Image contrast enhancement based on a local standard deviation model

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

Chang, Dah-Chung; Wu, Wen-Rong

1996-12-31

The adaptive contrast enhancement (ACE) algorithm is a widely used image enhancement method, which needs a contrast gain to adjust high frequency components of an image. In the literature, the gain is usually inversely proportional to the local standard deviation (LSD) or is a constant. But these cause two problems in practical applications, i.e., noise overenhancement and ringing artifact. In this paper a new gain is developed based on Hunt`s Gaussian image model to prevent the two defects. The new gain is a nonlinear function of LSD and has the desired characteristic emphasizing the LSD regions in which details aremore » concentrated. We have applied the new ACE algorithm to chest x-ray images and the simulations show the effectiveness of the proposed algorithm.« less

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

A novel image enhancement algorithm based on stationary wavelet transform for infrared thermography to the de-bonding defect in solid rocket motors

NASA Astrophysics Data System (ADS)

Liu, Tao; Zhang, Wei; Yan, Shaoze

2015-10-01

In this paper, a multi-scale image enhancement algorithm based on low-passing filtering and nonlinear transformation is proposed for infrared testing image of the de-bonding defect in solid propellant rocket motors. Infrared testing images with high-level noise and low contrast are foundations for identifying defects and calculating the defects size. In order to improve quality of the infrared image, according to distribution properties of the detection image, within framework of stationary wavelet transform, the approximation coefficients at suitable decomposition level is processed by index low-passing filtering by using Fourier transform, after that, the nonlinear transformation is applied to further process the figure to improve the picture contrast. To verify validity of the algorithm, the image enhancement algorithm is applied to infrared testing pictures of two specimens with de-bonding defect. Therein, one specimen is made of a type of high-strength steel, and the other is a type of carbon fiber composite. As the result shown, in the images processed by the image enhancement algorithm presented in the paper, most of noises are eliminated, and contrast between defect areas and normal area is improved greatly; in addition, by using the binary picture of the processed figure, the continuous defect edges can be extracted, all of which show the validity of the algorithm. The paper provides a well-performing image enhancement algorithm for the infrared thermography.

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.

Balance Contrast Enhancement using piecewise linear stretching

NASA Astrophysics Data System (ADS)

Rahavan, R. V.; Govil, R. C.

1993-04-01

Balance Contrast Enhancement is one of the techniques employed to produce color composites with increased color contrast. It equalizes the three images used for color composition in range and mean. This results in a color composite with large variation in hue. Here, it is shown that piecewise linear stretching can be used for performing the Balance Contrast Enhancement. In comparison with the Balance Contrast Enhancement Technique using parabolic segment as transfer function (BCETP), the method presented here is algorithmically simple, constraint-free and produces comparable results.

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.

A predictive software tool for optimal timing in contrast enhanced carotid MR angiography

NASA Astrophysics Data System (ADS)

Moghaddam, Abbas N.; Balawi, Tariq; Habibi, Reza; Panknin, Christoph; Laub, Gerhard; Ruehm, Stefan; Finn, J. Paul

2008-03-01

A clear understanding of the first pass dynamics of contrast agents in the vascular system is crucial in synchronizing data acquisition of 3D MR angiography (MRA) with arrival of the contrast bolus in the vessels of interest. We implemented a computational model to simulate contrast dynamics in the vessels using the theory of linear time-invariant systems. The algorithm calculates a patient-specific impulse response for the contrast concentration from time-resolved images following a small test bolus injection. This is performed for a specific region of interest and through deconvolution of the intensity curve using the long division method. Since high spatial resolution 3D MRA is not time-resolved, the method was validated on time-resolved arterial contrast enhancement in Multi Slice CT angiography. For 20 patients, the timing of the contrast enhancement of the main bolus was predicted by our algorithm from the response to the test bolus, and then for each case the predicted time of maximum intensity was compared to the corresponding time in the actual scan which resulted in an acceptable agreement. Furthermore, as a qualitative validation, the algorithm's predictions of the timing of the carotid MRA in 20 patients with high quality MRA were correlated with the actual timing of those studies. We conclude that the above algorithm can be used as a practical clinical tool to eliminate guesswork and to replace empiric formulae by a priori computation of patient-specific timing of data acquisition for MR angiography.

Cuckoo search algorithm based satellite image contrast and brightness enhancement using DWT-SVD.

PubMed

Bhandari, A K; Soni, V; Kumar, A; Singh, G K

2014-07-01

This paper presents a new contrast enhancement approach which is based on Cuckoo Search (CS) algorithm and DWT-SVD for quality improvement of the low contrast satellite images. The input image is decomposed into the four frequency subbands through Discrete Wavelet Transform (DWT), and CS algorithm used to optimize each subband of DWT and then obtains the singular value matrix of the low-low thresholded subband image and finally, it reconstructs the enhanced image by applying IDWT. The singular value matrix employed intensity information of the particular image, and any modification in the singular values changes the intensity of the given image. The experimental results show superiority of the proposed method performance in terms of PSNR, MSE, Mean and Standard Deviation over conventional and state-of-the-art techniques. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Application of the EM algorithm to radiographic images.

PubMed

Brailean, J C; Little, D; Giger, M L; Chen, C T; Sullivan, B J

1992-01-01

The expectation maximization (EM) algorithm has received considerable attention in the area of positron emitted tomography (PET) as a restoration and reconstruction technique. In this paper, the restoration capabilities of the EM algorithm when applied to radiographic images is investigated. This application does not involve reconstruction. The performance of the EM algorithm is quantitatively evaluated using a "perceived" signal-to-noise ratio (SNR) as the image quality metric. This perceived SNR is based on statistical decision theory and includes both the observer's visual response function and a noise component internal to the eye-brain system. For a variety of processing parameters, the relative SNR (ratio of the processed SNR to the original SNR) is calculated and used as a metric to compare quantitatively the effects of the EM algorithm with two other image enhancement techniques: global contrast enhancement (windowing) and unsharp mask filtering. The results suggest that the EM algorithm's performance is superior when compared to unsharp mask filtering and global contrast enhancement for radiographic images which contain objects smaller than 4 mm.

Fast and robust wavelet-based dynamic range compression and contrast enhancement model with color restoration

NASA Astrophysics Data System (ADS)

Unaldi, Numan; Asari, Vijayan K.; Rahman, Zia-ur

2009-05-01

Recently we proposed a wavelet-based dynamic range compression algorithm to improve the visual quality of digital images captured from high dynamic range scenes with non-uniform lighting conditions. The fast image enhancement algorithm that provides dynamic range compression, while preserving the local contrast and tonal rendition, is also a good candidate for real time video processing applications. Although the colors of the enhanced images produced by the proposed algorithm are consistent with the colors of the original image, the proposed algorithm fails to produce color constant results for some "pathological" scenes that have very strong spectral characteristics in a single band. The linear color restoration process is the main reason for this drawback. Hence, a different approach is required for the final color restoration process. In this paper the latest version of the proposed algorithm, which deals with this issue is presented. The results obtained by applying the algorithm to numerous natural images show strong robustness and high image quality.

Local adaptive contrast enhancement for color images

NASA Astrophysics Data System (ADS)

Dijk, Judith; den Hollander, Richard J. M.; Schavemaker, John G. M.; Schutte, Klamer

2007-04-01

A camera or display usually has a smaller dynamic range than the human eye. For this reason, objects that can be detected by the naked eye may not be visible in recorded images. Lighting is here an important factor; improper local lighting impairs visibility of details or even entire objects. When a human is observing a scene with different kinds of lighting, such as shadows, he will need to see details in both the dark and light parts of the scene. For grey value images such as IR imagery, algorithms have been developed in which the local contrast of the image is enhanced using local adaptive techniques. In this paper, we present how such algorithms can be adapted so that details in color images are enhanced while color information is retained. We propose to apply the contrast enhancement on color images by applying a grey value contrast enhancement algorithm to the luminance channel of the color signal. The color coordinates of the signal will remain the same. Care is taken that the saturation change is not too high. Gamut mapping is performed so that the output can be displayed on a monitor. The proposed technique can for instance be used by operators monitoring movements of people in order to detect suspicious behavior. To do this effectively, specific individuals should both be easy to recognize and track. This requires optimal local contrast, and is sometimes much helped by color when tracking a person with colored clothes. In such applications, enhanced local contrast in color images leads to more effective monitoring.

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.

Underwater image enhancement through depth estimation based on random forest

NASA Astrophysics Data System (ADS)

Tai, Shen-Chuan; Tsai, Ting-Chou; Huang, Jyun-Han

2017-11-01

Light absorption and scattering in underwater environments can result in low-contrast images with a distinct color cast. This paper proposes a systematic framework for the enhancement of underwater images. Light transmission is estimated using the random forest algorithm. RGB values, luminance, color difference, blurriness, and the dark channel are treated as features in training and estimation. Transmission is calculated using an ensemble machine learning algorithm to deal with a variety of conditions encountered in underwater environments. A color compensation and contrast enhancement algorithm based on depth information was also developed with the aim of improving the visual quality of underwater images. Experimental results demonstrate that the proposed scheme outperforms existing methods with regard to subjective visual quality as well as objective measurements.

Evaluating some computer exhancement algorithms that improve the visibility of cometary morphology

NASA Technical Reports Server (NTRS)

Larson, Stephen M.; Slaughter, Charles D.

1992-01-01

Digital enhancement of cometary images is a necessary tool in studying cometary morphology. Many image processing algorithms, some developed specifically for comets, have been used to enhance the subtle, low contrast coma and tail features. We compare some of the most commonly used algorithms on two different images to evaluate their strong and weak points, and conclude that there currently exists no single 'ideal' algorithm, although the radial gradient spatial filter gives the best overall result. This comparison should aid users in selecting the best algorithm to enhance particular features of interest.

Content-aware dark image enhancement through channel division.

PubMed

Rivera, Adin Ramirez; Ryu, Byungyong; Chae, Oksam

2012-09-01

The current contrast enhancement algorithms occasionally result in artifacts, overenhancement, and unnatural effects in the processed images. These drawbacks increase for images taken under poor illumination conditions. In this paper, we propose a content-aware algorithm that enhances dark images, sharpens edges, reveals details in textured regions, and preserves the smoothness of flat regions. The algorithm produces an ad hoc transformation for each image, adapting the mapping functions to each image's characteristics to produce the maximum enhancement. We analyze the contrast of the image in the boundary and textured regions, and group the information with common characteristics. These groups model the relations within the image, from which we extract the transformation functions. The results are then adaptively mixed, by considering the human vision system characteristics, to boost the details in the image. Results show that the algorithm can automatically process a wide range of images-e.g., mixed shadow and bright areas, outdoor and indoor lighting, and face images-without introducing artifacts, which is an improvement over many existing methods.

Temporal subtraction contrast-enhanced dedicated breast CT

NASA Astrophysics Data System (ADS)

Gazi, Peymon M.; Aminololama-Shakeri, Shadi; Yang, Kai; Boone, John M.

2016-09-01

The development of a framework of deformable image registration and segmentation for the purpose of temporal subtraction contrast-enhanced breast CT is described. An iterative histogram-based two-means clustering method was used for the segmentation. Dedicated breast CT images were segmented into background (air), adipose, fibroglandular and skin components. Fibroglandular tissue was classified as either normal or contrast-enhanced then divided into tiers for the purpose of categorizing degrees of contrast enhancement. A variant of the Demons deformable registration algorithm, intensity difference adaptive Demons (IDAD), was developed to correct for the large deformation forces that stemmed from contrast enhancement. In this application, the accuracy of the proposed method was evaluated in both mathematically-simulated and physically-acquired phantom images. Clinical usage and accuracy of the temporal subtraction framework was demonstrated using contrast-enhanced breast CT datasets from five patients. Registration performance was quantified using normalized cross correlation (NCC), symmetric uncertainty coefficient, normalized mutual information (NMI), mean square error (MSE) and target registration error (TRE). The proposed method outperformed conventional affine and other Demons variations in contrast enhanced breast CT image registration. In simulation studies, IDAD exhibited improvement in MSE (0-16%), NCC (0-6%), NMI (0-13%) and TRE (0-34%) compared to the conventional Demons approaches, depending on the size and intensity of the enhancing lesion. As lesion size and contrast enhancement levels increased, so did the improvement. The drop in the correlation between the pre- and post-contrast images for the largest enhancement levels in phantom studies is less than 1.2% (150 Hounsfield units). Registration error, measured by TRE, shows only submillimeter mismatches between the concordant anatomical target points in all patient studies. The algorithm was implemented using a parallel processing architecture resulting in rapid execution time for the iterative segmentation and intensity-adaptive registration techniques. Characterization of contrast-enhanced lesions is improved using temporal subtraction contrast-enhanced dedicated breast CT. Adaptation of Demons registration forces as a function of contrast-enhancement levels provided a means to accurately align breast tissue in pre- and post-contrast image acquisitions, improving subtraction results. Spatial subtraction of the aligned images yields useful diagnostic information with respect to enhanced lesion morphology and uptake.

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

Interobserver Agreement for Contrast-Enhanced Ultrasound (CEUS)-Based Standardized Algorithms for the Diagnosis of Hepatocellular Carcinoma in High-Risk Patients.

PubMed

Schellhaas, Barbara; Pfeifer, Lukas; Kielisch, Christian; Goertz, Ruediger Stephan; Neurath, Markus F; Strobel, Deike

2018-06-07

 This pilot study aimed at assessing interobserver agreement with two contrast-enhanced ultrasound (CEUS) algorithms for the diagnosis of hepatocellular carcinoma (HCC) in high-risk patients.  Focal liver lesions in 55 high-risk patients were assessed independently by three blinded observers with two standardized CEUS algorithms: ESCULAP (Erlanger Synopsis of Contrast-Enhanced Ultrasound for Liver Lesion Assessment in Patients at risk) and ACR-CEUS-LI-RADSv.2016 (American College of Radiology CEUS-Liver Imaging Reporting and Data System). Lesions were categorized according to size and ultrasound contrast enhancement in the arterial, portal-venous and late phase. Interobserver agreement for assessment of enhancement pattern and categorization was compared between both CEUS algorithms. Additionally, diagnostic accuracy for the definitive diagnosis of HCC was compared. Histology and/or CE-MRI and follow-up served as reference standards.  55 patients were included in the study (male/female, 44/ 11; mean age: 65.9 years). 90.9 % had cirrhosis. Histological findings were available in 39/55 lesions (70.9 %). Reference standard of the 55 lesions revealed 48 HCCs, 2 intrahepatic cholangiocellular carcinomas (ICCs), and 5 non-HCC-non-ICC lesions. Interobserver agreement was moderate to substantial for arterial phase hyperenhancement (ĸ = 0.53 - 0.67), and fair to moderate for contrast washout in the portal-venous or late phase (ĸ = 0.33 - 0.53). Concerning the CEUS-based algorithms, the interreader agreement was substantial for the ESCULAP category (ĸ = 0.64 - 0.68) and fair for the CEUS-LI-RADS ® category (ĸ = 0.3 - 0.39). Disagreement between observers was mostly due to different perception of washout.  Interobserver agreement is better for ESCULAP than for CEUS-LI-RADS ® . This is mostly due to the fact that perception of contrast washout varies between different observers. However, interobserver agreement is good for arterial phase hyperenhancement, which is the key diagnostic feature for the diagnosis of HCC with CEUS in the cirrhotic liver. © Georg Thieme Verlag KG Stuttgart · New York.

Retinex enhancement of infrared images.

PubMed

Li, Ying; He, Renjie; Xu, Guizhi; Hou, Changzhi; Sun, Yunyan; Guo, Lei; Rao, Liyun; Yan, Weili

2008-01-01

With the ability of imaging the temperature distribution of body, infrared imaging is promising in diagnostication and prognostication of diseases. However the poor quality of the raw original infrared images prevented applications and one of the essential problems is the low contrast appearance of the imagined object. In this paper, the image enhancement technique based on the Retinex theory is studied, which is a process that automatically retrieve the visual realism to images. The algorithms, including Frackle-McCann algorithm, McCann99 algorithm, single-scale Retinex algorithm, multi-scale Retinex algorithm and multi-scale Retinex algorithm with color restoration, are experienced to the enhancement of infrared images. The entropy measurements along with the visual inspection were compared and results shown the algorithms based on Retinex theory have the ability in enhancing the infrared image. Out of the algorithms compared, MSRCR demonstrated the best performance.

Stochastic HKMDHE: A multi-objective contrast enhancement algorithm

NASA Astrophysics Data System (ADS)

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

2018-02-01

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

New subtraction algorithms for evaluation of lesions on dynamic contrast-enhanced MR mammography.

PubMed

Choi, Byung Gil; Kim, Hak Hee; Kim, Euy Neyng; Kim, Bum-soo; Han, Ji-Youn; Yoo, Seung-Schik; Park, Seog Hee

2002-12-01

We report new subtraction algorithms for the detection of lesions in dynamic contrast-enhanced MR mammography(CE MRM). Twenty-five patients with suspicious breast lesions underwent dynamic CE MRM using 3D fast low-angle shot. After the acquisition of the T1-weighted scout images, dynamic images were acquired six times after the bolus injection of contrast media. Serial subtractions, step-by-step subtractions, and reverse subtractions, were performed. Two radiologists attempted to differentiate benign from malignant lesion in consensus. The sensitivity, specificity, and accuracy of the method leading to the differentiation of malignant tumor from benign lesions were 85.7, 100, and 96%, respectively. Subtraction images allowed for better visualization of the enhancement as well as its temporal pattern than visual inspection of dynamic images alone. Our findings suggest that the new subtraction algorithm is adequate for screening malignant breast lesions and can potentially replace the time-intensity profile analysis on user-selected regions of interest.

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

Novel techniques for enhancement and segmentation of acne vulgaris lesions.

PubMed

Malik, A S; Humayun, J; Kamel, N; Yap, F B-B

2014-08-01

More than 99% acne patients suffer from acne vulgaris. While diagnosing the severity of acne vulgaris lesions, dermatologists have observed inter-rater and intra-rater variability in diagnosis results. This is because during assessment, identifying lesion types and their counting is a tedious job for dermatologists. To make the assessment job objective and easier for dermatologists, an automated system based on image processing methods is proposed in this study. There are two main objectives: (i) to develop an algorithm for the enhancement of various acne vulgaris lesions; and (ii) to develop a method for the segmentation of enhanced acne vulgaris lesions. For the first objective, an algorithm is developed based on the theory of high dynamic range (HDR) images. The proposed algorithm uses local rank transform to generate the HDR images from a single acne image followed by the log transformation. Then, segmentation is performed by clustering the pixels based on Mahalanobis distance of each pixel from spectral models of acne vulgaris lesions. Two metrics are used to evaluate the enhancement of acne vulgaris lesions, i.e., contrast improvement factor (CIF) and image contrast normalization (ICN). The proposed algorithm is compared with two other methods. The proposed enhancement algorithm shows better result than both the other methods based on CIF and ICN. In addition, sensitivity and specificity are calculated for the segmentation results. The proposed segmentation method shows higher sensitivity and specificity than other methods. This article specifically discusses the contrast enhancement and segmentation for automated diagnosis system of acne vulgaris lesions. The results are promising that can be used for further classification of acne vulgaris lesions for final grading of the lesions. © 2013 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Temporal subtraction contrast-enhanced dedicated breast CT

PubMed Central

Gazi, Peymon M.; Aminololama-Shakeri, Shadi; Yang, Kai; Boone, John M.

