of sharpening. The final map is then created by combining a series of histogram-equalized intermediate maps. In the test cases shown, the maps produced in this way are found to have increased interpretabilitymore » and decreased model bias compared with the starting 2 m F obs- D F modelσ A-weighted map.« less

A natural-color mapping for single-band night-time image based on FPGA

NASA Astrophysics Data System (ADS)

Wang, Yilun; Qian, Yunsheng

2018-01-01

A natural-color mapping for single-band night-time image method based on FPGA can transmit the color of the reference image to single-band night-time image, which is consistent with human visual habits and can help observers identify the target. This paper introduces the processing of the natural-color mapping algorithm based on FPGA. Firstly, the image can be transformed based on histogram equalization, and the intensity features and standard deviation features of reference image are stored in SRAM. Then, the real-time digital images' intensity features and standard deviation features are calculated by FPGA. At last, FPGA completes the color mapping through matching pixels between images using the features in luminance channel.

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.

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.

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.

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.

Automated Segmentation of Light-Sheet Fluorescent Imaging to Characterize Experimental Doxorubicin-Induced Cardiac Injury and Repair.

PubMed

Packard, René R Sevag; Baek, Kyung In; Beebe, Tyler; Jen, Nelson; Ding, Yichen; Shi, Feng; Fei, Peng; Kang, Bong Jin; Chen, Po-Heng; Gau, Jonathan; Chen, Michael; Tang, Jonathan Y; Shih, Yu-Huan; Ding, Yonghe; Li, Debiao; Xu, Xiaolei; Hsiai, Tzung K

2017-08-17

This study sought to develop an automated segmentation approach based on histogram analysis of raw axial images acquired by light-sheet fluorescent imaging (LSFI) to establish rapid reconstruction of the 3-D zebrafish cardiac architecture in response to doxorubicin-induced injury and repair. Input images underwent a 4-step automated image segmentation process consisting of stationary noise removal, histogram equalization, adaptive thresholding, and image fusion followed by 3-D reconstruction. We applied this method to 3-month old zebrafish injected intraperitoneally with doxorubicin followed by LSFI at 3, 30, and 60 days post-injection. We observed an initial decrease in myocardial and endocardial cavity volumes at day 3, followed by ventricular remodeling at day 30, and recovery at day 60 (P < 0.05, n = 7-19). Doxorubicin-injected fish developed ventricular diastolic dysfunction and worsening global cardiac function evidenced by elevated E/A ratios and myocardial performance indexes quantified by pulsed-wave Doppler ultrasound at day 30, followed by normalization at day 60 (P < 0.05, n = 9-20). Treatment with the γ-secretase inhibitor, DAPT, to inhibit cleavage and release of Notch Intracellular Domain (NICD) blocked cardiac architectural regeneration and restoration of ventricular function at day 60 (P < 0.05, n = 6-14). Our approach provides a high-throughput model with translational implications for drug discovery and genetic modifiers of chemotherapy-induced cardiomyopathy.

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

Histogram and gray level co-occurrence matrix on gray-scale ultrasound images for diagnosing lymphocytic thyroiditis.

PubMed

Shin, Young Gyung; Yoo, Jaeheung; Kwon, Hyeong Ju; Hong, Jung Hwa; Lee, Hye Sun; Yoon, Jung Hyun; Kim, Eun-Kyung; Moon, Hee Jung; Han, Kyunghwa; Kwak, Jin Young

2016-08-01

The objective of the study was to evaluate whether texture analysis using histogram and gray level co-occurrence matrix (GLCM) parameters can help clinicians diagnose lymphocytic thyroiditis (LT) and differentiate LT according to pathologic grade. The background thyroid pathology of 441 patients was classified into no evidence of LT, chronic LT (CLT), and Hashimoto's thyroiditis (HT). Histogram and GLCM parameters were extracted from the regions of interest on ultrasound. The diagnostic performances of the parameters for diagnosing and differentiating LT were calculated. Of the histogram and GLCM parameters, the mean on histogram had the highest Az (0.63) and VUS (0.303). As the degrees of LT increased, the mean decreased and the standard deviation and entropy increased. The mean on histogram from gray-scale ultrasound showed the best diagnostic performance as a single parameter in differentiating LT according to pathologic grade as well as in diagnosing LT. Copyright © 2016 Elsevier Ltd. All rights reserved.

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.

Visibility enhancement of color images using Type-II fuzzy membership function

NASA Astrophysics Data System (ADS)

Singh, Harmandeep; Khehra, Baljit Singh

2018-04-01

Images taken in poor environmental conditions decrease the visibility and hidden information of digital images. Therefore, image enhancement techniques are necessary for improving the significant details of these images. An extensive review has shown that histogram-based enhancement techniques greatly suffer from over/under enhancement issues. Fuzzy-based enhancement techniques suffer from over/under saturated pixels problems. In this paper, a novel Type-II fuzzy-based image enhancement technique has been proposed for improving the visibility of images. The Type-II fuzzy logic can automatically extract the local atmospheric light and roughly eliminate the atmospheric veil in local detail enhancement. The proposed technique has been evaluated on 10 well-known weather degraded color images and is also compared with four well-known existing image enhancement techniques. The experimental results reveal that the proposed technique outperforms others regarding visible edge ratio, color gradients and number of saturated pixels.

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.

[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.

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).

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…

Automatic and quantitative measurement of laryngeal video stroboscopic images.

PubMed

Kuo, Chung-Feng Jeffrey; Kuo, Joseph; Hsiao, Shang-Wun; Lee, Chi-Lung; Lee, Jih-Chin; Ke, Bo-Han

2017-01-01

The laryngeal video stroboscope is an important instrument for physicians to analyze abnormalities and diseases in the glottal area. Stroboscope has been widely used around the world. However, without quantized indices, physicians can only make subjective judgment on glottal images. We designed a new laser projection marking module and applied it onto the laryngeal video stroboscope to provide scale conversion reference parameters for glottal imaging and to convert the physiological parameters of glottis. Image processing technology was used to segment the important image regions of interest. Information of the glottis was quantified, and the vocal fold image segmentation system was completed to assist clinical diagnosis and increase accuracy. Regarding image processing, histogram equalization was used to enhance glottis image contrast. The center weighted median filters image noise while retaining the texture of the glottal image. Statistical threshold determination was used for automatic segmentation of a glottal image. As the glottis image contains saliva and light spots, which are classified as the noise of the image, noise was eliminated by erosion, expansion, disconnection, and closure techniques to highlight the vocal area. We also used image processing to automatically identify an image of vocal fold region in order to quantify information from the glottal image, such as glottal area, vocal fold perimeter, vocal fold length, glottal width, and vocal fold angle. The quantized glottis image database was created to assist physicians in diagnosing glottis diseases more objectively.

Malignancy Detection on Mammography Using Dual Deep Convolutional Neural Networks and Genetically Discovered False Color Input Enhancement.

PubMed

Teare, Philip; Fishman, Michael; Benzaquen, Oshra; Toledano, Eyal; Elnekave, Eldad

2017-08-01

Breast cancer is the most prevalent malignancy in the US and the third highest cause of cancer-related mortality worldwide. Regular mammography screening has been attributed with doubling the rate of early cancer detection over the past three decades, yet estimates of mammographic accuracy in the hands of experienced radiologists remain suboptimal with sensitivity ranging from 62 to 87% and specificity from 75 to 91%. Advances in machine learning (ML) in recent years have demonstrated capabilities of image analysis which often surpass those of human observers. Here we present two novel techniques to address inherent challenges in the application of ML to the domain of mammography. We describe the use of genetic search of image enhancement methods, leading us to the use of a novel form of false color enhancement through contrast limited adaptive histogram equalization (CLAHE), as a method to optimize mammographic feature representation. We also utilize dual deep convolutional neural networks at different scales, for classification of full mammogram images and derivative patches combined with a random forest gating network as a novel architectural solution capable of discerning malignancy with a specificity of 0.91 and a specificity of 0.80. To our knowledge, this represents the first automatic stand-alone mammography malignancy detection algorithm with sensitivity and specificity performance similar to that of expert radiologists.

A novel algorithm to detect glaucoma risk using texton and local configuration pattern features extracted from fundus images.

PubMed

Acharya, U Rajendra; Bhat, Shreya; Koh, Joel E W; Bhandary, Sulatha V; Adeli, Hojjat

2017-09-01

Glaucoma is an optic neuropathy defined by characteristic damage to the optic nerve and accompanying visual field deficits. Early diagnosis and treatment are critical to prevent irreversible vision loss and ultimate blindness. Current techniques for computer-aided analysis of the optic nerve and retinal nerve fiber layer (RNFL) are expensive and require keen interpretation by trained specialists. Hence, an automated system is highly desirable for a cost-effective and accurate screening for the diagnosis of glaucoma. This paper presents a new methodology and a computerized diagnostic system. Adaptive histogram equalization is used to convert color images to grayscale images followed by convolution of these images with Leung-Malik (LM), Schmid (S), and maximum response (MR4 and MR8) filter banks. The basic microstructures in typical images are called textons. The convolution process produces textons. Local configuration pattern (LCP) features are extracted from these textons. The significant features are selected using a sequential floating forward search (SFFS) method and ranked using the statistical t-test. Finally, various classifiers are used for classification of images into normal and glaucomatous classes. A high classification accuracy of 95.8% is achieved using six features obtained from the LM filter bank and the k-nearest neighbor (kNN) classifier. A glaucoma integrative index (GRI) is also formulated to obtain a reliable and effective system. Copyright © 2017 Elsevier Ltd. All rights reserved.

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.

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.

Research of Daily Conversation Transmitting System Based on Mouth Part Pattern Recognition

NASA Astrophysics Data System (ADS)

Watanabe, Mutsumi; Nishi, Natsuko

The authors are developing a vision-based intension transfer technique by recognizing user’s face expressions and movements, to help free and convenient communications with aged or disabled persons who find difficulties in talking, discriminating small character prints and operating keyboards by hands and fingers. In this paper we report a prototype system, where layered daily conversations are successively selected by recognizing the transition in shape of user’s mouth parts using camera image sequences settled in front of the user. Four mouth part patterns are used in the system. A method that automatically recognizes these patterns by analyzing the intensity histogram data around the mouth region is newly developed. The confirmation of a selection on the way is executed by detecting the open and shut movements of mouth through the temporal change in intensity histogram data. The method has been installed in a desktop PC by VC++ programs. Experimental results of mouth shape pattern recognition by twenty-five persons have shown the effectiveness of the method.

Segmentation by fusion of histogram-based k-means clusters in different color spaces.

PubMed

Mignotte, Max

2008-05-01

This paper presents a new, simple, and efficient segmentation approach, based on a fusion procedure which aims at combining several segmentation maps associated to simpler partition models in order to finally get a more reliable and accurate segmentation result. The different label fields to be fused in our application are given by the same and simple (K-means based) clustering technique on an input image expressed in different color spaces. Our fusion strategy aims at combining these segmentation maps with a final clustering procedure using as input features, the local histogram of the class labels, previously estimated and associated to each site and for all these initial partitions. This fusion framework remains simple to implement, fast, general enough to be applied to various computer vision applications (e.g., motion detection and segmentation), and has been successfully applied on the Berkeley image database. The experiments herein reported in this paper illustrate the potential of this approach compared to the state-of-the-art segmentation methods recently proposed in the literature.

Three-dimensional volumetric gray-scale uterine cervix histogram prediction of days to delivery in full term pregnancy.

PubMed

Kim, Ji Youn; Kim, Hai-Joong; Hahn, Meong Hi; Jeon, Hye Jin; Cho, Geum Joon; Hong, Sun Chul; Oh, Min Jeong

2013-09-01

Our aim was to figure out whether volumetric gray-scale histogram difference between anterior and posterior cervix can indicate the extent of cervical consistency. We collected data of 95 patients who were appropriate for vaginal delivery with 36th to 37th weeks of gestational age from September 2010 to October 2011 in the Department of Obstetrics and Gynecology, Korea University Ansan Hospital. Patients were excluded who had one of the followings: Cesarean section, labor induction, premature rupture of membrane. Thirty-four patients were finally enrolled. The patients underwent evaluation of the cervix through Bishop score, cervical length, cervical volume, three-dimensional (3D) cervical volumetric gray-scale histogram. The interval days from the cervix evaluation to the delivery day were counted. We compared to 3D cervical volumetric gray-scale histogram, Bishop score, cervical length, cervical volume with interval days from the evaluation of the cervix to the delivery. Gray-scale histogram difference between anterior and posterior cervix was significantly correlated to days to delivery. Its correlation coefficient (R) was 0.500 (P = 0.003). The cervical length was significantly related to the days to delivery. The correlation coefficient (R) and P-value between them were 0.421 and 0.013. However, anterior lip histogram, posterior lip histogram, total cervical volume, Bishop score were not associated with days to delivery (P >0.05). By using gray-scale histogram difference between anterior and posterior cervix and cervical length correlated with the days to delivery. These methods can be utilized to better help predict a cervical consistency.

Histogram analysis of diffusion kurtosis imaging of nasopharyngeal carcinoma: Correlation between quantitative parameters and clinical stage.

PubMed

Xu, Xiao-Quan; Ma, Gao; Wang, Yan-Jun; Hu, Hao; Su, Guo-Yi; Shi, Hai-Bin; Wu, Fei-Yun

2017-07-18

To evaluate the correlation between histogram parameters derived from diffusion-kurtosis (DK) imaging and the clinical stage of nasopharyngeal carcinoma (NPC). High T-stage (T3/4) NPC showed significantly higher Kapp-mean (P = 0.018), Kapp-median (P = 0.029) and Kapp-90th (P = 0.003) than low T-stage (T1/2) NPC. High N-stage NPC (N2/3) showed significantly lower Dapp-mean (P = 0.002), Dapp-median (P = 0.002) and Dapp-10th (P < 0.001) than low N-stage NPC (N0/1). High AJCC-stage NPC (III/IV) showed significantly lower Dapp-10th (P = 0.038) than low AJCC-stage NPC (I/II). ROC analyses indicated that Kapp-90th was optimal for predicting high T-stage (AUC, 0.759; sensitivity, 0.842; specificity, 0.607), while Dapp-10th was best for predicting high N- and AJCC-stage (N-stage, AUC, 0.841; sensitivity, 0.875; specificity, 0.807; AJCC-stage, AUC, 0.671; sensitivity, 0.800; specificity, 0.588). DK imaging data of forty-seven consecutive NPC patients were retrospectively analyzed. Apparent diffusion for Gaussian distribution (Dapp) and apparent kurtosis coefficient (Kapp) were generated using diffusion-kurtosis model. Histogram parameters, including mean, median, 10th, 90th percentiles, skewness and kurtosis of Dapp and Kapp were calculated. Patients were divided into low and high T, N and clinical stage based on American Joint Committee on Cancer (AJCC) staging system. Differences of histogram parameters between low and high T, N and AJCC stages were compared using t test. Multiple receiver operating characteristic (ROC) curves were used to determine and compare the value of significant parameters in predicting high T, N and AJCC stage, respectively. DK imaging-derived parameters correlated well with clinical stage of NPC, therefore could serve as an adjunctive imaging technique for evaluating NPC.

CHOBS: Color Histogram of Block Statistics for Automatic Bleeding Detection in Wireless Capsule Endoscopy Video

PubMed Central

Ghosh, Tonmoy; Wahid, Khan A.

2018-01-01

Wireless capsule endoscopy (WCE) is the most advanced technology to visualize whole gastrointestinal (GI) tract in a non-invasive way. But the major disadvantage here, it takes long reviewing time, which is very laborious as continuous manual intervention is necessary. In order to reduce the burden of the clinician, in this paper, an automatic bleeding detection method for WCE video is proposed based on the color histogram of block statistics, namely CHOBS. A single pixel in WCE image may be distorted due to the capsule motion in the GI tract. Instead of considering individual pixel values, a block surrounding to that individual pixel is chosen for extracting local statistical features. By combining local block features of three different color planes of RGB color space, an index value is defined. A color histogram, which is extracted from those index values, provides distinguishable color texture feature. A feature reduction technique utilizing color histogram pattern and principal component analysis is proposed, which can drastically reduce the feature dimension. For bleeding zone detection, blocks are classified using extracted local features that do not incorporate any computational burden for feature extraction. From extensive experimentation on several WCE videos and 2300 images, which are collected from a publicly available database, a very satisfactory bleeding frame and zone detection performance is achieved in comparison to that obtained by some of the existing methods. In the case of bleeding frame detection, the accuracy, sensitivity, and specificity obtained from proposed method are 97.85%, 99.47%, and 99.15%, respectively, and in the case of bleeding zone detection, 95.75% of precision is achieved. The proposed method offers not only low feature dimension but also highly satisfactory bleeding detection performance, which even can effectively detect bleeding frame and zone in a continuous WCE video data. PMID:29468094

Texture operator for snow particle classification into snowflake and graupel

NASA Astrophysics Data System (ADS)

Nurzyńska, Karolina; Kubo, Mamoru; Muramoto, Ken-ichiro

2012-11-01

In order to improve the estimation of precipitation, the coefficients of Z-R relation should be determined for each snow type. Therefore, it is necessary to identify the type of falling snow. Consequently, this research addresses a problem of snow particle classification into snowflake and graupel in an automatic manner (as these types are the most common in the study region). Having correctly classified precipitation events, it is believed that it will be possible to estimate the related parameters accurately. The automatic classification system presented here describes the images with texture operators. Some of them are well-known from the literature: first order features, co-occurrence matrix, grey-tone difference matrix, run length matrix, and local binary pattern, but also a novel approach to design simple local statistic operators is introduced. In this work the following texture operators are defined: mean histogram, min-max histogram, and mean-variance histogram. Moreover, building a feature vector, which is based on the structure created in many from mentioned algorithms is also suggested. For classification, the k-nearest neighbourhood classifier was applied. The results showed that it is possible to achieve correct classification accuracy above 80% by most of the techniques. The best result of 86.06%, was achieved for operator built from a structure achieved in the middle stage of the co-occurrence matrix calculation. Next, it was noticed that describing an image with two texture operators does not improve the classification results considerably. In the best case the correct classification efficiency was 87.89% for a pair of texture operators created from local binary pattern and structure build in a middle stage of grey-tone difference matrix calculation. This also suggests that the information gathered by each texture operator is redundant. Therefore, the principal component analysis was applied in order to remove the unnecessary information and additionally reduce the length of the feature vectors. The improvement of the correct classification efficiency for up to 100% is possible for methods: min-max histogram, texture operator built from structure achieved in a middle stage of co-occurrence matrix calculation, texture operator built from a structure achieved in a middle stage of grey-tone difference matrix creation, and texture operator based on a histogram, when the feature vector stores 99% of initial information.

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…

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.

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.

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.

An Analysis of Measures Used to Evaluate the Air Force Critical Item Program

DTIC Science & Technology

1991-09-01

example of a histogram. Cause & Effect Diagram. The cause and effect diagram was introduced in 1953 by Dr. Kaoru Ishikawa in summarizing the opinions of...Personal Interview. Air Force Institute of Technology, School of Engineering, Wright-Patterson AFB OH, 24 April 1991. 31. Ishikawa , Dr. Kaoru . Guide to...collected. How the data are collected will determine which measurement techniques are appropriate. Ishikawa classifies data collection into five categories

SU-F-T-537: Prone Breast Accelerated Partial Breast Irradiation Using Non-Coplanar Volumetric Arc Therapy

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

Beninati, G; Barbiere, J; Godfrey, L

2016-06-15

Purpose: To demonstrate that Volumetric Modulated Arc Therapy (VMAT) can be an alternative technique to Brachytherapy Accelerated Partial Breast Irradiation (APBI) for treating large breasted women. The non-coplanar VMAT technique uses a commercially available couch and a small number of angles. This technique with the patient in the prone position can reduce high skin and critical structure doses in large breasted women, which are usually associated with Brachytherapy APBI. Methods: Philips Pinnacle treatment planning system with Smart Arc was used to plan a left sided laterally located excision cavity on a standard prone breast patient setup. Three thirty-degree arcs enteredmore » from the lateral side at respective couch angles of 345, 0, and 15 degrees. A fourth thirty degree arc beam entered from the medial side at a couch angle of 0 degrees. The arcs were selected to avoid critical structures as much as possible. A test run was then performed to verify that the beams did not collide with the patient nor support structures. NSABP B-39/RTOG 0413 protocol guidelines were used for dose prescription, normal tissue, and target definition. Results: Dose Volume Histogram analysis indicated that all parameters were equal or better than RTOG recommendations. Of particular note regarding the plan quality:1.(a) For a prescribed dose of 3850cGy the PTV-EVAL target volume receiving 100 percent of the dose(V100) was 93; protocol recommendation is V90 > 90 percent. (b) Maximum dose was 110 percent versus the allowed 120 percent .2. Uninvolved percentage of normal breast V100 and V50 were 17 and 47 versus allowed 35 and 60 percent respectively.3. For the skin, V100 was 5.7cc and the max dose to 0.1 cc was 4190cGy. Conclusion: Prone Breast non-coplanar VMAT APBI can achieve better skin cosmesis and lower critical structure doses than Brachytherapy APBI.« less

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.

Accelerated weight histogram method for exploring free energy landscapes

NASA Astrophysics Data System (ADS)

Lindahl, V.; Lidmar, J.; Hess, B.

2014-07-01

Calculating free energies is an important and notoriously difficult task for molecular simulations. The rapid increase in computational power has made it possible to probe increasingly complex systems, yet extracting accurate free energies from these simulations remains a major challenge. Fully exploring the free energy landscape of, say, a biological macromolecule typically requires sampling large conformational changes and slow transitions. Often, the only feasible way to study such a system is to simulate it using an enhanced sampling method. The accelerated weight histogram (AWH) method is a new, efficient extended ensemble sampling technique which adaptively biases the simulation to promote exploration of the free energy landscape. The AWH method uses a probability weight histogram which allows for efficient free energy updates and results in an easy discretization procedure. A major advantage of the method is its general formulation, making it a powerful platform for developing further extensions and analyzing its relation to already existing methods. Here, we demonstrate its efficiency and general applicability by calculating the potential of mean force along a reaction coordinate for both a single dimension and multiple dimensions. We make use of a non-uniform, free energy dependent target distribution in reaction coordinate space so that computational efforts are not wasted on physically irrelevant regions. We present numerical results for molecular dynamics simulations of lithium acetate in solution and chignolin, a 10-residue long peptide that folds into a β-hairpin. We further present practical guidelines for setting up and running an AWH simulation.

Accelerated weight histogram method for exploring free energy landscapes.

PubMed

Lindahl, V; Lidmar, J; Hess, B

2014-07-28

Calculating free energies is an important and notoriously difficult task for molecular simulations. The rapid increase in computational power has made it possible to probe increasingly complex systems, yet extracting accurate free energies from these simulations remains a major challenge. Fully exploring the free energy landscape of, say, a biological macromolecule typically requires sampling large conformational changes and slow transitions. Often, the only feasible way to study such a system is to simulate it using an enhanced sampling method. The accelerated weight histogram (AWH) method is a new, efficient extended ensemble sampling technique which adaptively biases the simulation to promote exploration of the free energy landscape. The AWH method uses a probability weight histogram which allows for efficient free energy updates and results in an easy discretization procedure. A major advantage of the method is its general formulation, making it a powerful platform for developing further extensions and analyzing its relation to already existing methods. Here, we demonstrate its efficiency and general applicability by calculating the potential of mean force along a reaction coordinate for both a single dimension and multiple dimensions. We make use of a non-uniform, free energy dependent target distribution in reaction coordinate space so that computational efforts are not wasted on physically irrelevant regions. We present numerical results for molecular dynamics simulations of lithium acetate in solution and chignolin, a 10-residue long peptide that folds into a β-hairpin. We further present practical guidelines for setting up and running an AWH simulation.

Binaural interaction in low-frequency neurons in inferior colliculus of the cat. II. Effects of changing rate and direction of interaural phase.

PubMed

Yin, T C; Kuwada, S

1983-10-01

We used the binaural beat stimulus to study the interaural phase sensitivity of inferior colliculus (IC) neurons in the cat. The binaural beat, produced by delivering tones of slightly different frequencies to the two ears, generates continuous and graded changes in interaural phase. Over 90% of the cells that exhibit a sensitivity to changes in the interaural delay also show a sensitivity to interaural phase disparities with the binaural beat. Cells respond with a burst of impulses with each complete cycle of the beat frequency. The period histogram obtained by binning the poststimulus time histogram on the beat frequency gives a measure of the interaural phase sensitivity of the cell. In general, there is good correspondence in the shapes of the period histograms generated from binaural beats and the interaural phase curves derived from interaural delays and in the mean interaural phase angle calculated from them. The magnitude of the beat frequency determines the rate of change of interaural phase and the sign determines the direction of phase change. While most cells respond in a phase-locked manner up to beat frequencies of 10 Hz, there are some cells tht will phase lock up to 80 Hz. Beat frequency and mean interaural phase angle are linearly related for most cells. Most cells respond equally in the two directions of phase change and with different rates of change, at least up to 10 Hz. However, some IC cells exhibit marked sensitivity to the speed of phase change, either responding more vigorously at low beat frequencies or at high beat frequencies. In addition, other cells demonstrate a clear directional sensitivity. The cells that show sensitivity to the direction and speed of phase changes would be expected to demonstrate a sensitivity to moving sound sources in the free field. Changes in the mean interaural phase of the binaural beat period histograms are used to determine the effects of changes in average and interaural intensity on the phase sensitivity of the cells. The effects of both forms of intensity variation are continuously distributed. The binaural beat offers a number of advantages for studying the interaural phase sensitivity of binaural cells. The dynamic characteristics of the interaural phase can be varied so that the speed and direction of phase change are under direct control. The data can be obtained in a much more efficient manner, as the binaural beat is about 10 times faster in terms of data collection than the interaural delay.

n-SIFT: n-dimensional scale invariant feature transform.