2016-01-01

Purpose To develop a framework of deformable image registration and segmentation for the purpose of temporal subtraction contrast-enhanced breast CT is described. Methods An iterative histogram-based two-means clustering method was used for the segmentation. Dedicated breast CT images were segmented into background (air), adipose, fibroglandular and skin components. Fibroglandular tissue was classified as either normal or contrast-enhanced then divided into tiers for the purpose of categorizing degrees of contrast enhancement. A variant of the Demons deformable registration algorithm, Intensity Difference Adaptive Demons (IDAD), was developed to correct for the large deformation forces that stemmed from contrast enhancement. In this application, the accuracy of the proposed method was evaluated in both mathematically-simulated and physically-acquired phantom images. Clinical usage and accuracy of the temporal subtraction framework was demonstrated using contrast-enhanced breast CT datasets from five patients. Registration performance was quantified using Normalized Cross Correlation (NCC), Symmetric Uncertainty Coefficient (SUC), Normalized Mutual Information (NMI), Mean Square Error (MSE) and Target Registration Error (TRE). Results The proposed method outperformed conventional affine and other Demons variations in contrast enhanced breast CT image registration. In simulation studies, IDAD exhibited improvement in MSE(0–16%), NCC (0–6%), NMI (0–13%) and TRE (0–34%) compared to the conventional Demons approaches, depending on the size and intensity of the enhancing lesion. As lesion size and contrast enhancement levels increased, so did the improvement. The drop in the correlation between the pre- and post-contrast images for the largest enhancement levels in phantom studies is less than 1.2% (150 Hounsfield units). Registration error, measured by TRE, shows only submillimeter mismatches between the concordant anatomical target points in all patient studies. The algorithm was implemented using a parallel processing architecture resulting in rapid execution time for the iterative segmentation and intensity-adaptive registration techniques. Conclusion Characterization of contrast-enhanced lesions is improved using temporal subtraction contrast-enhanced dedicated breast CT. Adaptation of Demons registration forces as a function of contrast-enhancement levels provided a means to accurately align breast tissue in pre- and post-contrast image acquisitions, improving subtraction results. Spatial subtraction of the aligned images yields useful diagnostic information with respect to enhanced lesion morphology and uptake. PMID:27494376

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.

Color enhancement and image defogging in HSI based on Retinex model

NASA Astrophysics Data System (ADS)

Gao, Han; Wei, Ping; Ke, Jun

2015-08-01

Retinex is a luminance perceptual algorithm based on color consistency. It has a good performance in color enhancement. But in some cases, the traditional Retinex algorithms, both Single-Scale Retinex(SSR) and Multi-Scale Retinex(MSR) in RGB color space, do not work well and will cause color deviation. To solve this problem, we present improved SSR and MSR algorithms. Compared to other Retinex algorithms, we implement Retinex algorithms in HSI(Hue, Saturation, Intensity) color space, and use a parameter αto improve quality of the image. Moreover, the algorithms presented in this paper has a good performance in image defogging. Contrasted with traditional Retinex algorithms, we use intensity channel to obtain reflection information of an image. The intensity channel is processed using a Gaussian center-surround image filter to get light information, which should be removed from intensity channel. After that, we subtract the light information from intensity channel to obtain the reflection image, which only includes the attribute of the objects in image. Using the reflection image and a parameter α, which is an arbitrary scale factor set manually, we improve the intensity channel, and complete the color enhancement. Our experiments show that this approach works well compared with existing methods for color enhancement. Besides a better performance in color deviation problem and image defogging, a visible improvement in the image quality for human contrast perception is also observed.

Infrared traffic image enhancement algorithm based on dark channel prior and gamma correction

NASA Astrophysics Data System (ADS)

Zheng, Lintao; Shi, Hengliang; Gu, Ming

2017-07-01

The infrared traffic image acquired by the intelligent traffic surveillance equipment has low contrast, little hierarchical differences in perceptions of image and the blurred vision effect. Therefore, infrared traffic image enhancement, being an indispensable key step, is applied to nearly all infrared imaging based traffic engineering applications. In this paper, we propose an infrared traffic image enhancement algorithm that is based on dark channel prior and gamma correction. In existing research dark channel prior, known as a famous image dehazing method, here is used to do infrared image enhancement for the first time. Initially, in the proposed algorithm, the original degraded infrared traffic image is transformed with dark channel prior as the initial enhanced result. A further adjustment based on the gamma curve is needed because initial enhanced result has lower brightness. Comprehensive validation experiments reveal that the proposed algorithm outperforms the current state-of-the-art algorithms.

An analytical optimization model for infrared image enhancement via local context

NASA Astrophysics Data System (ADS)

Xu, Yongjian; Liang, Kun; Xiong, Yiru; Wang, Hui

2017-12-01

The requirement for high-quality infrared images is constantly increasing in both military and civilian areas, and it is always associated with little distortion and appropriate contrast, while infrared images commonly have some shortcomings such as low contrast. In this paper, we propose a novel infrared image histogram enhancement algorithm based on local context. By constraining the enhanced image to have high local contrast, a regularized analytical optimization model is proposed to enhance infrared images. The local contrast is determined by evaluating whether two intensities are neighbors and calculating their differences. The comparison on 8-bit images shows that the proposed method can enhance the infrared images with more details and lower noise.

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.

Correction of Non-Linear Propagation Artifact in Contrast-Enhanced Ultrasound Imaging of Carotid Arteries: Methods and in Vitro Evaluation.

PubMed

Yildiz, Yesna O; Eckersley, Robert J; Senior, Roxy; Lim, Adrian K P; Cosgrove, David; Tang, Meng-Xing

2015-07-01

Non-linear propagation of ultrasound creates artifacts in contrast-enhanced ultrasound images that significantly affect both qualitative and quantitative assessments of tissue perfusion. This article describes the development and evaluation of a new algorithm to correct for this artifact. The correction is a post-processing method that estimates and removes non-linear artifact in the contrast-specific image using the simultaneously acquired B-mode image data. The method is evaluated on carotid artery flow phantoms with large and small vessels containing microbubbles of various concentrations at different acoustic pressures. The algorithm significantly reduces non-linear artifacts while maintaining the contrast signal from bubbles to increase the contrast-to-tissue ratio by up to 11 dB. Contrast signal from a small vessel 600 μm in diameter buried in tissue artifacts before correction was recovered after the correction. Copyright © 2015 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.

Investigation of self-adaptive LED surgical lighting based on entropy contrast enhancing method

NASA Astrophysics Data System (ADS)

Liu, Peng; Wang, Huihui; Zhang, Yaqin; Shen, Junfei; Wu, Rengmao; Zheng, Zhenrong; Li, Haifeng; Liu, Xu

2014-05-01

Investigation was performed to explore the possibility of enhancing contrast by varying the spectral distribution (SPD) of the surgical lighting. The illumination scenes with different SPDs were generated by the combination of a self-adaptive white light optimization method and the LED ceiling system, the images of biological sample are taken by a CCD camera and then processed by an 'Entropy' based contrast evaluation model which is proposed specific for surgery occasion. Compared with the neutral white LED based and traditional algorithm based image enhancing methods, the illumination based enhancing method turns out a better performance in contrast enhancing and improves the average contrast value about 9% and 6%, respectively. This low cost method is simple, practicable, and thus may provide an alternative solution for the expensive visual facility medical instruments.

Advanced Image Enhancement Method for Distant Vessels and Structures in Capsule Endoscopy

PubMed Central

Pedersen, Marius

2017-01-01

This paper proposes an advanced method for contrast enhancement of capsule endoscopic images, with the main objective to obtain sufficient information about the vessels and structures in more distant (or darker) parts of capsule endoscopic images. The proposed method (PM) combines two algorithms for the enhancement of darker and brighter areas of capsule endoscopic images, respectively. The half-unit weighted-bilinear algorithm (HWB) proposed in our previous work is used to enhance darker areas according to the darker map content of its HSV's component V. Enhancement of brighter areas is achieved thanks to the novel threshold weighted-bilinear algorithm (TWB) developed to avoid overexposure and enlargement of specular highlight spots while preserving the hue, in such areas. The TWB performs enhancement operations following a gradual increment of the brightness of the brighter map content of its HSV's component V. In other words, the TWB decreases its averaged weights as the intensity content of the component V increases. Extensive experimental demonstrations were conducted, and, based on evaluation of the reference and PM enhanced images, a gastroenterologist (Ø.H.) concluded that the PM enhanced images were the best ones based on the information about the vessels, contrast in the images, and the view or visibility of the structures in more distant parts of the capsule endoscopy images. PMID:29225668

Enhanced image fusion using directional contrast rules in fuzzy transform domain.

PubMed

Nandal, Amita; Rosales, Hamurabi Gamboa

2016-01-01

In this paper a novel image fusion algorithm based on directional contrast in fuzzy transform (FTR) domain is proposed. Input images to be fused are first divided into several non-overlapping blocks. The components of these sub-blocks are fused using directional contrast based fuzzy fusion rule in FTR domain. The fused sub-blocks are then transformed into original size blocks using inverse-FTR. Further, these inverse transformed blocks are fused according to select maximum based fusion rule for reconstructing the final fused image. The proposed fusion algorithm is both visually and quantitatively compared with other standard and recent fusion algorithms. Experimental results demonstrate that the proposed method generates better results than the other methods.

Very low cost real time histogram-based contrast enhancer utilizing fixed-point DSP processing

NASA Astrophysics Data System (ADS)

McCaffrey, Nathaniel J.; Pantuso, Francis P.

1998-03-01

A real time contrast enhancement system utilizing histogram- based algorithms has been developed to operate on standard composite video signals. This low-cost DSP based system is designed with fixed-point algorithms and an off-chip look up table (LUT) to reduce the cost considerably over other contemporary approaches. This paper describes several real- time contrast enhancing systems advanced at the Sarnoff Corporation for high-speed visible and infrared cameras. The fixed-point enhancer was derived from these high performance cameras. The enhancer digitizes analog video and spatially subsamples the stream to qualify the scene's luminance. Simultaneously, the video is streamed through a LUT that has been programmed with the previous calculation. Reducing division operations by subsampling reduces calculation- cycles and also allows the processor to be used with cameras of nominal resolutions. All values are written to the LUT during blanking so no frames are lost. The enhancer measures 13 cm X 6.4 cm X 3.2 cm, operates off 9 VAC and consumes 12 W. This processor is small and inexpensive enough to be mounted with field deployed security cameras and can be used for surveillance, video forensics and real- time medical imaging.

A new automated quantification algorithm for the detection and evaluation of focal liver lesions with contrast-enhanced ultrasound.

PubMed

Gatos, Ilias; Tsantis, Stavros; Spiliopoulos, Stavros; Skouroliakou, Aikaterini; Theotokas, Ioannis; Zoumpoulis, Pavlos; Hazle, John D; Kagadis, George C

2015-07-01

Detect and classify focal liver lesions (FLLs) from contrast-enhanced ultrasound (CEUS) imaging by means of an automated quantification algorithm. The proposed algorithm employs a sophisticated segmentation method to detect and contour focal lesions from 52 CEUS video sequences (30 benign and 22 malignant). Lesion detection involves wavelet transform zero crossings utilization as an initialization step to the Markov random field model toward the lesion contour extraction. After FLL detection across frames, time intensity curve (TIC) is computed which provides the contrast agents' behavior at all vascular phases with respect to adjacent parenchyma for each patient. From each TIC, eight features were automatically calculated and employed into the support vector machines (SVMs) classification algorithm in the design of the image analysis model. With regard to FLLs detection accuracy, all lesions detected had an average overlap value of 0.89 ± 0.16 with manual segmentations for all CEUS frame-subsets included in the study. Highest classification accuracy from the SVM model was 90.3%, misdiagnosing three benign and two malignant FLLs with sensitivity and specificity values of 93.1% and 86.9%, respectively. The proposed quantification system that employs FLLs detection and classification algorithms may be of value to physicians as a second opinion tool for avoiding unnecessary invasive procedures.

A review on brightness preserving contrast enhancement methods for digital image

NASA Astrophysics Data System (ADS)

Rahman, Md Arifur; Liu, Shilong; Li, Ruowei; Wu, Hongkun; Liu, San Chi; Jahan, Mahmuda Rawnak; Kwok, Ngaiming

2018-04-01

Image enhancement is an imperative step for many vision based applications. For image contrast enhancement, popular methods adopt the principle of spreading the captured intensities throughout the allowed dynamic range according to predefined distributions. However, these algorithms take little or no consideration into account of maintaining the mean brightness of the original scene, which is of paramount importance to carry the true scene illumination characteristics to the viewer. Though there have been significant amount of reviews on contrast enhancement methods published, updated review on overall brightness preserving image enhancement methods is still scarce. In this paper, a detailed survey is performed on those particular methods that specifically aims to maintain the overall scene illumination characteristics while enhancing the digital image.

Enhancement tuning and control for high dynamic range images in multi-scale locally adaptive contrast enhancement algorithms

NASA Astrophysics Data System (ADS)

Cvetkovic, Sascha D.; Schirris, Johan; de With, Peter H. N.

2009-01-01

For real-time imaging in surveillance applications, visibility of details is of primary importance to ensure customer confidence. If we display High Dynamic-Range (HDR) scenes whose contrast spans four or more orders of magnitude on a conventional monitor without additional processing, results are unacceptable. Compression of the dynamic range is therefore a compulsory part of any high-end video processing chain because standard monitors are inherently Low- Dynamic Range (LDR) devices with maximally two orders of display dynamic range. In real-time camera processing, many complex scenes are improved with local contrast enhancements, bringing details to the best possible visibility. In this paper, we show how a multi-scale high-frequency enhancement scheme, in which gain is a non-linear function of the detail energy, can be used for the dynamic range compression of HDR real-time video camera signals. We also show the connection of our enhancement scheme to the processing way of the Human Visual System (HVS). Our algorithm simultaneously controls perceived sharpness, ringing ("halo") artifacts (contrast) and noise, resulting in a good balance between visibility of details and non-disturbance of artifacts. The overall quality enhancement, suitable for both HDR and LDR scenes, is based on a careful selection of the filter types for the multi-band decomposition and a detailed analysis of the signal per frequency band.

View-sharing PROPELLER with pixel-based optimal blade selection: application on dynamic contrast-enhanced imaging.

PubMed

Chuang, Tzu-Chao; Huang, Hsuan-Hung; Chang, Hing-Chiu; Wu, Ming-Ting

2014-06-01

To achieve better spatial and temporal resolution of dynamic contrast-enhanced MR imaging, the concept of k-space data sharing, or view sharing, can be implemented for PROPELLER acquisition. As found in other view-sharing methods, the loss of high-resolution dynamics is possible for view-sharing PROPELLER (VS-Prop) due to the temporal smoothing effect. The degradation can be more severe when a narrow blade with less phase encoding steps is chosen in the acquisition for higher frame rate. In this study, an iterative algorithm termed pixel-based optimal blade selection (POBS) is proposed to allow spatially dependent selection of the rotating blades, to generate high-resolution dynamic images with minimal reconstruction artifacts. In the reconstruction of VS-Prop, the central k-space which dominates the image contrast is only provided by the target blade with the peripheral k-space contributed by a minimal number of consecutive rotating blades. To reduce the reconstruction artifacts, the set of neighboring blades exhibiting the closest image contrast with the target blade is picked by POBS algorithm. Numerical simulations and phantom experiments were conducted in this study to investigate the dynamic response and spatial profiles of images generated using our proposed method. In addition, dynamic contrast-enhanced cardiovascular imaging of healthy subjects was performed to demonstrate the feasibility and advantages. The simulation results show that POBS VS-Prop can provide timely dynamic response to rapid signal change, especially for a small region of interest or with the use of narrow blades. The POBS algorithm also demonstrates its capability to capture nonsimultaneous signal changes over the entire FOV. In addition, both phantom and in vivo experiments show that the temporal smoothing effect can be avoided by means of POBS, leading to higher wash-in slope of contrast enhancement after the bolus injection. With the satisfactory reconstruction quality provided by the POBS algorithm, VS-Prop acquisition technique may find useful clinical applications in DCE MR imaging studies where both spatial and temporal resolutions play important roles.

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.

Evaluation of the morphology structure of meibomian glands based on mask dodging method

NASA Astrophysics Data System (ADS)

Yan, Huangping; Zuo, Yingbo; Chen, Yisha; Chen, Yanping

2016-10-01

Low contrast and non-uniform illumination of infrared (IR) meibography images make the detection of meibomian glands challengeable. An improved Mask dodging algorithm is proposed. To overcome the shortage of low contrast using traditional Mask dodging method, a scale factor is used to enhance the image after subtracting background image from an original one. Meibomian glands are detected and the ratio of the meibomian gland area to the measurement area is calculated. The results show that the improved Mask algorithm has ideal dodging effect, which can eliminate non-uniform illumination and improve contrast of meibography images effectively.

High Energy Resolution Hyperspectral X-Ray Imaging for Low-Dose Contrast-Enhanced Digital Mammography.

PubMed

Pani, Silvia; Saifuddin, Sarene C; Ferreira, Filipa I M; Henthorn, Nicholas; Seller, Paul; Sellin, Paul J; Stratmann, Philipp; Veale, Matthew C; Wilson, Matthew D; Cernik, Robert J

2017-09-01

Contrast-enhanced digital mammography (CEDM) is an alternative to conventional X-ray mammography for imaging dense breasts. However, conventional approaches to CEDM require a double exposure of the patient, implying higher dose, and risk of incorrect image registration due to motion artifacts. A novel approach is presented, based on hyperspectral imaging, where a detector combining positional and high-resolution spectral information (in this case based on Cadmium Telluride) is used. This allows simultaneous acquisition of the two images required for CEDM. The approach was tested on a custom breast-equivalent phantom containing iodinated contrast agent (Niopam 150®). Two algorithms were used to obtain images of the contrast agent distribution: K-edge subtraction (KES), providing images of the distribution of the contrast agent with the background structures removed, and a dual-energy (DE) algorithm, providing an iodine-equivalent image and a water-equivalent image. The high energy resolution of the detector allowed the selection of two close-by energies, maximising the signal in KES images, and enhancing the visibility of details with the low surface concentration of contrast agent. DE performed consistently better than KES in terms of contrast-to-noise ratio of the details; moreover, it allowed a correct reconstruction of the surface concentration of the contrast agent in the iodine image. Comparison with CEDM with a conventional detector proved the superior performance of hyperspectral CEDM in terms of the image quality/dose tradeoff.

Image quality enhancement in low-light-level ghost imaging using modified compressive sensing method

NASA Astrophysics Data System (ADS)

Shi, Xiaohui; Huang, Xianwei; Nan, Suqin; Li, Hengxing; Bai, Yanfeng; Fu, Xiquan

2018-04-01

Detector noise has a significantly negative impact on ghost imaging at low light levels, especially for existing recovery algorithm. Based on the characteristics of the additive detector noise, a method named modified compressive sensing ghost imaging is proposed to reduce the background imposed by the randomly distributed detector noise at signal path. Experimental results show that, with an appropriate choice of threshold value, modified compressive sensing ghost imaging algorithm can dramatically enhance the contrast-to-noise ratio of the object reconstruction significantly compared with traditional ghost imaging and compressive sensing ghost imaging methods. The relationship between the contrast-to-noise ratio of the reconstruction image and the intensity ratio (namely, the average signal intensity to average noise intensity ratio) for the three reconstruction algorithms are also discussed. This noise suppression imaging technique will have great applications in remote-sensing and security areas.

A Rare Case of Malignant Melanoma of the Mandible: 
CT and MRI Findings.

PubMed

Ogura, Ichiro; Sasaki, Yoshihiko; Kameta, Ayako; Sue, Mikiko; Oda, Takaaki

Malignant melanoma of the mandibular gingiva is extremely rare. It is a malignant tumour of melanocytes or their precursor cells, and often misinterpreted as a benign pigmented process. A few reports have described computed tomography (CT) and magnetic resonance imaging (MRI) findings of malignant melanoma in the oral cavity. We report a rare case of malignant melanoma of the mandible and the related CT and MRI findings. Soft tissue algorithm contrast-enhanced CT showed an expansile mass and irregular destruction of alveolar bone in the right side of the mandibular molar area. MR images showed an enhancing mass and the tumour had a low to intermediate signal intensity and a high-signal intensity. Soft tissue algorithm contrast-enhanced CT and MR images showed lymphadenopathy involving the submandibular lymph nodes. Histopathological examination confirmed the diagnosis of malignant melanoma.

Diagnostic accuracy of contrast-enhanced ultrasound for the differential diagnosis of hepatocellular carcinoma: ESCULAP versus CEUS-LI-RADS.

PubMed

Schellhaas, Barbara; Görtz, Ruediger S; Pfeifer, Lukas; Kielisch, Christian; Neurath, Markus F; Strobel, Deike

2017-09-01

A comparison is made of two contrast-enhanced ultrasound (CEUS) algorithms for the diagnosis of hepatocellular carcinoma (HCC) in high-risk patients: Erlanger Synopsis of Contrast-enhanced Ultrasound for Liver lesion Assessment in Patients at Risk (ESCULAP) and American College of Radiology Contrast-Enhanced Ultrasound-Liver Imaging Reporting and Data System (ACR-CEUS-LI-RADSv.2016). Focal liver lesions in 100 high-risk patients were assessed using both CEUS algorithms (ESCULAP and CEUS-LI-RADSv.2016) for a direct comparison. Lesions were categorized according to size and contrast enhancement in the arterial, portal venous and late phases.For the definite diagnosis of HCC, categories ESCULAP-4, ESCULAP-Tr and ESCULAP-V and CEUS-LI-RADS-LR-5, LR-Tr and LR-5-V were compared. In addition, CEUS-LI-RADS-category LR-M (definitely/probably malignant, but not specific for HCC) and ESCULAP-category C [intrahepatic cholangiocellular carcinoma (ICC)] were compared.Histology, CE-computed tomography and CE-MRI served as reference standards. The reference standard among 100 lesions included 87 HCCs, six ICCs and seven non-HCC-non-ICC-lesions. For the diagnosis of HCC, the diagnostic accuracy of CEUS was significantly higher with ESCULAP versus CEUS-LI-RADS (94.3%/72.4%; p<0.01). Sensitivity, specificity and positive predictive value (PPV) and negative predictive value for ESCULAP/CEUS-LI-RADS were 94.3%/72.4%; 61.5%/69.2%; 94.3%/94%; and 61.5%/27.3%, respectively.The diagnostic accuracy for ICC (LR-M/ESCULAP-C) was identical with both algorithms (50%), with higher PPV for ESCULAP-C versus LR-M (75 vs. 50%). CEUS-based algorithms contribute toward standardized assessment and reporting of HCC-suspect lesions in high-risk patients. ESCULAP shows significantly higher diagnostic accuracy, sensitivity and negative predictive value with no loss of specificity compared with CEUS-LI-RADS. Both algorithms have an excellent PPV. Arterial hyperenhancement is the key feature for the diagnosis of HCC with CEUS. Washout should not be a necessary prerequisite for the diagnosis of definite HCC. CEUS-LI-RADS in its current version is inferior to ESCULAP for the noninvasive diagnosis of HCC. There are two ways to improve CEUS-LI-RADS: firstly, combination of the categories LR-4 and LR-5 for the diagnosis of definite HCC, and secondly, use of subtotal infiltration of a liver lobe as an additional feature.

Contrast features of breast cancer in frequency-domain laser scanning mammography

NASA Astrophysics Data System (ADS)

Moesta, K. Thomas; Fantini, Sergio; Jess, Helge; Totkas, Susan; Franceschini, Maria-Angela; Kaschke, Michael; Schlag, Peter M.

1998-04-01

Frequency-domain optical mammography has been advocated to improve contrast and thus cancer detectability in breast transillumination. To the best of our knowledge, this report provides the first systematic clinical results of a frequency-domain laser scanning mammograph (FLM). The instrument provides monochromatic light at 690 and 810 nm, whose intensity is modulated at 110.0008 MHz, respectively. The breast is scanned by stepwise positioning of source and detector, and amplitude and phase for both wavelengths are measured by a photomultiplier tube using heterodyne detection. Images are formed representing amplitude or phase data on linear gray scales. Furthermore, various algorithms carrying on more than one signal were essayed. Twenty visible cancers out of 25 cancers in the first 59 investigations were analyzed for their quantitative contrast with respect to the whole breast or to defined reference areas. Contrast definitions refer to the signal itself, to the signal noise, or were based on nonparametric comparison. The amplitude signal provides better contrast than the phase signal. Ratio images between red and IR amplitudes gave variable results; in some cases the tumor contrast was canceled. The algorithms to determine (mu) a and (mu) sPRM from amplitude and phase data did not significantly improve upon objective contrast. The N algorithm, using the phase signal to flatten the amplitude signal did significantly improve upon contrast according to contrast definitions 1 and 2, however, did not improve upon nonparametric contrast. Thus, with the current instrumentation, the phase signal is helpful to correct for the complex and variable geometry of the breast. However, an independent informational content for tumor differentiation could not be determined. The flat field algorithm did greatly enhance optical contrast in comparison with amplitude or amplitude ratio images. Further evaluation of FLM will have to be based on the N-algorithm images.

Correction of oral contrast artifacts in CT-based attenuation correction of PET images using an automated segmentation algorithm.

PubMed

Ahmadian, Alireza; Ay, Mohammad R; Bidgoli, Javad H; Sarkar, Saeed; Zaidi, Habib

2008-10-01

Oral contrast is usually administered in most X-ray computed tomography (CT) examinations of the abdomen and the pelvis as it allows more accurate identification of the bowel and facilitates the interpretation of abdominal and pelvic CT studies. However, the misclassification of contrast medium with high-density bone in CT-based attenuation correction (CTAC) is known to generate artifacts in the attenuation map (mumap), thus resulting in overcorrection for attenuation of positron emission tomography (PET) images. In this study, we developed an automated algorithm for segmentation and classification of regions containing oral contrast medium to correct for artifacts in CT-attenuation-corrected PET images using the segmented contrast correction (SCC) algorithm. The proposed algorithm consists of two steps: first, high CT number object segmentation using combined region- and boundary-based segmentation and second, object classification to bone and contrast agent using a knowledge-based nonlinear fuzzy classifier. Thereafter, the CT numbers of pixels belonging to the region classified as contrast medium are substituted with their equivalent effective bone CT numbers using the SCC algorithm. The generated CT images are then down-sampled followed by Gaussian smoothing to match the resolution of PET images. A piecewise calibration curve was then used to convert CT pixel values to linear attenuation coefficients at 511 keV. The visual assessment of segmented regions performed by an experienced radiologist confirmed the accuracy of the segmentation and classification algorithms for delineation of contrast-enhanced regions in clinical CT images. The quantitative analysis of generated mumaps of 21 clinical CT colonoscopy datasets showed an overestimation ranging between 24.4% and 37.3% in the 3D-classified regions depending on their volume and the concentration of contrast medium. Two PET/CT studies known to be problematic demonstrated the applicability of the technique in clinical setting. More importantly, correction of oral contrast artifacts improved the readability and interpretation of the PET scan and showed substantial decrease of the SUV (104.3%) after correction. An automated segmentation algorithm for classification of irregular shapes of regions containing contrast medium was developed for wider applicability of the SCC algorithm for correction of oral contrast artifacts during the CTAC procedure. The algorithm is being refined and further validated in clinical setting.

Automated vessel segmentation using cross-correlation and pooled covariance matrix analysis.

PubMed

Du, Jiang; Karimi, Afshin; Wu, Yijing; Korosec, Frank R; Grist, Thomas M; Mistretta, Charles A

2011-04-01

Time-resolved contrast-enhanced magnetic resonance angiography (CE-MRA) provides contrast dynamics in the vasculature and allows vessel segmentation based on temporal correlation analysis. Here we present an automated vessel segmentation algorithm including automated generation of regions of interest (ROIs), cross-correlation and pooled sample covariance matrix analysis. The dynamic images are divided into multiple equal-sized regions. In each region, ROIs for artery, vein and background are generated using an iterative thresholding algorithm based on the contrast arrival time map and contrast enhancement map. Region-specific multi-feature cross-correlation analysis and pooled covariance matrix analysis are performed to calculate the Mahalanobis distances (MDs), which are used to automatically separate arteries from veins. This segmentation algorithm is applied to a dual-phase dynamic imaging acquisition scheme where low-resolution time-resolved images are acquired during the dynamic phase followed by high-frequency data acquisition at the steady-state phase. The segmented low-resolution arterial and venous images are then combined with the high-frequency data in k-space and inverse Fourier transformed to form the final segmented arterial and venous images. Results from volunteer and patient studies demonstrate the advantages of this automated vessel segmentation and dual phase data acquisition technique. Copyright © 2011 Elsevier Inc. All rights reserved.

EROS Data Center Landsat digital enhancement techniques and imagery availability

USGS Publications Warehouse

Rohde, Wayne G.; Lo, Jinn Kai; Pohl, Russell A.

1978-01-01

The US Geological Survey's EROS Data Center (EDC) is experimenting with the production of digitally enhanced Landsat imagery. Advanced digital image processing techniques are used to perform geometric and radiometric corrections and to perform contrast and edge enhancements. The enhanced image product is produced from digitally preprocessed Landsat computer compatible tapes (CCTs) on a laser beam film recording system. Landsat CCT data have several geometric distortions which are corrected when NASA produces the standard film products. When producing film images from CCT's, geometric correction of the data is required. The EDC Digital Image Enhancement System (EDIES) compensates for geometric distortions introduced by Earth's rotation, variable line length, non-uniform mirror scan velocity, and detector misregistration. Radiometric anomalies such as bad data lines and striping are common to many Landsat film products and are also in the CCT data. Bad data lines or line segments with more than 150 contiguous bad pixels are corrected by inserting data from the previous line in place of the bad data. Striping, caused by variations in detector gain and offset, is removed with a destriping algorithm applied after digitally enhancing the data. Image enhancement is performed by applying a linear contrast stretch and an edge enhancement algorithm. The linear contrast enhancement algorithm is designed to expand digitally the full range of useful data recorded on the CCT over the range of 256 digital counts. This minimizes the effect of atmospheric scattering and saturates the relative brightness of highly reflecting features such as clouds or snow. It is the intent that no meaningful terrain data are eliminated by the digital processing. The edge enhancement algorithm is designed to enhance boundaries between terrain features that exhibit subtle differences in brightness values along edges of features. After the digital data have been processed, data for each Landsat band are recorded on black-and-white film with a laser beam film recorder (LBR). The LBR corrects for aspect ratio distortions as the digital data are recorded on the recording film over a preselected density range. Positive transparencies of MSS bands 4, 5, and 7 produced by the LBR are used to make color composite transparencies. Color film positives are made photographically from first generation black-and-white products generated on the LBR.

Human body region enhancement method based on Kinect infrared imaging

NASA Astrophysics Data System (ADS)

Yang, Lei; Fan, Yubo; Song, Xiaowei; Cai, Wenjing

2016-10-01

To effectively improve the low contrast of human body region in the infrared images, a combing method of several enhancement methods is utilized to enhance the human body region. Firstly, for the infrared images acquired by Kinect, in order to improve the overall contrast of the infrared images, an Optimal Contrast-Tone Mapping (OCTM) method with multi-iterations is applied to balance the contrast of low-luminosity infrared images. Secondly, to enhance the human body region better, a Level Set algorithm is employed to improve the contour edges of human body region. Finally, to further improve the human body region in infrared images, Laplacian Pyramid decomposition is adopted to enhance the contour-improved human body region. Meanwhile, the background area without human body region is processed by bilateral filtering to improve the overall effect. With theoretical analysis and experimental verification, the results show that the proposed method could effectively enhance the human body region of such infrared images.

Sum of top-hat transform based algorithm for vessel enhancement in MRA images

NASA Astrophysics Data System (ADS)

Ouazaa, Hibet-Allah; Jlassi, Hajer; Hamrouni, Kamel

2018-04-01

The Magnetic Resonance Angiography (MRA) is rich with information's. But, they suffer from poor contrast, illumination and noise. Thus, it is required to enhance the images. But, these significant information can be lost if improper techniques are applied. Therefore, in this paper, we propose a new method of enhancement. We applied firstly the CLAHE method to increase the contrast of the image. Then, we applied the sum of Top-Hat Transform to increase the brightness of vessels. It is performed with the structuring element oriented in different angles. The methodology is tested and evaluated on the publicly available database BRAINIX. And, we used the measurement methods MSE (Mean Square Error), PSNR (Peak Signal to Noise Ratio) and SNR (Signal to Noise Ratio) for the evaluation. The results demonstrate that the proposed method could efficiently enhance the image details and is comparable with state of the art algorithms. Hence, the proposed method could be broadly used in various applications.

A real-time MTFC algorithm of space remote-sensing camera based on FPGA

NASA Astrophysics Data System (ADS)

Zhao, Liting; Huang, Gang; Lin, Zhe

2018-01-01

A real-time MTFC algorithm of space remote-sensing camera based on FPGA was designed. The algorithm can provide real-time image processing to enhance image clarity when the remote-sensing camera running on-orbit. The image restoration algorithm adopted modular design. The MTF measurement calculation module on-orbit had the function of calculating the edge extension function, line extension function, ESF difference operation, normalization MTF and MTFC parameters. The MTFC image filtering and noise suppression had the function of filtering algorithm and effectively suppressing the noise. The algorithm used System Generator to design the image processing algorithms to simplify the design structure of system and the process redesign. The image gray gradient dot sharpness edge contrast and median-high frequency were enhanced. The image SNR after recovery reduced less than 1 dB compared to the original image. The image restoration system can be widely used in various fields.

Multiscale image contrast amplification (MUSICA)

NASA Astrophysics Data System (ADS)

Vuylsteke, Pieter; Schoeters, Emile P.

1994-05-01

This article presents a novel approach to the problem of detail contrast enhancement, based on multiresolution representation of the original image. The image is decomposed into a weighted sum of smooth, localized, 2D basis functions at multiple scales. Each transform coefficient represents the amount of local detail at some specific scale and at a specific position in the image. Detail contrast is enhanced by non-linear amplification of the transform coefficients. An inverse transform is then applied to the modified coefficients. This yields a uniformly contrast- enhanced image without artefacts. The MUSICA-algorithm is being applied routinely to computed radiography images of chest, skull, spine, shoulder, pelvis, extremities, and abdomen examinations, with excellent acceptance. It is useful for a wide range of applications in the medical, graphical, and industrial area.

Brightness-preserving fuzzy contrast enhancement scheme for the detection and classification of diabetic retinopathy disease.

PubMed

Datta, Niladri Sekhar; Dutta, Himadri Sekhar; Majumder, Koushik

2016-01-01

The contrast enhancement of retinal image plays a vital role for the detection of microaneurysms (MAs), which are an early sign of diabetic retinopathy disease. A retinal image contrast enhancement method has been presented to improve the MA detection technique. The success rate on low-contrast noisy retinal image analysis shows the importance of the proposed method. Overall, 587 retinal input images are tested for performance analysis. The average sensitivity and specificity are obtained as 95.94% and 99.21%, respectively. The area under curve is found as 0.932 for the receiver operating characteristics analysis. The classifications of diabetic retinopathy disease are also performed here. The experimental results show that the overall MA detection method performs better than the current state-of-the-art MA detection algorithms.

Quantitative assessment of tumor angiogenesis using real-time motion-compensated contrast-enhanced ultrasound imaging

PubMed Central

Pysz, Marybeth A.; Guracar, Ismayil; Foygel, Kira; Tian, Lu; Willmann, Jürgen K.

2015-01-01

Purpose To develop and test a real-time motion compensation algorithm for contrast-enhanced ultrasound imaging of tumor angiogenesis on a clinical ultrasound system. Materials and methods The Administrative Institutional Panel on Laboratory Animal Care approved all experiments. A new motion correction algorithm measuring the sum of absolute differences in pixel displacements within a designated tracking box was implemented in a clinical ultrasound machine. In vivo angiogenesis measurements (expressed as percent contrast area) with and without motion compensated maximum intensity persistence (MIP) ultrasound imaging were analyzed in human colon cancer xenografts (n = 64) in mice. Differences in MIP ultrasound imaging signal with and without motion compensation were compared and correlated with displacements in x- and y-directions. The algorithm was tested in an additional twelve colon cancer xenograft-bearing mice with (n = 6) and without (n = 6) anti-vascular therapy (ASA-404). In vivo MIP percent contrast area measurements were quantitatively correlated with ex vivo microvessel density (MVD) analysis. Results MIP percent contrast area was significantly different (P < 0.001) with and without motion compensation. Differences in percent contrast area correlated significantly (P < 0.001) with x- and y-displacements. MIP percent contrast area measurements were more reproducible with motion compensation (ICC = 0.69) than without (ICC = 0.51) on two consecutive ultrasound scans. Following anti-vascular therapy, motion-compensated MIP percent contrast area significantly (P = 0.03) decreased by 39.4 ± 14.6 % compared to non-treated mice and correlated well with ex vivo MVD analysis (Rho = 0.70; P = 0.05). Conclusion Real-time motion-compensated MIP ultrasound imaging allows reliable and accurate quantification and monitoring of angiogenesis in tumors exposed to breathing-induced motion artifacts. PMID:22535383

Quantitative assessment of tumor angiogenesis using real-time motion-compensated contrast-enhanced ultrasound imaging.

PubMed

Pysz, Marybeth A; Guracar, Ismayil; Foygel, Kira; Tian, Lu; Willmann, Jürgen K

2012-09-01

To develop and test a real-time motion compensation algorithm for contrast-enhanced ultrasound imaging of tumor angiogenesis on a clinical ultrasound system. The Administrative Institutional Panel on Laboratory Animal Care approved all experiments. A new motion correction algorithm measuring the sum of absolute differences in pixel displacements within a designated tracking box was implemented in a clinical ultrasound machine. In vivo angiogenesis measurements (expressed as percent contrast area) with and without motion compensated maximum intensity persistence (MIP) ultrasound imaging were analyzed in human colon cancer xenografts (n = 64) in mice. Differences in MIP ultrasound imaging signal with and without motion compensation were compared and correlated with displacements in x- and y-directions. The algorithm was tested in an additional twelve colon cancer xenograft-bearing mice with (n = 6) and without (n = 6) anti-vascular therapy (ASA-404). In vivo MIP percent contrast area measurements were quantitatively correlated with ex vivo microvessel density (MVD) analysis. MIP percent contrast area was significantly different (P < 0.001) with and without motion compensation. Differences in percent contrast area correlated significantly (P < 0.001) with x- and y-displacements. MIP percent contrast area measurements were more reproducible with motion compensation (ICC = 0.69) than without (ICC = 0.51) on two consecutive ultrasound scans. Following anti-vascular therapy, motion-compensated MIP percent contrast area significantly (P = 0.03) decreased by 39.4 ± 14.6 % compared to non-treated mice and correlated well with ex vivo MVD analysis (Rho = 0.70; P = 0.05). Real-time motion-compensated MIP ultrasound imaging allows reliable and accurate quantification and monitoring of angiogenesis in tumors exposed to breathing-induced motion artifacts.

Segmentation methods for breast vasculature in dual-energy contrast-enhanced digital breast tomosynthesis

NASA Astrophysics Data System (ADS)

Lau, Kristen C.; Lee, Hyo Min; Singh, Tanushriya; Maidment, Andrew D. A.

2015-03-01

Dual-energy contrast-enhanced digital breast tomosynthesis (DE CE-DBT) uses an iodinated contrast agent to image the three-dimensional breast vasculature. The University of Pennsylvania has an ongoing DE CE-DBT clinical study in patients with known breast cancers. The breast is compressed continuously and imaged at four time points (1 pre-contrast; 3 post-contrast). DE images are obtained by a weighted logarithmic subtraction of the high-energy (HE) and low-energy (LE) image pairs. Temporal subtraction of the post-contrast DE images from the pre-contrast DE image is performed to analyze iodine uptake. Our previous work investigated image registration methods to correct for patient motion, enhancing the evaluation of vascular kinetics. In this project we investigate a segmentation algorithm which identifies blood vessels in the breast from our temporal DE subtraction images. Anisotropic diffusion filtering, Gabor filtering, and morphological filtering are used for the enhancement of vessel features. Vessel labeling methods are then used to distinguish vessel and background features successfully. Statistical and clinical evaluations of segmentation accuracy in DE-CBT images are ongoing.

Fast algorithm of low power image reformation for OLED display

NASA Astrophysics Data System (ADS)

Lee, Myungwoo; Kim, Taewhan

2014-04-01

We propose a fast algorithm of low-power image reformation for organic light-emitting diode (OLED) display. The proposed algorithm scales the image histogram in a way to reduce power consumption in OLED display by remapping the gray levels of the pixels in the image based on the fast analysis of the histogram of the input image while maintaining contrast of the image. The key idea is that a large number of gray levels are never used in the images and these gray levels can be effectively exploited to reduce power consumption. On the other hand, to maintain the image contrast the gray level remapping is performed by taking into account the object size in the image to which each gray level is applied, that is, reforming little for the gray levels in the objects of large size. Through experiments with 24 Kodak images, it is shown that our proposed algorithm is able to reduce the power consumption by 10% even with 9% contrast enhancement. Our algorithm runs in a linear time so that it can be applied to moving pictures with high resolution.