PubMed

Cheung, Warren; Hamarneh, Ghassan

2009-09-01

We propose the n-dimensional scale invariant feature transform (n-SIFT) method for extracting and matching salient features from scalar images of arbitrary dimensionality, and compare this method's performance to other related features. The proposed features extend the concepts used for 2-D scalar images in the computer vision SIFT technique for extracting and matching distinctive scale invariant features. We apply the features to images of arbitrary dimensionality through the use of hyperspherical coordinates for gradients and multidimensional histograms to create the feature vectors. We analyze the performance of a fully automated multimodal medical image matching technique based on these features, and successfully apply the technique to determine accurate feature point correspondence between pairs of 3-D MRI images and dynamic 3D + time CT data.

Variability in the microcanonical cascades parameters among gauges of urban precipitation monitoring network

NASA Astrophysics Data System (ADS)

Licznar, Paweł; Rupp, David; Adamowski, Witold

2013-04-01

In the fall of 2008, Municipal Water Supply and Sewerage Company (MWSSC) in Warsaw began operating the first large precipitation monitoring network dedicated to urban hydrology in Poland. The process of establishing the network as well as the preliminary phase of its operation, raised a number of questions concerning optimal gauge location and density and revealed the urgent need for new data processing techniques. When considering the full-field precipitation as input to hydrodynamic models of stormwater and combined sewage systems, standard processing techniques developed previously for single gauges and concentrating mainly on the analysis of maximum rainfall rates and intensity-duration-frequency (IDF) curves development were found inadequate. We used a multifractal rainfall modeling framework based on microcanonical multiplicative random cascades to analyze properties of Warsaw precipitation. We calculated breakdown coefficients (BDC) for the hierarchy of timescales from λ=1 (5-min) up to λ=128 (1280-min) for all 25 gauges in the network. At small timescales histograms of BDCs were strongly deformed due to the recording precision of rainfall amounts. A randomization procedure statistically removed the artifacts due to precision errors in the original series. At large timescales BDC values were sparse due to relatively short period of observations (2008-2011). An algorithm with a moving window was proposed to increase the number of BDC values at large timescales and to smooth their histograms. The resulting empirical BDC histograms were modeled by a theoretical "2N-B" distribution, which combined 2 separate normal (N) distributions and one beta (B) distribution. A clear evolution of BDC histograms from a 2N-B distribution for small timescales to a N-B distributions for intermediate timescales and finally to a single beta distributions for large timescales was observed for all gauges. Cluster analysis revealed close patterns of BDC distributions among almost all gauges and timescales with exception of two gauges located at the city limits (one gauge was located on the Okęcie airport). We evaluated the performance of the microcanonical cascades at disaggregating 1280-min (quasi daily precipitation totals) into 5-min rainfall data for selected gauges. Synthetic time series were analyzed with respect to their intermittency and variability of rainfall intensities and compared to observational series. We showed that microcanonical cascades models could be used in practice for generating synthetic rainfall time series suitable as input to urban hydrology models in Warsaw.

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…

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.

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.

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.

Cluster analysis based on dimensional information with applications to feature selection and classification

NASA Technical Reports Server (NTRS)

Eigen, D. J.; Fromm, F. R.; Northouse, R. A.

1974-01-01

A new clustering algorithm is presented that is based on dimensional information. The algorithm includes an inherent feature selection criterion, which is discussed. Further, a heuristic method for choosing the proper number of intervals for a frequency distribution histogram, a feature necessary for the algorithm, is presented. The algorithm, although usable as a stand-alone clustering technique, is then utilized as a global approximator. Local clustering techniques and configuration of a global-local scheme are discussed, and finally the complete global-local and feature selector configuration is shown in application to a real-time adaptive classification scheme for the analysis of remote sensed multispectral scanner data.

Single Photon Counting Performance and Noise Analysis of CMOS SPAD-Based Image Sensors.

PubMed

Dutton, Neale A W; Gyongy, Istvan; Parmesan, Luca; Henderson, Robert K

2016-07-20

SPAD-based solid state CMOS image sensors utilising analogue integrators have attained deep sub-electron read noise (DSERN) permitting single photon counting (SPC) imaging. A new method is proposed to determine the read noise in DSERN image sensors by evaluating the peak separation and width (PSW) of single photon peaks in a photon counting histogram (PCH). The technique is used to identify and analyse cumulative noise in analogue integrating SPC SPAD-based pixels. The DSERN of our SPAD image sensor is exploited to confirm recent multi-photon threshold quanta image sensor (QIS) theory. Finally, various single and multiple photon spatio-temporal oversampling techniques are reviewed.

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.

Grating interferometry-based phase microtomography of atherosclerotic human arteries

NASA Astrophysics Data System (ADS)

Buscema, Marzia; Holme, Margaret N.; Deyhle, Hans; Schulz, Georg; Schmitz, Rüdiger; Thalmann, Peter; Hieber, Simone E.; Chicherova, Natalia; Cattin, Philippe C.; Beckmann, Felix; Herzen, Julia; Weitkamp, Timm; Saxer, Till; Müller, Bert

2014-09-01

Cardiovascular diseases are the number one cause of death and morbidity in the world. Understanding disease development in terms of lumen morphology and tissue composition of constricted arteries is essential to improve treatment and patient outcome. X-ray tomography provides non-destructive three-dimensional data with micrometer-resolution. However, a common problem is simultaneous visualization of soft and hard tissue-containing specimens, such as atherosclerotic human coronary arteries. Unlike absorption based techniques, where X-ray absorption strongly depends on atomic number and tissue density, phase contrast methods such as grating interferometry have significant advantages as the phase shift is only a linear function of the atomic number. We demonstrate that grating interferometry-based phase tomography is a powerful method to three-dimensionally visualize a variety of anatomical features in atherosclerotic human coronary arteries, including plaque, muscle, fat, and connective tissue. Three formalin-fixed, human coronary arteries were measured using advanced laboratory μCT. While this technique gives information about plaque morphology, it is impossible to extract the lumen morphology. Therefore, selected regions were measured using grating based phase tomography, sinograms were treated with a wavelet-Fourier filter to remove ring artifacts, and reconstructed data were processed to allow extraction of vessel lumen morphology. Phase tomography data in combination with conventional laboratory μCT data of the same specimen shows potential, through use of a joint histogram, to identify more tissue types than either technique alone. Such phase tomography data was also rigidly registered to subsequently decalcified arteries that were histologically sectioned, although the quality of registration was insufficient for joint histogram analysis.

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.

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

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.

Image correlation and sampling study

NASA Technical Reports Server (NTRS)

Popp, D. J.; Mccormack, D. S.; Sedwick, J. L.

1972-01-01

The development of analytical approaches for solving image correlation and image sampling of multispectral data is discussed. Relevant multispectral image statistics which are applicable to image correlation and sampling are identified. The general image statistics include intensity mean, variance, amplitude histogram, power spectral density function, and autocorrelation function. The translation problem associated with digital image registration and the analytical means for comparing commonly used correlation techniques are considered. General expressions for determining the reconstruction error for specific image sampling strategies are developed.

SU-F-J-13: Choosing An IMRT Technique in the Treatment of Head and Neck Cancer with Daily Localization Uncertainties

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

Lin, T; Wang, L; Galloway, T

Purpose: Head and Neck cancer treatment with IMRT/VMAT has two choices: split-filed IMRT(SFI), in which the LAN is treated with a separate anterior field and the extended whole-field IMRT(WFI) in which LAN is included with the IMRT/VMAT field. This study shows that under the same dose limit criteria, choosing the technique becomes a critical issue if daily localization and immobilization altered the dose distribution. Methods: Nine common head-and-neck cancer cases were chosen to illustrate how the daily localization and immobilization uncertainties affect to choose between SFI and WFI. Both SFI and WFI at upper target coverage were generated with VMAT.more » For each case, the same planning criteria were applied to the target and critical structures; therefore, similar target coverage and dose falloff can be observed in both techniques. Thirty days of kV cone beam CT(CBCT) images on each case were also delineated with contralateral and ipsilateral target as well as larynx as critical structure. About 300 CBCT images with daily delivered doses were analyzed and compared in a form of dose-volume histograms. Results: While both plans for SFI and WFI with VMAT planning utilized and meet the criteria of D95>prescription dose and for not-involved larynx with mean dose <35Gy and V55<10%, the daily localization and immobilization has a great contribution to the resulted dose delivery. With WFI, the better daily contralateral and ipsilateral neck target coverage can reflect a simpler or shorter localization; however, a much superior avoidance (WFI: mean dose a 42.5Gy; SFI: mean dose a 18.9Gy) of the non-involved larynx from the SFI is preferred. Conclusion: Dosimetrically, SFI and WFI are equally well for head and Neck cancer treatment with VMAT technique; however, if considering the contribution of daily localization(CBCT) method uncertainties, SFI is better with sparing non-involved larynx and WFI has better target coverage.« less

Computerized evaluation of holographic interferograms for fatigue crack detection in riveted lap joints

NASA Astrophysics Data System (ADS)

Zhou, Xiang

Using an innovative portable holographic inspection and testing system (PHITS) developed at the Australian Defence Force Academy, fatigue cracks in riveted lap joints can be detected by visually inspecting the abnormal fringe changes recorded on holographic interferograms. In this thesis, for automatic crack detection, some modern digital image processing techniques are investigated and applied to holographic interferogram evaluation. Fringe analysis algorithms are developed for identification of the crack-induced fringe changes. Theoretical analysis of PHITS and riveted lap joints and two typical experiments demonstrate that the fatigue cracks in lightly-clamped joints induce two characteristic fringe changes: local fringe discontinuities at the cracking sites; and the global crescent fringe distribution near to the edge of the rivet hole. Both of the fringe features are used for crack detection in this thesis. As a basis of the fringe feature extraction, an algorithm for local fringe orientation calculation is proposed. For high orientation accuracy and computational efficiency, Gaussian gradient filtering and neighboring direction averaging are used to minimize the effects of image background variations and random noise. The neighboring direction averaging is also used to approximate the fringe directions in centerlines of bright and dark fringes. Experimental results indicate that for high orientation accuracy the scales of the Gaussian filter and neighboring direction averaging should be chosen according to the local fringe spacings. The orientation histogram technique is applied to detect the local fringe discontinuity due to the fatigue cracks. The Fourier descriptor technique is used to characterize the global fringe distribution change from a circular to a crescent distribution with the fatigue crack growth. Experiments and computer simulations are conducted to analyze the detectability and reliability of crack detection using the two techniques. Results demonstrate that the Fourier descriptor technique is more promising in the detection of the short cracks near the edge of the rivet head. However, it is not as reliable as the fringe orientation technique for detection of the long through cracks. For reliability, both techniques should be used in practical crack detection. Neither the Fourier descriptor technique nor the orientation histogram technique have been previously applied to holographic interferometry. While this work related primarily to interferograms of cracked rivets, the techniques would be readily applied to other areas of fringe pattern analysis.

Introducing the concept of centergram. A new tool to squeeze data from separation techniques-mass spectrometry couplings.

PubMed

Erny, Guillaume L; Simó, Carolina; Cifuentes, Alejandro; Esteves, Valdemar I

2014-02-21

In separation techniques hyphenated to mass spectrometry (MS) the bulk from the separation step is continuously flowing into the mass spectrometer where the compounds, arriving at each separation time, are ionized and further separated based on their m/z ratio. An MS detector is recognized as being a universal detector, although it can also be a very selective instrument. In spite of these advantages, classical two dimensional representations from these hyphenated systems, such as those based on the base peak of electropherogram/chromatogram or on the total ion of electropherogram/chromatogram, usually hide a large number of features that if correctly assessed will show the presence of co-migrating species and/or the low abundant ones. The uses of peak picking algorithms to detect and measure as many peaks as possible from a dataset allow extracting much more information. However, a single migrating compound usually produces a multiplicity of ions, making difficult to differentiate peaks generated by the same compound from other peaks due e.g., to closely co-migrating/eluting species. In this work, a new representation is proposed and its usefulness demonstrated with experimental data from capillary electrophoresis-hyphenated to a time of flight mass spectrometer via an electrospray interface. This representation, called centergram, is obtained after using a peak picking methodology that detects electrophoretic peaks of single ions and measure their positions. The centergram is the histogram (i.e. the count of the number of observations that fall into each one of the intervals, known as bins, as determined by the user) of the measured positions. The intensity of the bars in this histogram will indicate the amount of peaks in the whole dataset whose centers are within each interval. As a compound that has been separated and has entered the MS instrument will produce multiple images at the same position along the m/z dimension, the centergram will exhibit a series of intense bars around the migration time. Those bars will allow defining a centergram peak whose area will be proportional to the number of different types of ions that have been generated in the ionization chamber, the position will be equal to the migration/retention time of the parent compounds and the width will depend on the precision in the measurement of the peak positions. The efficiency of this peak is determined to be up to thirty times higher than the equivalent peak in the classical base peak electropherogram allowing detecting easily co-migrating peaks or the presence of compounds at very low abundance. The number of peaks detected by using this new tool called centergram was increased by more than a factor of 3 compared to the standard representations. Copyright © 2014 Elsevier B.V. All rights reserved.

Automatic dynamic range adjustment for ultrasound B-mode imaging.

PubMed

Lee, Yeonhwa; Kang, Jinbum; Yoo, Yangmo

2015-02-01

In medical ultrasound imaging, dynamic range (DR) is defined as the difference between the maximum and minimum values of the displayed signal to display and it is one of the most essential parameters that determine its image quality. Typically, DR is given with a fixed value and adjusted manually by operators, which leads to low clinical productivity and high user dependency. Furthermore, in 3D ultrasound imaging, DR values are unable to be adjusted during 3D data acquisition. A histogram matching method, which equalizes the histogram of an input image based on that from a reference image, can be applied to determine the DR value. However, it could be lead to an over contrasted image. In this paper, a new Automatic Dynamic Range Adjustment (ADRA) method is presented that adaptively adjusts the DR value by manipulating input images similar to a reference image. The proposed ADRA method uses the distance ratio between the log average and each extreme value of a reference image. To evaluate the performance of the ADRA method, the similarity between the reference and input images was measured by computing a correlation coefficient (CC). In in vivo experiments, the CC values were increased by applying the ADRA method from 0.6872 to 0.9870 and from 0.9274 to 0.9939 for kidney and liver data, respectively, compared to the fixed DR case. In addition, the proposed ADRA method showed to outperform the histogram matching method with in vivo liver and kidney data. When using 3D abdominal data with 70 frames, while the CC value from the ADRA method is slightly increased (i.e., 0.6%), the proposed method showed improved image quality in the c-plane compared to its fixed counterpart, which suffered from a shadow artifact. These results indicate that the proposed method can enhance image quality in 2D and 3D ultrasound B-mode imaging by improving the similarity between the reference and input images while eliminating unnecessary manual interaction by the user. Copyright © 2014 Elsevier B.V. All rights reserved.

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.

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.

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

Performance analysis of a dual-tree algorithm for computing spatial distance histograms

PubMed Central

Chen, Shaoping; Tu, Yi-Cheng; Xia, Yuni

2011-01-01

Many scientific and engineering fields produce large volume of spatiotemporal data. The storage, retrieval, and analysis of such data impose great challenges to database systems design. Analysis of scientific spatiotemporal data often involves computing functions of all point-to-point interactions. One such analytics, the Spatial Distance Histogram (SDH), is of vital importance to scientific discovery. Recently, algorithms for efficient SDH processing in large-scale scientific databases have been proposed. These algorithms adopt a recursive tree-traversing strategy to process point-to-point distances in the visited tree nodes in batches, thus require less time when compared to the brute-force approach where all pairwise distances have to be computed. Despite the promising experimental results, the complexity of such algorithms has not been thoroughly studied. In this paper, we present an analysis of such algorithms based on a geometric modeling approach. The main technique is to transform the analysis of point counts into a problem of quantifying the area of regions where pairwise distances can be processed in batches by the algorithm. From the analysis, we conclude that the number of pairwise distances that are left to be processed decreases exponentially with more levels of the tree visited. This leads to the proof of a time complexity lower than the quadratic time needed for a brute-force algorithm and builds the foundation for a constant-time approximate algorithm. Our model is also general in that it works for a wide range of point spatial distributions, histogram types, and space-partitioning options in building the tree. PMID:21804753

Bennett's acceptance ratio and histogram analysis methods enhanced by umbrella sampling along a reaction coordinate in configurational space.

PubMed

Kim, Ilsoo; Allen, Toby W

2012-04-28

Free energy perturbation, a method for computing the free energy difference between two states, is often combined with non-Boltzmann biased sampling techniques in order to accelerate the convergence of free energy calculations. Here we present a new extension of the Bennett acceptance ratio (BAR) method by combining it with umbrella sampling (US) along a reaction coordinate in configurational space. In this approach, which we call Bennett acceptance ratio with umbrella sampling (BAR-US), the conditional histogram of energy difference (a mapping of the 3N-dimensional configurational space via a reaction coordinate onto 1D energy difference space) is weighted for marginalization with the associated population density along a reaction coordinate computed by US. This procedure produces marginal histograms of energy difference, from forward and backward simulations, with higher overlap in energy difference space, rendering free energy difference estimations using BAR statistically more reliable. In addition to BAR-US, two histogram analysis methods, termed Bennett overlapping histograms with US (BOH-US) and Bennett-Hummer (linear) least square with US (BHLS-US), are employed as consistency and convergence checks for free energy difference estimation by BAR-US. The proposed methods (BAR-US, BOH-US, and BHLS-US) are applied to a 1-dimensional asymmetric model potential, as has been used previously to test free energy calculations from non-equilibrium processes. We then consider the more stringent test of a 1-dimensional strongly (but linearly) shifted harmonic oscillator, which exhibits no overlap between two states when sampled using unbiased Brownian dynamics. We find that the efficiency of the proposed methods is enhanced over the original Bennett's methods (BAR, BOH, and BHLS) through fast uniform sampling of energy difference space via US in configurational space. We apply the proposed methods to the calculation of the electrostatic contribution to the absolute solvation free energy (excess chemical potential) of water. We then address the controversial issue of ion selectivity in the K(+) ion channel, KcsA. We have calculated the relative binding affinity of K(+) over Na(+) within a binding site of the KcsA channel for which different, though adjacent, K(+) and Na(+) configurations exist, ideally suited to these US-enhanced methods. Our studies demonstrate that the significant improvements in free energy calculations obtained using the proposed methods can have serious consequences for elucidating biological mechanisms and for the interpretation of experimental data.

GPU Acceleration of Mean Free Path Based Kernel Density Estimators for Monte Carlo Neutronics Simulations

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

Burke, TImothy P.; Kiedrowski, Brian C.; Martin, William R.

Kernel Density Estimators (KDEs) are a non-parametric density estimation technique that has recently been applied to Monte Carlo radiation transport simulations. Kernel density estimators are an alternative to histogram tallies for obtaining global solutions in Monte Carlo tallies. With KDEs, a single event, either a collision or particle track, can contribute to the score at multiple tally points with the uncertainty at those points being independent of the desired resolution of the solution. Thus, KDEs show potential for obtaining estimates of a global solution with reduced variance when compared to a histogram. Previously, KDEs have been applied to neutronics formore » one-group reactor physics problems and fixed source shielding applications. However, little work was done to obtain reaction rates using KDEs. This paper introduces a new form of the MFP KDE that is capable of handling general geometries. Furthermore, extending the MFP KDE to 2-D problems in continuous energy introduces inaccuracies to the solution. An ad-hoc solution to these inaccuracies is introduced that produces errors smaller than 4% at material interfaces.« less

Directional Histogram Ratio at Random Probes: A Local Thresholding Criterion for Capillary Images

PubMed Central

Lu, Na; Silva, Jharon; Gu, Yu; Gerber, Scott; Wu, Hulin; Gelbard, Harris; Dewhurst, Stephen; Miao, Hongyu

2013-01-01

With the development of micron-scale imaging techniques, capillaries can be conveniently visualized using methods such as two-photon and whole mount microscopy. However, the presence of background staining, leaky vessels and the diffusion of small fluorescent molecules can lead to significant complexity in image analysis and loss of information necessary to accurately quantify vascular metrics. One solution to this problem is the development of accurate thresholding algorithms that reliably distinguish blood vessels from surrounding tissue. Although various thresholding algorithms have been proposed, our results suggest that without appropriate pre- or post-processing, the existing approaches may fail to obtain satisfactory results for capillary images that include areas of contamination. In this study, we propose a novel local thresholding algorithm, called directional histogram ratio at random probes (DHR-RP). This method explicitly considers the geometric features of tube-like objects in conducting image binarization, and has a reliable performance in distinguishing small vessels from either clean or contaminated background. Experimental and simulation studies suggest that our DHR-RP algorithm is superior over existing thresholding methods. PMID:23525856

Multifractal diffusion entropy analysis: Optimal bin width of probability histograms

NASA Astrophysics Data System (ADS)

Jizba, Petr; Korbel, Jan

2014-11-01

In the framework of Multifractal Diffusion Entropy Analysis we propose a method for choosing an optimal bin-width in histograms generated from underlying probability distributions of interest. The method presented uses techniques of Rényi’s entropy and the mean squared error analysis to discuss the conditions under which the error in the multifractal spectrum estimation is minimal. We illustrate the utility of our approach by focusing on a scaling behavior of financial time series. In particular, we analyze the S&P500 stock index as sampled at a daily rate in the time period 1950-2013. In order to demonstrate a strength of the method proposed we compare the multifractal δ-spectrum for various bin-widths and show the robustness of the method, especially for large values of q. For such values, other methods in use, e.g., those based on moment estimation, tend to fail for heavy-tailed data or data with long correlations. Connection between the δ-spectrum and Rényi’s q parameter is also discussed and elucidated on a simple example of multiscale time series.

A Study of Hand Back Skin Texture Patterns for Personal Identification and Gender Classification

PubMed Central

Xie, Jin; Zhang, Lei; You, Jane; Zhang, David; Qu, Xiaofeng

2012-01-01

Human hand back skin texture (HBST) is often consistent for a person and distinctive from person to person. In this paper, we study the HBST pattern recognition problem with applications to personal identification and gender classification. A specially designed system is developed to capture HBST images, and an HBST image database was established, which consists of 1,920 images from 80 persons (160 hands). An efficient texton learning based method is then presented to classify the HBST patterns. First, textons are learned in the space of filter bank responses from a set of training images using the l1 -minimization based sparse representation (SR) technique. Then, under the SR framework, we represent the feature vector at each pixel over the learned dictionary to construct a representation coefficient histogram. Finally, the coefficient histogram is used as skin texture feature for classification. Experiments on personal identification and gender classification are performed by using the established HBST database. The results show that HBST can be used to assist human identification and gender classification. PMID:23012512

On the equivalence of the RTI and SVM approaches to time correlated analysis

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

Croft, S.; Favalli, A.; Henzlova, D.

2014-11-21

Recently two papers on how to perform passive neutron auto-correlation analysis on time gated histograms formed from pulse train data, generically called time correlation analysis (TCA), have appeared in this journal [1,2]. For those of us working in international nuclear safeguards these treatments are of particular interest because passive neutron multiplicity counting is a widely deployed technique for the quantification of plutonium. The purpose of this letter is to show that the skewness-variance-mean (SVM) approach developed in [1] is equivalent in terms of assay capability to the random trigger interval (RTI) analysis laid out in [2]. Mathematically we could alsomore » use other numerical ways to extract the time correlated information from the histogram data including for example what we might call the mean, mean square, and mean cube approach. The important feature however, from the perspective of real world applications, is that the correlated information extracted is the same, and subsequently gets interpreted in the same way based on the same underlying physics model.« less

Approach to Privacy-Preserve Data in Two-Tiered Wireless Sensor Network Based on Linear System and Histogram

NASA Astrophysics Data System (ADS)

Dang, Van H.; Wohlgemuth, Sven; Yoshiura, Hiroshi; Nguyen, Thuc D.; Echizen, Isao

Wireless sensor network (WSN) has been one of key technologies for the future with broad applications from the military to everyday life [1,2,3,4,5]. There are two kinds of WSN model models with sensors for sensing data and a sink for receiving and processing queries from users; and models with special additional nodes capable of storing large amounts of data from sensors and processing queries from the sink. Among the latter type, a two-tiered model [6,7] has been widely adopted because of its storage and energy saving benefits for weak sensors, as proved by the advent of commercial storage node products such as Stargate [8] and RISE. However, by concentrating storage in certain nodes, this model becomes more vulnerable to attack. Our novel technique, called zip-histogram, contributes to solving the problems of previous studies [6,7] by protecting the stored data's confidentiality and integrity (including data from the sensor and queries from the sink) against attackers who might target storage nodes in two-tiered WSNs.

Supervised retinal vessel segmentation from color fundus images based on matched filtering and AdaBoost classifier.