Efficient image enhancement using sparse source separation in the Retinex theory

NASA Astrophysics Data System (ADS)

Yoon, Jongsu; Choi, Jangwon; Choe, Yoonsik

2017-11-01

Color constancy is the feature of the human vision system (HVS) that ensures the relative constancy of the perceived color of objects under varying illumination conditions. The Retinex theory of machine vision systems is based on the HVS. Among Retinex algorithms, the physics-based algorithms are efficient; however, they generally do not satisfy the local characteristics of the original Retinex theory because they eliminate global illumination from their optimization. We apply the sparse source separation technique to the Retinex theory to present a physics-based algorithm that satisfies the locality characteristic of the original Retinex theory. Previous Retinex algorithms have limited use in image enhancement because the total variation Retinex results in an overly enhanced image and the sparse source separation Retinex cannot completely restore the original image. In contrast, our proposed method preserves the image edge and can very nearly replicate the original image without any special operation.

Effect of various digital processing algorithms on the measurement accuracy of endodontic file length.

PubMed

Kal, Betül Ilhan; Baksi, B Güniz; Dündar, Nesrin; Sen, Bilge Hakan

2007-02-01

The aim of this study was to compare the accuracy of endodontic file lengths after application of various image enhancement modalities. Endodontic files of three different ISO sizes were inserted in 20 single-rooted extracted permanent mandibular premolar teeth and standardized images were obtained. Original digital images were then enhanced using five processing algorithms. Six evaluators measured the length of each file on each image. The measurements from each processing algorithm and each file size were compared using repeated measures ANOVA and Bonferroni tests (P = 0.05). Paired t test was performed to compare the measurements with the true lengths of the files (P = 0.05). All of the processing algorithms provided significantly shorter measurements than the true length of each file size (P < 0.05). The threshold enhancement modality produced significantly higher mean error values (P < 0.05), while there was no significant difference among the other enhancement modalities (P > 0.05). Decrease in mean error value was observed with increasing file size (P < 0.05). Invert, contrast/brightness and edge enhancement algorithms may be recommended for accurate file length measurements when utilizing storage phosphor plates.

Automatic segmentation of invasive breast carcinomas from dynamic contrast-enhanced MRI using time series analysis.

PubMed

Jayender, Jagadaeesan; Chikarmane, Sona; Jolesz, Ferenc A; Gombos, Eva

2014-08-01

To accurately segment invasive ductal carcinomas (IDCs) from dynamic contrast-enhanced MRI (DCE-MRI) using time series analysis based on linear dynamic system (LDS) modeling. Quantitative segmentation methods based on black-box modeling and pharmacokinetic modeling are highly dependent on imaging pulse sequence, timing of bolus injection, arterial input function, imaging noise, and fitting algorithms. We modeled the underlying dynamics of the tumor by an LDS and used the system parameters to segment the carcinoma on the DCE-MRI. Twenty-four patients with biopsy-proven IDCs were analyzed. The lesions segmented by the algorithm were compared with an expert radiologist's segmentation and the output of a commercial software, CADstream. The results are quantified in terms of the accuracy and sensitivity of detecting the lesion and the amount of overlap, measured in terms of the Dice similarity coefficient (DSC). The segmentation algorithm detected the tumor with 90% accuracy and 100% sensitivity when compared with the radiologist's segmentation and 82.1% accuracy and 100% sensitivity when compared with the CADstream output. The overlap of the algorithm output with the radiologist's segmentation and CADstream output, computed in terms of the DSC was 0.77 and 0.72, respectively. The algorithm also shows robust stability to imaging noise. Simulated imaging noise with zero mean and standard deviation equal to 25% of the base signal intensity was added to the DCE-MRI series. The amount of overlap between the tumor maps generated by the LDS-based algorithm from the noisy and original DCE-MRI was DSC = 0.95. The time-series analysis based segmentation algorithm provides high accuracy and sensitivity in delineating the regions of enhanced perfusion corresponding to tumor from DCE-MRI. © 2013 Wiley Periodicals, Inc.

Recent advances in the imaging of hepatocellular carcinoma. From ultrasound to positron emission tomography scan.

PubMed

Camaggi, Valeria; Piscaglia, Fabio; Bolondi, Luigi

2007-07-01

Recent advances in imaging techniques for hepatocellular carcinoma (HCC) offer the possibility of investigating contrast perfusion of liver nodules in cirrhosis. It is now accepted that a non-invasive diagnosis of HCC can be established based on the vascular pattern obtained with pure blood pool contrast agents. The diagnostic pattern consists of contrast enhancement in the arterial phase, indicative of arterial hypervascularization, followed by contrast wash out in the portal and late phases, which leads the nodule to show the same, or, more specifically, a lower contrast signal than the surrounding parenchyma. Such patterns can be obtained by CT, MRI and, more recently, by real time Contrast Enhanced Ultrasonography with second-generation ultrasound contrast agents. A typical vascular pattern in a nodule perceptible also without contrast is highly specific for HCC, so that non-invasive diagnostic algorithms have been developed and recently updated.

Computerized detection of breast lesions in multi-centre and multi-instrument DCE-MR data using 3D principal component maps and template matching

NASA Astrophysics Data System (ADS)

Ertas, Gokhan; Doran, Simon; Leach, Martin O.

2011-12-01

In this study, we introduce a novel, robust and accurate computerized algorithm based on volumetric principal component maps and template matching that facilitates lesion detection on dynamic contrast-enhanced MR. The study dataset comprises 24 204 contrast-enhanced breast MR images corresponding to 4034 axial slices from 47 women in the UK multi-centre study of MRI screening for breast cancer and categorized as high risk. The scans analysed here were performed on six different models of scanner from three commercial vendors, sited in 13 clinics around the UK. 1952 slices from this dataset, containing 15 benign and 13 malignant lesions, were used for training. The remaining 2082 slices, with 14 benign and 12 malignant lesions, were used for test purposes. To prevent false positives being detected from other tissues and regions of the body, breast volumes are segmented from pre-contrast images using a fast semi-automated algorithm. Principal component analysis is applied to the centred intensity vectors formed from the dynamic contrast-enhanced T1-weighted images of the segmented breasts, followed by automatic thresholding to eliminate fatty tissues and slowly enhancing normal parenchyma and a convolution and filtering process to minimize artefacts from moderately enhanced normal parenchyma and blood vessels. Finally, suspicious lesions are identified through a volumetric sixfold neighbourhood connectivity search and calculation of two morphological features: volume and volumetric eccentricity, to exclude highly enhanced blood vessels, nipples and normal parenchyma and to localize lesions. This provides satisfactory lesion localization. For a detection sensitivity of 100%, the overall false-positive detection rate of the system is 1.02/lesion, 1.17/case and 0.08/slice, comparing favourably with previous studies. This approach may facilitate detection of lesions in multi-centre and multi-instrument dynamic contrast-enhanced breast MR data.

Comparison study of reconstruction algorithms for prototype digital breast tomosynthesis using various breast phantoms.

PubMed

Kim, Ye-seul; Park, Hye-suk; Lee, Haeng-Hwa; Choi, Young-Wook; Choi, Jae-Gu; Kim, Hak Hee; Kim, Hee-Joung

2016-02-01

Digital breast tomosynthesis (DBT) is a recently developed system for three-dimensional imaging that offers the potential to reduce the false positives of mammography by preventing tissue overlap. Many qualitative evaluations of digital breast tomosynthesis were previously performed by using a phantom with an unrealistic model and with heterogeneous background and noise, which is not representative of real breasts. The purpose of the present work was to compare reconstruction algorithms for DBT by using various breast phantoms; validation was also performed by using patient images. DBT was performed by using a prototype unit that was optimized for very low exposures and rapid readout. Three algorithms were compared: a back-projection (BP) algorithm, a filtered BP (FBP) algorithm, and an iterative expectation maximization (EM) algorithm. To compare the algorithms, three types of breast phantoms (homogeneous background phantom, heterogeneous background phantom, and anthropomorphic breast phantom) were evaluated, and clinical images were also reconstructed by using the different reconstruction algorithms. The in-plane image quality was evaluated based on the line profile and the contrast-to-noise ratio (CNR), and out-of-plane artifacts were evaluated by means of the artifact spread function (ASF). Parenchymal texture features of contrast and homogeneity were computed based on reconstructed images of an anthropomorphic breast phantom. The clinical images were studied to validate the effect of reconstruction algorithms. The results showed that the CNRs of masses reconstructed by using the EM algorithm were slightly higher than those obtained by using the BP algorithm, whereas the FBP algorithm yielded much lower CNR due to its high fluctuations of background noise. The FBP algorithm provides the best conspicuity for larger calcifications by enhancing their contrast and sharpness more than the other algorithms; however, in the case of small-size and low-contrast microcalcifications, the FBP reduced detectability due to its increased noise. The EM algorithm yielded high conspicuity for both microcalcifications and masses and yielded better ASFs in terms of the full width at half maximum. The higher contrast and lower homogeneity in terms of texture analysis were shown in FBP algorithm than in other algorithms. The patient images using the EM algorithm resulted in high visibility of low-contrast mass with clear border. In this study, we compared three reconstruction algorithms by using various kinds of breast phantoms and patient cases. Future work using these algorithms and considering the type of the breast and the acquisition techniques used (e.g., angular range, dose distribution) should include the use of actual patients or patient-like phantoms to increase the potential for practical applications.

Single underwater image enhancement based on color cast removal and visibility restoration

NASA Astrophysics Data System (ADS)

Li, Chongyi; Guo, Jichang; Wang, Bo; Cong, Runmin; Zhang, Yan; Wang, Jian

2016-05-01

Images taken under underwater condition usually have color cast and serious loss of contrast and visibility. Degraded underwater images are inconvenient for observation and analysis. In order to address these problems, an underwater image-enhancement method is proposed. A simple yet effective underwater image color cast removal algorithm is first presented based on the optimization theory. Then, based on the minimum information loss principle and inherent relationship of medium transmission maps of three color channels in an underwater image, an effective visibility restoration algorithm is proposed to recover visibility, contrast, and natural appearance of degraded underwater images. To evaluate the performance of the proposed method, qualitative comparison, quantitative comparison, and color accuracy test are conducted. Experimental results demonstrate that the proposed method can effectively remove color cast, improve contrast and visibility, and recover natural appearance of degraded underwater images. Additionally, the proposed method is comparable to and even better than several state-of-the-art methods.

A parameterized logarithmic image processing method with Laplacian of Gaussian filtering for lung nodule enhancement in chest radiographs.

PubMed

Chen, Sheng; Yao, Liping; Chen, Bao

2016-11-01

The enhancement of lung nodules in chest radiographs (CXRs) plays an important role in the manual as well as computer-aided detection (CADe) lung cancer. In this paper, we proposed a parameterized logarithmic image processing (PLIP) method combined with the Laplacian of a Gaussian (LoG) filter to enhance lung nodules in CXRs. We first applied several LoG filters with varying parameters to an original CXR to enhance the nodule-like structures as well as the edges in the image. We then applied the PLIP model, which can enhance lung nodule images with high contrast and was beneficial in extracting effective features for nodule detection in the CADe scheme. Our method combined the advantages of both the PLIP algorithm and the LoG algorithm, which can enhance lung nodules in chest radiographs with high contrast. To test our nodule enhancement method, we tested a CADe scheme, with a relatively high performance in nodule detection, using a publically available database containing 140 nodules in 140 CXRs enhanced through our nodule enhancement method. The CADe scheme attained a sensitivity of 81 and 70 % with an average of 5.0 frame rate (FP) and 2.0 FP, respectively, in a leave-one-out cross-validation test. By contrast, the CADe scheme based on the original image recorded a sensitivity of 77 and 63 % at 5.0 FP and 2.0 FP, respectively. We introduced the measurement of enhancement by entropy evaluation to objectively assess our method. Experimental results show that the proposed method obtains an effective enhancement of lung nodules in CXRs for both radiologists and CADe schemes.

Machine learning based brain tumour segmentation on limited data using local texture and abnormality.

PubMed

Bonte, Stijn; Goethals, Ingeborg; Van Holen, Roel

2018-05-07

Brain tumour segmentation in medical images is a very challenging task due to the large variety in tumour shape, position, appearance, scanning modalities and scanning parameters. Most existing segmentation algorithms use information from four different MRI-sequences, but since this is often not available, there is need for a method able to delineate the different tumour tissues based on a minimal amount of data. We present a novel approach using a Random Forests model combining voxelwise texture and abnormality features on a contrast-enhanced T1 and FLAIR MRI. We transform the two scans into 275 feature maps. A random forest model next calculates the probability to belong to 4 tumour classes or 5 normal classes. Afterwards, a dedicated voxel clustering algorithm provides the final tumour segmentation. We trained our method on the BraTS 2013 database and validated it on the larger BraTS 2017 dataset. We achieve median Dice scores of 40.9% (low-grade glioma) and 75.0% (high-grade glioma) to delineate the active tumour, and 68.4%/80.1% for the total abnormal region including edema. Our fully automated brain tumour segmentation algorithm is able to delineate contrast enhancing tissue and oedema with high accuracy based only on post-contrast T1-weighted and FLAIR MRI, whereas for non-enhancing tumour tissue and necrosis only moderate results are obtained. This makes the method especially suitable for high-grade glioma. Copyright © 2018 Elsevier Ltd. All rights reserved.

SU-E-I-82: Improving CT Image Quality for Radiation Therapy Using Iterative Reconstruction Algorithms and Slightly Increasing Imaging Doses

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

Noid, G; Chen, G; Tai, A

2014-06-01

Purpose: Iterative reconstruction (IR) algorithms are developed to improve CT image quality (IQ) by reducing noise without diminishing spatial resolution or contrast. For CT in radiation therapy (RT), slightly increasing imaging dose to improve IQ may be justified if it can substantially enhance structure delineation. The purpose of this study is to investigate and to quantify the IQ enhancement as a result of increasing imaging doses and using IR algorithms. Methods: CT images were acquired for phantoms, built to evaluate IQ metrics including spatial resolution, contrast and noise, with a variety of imaging protocols using a CT scanner (Definition ASmore » Open, Siemens) installed inside a Linac room. Representative patients were scanned once the protocols were optimized. Both phantom and patient scans were reconstructed using the Sinogram Affirmed Iterative Reconstruction (SAFIRE) and the Filtered Back Projection (FBP) methods. IQ metrics of the obtained CTs were compared. Results: IR techniques are demonstrated to preserve spatial resolution as measured by the point spread function and reduce noise in comparison to traditional FBP. Driven by the reduction in noise, the contrast to noise ratio is doubled by adopting the highest SAFIRE strength. As expected, increasing imaging dose reduces noise for both SAFIRE and FBP reconstructions. The contrast to noise increases from 3 to 5 by increasing the dose by a factor of 4. Similar IQ improvement was observed on the CTs for selected patients with pancreas and prostrate cancers. Conclusion: The IR techniques produce a measurable enhancement to CT IQ by reducing the noise. Increasing imaging dose further reduces noise independent of the IR techniques. The improved CT enables more accurate delineation of tumors and/or organs at risk during RT planning and delivery guidance.« less

Global Contrast Based Salient Region Detection.

PubMed

Cheng, Ming-Ming; Mitra, Niloy J; Huang, Xiaolei; Torr, Philip H S; Hu, Shi-Min

2015-03-01

Automatic estimation of salient object regions across images, without any prior assumption or knowledge of the contents of the corresponding scenes, enhances many computer vision and computer graphics applications. We introduce a regional contrast based salient object detection algorithm, which simultaneously evaluates global contrast differences and spatial weighted coherence scores. The proposed algorithm is simple, efficient, naturally multi-scale, and produces full-resolution, high-quality saliency maps. These saliency maps are further used to initialize a novel iterative version of GrabCut, namely SaliencyCut, for high quality unsupervised salient object segmentation. We extensively evaluated our algorithm using traditional salient object detection datasets, as well as a more challenging Internet image dataset. Our experimental results demonstrate that our algorithm consistently outperforms 15 existing salient object detection and segmentation methods, yielding higher precision and better recall rates. We also show that our algorithm can be used to efficiently extract salient object masks from Internet images, enabling effective sketch-based image retrieval (SBIR) via simple shape comparisons. Despite such noisy internet images, where the saliency regions are ambiguous, our saliency guided image retrieval achieves a superior retrieval rate compared with state-of-the-art SBIR methods, and additionally provides important target object region information.

Effect of contrast leakage on the detection of abnormal brain tumor vasculature in high-grade glioma.

PubMed

LaViolette, Peter S; Daun, Mitchell K; Paulson, Eric S; Schmainda, Kathleen M

2014-02-01

Abnormal brain tumor vasculature has recently been highlighted by a dynamic susceptibility contrast (DSC) MRI processing technique. The technique uses independent component analysis (ICA) to separate arterial and venous perfusion. The overlap of the two, i.e. arterio-venous overlap or AVOL, preferentially occurs in brain tumors and predicts response to anti-angiogenic therapy. The effects of contrast agent leakage on the AVOL biomarker have yet to be established. DSC was acquired during two separate contrast boluses in ten patients undergoing clinical imaging for brain tumor diagnosis. Three components were modeled with ICA, which included the arterial and venous components. The percentage of each component as well as a third component were determined within contrast enhancing tumor and compared. AVOL within enhancing tumor was also compared between doses. The percentage of enhancing tumor classified as not arterial or venous and instead into a third component with contrast agent leakage apparent in the time-series was significantly greater for the first contrast dose compared to the second. The amount of AVOL detected within enhancing tumor was also significantly greater with the second dose compared to the first. Contrast leakage results in large signal variance classified as a separate component by the ICA algorithm. The use of a second dose mitigates the effect and allows measurement of AVOL within enhancement.

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

PubMed

Alavijeh, Fatemeh Shahsavari; Mahdavi-Nasab, Homayoun

2015-01-01

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

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

PubMed Central

Alavijeh, Fatemeh Shahsavari; Mahdavi-Nasab, Homayoun

2015-01-01

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

Wavelet-based algorithm to the evaluation of contrasted hepatocellular carcinoma in CT-images after transarterial chemoembolization.

PubMed

Alvarez, Matheus; de Pina, Diana Rodrigues; Romeiro, Fernando Gomes; Duarte, Sérgio Barbosa; Miranda, José Ricardo de Arruda

2014-07-26

Hepatocellular carcinoma is a primary tumor of the liver and involves different treatment modalities according to the tumor stage. After local therapies, the tumor evaluation is based on the mRECIST criteria, which involves the measurement of the maximum diameter of the viable lesion. This paper describes a computed methodology to measure through the contrasted area of the lesions the maximum diameter of the tumor by a computational algorithm. 63 computed tomography (CT) slices from 23 patients were assessed. Non-contrasted liver and HCC typical nodules were evaluated, and a virtual phantom was developed for this purpose. Optimization of the algorithm detection and quantification was made using the virtual phantom. After that, we compared the algorithm findings of maximum diameter of the target lesions against radiologist measures. Computed results of the maximum diameter are in good agreement with the results obtained by radiologist evaluation, indicating that the algorithm was able to detect properly the tumor limits. A comparison of the estimated maximum diameter by radiologist versus the algorithm revealed differences on the order of 0.25 cm for large-sized tumors (diameter > 5 cm), whereas agreement lesser than 1.0 cm was found for small-sized tumors. Differences between algorithm and radiologist measures were accurate for small-sized tumors with a trend to a small decrease for tumors greater than 5 cm. Therefore, traditional methods for measuring lesion diameter should be complemented non-subjective measurement methods, which would allow a more correct evaluation of the contrast-enhanced areas of HCC according to the mRECIST criteria.

Bidirectional Contrast agent leakage correction of dynamic susceptibility contrast (DSC)-MRI improves cerebral blood volume estimation and survival prediction in recurrent glioblastoma treated with bevacizumab.

PubMed

Leu, Kevin; Boxerman, Jerrold L; Lai, Albert; Nghiemphu, Phioanh L; Pope, Whitney B; Cloughesy, Timothy F; Ellingson, Benjamin M

2016-11-01

To evaluate a leakage correction algorithm for T 1 and T2* artifacts arising from contrast agent extravasation in dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) that accounts for bidirectional contrast agent flux and compare relative cerebral blood volume (CBV) estimates and overall survival (OS) stratification from this model to those made with the unidirectional and uncorrected models in patients with recurrent glioblastoma (GBM). We determined median rCBV within contrast-enhancing tumor before and after bevacizumab treatment in patients (75 scans on 1.5T, 19 scans on 3.0T) with recurrent GBM without leakage correction and with application of the unidirectional and bidirectional leakage correction algorithms to determine whether rCBV stratifies OS. Decreased post-bevacizumab rCBV from baseline using the bidirectional leakage correction algorithm significantly correlated with longer OS (Cox, P = 0.01), whereas rCBV change using the unidirectional model (P = 0.43) or the uncorrected rCBV values (P = 0.28) did not. Estimates of rCBV computed with the two leakage correction algorithms differed on average by 14.9%. Accounting for T 1 and T2* leakage contamination in DSC-MRI using a two-compartment, bidirectional rather than unidirectional exchange model might improve post-bevacizumab survival stratification in patients with recurrent GBM. J. Magn. Reson. Imaging 2016;44:1229-1237. © 2016 International Society for Magnetic Resonance in Medicine.

Half-unit weighted bilinear algorithm for image contrast enhancement in capsule endoscopy

NASA Astrophysics Data System (ADS)

Rukundo, Olivier

2018-04-01

This paper proposes a novel enhancement method based exclusively on the bilinear interpolation algorithm for capsule endoscopy images. The proposed method does not convert the original RBG image components to HSV or any other color space or model; instead, it processes directly RGB components. In each component, a group of four adjacent pixels and half-unit weight in the bilinear weighting function are used to calculate the average pixel value, identical for each pixel in that particular group. After calculations, groups of identical pixels are overlapped successively in horizontal and vertical directions to achieve a preliminary-enhanced image. The final-enhanced image is achieved by halving the sum of the original and preliminary-enhanced image pixels. Quantitative and qualitative experiments were conducted focusing on pairwise comparisons between original and enhanced images. Final-enhanced images have generally the best diagnostic quality and gave more details about the visibility of vessels and structures in capsule endoscopy images.

An automated skin segmentation of Breasts in Dynamic Contrast-Enhanced Magnetic Resonance Imaging.

PubMed

Lee, Chia-Yen; Chang, Tzu-Fang; Chang, Nai-Yun; Chang, Yeun-Chung

2018-04-18

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is used to diagnose breast disease. Obtaining anatomical information from DCE-MRI requires the skin be manually removed so that blood vessels and tumors can be clearly observed by physicians and radiologists; this requires considerable manpower and time. We develop an automated skin segmentation algorithm where the surface skin is removed rapidly and correctly. The rough skin area is segmented by the active contour model, and analyzed in segments according to the continuity of the skin thickness for accuracy. Blood vessels and mammary glands are retained, which remedies the defect of removing some blood vessels in active contours. After three-dimensional imaging, the DCE-MRIs without the skin can be used to see internal anatomical information for clinical applications. The research showed the Dice's coefficients of the 3D reconstructed images using the proposed algorithm and the active contour model for removing skins are 93.2% and 61.4%, respectively. The time performance of segmenting skins automatically is about 165 times faster than manually. The texture information of the tumors position with/without the skin is compared by the paired t-test yielded all p < 0.05, which suggested the proposed algorithm may enhance observability of tumors at the significance level of 0.05.

Improved artificial neural networks in prediction of malignancy of lesions in contrast-enhanced MR-mammography.

PubMed

Vomweg, T W; Buscema, M; Kauczor, H U; Teifke, A; Intraligi, M; Terzi, S; Heussel, C P; Achenbach, T; Rieker, O; Mayer, D; Thelen, M

2003-09-01

The aim of this study was to evaluate the capability of improved artificial neural networks (ANN) and additional novel training methods in distinguishing between benign and malignant breast lesions in contrast-enhanced magnetic resonance-mammography (MRM). A total of 604 histologically proven cases of contrast-enhanced lesions of the female breast at MRI were analyzed. Morphological, dynamic and clinical parameters were collected and stored in a database. The data set was divided into several groups using random or experimental methods [Training & Testing (T&T) algorithm] to train and test different ANNs. An additional novel computer program for input variable selection was applied. Sensitivity and specificity were calculated and compared with a statistical method and an expert radiologist. After optimization of the distribution of cases among the training and testing sets by the T & T algorithm and the reduction of input variables by the Input Selection procedure a highly sophisticated ANN achieved a sensitivity of 93.6% and a specificity of 91.9% in predicting malignancy of lesions within an independent prediction sample set. The best statistical method reached a sensitivity of 90.5% and a specificity of 68.9%. An expert radiologist performed better than the statistical method but worse than the ANN (sensitivity 92.1%, specificity 85.6%). Features extracted out of dynamic contrast-enhanced MRM and additional clinical data can be successfully analyzed by advanced ANNs. The quality of the resulting network strongly depends on the training methods, which are improved by the use of novel training tools. The best results of an improved ANN outperform expert radiologists.

A biomechanical modeling guided simultaneous motion estimation and image reconstruction technique (SMEIR-Bio) for 4D-CBCT reconstruction

NASA Astrophysics Data System (ADS)

Huang, Xiaokun; Zhang, You; Wang, Jing

2017-03-01

Four-dimensional (4D) cone-beam computed tomography (CBCT) enables motion tracking of anatomical structures and removes artifacts introduced by motion. However, the imaging time/dose of 4D-CBCT is substantially longer/higher than traditional 3D-CBCT. We previously developed a simultaneous motion estimation and image reconstruction (SMEIR) algorithm, to reconstruct high-quality 4D-CBCT from limited number of projections to reduce the imaging time/dose. However, the accuracy of SMEIR is limited in reconstructing low-contrast regions with fine structure details. In this study, we incorporate biomechanical modeling into the SMEIR algorithm (SMEIR-Bio), to improve the reconstruction accuracy at low-contrast regions with fine details. The efficacy of SMEIR-Bio is evaluated using 11 lung patient cases and compared to that of the original SMEIR algorithm. Qualitative and quantitative comparisons showed that SMEIR-Bio greatly enhances the accuracy of reconstructed 4D-CBCT volume in low-contrast regions, which can potentially benefit multiple clinical applications including the treatment outcome analysis.

Enhancement of morphological and vascular features in OCT images using a modified Bayesian residual transform

PubMed Central

Tan, Bingyao; Wong, Alexander; Bizheva, Kostadinka

2018-01-01

A novel image processing algorithm based on a modified Bayesian residual transform (MBRT) was developed for the enhancement of morphological and vascular features in optical coherence tomography (OCT) and OCT angiography (OCTA) images. The MBRT algorithm decomposes the original OCT image into multiple residual images, where each image presents information at a unique scale. Scale selective residual adaptation is used subsequently to enhance morphological features of interest, such as blood vessels and tissue layers, and to suppress irrelevant image features such as noise and motion artefacts. The performance of the proposed MBRT algorithm was tested on a series of cross-sectional and enface OCT and OCTA images of retina and brain tissue that were acquired in-vivo. Results show that the MBRT reduces speckle noise and motion-related imaging artefacts locally, thus improving significantly the contrast and visibility of morphological features in the OCT and OCTA images. PMID:29760996

Investigation of contrast-enhanced subtracted breast CT images with MAP-EM based on projection-based weighting imaging.

PubMed

Zhou, Zhengdong; Guan, Shaolin; Xin, Runchao; Li, Jianbo

2018-06-01

Contrast-enhanced subtracted breast computer tomography (CESBCT) images acquired using energy-resolved photon counting detector can be helpful to enhance the visibility of breast tumors. In such technology, one challenge is the limited number of photons in each energy bin, thereby possibly leading to high noise in separate images from each energy bin, the projection-based weighted image, and the subtracted image. In conventional low-dose CT imaging, iterative image reconstruction provides a superior signal-to-noise compared with the filtered back projection (FBP) algorithm. In this paper, maximum a posteriori expectation maximization (MAP-EM) based on projection-based weighting imaging for reconstruction of CESBCT images acquired using an energy-resolving photon counting detector is proposed, and its performance was investigated in terms of contrast-to-noise ratio (CNR). The simulation study shows that MAP-EM based on projection-based weighting imaging can improve the CNR in CESBCT images by 117.7%-121.2% compared with FBP based on projection-based weighting imaging method. When compared with the energy-integrating imaging that uses the MAP-EM algorithm, projection-based weighting imaging that uses the MAP-EM algorithm can improve the CNR of CESBCT images by 10.5%-13.3%. In conclusion, MAP-EM based on projection-based weighting imaging shows significant improvement the CNR of the CESBCT image compared with FBP based on projection-based weighting imaging, and MAP-EM based on projection-based weighting imaging outperforms MAP-EM based on energy-integrating imaging for CESBCT imaging.

Local ablation therapy with contrast-enhanced ultrasonography for hepatocellular carcinoma: a practical review

PubMed Central

Kim, Tae Kyoung; Khalili, Korosh; Jang, Hyun-Jung

2015-01-01

A successful program for local ablation therapy for hepatocellular carcinoma (HCC) requires extensive imaging support for diagnosis and localization of HCC, imaging guidance for the ablation procedures, and post-treatment monitoring. Contrast-enhanced ultrasonography (CEUS) has several advantages over computed tomography/magnetic resonance imaging (CT/MRI), including real-time imaging capability, sensitive detection of arterial-phase hypervascularity and washout, no renal excretion, no ionizing radiation, repeatability, excellent patient compliance, and relatively low cost. CEUS is useful for image guidance for isoechoic lesions. While contrast-enhanced CT/MRI is the standard method for the diagnosis of HCC and post-ablation monitoring, CEUS is useful when CT/MRI findings are indeterminate or CT/MRI is contraindicated. This article provides a practical review of the role of CEUS in imaging algorithms for pre- and post-ablation therapy for HCC. PMID:26169081

Light-extraction enhancement for light-emitting diodes: a firefly-inspired structure refined by the genetic algorithm

NASA Astrophysics Data System (ADS)

Bay, Annick; Mayer, Alexandre

2014-09-01

The efficiency of light-emitting diodes (LED) has increased significantly over the past few years, but the overall efficiency is still limited by total internal reflections due to the high dielectric-constant contrast between the incident and emergent media. The bioluminescent organ of fireflies gave incentive for light-extraction enhance-ment studies. A specific factory-roof shaped structure was shown, by means of light-propagation simulations and measurements, to enhance light extraction significantly. In order to achieve a similar effect for light-emitting diodes, the structure needs to be adapted to the specific set-up of LEDs. In this context simulations were carried out to determine the best geometrical parameters. In the present work, the search for a geometry that maximizes the extraction of light has been conducted by using a genetic algorithm. The idealized structure considered previously was generalized to a broader variety of shapes. The genetic algorithm makes it possible to search simultaneously over a wider range of parameters. It is also significantly less time-consuming than the previous approach that was based on a systematic scan on parameters. The results of the genetic algorithm show that (1) the calculations can be performed in a smaller amount of time and (2) the light extraction can be enhanced even more significantly by using optimal parameters determined by the genetic algorithm for the generalized structure. The combination of the genetic algorithm with the Rigorous Coupled Waves Analysis method constitutes a strong simulation tool, which provides us with adapted designs for enhancing light extraction from light-emitting diodes.

Method of Improved Fuzzy Contrast Combined Adaptive Threshold in NSCT for Medical Image Enhancement

PubMed Central

Yang, Jie; Kasabov, Nikola

2017-01-01

Noises and artifacts are introduced to medical images due to acquisition techniques and systems. This interference leads to low contrast and distortion in images, which not only impacts the effectiveness of the medical image but also seriously affects the clinical diagnoses. This paper proposes an algorithm for medical image enhancement based on the nonsubsampled contourlet transform (NSCT), which combines adaptive threshold and an improved fuzzy set. First, the original image is decomposed into the NSCT domain with a low-frequency subband and several high-frequency subbands. Then, a linear transformation is adopted for the coefficients of the low-frequency component. An adaptive threshold method is used for the removal of high-frequency image noise. Finally, the improved fuzzy set is used to enhance the global contrast and the Laplace operator is used to enhance the details of the medical images. Experiments and simulation results show that the proposed method is superior to existing methods of image noise removal, improves the contrast of the image significantly, and obtains a better visual effect. PMID:28744464

Signal enhancement ratio (SER) quantified from breast DCE-MRI and breast cancer risk

NASA Astrophysics Data System (ADS)

Wu, Shandong; Kurland, Brenda F.; Berg, Wendie A.; Zuley, Margarita L.; Jankowitz, Rachel C.; Sumkin, Jules; Gur, David

2015-03-01

Breast magnetic resonance imaging (MRI) is recommended as an adjunct to mammography for women who are considered at elevated risk of developing breast cancer. As a key component of breast MRI, dynamic contrast-enhanced MRI (DCE-MRI) uses a contrast agent to provide high intensity contrast between breast tissues, making it sensitive to tissue composition and vascularity. Breast DCE-MRI characterizes certain physiologic properties of breast tissue that are potentially related to breast cancer risk. Studies have shown that increased background parenchymal enhancement (BPE), which is the contrast enhancement occurring in normal cancer-unaffected breast tissues in post-contrast sequences, predicts increased breast cancer risk. Signal enhancement ratio (SER) computed from pre-contrast and post-contrast sequences in DCE-MRI measures change in signal intensity due to contrast uptake over time and is a measure of contrast enhancement kinetics. SER quantified in breast tumor has been shown potential as a biomarker for characterizing tumor response to treatments. In this work we investigated the relationship between quantitative measures of SER and breast cancer risk. A pilot retrospective case-control study was performed using a cohort of 102 women, consisting of 51 women who had diagnosed with unilateral breast cancer and 51 matched controls (by age and MRI date) with a unilateral biopsy-proven benign lesion. SER was quantified using fully-automated computerized algorithms and three SER-derived quantitative volume measures were compared between the cancer cases and controls using logistic regression analysis. Our preliminary results showed that SER is associated with breast cancer risk, after adjustment for the Breast Imaging Reporting and Data System (BI-RADS)-based mammographic breast density measures. This pilot study indicated that SER has potential for use as a risk factor for breast cancer risk assessment in women at elevated risk of developing breast cancer.

Color reproduction and processing algorithm based on real-time mapping for endoscopic images.

PubMed

Khan, Tareq H; Mohammed, Shahed K; Imtiaz, Mohammad S; Wahid, Khan A

2016-01-01

In this paper, we present a real-time preprocessing algorithm for image enhancement for endoscopic images. A novel dictionary based color mapping algorithm is used for reproducing the color information from a theme image. The theme image is selected from a nearby anatomical location. A database of color endoscopy image for different location is prepared for this purpose. The color map is dynamic as its contents change with the change of the theme image. This method is used on low contrast grayscale white light images and raw narrow band images to highlight the vascular and mucosa structures and to colorize the images. It can also be applied to enhance the tone of color images. The statistic visual representation and universal image quality measures show that the proposed method can highlight the mucosa structure compared to other methods. The color similarity has been verified using Delta E color difference, structure similarity index, mean structure similarity index and structure and hue similarity. The color enhancement was measured using color enhancement factor that shows considerable improvements. The proposed algorithm has low and linear time complexity, which results in higher execution speed than other related works.

Retooling Laser Speckle Contrast Analysis Algorithm to Enhance Non-Invasive High Resolution Laser Speckle Functional Imaging of Cutaneous Microcirculation

NASA Astrophysics Data System (ADS)

Gnyawali, Surya C.; Blum, Kevin; Pal, Durba; Ghatak, Subhadip; Khanna, Savita; Roy, Sashwati; Sen, Chandan K.

2017-01-01

Cutaneous microvasculopathy complicates wound healing. Functional assessment of gated individual dermal microvessels is therefore of outstanding interest. Functional performance of laser speckle contrast imaging (LSCI) systems is compromised by motion artefacts. To address such weakness, post-processing of stacked images is reported. We report the first post-processing of binary raw data from a high-resolution LSCI camera. Sharp images of low-flowing microvessels were enabled by introducing inverse variance in conjunction with speckle contrast in Matlab-based program code. Extended moving window averaging enhanced signal-to-noise ratio. Functional quantitative study of blood flow kinetics was performed on single gated microvessels using a free hand tool. Based on detection of flow in low-flow microvessels, a new sharp contrast image was derived. Thus, this work presents the first distinct image with quantitative microperfusion data from gated human foot microvasculature. This versatile platform is applicable to study a wide range of tissue systems including fine vascular network in murine brain without craniotomy as well as that in the murine dorsal skin. Importantly, the algorithm reported herein is hardware agnostic and is capable of post-processing binary raw data from any camera source to improve the sensitivity of functional flow data above and beyond standard limits of the optical system.

Retooling Laser Speckle Contrast Analysis Algorithm to Enhance Non-Invasive High Resolution Laser Speckle Functional Imaging of Cutaneous Microcirculation

PubMed Central

Gnyawali, Surya C.; Blum, Kevin; Pal, Durba; Ghatak, Subhadip; Khanna, Savita; Roy, Sashwati; Sen, Chandan K.

2017-01-01

Cutaneous microvasculopathy complicates wound healing. Functional assessment of gated individual dermal microvessels is therefore of outstanding interest. Functional performance of laser speckle contrast imaging (LSCI) systems is compromised by motion artefacts. To address such weakness, post-processing of stacked images is reported. We report the first post-processing of binary raw data from a high-resolution LSCI camera. Sharp images of low-flowing microvessels were enabled by introducing inverse variance in conjunction with speckle contrast in Matlab-based program code. Extended moving window averaging enhanced signal-to-noise ratio. Functional quantitative study of blood flow kinetics was performed on single gated microvessels using a free hand tool. Based on detection of flow in low-flow microvessels, a new sharp contrast image was derived. Thus, this work presents the first distinct image with quantitative microperfusion data from gated human foot microvasculature. This versatile platform is applicable to study a wide range of tissue systems including fine vascular network in murine brain without craniotomy as well as that in the murine dorsal skin. Importantly, the algorithm reported herein is hardware agnostic and is capable of post-processing binary raw data from any camera source to improve the sensitivity of functional flow data above and beyond standard limits of the optical system. PMID:28106129

Fuzzy entropy thresholding and multi-scale morphological approach for microscopic image enhancement

NASA Astrophysics Data System (ADS)

Zhou, Jiancan; Li, Yuexiang; Shen, Linlin

2017-07-01

Microscopic images provide lots of useful information for modern diagnosis and biological research. However, due to the unstable lighting condition during image capturing, two main problems, i.e., high-level noises and low image contrast, occurred in the generated cell images. In this paper, a simple but efficient enhancement framework is proposed to address the problems. The framework removes image noises using a hybrid method based on wavelet transform and fuzzy-entropy, and enhances the image contrast with an adaptive morphological approach. Experiments on real cell dataset were made to assess the performance of proposed framework. The experimental results demonstrate that our proposed enhancement framework increases the cell tracking accuracy to an average of 74.49%, which outperforms the benchmark algorithm, i.e., 46.18%.

Phase gradient imaging for positive contrast generation to superparamagnetic iron oxide nanoparticle-labeled targets in magnetic resonance imaging.

PubMed

Zhu, Haitao; Demachi, Kazuyuki; Sekino, Masaki

2011-09-01

Positive contrast imaging methods produce enhanced signal at large magnetic field gradient in magnetic resonance imaging. Several postprocessing algorithms, such as susceptibility gradient mapping and phase gradient mapping methods, have been applied for positive contrast generation to detect the cells targeted by superparamagnetic iron oxide nanoparticles. In the phase gradient mapping methods, smoothness condition has to be satisfied to keep the phase gradient unwrapped. Moreover, there has been no discussion about the truncation artifact associated with the algorithm of differentiation that is performed in k-space by the multiplication with frequency value. In this work, phase gradient methods are discussed by considering the wrapping problem when the smoothness condition is not satisfied. A region-growing unwrapping algorithm is used in the phase gradient image to solve the problem. In order to reduce the truncation artifact, a cosine function is multiplied in the k-space to eliminate the abrupt change at the boundaries. Simulation, phantom and in vivo experimental results demonstrate that the modified phase gradient mapping methods may produce improved positive contrast effects by reducing truncation or wrapping artifacts. Copyright © 2011 Elsevier Inc. All rights reserved.