PubMed

Memari, Nogol; Ramli, Abd Rahman; Bin Saripan, M Iqbal; Mashohor, Syamsiah; Moghbel, Mehrdad

2017-01-01

The structure and appearance of the blood vessel network in retinal fundus images is an essential part of diagnosing various problems associated with the eyes, such as diabetes and hypertension. In this paper, an automatic retinal vessel segmentation method utilizing matched filter techniques coupled with an AdaBoost classifier is proposed. The fundus image is enhanced using morphological operations, the contrast is increased using contrast limited adaptive histogram equalization (CLAHE) method and the inhomogeneity is corrected using Retinex approach. Then, the blood vessels are enhanced using a combination of B-COSFIRE and Frangi matched filters. From this preprocessed image, different statistical features are computed on a pixel-wise basis and used in an AdaBoost classifier to extract the blood vessel network inside the image. Finally, the segmented images are postprocessed to remove the misclassified pixels and regions. The proposed method was validated using publicly accessible Digital Retinal Images for Vessel Extraction (DRIVE), Structured Analysis of the Retina (STARE) and Child Heart and Health Study in England (CHASE_DB1) datasets commonly used for determining the accuracy of retinal vessel segmentation methods. The accuracy of the proposed segmentation method was comparable to other state of the art methods while being very close to the manual segmentation provided by the second human observer with an average accuracy of 0.972, 0.951 and 0.948 in DRIVE, STARE and CHASE_DB1 datasets, respectively.

An improved protocol for optical projection tomography imaging reveals lobular heterogeneities in pancreatic islet and β-cell mass distribution

PubMed Central

2011-01-01

Optical projection tomography (OPT) imaging is a powerful tool for three-dimensional imaging of gene and protein distribution patterns in biomedical specimens. We have previously demonstrated the possibility, by this technique, to extract information of the spatial and quantitative distribution of the islets of Langerhans in the intact mouse pancreas. In order to further increase the sensitivity of OPT imaging for this type of assessment, we have developed a protocol implementing a computational statistical approach: contrast limited adaptive histogram equalization (CLAHE). We demonstrate that this protocol significantly increases the sensitivity of OPT imaging for islet detection, helps preserve islet morphology and diminish subjectivity in thresholding for tomographic reconstruction. When applied to studies of the pancreas from healthy C57BL/6 mice, our data reveal that, at least in this strain, the pancreas harbors substantially more islets than has previously been reported. Further, we provide evidence that the gastric, duodenal and splenic lobes of the pancreas display dramatic differences in total and relative islet and β-cell mass distribution. This includes a 75% higher islet density in the gastric lobe as compared to the splenic lobe and a higher relative volume of insulin producing cells in the duodenal lobe as compared to the other lobes. Altogether, our data show that CLAHE substantially improves OPT based assessments of the islets of Langerhans and that lobular origin must be taken into careful consideration in quantitative and spatial assessments of the pancreas. PMID:21633198

Single Photon Counting Performance and Noise Analysis of CMOS SPAD-Based Image Sensors

PubMed Central

Dutton, Neale A. W.; Gyongy, Istvan; Parmesan, Luca; Henderson, Robert K.

2016-01-01

SPAD-based solid state CMOS image sensors utilising analogue integrators have attained deep sub-electron read noise (DSERN) permitting single photon counting (SPC) imaging. A new method is proposed to determine the read noise in DSERN image sensors by evaluating the peak separation and width (PSW) of single photon peaks in a photon counting histogram (PCH). The technique is used to identify and analyse cumulative noise in analogue integrating SPC SPAD-based pixels. The DSERN of our SPAD image sensor is exploited to confirm recent multi-photon threshold quanta image sensor (QIS) theory. Finally, various single and multiple photon spatio-temporal oversampling techniques are reviewed. PMID:27447643

Image enhancement software for underwater recovery operations: User's manual

NASA Astrophysics Data System (ADS)

Partridge, William J.; Therrien, Charles W.

1989-06-01

This report describes software for performing image enhancement on live or recorded video images. The software was developed for operational use during underwater recovery operations at the Naval Undersea Warfare Engineering Station. The image processing is performed on an IBM-PC/AT compatible computer equipped with hardware to digitize and display video images. The software provides the capability to provide contrast enhancement and other similar functions in real time through hardware lookup tables, to automatically perform histogram equalization, to capture one or more frames and average them or apply one of several different processing algorithms to a captured frame. The report is in the form of a user manual for the software and includes guided tutorial and reference sections. A Digital Image Processing Primer in the appendix serves to explain the principle concepts that are used in the image processing.

A long-term target detection approach in infrared image sequence

NASA Astrophysics Data System (ADS)

Li, Hang; Zhang, Qi; Li, Yuanyuan; Wang, Liqiang

2015-12-01

An automatic target detection method used in long term infrared (IR) image sequence from a moving platform is proposed. Firstly, based on non-linear histogram equalization, target candidates are coarse-to-fine segmented by using two self-adapt thresholds generated in the intensity space. Then the real target is captured via two different selection approaches. At the beginning of image sequence, the genuine target with litter texture is discriminated from other candidates by using contrast-based confidence measure. On the other hand, when the target becomes larger, we apply online EM method to iteratively estimate and update the distributions of target's size and position based on the prior detection results, and then recognize the genuine one which satisfies both the constraints of size and position. Experimental results demonstrate that the presented method is accurate, robust and efficient.

Component Pin Recognition Using Algorithms Based on Machine Learning

NASA Astrophysics Data System (ADS)

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

2018-04-01

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

Support vector machine for breast cancer classification using diffusion-weighted MRI histogram features: Preliminary study.

PubMed

Vidić, Igor; Egnell, Liv; Jerome, Neil P; Teruel, Jose R; Sjøbakk, Torill E; Østlie, Agnes; Fjøsne, Hans E; Bathen, Tone F; Goa, Pål Erik

2018-05-01

Diffusion-weighted MRI (DWI) is currently one of the fastest developing MRI-based techniques in oncology. Histogram properties from model fitting of DWI are useful features for differentiation of lesions, and classification can potentially be improved by machine learning. To evaluate classification of malignant and benign tumors and breast cancer subtypes using support vector machine (SVM). Prospective. Fifty-one patients with benign (n = 23) and malignant (n = 28) breast tumors (26 ER+, whereof six were HER2+). Patients were imaged with DW-MRI (3T) using twice refocused spin-echo echo-planar imaging with echo time / repetition time (TR/TE) = 9000/86 msec, 90 × 90 matrix size, 2 × 2 mm in-plane resolution, 2.5 mm slice thickness, and 13 b-values. Apparent diffusion coefficient (ADC), relative enhanced diffusivity (RED), and the intravoxel incoherent motion (IVIM) parameters diffusivity (D), pseudo-diffusivity (D*), and perfusion fraction (f) were calculated. The histogram properties (median, mean, standard deviation, skewness, kurtosis) were used as features in SVM (10-fold cross-validation) for differentiation of lesions and subtyping. Accuracies of the SVM classifications were calculated to find the combination of features with highest prediction accuracy. Mann-Whitney tests were performed for univariate comparisons. For benign versus malignant tumors, univariate analysis found 11 histogram properties to be significant differentiators. Using SVM, the highest accuracy (0.96) was achieved from a single feature (mean of RED), or from three feature combinations of IVIM or ADC. Combining features from all models gave perfect classification. No single feature predicted HER2 status of ER + tumors (univariate or SVM), although high accuracy (0.90) was achieved with SVM combining several features. Importantly, these features had to include higher-order statistics (kurtosis and skewness), indicating the importance to account for heterogeneity. Our findings suggest that SVM, using features from a combination of diffusion models, improves prediction accuracy for differentiation of benign versus malignant breast tumors, and may further assist in subtyping of breast cancer. 3 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2018;47:1205-1216. © 2017 International Society for Magnetic Resonance in Medicine.

Water quality analysis in rivers with non-parametric probability distributions and fuzzy inference systems: application to the Cauca River, Colombia.

PubMed

Ocampo-Duque, William; Osorio, Carolina; Piamba, Christian; Schuhmacher, Marta; Domingo, José L

2013-02-01

The integration of water quality monitoring variables is essential in environmental decision making. Nowadays, advanced techniques to manage subjectivity, imprecision, uncertainty, vagueness, and variability are required in such complex evaluation process. We here propose a probabilistic fuzzy hybrid model to assess river water quality. Fuzzy logic reasoning has been used to compute a water quality integrative index. By applying a Monte Carlo technique, based on non-parametric probability distributions, the randomness of model inputs was estimated. Annual histograms of nine water quality variables were built with monitoring data systematically collected in the Colombian Cauca River, and probability density estimations using the kernel smoothing method were applied to fit data. Several years were assessed, and river sectors upstream and downstream the city of Santiago de Cali, a big city with basic wastewater treatment and high industrial activity, were analyzed. The probabilistic fuzzy water quality index was able to explain the reduction in water quality, as the river receives a larger number of agriculture, domestic, and industrial effluents. The results of the hybrid model were compared to traditional water quality indexes. The main advantage of the proposed method is that it considers flexible boundaries between the linguistic qualifiers used to define the water status, being the belongingness of water quality to the diverse output fuzzy sets or classes provided with percentiles and histograms, which allows classify better the real water condition. The results of this study show that fuzzy inference systems integrated to stochastic non-parametric techniques may be used as complementary tools in water quality indexing methodologies. Copyright © 2012 Elsevier Ltd. All rights reserved.

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

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).

Pixel-based skin segmentation in psoriasis images.

PubMed

George, Y; Aldeen, M; Garnavi, R

2016-08-01

In this paper, we present a detailed comparison study of skin segmentation methods for psoriasis images. Different techniques are modified and then applied to a set of psoriasis images acquired from the Royal Melbourne Hospital, Melbourne, Australia, with aim of finding the best technique suited for application to psoriasis images. We investigate the effect of different colour transformations on skin detection performance. In this respect, explicit skin thresholding is evaluated with three different decision boundaries (CbCr, HS and rgHSV). Histogram-based Bayesian classifier is applied to extract skin probability maps (SPMs) for different colour channels. This is then followed by using different approaches to find a binary skin map (SM) image from the SPMs. The approaches used include binary decision tree (DT) and Otsu's thresholding. Finally, a set of morphological operations are implemented to refine the resulted SM image. The paper provides detailed analysis and comparison of the performance of the Bayesian classifier in five different colour spaces (YCbCr, HSV, RGB, XYZ and CIELab). The results show that histogram-based Bayesian classifier is more effective than explicit thresholding, when applied to psoriasis images. It is also found that decision boundary CbCr outperforms HS and rgHSV. Another finding is that the SPMs of Cb, Cr, H and B-CIELab colour bands yield the best SMs for psoriasis images. In this study, we used a set of 100 psoriasis images for training and testing the presented methods. True Positive (TP) and True Negative (TN) are used as statistical evaluation measures.

Computerized image analysis: estimation of breast density on mammograms

NASA Astrophysics Data System (ADS)

Zhou, Chuan; Chan, Heang-Ping; Petrick, Nicholas; Sahiner, Berkman; Helvie, Mark A.; Roubidoux, Marilyn A.; Hadjiiski, Lubomir M.; Goodsitt, Mitchell M.

2000-06-01

An automated image analysis tool is being developed for estimation of mammographic breast density, which may be useful for risk estimation or for monitoring breast density change in a prevention or intervention program. A mammogram is digitized using a laser scanner and the resolution is reduced to a pixel size of 0.8 mm X 0.8 mm. Breast density analysis is performed in three stages. First, the breast region is segmented from the surrounding background by an automated breast boundary-tracking algorithm. Second, an adaptive dynamic range compression technique is applied to the breast image to reduce the range of the gray level distribution in the low frequency background and to enhance the differences in the characteristic features of the gray level histogram for breasts of different densities. Third, rule-based classification is used to classify the breast images into several classes according to the characteristic features of their gray level histogram. For each image, a gray level threshold is automatically determined to segment the dense tissue from the breast region. The area of segmented dense tissue as a percentage of the breast area is then estimated. In this preliminary study, we analyzed the interobserver variation of breast density estimation by two experienced radiologists using BI-RADS lexicon. The radiologists' visually estimated percent breast densities were compared with the computer's calculation. The results demonstrate the feasibility of estimating mammographic breast density using computer vision techniques and its potential to improve the accuracy and reproducibility in comparison with the subjective visual assessment by radiologists.

Study of a co-designed decision feedback equalizer, deinterleaver, and decoder

NASA Technical Reports Server (NTRS)

Peile, Robert E.; Welch, Loyd

1990-01-01

A technique that promises better quality data from band limited channels at lower received power in digital transmission systems is presented. Data transmission, in such systems often suffers from intersymbol interference (ISI) and noise. Two separate techniques, channel coding and equalization, have caused considerable advances in the state of communication systems and both concern themselves with removing the undesired effects of a communication channel. Equalizers mitigate the ISI whereas coding schemes are used to incorporate error-correction. In the past, most of the research in these two areas has been carried out separately. However, the individual techniques have strengths and weaknesses that are complementary in many applications: an integrated approach realizes gains in excess to that of a simple juxtaposition. Coding schemes have been successfully used in cascade with linear equalizers which in the absence of ISI provide excellent performance. However, when both ISI and the noise level are relatively high, nonlinear receivers like the decision feedback equalizer (DFE) perform better. The DFE has its drawbacks: it suffers from error propagation. The technique presented here takes advantage of interleaving to integrate the two approaches so that the error propagation in DFE can be reduced with the help of error correction provided by the decoder. The results of simulations carried out for both, binary, and non-binary, channels confirm that significant gain can be obtained by codesigning equalizer and decoder. Although, systems with time-invariant channels and simple DFE having linear filters were looked into, the technique is fairly general and can easily be modified for more sophisticated equalizers to obtain even larger gains.

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.

Objective evaluation of linear and nonlinear tomosynthetic reconstruction algorithms

NASA Astrophysics Data System (ADS)

Webber, Richard L.; Hemler, Paul F.; Lavery, John E.

2000-04-01

This investigation objectively tests five different tomosynthetic reconstruction methods involving three different digital sensors, each used in a different radiologic application: chest, breast, and pelvis, respectively. The common task was to simulate a specific representative projection for each application by summation of appropriately shifted tomosynthetically generated slices produced by using the five algorithms. These algorithms were, respectively, (1) conventional back projection, (2) iteratively deconvoluted back projection, (3) a nonlinear algorithm similar to back projection, except that the minimum value from all of the component projections for each pixel is computed instead of the average value, (4) a similar algorithm wherein the maximum value was computed instead of the minimum value, and (5) the same type of algorithm except that the median value was computed. Using these five algorithms, we obtained data from each sensor-tissue combination, yielding three factorially distributed series of contiguous tomosynthetic slices. The respective slice stacks then were aligned orthogonally and averaged to yield an approximation of a single orthogonal projection radiograph of the complete (unsliced) tissue thickness. Resulting images were histogram equalized, and actual projection control images were subtracted from their tomosynthetically synthesized counterparts. Standard deviations of the resulting histograms were recorded as inverse figures of merit (FOMs). Visual rankings of image differences by five human observers of a subset (breast data only) also were performed to determine whether their subjective observations correlated with homologous FOMs. Nonparametric statistical analysis of these data demonstrated significant differences (P > 0.05) between reconstruction algorithms. The nonlinear minimization reconstruction method nearly always outperformed the other methods tested. Observer rankings were similar to those measured objectively.

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.

[Research on K-means clustering segmentation method for MRI brain image based on selecting multi-peaks in gray histogram].

PubMed

Chen, Zhaoxue; Yu, Haizhong; Chen, Hao

2013-12-01

To solve the problem of traditional K-means clustering in which initial clustering centers are selected randomly, we proposed a new K-means segmentation algorithm based on robustly selecting 'peaks' standing for White Matter, Gray Matter and Cerebrospinal Fluid in multi-peaks gray histogram of MRI brain image. The new algorithm takes gray value of selected histogram 'peaks' as the initial K-means clustering center and can segment the MRI brain image into three parts of tissue more effectively, accurately, steadily and successfully. Massive experiments have proved that the proposed algorithm can overcome many shortcomings caused by traditional K-means clustering method such as low efficiency, veracity, robustness and time consuming. The histogram 'peak' selecting idea of the proposed segmentootion method is of more universal availability.

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.

The magnifying glass - A feature space local expansion for visual analysis. [and image enhancement

NASA Technical Reports Server (NTRS)

Juday, R. D.

1981-01-01

The Magnifying Glass Transformation (MGT) technique is proposed, as a multichannel spectral operation yielding visual imagery which is enhanced in a specified spectral vicinity, guided by the statistics of training samples. An application example is that in which the discrimination among spectral neighbors within an interactive display may be increased without altering distant object appearances or overall interpretation. A direct histogram specification technique is applied to the channels within the multispectral image so that a subset of the spectral domain occupies an increased fraction of the domain. The transformation is carried out by obtaining the training information, establishing the condition of the covariance matrix, determining the influenced solid, and initializing the lookup table. Finally, the image is transformed.

Method for star identification using neural networks

NASA Astrophysics Data System (ADS)

Lindsey, Clark S.; Lindblad, Thomas; Eide, Age J.

1997-04-01

Identification of star constellations with an onboard star tracker provides the highest precision of all attitude determination techniques for spacecraft. A method for identification of star constellations inspired by neural network (NNW) techniques is presented. It compares feature vectors derived from histograms of distances to multiple stars around the unknown star. The NNW method appears most robust with respect to position noise and would require a smaller database than conventional methods, especially for small fields of view. The neural network method is quite slow when performed on a sequential (serial) processor, but would provide very high speed if implemented in special hardware. Such hardware solutions could also yield lower low weight and low power consumption, both important features for small satellites.

Automatic video segmentation and indexing

NASA Astrophysics Data System (ADS)

Chahir, Youssef; Chen, Liming

1999-08-01

Indexing is an important aspect of video database management. Video indexing involves the analysis of video sequences, which is a computationally intensive process. However, effective management of digital video requires robust indexing techniques. The main purpose of our proposed video segmentation is twofold. Firstly, we develop an algorithm that identifies camera shot boundary. The approach is based on the use of combination of color histograms and block-based technique. Next, each temporal segment is represented by a color reference frame which specifies the shot similarities and which is used in the constitution of scenes. Experimental results using a variety of videos selected in the corpus of the French Audiovisual National Institute are presented to demonstrate the effectiveness of performing shot detection, the content characterization of shots and the scene constitution.

Automated segmentation and isolation of touching cell nuclei in cytopathology smear images of pleural effusion using distance transform watershed method

NASA Astrophysics Data System (ADS)

Win, Khin Yadanar; Choomchuay, Somsak; Hamamoto, Kazuhiko

2017-06-01

The automated segmentation of cell nuclei is an essential stage in the quantitative image analysis of cell nuclei extracted from smear cytology images of pleural fluid. Cell nuclei can indicate cancer as the characteristics of cell nuclei are associated with cells proliferation and malignancy in term of size, shape and the stained color. Nevertheless, automatic nuclei segmentation has remained challenging due to the artifacts caused by slide preparation, nuclei heterogeneity such as the poor contrast, inconsistent stained color, the cells variation, and cells overlapping. In this paper, we proposed a watershed-based method that is capable to segment the nuclei of the variety of cells from cytology pleural fluid smear images. Firstly, the original image is preprocessed by converting into the grayscale image and enhancing by adjusting and equalizing the intensity using histogram equalization. Next, the cell nuclei are segmented using OTSU thresholding as the binary image. The undesirable artifacts are eliminated using morphological operations. Finally, the distance transform based watershed method is applied to isolate the touching and overlapping cell nuclei. The proposed method is tested with 25 Papanicolaou (Pap) stained pleural fluid images. The accuracy of our proposed method is 92%. The method is relatively simple, and the results are very promising.

MRI volumetry of prefrontal cortex

NASA Astrophysics Data System (ADS)

Sheline, Yvette I.; Black, Kevin J.; Lin, Daniel Y.; Pimmel, Joseph; Wang, Po; Haller, John W.; Csernansky, John G.; Gado, Mokhtar; Walkup, Ronald K.; Brunsden, Barry S.; Vannier, Michael W.

1995-05-01

Prefrontal cortex volumetry by brain magnetic resonance (MR) is required to estimate changes postulated to occur in certain psychiatric and neurologic disorders. A semiautomated method with quantitative characterization of its performance is sought to reliably distinguish small prefrontal cortex volume changes within individuals and between groups. Stereological methods were tested by a blinded comparison of measurements applied to 3D MR scans obtained using an MPRAGE protocol. Fixed grid stereologic methods were used to estimate prefrontal cortex volumes on a graphic workstation, after the images are scaled from 16 to 8 bits using a histogram method. In addition images were resliced into coronal sections perpendicular to the bicommissural plane. Prefrontal cortex volumes were defined as all sections of the frontal lobe anterior to the anterior commissure. Ventricular volumes were excluded. Stereological measurement yielded high repeatability and precision, and was time efficient for the raters. The coefficient of error was

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.

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)

Video enhancement workbench: an operational real-time video image processing system

NASA Astrophysics Data System (ADS)

Yool, Stephen R.; Van Vactor, David L.; Smedley, Kirk G.

1993-01-01

Video image sequences can be exploited in real-time, giving analysts rapid access to information for military or criminal investigations. Video-rate dynamic range adjustment subdues fluctuations in image intensity, thereby assisting discrimination of small or low- contrast objects. Contrast-regulated unsharp masking enhances differentially shadowed or otherwise low-contrast image regions. Real-time removal of localized hotspots, when combined with automatic histogram equalization, may enhance resolution of objects directly adjacent. In video imagery corrupted by zero-mean noise, real-time frame averaging can assist resolution and location of small or low-contrast objects. To maximize analyst efficiency, lengthy video sequences can be screened automatically for low-frequency, high-magnitude events. Combined zoom, roam, and automatic dynamic range adjustment permit rapid analysis of facial features captured by video cameras recording crimes in progress. When trying to resolve small objects in murky seawater, stereo video places the moving imagery in an optimal setting for human interpretation.

Lead concentration distribution and source tracing of urban/suburban aquatic sediments in two typical famous tourist cities: Haikou and Sanya, China.

PubMed

Dong, Zhicheng; Bao, Zhengyu; Wu, Guoai; Fu, Yangrong; Yang, Yi

2010-11-01

The content and spatial distribution of lead in the aquatic systems in two Chinese tropical cities in Hainan province (Haikou and Sanyan) show an unequal distribution of lead between the urban and the suburban areas. The lead content is significantly higher (72.3 mg/kg) in the urban area than the suburbs (15.0 mg/kg) in Haikou, but quite equal in Sanya (41.6 and 43.9 mg/kg). The frequency distribution histograms suggest that the lead in Haikou and in Sanya derives from different natural and/or anthropogenic sources. The isotopic compositions indicate that urban sediment lead in Haikou originates mainly from anthropogenic sources (automobile exhaust, atmospheric deposition, etc.) which contribute much more than the natural sources, while natural lead (basalt and sea sands) is still dominant in the suburban areas in Haikou. In Sanya, the primary source is natural (soils and sea sands).

Image contrast enhancement with brightness preservation using an optimal gamma correction and weighted sum approach

NASA Astrophysics Data System (ADS)

Jiang, G.; Wong, C. Y.; Lin, S. C. F.; Rahman, M. A.; Ren, T. R.; Kwok, Ngaiming; Shi, Haiyan; Yu, Ying-Hao; Wu, Tonghai

2015-04-01

The enhancement of image contrast and preservation of image brightness are two important but conflicting objectives in image restoration. Previous attempts based on linear histogram equalization had achieved contrast enhancement, but exact preservation of brightness was not accomplished. A new perspective is taken here to provide balanced performance of contrast enhancement and brightness preservation simultaneously by casting the quest of such solution to an optimization problem. Specifically, the non-linear gamma correction method is adopted to enhance the contrast, while a weighted sum approach is employed for brightness preservation. In addition, the efficient golden search algorithm is exploited to determine the required optimal parameters to produce the enhanced images. Experiments are conducted on natural colour images captured under various indoor, outdoor and illumination conditions. Results have shown that the proposed method outperforms currently available methods in contrast to enhancement and brightness preservation.

Automated Detection of Diabetic Retinopathy using Deep Learning.

PubMed

Lam, Carson; Yi, Darvin; Guo, Margaret; Lindsey, Tony

2018-01-01

Diabetic retinopathy is a leading cause of blindness among working-age adults. Early detection of this condition is critical for good prognosis. In this paper, we demonstrate the use of convolutional neural networks (CNNs) on color fundus images for the recognition task of diabetic retinopathy staging. Our network models achieved test metric performance comparable to baseline literature results, with validation sensitivity of 95%. We additionally explored multinomial classification models, and demonstrate that errors primarily occur in the misclassification of mild disease as normal due to the CNNs inability to detect subtle disease features. We discovered that preprocessing with contrast limited adaptive histogram equalization and ensuring dataset fidelity by expert verification of class labels improves recognition of subtle features. Transfer learning on pretrained GoogLeNet and AlexNet models from ImageNet improved peak test set accuracies to 74.5%, 68.8%, and 57.2% on 2-ary, 3-ary, and 4-ary classification models, respectively.

Retinex based low-light image enhancement using guided filtering and variational framework

NASA Astrophysics Data System (ADS)

Zhang, Shi; Tang, Gui-jin; Liu, Xiao-hua; Luo, Su-huai; Wang, Da-dong

2018-03-01

A new image enhancement algorithm based on Retinex theory is proposed to solve the problem of bad visual effect of an image in low-light conditions. First, an image is converted from the RGB color space to the HSV color space to get the V channel. Next, the illuminations are respectively estimated by the guided filtering and the variational framework on the V channel and combined into a new illumination by average gradient. The new reflectance is calculated using V channel and the new illumination. Then a new V channel obtained by multiplying the new illumination and reflectance is processed with contrast limited adaptive histogram equalization (CLAHE). Finally, the new image in HSV space is converted back to RGB space to obtain the enhanced image. Experimental results show that the proposed method has better subjective quality and objective quality than existing methods.

Nondestructive Detection of the Internalquality of Apple Using X-Ray and Machine Vision

NASA Astrophysics Data System (ADS)

Yang, Fuzeng; Yang, Liangliang; Yang, Qing; Kang, Likui

The internal quality of apple is impossible to be detected by eyes in the procedure of sorting, which could reduce the apple’s quality reaching market. This paper illustrates an instrument using X-ray and machine vision. The following steps were introduced to process the X-ray image in order to determine the mould core apple. Firstly, lifting wavelet transform was used to get a low frequency image and three high frequency images. Secondly, we enhanced the low frequency image through image’s histogram equalization. Then, the edge of each apple's image was detected using canny operator. Finally, a threshold was set to clarify mould core and normal apple according to the different length of the apple core’s diameter. The experimental results show that this method could on-line detect the mould core apple with less time consuming, less than 0.03 seconds per apple, and the accuracy could reach 92%.

Fluorescent Microscopy Enhancement Using Imaging

NASA Astrophysics Data System (ADS)

Conrad, Morgan P.; Reck tenwald, Diether J.; Woodhouse, Bryan S.

1986-06-01

To enhance our capabilities for observing fluorescent stains in biological systems, we are developing a low cost imaging system based around an IBM AT microcomputer and a commercial image capture board compatible with a standard RS-170 format video camera. The image is digitized in real time with 256 grey levels, while being displayed and also stored in memory. The software allows for interactive processing of the data, such as histogram equalization or pseudocolor enhancement of the display. The entire image, or a quadrant thereof, can be averaged over time to improve the signal to noise ratio. Images may be stored to disk for later use or comparison. The camera may be selected for better response in the UV or near IR. Combined with signal averaging, this increases the sensitivity relative to that of the human eye, while still allowing for the fluorescence distribution on either the surface or internal cytoskeletal structure to be observed.

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 technique for locating function roots and for satisfying equality constraints in optimization

NASA Technical Reports Server (NTRS)

Sobieszczanski-Sobieski, Jaroslaw

1991-01-01

A new technique for locating simultaneous roots of a set of functions is described. The technique is based on the property of the Kreisselmeier-Steinhauser function which descends to a minimum at each root location. It is shown that the ensuing algorithm may be merged into any nonlinear programming method for solving optimization problems with equality constraints.

A technique for locating function roots and for satisfying equality constraints in optimization

NASA Technical Reports Server (NTRS)

Sobieszczanski-Sobieski, J.

1992-01-01

A new technique for locating simultaneous roots of a set of functions is described. The technique is based on the property of the Kreisselmeier-Steinhauser function which descends to a minimum at each root location. It is shown that the ensuing algorithm may be merged into any nonlinear programming method for solving optimization problems with equality constraints.

DNA flow cytometric analysis of primary operable breast cancer. Relation of ploidy and S-phase fraction to outcome of patients in NSABP B-04.

PubMed

Fisher, B; Gunduz, N; Costantino, J; Fisher, E R; Redmond, C; Mamounas, E P; Siderits, R

1991-10-01

Between 1971 and 1974, 1665 women with primary operable breast cancer were randomized into a National Surgical Adjuvant Breast and Bowel Project (NSABP) trial (B-04) conducted to evaluate the effectiveness of several different regimens of surgical and radiation therapy. No systemic therapy was given. Cells from archival paraffin-embedded tumor tissue taken from 398 patients were analyzed for ploidy and S-phase fraction (SPF) using flow cytometry. Characteristics and outcome of patients with satisfactory DNA histograms were comparable to those from whom no satisfactory cytometric studies were available. In patients with diploid tumors (43%), the mean SPF was 3.4% +/- 2.3%; in the aneuploid population (57%), the SPF was 7.9% +/- 6.3%. Only 29.9% +/- 17.3% of cells in aneuploid tumors were aneuploid. Diploid tumors were more likely than aneuploid tumors to be of good nuclear grade (P less than 0.001) and smaller size (P equals 0.03). More tumors with high SPF were of poor nuclear grade than were tumors with low SPF (P equals 0.002). No significant difference in 10-year disease-free survival (P equals 0.3) or survival (P equals 0.1) was found between women with diploid or aneuploid tumors. Patients with low SPF tumors had a 13% better disease-free survival (P equals 0.0006) than those with a high SPF and a 14% better survival (P equals 0.007) at 10 years than patients with high SPF tumors. After adjustment for clinical tumor size, the difference in both disease-free survival and survival between patients with high and low SPF tumors was only 10% (P equals 0.04 and 0.08, respectively). Although SPF was found to be of independent prognostic significance for disease-free survival and marginal significance for survival, it did not detect patients with such a good prognosis as to preclude their receiving chemotherapy. The overall survival of patients with low SPF was only 53% at 10 years. These findings and those of others indicate that additional studies are necessary before tumor ploidy and SPF can be used to select patients who should or should not receive systemic therapy.

Independent component analysis based digital signal processing in coherent optical fiber communication systems

NASA Astrophysics Data System (ADS)

Li, Xiang; Luo, Ming; Qiu, Ying; Alphones, Arokiaswami; Zhong, Wen-De; Yu, Changyuan; Yang, Qi

2018-02-01

In this paper, channel equalization techniques for coherent optical fiber transmission systems based on independent component analysis (ICA) are reviewed. The principle of ICA for blind source separation is introduced. The ICA based channel equalization after both single-mode fiber and few-mode fiber transmission for single-carrier and orthogonal frequency division multiplexing (OFDM) modulation formats are investigated, respectively. The performance comparisons with conventional channel equalization techniques are discussed.

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.

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.

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.

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.

Motion compensation in digital subtraction angiography using graphics hardware.

PubMed

Deuerling-Zheng, Yu; Lell, Michael; Galant, Adam; Hornegger, Joachim

2006-07-01

An inherent disadvantage of digital subtraction angiography (DSA) is its sensitivity to patient motion which causes artifacts in the subtraction images. These artifacts could often reduce the diagnostic value of this technique. Automated, fast and accurate motion compensation is therefore required. To cope with this requirement, we first examine a method explicitly designed to detect local motions in DSA. Then, we implement a motion compensation algorithm by means of block matching on modern graphics hardware. Both methods search for maximal local similarity by evaluating a histogram-based measure. In this context, we are the first who have mapped an optimizing search strategy on graphics hardware while paralleling block matching. Moreover, we provide an innovative method for creating histograms on graphics hardware with vertex texturing and frame buffer blending. It turns out that both methods can effectively correct the artifacts in most case, as the hardware implementation of block matching performs much faster: the displacements of two 1024 x 1024 images can be calculated at 3 frames/s with integer precision or 2 frames/s with sub-pixel precision. Preliminary clinical evaluation indicates that the computation with integer precision could already be sufficient.

Redshift data and statistical inference

NASA Technical Reports Server (NTRS)

Newman, William I.; Haynes, Martha P.; Terzian, Yervant

1994-01-01

Frequency histograms and the 'power spectrum analysis' (PSA) method, the latter developed by Yu & Peebles (1969), have been widely employed as techniques for establishing the existence of periodicities. We provide a formal analysis of these two classes of methods, including controlled numerical experiments, to better understand their proper use and application. In particular, we note that typical published applications of frequency histograms commonly employ far greater numbers of class intervals or bins than is advisable by statistical theory sometimes giving rise to the appearance of spurious patterns. The PSA method generates a sequence of random numbers from observational data which, it is claimed, is exponentially distributed with unit mean and variance, essentially independent of the distribution of the original data. We show that the derived random processes is nonstationary and produces a small but systematic bias in the usual estimate of the mean and variance. Although the derived variable may be reasonably described by an exponential distribution, the tail of the distribution is far removed from that of an exponential, thereby rendering statistical inference and confidence testing based on the tail of the distribution completely unreliable. Finally, we examine a number of astronomical examples wherein these methods have been used giving rise to widespread acceptance of statistically unconfirmed conclusions.

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).

Comparison of Boltzmann and Gibbs entropies for the analysis of single-chain phase transitions

NASA Astrophysics Data System (ADS)

Shakirov, T.; Zablotskiy, S.; Böker, A.; Ivanov, V.; Paul, W.

2017-03-01

In the last 10 years, flat histogram Monte Carlo simulations have contributed strongly to our understanding of the phase behavior of simple generic models of polymers. These simulations result in an estimate for the density of states of a model system. To connect this result with thermodynamics, one has to relate the density of states to the microcanonical entropy. In a series of publications, Dunkel, Hilbert and Hänggi argued that it would lead to a more consistent thermodynamic description of small systems, when one uses the Gibbs definition of entropy instead of the Boltzmann one. The latter is the logarithm of the density of states at a certain energy, the former is the logarithm of the integral of the density of states over all energies smaller than or equal to this energy. We will compare the predictions using these two definitions for two polymer models, a coarse-grained model of a flexible-semiflexible multiblock copolymer and a coarse-grained model of the protein poly-alanine. Additionally, it is important to note that while Monte Carlo techniques are normally concerned with the configurational energy only, the microcanonical ensemble is defined for the complete energy. We will show how taking the kinetic energy into account alters the predictions from the analysis. Finally, the microcanonical ensemble is supposed to represent a closed mechanical N-particle system. But due to Galilei invariance such a system has two additional conservation laws, in general: momentum and angular momentum. We will also show, how taking these conservation laws into account alters the results.

Score-level fusion of two-dimensional and three-dimensional palmprint for personal recognition systems

NASA Astrophysics Data System (ADS)

Chaa, Mourad; Boukezzoula, Naceur-Eddine; Attia, Abdelouahab

2017-01-01

Two types of scores extracted from two-dimensional (2-D) and three-dimensional (3-D) palmprint for personal recognition systems are merged, introducing a local image descriptor for 2-D palmprint-based recognition systems, named bank of binarized statistical image features (B-BSIF). The main idea of B-BSIF is that the extracted histograms from the binarized statistical image features (BSIF) code images (the results of applying the different BSIF descriptor size with the length 12) are concatenated into one to produce a large feature vector. 3-D palmprint contains the depth information of the palm surface. The self-quotient image (SQI) algorithm is applied for reconstructing illumination-invariant 3-D palmprint images. To extract discriminative Gabor features from SQI images, Gabor wavelets are defined and used. Indeed, the dimensionality reduction methods have shown their ability in biometrics systems. Given this, a principal component analysis (PCA)+linear discriminant analysis (LDA) technique is employed. For the matching process, the cosine Mahalanobis distance is applied. Extensive experiments were conducted on a 2-D and 3-D palmprint database with 10,400 range images from 260 individuals. Then, a comparison was made between the proposed algorithm and other existing methods in the literature. Results clearly show that the proposed framework provides a higher correct recognition rate. Furthermore, the best results were obtained by merging the score of B-BSIF descriptor with the score of the SQI+Gabor wavelets+PCA+LDA method, yielding an equal error rate of 0.00% and a recognition rate of rank-1=100.00%.

Structure-activity studies with carboxy- and amino-terminal fragments of neurotensin on hypothalamic neurons in vitro.

PubMed

Baldino, F; Davis, L G; Wolfson, B

1985-09-09

The purpose of this study was to determine the structural requirements for the activity of neurotensin (NT1-13) on preoptic/anterior hypothalamic (POAH) neurons in vitro. Standard explant culture electrophysiological techniques were employed. NT was administered to POAH cultures through the superfusion fluid, or, to the vicinity of individual neurons by pressure ejection (0.5-10 psi) from micropipettes. Computer-generated, peri-event histograms were used to quantitate neuronal responses. Pressure ejection of NT1-13 (50 pM to 1 microM) consistently produced an excitatory effect on 30 of 42 neurons. The remaining cells were either inhibited or unaffected. Application of the C-terminal hexapeptide, NT8-13, but not the N-terminal octapeptide, NT1-8 (less than or equal to 1 mM), produced an excitatory response in 21 of 30 neurons, but was less potent than NT1-13. Application of an N-acetylated NT8-13 fragment (NTAC8-13) produced a response that was similar to that produced by NT8-13. The excitatory effects of NT1-13 and NT8-13 were maintained in medium which effectively blocked synaptic transmission (0 mM Ca2+/12 mM Mg2+ 1 mM EGTA). These data indicate that the C-terminal hexapeptide, but not the N-terminal octapeptide, produces a dose-related, excitatory effect on single neurons in the POAH in vitro. The persistence of these effects in Ca2+-free medium supports a postsynaptic site of action for these peptides.

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.

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

Optimizing a machine learning based glioma grading system using multi-parametric MRI histogram and texture features

PubMed Central

Hu, Yu-Chuan; Li, Gang; Yang, Yang; Han, Yu; Sun, Ying-Zhi; Liu, Zhi-Cheng; Tian, Qiang; Han, Zi-Yang; Liu, Le-De; Hu, Bin-Quan; Qiu, Zi-Yu; Wang, Wen; Cui, Guang-Bin

2017-01-01

Current machine learning techniques provide the opportunity to develop noninvasive and automated glioma grading tools, by utilizing quantitative parameters derived from multi-modal magnetic resonance imaging (MRI) data. However, the efficacies of different machine learning methods in glioma grading have not been investigated.A comprehensive comparison of varied machine learning methods in differentiating low-grade gliomas (LGGs) and high-grade gliomas (HGGs) as well as WHO grade II, III and IV gliomas based on multi-parametric MRI images was proposed in the current study. The parametric histogram and image texture attributes of 120 glioma patients were extracted from the perfusion, diffusion and permeability parametric maps of preoperative MRI. Then, 25 commonly used machine learning classifiers combined with 8 independent attribute selection methods were applied and evaluated using leave-one-out cross validation (LOOCV) strategy. Besides, the influences of parameter selection on the classifying performances were investigated. We found that support vector machine (SVM) exhibited superior performance to other classifiers. By combining all tumor attributes with synthetic minority over-sampling technique (SMOTE), the highest classifying accuracy of 0.945 or 0.961 for LGG and HGG or grade II, III and IV gliomas was achieved. Application of Recursive Feature Elimination (RFE) attribute selection strategy further improved the classifying accuracies. Besides, the performances of LibSVM, SMO, IBk classifiers were influenced by some key parameters such as kernel type, c, gama, K, etc. SVM is a promising tool in developing automated preoperative glioma grading system, especially when being combined with RFE strategy. Model parameters should be considered in glioma grading model optimization. PMID:28599282

Cytochrome c conformations resolved by the photon counting histogram: Watching the alkaline transition with single-molecule sensitivity

PubMed Central

Perroud, Thomas D.; Bokoch, Michael P.; Zare, Richard N.

2005-01-01

We apply the photon counting histogram (PCH) model, a fluorescence technique with single-molecule sensitivity, to study pH-induced conformational changes of cytochrome c. PCH is able to distinguish different protein conformations based on the brightness of a fluorophore sensitive to its local environment. We label cytochrome c through its single free cysteine with tetramethylrhodamine-5-maleimide (TMR), a fluorophore with specific brightnesses that we associate with specific protein conformations. Ensemble measurements demonstrate two different fluorescence responses with increasing pH: (i) a decrease in fluorescence intensity caused by the alkaline transition of cytochrome c (pH 7.0–9.5), and (ii) an increase in intensity when the protein unfolds (pH 9.5–10.8). The magnitudes of these two responses depend strongly on the molar ratio of TMR used to label cytochrome c. Using PCH we determine that this effect arises from the proportion of a nonfunctional conformation in the sample, which can be differentiated from the functional conformation. We further determine the causes of each ensemble fluorescence response: (i) during the alkaline transition, the fluorophore enters a dark state and discrete conformations are observed, and (ii) as cytochrome c unfolds, the fluorophore incrementally brightens, but discrete conformations are no longer resolved. Moreover, we also show that functional TMR-cytochrome c undergoes a response of identical magnitude regardless of the proportion of nonfunctional protein in the sample. As expected for a technique with single-molecule sensitivity, we demonstrate that PCH can directly observe the most relevant conformation, unlike ensemble fluorometry. PMID:16314563

Optimizing a machine learning based glioma grading system using multi-parametric MRI histogram and texture features.

PubMed

Zhang, Xin; Yan, Lin-Feng; Hu, Yu-Chuan; Li, Gang; Yang, Yang; Han, Yu; Sun, Ying-Zhi; Liu, Zhi-Cheng; Tian, Qiang; Han, Zi-Yang; Liu, Le-De; Hu, Bin-Quan; Qiu, Zi-Yu; Wang, Wen; Cui, Guang-Bin

2017-07-18

Current machine learning techniques provide the opportunity to develop noninvasive and automated glioma grading tools, by utilizing quantitative parameters derived from multi-modal magnetic resonance imaging (MRI) data. However, the efficacies of different machine learning methods in glioma grading have not been investigated.A comprehensive comparison of varied machine learning methods in differentiating low-grade gliomas (LGGs) and high-grade gliomas (HGGs) as well as WHO grade II, III and IV gliomas based on multi-parametric MRI images was proposed in the current study. The parametric histogram and image texture attributes of 120 glioma patients were extracted from the perfusion, diffusion and permeability parametric maps of preoperative MRI. Then, 25 commonly used machine learning classifiers combined with 8 independent attribute selection methods were applied and evaluated using leave-one-out cross validation (LOOCV) strategy. Besides, the influences of parameter selection on the classifying performances were investigated. We found that support vector machine (SVM) exhibited superior performance to other classifiers. By combining all tumor attributes with synthetic minority over-sampling technique (SMOTE), the highest classifying accuracy of 0.945 or 0.961 for LGG and HGG or grade II, III and IV gliomas was achieved. Application of Recursive Feature Elimination (RFE) attribute selection strategy further improved the classifying accuracies. Besides, the performances of LibSVM, SMO, IBk classifiers were influenced by some key parameters such as kernel type, c, gama, K, etc. SVM is a promising tool in developing automated preoperative glioma grading system, especially when being combined with RFE strategy. Model parameters should be considered in glioma grading model optimization.

TH-E-BRF-09: Gaussian Mixture Model Analysis of Radiation-Induced Parotid-Gland Injury: An Ultrasound Study of Acute and Late Xerostomia in Head-And-Neck Radiotherapy

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

Liu, T; Yu, D; Beitler, J

Purpose: Xerostomia (dry mouth), secondary to parotid-gland injury, is a distressing side-effect in head-and-neck radiotherapy (RT). This study's purpose is to develop a novel ultrasound technique to quantitatively evaluate post-RT parotid-gland injury. Methods: Recent ultrasound studies have shown that healthy parotid glands exhibit homogeneous echotexture, whereas post-RT parotid glands are often heterogeneous, with multiple hypoechoic (inflammation) or hyperechoic (fibrosis) regions. We propose to use a Gaussian mixture model to analyze the ultrasonic echo-histogram of the parotid glands. An IRB-approved clinical study was conducted: (1) control-group: 13 healthy-volunteers, served as the control; (2) acutetoxicity group − 20 patients (mean age: 62.5more » ± 8.9 years, follow-up: 2.0±0.8 months); and (3) late-toxicity group − 18 patients (mean age: 60.7 ± 7.3 years, follow-up: 20.1±10.4 months). All patients experienced RTOG grade 1 or 2 salivary-gland toxicity. Each participant underwent an ultrasound scan (10 MHz) of the bilateral parotid glands. An echo-intensity histogram was derived for each parotid and a Gaussian mixture model was used to fit the histogram using expectation maximization (EM) algorithm. The quality of the fitting was evaluated with the R-squared value. Results: (1) Controlgroup: all parotid glands fitted well with one Gaussian component, with a mean intensity of 79.8±4.9 (R-squared>0.96). (2) Acute-toxicity group: 37 of the 40 post-RT parotid glands fitted well with two Gaussian components, with a mean intensity of 42.9±7.4, 73.3±12.2 (R-squared>0.95). (3) Latetoxicity group: 32 of the 36 post-RT parotid fitted well with 3 Gaussian components, with mean intensities of 49.7±7.6, 77.2±8.7, and 118.6±11.8 (R-squared>0.98). Conclusion: RT-associated parotid-gland injury is common in head-and-neck RT, but challenging to assess. This work has demonstrated that the Gaussian mixture model of the echo-histogram could quantify acute and late toxicity of the parotid glands. This study provides meaningful preliminary data from future observational and interventional clinical research.« less

X-ray beam equalization for digital fluoroscopy

NASA Astrophysics Data System (ADS)

Molloi, Sabee Y.; Tang, Jerry; Marcin, Martin R.; Zhou, Yifang; Anvar, Behzad

1996-04-01

The concept of radiographic equalization has previously been investigated. However, a suitable technique for digital fluoroscopic applications has not been developed. The previously reported scanning equalization techniques cannot be applied to fluoroscopic applications due to their exposure time limitations. On the other hand, area beam equalization techniques are more suited for digital fluoroscopic applications. The purpose of this study is to develop an x- ray beam equalization technique for digital fluoroscopic applications that will produce an equalized radiograph with minimal image artifacts and tube loading. Preliminary unequalized images of a humanoid chest phantom were acquired using a digital fluoroscopic system. Using this preliminary image as a guide, an 8 by 8 array of square pistons were used to generate masks in a mold with CeO2. The CeO2 attenuator thicknesses were calculated using the gray level information from the unequalized image. The generated mask was positioned close to the focal spot (magnification of 8.0) in order to minimize edge artifacts from the mask. The masks were generated manually in order to investigate the piston and matrix size requirements. The development of an automated version of mask generation and positioning is in progress. The results of manual mask generation and positioning show that it is possible to generate equalized radiographs with minimal perceptible artifacts. The equalization of x-ray transmission across the field exiting from the object significantly improved the image quality by preserving local contrast throughout the image. Furthermore, the reduction in dynamic range significantly reduced the effect of x-ray scatter and veiling glare from high transmission to low transmission areas. Also, the x-ray tube loading due to the mask assembly itself was negligible. In conclusion it is possible to produce area beam compensation that will be compatible with digital fluoroscopy with minimal compensation artifacts. The compensation process produces an image with equalized signal to noise ratio in all parts of the image.

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

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.

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.

Comparing stochastic proton interactions simulated using TOPAS-nBio to experimental data from fluorescent nuclear track detectors

NASA Astrophysics Data System (ADS)

Underwood, T. S. A.; Sung, W.; McFadden, C. H.; McMahon, S. J.; Hall, D. C.; McNamara, A. L.; Paganetti, H.; Sawakuchi, G. O.; Schuemann, J.

2017-04-01

Whilst Monte Carlo (MC) simulations of proton energy deposition have been well-validated at the macroscopic level, their microscopic validation remains lacking. Equally, no gold-standard yet exists for experimental metrology of individual proton tracks. In this work we compare the distributions of stochastic proton interactions simulated using the TOPAS-nBio MC platform against confocal microscope data for Al2O3:C,Mg fluorescent nuclear track detectors (FNTDs). We irradiated 8× 4× 0.5 mm3 FNTD chips inside a water phantom, positioned at seven positions along a pristine proton Bragg peak with a range in water of 12 cm. MC simulations were implemented in two stages: (1) using TOPAS to model the beam properties within a water phantom and (2) using TOPAS-nBio with Geant4-DNA physics to score particle interactions through a water surrogate of Al2O3:C,Mg. The measured median track integrated brightness (IB) was observed to be strongly correlated to both (i) voxelized track-averaged linear energy transfer (LET) and (ii) frequency mean microdosimetric lineal energy, \\overline{{{y}F}} , both simulated in pure water. Histograms of FNTD track IB were compared against TOPAS-nBio histograms of the number of terminal electrons per proton, scored in water with mass-density scaled to mimic Al2O3:C,Mg. Trends between exposure depths observed in TOPAS-nBio simulations were experimentally replicated in the study of FNTD track IB. Our results represent an important first step towards the experimental validation of MC simulations on the sub-cellular scale and suggest that FNTDs can enable experimental study of the microdosimetric properties of individual proton tracks.

Comparing stochastic proton interactions simulated using TOPAS-nBio to experimental data from fluorescent nuclear track detectors.

PubMed

Underwood, T S A; Sung, W; McFadden, C H; McMahon, S J; Hall, D C; McNamara, A L; Paganetti, H; Sawakuchi, G O; Schuemann, J

2017-04-21

Whilst Monte Carlo (MC) simulations of proton energy deposition have been well-validated at the macroscopic level, their microscopic validation remains lacking. Equally, no gold-standard yet exists for experimental metrology of individual proton tracks. In this work we compare the distributions of stochastic proton interactions simulated using the TOPAS-nBio MC platform against confocal microscope data for Al 2 O 3 :C,Mg fluorescent nuclear track detectors (FNTDs). We irradiated [Formula: see text] mm 3 FNTD chips inside a water phantom, positioned at seven positions along a pristine proton Bragg peak with a range in water of 12 cm. MC simulations were implemented in two stages: (1) using TOPAS to model the beam properties within a water phantom and (2) using TOPAS-nBio with Geant4-DNA physics to score particle interactions through a water surrogate of Al 2 O 3 :C,Mg. The measured median track integrated brightness (IB) was observed to be strongly correlated to both (i) voxelized track-averaged linear energy transfer (LET) and (ii) frequency mean microdosimetric lineal energy, [Formula: see text], both simulated in pure water. Histograms of FNTD track IB were compared against TOPAS-nBio histograms of the number of terminal electrons per proton, scored in water with mass-density scaled to mimic Al 2 O 3 :C,Mg. Trends between exposure depths observed in TOPAS-nBio simulations were experimentally replicated in the study of FNTD track IB. Our results represent an important first step towards the experimental validation of MC simulations on the sub-cellular scale and suggest that FNTDs can enable experimental study of the microdosimetric properties of individual proton tracks.