Local contrast-enhanced MR images via high dynamic range processing.

PubMed

Chandra, Shekhar S; Engstrom, Craig; Fripp, Jurgen; Neubert, Ales; Jin, Jin; Walker, Duncan; Salvado, Olivier; Ho, Charles; Crozier, Stuart

2018-09-01

To develop a local contrast-enhancing and feature-preserving high dynamic range (HDR) image processing algorithm for multichannel and multisequence MR images of multiple body regions and tissues, and to evaluate its performance for structure visualization, bias field (correction) mitigation, and automated tissue segmentation. A multiscale-shape and detail-enhancement HDR-MRI algorithm is applied to data sets of multichannel and multisequence MR images of the brain, knee, breast, and hip. In multisequence 3T hip images, agreement between automatic cartilage segmentations and corresponding synthesized HDR-MRI series were computed for mean voxel overlap established from manual segmentations for a series of cases. Qualitative comparisons between the developed HDR-MRI and standard synthesis methods were performed on multichannel 7T brain and knee data, and multisequence 3T breast and knee data. The synthesized HDR-MRI series provided excellent enhancement of fine-scale structure from multiple scales and contrasts, while substantially reducing bias field effects in 7T brain gradient echo, T 1 and T 2 breast images and 7T knee multichannel images. Evaluation of the HDR-MRI approach on 3T hip multisequence images showed superior outcomes for automatic cartilage segmentations with respect to manual segmentation, particularly around regions with hyperintense synovial fluid, across a set of 3D sequences. The successful combination of multichannel/sequence MR images into a single-fused HDR-MR image format provided consolidated visualization of tissues within 1 omnibus image, enhanced definition of thin, complex anatomical structures in the presence of variable or hyperintense signals, and improved tissue (cartilage) segmentation outcomes. © 2018 International Society for Magnetic Resonance in Medicine.

A novel ship CFAR detection algorithm based on adaptive parameter enhancement and wake-aided detection in SAR images

NASA Astrophysics Data System (ADS)

Meng, Siqi; Ren, Kan; Lu, Dongming; Gu, Guohua; Chen, Qian; Lu, Guojun

2018-03-01

Synthetic aperture radar (SAR) is an indispensable and useful method for marine monitoring. With the increase of SAR sensors, high resolution images can be acquired and contain more target structure information, such as more spatial details etc. This paper presents a novel adaptive parameter transform (APT) domain constant false alarm rate (CFAR) to highlight targets. The whole method is based on the APT domain value. Firstly, the image is mapped to the new transform domain by the algorithm. Secondly, the false candidate target pixels are screened out by the CFAR detector to highlight the target ships. Thirdly, the ship pixels are replaced by the homogeneous sea pixels. And then, the enhanced image is processed by Niblack algorithm to obtain the wake binary image. Finally, normalized Hough transform (NHT) is used to detect wakes in the binary image, as a verification of the presence of the ships. Experiments on real SAR images validate that the proposed transform does enhance the target structure and improve the contrast of the image. The algorithm has a good performance in the ship and ship wake detection.

Dynamic cone beam CT angiography of carotid and cerebral arteries using canine model

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

Cai Weixing; Zhao Binghui; Conover, David

2012-01-15

Purpose: This research is designed to develop and evaluate a flat-panel detector-based dynamic cone beam CT system for dynamic angiography imaging, which is able to provide both dynamic functional information and dynamic anatomic information from one multirevolution cone beam CT scan. Methods: A dynamic cone beam CT scan acquired projections over four revolutions within a time window of 40 s after contrast agent injection through a femoral vein to cover the entire wash-in and wash-out phases. A dynamic cone beam CT reconstruction algorithm was utilized and a novel recovery method was developed to correct the time-enhancement curve of contrast flow.more » From the same data set, both projection-based subtraction and reconstruction-based subtraction approaches were utilized and compared to remove the background tissues and visualize the 3D vascular structure to provide the dynamic anatomic information. Results: Through computer simulations, the new recovery algorithm for dynamic time-enhancement curves was optimized and showed excellent accuracy to recover the actual contrast flow. Canine model experiments also indicated that the recovered time-enhancement curves from dynamic cone beam CT imaging agreed well with that of an IV-digital subtraction angiography (DSA) study. The dynamic vascular structures reconstructed using both projection-based subtraction and reconstruction-based subtraction were almost identical as the differences between them were comparable to the background noise level. At the enhancement peak, all the major carotid and cerebral arteries and the Circle of Willis could be clearly observed. Conclusions: The proposed dynamic cone beam CT approach can accurately recover the actual contrast flow, and dynamic anatomic imaging can be obtained with high isotropic 3D resolution. This approach is promising for diagnosis and treatment planning of vascular diseases and strokes.« less

Retinal vessel segmentation on SLO image

PubMed Central

Xu, Juan; Ishikawa, Hiroshi; Wollstein, Gadi; Schuman, Joel S.

2010-01-01

A scanning laser ophthalmoscopy (SLO) image, taken from optical coherence tomography (OCT), usually has lower global/local contrast and more noise compared to the traditional retinal photograph, which makes the vessel segmentation challenging work. A hybrid algorithm is proposed to efficiently solve these problems by fusing several designed methods, taking the advantages of each method and reducing the error measurements. The algorithm has several steps consisting of image preprocessing, thresholding probe and weighted fusing. Four different methods are first designed to transform the SLO image into feature response images by taking different combinations of matched filter, contrast enhancement and mathematical morphology operators. A thresholding probe algorithm is then applied on those response images to obtain four vessel maps. Weighted majority opinion is used to fuse these vessel maps and generate a final vessel map. The experimental results showed that the proposed hybrid algorithm could successfully segment the blood vessels on SLO images, by detecting the major and small vessels and suppressing the noises. The algorithm showed substantial potential in various clinical applications. The use of this method can be also extended to medical image registration based on blood vessel location. PMID:19163149

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

PubMed

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

2017-04-01

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

Novel cooperative neural fusion algorithms for image restoration and image fusion.

PubMed

Xia, Youshen; Kamel, Mohamed S

2007-02-01

To deal with the problem of restoring degraded images with non-Gaussian noise, this paper proposes a novel cooperative neural fusion regularization (CNFR) algorithm for image restoration. Compared with conventional regularization algorithms for image restoration, the proposed CNFR algorithm can relax need of the optimal regularization parameter to be estimated. Furthermore, to enhance the quality of restored images, this paper presents a cooperative neural fusion (CNF) algorithm for image fusion. Compared with existing signal-level image fusion algorithms, the proposed CNF algorithm can greatly reduce the loss of contrast information under blind Gaussian noise environments. The performance analysis shows that the proposed two neural fusion algorithms can converge globally to the robust and optimal image estimate. Simulation results confirm that in different noise environments, the proposed two neural fusion algorithms can obtain a better image estimate than several well known image restoration and image fusion methods.

Contrast improvement of continuous wave diffuse optical tomography reconstruction by hybrid approach using least square and genetic algorithm

NASA Astrophysics Data System (ADS)

Patra, Rusha; Dutta, Pranab K.

2015-07-01

Reconstruction of the absorption coefficient of tissue with good contrast is of key importance in functional diffuse optical imaging. A hybrid approach using model-based iterative image reconstruction and a genetic algorithm is proposed to enhance the contrast of the reconstructed image. The proposed method yields an observed contrast of 98.4%, mean square error of 0.638×10-3, and object centroid error of (0.001 to 0.22) mm. Experimental validation of the proposed method has also been provided with tissue-like phantoms which shows a significant improvement in image quality and thus establishes the potential of the method for functional diffuse optical tomography reconstruction with continuous wave setup. A case study of finger joint imaging is illustrated as well to show the prospect of the proposed method in clinical diagnosis. The method can also be applied to the concentration measurement of a region of interest in a turbid medium.

High sensitivity contrast enhanced optical coherence tomography for functional in vivo imaging

NASA Astrophysics Data System (ADS)

Liba, Orly; SoRelle, Elliott D.; Sen, Debasish; de la Zerda, Adam

2017-02-01

In this study, we developed and applied highly-scattering large gold nanorods (LGNRs) and custom spectral detection algorithms for high sensitivity contrast-enhanced optical coherence tomography (OCT). We were able to detect LGNRs at a concentration as low as 50 pM in blood. We used this approach for noninvasive 3D imaging of blood vessels deep in solid tumors in living mice. Additionally, we demonstrated multiplexed imaging of spectrally-distinct LGNRs that enabled observations of functional drainage in lymphatic networks. This method, which we call MOZART, provides a platform for molecular imaging and characterization of tissue noninvasively at cellular resolution.

Speech Enhancement, Gain, and Noise Spectrum Adaptation Using Approximate Bayesian Estimation

PubMed Central

Hao, Jiucang; Attias, Hagai; Nagarajan, Srikantan; Lee, Te-Won; Sejnowski, Terrence J.

2010-01-01

This paper presents a new approximate Bayesian estimator for enhancing a noisy speech signal. The speech model is assumed to be a Gaussian mixture model (GMM) in the log-spectral domain. This is in contrast to most current models in frequency domain. Exact signal estimation is a computationally intractable problem. We derive three approximations to enhance the efficiency of signal estimation. The Gaussian approximation transforms the log-spectral domain GMM into the frequency domain using minimal Kullback–Leiber (KL)-divergency criterion. The frequency domain Laplace method computes the maximum a posteriori (MAP) estimator for the spectral amplitude. Correspondingly, the log-spectral domain Laplace method computes the MAP estimator for the log-spectral amplitude. Further, the gain and noise spectrum adaptation are implemented using the expectation–maximization (EM) algorithm within the GMM under Gaussian approximation. The proposed algorithms are evaluated by applying them to enhance the speeches corrupted by the speech-shaped noise (SSN). The experimental results demonstrate that the proposed algorithms offer improved signal-to-noise ratio, lower word recognition error rate, and less spectral distortion. PMID:20428253

On the assessment of spatial resolution of PET systems with iterative image reconstruction

NASA Astrophysics Data System (ADS)

Gong, Kuang; Cherry, Simon R.; Qi, Jinyi

2016-03-01

Spatial resolution is an important metric for performance characterization in PET systems. Measuring spatial resolution is straightforward with a linear reconstruction algorithm, such as filtered backprojection, and can be performed by reconstructing a point source scan and calculating the full-width-at-half-maximum (FWHM) along the principal directions. With the widespread adoption of iterative reconstruction methods, it is desirable to quantify the spatial resolution using an iterative reconstruction algorithm. However, the task can be difficult because the reconstruction algorithms are nonlinear and the non-negativity constraint can artificially enhance the apparent spatial resolution if a point source image is reconstructed without any background. Thus, it was recommended that a background should be added to the point source data before reconstruction for resolution measurement. However, there has been no detailed study on the effect of the point source contrast on the measured spatial resolution. Here we use point source scans from a preclinical PET scanner to investigate the relationship between measured spatial resolution and the point source contrast. We also evaluate whether the reconstruction of an isolated point source is predictive of the ability of the system to resolve two adjacent point sources. Our results indicate that when the point source contrast is below a certain threshold, the measured FWHM remains stable. Once the contrast is above the threshold, the measured FWHM monotonically decreases with increasing point source contrast. In addition, the measured FWHM also monotonically decreases with iteration number for maximum likelihood estimate. Therefore, when measuring system resolution with an iterative reconstruction algorithm, we recommend using a low-contrast point source and a fixed number of iterations.

Swarm Intelligence for Optimizing Hybridized Smoothing Filter in Image Edge Enhancement

NASA Astrophysics Data System (ADS)

Rao, B. Tirumala; Dehuri, S.; Dileep, M.; Vindhya, A.

In this modern era, image transmission and processing plays a major role. It would be impossible to retrieve information from satellite and medical images without the help of image processing techniques. Edge enhancement is an image processing step that enhances the edge contrast of an image or video in an attempt to improve its acutance. Edges are the representations of the discontinuities of image intensity functions. For processing these discontinuities in an image, a good edge enhancement technique is essential. The proposed work uses a new idea for edge enhancement using hybridized smoothening filters and we introduce a promising technique of obtaining best hybrid filter using swarm algorithms (Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO)) to search for an optimal sequence of filters from among a set of rather simple, representative image processing filters. This paper deals with the analysis of the swarm intelligence techniques through the combination of hybrid filters generated by these algorithms for image edge enhancement.

Quantitative assessment of placental perfusion by contrast-enhanced ultrasound in macaques and human subjects

PubMed Central

Roberts, Victoria HJ; Lo, Jamie O; Salati, Jennifer A; Lewandowski, Katherine S; Lindner, Jonathan R; Morgan, Terry K; Frias, Antonio E

2016-01-01

Background The utero-placental vascular supply is a critical determinant of placental function and fetal growth. Current methods for the in vivo assessment of placental blood flow are limited. Objective Here we demonstrate the feasibility of utilizing contrast-enhanced ultrasound to visualize and quantify perfusion kinetics in the intervillous space of the primate placenta. Study design Pregnant Japanese macaques were studied at mid second trimester and in the early third trimester. Markers of injury were assessed in placenta samples from animals with or without contrast-enhanced ultrasound exposure (n=6/group). Human subjects were recruited immediately prior to scheduled first trimester pregnancy termination. All studies were performed with maternal intravenous infusion of lipid-shelled octofluoropropane microbubbles with image acquisition using a multipulse contrast-specific algorithm with destruction-replenishment analysis of signal intensity for assessment of perfusion. Results In macaques, rate of perfusion in the intervillous space was increased with advancing gestation. No evidence of microvascular hemorrhage or acute inflammation was found in placental villous tissue and expression levels of caspase-3, nitrotyrosine and HSP70 as markers of apoptosis, nitrative and oxidative stress respectively were unchanged by contrast-enhanced ultrasound exposure. In humans, placental perfusion was visualized at 11wks gestation and preliminary data reveal regional differences in intervillous space perfusion within an individual placenta. By electron microscopy, we demonstrate no evidence of ultrastructure damage to the microvilli on the syncytiotrophoblast following first trimester ultrasound studies. Conclusions Use of contrast-enhanced ultrasound did not result in placental structural damage, and was able to identify intervillous space perfusion rate differences within a placenta. Contrast-enhanced ultrasound may offer a safe clinical tool for the identification of pregnancies at-risk for vascular insufficiency; early recognition may facilitate intervention and improved pregnancy outcomes. PMID:26928151

Morphological rational multi-scale algorithm for color contrast enhancement

NASA Astrophysics Data System (ADS)

Peregrina-Barreto, Hayde; Terol-Villalobos, Iván R.

2010-01-01

Contrast enhancement main goal consists on improving the image visual appearance but also it is used for providing a transformed image in order to segment it. In mathematical morphology several works have been derived from the framework theory for contrast enhancement proposed by Meyer and Serra. However, when working with images with a wide range of scene brightness, as for example when strong highlights and deep shadows appear in the same image, the proposed morphological methods do not allow the enhancement. In this work, a rational multi-scale method, which uses a class of morphological connected filters called filters by reconstruction, is proposed. Granulometry is used by finding the more accurate scales for filters and with the aim of avoiding the use of other little significant scales. The CIE-u'v'Y' space was used to introduce our results since it takes into account the Weber's Law and by avoiding the creation of new colors it permits to modify the luminance values without affecting the hue. The luminance component ('Y) is enhanced separately using the proposed method, next it is used for enhancing the chromatic components (u', v') by means of the center of gravity law of color mixing.

Modified global and modified linear contrast stretching algorithms: new colour contrast enhancement techniques for microscopic analysis of malaria slide images.

PubMed

Abdul-Nasir, Aimi Salihah; Mashor, Mohd Yusoff; Mohamed, Zeehaida

2012-01-01

Malaria is one of the serious global health problem, causing widespread sufferings and deaths in various parts of the world. With the large number of cases diagnosed over the year, early detection and accurate diagnosis which facilitates prompt treatment is an essential requirement to control malaria. For centuries now, manual microscopic examination of blood slide remains the gold standard for malaria diagnosis. However, low contrast of the malaria and variable smears quality are some factors that may influence the accuracy of interpretation by microbiologists. In order to reduce this problem, this paper aims to investigate the performance of the proposed contrast enhancement techniques namely, modified global and modified linear contrast stretching as well as the conventional global and linear contrast stretching that have been applied on malaria images of P. vivax species. The results show that the proposed modified global and modified linear contrast stretching techniques have successfully increased the contrast of the parasites and the infected red blood cells compared to the conventional global and linear contrast stretching. Hence, the resultant images would become useful to microbiologists for identification of various stages and species of malaria.

Model and reconstruction of a K-edge contrast agent distribution with an X-ray photon-counting detector

PubMed Central

Meng, Bo; Cong, Wenxiang; Xi, Yan; De Man, Bruno; Yang, Jian; Wang, Ge

2017-01-01

Contrast-enhanced computed tomography (CECT) helps enhance the visibility for tumor imaging. When a high-Z contrast agent interacts with X-rays across its K-edge, X-ray photoelectric absorption would experience a sudden increment, resulting in a significant difference of the X-ray transmission intensity between the left and right energy windows of the K-edge. Using photon-counting detectors, the X-ray intensity data in the left and right windows of the K-edge can be measured simultaneously. The differential information of the two kinds of intensity data reflects the contrast-agent concentration distribution. K-edge differences between various matters allow opportunities for the identification of contrast agents in biomedical applications. In this paper, a general radon transform is established to link the contrast-agent concentration to X-ray intensity measurement data. An iterative algorithm is proposed to reconstruct a contrast-agent distribution and tissue attenuation background simultaneously. Comprehensive numerical simulations are performed to demonstrate the merits of the proposed method over the existing K-edge imaging methods. Our results show that the proposed method accurately quantifies a distribution of a contrast agent, optimizing the contrast-to-noise ratio at a high dose efficiency. PMID:28437900

Advances in parameter estimation techniques applied to flexible structures

NASA Technical Reports Server (NTRS)

Maben, Egbert; Zimmerman, David C.

1994-01-01

In this work, various parameter estimation techniques are investigated in the context of structural system identification utilizing distributed parameter models and 'measured' time-domain data. Distributed parameter models are formulated using the PDEMOD software developed by Taylor. Enhancements made to PDEMOD for this work include the following: (1) a Wittrick-Williams based root solving algorithm; (2) a time simulation capability; and (3) various parameter estimation algorithms. The parameter estimations schemes will be contrasted using the NASA Mini-Mast as the focus structure.

Segmentation of knee MRI using structure enhanced local phase filtering

NASA Astrophysics Data System (ADS)

Lim, Mikhiel; Hacihaliloglu, Ilker

2016-03-01

The segmentation of bone surfaces from magnetic resonance imaging (MRI) data has applications in the quanti- tative measurement of knee osteoarthritis, surgery planning for patient specific total knee arthroplasty and its subsequent fabrication of artificial implants. However, due to the problems associated with MRI imaging such as low contrast between bone and surrounding tissues, noise, bias fields, and the partial volume effect, segmentation of bone surfaces continues to be a challenging operation. In this paper, a new framework is presented for the enhancement of knee MRI scans prior to segmentation in order to obtain high contrast bone images. During the first stage, a new contrast enhanced relative total variation (RTV) regularization method is used in order to remove textural noise from the bone structures and surrounding soft tissue interface. This salient bone edge information is further enhanced using a sparse gradient counting method based on L0 gradient minimization, which globally controls how many non-zero gradients are resulted in order to approximate prominent bone structures in a structure-sparsity-management manner. The last stage of the framework involves incorporation of local phase bone boundary information in order to provide an intensity invariant enhancement of contrast between the bone and surrounding soft tissue. The enhanced images are segmented using a fast random walker algorithm. Validation against expert segmentation was performed on 10 clinical knee MRI images, and achieved a mean dice similarity coefficient (DSC) of 0.975.

Application and assessment of a robust elastic motion correction algorithm to dynamic MRI.