Application of the Minkowski-functionals for automated pattern classification of breast parenchyma depicted by digital mammography

NASA Astrophysics Data System (ADS)

Boehm, Holger F.; Fischer, Tanja; Riosk, Dororthea; Britsch, Stefanie; Reiser, Maximilian

2008-03-01

With an estimated life-time-risk of about 10%, breast cancer is the most common cancer among women in western societies. Extensive mammography-screening programs have been implemented for diagnosis of the disease at an early stage. Several algorithms for computer-aided detection (CAD) have been proposed to help radiologists manage the increasing number of mammographic image-data and identify new cases of cancer. However, a major issue with most CAD-solutions is the fact that performance strongly depends on the structure and density of the breast tissue. Prior information about the global tissue quality in a patient would be helpful for selecting the most effective CAD-approach in order to increase the sensitivity of lesion-detection. In our study, we propose an automated method for textural evaluation of digital mammograms using the Minkowski Functionals in 2D. 80 mammograms are consensus-classified by two experienced readers as fibrosis, involution/atrophy, or normal. For each case, the topology of graylevel distribution is evaluated within a retromamillary image-section of 512 x 512 pixels. In addition, we obtain parameters from the graylevel-histogram (20th percentile, median and mean graylevel intensity). As a result, correct classification of the mammograms based on the densitometic parameters is achieved in between 38 and 48%, whereas topological analysis increases the rate to 83%. The findings demonstrate the effectiveness of the proposed algorithm. Compared to features obtained from graylevel histograms and comparable studies, we draw the conclusion that the presented method performs equally good or better. Our future work will be focused on the characterization of the mammographic tissue according to the Breast Imaging Reporting and Data System (BI-RADS). Moreover, other databases will be tested for an in-depth evaluation of the efficiency of our proposal.

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.

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.

Histogram analysis of apparent diffusion coefficient maps for assessing thymic epithelial tumours: correlation with world health organization classification and clinical staging.

PubMed

Kong, Ling-Yan; Zhang, Wei; Zhou, Yue; Xu, Hai; Shi, Hai-Bin; Feng, Qing; Xu, Xiao-Quan; Yu, Tong-Fu

2018-04-01

To investigate the value of apparent diffusion coefficients (ADCs) histogram analysis for assessing World Health Organization (WHO) pathological classification and Masaoka clinical stages of thymic epithelial tumours. 37 patients with histologically confirmed thymic epithelial tumours were enrolled. ADC measurements were performed using hot-spot ROI (ADC HS-ROI ) and histogram-based approach. ADC histogram parameters included mean ADC (ADC mean ), median ADC (ADC median ), 10 and 90 percentile of ADC (ADC 10 and ADC 90 ), kurtosis and skewness. One-way ANOVA, independent-sample t-test, and receiver operating characteristic were used for statistical analyses. There were significant differences in ADC mean , ADC median , ADC 10 , ADC 90 and ADC HS-ROI among low-risk thymoma (type A, AB, B1; n = 14), high-risk thymoma (type B2, B3; n = 9) and thymic carcinoma (type C, n = 14) groups (all p-values <0.05), while no significant difference in skewness (p = 0.181) and kurtosis (p = 0.088). ADC 10 showed best differentiating ability (cut-off value, ≤0.689 × 10 -3 mm 2 s -1 ; AUC, 0.957; sensitivity, 95.65%; specificity, 92.86%) for discriminating low-risk thymoma from high-risk thymoma and thymic carcinoma. Advanced Masaoka stages (Stage III and IV; n = 24) tumours showed significant lower ADC parameters and higher kurtosis than early Masaoka stage (Stage I and II; n = 13) tumours (all p-values <0.05), while no significant difference on skewness (p = 0.063). ADC 10 showed best differentiating ability (cut-off value, ≤0.689 × 10 -3 mm 2 s -1 ; AUC, 0.913; sensitivity, 91.30%; specificity, 85.71%) for discriminating advanced and early Masaoka stage epithelial tumours. ADC histogram analysis may assist in assessing the WHO pathological classification and Masaoka clinical stages of thymic epithelial tumours. Advances in knowledge: 1. ADC histogram analysis could help to assess WHO pathological classification of thymic epithelial tumours. 2. ADC histogram analysis could help to evaluate Masaoka clinical stages of thymic epithelial tumours. 3. ADC 10 might be a promising imaging biomarker for assessing and characterizing thymic epithelial tumours.

Utility of whole-lesion ADC histogram metrics for assessing the malignant potential of pancreatic intraductal papillary mucinous neoplasms (IPMNs).

PubMed

Hoffman, David H; Ream, Justin M; Hajdu, Christina H; Rosenkrantz, Andrew B

2017-04-01

To evaluate whole-lesion ADC histogram metrics for assessing the malignant potential of pancreatic intraductal papillary mucinous neoplasms (IPMNs), including in comparison with conventional MRI features. Eighteen branch-duct IPMNs underwent MRI with DWI prior to resection (n = 16) or FNA (n = 2). A blinded radiologist placed 3D volumes-of-interest on the entire IPMN on the ADC map, from which whole-lesion histogram metrics were generated. The reader also assessed IPMN size, mural nodularity, and adjacent main-duct dilation. Benign (low-to-intermediate grade dysplasia; n = 10) and malignant (high-grade dysplasia or invasive adenocarcinoma; n = 8) IPMNs were compared. Whole-lesion ADC histogram metrics demonstrating significant differences between benign and malignant IPMNs were: entropy (5.1 ± 0.2 vs. 5.4 ± 0.2; p = 0.01, AUC = 86%); mean of the bottom 10th percentile (2.2 ± 0.4 vs. 1.6 ± 0.7; p = 0.03; AUC = 81%); and mean of the 10-25th percentile (2.8 ± 0.4 vs. 2.3 ± 0.6; p = 0.04; AUC = 79%). The overall mean ADC, skewness, and kurtosis were not significantly different between groups (p ≥ 0.06; AUC = 50-78%). For entropy (highest performing histogram metric), an optimal threshold of >5.3 achieved a sensitivity of 100%, a specificity of 70%, and an accuracy of 83% for predicting malignancy. No significant difference (p = 0.18-0.64) was observed between benign and malignant IPMNs for cyst size ≥3 cm, adjacent main-duct dilatation, or mural nodule. At multivariable analysis of entropy in combination with all other ADC histogram and conventional MRI features, entropy was the only significant independent predictor of malignancy (p = 0.004). Although requiring larger studies, ADC entropy obtained from 3D whole-lesion histogram analysis may serve as a biomarker for identifying the malignant potential of IPMNs, independent of conventional MRI features.

Clarification to "Examining Rater Errors in the Assessment of Written Composition with a Many-Faceted Rasch Model."

ERIC Educational Resources Information Center

Englehard, George, Jr.

1996-01-01

Data presented in figure three of the article cited may be misleading in that the automatic scaling procedure used by the computer program that generated the histogram highlighted spikes that would look different with different histogram methods. (SLD)

Using Computer Graphics in Statistics.

ERIC Educational Resources Information Center

Kerley, Lyndell M.

1990-01-01

Described is software which allows a student to use simulation to produce analytical output as well as graphical results. The results include a frequency histogram of a selected population distribution, a frequency histogram of the distribution of the sample means, and test the normality distributions of the sample means. (KR)

An analysis of automatic human detection and tracking

NASA Astrophysics Data System (ADS)

Demuth, Philipe R.; Cosmo, Daniel L.; Ciarelli, Patrick M.

2015-12-01

This paper presents an automatic method to detect and follow people on video streams. This method uses two techniques to determine the initial position of the person at the beginning of the video file: one based on optical flow and the other one based on Histogram of Oriented Gradients (HOG). After defining the initial bounding box, tracking is done using four different trackers: Median Flow tracker, TLD tracker, Mean Shift tracker and a modified version of the Mean Shift tracker using HSV color space. The results of the methods presented in this paper are then compared at the end of the paper.

Assessment of pancreas cells

NASA Technical Reports Server (NTRS)

Vanoss, C. J.

1978-01-01

Pancreatic islets were obtained from guinea pig pancreas by the collagenase method and kept alive in tissue culture prior to further studies. Pancreas cell morphology was studied by standard histochemical techniques using light microscopy. Preparative vertical electrophoresis-levitation of dispersed fetal guinea pig pancreas cells was conducted in phosphate buffer containing a heavy water (D20) gradient which does not cause clumping of cells or alter the osmolarity of the buffers. The faster migrating fractions tended to be enriched in beta-cell content. Alpha and delta cells were found to some degree in most fractions. A histogram showing the cell count distribution is included.

A case of EDTA-dependent pseudothrombocytopenia: simple recognition of an underdiagnosed and misleading phenomenon

PubMed Central

2014-01-01

Background EDTA-dependent pseudothrombocytopenia (EDTA-PTCP) is a common laboratory phenomenon with a prevalence ranging from 0.1-2% in hospitalized patients to 15-17% in outpatients evaluated for isolated thrombocytopenia. Despite its harmlessness, EDTA-PTCP frequently leads to time-consuming, costly and even invasive diagnostic investigations. EDTA-PTCP is often overlooked because blood smears are not evaluated visually in routine practice and histograms as well as warning flags of hematology analyzers are not interpreted correctly. Nonetheless, EDTA-PTCP may be diagnosed easily even by general practitioners without any experiences in blood film examinations. This is the first report illustrating the typical patterns of a platelet (PLT) and white blood cell (WBC) histograms of hematology analyzers. Case presentation A 37-year-old female patient of Caucasian origin was referred with suspected acute leukemia and the crew of the emergency unit arranged extensive investigations for work-up. However, examination of EDTA blood sample revealed atypical lymphocytes and an isolated thrombocytopenia together with typical patterns of WBC and PLT histograms: a serrated curve of the platelet histogram and a peculiar peak on the left side of the WBC histogram. EDTA-PTCP was confirmed by a normal platelet count when examining citrated blood. Conclusion Awareness of typical PLT and WBC patterns may alert to the presence of EDTA-PTCP in routine laboratory practice helping to avoid unnecessary investigations and over-treatment. PMID:24808761

Bone mass density estimation: Archimede’s principle versus automatic X-ray histogram and edge detection technique in ovariectomized rats treated with germinated brown rice bioactives

PubMed Central

Muhammad, Sani Ismaila; Maznah, Ismail; Mahmud, Rozi Binti; Esmaile, Maher Faik; Zuki, Abu Bakar Zakaria

2013-01-01

Background Bone mass density is an important parameter used in the estimation of the severity and depth of lesions in osteoporosis. Estimation of bone density using existing methods in experimental models has its advantages as well as drawbacks. Materials and methods In this study, the X-ray histogram edge detection technique was used to estimate the bone mass density in ovariectomized rats treated orally with germinated brown rice (GBR) bioactives, and the results were compared with estimated results obtained using Archimede’s principle. New bone cell proliferation was assessed by histology and immunohistochemical reaction using polyclonal nuclear antigen. Additionally, serum alkaline phosphatase activity, serum and bone calcium and zinc concentrations were detected using a chemistry analyzer and atomic absorption spectroscopy. Rats were divided into groups of six as follows: sham (nonovariectomized, nontreated); ovariectomized, nontreated; and ovariectomized and treated with estrogen, or Remifemin®, GBR-phenolics, acylated steryl glucosides, gamma oryzanol, and gamma amino-butyric acid extracted from GBR at different doses. Results Our results indicate a significant increase in alkaline phosphatase activity, serum and bone calcium, and zinc and ash content in the treated groups compared with the ovariectomized nontreated group (P < 0.05). Bone density increased significantly (P < 0.05) in groups treated with estrogen, GBR, Remifemin®, and gamma oryzanol compared to the ovariectomized nontreated group. Histological sections revealed more osteoblasts in the treated groups when compared with the untreated groups. A polyclonal nuclear antigen reaction showing proliferating new cells was observed in groups treated with estrogen, Remifemin®, GBR, acylated steryl glucosides, and gamma oryzanol. There was a good correlation between bone mass densities estimated using Archimede’s principle and the edge detection technique between the treated groups (r2 = 0.737, P = 0.004). Conclusion Our study shows that GBR bioactives increase bone density, which might be via the activation of zinc formation and increased calcium content, and that X-ray edge detection technique is effective in the measurement of bone density and can be employed effectively in this respect. PMID:24187491

Probability Distribution Extraction from TEC Estimates based on Kernel Density Estimation

NASA Astrophysics Data System (ADS)

Demir, Uygar; Toker, Cenk; Çenet, Duygu

2016-07-01

Statistical analysis of the ionosphere, specifically the Total Electron Content (TEC), may reveal important information about its temporal and spatial characteristics. One of the core metrics that express the statistical properties of a stochastic process is its Probability Density Function (pdf). Furthermore, statistical parameters such as mean, variance and kurtosis, which can be derived from the pdf, may provide information about the spatial uniformity or clustering of the electron content. For example, the variance differentiates between a quiet ionosphere and a disturbed one, whereas kurtosis differentiates between a geomagnetic storm and an earthquake. Therefore, valuable information about the state of the ionosphere (and the natural phenomena that cause the disturbance) can be obtained by looking at the statistical parameters. In the literature, there are publications which try to fit the histogram of TEC estimates to some well-known pdf.s such as Gaussian, Exponential, etc. However, constraining a histogram to fit to a function with a fixed shape will increase estimation error, and all the information extracted from such pdf will continue to contain this error. In such techniques, it is highly likely to observe some artificial characteristics in the estimated pdf which is not present in the original data. In the present study, we use the Kernel Density Estimation (KDE) technique to estimate the pdf of the TEC. KDE is a non-parametric approach which does not impose a specific form on the TEC. As a result, better pdf estimates that almost perfectly fit to the observed TEC values can be obtained as compared to the techniques mentioned above. KDE is particularly good at representing the tail probabilities, and outliers. We also calculate the mean, variance and kurtosis of the measured TEC values. The technique is applied to the ionosphere over Turkey where the TEC values are estimated from the GNSS measurement from the TNPGN-Active (Turkish National Permanent GNSS Network) network. This study is supported by by TUBITAK 115E915 and Joint TUBITAK 114E092 and AS CR14/001 projects.

Gaze Fluctuations Are Not Additively Decomposable: Reply to Bogartz and Staub

ERIC Educational Resources Information Center

Kelty-Stephen, Damian G.; Mirman, Daniel

2013-01-01

Our previous work interpreted single-lognormal fits to inter-gaze distance (i.e., "gaze steps") histograms as evidence of multiplicativity and hence interactions across scales in visual cognition. Bogartz and Staub (2012) proposed that gaze steps are additively decomposable into fixations and saccades, matching the histograms better and…

Trinucleotide's quadruplet symmetries and natural symmetry law of DNA creation ensuing Chargaff's second parity rule.

PubMed

Rosandić, Marija; Vlahović, Ines; Glunčić, Matko; Paar, Vladimir

2016-07-01

For almost 50 years the conclusive explanation of Chargaff's second parity rule (CSPR), the equality of frequencies of nucleotides A=T and C=G or the equality of direct and reverse complement trinucleotides in the same DNA strand, has not been determined yet. Here, we relate CSPR to the interstrand mirror symmetry in 20 symbolic quadruplets of trinucleotides (direct, reverse complement, complement, and reverse) mapped to double-stranded genome. The symmetries of Q-box corresponding to quadruplets can be obtained as a consequence of Watson-Crick base pairing and CSPR together. Alternatively, assuming Natural symmetry law for DNA creation that each trinucleotide in one strand of DNA must simultaneously appear also in the opposite strand automatically leads to Q-box direct-reverse mirror symmetry which in conjunction with Watson-Crick base pairing generates CSPR. We demonstrate quadruplet's symmetries in chromosomes of wide range of organisms, from Escherichia coli to Neanderthal and human genomes, introducing novel quadruplet-frequency histograms and 3D-diagrams with combined interstrand frequencies. These "landscapes" are mutually similar in all mammals, including extinct Neanderthals, and somewhat different in most of older species. In human chromosomes 1-12, and X, Y the "landscapes" are almost identical and slightly different in the remaining smaller and telocentric chromosomes. Quadruplet frequencies could provide a new robust tool for characterization and classification of genomes and their evolutionary trajectories.

The use of linear programming techniques to design optimal digital filters for pulse shaping and channel equalization

NASA Technical Reports Server (NTRS)

Houts, R. C.; Burlage, D. W.

1972-01-01

A time domain technique is developed to design finite-duration impulse response digital filters using linear programming. Two related applications of this technique in data transmission systems are considered. The first is the design of pulse shaping digital filters to generate or detect signaling waveforms transmitted over bandlimited channels that are assumed to have ideal low pass or bandpass characteristics. The second is the design of digital filters to be used as preset equalizers in cascade with channels that have known impulse response characteristics. Example designs are presented which illustrate that excellent waveforms can be generated with frequency-sampling filters and the ease with which digital transversal filters can be designed for preset equalization.

Equalizer design techniques for dispersive cables with application to the SPS wideband kicker

NASA Astrophysics Data System (ADS)

Platt, Jason; Hofle, Wolfgang; Pollock, Kristin; Fox, John

2017-10-01

A wide-band vertical instability feedback control system in development at CERN requires 1-1.5 GHz of bandwidth for the entire processing chain, from the beam pickups through the feedback signal digital processing to the back-end power amplifiers and kicker structures. Dispersive effects in cables, amplifiers, pickup and kicker elements can result in distortions in the time domain signal as it proceeds through the processing system, and deviations from linear phase response reduce the allowable bandwidth for the closed-loop feedback system. We have developed an equalizer analog circuit that compensates for these dispersive effects. Here we present a design technique for the construction of an analog equalizer that incorporates the effect of parasitic circuit elements in the equalizer to increase the fidelity of the implemented equalizer. Finally, we show results from the measurement of an assembled backend equalizer that corrects for dispersive elements in the cables over a bandwidth of 10-1000 MHz.

A successive overrelaxation iterative technique for an adaptive equalizer

NASA Technical Reports Server (NTRS)

Kosovych, O. S.

1973-01-01

An adaptive strategy for the equalization of pulse-amplitude-modulated signals in the presence of intersymbol interference and additive noise is reported. The successive overrelaxation iterative technique is used as the algorithm for the iterative adjustment of the equalizer coefficents during a training period for the minimization of the mean square error. With 2-cyclic and nonnegative Jacobi matrices substantial improvement is demonstrated in the rate of convergence over the commonly used gradient techniques. The Jacobi theorems are also extended to nonpositive Jacobi matrices. Numerical examples strongly indicate that the improvements obtained for the special cases are possible for general channel characteristics. The technique is analytically demonstrated to decrease the mean square error at each iteration for a large range of parameter values for light or moderate intersymbol interference and for small intervals for general channels. Analytically, convergence of the relaxation algorithm was proven in a noisy environment and the coefficient variance was demonstrated to be bounded.

MRI intensity nonuniformity correction using simultaneously spatial and gray-level histogram information.

PubMed

Milles, Julien; Zhu, Yue Min; Gimenez, Gérard; Guttmann, Charles R G; Magnin, Isabelle E

2007-03-01

A novel approach for correcting intensity nonuniformity in magnetic resonance imaging (MRI) is presented. This approach is based on the simultaneous use of spatial and gray-level histogram information. Spatial information about intensity nonuniformity is obtained using cubic B-spline smoothing. Gray-level histogram information of the image corrupted by intensity nonuniformity is exploited from a frequential point of view. The proposed correction method is illustrated using both physical phantom and human brain images. The results are consistent with theoretical prediction, and demonstrate a new way of dealing with intensity nonuniformity problems. They are all the more significant as the ground truth on intensity nonuniformity is unknown in clinical images.

An effective image classification method with the fusion of invariant feature and a new color descriptor

NASA Astrophysics Data System (ADS)

Mansourian, Leila; Taufik Abdullah, Muhamad; Nurliyana Abdullah, Lili; Azman, Azreen; Mustaffa, Mas Rina

2017-02-01

Pyramid Histogram of Words (PHOW), combined Bag of Visual Words (BoVW) with the spatial pyramid matching (SPM) in order to add location information to extracted features. However, different PHOW extracted from various color spaces, and they did not extract color information individually, that means they discard color information, which is an important characteristic of any image that is motivated by human vision. This article, concatenated PHOW Multi-Scale Dense Scale Invariant Feature Transform (MSDSIFT) histogram and a proposed Color histogram to improve the performance of existing image classification algorithms. Performance evaluation on several datasets proves that the new approach outperforms other existing, state-of-the-art methods.

Web servlet-assisted, dial-in flow cytometry data analysis.

PubMed

Battye, F

2001-02-01

The obvious benefits of centralized data storage notwithstanding, the size of modern flow cytometry data files discourages their transmission over commonly used telephone modem connections. The proposed solution is to install at the central location a web servlet that can extract compact data arrays, of a form dependent on the requested display type, from the stored files and transmit them to a remote client computer program for display. A client program and a web servlet, both written in the Java programming language, were designed to communicate over standard network connections. The client program creates familiar numerical and graphical display types and allows the creation of gates from combinations of user-defined regions. Data compression techniques further reduce transmission times for data arrays that are already much smaller than the data file itself. For typical data files, network transmission times were reduced more than 700-fold for extraction of one-dimensional (1-D) histograms, between 18 and 120-fold for 2-D histograms, and 6-fold for color-coded dot plots. Numerous display formats are possible without further access to the data file. This scheme enables telephone modem access to centrally stored data without restricting flexibility of display format or preventing comparisons with locally stored files. Copyright 2001 Wiley-Liss, Inc.

Underwater image enhancement based on the dark channel prior and attenuation compensation

NASA Astrophysics Data System (ADS)

Guo, Qingwen; Xue, Lulu; Tang, Ruichun; Guo, Lingrui

2017-10-01

Aimed at the two problems of underwater imaging, fog effect and color cast, an Improved Segmentation Dark Channel Prior (ISDCP) defogging method is proposed to solve the fog effects caused by physical properties of water. Due to mass refraction of light in the process of underwater imaging, fog effects would lead to image blurring. And color cast is closely related to different degree of attenuation while light with different wavelengths is traveling in water. The proposed method here integrates the ISDCP and quantitative histogram stretching techniques into the image enhancement procedure. Firstly, the threshold value is set during the refinement process of the transmission maps to identify the original mismatching, and to conduct the differentiated defogging process further. Secondly, a method of judging the propagating distance of light is adopted to get the attenuation degree of energy during the propagation underwater. Finally, the image histogram is stretched quantitatively in Red-Green-Blue channel respectively according to the degree of attenuation in each color channel. The proposed method ISDCP can reduce the computational complexity and improve the efficiency in terms of defogging effect to meet the real-time requirements. Qualitative and quantitative comparison for several different underwater scenes reveals that the proposed method can significantly improve the visibility compared with previous methods.

Comparison of imaging characteristics of multiple-beam equalization and storage phosphor direct digitizer radiographic systems

NASA Astrophysics Data System (ADS)

Sankaran, A.; Chuang, Keh-Shih; Yonekawa, Hisashi; Huang, H. K.

1992-06-01

The imaging characteristics of two chest radiographic equipment, Advanced Multiple Beam Equalization Radiography (AMBER) and Konica Direct Digitizer [using a storage phosphor (SP) plate] systems have been compared. The variables affecting image quality and the computer display/reading systems used are detailed. Utilizing specially designed wedge, geometric, and anthropomorphic phantoms, studies were conducted on: exposure and energy response of detectors; nodule detectability; different exposure techniques; various look- up tables (LUTs), gray scale displays and laser printers. Methods for scatter estimation and reduction were investigated. It is concluded that AMBER with screen-film and equalization techniques provides better nodule detectability than SP plates. However, SP plates have other advantages such as flexibility in the selection of exposure techniques, image processing features, and excellent sensitivity when combined with optimum reader operating modes. The equalization feature of AMBER provides better nodule detectability under the denser regions of the chest. Results of diagnostic accuracy are demonstrated with nodule detectability plots and analysis of images obtained with phantoms.

A New Quantum Watermarking Based on Quantum Wavelet Transforms

NASA Astrophysics Data System (ADS)

Heidari, Shahrokh; Naseri, Mosayeb; Gheibi, Reza; Baghfalaki, Masoud; Rasoul Pourarian, Mohammad; Farouk, Ahmed

2017-06-01

Quantum watermarking is a technique to embed specific information, usually the owner’s identification, into quantum cover data such for copyright protection purposes. In this paper, a new scheme for quantum watermarking based on quantum wavelet transforms is proposed which includes scrambling, embedding and extracting procedures. The invisibility and robustness performances of the proposed watermarking method is confirmed by simulation technique. The invisibility of the scheme is examined by the peak-signal-to-noise ratio (PSNR) and the histogram calculation. Furthermore the robustness of the scheme is analyzed by the Bit Error Rate (BER) and the Correlation Two-Dimensional (Corr 2-D) calculation. The simulation results indicate that the proposed watermarking scheme indicate not only acceptable visual quality but also a good resistance against different types of attack. Supported by Kermanshah Branch, Islamic Azad University, Kermanshah, Iran

Microprocessor-Based Neural-Pulse-Wave Analyzer

NASA Technical Reports Server (NTRS)

Kojima, G. K.; Bracchi, F.

1983-01-01

Microprocessor-based system analyzes amplitudes and rise times of neural waveforms. Displaying histograms of measured parameters helps researchers determine how many nerves contribute to signal and specify waveform characteristics of each. Results are improved noise rejection, full or partial separation of overlapping peaks, and isolation and identification of related peaks in different histograms. 2

Histogram-based automatic thresholding for bruise detection of apples by structured-illumination reflectance imaging

USDA-ARS?s Scientific Manuscript database

Thresholding is an important step in the segmentation of image features, and the existing methods are not all effective when the image histogram exhibits a unimodal pattern, which is common in defect detection of fruit. This study was aimed at developing a general automatic thresholding methodology ...