PubMed

Herrmann, K-H; Wurdinger, S; Fischer, D R; Krumbein, I; Schmitt, M; Hermosillo, G; Chaudhuri, K; Krishnan, A; Salganicoff, M; Kaiser, W A; Reichenbach, J R

2007-01-01

The purpose of this study was to assess the performance of a new motion correction algorithm. Twenty-five dynamic MR mammography (MRM) data sets and 25 contrast-enhanced three-dimensional peripheral MR angiographic (MRA) data sets which were affected by patient motion of varying severeness were selected retrospectively from routine examinations. Anonymized data were registered by a new experimental elastic motion correction algorithm. The algorithm works by computing a similarity measure for the two volumes that takes into account expected signal changes due to the presence of a contrast agent while penalizing other signal changes caused by patient motion. A conjugate gradient method is used to find the best possible set of motion parameters that maximizes the similarity measures across the entire volume. Images before and after correction were visually evaluated and scored by experienced radiologists with respect to reduction of motion, improvement of image quality, disappearance of existing lesions or creation of artifactual lesions. It was found that the correction improves image quality (76% for MRM and 96% for MRA) and diagnosability (60% for MRM and 96% for MRA).

On Optimizing H. 264/AVC Rate Control by Improving R-D Model and Incorporating HVS Characteristics

NASA Astrophysics Data System (ADS)

Zhu, Zhongjie; Wang, Yuer; Bai, Yongqiang; Jiang, Gangyi

2010-12-01

The state-of-the-art JVT-G012 rate control algorithm of H.264 is improved from two aspects. First, the quadratic rate-distortion (R-D) model is modified based on both empirical observations and theoretical analysis. Second, based on the existing physiological and psychological research findings of human vision, the rate control algorithm is optimized by incorporating the main characteristics of the human visual system (HVS) such as contrast sensitivity, multichannel theory, and masking effect. Experiments are conducted, and experimental results show that the improved algorithm can simultaneously enhance the overall subjective visual quality and improve the rate control precision effectively.

Application of low dose radiation and low concentration contrast media in enhanced CT scans in children with congenital heart disease.

PubMed

Liu, Zhimin; Song, Lei; Yu, Tong; Gao, Jun; Zhang, Qifeng; Jiang, Ling; Liu, Yong; Peng, Yun

2016-09-01

The aim of this study was to explore the feasibility of using low dose radiation and low concentration contrast media in enhanced CT examinations in children with congenital heart disease. Ninety patients with congenital heart disease were randomly divided into three groups of 30 patients each who underwent contrast-enhanced cardiac scans on a Discovery CT750 HD scanner. Group A received 270 mg I/mL iodixanol, and group B received 320 mg I/mL iodixanol contrast media and was scanned with prospective ECG triggering mode. Group C received 320 mg I/mL iodixanol and was scanned with conventional retrospective ECG gating mode. The same weight-based contrast injection protocol was used for all three groups. Images were reconstructed using a 30% adaptive statistical iterative reconstruction (ASIR) algorithm and a 50% ASIR in groups A and B and a 30% ASIR in group C. The subjective and objective image quality evaluations, diagnostic accuracies, radiation doses and amounts of contrast media in the three groups were measured and compared. All images in the three groups met the diagnostic requirements, with the same diagnostic accuracy and image quality scores greater than 3 in a 4-point scoring system. However, ventricular enhancement and the objective noise, signal-to-noise ratio, contrast-to-noise ratio and subjective image quality scores in group C were better than those in groups A and B (all P<.001). The effective radiation dose in groups A and B was 84% lower than that in group C (P<.001); group A received the lowest contrast dose (14% lower than that of groups B and C). Enhanced CT scan images with low dose radiation and low concentration contrast media can meet the diagnostic requirements for examining children with congenital heart disease while reducing the potential risk of radiation damage and contrast-induced nephropathy. © 2016 John Wiley & Sons Ltd.

An Algorithm of Image Heterogeneity with Contrast-Enhanced Ultrasound in Differential Diagnosis of Solid Thyroid Nodules.

PubMed

Jin, Lifang; Xu, Changsong; Xie, Xueqian; Li, Fan; Lv, Xiuhong; Du, Lianfang

2017-01-01

Enhancement heterogeneity on contrast-enhanced ultrasonography (CEUS) is used to differentiate between benign and malignant thyroid nodules. In this study, we used an algorithm to quantify enhancement heterogeneity of solid thyroid nodules on CEUS. The heterogeneity value (HV) is calculated as standard deviation/mean intensity × 100 (using Adobe Photoshop). The heterogeneity ratio (HR) is calculated as the ratio of the HV of the nodule to that of the surrounding parenchyma. Three phases-ascending, peak and descending phases-were studied. HV values at ascending (HV a ) and peak (HV p ) phases were significantly higher in malignant nodules than in benign nodules (95.57 ± 43.87 vs. 73.06 ± 44.04, p = 0.009, and 32.53 ± 10.73 vs. 26.44 ± 8.25, p = 0.002, respectively). HR a , HR p and HR d were significantly higher in malignant nodules than in benign nodules (1.93 ± 1.03 vs. 1.00 ± 0.47, p = 0.000, 1.43 ± 0.51 vs. 1.09 ± 0.28, p = 0.000, and 1.33 ± 0.40 vs. 1.08 ± 0.33, p = 0.001, respectively). HR a achieved optimal diagnostic performance on receiver operating characteristic curve analysis. The algorithm used for assessment of image heterogeneity on CEUS examination may be a useful adjunct to conventional ultrasound for differential diagnosis of solid thyroid nodules. Copyright © 2016 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.

Contrast adaptive total p-norm variation minimization approach to CT reconstruction for artifact reduction in reduced-view brain perfusion CT

NASA Astrophysics Data System (ADS)

Kim, Chang-Won; Kim, Jong-Hyo

2011-03-01

Perfusion CT (PCT) examinations are getting more frequently used for diagnosis of acute brain diseases such as hemorrhage and infarction, because the functional map images it produces such as regional cerebral blood flow (rCBF), regional cerebral blood volume (rCBV), and mean transit time (MTT) may provide critical information in the emergency work-up of patient care. However, a typical PCT scans the same slices several tens of times after injection of contrast agent, which leads to much increased radiation dose and is inevitability of growing concern for radiation-induced cancer risk. Reducing the number of views in projection in combination of TV minimization reconstruction technique is being regarded as an option for radiation reduction. However, reconstruction artifacts due to insufficient number of X-ray projections become problematic especially when high contrast enhancement signals are present or patient's motion occurred. In this study, we present a novel reconstruction technique using contrast-adaptive TpV minimization that can reduce reconstruction artifacts effectively by using different p-norms in high contrast and low contrast objects. In the proposed method, high contrast components are first reconstructed using thresholded projection data and low p-norm total variation to reflect sparseness in both projection and reconstruction spaces. Next, projection data are modified to contain only low contrast objects by creating projection data of reconstructed high contrast components and subtracting them from original projection data. Then, the low contrast projection data are reconstructed by using relatively high p-norm TV minimization technique, and are combined with the reconstructed high contrast component images to produce final reconstructed images. The proposed algorithm was applied to numerical phantom and a clinical data set of brain PCT exam, and the resultant images were compared with those using filtered back projection (FBP) and conventional TV reconstruction algorithm. Our results show the potential of the proposed algorithm for image quality improvement, which in turn may lead to dose reduction.

Using pixel intensity as a self-regulating threshold for deterministic image sampling in Milano Retinex: the T-Rex algorithm

NASA Astrophysics Data System (ADS)

Lecca, Michela; Modena, Carla Maria; Rizzi, Alessandro

2018-01-01

Milano Retinexes are spatial color algorithms, part of the Retinex family, usually employed for image enhancement. They modify the color of each pixel taking into account the surrounding colors and their position, catching in this way the local spatial color distribution relevant to image enhancement. We present T-Rex (from the words threshold and Retinex), an implementation of Milano Retinex, whose main novelty is the use of the pixel intensity as a self-regulating threshold to deterministically sample local color information. The experiments, carried out on real-world pictures, show that T-Rex image enhancement performance are in line with those of the Milano Retinex family: T-Rex increases the brightness, the contrast, and the flatness of the channel distributions of the input image, making more intelligible the content of pictures acquired under difficult light conditions.

DSP Implementation of the Retinex Image Enhancement Algorithm

NASA Technical Reports Server (NTRS)

Hines, Glenn; Rahman, Zia-Ur; Jobson, Daniel; Woodell, Glenn

2004-01-01

The Retinex is a general-purpose image enhancement algorithm that is used to produce good visual representations of scenes. It performs a non-linear spatial/spectral transform that synthesizes strong local contrast enhancement and color constancy. A real-time, video frame rate implementation of the Retinex is required to meet the needs of various potential users. Retinex processing contains a relatively large number of complex computations, thus to achieve real-time performance using current technologies requires specialized hardware and software. In this paper we discuss the design and development of a digital signal processor (DSP) implementation of the Retinex. The target processor is a Texas Instruments TMS320C6711 floating point DSP. NTSC video is captured using a dedicated frame-grabber card, Retinex processed, and displayed on a standard monitor. We discuss the optimizations used to achieve real-time performance of the Retinex and also describe our future plans on using alternative architectures.

SU-D-12A-02: DeTECT, a Method to Enhance Soft Tissue Contrast From Mega Voltage CT

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

Sheng, K; Gou, S; Qi, S

Purpose: MVCT images have been used on TomoTherapy system to align patients based on bony anatomies but its usefulness for soft tissue registration, delineation and adaptive radiation therapy is severely limited due to minimal photoelectric interaction and prominent presence of noise resulting from low detector quantum efficiency of megavoltage x-rays. We aim to utilize a non-local means denoising method and texture analysis to recover the soft tissue information for MVCT. Methods: A block matching 3D (BM3D) algorithm was adapted to reduce the noise while keeping the texture information of the MVCT images. BM3D is an imaging denoising algorithm developed frommore » non-local means methods. BM3D additionally creates 3D groups by stacking 2D patches by the order of similarity. 3D denoising operation is then performed. The resultant 3D group is inversely transformed back to 2D images. In this study, BM3D was applied to MVCT images of a CT quality phantom, a head and neck and a prostate patient. Following denoising, imaging texture was enhanced to create the denoised and texture enhanced CT (DeTECT). Results: The original MVCT images show prevalent noise and poor soft tissue contrast. By applying BM3D denoising and texture enhancement, all MVCT images show remarkable improvements. For the phantom, the contrast to noise ratio for the low contrast plug was improved from 2.2 to 13.1 without compromising line pair conspicuity. For the head and neck patient, the lymph nodes and vein in the carotid space inconspicuous in the original MVCT image becomes highly visible in DeTECT. For the prostate patient, the boundary between the bladder and the prostate in the original MVCT is successfully recovered. Both results are visually validated by kVCT images of the corresponding patients. Conclusion: DeTECT showed the promise to drastically improve the soft tissue contrast of MVCT for image guided radiotherapy and adaptive radiotherapy.« less

WE-E-17A-06: Assessing the Scale of Tumor Heterogeneity by Complete Hierarchical Segmentation On MRI

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

Gensheimer, M; Trister, A; Ermoian, R

2014-06-15

Purpose: In many cancers, intratumoral heterogeneity exists in vascular and genetic structure. We developed an algorithm which uses clinical imaging to interrogate different scales of heterogeneity. We hypothesize that heterogeneity of perfusion at large distance scales may correlate with propensity for disease recurrence. We applied the algorithm to initial diagnosis MRI of rhabdomyosarcoma patients to predict recurrence. Methods: The Spatial Heterogeneity Analysis by Recursive Partitioning (SHARP) algorithm recursively segments the tumor image. The tumor is repeatedly subdivided, with each dividing line chosen to maximize signal intensity difference between the two subregions. This process continues to the voxel level, producing segmentsmore » at multiple scales. Heterogeneity is measured by comparing signal intensity histograms between each segmented region and the adjacent region. We measured the scales of contrast enhancement heterogeneity of the primary tumor in 18 rhabdomyosarcoma patients. Using Cox proportional hazards regression, we explored the influence of heterogeneity parameters on relapse-free survival (RFS). To compare with existing methods, fractal and Haralick texture features were also calculated. Results: The complete segmentation produced by SHARP allows extraction of diverse features, including the amount of heterogeneity at various distance scales, the area of the tumor with the most heterogeneity at each scale, and for a given point in the tumor, the heterogeneity at different scales. 10/18 rhabdomyosarcoma patients suffered disease recurrence. On contrast-enhanced MRI, larger scale of maximum signal intensity heterogeneity, relative to tumor diameter, predicted for shorter RFS (p=0.05). Fractal dimension, fractal fit, and three Haralick features did not predict RFS (p=0.09-0.90). Conclusion: SHARP produces an automatic segmentation of tumor regions and reports the amount of heterogeneity at various distance scales. In rhabdomyosarcoma, RFS was shorter when the primary tumor exhibited larger scale of heterogeneity on contrast-enhanced MRI. If validated on a larger dataset, this imaging biomarker could be useful to help personalize treatment.« less

Metal artefact reduction with cone beam CT: an in vitro study

PubMed Central

Bechara, BB; Moore, WS; McMahan, CA; Noujeim, M

2012-01-01

Background Metal in a patient's mouth has been shown to cause artefacts that can interfere with the diagnostic quality of cone beam CT. Recently, a manufacturer has made an algorithm and software available which reduces metal streak artefact (Picasso Master 3D® machine; Vatech, Hwaseong, Republic of Korea). Objectives The purpose of this investigation was to determine whether or not the metal artefact reduction algorithm was effective and enhanced the contrast-to-noise ratio. Methods A phantom was constructed incorporating three metallic beads and three epoxy resin-based bone substitutes to simulate bone next to metal. The phantom was placed in the centre of the field of view and at the periphery. 10 data sets were acquired at 50–90 kVp. The images obtained were analysed using a public domain software ImageJ (NIH Image, Bethesda, MD). Profile lines were used to evaluate grey level changes and area histograms were used to evaluate contrast. The contrast-to-noise ratio was calculated. Results The metal artefact reduction option reduced grey value variation and increased the contrast-to-noise ratio. The grey value varied least when the phantom was in the middle of the volume and the metal artefact reduction was activated. The image quality improved as the peak kilovoltage increased. Conclusion Better images of a phantom were obtained when the metal artefact reduction algorithm was used. PMID:22241878

Non-contrast-enhanced perfusion and ventilation assessment of the human lung by means of fourier decomposition in proton MRI.

PubMed

Bauman, Grzegorz; Puderbach, Michael; Deimling, Michael; Jellus, Vladimir; Chefd'hotel, Christophe; Dinkel, Julien; Hintze, Christian; Kauczor, Hans-Ulrich; Schad, Lothar R

2009-09-01

Assessment of regional lung perfusion and ventilation has significant clinical value for the diagnosis and follow-up of pulmonary diseases. In this work a new method of non-contrast-enhanced functional lung MRI (not dependent on intravenous or inhalative contrast agents) is proposed. A two-dimensional (2D) true fast imaging with steady precession (TrueFISP) pulse sequence (TR/TE = 1.9 ms/0.8 ms, acquisition time [TA] = 112 ms/image) was implemented on a 1.5T whole-body MR scanner. The imaging protocol comprised sets of 198 lung images acquired with an imaging rate of 3.33 images/s in coronal and sagittal view. No electrocardiogram (ECG) or respiratory triggering was used. A nonrigid image registration algorithm was applied to compensate for respiratory motion. Rapid data acquisition allowed observing intensity changes in corresponding lung areas with respect to the cardiac and respiratory frequencies. After a Fourier analysis along the time domain, two spectral lines corresponding to both frequencies were used to calculate the perfusion- and ventilation-weighted images. The described method was applied in preliminary studies on volunteers and patients showing clinical relevance to obtain non-contrast-enhanced perfusion and ventilation data.

Enhancing resolution and contrast in second-harmonic generation microscopy using an advanced maximum likelihood estimation restoration method

NASA Astrophysics Data System (ADS)

Sivaguru, Mayandi; Kabir, Mohammad M.; Gartia, Manas Ranjan; Biggs, David S. C.; Sivaguru, Barghav S.; Sivaguru, Vignesh A.; Berent, Zachary T.; Wagoner Johnson, Amy J.; Fried, Glenn A.; Liu, Gang Logan; Sadayappan, Sakthivel; Toussaint, Kimani C.

2017-02-01

Second-harmonic generation (SHG) microscopy is a label-free imaging technique to study collagenous materials in extracellular matrix environment with high resolution and contrast. However, like many other microscopy techniques, the actual spatial resolution achievable by SHG microscopy is reduced by out-of-focus blur and optical aberrations that degrade particularly the amplitude of the detectable higher spatial frequencies. Being a two-photon scattering process, it is challenging to define a point spread function (PSF) for the SHG imaging modality. As a result, in comparison with other two-photon imaging systems like two-photon fluorescence, it is difficult to apply any PSF-engineering techniques to enhance the experimental spatial resolution closer to the diffraction limit. Here, we present a method to improve the spatial resolution in SHG microscopy using an advanced maximum likelihood estimation (AdvMLE) algorithm to recover the otherwise degraded higher spatial frequencies in an SHG image. Through adaptation and iteration, the AdvMLE algorithm calculates an improved PSF for an SHG image and enhances the spatial resolution by decreasing the full-width-at-halfmaximum (FWHM) by 20%. Similar results are consistently observed for biological tissues with varying SHG sources, such as gold nanoparticles and collagen in porcine feet tendons. By obtaining an experimental transverse spatial resolution of 400 nm, we show that the AdvMLE algorithm brings the practical spatial resolution closer to the theoretical diffraction limit. Our approach is suitable for adaptation in micro-nano CT and MRI imaging, which has the potential to impact diagnosis and treatment of human diseases.

Tumor-Cut: segmentation of brain tumors on contrast enhanced MR images for radiosurgery applications.

PubMed

Hamamci, Andac; Kucuk, Nadir; Karaman, Kutlay; Engin, Kayihan; Unal, Gozde

2012-03-01

In this paper, we present a fast and robust practical tool for segmentation of solid tumors with minimal user interaction to assist clinicians and researchers in radiosurgery planning and assessment of the response to the therapy. Particularly, a cellular automata (CA) based seeded tumor segmentation method on contrast enhanced T1 weighted magnetic resonance (MR) images, which standardizes the volume of interest (VOI) and seed selection, is proposed. First, we establish the connection of the CA-based segmentation to the graph-theoretic methods to show that the iterative CA framework solves the shortest path problem. In that regard, we modify the state transition function of the CA to calculate the exact shortest path solution. Furthermore, a sensitivity parameter is introduced to adapt to the heterogeneous tumor segmentation problem, and an implicit level set surface is evolved on a tumor probability map constructed from CA states to impose spatial smoothness. Sufficient information to initialize the algorithm is gathered from the user simply by a line drawn on the maximum diameter of the tumor, in line with the clinical practice. Furthermore, an algorithm based on CA is presented to differentiate necrotic and enhancing tumor tissue content, which gains importance for a detailed assessment of radiation therapy response. Validation studies on both clinical and synthetic brain tumor datasets demonstrate 80%-90% overlap performance of the proposed algorithm with an emphasis on less sensitivity to seed initialization, robustness with respect to different and heterogeneous tumor types, and its efficiency in terms of computation time.

Contrast enhancement in EIT imaging of the brain.

PubMed

Nissinen, A; Kaipio, J P; Vauhkonen, M; Kolehmainen, V

2016-01-01

We consider electrical impedance tomography (EIT) imaging of the brain. The brain is surrounded by the poorly conducting skull which has low conductivity compared to the brain. The skull layer causes a partial shielding effect which leads to weak sensitivity for the imaging of the brain tissue. In this paper we propose an approach based on the Bayesian approximation error approach, to enhance the contrast in brain imaging. With this approach, both the (uninteresting) geometry and the conductivity of the skull are embedded in the approximation error statistics, which leads to a computationally efficient algorithm that is able to detect features such as internal haemorrhage with significantly increased sensitivity and specificity. We evaluate the approach with simulations and phantom data.

Using Correlative Properties of Neighboring Pixels to Enhance Contrast-to-Noise Ratio of Abnormal Hippocampus in Patients With Intractable Epilepsy and Mesial Temporal Sclerosis.