Evaluation of digital radiography practice using exposure index tracking

PubMed Central

Zhou, Yifang; Allahverdian, Janet; Nute, Jessica L.; Lee, Christina

2016-01-01

Some digital radiography (DR) detectors and software allow for remote download of exam statistics, including image reject status, body part, projection, and exposure index (EI). The ability to have automated data collection from multiple DR units is conducive to a quality control (QC) program monitoring institutional radiographic exposures. We have implemented such a QC program with the goal to identify outliers in machine radiation output and opportunities for improvement in radiation dose levels. We studied the QC records of four digital detectors in greater detail on a monthly basis for one year. Although individual patient entrance skin exposure varied, the radiation dose levels to the detectors were made to be consistent via phototimer recalibration. The exposure data stored on each digital detector were periodically downloaded in a spreadsheet format for analysis. EI median and standard deviation were calculated for each protocol (by body part) and EI histograms were created for torso protocols. When histograms of EI values for different units were compared, we observed differences up to 400 in average EI (representing 60% difference in radiation levels to the detector) between units nominally calibrated to the same EI. We identified distinct components of the EI distributions, which in some cases, had mean EI values 300 apart. Peaks were observed at the current calibrated EI, a previously calibrated EI, and an EI representing computed radiography (CR) techniques. Our findings in this ongoing project have allowed us to make useful interventions, from emphasizing the use of phototimers instead of institutional memory of manual techniques to improvements in our phototimer calibration. We believe that this QC program can be implemented at other sites and can reveal problems with radiation levels in the aggregate that are difficult to identify on a case‐by‐case basis. PACS number(s): 87.59.bf PMID:27929507

Principal component analysis-based pattern analysis of dose-volume histograms and influence on rectal toxicity.

PubMed

Söhn, Matthias; Alber, Markus; Yan, Di

2007-09-01

The variability of dose-volume histogram (DVH) shapes in a patient population can be quantified using principal component analysis (PCA). We applied this to rectal DVHs of prostate cancer patients and investigated the correlation of the PCA parameters with late bleeding. PCA was applied to the rectal wall DVHs of 262 patients, who had been treated with a four-field box, conformal adaptive radiotherapy technique. The correlated changes in the DVH pattern were revealed as "eigenmodes," which were ordered by their importance to represent data set variability. Each DVH is uniquely characterized by its principal components (PCs). The correlation of the first three PCs and chronic rectal bleeding of Grade 2 or greater was investigated with uni- and multivariate logistic regression analyses. Rectal wall DVHs in four-field conformal RT can primarily be represented by the first two or three PCs, which describe approximately 94% or 96% of the DVH shape variability, respectively. The first eigenmode models the total irradiated rectal volume; thus, PC1 correlates to the mean dose. Mode 2 describes the interpatient differences of the relative rectal volume in the two- or four-field overlap region. Mode 3 reveals correlations of volumes with intermediate doses ( approximately 40-45 Gy) and volumes with doses >70 Gy; thus, PC3 is associated with the maximal dose. According to univariate logistic regression analysis, only PC2 correlated significantly with toxicity. However, multivariate logistic regression analysis with the first two or three PCs revealed an increased probability of bleeding for DVHs with more than one large PC. PCA can reveal the correlation structure of DVHs for a patient population as imposed by the treatment technique and provide information about its relationship to toxicity. It proves useful for augmenting normal tissue complication probability modeling approaches.

Deep convolutional neural networks for automatic classification of gastric carcinoma using whole slide images in digital histopathology.

PubMed

Sharma, Harshita; Zerbe, Norman; Klempert, Iris; Hellwich, Olaf; Hufnagl, Peter

2017-11-01

Deep learning using convolutional neural networks is an actively emerging field in histological image analysis. This study explores deep learning methods for computer-aided classification in H&E stained histopathological whole slide images of gastric carcinoma. An introductory convolutional neural network architecture is proposed for two computerized applications, namely, cancer classification based on immunohistochemical response and necrosis detection based on the existence of tumor necrosis in the tissue. Classification performance of the developed deep learning approach is quantitatively compared with traditional image analysis methods in digital histopathology requiring prior computation of handcrafted features, such as statistical measures using gray level co-occurrence matrix, Gabor filter-bank responses, LBP histograms, gray histograms, HSV histograms and RGB histograms, followed by random forest machine learning. Additionally, the widely known AlexNet deep convolutional framework is comparatively analyzed for the corresponding classification problems. The proposed convolutional neural network architecture reports favorable results, with an overall classification accuracy of 0.6990 for cancer classification and 0.8144 for necrosis detection. Copyright © 2017 Elsevier Ltd. All rights reserved.

Distribution of a suite of elements including arsenic and mercury in Alabama coal

USGS Publications Warehouse

Goldhaber, Martin B.; Bigelow, R.C.; Hatch, J.R.; Pashin, J.C.

2000-01-01

Arsenic and other elements are unusually abundant in Alabama coal. This conclusion is based on chemical analyses of coal in the U.S. Geological Survey's National Coal Resources Data System (NCRDS; Bragg and others, 1994). According to NCRDS data, the average concentration of arsenic in Alabama coal (72 ppm) is three times higher than is the average for all U.S. coal (24 ppm). Of the U.S. coal analyses for arsenic that are at least 3 standard deviations above the mean, approximately 90% are from the coal fields of Alabama. Figure 1 contrasts the abundance of arsenic in coal of the Warrior field of Alabama (histogram C) with that of coal of the Powder River Basin, Wyoming (histogram A), and the Eastern Interior Province including the Illinois Basin and nearby areas (histogram B). The Warrior field is by far the largest in Alabama. On the histogram, the large 'tail' of very high values (> 200 ppm) in the Warrior coal contrasts with the other two regions that have very few analyses greater than 200 ppm.

Real-Time Tracking by Double Templates Matching Based on Timed Motion History Image with HSV Feature

PubMed Central

Li, Zhiyong; Li, Pengfei; Yu, Xiaoping; Hashem, Mervat

2014-01-01

It is a challenge to represent the target appearance model for moving object tracking under complex environment. This study presents a novel method with appearance model described by double templates based on timed motion history image with HSV color histogram feature (tMHI-HSV). The main components include offline template and online template initialization, tMHI-HSV-based candidate patches feature histograms calculation, double templates matching (DTM) for object location, and templates updating. Firstly, we initialize the target object region and calculate its HSV color histogram feature as offline template and online template. Secondly, the tMHI-HSV is used to segment the motion region and calculate these candidate object patches' color histograms to represent their appearance models. Finally, we utilize the DTM method to trace the target and update the offline template and online template real-timely. The experimental results show that the proposed method can efficiently handle the scale variation and pose change of the rigid and nonrigid objects, even in illumination change and occlusion visual environment. PMID:24592185

Adjustments for the display of quantized ion channel dwell times in histograms with logarithmic bins.

PubMed

Stark, J A; Hladky, S B

2000-02-01

Dwell-time histograms are often plotted as part of patch-clamp investigations of ion channel currents. The advantages of plotting these histograms with a logarithmic time axis were demonstrated by, J. Physiol. (Lond.). 378:141-174), Pflügers Arch. 410:530-553), and, Biophys. J. 52:1047-1054). Sigworth and Sine argued that the interpretation of such histograms is simplified if the counts are presented in a manner similar to that of a probability density function. However, when ion channel records are recorded as a discrete time series, the dwell times are quantized. As a result, the mapping of dwell times to logarithmically spaced bins is highly irregular; bins may be empty, and significant irregularities may extend beyond the duration of 100 samples. Using simple approximations based on the nature of the binning process and the transformation rules for probability density functions, we develop adjustments for the display of the counts to compensate for this effect. Tests with simulated data suggest that this procedure provides a faithful representation of the data.

Qualitative evaluations and comparisons of six night-vision colorization methods

NASA Astrophysics Data System (ADS)

Zheng, Yufeng; Reese, Kristopher; Blasch, Erik; McManamon, Paul

2013-05-01

Current multispectral night vision (NV) colorization techniques can manipulate images to produce colorized images that closely resemble natural scenes. The colorized NV images can enhance human perception by improving observer object classification and reaction times especially for low light conditions. This paper focuses on the qualitative (subjective) evaluations and comparisons of six NV colorization methods. The multispectral images include visible (Red-Green- Blue), near infrared (NIR), and long wave infrared (LWIR) images. The six colorization methods are channel-based color fusion (CBCF), statistic matching (SM), histogram matching (HM), joint-histogram matching (JHM), statistic matching then joint-histogram matching (SM-JHM), and the lookup table (LUT). Four categries of quality measurements are used for the qualitative evaluations, which are contrast, detail, colorfulness, and overall quality. The score of each measurement is rated from 1 to 3 scale to represent low, average, and high quality, respectively. Specifically, high contrast (of rated score 3) means an adequate level of brightness and contrast. The high detail represents high clarity of detailed contents while maintaining low artifacts. The high colorfulness preserves more natural colors (i.e., closely resembles the daylight image). Overall quality is determined from the NV image compared to the reference image. Nine sets of multispectral NV images were used in our experiments. For each set, the six colorized NV images (produced from NIR and LWIR images) are concurrently presented to users along with the reference color (RGB) image (taken at daytime). A total of 67 subjects passed a screening test ("Ishihara Color Blindness Test") and were asked to evaluate the 9-set colorized images. The experimental results showed the quality order of colorization methods from the best to the worst: CBCF < SM < SM-JHM < LUT < JHM < HM. It is anticipated that this work will provide a benchmark for NV colorization and for quantitative evaluation using an objective metric such as objective evaluation index (OEI).

Nanomechanical characterization of heterogeneous and hierarchical biomaterials and tissues using nanoindentation: the role of finite mixture models.

PubMed

Zadpoor, Amir A

2015-03-01

Mechanical characterization of biological tissues and biomaterials at the nano-scale is often performed using nanoindentation experiments. The different constituents of the characterized materials will then appear in the histogram that shows the probability of measuring a certain range of mechanical properties. An objective technique is needed to separate the probability distributions that are mixed together in such a histogram. In this paper, finite mixture models (FMMs) are proposed as a tool capable of performing such types of analysis. Finite Gaussian mixture models assume that the measured probability distribution is a weighted combination of a finite number of Gaussian distributions with separate mean and standard deviation values. Dedicated optimization algorithms are available for fitting such a weighted mixture model to experimental data. Moreover, certain objective criteria are available to determine the optimum number of Gaussian distributions. In this paper, FMMs are used for interpreting the probability distribution functions representing the distributions of the elastic moduli of osteoarthritic human cartilage and co-polymeric microspheres. As for cartilage experiments, FMMs indicate that at least three mixture components are needed for describing the measured histogram. While the mechanical properties of the softer mixture components, often assumed to be associated with Glycosaminoglycans, were found to be more or less constant regardless of whether two or three mixture components were used, those of the second mixture component (i.e. collagen network) considerably changed depending on the number of mixture components. Regarding the co-polymeric microspheres, the optimum number of mixture components estimated by the FMM theory, i.e. 3, nicely matches the number of co-polymeric components used in the structure of the polymer. The computer programs used for the presented analyses are made freely available online for other researchers to use. Copyright © 2014 Elsevier B.V. All rights reserved.

Hybrid acousto-optic and digital equalization for microwave digital radio channels

NASA Astrophysics Data System (ADS)

Anderson, C. S.; Vanderlugt, A.

1990-11-01

Digital radio transmission systems use complex modulation schemes that require powerful signal-processing techniques to correct channel distortions and to minimize BERs. This paper proposes combining the computation power of acoustooptic processing and the accuracy of digital processing to produce a hybrid channel equalizer that exceeds the performance of digital equalization alone. Analysis shows that a hybrid equalizer for 256-level quadrature amplitude modulation (QAM) performs better than a digital equalizer for 64-level QAM.

Assessment of Arterial Wall Enhancement for Differentiation of Parent Artery Disease from Small Artery Disease: Comparison between Histogram Analysis and Visual Analysis on 3-Dimensional Contrast-Enhanced T1-Weighted Turbo Spin Echo MR Images at 3T.

PubMed

Jang, Jinhee; Kim, Tae-Won; Hwang, Eo-Jin; Choi, Hyun Seok; Koo, Jaseong; Shin, Yong Sam; Jung, So-Lyung; Ahn, Kook-Jin; Kim, Bum-Soo

2017-01-01

The purpose of this study was to compare the histogram analysis and visual scores in 3T MRI assessment of middle cerebral arterial wall enhancement in patients with acute stroke, for the differentiation of parent artery disease (PAD) from small artery disease (SAD). Among the 82 consecutive patients in a tertiary hospital for one year, 25 patients with acute infarcts in middle cerebral artery (MCA) territory were included in this study including 15 patients with PAD and 10 patients with SAD. Three-dimensional contrast-enhanced T1-weighted turbo spin echo MR images with black-blood preparation at 3T were analyzed both qualitatively and quantitatively. The degree of MCA stenosis, and visual and histogram assessments on MCA wall enhancement were evaluated. A statistical analysis was performed to compare diagnostic accuracy between qualitative and quantitative metrics. The degree of stenosis, visual enhancement score, geometric mean (GM), and the 90th percentile (90P) value from the histogram analysis were significantly higher in PAD than in SAD ( p = 0.006 for stenosis, < 0.001 for others). The receiver operating characteristic curve area of GM and 90P were 1 (95% confidence interval [CI], 0.86-1.00). A histogram analysis of a relevant arterial wall enhancement allows differentiation between PAD and SAD in patients with acute stroke within the MCA territory.

Utility of histogram analysis of apparent diffusion coefficient maps obtained using 3.0T MRI for distinguishing uterine carcinosarcoma from endometrial carcinoma.

PubMed

Takahashi, Masahiro; Kozawa, Eito; Tanisaka, Megumi; Hasegawa, Kousei; Yasuda, Masanori; Sakai, Fumikazu

2016-06-01

We explored the role of histogram analysis of apparent diffusion coefficient (ADC) maps for discriminating uterine carcinosarcoma and endometrial carcinoma. We retrospectively evaluated findings in 13 patients with uterine carcinosarcoma and 50 patients with endometrial carcinoma who underwent diffusion-weighted imaging (b = 0, 500, 1000 s/mm(2) ) at 3T with acquisition of corresponding ADC maps. We derived histogram data from regions of interest drawn on all slices of the ADC maps in which tumor was visualized, excluding areas of necrosis and hemorrhage in the tumor. We used the Mann-Whitney test to evaluate the capacity of histogram parameters (mean ADC value, 5th to 95th percentiles, skewness, kurtosis) to discriminate uterine carcinosarcoma and endometrial carcinoma and analyzed the receiver operating characteristic (ROC) curve to determine the optimum threshold value for each parameter and its corresponding sensitivity and specificity. Carcinosarcomas demonstrated significantly higher mean vales of ADC, 95th, 90th, 75th, 50th, 25th percentiles and kurtosis than endometrial carcinomas (P < 0.05). ROC curve analysis of the 75th percentile yielded the best area under the ROC curve (AUC; 0.904), sensitivity of 100%, and specificity of 78.0%, with a cutoff value of 1.034 × 10(-3) mm(2) /s. Histogram analysis of ADC maps might be helpful for discriminating uterine carcinosarcomas and endometrial carcinomas. J. Magn. Reson. Imaging 2016;43:1301-1307. © 2015 Wiley Periodicals, Inc.

Characterization of testicular germ cell tumors: Whole-lesion histogram analysis of the apparent diffusion coefficient at 3T.

PubMed

Min, Xiangde; Feng, Zhaoyan; Wang, Liang; Cai, Jie; Yan, Xu; Li, Basen; Ke, Zan; Zhang, Peipei; You, Huijuan

2018-01-01

To assess the values of parameters derived from whole-lesion histograms of the apparent diffusion coefficient (ADC) at 3T for the characterization of testicular germ cell tumors (TGCTs). A total of 24 men with TGCTs underwent 3T diffusion-weighted imaging. Fourteen tumors were pathologically confirmed as seminomas, and ten tumors were pathologically confirmed as nonseminomas. Whole-lesion histogram analysis of the ADC values was performed. A Mann-Whitney U test was employed to compare the differences in ADC histogram parameters between seminomas and nonseminomas. Receiver operating characteristic analysis was used to identify the cutoff values for each parameter for differentiating seminomas from nonseminomas; furthermore, the area under the curve (AUC) was calculated to evaluate the diagnostic accuracy. The median of 10th, 25th, 50th, 75th, and 90th percentiles and mean, minimum and maximum ADC values were all significantly reduced for seminomas compared with nonseminomas (p<0.05 for all). In contrast, the median of kurtosis and skewness of ADC values of seminomas were both significantly increased compared with those of nonseminomas (p=0.003 and 0.001, respectively). For differentiating nonseminomas from seminomas, the 10th percentile ADC yielded the highest AUC with a sensitivity and specificity of 100% and 92.86%, respectively. Whole-lesion histogram analysis of ADCs might be used for preoperative characterization of TGCTs. Copyright © 2017 Elsevier B.V. All rights reserved.

Quantification and classification of neuronal responses in kernel-smoothed peristimulus time histograms

PubMed Central

Fried, Itzhak; Koch, Christof

2014-01-01

Peristimulus time histograms are a widespread form of visualizing neuronal responses. Kernel convolution methods transform these histograms into a smooth, continuous probability density function. This provides an improved estimate of a neuron's actual response envelope. We here develop a classifier, called the h-coefficient, to determine whether time-locked fluctuations in the firing rate of a neuron should be classified as a response or as random noise. Unlike previous approaches, the h-coefficient takes advantage of the more precise response envelope estimation provided by the kernel convolution method. The h-coefficient quantizes the smoothed response envelope and calculates the probability of a response of a given shape to occur by chance. We tested the efficacy of the h-coefficient in a large data set of Monte Carlo simulated smoothed peristimulus time histograms with varying response amplitudes, response durations, trial numbers, and baseline firing rates. Across all these conditions, the h-coefficient significantly outperformed more classical classifiers, with a mean false alarm rate of 0.004 and a mean hit rate of 0.494. We also tested the h-coefficient's performance in a set of neuronal responses recorded in humans. The algorithm behind the h-coefficient provides various opportunities for further adaptation and the flexibility to target specific parameters in a given data set. Our findings confirm that the h-coefficient can provide a conservative and powerful tool for the analysis of peristimulus time histograms with great potential for future development. PMID:25475352

Using color histogram normalization for recovering chromatic illumination-changed images.

PubMed

Pei, S C; Tseng, C L; Wu, C C

2001-11-01

We propose a novel image-recovery method using the covariance matrix of the red-green-blue (R-G-B) color histogram and tensor theories. The image-recovery method is called the color histogram normalization algorithm. It is known that the color histograms of an image taken under varied illuminations are related by a general affine transformation of the R-G-B coordinates when the illumination is changed. We propose a simplified affine model for application with illumination variation. This simplified affine model considers the effects of only three basic forms of distortion: translation, scaling, and rotation. According to this principle, we can estimate the affine transformation matrix necessary to recover images whose color distributions are varied as a result of illumination changes. We compare the normalized color histogram of the standard image with that of the tested image. By performing some operations of simple linear algebra, we can estimate the matrix of the affine transformation between two images under different illuminations. To demonstrate the performance of the proposed algorithm, we divide the experiments into two parts: computer-simulated images and real images corresponding to illumination changes. Simulation results show that the proposed algorithm is effective for both types of images. We also explain the noise-sensitive skew-rotation estimation that exists in the general affine model and demonstrate that the proposed simplified affine model without the use of skew rotation is better than the general affine model for such applications.

The impact of slice-reduced computed tomography on histogram-based densitometry assessment of lung fibrosis in patients with systemic sclerosis.

PubMed

Nguyen-Kim, Thi Dan Linh; Maurer, Britta; Suliman, Yossra A; Morsbach, Fabian; Distler, Oliver; Frauenfelder, Thomas

2018-04-01

To evaluate usability of slice-reduced sequential computed tomography (CT) compared to standard high-resolution CT (HRCT) in patients with systemic sclerosis (SSc) for qualitative and quantitative assessment of interstitial lung disease (ILD) with respect to (I) detection of lung parenchymal abnormalities, (II) qualitative and semiquantitative visual assessment, (III) quantification of ILD by histograms and (IV) accuracy for the 20%-cut off discrimination. From standard chest HRCT of 60 SSc patients sequential 9-slice-computed tomography (reduced HRCT) was retrospectively reconstructed. ILD was assessed by visual scoring and quantitative histogram parameters. Results from standard and reduced HRCT were compared using non-parametric tests and analysed by univariate linear regression analyses. With respect to the detection of parenchymal abnormalities, only the detection of intrapulmonary bronchiectasis was significantly lower in reduced HRCT compared to standard HRCT (P=0.039). No differences were found comparing visual scores for fibrosis severity and extension from standard and reduced HRCT (P=0.051-0.073). All scores correlated significantly (P<0.001) to histogram parameters derived from both, standard and reduced HRCT. Significant higher values of kurtosis and skewness for reduced HRCT were found (both P<0.001). In contrast to standard HRCT histogram parameters from reduced HRCT showed significant discrimination at cut-off 20% fibrosis (sensitivity 88% kurtosis and skewness; specificity 81% kurtosis and 86% skewness; cut-off kurtosis ≤26, cut-off skewness ≤4; both P<0.001). Reduced HRCT is a robust method to assess lung fibrosis in SSc with minimal radiation dose with no difference in scoring assessment of lung fibrosis severity and extension in comparison to standard HRCT. In contrast to standard HRCT histogram parameters derived from the approach of reduced HRCT could discriminate at a threshold of 20% lung fibrosis with high sensitivity and specificity. Hence it might be used to detect early disease progression of lung fibrosis in context of monitoring and treatment of SSc patients.

Histogram analysis of ADC in rectal cancer: associations with different histopathological findings including expression of EGFR, Hif1-alpha, VEGF, p53, PD1, and KI 67. A preliminary study.

PubMed

Meyer, Hans Jonas; Höhn, Annekathrin; Surov, Alexey

2018-04-06

Functional imaging modalities like Diffusion-weighted imaging are increasingly used to predict tumor behavior like cellularity and vascularity in different tumors. Histogram analysis is an emergent imaging analysis, in which every voxel is used to obtain a histogram and therefore statistically information about tumors can be provided. The purpose of this study was to elucidate possible associations between ADC histogram parameters and several immunhistochemical features in rectal cancer. Overall, 11 patients with histologically proven rectal cancer were included into the study. There were 2 (18.18%) females and 9 males with a mean age of 67.1 years. KI 67-index, expression of p53, EGFR, VEGF, and Hif1-alpha were semiautomatically estimated. The tumors were divided into PD1-positive and PD1-negative lesions. ADC histogram analysis was performed as a whole lesion measurement using an in-house matlab application. Spearman's correlation analysis revealed a strong correlation between EGFR expression and ADCmax (p=0.72, P=0.02). None of the vascular parameters (VEGF, Hif1-alpha) correlated with ADC parameters. Kurtosis and skewness correlated inversely with p53 expression (p=-0.64, P=0.03 and p=-0.81, P=0.002, respectively). ADCmedian and ADCmode correlated with Ki67 (p=-0.62, P=0.04 and p=-0.65, P=0.03, respectively). PD1-positive tumors showed statistically significant lower ADCmax values in comparison to PD1-negative tumors, 1.93 ± 0.36 vs 2.32 ± 0.47×10 -3 mm 2 /s, p=0.04. Several associations were identified between histogram parameter derived from ADC maps and EGFR, KI 67 and p53 expression in rectal cancer. Furthermore, ADCmax was different between PD1 positive and PD1 negative tumors indicating an important role of ADC parameters for possible future treatment prediction.

Histogram analysis of ADC in rectal cancer: associations with different histopathological findings including expression of EGFR, Hif1-alpha, VEGF, p53, PD1, and KI 67. A preliminary study

PubMed Central

Meyer, Hans Jonas; Höhn, Annekathrin; Surov, Alexey

2018-01-01

Functional imaging modalities like Diffusion-weighted imaging are increasingly used to predict tumor behavior like cellularity and vascularity in different tumors. Histogram analysis is an emergent imaging analysis, in which every voxel is used to obtain a histogram and therefore statistically information about tumors can be provided. The purpose of this study was to elucidate possible associations between ADC histogram parameters and several immunhistochemical features in rectal cancer. Overall, 11 patients with histologically proven rectal cancer were included into the study. There were 2 (18.18%) females and 9 males with a mean age of 67.1 years. KI 67-index, expression of p53, EGFR, VEGF, and Hif1-alpha were semiautomatically estimated. The tumors were divided into PD1-positive and PD1-negative lesions. ADC histogram analysis was performed as a whole lesion measurement using an in-house matlab application. Spearman's correlation analysis revealed a strong correlation between EGFR expression and ADCmax (p=0.72, P=0.02). None of the vascular parameters (VEGF, Hif1-alpha) correlated with ADC parameters. Kurtosis and skewness correlated inversely with p53 expression (p=-0.64, P=0.03 and p=-0.81, P=0.002, respectively). ADCmedian and ADCmode correlated with Ki67 (p=-0.62, P=0.04 and p=-0.65, P=0.03, respectively). PD1-positive tumors showed statistically significant lower ADCmax values in comparison to PD1-negative tumors, 1.93 ± 0.36 vs 2.32 ± 0.47×10−3mm2/s, p=0.04. Several associations were identified between histogram parameter derived from ADC maps and EGFR, KI 67 and p53 expression in rectal cancer. Furthermore, ADCmax was different between PD1 positive and PD1 negative tumors indicating an important role of ADC parameters for possible future treatment prediction. PMID:29719621

SU-C-207A-07: Cumulative 18F-FDG Uptake Histogram Relative to Radiation Dose Volume Histogram of Lung After IMRT Or PSPT and Their Association with Radiation Pneumonitis

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

Shusharina, N; Choi, N; Bortfeld, T

2016-06-15

Purpose: To determine whether the difference in cumulative 18F-FDG uptake histogram of lung treated with either IMRT or PSPT is associated with radiation pneumonitis (RP) in patients with inoperable stage II and III NSCLC. Methods: We analyzed 24 patients from a prospective randomized trial to compare IMRT (n=12) with vs. PSPT (n=12) for inoperable NSCLC. All patients underwent PET-CT imaging between 35 and 88 days post-therapy. Post-treatment PET-CT was aligned with planning 4D CT to establish a voxel-to-voxel correspondence between post-treatment PET and planning dose images. 18F-FDG uptake as a function of radiation dose to normal lung was obtained formore » each patient. Distribution of the standard uptake value (SUV) was analyzed using a volume histogram method. The image quantitative characteristics and DVH measures were correlated with clinical symptoms of pneumonitis. Results: Patients with RP were present in both groups: 5 in the IMRT and 6 in the PSPT. The analysis of cumulative SUV histograms showed significantly higher relative volumes of the normal lung having higher SUV uptake in the PSPT patients for both symptomatic and asymptomatic cases (VSUV=2: 10% for IMRT vs 16% for proton RT and VSUV=1: 10% for IMRT vs 23% for proton RT). In addition, the SUV histograms for symptomatic cases in PSPT patients exhibited a significantly longer tail at the highest SUV. The absolute volume of the lung receiving the dose >70 Gy was larger in the PSPT patients. Conclusion: 18F-FDG uptake – radiation dose response correlates with RP in both groups of patients by means of the linear regression slope. SUV is higher for the PSPT patients for both symptomatic and asymptomatic cases. Higher uptake after PSPT patients is explained by larger volumes of the lung receiving high radiation dose.« less

LinoSPAD: a time-resolved 256×1 CMOS SPAD line sensor system featuring 64 FPGA-based TDC channels running at up to 8.5 giga-events per second

NASA Astrophysics Data System (ADS)

Burri, Samuel; Homulle, Harald; Bruschini, Claudio; Charbon, Edoardo

2016-04-01

LinoSPAD is a reconfigurable camera sensor with a 256×1 CMOS SPAD (single-photon avalanche diode) pixel array connected to a low cost Xilinx Spartan 6 FPGA. The LinoSPAD sensor's line of pixels has a pitch of 24 μm and 40% fill factor. The FPGA implements an array of 64 TDCs and histogram engines capable of processing up to 8.5 giga-photons per second. The LinoSPAD sensor measures 1.68 mm×6.8 mm and each pixel has a direct digital output to connect to the FPGA. The chip is bonded on a carrier PCB to connect to the FPGA motherboard. 64 carry chain based TDCs sampled at 400 MHz can generate a timestamp every 7.5 ns with a mean time resolution below 25 ps per code. The 64 histogram engines provide time-of-arrival histograms covering up to 50 ns. An alternative mode allows the readout of 28 bit timestamps which have a range of up to 4.5 ms. Since the FPGA TDCs have considerable non-linearity we implemented a correction module capable of increasing histogram linearity at real-time. The TDC array is interfaced to a computer using a super-speed USB3 link to transfer over 150k histograms per second for the 12.5 ns reference period used in our characterization. After characterization and subsequent programming of the post-processing we measure an instrument response histogram shorter than 100 ps FWHM using a strong laser pulse with 50 ps FWHM. A timing resolution that when combined with the high fill factor makes the sensor well suited for a wide variety of applications from fluorescence lifetime microscopy over Raman spectroscopy to 3D time-of-flight.

Isobio software: biological dose distribution and biological dose volume histogram from physical dose conversion using linear-quadratic-linear model.

PubMed

Jaikuna, Tanwiwat; Khadsiri, Phatchareewan; Chawapun, Nisa; Saekho, Suwit; Tharavichitkul, Ekkasit

2017-02-01

To develop an in-house software program that is able to calculate and generate the biological dose distribution and biological dose volume histogram by physical dose conversion using the linear-quadratic-linear (LQL) model. The Isobio software was developed using MATLAB version 2014b to calculate and generate the biological dose distribution and biological dose volume histograms. The physical dose from each voxel in treatment planning was extracted through Computational Environment for Radiotherapy Research (CERR), and the accuracy was verified by the differentiation between the dose volume histogram from CERR and the treatment planning system. An equivalent dose in 2 Gy fraction (EQD 2 ) was calculated using biological effective dose (BED) based on the LQL model. The software calculation and the manual calculation were compared for EQD 2 verification with pair t -test statistical analysis using IBM SPSS Statistics version 22 (64-bit). Two and three-dimensional biological dose distribution and biological dose volume histogram were displayed correctly by the Isobio software. Different physical doses were found between CERR and treatment planning system (TPS) in Oncentra, with 3.33% in high-risk clinical target volume (HR-CTV) determined by D 90% , 0.56% in the bladder, 1.74% in the rectum when determined by D 2cc , and less than 1% in Pinnacle. The difference in the EQD 2 between the software calculation and the manual calculation was not significantly different with 0.00% at p -values 0.820, 0.095, and 0.593 for external beam radiation therapy (EBRT) and 0.240, 0.320, and 0.849 for brachytherapy (BT) in HR-CTV, bladder, and rectum, respectively. The Isobio software is a feasible tool to generate the biological dose distribution and biological dose volume histogram for treatment plan evaluation in both EBRT and BT.

Diagnostic accuracy of ultrasonic histogram features to evaluate radiation toxicity of the parotid glands: a clinical study of xerostomia following head-and-neck cancer radiotherapy.

PubMed

Yang, Xiaofeng; Tridandapani, Srini; Beitler, Jonathan J; Yu, David S; Chen, Zhengjia; Kim, Sungjin; Bruner, Deborah W; Curran, Walter J; Liu, Tian

2014-10-01

To investigate the diagnostic accuracy of ultrasound histogram features in the quantitative assessment of radiation-induced parotid gland injury and to identify potential imaging biomarkers for radiation-induced xerostomia (dry mouth)-the most common and debilitating side effect after head-and-neck radiotherapy (RT). Thirty-four patients, who have developed xerostomia after RT for head-and-neck cancer, were enrolled. Radiation-induced xerostomia was defined by the Radiation Therapy Oncology Group/European Organization for Research and Treatment of Cancer morbidity scale. Ultrasound scans were performed on each patient's parotids bilaterally. The 34 patients were stratified into the acute-toxicity groups (16 patients, ≤ 3 months after treatment) and the late-toxicity group (18 patients, > 3 months after treatment). A separate control group of 13 healthy volunteers underwent similar ultrasound scans of their parotid glands. Six sonographic features were derived from the echo-intensity histograms to assess acute and late toxicity of the parotid glands. The quantitative assessments were compared to a radiologist's clinical evaluations. The diagnostic accuracy of these ultrasonic histogram features was evaluated with the receiver operating characteristic (ROC) curve. With an area under the ROC curve greater than 0.90, several histogram features demonstrated excellent diagnostic accuracy for evaluation of acute and late toxicity of parotid glands. Significant differences (P < .05) in all six sonographic features were demonstrated between the control, acute-toxicity, and late-toxicity groups. However, subjective radiologic evaluation cannot distinguish between acute and late toxicity of parotid glands. We demonstrated that ultrasound histogram features could be used to measure acute and late toxicity of the parotid glands after head-and-neck cancer RT, which may be developed into a low-cost imaging method for xerostomia monitoring and assessment. Copyright © 2014 AUR. Published by Elsevier Inc. All rights reserved.

MCNP Output Data Analysis with ROOT (MODAR)

NASA Astrophysics Data System (ADS)

Carasco, C.

2010-06-01

MCNP Output Data Analysis with ROOT (MODAR) is a tool based on CERN's ROOT software. MODAR has been designed to handle time-energy data issued by MCNP simulations of neutron inspection devices using the associated particle technique. MODAR exploits ROOT's Graphical User Interface and functionalities to visualize and process MCNP simulation results in a fast and user-friendly way. MODAR allows to take into account the detection system time resolution (which is not possible with MCNP) as well as detectors energy response function and counting statistics in a straightforward way. Program summaryProgram title: MODAR Catalogue identifier: AEGA_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEGA_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 155 373 No. of bytes in distributed program, including test data, etc.: 14 815 461 Distribution format: tar.gz Programming language: C++ Computer: Most Unix workstations and PC Operating system: Most Unix systems, Linux and windows, provided the ROOT package has been installed. Examples where tested under Suse Linux and Windows XP. RAM: Depends on the size of the MCNP output file. The example presented in the article, which involves three two-dimensional 139×740 bins histograms, allocates about 60 MB. These data are running under ROOT and include consumption by ROOT itself. Classification: 17.6 External routines: ROOT version 5.24.00 ( http://root.cern.ch/drupal/) Nature of problem: The output of an MCNP simulation is an ASCII file. The data processing is usually performed by copying and pasting the relevant parts of the ASCII file into Microsoft Excel. Such an approach is satisfactory when the quantity of data is small but is not efficient when the size of the simulated data is large, for example when time-energy correlations are studied in detail such as in problems involving the associated particle technique. In addition, since the finite time resolution of the simulated detector cannot be modeled with MCNP, systems in which time-energy correlation is crucial cannot be described in a satisfactory way. Finally, realistic particle energy deposit in detectors is calculated with MCNP in a two-step process involving type-5 then type-8 tallies. In the first step, the photon flux energy spectrum associated to a time region is selected and serves as a source energy distribution for the second step. Thus, several files must be manipulated before getting the result, which can be time consuming if one needs to study several time regions or different detectors performances. In the same way, modeling counting statistics obtained in a limited acquisition time requires several steps and can also be time consuming. Solution method: In order to overcome the previous limitations, the MODAR C++ code has been written to make use of CERN's ROOT data analysis software. MCNP output data are read from the MCNP output file with dedicated routines. Two-dimensional histograms are filled and can be handled efficiently within the ROOT framework. To keep a user friendly analysis tool, all processing and data display can be done by means of ROOT Graphical User Interface. Specific routines have been written to include detectors finite time resolution and energy response function as well as counting statistics in a straightforward way. Additional comments: The possibility of adding tallies has also been incorporated in MODAR in order to describe systems in which the signal from several detectors can be summed. Moreover, MODAR can be adapted to handle other problems involving two-dimensional data. Running time: The CPU time needed to smear a two-dimensional histogram depends on the size of the histogram. In the presented example, the time-energy smearing of one of the 139×740 two-dimensional histograms takes 3 minutes with a DELL computer equipped with INTEL Core 2.

A smart technique for attendance system to recognize faces through parallelism

NASA Astrophysics Data System (ADS)

Prabhavathi, B.; Tanuja, V.; Madhu Viswanatham, V.; Rajashekhara Babu, M.

2017-11-01

Major part of recognising a person is face with the help of image processing techniques we can exploit the physical features of a person. In the old approach method that is used in schools and colleges it is there that the professor calls the student name and then the attendance for the students marked. Here in paper want to deviate from the old approach and go with the new approach by using techniques that are there in image processing. In this paper we presenting spontaneous presence for students in classroom. At first classroom image has been in use and after that image is kept in data record. For the images that are stored in the database we apply system algorithm which includes steps such as, histogram classification, noise removal, face detection and face recognition methods. So by using these steps we detect the faces and then compare it with the database. The attendance gets marked automatically if the system recognizes the faces.

Subtype differentiation of renal tumors using voxel-based histogram analysis of intravoxel incoherent motion parameters.

PubMed

Gaing, Byron; Sigmund, Eric E; Huang, William C; Babb, James S; Parikh, Nainesh S; Stoffel, David; Chandarana, Hersh

2015-03-01

The aim of this study was to determine if voxel-based histogram analysis of intravoxel incoherent motion imaging (IVIM) parameters can differentiate various subtypes of renal tumors, including benign and malignant lesions. A total of 44 patients with renal tumors who underwent surgery and had histopathology available were included in this Health Insurance Portability and Accountability Act-compliant, institutional review board-approved, single-institution prospective study. In addition to routine renal magnetic resonance imaging examination performed on a 1.5-T system, all patients were imaged with axial diffusion-weighted imaging using 8 b values (range, 0-800 s/mm). A biexponential model was fitted to the diffusion signal data using a segmented algorithm to extract the IVIM parameters perfusion fraction (fp), tissue diffusivity (Dt), and pseudodiffusivity (Dp) for each voxel. Mean and histogram measures of heterogeneity (standard deviation, skewness, and kurtosis) of IVIM parameters were correlated with pathology results of tumor subtype using unequal variance t tests to compare subtypes in terms of each measure. Correction for multiple comparisons was accomplished using the Tukey honestly significant difference procedure. A total of 44 renal tumors including 23 clear cell (ccRCC), 4 papillary (pRCC), 5 chromophobe, and 5 cystic renal cell carcinomas, as well as benign lesions, 4 oncocytomas (Onc) and 3 angiomyolipomas (AMLs), were included in our analysis. Mean IVIM parameters fp and Dt differentiated 8 of 15 pairs of renal tumors. Histogram analysis of IVIM parameters differentiated 9 of 15 subtype pairs. One subtype pair (ccRCC vs pRCC) was differentiated by mean analysis but not by histogram analysis. However, 2 other subtype pairs (AML vs Onc and ccRCC vs Onc) were differentiated by histogram distribution parameters exclusively. The standard deviation of Dt [σ(Dt)] differentiated ccRCC (0.362 ± 0.136 × 10 mm/s) from AML (0.199 ± 0.043 × 10 mm/s) (P = 0.002). Kurtosis of fp separated Onc (2.767 ± 1.299) from AML (-0.325 ± 0.279; P = 0.001), ccRCC (0.612 ± 1.139; P = 0.042), and pRCC (0.308 ± 0.730; P = 0.025). Intravoxel incoherent motion imaging parameters with inclusion of histogram measures of heterogeneity can help differentiate malignant from benign lesions as well as various subtypes of renal cancers.

Enhancing the pictorial content of digital holograms at 100 frames per second.

PubMed

Tsang, P W M; Poon, T-C; Cheung, K W K

2012-06-18

We report a low complexity, non-iterative method for enhancing the sharpness, brightness, and contrast of the pictorial content that is recorded in a digital hologram, without the need of re-generating the latter from the original object scene. In our proposed method, the hologram is first back-projected to a 2-D virtual diffraction plane (VDP) which is located at close proximity to the original object points. Next the field distribution on the VDP, which shares similar optical properties as the object scene, is enhanced. Subsequently, the processed VDP is expanded into a full hologram. We demonstrate two types of enhancement: a modified histogram equalization to improve the brightness and contrast, and localized high-boost-filtering (LHBF) to increase the sharpness. Experiment results have demonstrated that our proposed method is capable of enhancing a 2048x2048 hologram at a rate of around 100 frames per second. To the best of our knowledge, this is the first time real-time image enhancement is considered in the context of digital holography.

Optimal nonlinear codes for the perception of natural colours.

PubMed

von der Twer, T; MacLeod, D I

2001-08-01

We discuss how visual nonlinearity can be optimized for the precise representation of environmental inputs. Such optimization leads to neural signals with a compressively nonlinear input-output function the gradient of which is matched to the cube root of the probability density function (PDF) of the environmental input values (and not to the PDF directly as in histogram equalization). Comparisons between theory and psychophysical and electrophysiological data are roughly consistent with the idea that parvocellular (P) cells are optimized for precision representation of colour: their contrast-response functions span a range appropriately matched to the environmental distribution of natural colours along each dimension of colour space. Thus P cell codes for colour may have been selected to minimize error in the perceptual estimation of stimulus parameters for natural colours. But magnocellular (M) cells have a much stronger than expected saturating nonlinearity; this supports the view that the function of M cells is mainly to detect boundaries rather than to specify contrast or lightness.

An application of viola jones method for face recognition for absence process efficiency

NASA Astrophysics Data System (ADS)

Rizki Damanik, Rudolfo; Sitanggang, Delima; Pasaribu, Hendra; Siagian, Hendrik; Gulo, Frisman

2018-04-01

Absence was a list of documents that the company used to record the attendance time of each employee. The most common problem in a fingerprint machine is the identification of a slow sensor or a sensor not recognizing a finger. The employees late to work because they get difficulties at fingerprint system, they need about 3 – 5 minutes to absence when the condition of finger is wet or not fit. To overcome this problem, this research tried to utilize facial recognition for attendance process. The method used for facial recognition was Viola Jones. Through the processing phase of the RGB face image was converted into a histogram equalization face image for the next stage of recognition. The result of this research was the absence process could be done less than 1 second with a maximum slope of ± 700 and a distance of 20-200 cm. After implement facial recognition the process of absence is more efficient, just take less 1 minute to absence.

Boundary segmentation for fluorescence microscopy using steerable filters

NASA Astrophysics Data System (ADS)

Ho, David Joon; Salama, Paul; Dunn, Kenneth W.; Delp, Edward J.

2017-02-01

Fluorescence microscopy is used to image multiple subcellular structures in living cells which are not readily observed using conventional optical microscopy. Moreover, two-photon microscopy is widely used to image structures deeper in tissue. Recent advancement in fluorescence microscopy has enabled the generation of large data sets of images at different depths, times, and spectral channels. Thus, automatic object segmentation is necessary since manual segmentation would be inefficient and biased. However, automatic segmentation is still a challenging problem as regions of interest may not have well defined boundaries as well as non-uniform pixel intensities. This paper describes a method for segmenting tubular structures in fluorescence microscopy images of rat kidney and liver samples using adaptive histogram equalization, foreground/background segmentation, steerable filters to capture directional tendencies, and connected-component analysis. The results from several data sets demonstrate that our method can segment tubular boundaries successfully. Moreover, our method has better performance when compared to other popular image segmentation methods when using ground truth data obtained via manual segmentation.

A CCD-based reader combined with CdS quantum dot-labeled lateral flow strips for ultrasensitive quantitative detection of CagA

NASA Astrophysics Data System (ADS)

Gui, Chen; Wang, Kan; Li, Chao; Dai, Xuan; Cui, Daxiang

2014-02-01

Immunochromatographic assays are widely used to detect many analytes. CagA is proved to be associated closely with initiation of gastric carcinoma. Here, we reported that a charge-coupled device (CCD)-based test strip reader combined with CdS quantum dot-labeled lateral flow strips for quantitative detection of CagA was developed, which used 365-nm ultraviolet LED as the excitation light source, and captured the test strip images through an acquisition module. Then, the captured image was transferred to the computer and was processed by a software system. A revised weighted threshold histogram equalization (WTHE) image processing algorithm was applied to analyze the result. CdS quantum dot-labeled lateral flow strips for detection of CagA were prepared. One hundred sera samples from clinical patients with gastric cancer and healthy people were prepared for detection, which demonstrated that the device could realize rapid, stable, and point-of-care detection, with a sensitivity of 20 pg/mL.

A semi-blind logo watermarking scheme for color images by comparison and modification of DFT coefficients

NASA Astrophysics Data System (ADS)

Kusyk, Janusz; Eskicioglu, Ahmet M.

2005-10-01

Digital watermarking is considered to be a major technology for the protection of multimedia data. Some of the important applications are broadcast monitoring, copyright protection, and access control. In this paper, we present a semi-blind watermarking scheme for embedding a logo in color images using the DFT domain. After computing the DFT of the luminance layer of the cover image, the magnitudes of DFT coefficients are compared, and modified. A given watermark is embedded in three frequency bands: Low, middle, and high. Our experiments show that the watermarks extracted from the lower frequencies have the best visual quality for low pass filtering, adding Gaussian noise, JPEG compression, resizing, rotation, and scaling, and the watermarks extracted from the higher frequencies have the best visual quality for cropping, intensity adjustment, histogram equalization, and gamma correction. Extractions from the fragmented and translated image are identical to extractions from the unattacked watermarked image. The collusion and rewatermarking attacks do not provide the hacker with useful tools.

MCNP output data analysis with ROOT (MODAR)

NASA Astrophysics Data System (ADS)

Carasco, C.

2010-12-01

MCNP Output Data Analysis with ROOT (MODAR) is a tool based on CERN's ROOT software. MODAR has been designed to handle time-energy data issued by MCNP simulations of neutron inspection devices using the associated particle technique. MODAR exploits ROOT's Graphical User Interface and functionalities to visualize and process MCNP simulation results in a fast and user-friendly way. MODAR allows to take into account the detection system time resolution (which is not possible with MCNP) as well as detectors energy response function and counting statistics in a straightforward way. New version program summaryProgram title: MODAR Catalogue identifier: AEGA_v1_1 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEGA_v1_1.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 150 927 No. of bytes in distributed program, including test data, etc.: 4 981 633 Distribution format: tar.gz Programming language: C++ Computer: Most Unix workstations and PCs Operating system: Most Unix systems, Linux and windows, provided the ROOT package has been installed. Examples where tested under Suse Linux and Windows XP. RAM: Depends on the size of the MCNP output file. The example presented in the article, which involves three two dimensional 139×740 bins histograms, allocates about 60 MB. These data are running under ROOT and include consumption by ROOT itself. Classification: 17.6 Catalogue identifier of previous version: AEGA_v1_0 Journal reference of previous version: Comput. Phys. Comm. 181 (2010) 1161 External routines: ROOT version 5.24.00 ( http://root.cern.ch/drupal/) Does the new version supersede the previous version?: Yes Nature of problem: The output of a MCNP simulation is an ascii file. The data processing is usually performed by copying and pasting the relevant parts of the ascii file into Microsoft Excel. Such an approach is satisfactory when the quantity of data is small but is not efficient when the size of the simulated data is large, for example when time-energy correlations are studied in detail such as in problems involving the associated particle technique. In addition, since the finite time resolution of the simulated detector cannot be modeled with MCNP, systems in which time-energy correlation is crucial cannot be described in a satisfactory way. Finally, realistic particle energy deposit in detectors is calculated with MCNP in a two step process involving type-5 then type-8 tallies. In the first step, the photon flux energy spectrum associated to a time region is selected and serves as a source energy distribution for the second step. Thus, several files must be manipulated before getting the result, which can be time consuming if one needs to study several time regions or different detectors performances. In the same way, modeling counting statistics obtained in a limited acquisition time requires several steps and can also be time consuming. Solution method: In order to overcome the previous limitations, the MODAR C++ code has been written to make use of CERN's ROOT data analysis software. MCNP output data are read from the MCNP output file with dedicated routines. Two dimensional histograms are filled and can be handled efficiently within the ROOT framework. To keep a user friendly analysis tool, all processing and data display can be done by means of ROOT Graphical User Interface. Specific routines have been written to include detectors finite time resolution and energy response function as well as counting statistics in a straightforward way. Reasons for new version: For applications involving the Associate Particle Technique, a large number of gamma rays are produced by the fast neutrons interactions. To study the energy spectra, it is useful to identify the gamma-ray energy peaks in a straightforward way. Therefore, the possibility to show gamma rays corresponding to specific reactions has been added in MODAR. Summary of revisions: It is possible to use a gamma ray database to better identify in the energy spectra gamma ray peaks with their first and second escapes. Histograms can be scaled by the number of source particle to evaluate the number of counts that is expected without statistical uncertainties. Additional comments: The possibility of adding tallies has also been incorporated in MODAR in order to describe systems in which the signal from several detectors can be summed. Moreover, MODAR can be adapted to handle other problems involving two dimensional data. Running time: The CPU time needed to smear a two dimensional histogram depends on the size of the histogram. In the presented example, the time-energy smearing of one of the 139×740 two dimensional histograms takes 3 minutes with a DELL computer equipped with INTEL Core 2.

Geostatistical conditional simulation for the assessment of contaminated land by abandoned heavy metal mining.

PubMed

Ersoy, Adem; Yunsel, Tayfun Yusuf; Atici, Umit

2008-02-01

Abandoned mine workings can undoubtedly cause varying degrees of contamination of soil with heavy metals such as lead and zinc has occurred on a global scale. Exposure to these elements may cause to harm human health and environment. In the study, a total of 269 soil samples were collected at 1, 5, and 10 m regular grid intervals of 100 x 100 m area of Carsington Pasture in the UK. Cell declustering technique was applied to the data set due to no statistical representativity. Directional experimental semivariograms of the elements for the transformed data showed that both geometric and zonal anisotropy exists in the data. The most evident spatial dependence structure of the continuity for the directional experimental semivariogram, characterized by spherical and exponential models of Pb and Zn were obtained. This study reports the spatial distribution and uncertainty of Pb and Zn concentrations in soil at the study site using a probabilistic approach. The approach was based on geostatistical sequential Gaussian simulation (SGS), which is used to yield a series of conditional images characterized by equally probable spatial distributions of the heavy elements concentrations across the area. Postprocessing of many simulations allowed the mapping of contaminated and uncontaminated areas, and provided a model for the uncertainty in the spatial distribution of element concentrations. Maps of the simulated Pb and Zn concentrations revealed the extent and severity of contamination. SGS was validated by statistics, histogram, variogram reproduction, and simulation errors. The maps of the elements might be used in the remediation studies, help decision-makers and others involved in the abandoned heavy metal mining site in the world.

Implementation of a dose gradient method into optimization of dose distribution in prostate cancer 3D-CRT plans

PubMed Central

Giżyńska, Marta K.; Kukołowicz, Paweł F.; Kordowski, Paweł

2014-01-01

Aim The aim of this work is to present a method of beam weight and wedge angle optimization for patients with prostate cancer. Background 3D-CRT is usually realized with forward planning based on a trial and error method. Several authors have published a few methods of beam weight optimization applicable to the 3D-CRT. Still, none on these methods is in common use. Materials and methods Optimization is based on the assumption that the best plan is achieved if dose gradient at ICRU point is equal to zero. Our optimization algorithm requires beam quality index, depth of maximum dose, profiles of wedged fields and maximum dose to femoral heads. The method was tested for 10 patients with prostate cancer, treated with the 3-field technique. Optimized plans were compared with plans prepared by 12 experienced planners. Dose standard deviation in target volume, and minimum and maximum doses were analyzed. Results The quality of plans obtained with the proposed optimization algorithms was comparable to that prepared by experienced planners. Mean difference in target dose standard deviation was 0.1% in favor of the plans prepared by planners for optimization of beam weights and wedge angles. Introducing a correction factor for patient body outline for dose gradient at ICRU point improved dose distribution homogeneity. On average, a 0.1% lower standard deviation was achieved with the optimization algorithm. No significant difference in mean dose–volume histogram for the rectum was observed. Conclusions Optimization shortens very much time planning. The average planning time was 5 min and less than a minute for forward and computer optimization, respectively. PMID:25337411

Students' Misconceptions in Interpreting Center and Variability of Data Represented via Histograms and Stem-and-Leaf Plots

ERIC Educational Resources Information Center

Cooper, Linda L.; Shore, Felice S.

2008-01-01

This paper identifies and discusses misconceptions that students have in making judgments of center and variability when data are presented graphically. An assessment addressing interpreting center and variability in histograms and stem-and-leaf plots was administered to, and follow-up interviews were conducted with, undergraduates enrolled in…

Texture and phase analysis of deformed SUS304 by using HIPPO

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

Takajo, Shigehiro; Vogel, Sven C.

2016-11-15

These slides represent the author's research activity at Los Alamos National Laboratory (LANL), which is about texture and phase analysis of deformed SUS304 by using HIPPO. The following topics are covered: diffraction histogram at each sample position, diffraction histogram (all bank data averaged), possiblity of ε-phase, MAUD analysis with including ε-phase.

Shift-Invariant Image Reconstruction of Speckle-Degraded Images Using Bispectrum Estimation

DTIC Science & Technology

1990-05-01

process with the requisite negative exponential pelf. I call this model the Negative Exponential Model ( NENI ). The NENI flowchart is seen in Figure 6...Figure ]3d-g. Statistical Histograms and Phase for the RPj NG EXP FDF MULT METHOD FILuteC 14a. Truth Object Speckled Via the NENI HISTOGRAM OF SPECKLE

Damage Proxy Map from Interferometric Synthetic Aperture Radar Coherence

NASA Technical Reports Server (NTRS)

Webb, Frank H. (Inventor); Yun, Sang-Ho (Inventor); Fielding, Eric Jameson (Inventor); Simons, Mark (Inventor)

2015-01-01

A method, apparatus, and article of manufacture provide the ability to generate a damage proxy map. A master coherence map and a slave coherence map, for an area prior and subsequent to (including) a damage event are obtained. The slave coherence map is registered to the master coherence map. Pixel values of the slave coherence map are modified using histogram matching to provide a first histogram of the master coherence map that exactly matches a second histogram of the slave coherence map. A coherence difference between the slave coherence map and the master coherence map is computed to produce a damage proxy map. The damage proxy map is displayed with the coherence difference displayed in a visually distinguishable manner.

Multimodal Image Registration through Simultaneous Segmentation.

PubMed

Aganj, Iman; Fischl, Bruce

2017-11-01

Multimodal image registration facilitates the combination of complementary information from images acquired with different modalities. Most existing methods require computation of the joint histogram of the images, while some perform joint segmentation and registration in alternate iterations. In this work, we introduce a new non-information-theoretical method for pairwise multimodal image registration, in which the error of segmentation - using both images - is considered as the registration cost function. We empirically evaluate our method via rigid registration of multi-contrast brain magnetic resonance images, and demonstrate an often higher registration accuracy in the results produced by the proposed technique, compared to those by several existing methods.

Event-Based Processing of Neutron Scattering Data

DOE PAGES

Peterson, Peter F.; Campbell, Stuart I.; Reuter, Michael A.; ...

2015-09-16

Many of the world's time-of-flight spallation neutrons sources are migrating to the recording of individual neutron events. This provides for new opportunities in data processing, the least of which is to filter the events based on correlating them with logs of sample environment and other ancillary equipment. This paper will describe techniques for processing neutron scattering data acquired in event mode that preserve event information all the way to a final spectrum, including any necessary corrections or normalizations. This results in smaller final errors, while significantly reducing processing time and memory requirements in typical experiments. Results with traditional histogramming techniquesmore » will be shown for comparison.« less

Detecting duplicate biological entities using Shortest Path Edit Distance.

PubMed

Rudniy, Alex; Song, Min; Geller, James

2010-01-01

Duplicate entity detection in biological data is an important research task. In this paper, we propose a novel and context-sensitive Shortest Path Edit Distance (SPED) extending and supplementing our previous work on Markov Random Field-based Edit Distance (MRFED). SPED transforms the edit distance computational problem to the calculation of the shortest path among two selected vertices of a graph. We produce several modifications of SPED by applying Levenshtein, arithmetic mean, histogram difference and TFIDF techniques to solve subtasks. We compare SPED performance to other well-known distance algorithms for biological entity matching. The experimental results show that SPED produces competitive outcomes.

Automatic measurement of images on astrometric plates

NASA Astrophysics Data System (ADS)

Ortiz Gil, A.; Lopez Garcia, A.; Martinez Gonzalez, J. M.; Yershov, V.

1994-04-01

We present some results on the process of automatic detection and measurement of objects in overlapped fields of astrometric plates. The main steps of our algorithm are the following: determination of the Scale and Tilt between charge coupled devices (CCD) and microscope coordinate systems and estimation of signal-to-noise ratio in each field;--image identification and improvement of its position and size;--image final centering;--image selection and storage. Several parameters allow the use of variable criteria for image identification, characterization and selection. Problems related with faint images and crowded fields will be approached by special techniques (morphological filters, histogram properties and fitting models).

Properties of hypothesis testing techniques and (Bayesian) model selection for exploration-based and theory-based (order-restricted) hypotheses.

PubMed

Kuiper, Rebecca M; Nederhoff, Tim; Klugkist, Irene

2015-05-01

In this paper, the performance of six types of techniques for comparisons of means is examined. These six emerge from the distinction between the method employed (hypothesis testing, model selection using information criteria, or Bayesian model selection) and the set of hypotheses that is investigated (a classical, exploration-based set of hypotheses containing equality constraints on the means, or a theory-based limited set of hypotheses with equality and/or order restrictions). A simulation study is conducted to examine the performance of these techniques. We demonstrate that, if one has specific, a priori specified hypotheses, confirmation (i.e., investigating theory-based hypotheses) has advantages over exploration (i.e., examining all possible equality-constrained hypotheses). Furthermore, examining reasonable order-restricted hypotheses has more power to detect the true effect/non-null hypothesis than evaluating only equality restrictions. Additionally, when investigating more than one theory-based hypothesis, model selection is preferred over hypothesis testing. Because of the first two results, we further examine the techniques that are able to evaluate order restrictions in a confirmatory fashion by examining their performance when the homogeneity of variance assumption is violated. Results show that the techniques are robust to heterogeneity when the sample sizes are equal. When the sample sizes are unequal, the performance is affected by heterogeneity. The size and direction of the deviations from the baseline, where there is no heterogeneity, depend on the effect size (of the means) and on the trend in the group variances with respect to the ordering of the group sizes. Importantly, the deviations are less pronounced when the group variances and sizes exhibit the same trend (e.g., are both increasing with group number). © 2014 The British Psychological Society.

Whole-tumor apparent diffusion coefficient (ADC) histogram analysis to differentiate benign peripheral neurogenic tumors from soft tissue sarcomas.

PubMed

Nakajo, Masanori; Fukukura, Yoshihiko; Hakamada, Hiroto; Yoneyama, Tomohide; Kamimura, Kiyohisa; Nagano, Satoshi; Nakajo, Masayuki; Yoshiura, Takashi

2018-02-22

Apparent diffusion coefficient (ADC) histogram analyses have been used to differentiate tumor grades and predict therapeutic responses in various anatomic sites with moderate success. To determine the ability of diffusion-weighted imaging (DWI) with a whole-tumor ADC histogram analysis to differentiate benign peripheral neurogenic tumors (BPNTs) from soft tissue sarcomas (STSs). Retrospective study, single institution. In all, 25 BPNTs and 31 STSs. Two-b value DWI (b-values = 0, 1000s/mm 2 ) was at 3.0T. The histogram parameters of whole-tumor for ADC were calculated by two radiologists and compared between BPNTs and STSs. Nonparametric tests were performed for comparisons between BPNTs and STSs. P < 0.05 was considered statistically significant. The ability of each parameter to differentiate STSs from BPNTs was evaluated using area under the curve (AUC) values derived from a receiver operating characteristic curve analysis. The mean ADC and all percentile parameters were significantly lower in STSs than in BPNTs (P < 0.001-0.009), with AUCs of 0.703-0.773. However, the coefficient of variation (P = 0.020 and AUC = 0.682) and skewness (P = 0.012 and AUC = 0.697) were significantly higher in STSs than in BPNTs. Kurtosis (P = 0.295) and entropy (P = 0.604) did not differ significantly between BPNTs and STSs. Whole-tumor ADC histogram parameters except kurtosis and entropy differed significantly between BPNTs and STSs. 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018. © 2018 International Society for Magnetic Resonance in Medicine.

Histogram Matching Extends Acceptable Signal Strength Range on Optical Coherence Tomography Images

PubMed Central

Chen, Chieh-Li; Ishikawa, Hiroshi; Wollstein, Gadi; Bilonick, Richard A.; Sigal, Ian A.; Kagemann, Larry; Schuman, Joel S.

2015-01-01

Purpose. We minimized the influence of image quality variability, as measured by signal strength (SS), on optical coherence tomography (OCT) thickness measurements using the histogram matching (HM) method. Methods. We scanned 12 eyes from 12 healthy subjects with the Cirrus HD-OCT device to obtain a series of OCT images with a wide range of SS (maximal range, 1–10) at the same visit. For each eye, the histogram of an image with the highest SS (best image quality) was set as the reference. We applied HM to the images with lower SS by shaping the input histogram into the reference histogram. Retinal nerve fiber layer (RNFL) thickness was automatically measured before and after HM processing (defined as original and HM measurements), and compared to the device output (device measurements). Nonlinear mixed effects models were used to analyze the relationship between RNFL thickness and SS. In addition, the lowest tolerable SSs, which gave the RNFL thickness within the variability margin of manufacturer recommended SS range (6–10), were determined for device, original, and HM measurements. Results. The HM measurements showed less variability across a wide range of image quality than the original and device measurements (slope = 1.17 vs. 4.89 and 1.72 μm/SS, respectively). The lowest tolerable SS was successfully reduced to 4.5 after HM processing. Conclusions. The HM method successfully extended the acceptable SS range on OCT images. This would qualify more OCT images with low SS for clinical assessment, broadening the OCT application to a wider range of subjects. PMID:26066749

Histogram analysis of diffusion kurtosis imaging derived maps may distinguish between low and high grade gliomas before surgery.

PubMed

Qi, Xi-Xun; Shi, Da-Fa; Ren, Si-Xie; Zhang, Su-Ya; Li, Long; Li, Qing-Chang; Guan, Li-Ming

2018-04-01

To investigate the value of histogram analysis of diffusion kurtosis imaging (DKI) maps in the evaluation of glioma grading. A total of 39 glioma patients who underwent preoperative magnetic resonance imaging (MRI) were classified into low-grade (13 cases) and high-grade (26 cases) glioma groups. Parametric DKI maps were derived, and histogram metrics between low- and high-grade gliomas were analysed. The optimum diagnostic thresholds of the parameters, area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were achieved using a receiver operating characteristic (ROC). Significant differences were observed not only in 12 metrics of histogram DKI parameters (P<0.05), but also in mean diffusivity (MD) and mean kurtosis (MK) values, including age as a covariate (F=19.127, P<0.001 and F=20.894, P<0.001, respectively), between low- and high-grade gliomas. Mean MK was the best independent predictor of differentiating glioma grades (B=18.934, 22.237 adjusted for age, P<0.05). The partial correlation coefficient between fractional anisotropy (FA) and kurtosis fractional anisotropy (KFA) was 0.675 (P<0.001). The AUC of the mean MK, sensitivity, and specificity were 0.925, 88.5% and 84.6%, respectively. DKI parameters can effectively distinguish between low- and high-grade gliomas. Mean MK is the best independent predictor of differentiating glioma grades. • DKI is a new and important method. • DKI can provide additional information on microstructural architecture. • Histogram analysis of DKI may be more effective in glioma grading.

Whole-tumour diffusion kurtosis MR imaging histogram analysis of rectal adenocarcinoma: Correlation with clinical pathologic prognostic factors.

PubMed

Cui, Yanfen; Yang, Xiaotang; Du, Xiaosong; Zhuo, Zhizheng; Xin, Lei; Cheng, Xintao

2018-04-01

To investigate potential relationships between diffusion kurtosis imaging (DKI)-derived parameters using whole-tumour volume histogram analysis and clinicopathological prognostic factors in patients with rectal adenocarcinoma. 79 consecutive patients who underwent MRI examination with rectal adenocarcinoma were retrospectively evaluated. Parameters D, K and conventional ADC were measured using whole-tumour volume histogram analysis. Student's t-test or Mann-Whitney U-test, receiver operating characteristic curves and Spearman's correlation were used for statistical analysis. Almost all the percentile metrics of K were correlated positively with nodal involvement, higher histological grades, the presence of lymphangiovascular invasion (LVI) and circumferential margin (CRM) (p<0.05), with the exception of between K 10th , K 90th and histological grades. In contrast, significant negative correlations were observed between 25th, 50th percentiles and mean values of ADC and D, as well as ADC 10th , with tumour T stages (p< 0.05). Meanwhile, lower 75th and 90th percentiles of ADC and D values were also correlated inversely with nodal involvement (p< 0.05). K mean showed a relatively higher area under the curve (AUC) and higher specificity than other percentiles for differentiation of lesions with nodal involvement. DKI metrics with whole-tumour volume histogram analysis, especially K parameters, were associated with important prognostic factors of rectal cancer. • K correlated positively with some important prognostic factors of rectal cancer. • K mean showed higher AUC and specificity for differentiation of nodal involvement. • DKI metrics with whole-tumour volume histogram analysis depicted tumour heterogeneity.

Apparent diffusion coefficient histogram metrics correlate with survival in diffuse intrinsic pontine glioma: a report from the Pediatric Brain Tumor Consortium

PubMed Central

Poussaint, Tina Young; Vajapeyam, Sridhar; Ricci, Kelsey I.; Panigrahy, Ashok; Kocak, Mehmet; Kun, Larry E.; Boyett, James M.; Pollack, Ian F.; Fouladi, Maryam

2016-01-01

Background Diffuse intrinsic pontine glioma (DIPG) is associated with poor survival regardless of therapy. We used volumetric apparent diffusion coefficient (ADC) histogram metrics to determine associations with progression-free survival (PFS) and overall survival (OS) at baseline and after radiation therapy (RT). Methods Baseline and post-RT quantitative ADC histograms were generated from fluid-attenuated inversion recovery (FLAIR) images and enhancement regions of interest. Metrics assessed included number of peaks (ie, unimodal or bimodal), mean and median ADC, standard deviation, mode, skewness, and kurtosis. Results Based on FLAIR images, the majority of tumors had unimodal peaks with significantly shorter average survival. Pre-RT FLAIR mean, mode, and median values were significantly associated with decreased risk of progression; higher pre-RT ADC values had longer PFS on average. Pre-RT FLAIR skewness and standard deviation were significantly associated with increased risk of progression; higher pre-RT FLAIR skewness and standard deviation had shorter PFS. Nonenhancing tumors at baseline showed higher ADC FLAIR mean values, lower kurtosis, and higher PFS. For enhancing tumors at baseline, bimodal enhancement histograms had much worse PFS and OS than unimodal cases and significantly lower mean peak values. Enhancement in tumors only after RT led to significantly shorter PFS and OS than in patients with baseline or no baseline enhancement. Conclusions ADC histogram metrics in DIPG demonstrate significant correlations between diffusion metrics and survival, with lower diffusion values (increased cellularity), increased skewness, and enhancement associated with shorter survival, requiring future investigations in large DIPG clinical trials. PMID:26487690

Proton beam radiotherapy versus fractionated stereotactic radiotherapy for uveal melanomas: A comparative study.

PubMed

Weber, Damien C; Bogner, Joachim; Verwey, Jorn; Georg, Dietmar; Dieckmann, Karin; Escudé, Lluis; Caro, Monica; Pötter, Richard; Goitein, Gudrun; Lomax, Antony J; Miralbell, Raymond

2005-10-01

A comparative treatment planning study was undertaken between proton and photon therapy in uveal melanoma to assess the potential benefits and limitations of these treatment modalities. A fixed proton horizontal beam (OPTIS) and intensity-modulated spot-scanning proton therapy (IMPT), with multiple noncoplanar beam arrangements, was compared with linear accelerator-based stereotactic radiotherapy (SRT), using a static and a dynamic micromultileaf collimator and intensity-modulated RT (IMRS). A planning CT scan was performed on a brain metastasis patient, with a 3-mm acquisition slice spacing and the patient looking at a luminous spot with the eyes in three different positions (neutral and 25 degrees right and left). Four different gross tumor volumes were defined for each treatment technique. These target scenarios represented different locations (involving vs. not involving the macula and temporal vs. nasal) and volumes (10 x 6 mm vs. 16 x 10 mm) to challenge the proton and photon treatment techniques. The planning target volume was defined as the gross tumor volume plus 2 mm laterally and 3 mm craniocaudally for both modalities. A dose homogeneity of 95-99% of the planning target volume was used as the "goal" for all techniques. The dose constraint (maximum) for the organs at risk (OARs) for both the proton and the SRT photon plans was 27.5, 22.5, 20, and 9 CGE-Gy for the optic apparatus, retina, lacrimal gland, and lens, respectively. The dose to the planning target volume was 50 CGE-Gy in 10 CGE-Gy daily fractions. The plans for proton and photon therapy were computed using the Paul Scherrer Institute and BrainSCAN, version 5.2 (BrainLAB, Heimstetten, Germany) treatment planning systems, respectively. Tumor and OARs dose-volume histograms were calculated. The results were analyzed using the dose-volume histogram parameters, conformity index (CI(95%)), and inhomogeneity coefficient. Target coverage of all simulated uveal melanomas was equally conformal with the photon and proton modalities. The median CI(95%) value was 1.74, 1.86, and 1.83 for the static, dynamic, and IMSRT plans, respectively. With proton planning, the median CI(95%) was 1.88 for OPTIS and substantially improved with IMPT in some tumor cases (median CI(95%), 1.29). The tumor dose homogeneity in the proton plans was, however, always better than with SRT photon planning (median inhomogeneity coefficient 0.1 and 0.15 vs. 0.46, 0.41, and 0.23 for the OPTIS and IMPT vs. the static, dynamic, and IMSRT plans, respectively). Compared with the photon plans, the use of protons did not lead to a substantial reduction in the homolateral OAR total integral dose in the low- to high-dose level, except for the lacrimal gland. The median maximal dose and dose at the 10% volume with the static, dynamic, and IMSRT plans was 33-30.8, 31.8-28, and 35.8-49 Gy, respectively, for the lacrimal gland, a critical organ. For protons, only the OPTIS plans were better, with a median maximal dose and dose at the 10% volume using OPTIS and IMPT of 19.2 and 8.8 and 25.6 and 23.6 CGE, respectively. The contralateral OARs were completely spared with the proton plans, but the median dose delivered to these structures was 1.2 Gy (range, 0-6.3 Gy) with the SRT photon plans. These results suggest that the use of SRT photon techniques, compared with protons, can result in similar levels of dose conformation. IMPT did not increase the degree of conformality for this small tumor. Tumor dose inhomogeneity was, however, always increased with photon planning. Except for the lacrimal gland, the use of protons, with or without intensity modulation, did not increase homolateral OAR dose sparing. The dose to all the contralateral OARs was, however, completely eliminated with proton planning.

A minimax technique for time-domain design of preset digital equalizers using linear programming

NASA Technical Reports Server (NTRS)

Vaughn, G. L.; Houts, R. C.

1975-01-01

A linear programming technique is presented for the design of a preset finite-impulse response (FIR) digital filter to equalize the intersymbol interference (ISI) present in a baseband channel with known impulse response. A minimax technique is used which minimizes the maximum absolute error between the actual received waveform and a specified raised-cosine waveform. Transversal and frequency-sampling FIR digital filters are compared as to the accuracy of the approximation, the resultant ISI and the transmitted energy required. The transversal designs typically have slightly better waveform accuracy for a given distortion; however, the frequency-sampling equalizer uses fewer multipliers and requires less transmitted energy. A restricted transversal design is shown to use the least number of multipliers at the cost of a significant increase in energy and loss of waveform accuracy at the receiver.

Issues around Creating a Reusable Learning Object to Support Statistics Teaching

ERIC Educational Resources Information Center

Gilchrist, Mollie

2007-01-01

Although our health professional students have some experience of simple charts, such as pie and bar, and some intuition of histograms, they do not appear to have much knowledge or understanding about box and whisker plots and their relation to the data they are describing or compared to histograms. The boxplot is a versatile charting tool, useful…

ON THE THEORY AND PROCEDURE FOR CONSTRUCTING A MINIMAL-LENGTH, AREA-CONSERVING FREQUENCY POLYGON FROM GROUPED DATA.

ERIC Educational Resources Information Center

CASE, C. MARSTON

THIS PAPER IS CONCERNED WITH GRAPHIC PRESENTATION AND ANALYSIS OF GROUPED OBSERVATIONS. IT PRESENTS A METHOD AND SUPPORTING THEORY FOR THE CONSTRUCTION OF AN AREA-CONSERVING, MINIMAL LENGTH FREQUENCY POLYGON CORRESPONDING TO A GIVEN HISTOGRAM. TRADITIONALLY, THE CONCEPT OF A FREQUENCY POLYGON CORRESPONDING TO A GIVEN HISTOGRAM HAS REFERRED TO THAT…

Methods for Determining Particle Size Distributions from Nuclear Detonations.

DTIC Science & Technology

1987-03-01

Debris . . . 30 IV. Summary of Sample Preparation Method . . . . 35 V. Set Parameters for PCS ... ........... 39 VI. Analysis by Vendors...54 XV. Results From Brookhaven Analysis Using The Method of Cumulants ... ........... . 54 XVI. Results From Brookhaven Analysis of Sample...R-3 Using Histogram Method ......... .55 XVII. Results From Brookhaven Analysis of Sample R-8 Using Histogram Method ........... 56 XVIII.TEM Particle

Diffusion profiling of tumor volumes using a histogram approach can predict proliferation and further microarchitectural features in medulloblastoma.

PubMed

Schob, Stefan; Beeskow, Anne; Dieckow, Julia; Meyer, Hans-Jonas; Krause, Matthias; Frydrychowicz, Clara; Hirsch, Franz-Wolfgang; Surov, Alexey

2018-05-31

Medulloblastomas are the most common central nervous system tumors in childhood. Treatment and prognosis strongly depend on histology and transcriptomic profiling. However, the proliferative potential also has prognostical value. Our study aimed to investigate correlations between histogram profiling of diffusion-weighted images and further microarchitectural features. Seven patients (age median 14.6 years, minimum 2 years, maximum 20 years; 5 male, 2 female) were included in this retrospective study. Using a Matlab-based analysis tool, histogram analysis of whole apparent diffusion coefficient (ADC) volumes was performed. ADC entropy revealed a strong inverse correlation with the expression of the proliferation marker Ki67 (r = - 0.962, p = 0.009) and with total nuclear area (r = - 0.888, p = 0.044). Furthermore, ADC percentiles, most of all ADCp90, showed significant correlations with Ki67 expression (r = 0.902, p = 0.036). Diffusion histogram profiling of medulloblastomas provides valuable in vivo information which potentially can be used for risk stratification and prognostication. First of all, entropy revealed to be the most promising imaging biomarker. However, further studies are warranted.

Whole-lesion ADC histogram and texture analysis in predicting recurrence of cervical cancer treated with CCRT.

PubMed

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

2017-11-03

To explore the value of whole-lesion apparent diffusion coefficient (ADC) histogram and texture analysis in predicting tumor recurrence of advanced cervical cancer treated with concurrent chemo-radiotherapy (CCRT). 36 women with pathologically confirmed advanced cervical squamous carcinomas were enrolled in this prospective study. 3.0 T pelvic MR examinations including diffusion weighted imaging (b = 0, 800 s/mm 2 ) were performed before CCRT (pre-CCRT) and at the end of 2nd week of CCRT (mid-CCRT). ADC histogram and texture features were derived from the whole volume of cervical cancers. With a mean follow-up of 25 months (range, 11 ∼ 43), 10/36 (27.8%) patients ended with recurrence. Pre-CCRT 75th, 90th, correlation, autocorrelation and mid-CCRT ADC mean , 10th, 25th, 50th, 75th, 90th, autocorrelation can effectively differentiate the recurrence from nonrecurrence group with area under the curve ranging from 0.742 to 0.850 (P values range, 0.001 ∼ 0.038). Pre- and mid-treatment whole-lesion ADC histogram and texture analysis hold great potential in predicting tumor recurrence of advanced cervical cancer treated with CCRT.