PubMed

Parsons, Matthew S; Sharma, Aseem; Hildebolt, Charles

2018-06-12

To test whether an image-processing algorithm can aid in visualization of mesial temporal sclerosis on magnetic resonance imaging by selectively increasing contrast-to-noise ratio (CNR) between abnormal hippocampus and normal brain. In this Institutional Review Board-approved and Health Insurance Portability and Accountability Act-compliant study, baseline coronal fluid-attenuated inversion recovery images of 18 adults (10 females, eight males; mean age 41.2 years) with proven mesial temporal sclerosis were processed using a custom algorithm to produce corresponding enhanced images. Average (Hmean) and maximum (Hmax) CNR for abnormal hippocampus were calculated relative to normal ipsilateral white matter. CNR values for normal gray matter (GM) were similarly calculated using ipsilateral cingulate gyrus as the internal control. To evaluate effect of image processing on visual conspicuity of hippocampal signal alteration, a neuroradiologist masked to the side of hippocampal abnormality rated signal intensity (SI) of hippocampi on baseline and enhanced images using a five-point scale (definitely abnormal to definitely normal). Differences in Hmean, Hmax, GM, and SI ratings for abnormal hippocampi on baseline and enhanced images were assessed for statistical significance. Both Hmean and Hmax were significantly higher in enhanced images as compared to baseline images (p < 0.0001 for both). There was no significant difference in the GM between baseline and enhanced images (p = 0.9375). SI ratings showed a more confident identification of abnormality on enhanced images (p = 0.0001). Image-processing resulted in increased CNR of abnormal hippocampus without affecting the CNR of normal gray matter. This selective increase in conspicuity of abnormal hippocampus was associated with more confident identification of hippocampal signal alteration. Copyright © 2018 Academic Radiology. Published by Elsevier Inc. All rights reserved.

Blood vessel segmentation in color fundus images based on regional and Hessian features.

PubMed

Shah, Syed Ayaz Ali; Tang, Tong Boon; Faye, Ibrahima; Laude, Augustinus

2017-08-01

To propose a new algorithm of blood vessel segmentation based on regional and Hessian features for image analysis in retinal abnormality diagnosis. Firstly, color fundus images from the publicly available database DRIVE were converted from RGB to grayscale. To enhance the contrast of the dark objects (blood vessels) against the background, the dot product of the grayscale image with itself was generated. To rectify the variation in contrast, we used a 5 × 5 window filter on each pixel. Based on 5 regional features, 1 intensity feature and 2 Hessian features per scale using 9 scales, we extracted a total of 24 features. A linear minimum squared error (LMSE) classifier was trained to classify each pixel into a vessel or non-vessel pixel. The DRIVE dataset provided 20 training and 20 test color fundus images. The proposed algorithm achieves a sensitivity of 72.05% with 94.79% accuracy. Our proposed algorithm achieved higher accuracy (0.9206) at the peripapillary region, where the ocular manifestations in the microvasculature due to glaucoma, central retinal vein occlusion, etc. are most obvious. This supports the proposed algorithm as a strong candidate for automated vessel segmentation.

Computer image processing - The Viking experience. [digital enhancement techniques

NASA Technical Reports Server (NTRS)

Green, W. B.

1977-01-01

Computer processing of digital imagery from the Viking mission to Mars is discussed, with attention given to subjective enhancement and quantitative processing. Contrast stretching and high-pass filtering techniques of subjective enhancement are described; algorithms developed to determine optimal stretch and filtering parameters are also mentioned. In addition, geometric transformations to rectify the distortion of shapes in the field of view and to alter the apparent viewpoint of the image are considered. Perhaps the most difficult problem in quantitative processing of Viking imagery was the production of accurate color representations of Orbiter and Lander camera images.

A Flexible Method for Multi-Material Decomposition of Dual-Energy CT Images.

PubMed

Mendonca, Paulo R S; Lamb, Peter; Sahani, Dushyant V

2014-01-01

The ability of dual-energy computed-tomographic (CT) systems to determine the concentration of constituent materials in a mixture, known as material decomposition, is the basis for many of dual-energy CT's clinical applications. However, the complex composition of tissues and organs in the human body poses a challenge for many material decomposition methods, which assume the presence of only two, or at most three, materials in the mixture. We developed a flexible, model-based method that extends dual-energy CT's core material decomposition capability to handle more complex situations, in which it is necessary to disambiguate among and quantify the concentration of a larger number of materials. The proposed method, named multi-material decomposition (MMD), was used to develop two image analysis algorithms. The first was virtual unenhancement (VUE), which digitally removes the effect of contrast agents from contrast-enhanced dual-energy CT exams. VUE has the ability to reduce patient dose and improve clinical workflow, and can be used in a number of clinical applications such as CT urography and CT angiography. The second algorithm developed was liver-fat quantification (LFQ), which accurately quantifies the fat concentration in the liver from dual-energy CT exams. LFQ can form the basis of a clinical application targeting the diagnosis and treatment of fatty liver disease. Using image data collected from a cohort consisting of 50 patients and from phantoms, the application of MMD to VUE and LFQ yielded quantitatively accurate results when compared against gold standards. Furthermore, consistent results were obtained across all phases of imaging (contrast-free and contrast-enhanced). This is of particular importance since most clinical protocols for abdominal imaging with CT call for multi-phase imaging. We conclude that MMD can successfully form the basis of a number of dual-energy CT image analysis algorithms, and has the potential to improve the clinical utility of dual-energy CT in disease management.

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.

Semiautomated hybrid algorithm for estimation of three-dimensional liver surface in CT using dynamic cellular automata and level-sets

PubMed Central

Dakua, Sarada Prasad; Abinahed, Julien; Al-Ansari, Abdulla

2015-01-01

Abstract. Liver segmentation continues to remain a major challenge, largely due to its intense complexity with surrounding anatomical structures (stomach, kidney, and heart), high noise level and lack of contrast in pathological computed tomography (CT) data. We present an approach to reconstructing the liver surface in low contrast CT. The main contributions are: (1) a stochastic resonance-based methodology in discrete cosine transform domain is developed to enhance the contrast of pathological liver images, (2) a new formulation is proposed to prevent the object boundary, resulting from the cellular automata method, from leaking into the surrounding areas of similar intensity, and (3) a level-set method is suggested to generate intermediate segmentation contours from two segmented slices distantly located in a subject sequence. We have tested the algorithm on real datasets obtained from two sources, Hamad General Hospital and medical image computing and computer-assisted interventions grand challenge workshop. Various parameters in the algorithm, such as w, Δt, z, α, μ, α1, and α2, play imperative roles, thus their values are precisely selected. Both qualitative and quantitative evaluation performed on liver data show promising segmentation accuracy when compared with ground truth data reflecting the potential of the proposed method. PMID:26158101

Dual-energy contrast enhanced digital breast tomosynthesis: concept, method, and evaluation on phantoms

NASA Astrophysics Data System (ADS)

Puong, Sylvie; Patoureaux, Fanny; Iordache, Razvan; Bouchevreau, Xavier; Muller, Serge

2007-03-01

In this paper, we present the development of dual-energy Contrast-Enhanced Digital Breast Tomosynthesis (CEDBT). A method to produce background clutter-free slices from a set of low and high-energy projections is introduced, along with a scheme for the determination of the optimal low and high-energy techniques. Our approach consists of a dual-energy recombination of the projections, with an algorithm that has proven its performance in Contrast-Enhanced Digital Mammography1 (CEDM), followed by an iterative volume reconstruction. The aim is to eliminate the anatomical background clutter and to reconstruct slices where the gray level is proportional to the local iodine volumetric concentration. Optimization of the low and high-energy techniques is performed by minimizing the total glandular dose to reach a target iodine Signal Difference to Noise Ratio (SDNR) in the slices. In this study, we proved that this optimization could be done on the projections, by consideration of the SDNR in the projections instead of the SDNR in the slices, and verified this with phantom measurements. We also discuss some limitations of dual-energy CEDBT, due to the restricted angular range for the projection views, and to the presence of scattered radiation. Experiments on textured phantoms with iodine inserts were conducted to assess the performance of dual-energy CEDBT. Texture contrast was nearly completely removed and the iodine signal was enhanced in the slices.

Creating an anthropomorphic digital MR phantom—an extensible tool for comparing and evaluating quantitative imaging algorithms

NASA Astrophysics Data System (ADS)

Bosca, Ryan J.; Jackson, Edward F.

2016-01-01

Assessing and mitigating the various sources of bias and variance associated with image quantification algorithms is essential to the use of such algorithms in clinical research and practice. Assessment is usually accomplished with grid-based digital reference objects (DRO) or, more recently, digital anthropomorphic phantoms based on normal human anatomy. Publicly available digital anthropomorphic phantoms can provide a basis for generating realistic model-based DROs that incorporate the heterogeneity commonly found in pathology. Using a publicly available vascular input function (VIF) and digital anthropomorphic phantom of a normal human brain, a methodology was developed to generate a DRO based on the general kinetic model (GKM) that represented realistic and heterogeneously enhancing pathology. GKM parameters were estimated from a deidentified clinical dynamic contrast-enhanced (DCE) MRI exam. This clinical imaging volume was co-registered with a discrete tissue model, and model parameters estimated from clinical images were used to synthesize a DCE-MRI exam that consisted of normal brain tissues and a heterogeneously enhancing brain tumor. An example application of spatial smoothing was used to illustrate potential applications in assessing quantitative imaging algorithms. A voxel-wise Bland-Altman analysis demonstrated negligible differences between the parameters estimated with and without spatial smoothing (using a small radius Gaussian kernel). In this work, we reported an extensible methodology for generating model-based anthropomorphic DROs containing normal and pathological tissue that can be used to assess quantitative imaging algorithms.

Non-preconditioned conjugate gradient on cell and FPGA based hybrid supercomputer nodes

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

Dubois, David H; Dubois, Andrew J; Boorman, Thomas M

2009-01-01

This work presents a detailed implementation of a double precision, non-preconditioned, Conjugate Gradient algorithm on a Roadrunner heterogeneous supercomputer node. These nodes utilize the Cell Broadband Engine Architecture{sup TM} in conjunction with x86 Opteron{sup TM} processors from AMD. We implement a common Conjugate Gradient algorithm, on a variety of systems, to compare and contrast performance. Implementation results are presented for the Roadrunner hybrid supercomputer, SRC Computers, Inc. MAPStation SRC-6 FPGA enhanced hybrid supercomputer, and AMD Opteron only. In all hybrid implementations wall clock time is measured, including all transfer overhead and compute timings.

Non-preconditioned conjugate gradient on cell and FPCA-based hybrid supercomputer nodes

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

Dubois, David H; Dubois, Andrew J; Boorman, Thomas M

2009-03-10

This work presents a detailed implementation of a double precision, Non-Preconditioned, Conjugate Gradient algorithm on a Roadrunner heterogeneous supercomputer node. These nodes utilize the Cell Broadband Engine Architecture{trademark} in conjunction with x86 Opteron{trademark} processors from AMD. We implement a common Conjugate Gradient algorithm, on a variety of systems, to compare and contrast performance. Implementation results are presented for the Roadrunner hybrid supercomputer, SRC Computers, Inc. MAPStation SRC-6 FPGA enhanced hybrid supercomputer, and AMD Opteron only. In all hybrid implementations wall clock time is measured, including all transfer overhead and compute timings.

Respiratory motion compensation algorithm of ultrasound hepatic perfusion data acquired in free-breathing

NASA Astrophysics Data System (ADS)

Wu, Kaizhi; Zhang, Xuming; Chen, Guangxie; Weng, Fei; Ding, Mingyue

2013-10-01

Images acquired in free breathing using contrast enhanced ultrasound exhibit a periodic motion that needs to be compensated for if a further accurate quantification of the hepatic perfusion analysis is to be executed. In this work, we present an algorithm to compensate the respiratory motion by effectively combining the PCA (Principal Component Analysis) method and block matching method. The respiratory kinetics of the ultrasound hepatic perfusion image sequences was firstly extracted using the PCA method. Then, the optimal phase of the obtained respiratory kinetics was detected after normalizing the motion amplitude and determining the image subsequences of the original image sequences. The image subsequences were registered by the block matching method using cross-correlation as the similarity. Finally, the motion-compensated contrast images can be acquired by using the position mapping and the algorithm was evaluated by comparing the TICs extracted from the original image sequences and compensated image subsequences. Quantitative comparisons demonstrated that the average fitting error estimated of ROIs (region of interest) was reduced from 10.9278 +/- 6.2756 to 5.1644 +/- 3.3431 after compensating.

Improved fuzzy clustering algorithms in segmentation of DC-enhanced breast MRI.

PubMed

Kannan, S R; Ramathilagam, S; Devi, Pandiyarajan; Sathya, A

2012-02-01

Segmentation of medical images is a difficult and challenging problem due to poor image contrast and artifacts that result in missing or diffuse organ/tissue boundaries. Many researchers have applied various techniques however fuzzy c-means (FCM) based algorithms is more effective compared to other methods. The objective of this work is to develop some robust fuzzy clustering segmentation systems for effective segmentation of DCE - breast MRI. This paper obtains the robust fuzzy clustering algorithms by incorporating kernel methods, penalty terms, tolerance of the neighborhood attraction, additional entropy term and fuzzy parameters. The initial centers are obtained using initialization algorithm to reduce the computation complexity and running time of proposed algorithms. Experimental works on breast images show that the proposed algorithms are effective to improve the similarity measurement, to handle large amount of noise, to have better results in dealing the data corrupted by noise, and other artifacts. The clustering results of proposed methods are validated using Silhouette Method.

Speckle Noise Reduction in Optical Coherence Tomography Using Two-dimensional Curvelet-based Dictionary Learning.

PubMed

Esmaeili, Mahdad; Dehnavi, Alireza Mehri; Rabbani, Hossein; Hajizadeh, Fedra

2017-01-01

The process of interpretation of high-speed optical coherence tomography (OCT) images is restricted due to the large speckle noise. To address this problem, this paper proposes a new method using two-dimensional (2D) curvelet-based K-SVD algorithm for speckle noise reduction and contrast enhancement of intra-retinal layers of 2D spectral-domain OCT images. For this purpose, we take curvelet transform of the noisy image. In the next step, noisy sub-bands of different scales and rotations are separately thresholded with an adaptive data-driven thresholding method, then, each thresholded sub-band is denoised based on K-SVD dictionary learning with a variable size initial dictionary dependent on the size of curvelet coefficients' matrix in each sub-band. We also modify each coefficient matrix to enhance intra-retinal layers, with noise suppression at the same time. We demonstrate the ability of the proposed algorithm in speckle noise reduction of 100 publically available OCT B-scans with and without non-neovascular age-related macular degeneration (AMD), and improvement of contrast-to-noise ratio from 1.27 to 5.12 and mean-to-standard deviation ratio from 3.20 to 14.41 are obtained.

Investigation of optimal parameters for penalized maximum-likelihood reconstruction applied to iodinated contrast-enhanced breast CT

NASA Astrophysics Data System (ADS)

Makeev, Andrey; Ikejimba, Lynda; Lo, Joseph Y.; Glick, Stephen J.

2016-03-01

Although digital mammography has reduced breast cancer mortality by approximately 30%, sensitivity and specificity are still far from perfect. In particular, the performance of mammography is especially limited for women with dense breast tissue. Two out of every three biopsies performed in the U.S. are unnecessary, thereby resulting in increased patient anxiety, pain, and possible complications. One promising tomographic breast imaging method that has recently been approved by the FDA is dedicated breast computed tomography (BCT). However, visualizing lesions with BCT can still be challenging for women with dense breast tissue due to the minimal contrast for lesions surrounded by fibroglandular tissue. In recent years there has been renewed interest in improving lesion conspicuity in x-ray breast imaging by administration of an iodinated contrast agent. Due to the fully 3-D imaging nature of BCT, as well as sub-optimal contrast enhancement while the breast is under compression with mammography and breast tomosynthesis, dedicated BCT of the uncompressed breast is likely to offer the best solution for injected contrast-enhanced x-ray breast imaging. It is well known that use of statistically-based iterative reconstruction in CT results in improved image quality at lower radiation dose. Here we investigate possible improvements in image reconstruction for BCT, by optimizing free regularization parameter in method of maximum likelihood and comparing its performance with clinical cone-beam filtered backprojection (FBP) algorithm.

Advanced image fusion algorithms for Gamma Knife treatment planning. Evaluation and proposal for clinical use.

PubMed

Apostolou, N; Papazoglou, Th; Koutsouris, D

2006-01-01

Image fusion is a process of combining information from multiple sensors. It is a useful tool implemented in the treatment planning programme of Gamma Knife Radiosurgery. In this paper we evaluate advanced image fusion algorithms for Matlab platform and head images. We develop nine level grayscale image fusion methods: average, principal component analysis (PCA), discrete wavelet transform (DWT) and Laplacian, filter - subtract - decimate (FSD), contrast, gradient, morphological pyramid and a shift invariant discrete wavelet transform (SIDWT) method in Matlab platform. We test these methods qualitatively and quantitatively. The quantitative criteria we use are the Root Mean Square Error (RMSE), the Mutual Information (MI), the Standard Deviation (STD), the Entropy (H), the Difference Entropy (DH) and the Cross Entropy (CEN). The qualitative are: natural appearance, brilliance contrast, presence of complementary features and enhancement of common features. Finally we make clinically useful suggestions.

Citrus fruit recognition using color image analysis

NASA Astrophysics Data System (ADS)

Xu, Huirong; Ying, Yibin

2004-10-01

An algorithm for the automatic recognition of citrus fruit on the tree was developed. Citrus fruits have different color with leaves and branches portions. Fifty-three color images with natural citrus-grove scenes were digitized and analyzed for red, green, and blue (RGB) color content. The color characteristics of target surfaces (fruits, leaves, or branches) were extracted using the range of interest (ROI) tool. Several types of contrast color indices were designed and tested. In this study, the fruit image was enhanced using the (R-B) contrast color index because results show that the fruit have the highest color difference among the objects in the image. A dynamic threshold function was derived from this color model and used to distinguish citrus fruit from background. The results show that the algorithm worked well under frontlighting or backlighting condition. However, there are misclassifications when the fruit or the background is under a brighter sunlight.

A parallelizable real-time motion tracking algorithm with applications to ultrasonic strain imaging.

PubMed

Jiang, J; Hall, T J

2007-07-07

Ultrasound-based mechanical strain imaging systems utilize signals from conventional diagnostic ultrasound systems to image tissue elasticity contrast that provides new diagnostically valuable information. Previous works (Hall et al 2003 Ultrasound Med. Biol. 29 427, Zhu and Hall 2002 Ultrason. Imaging 24 161) demonstrated that uniaxial deformation with minimal elevation motion is preferred for breast strain imaging and real-time strain image feedback to operators is important to accomplish this goal. The work reported here enhances the real-time speckle tracking algorithm with two significant modifications. One fundamental change is that the proposed algorithm is a column-based algorithm (a column is defined by a line of data parallel to the ultrasound beam direction, i.e. an A-line), as opposed to a row-based algorithm (a row is defined by a line of data perpendicular to the ultrasound beam direction). Then, displacement estimates from its adjacent columns provide good guidance for motion tracking in a significantly reduced search region to reduce computational cost. Consequently, the process of displacement estimation can be naturally split into at least two separated tasks, computed in parallel, propagating outward from the center of the region of interest (ROI). The proposed algorithm has been implemented and optimized in a Windows system as a stand-alone ANSI C++ program. Results of preliminary tests, using numerical and tissue-mimicking phantoms, and in vivo tissue data, suggest that high contrast strain images can be consistently obtained with frame rates (10 frames s(-1)) that exceed our previous methods.

TDAAPS 2: Acoustic Wave Propagation in Attenuative Moving Media

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

Preston, Leiph A.

This report outlines recent enhancements to the TDAAPS algorithm first described by Symons et al., 2005. One of the primary additions to the code is the ability to specify an attenuative media using standard linear fluid mechanisms to match reasonably general frequency versus loss curves, including common frequency versus loss curves for the atmosphere and seawater. Other improvements that will be described are the addition of improved numerical boundary conditions via various forms of Perfectly Matched Layers, enhanced accuracy near high contrast media interfaces, and improved physics options.

Improved diagnostic differentiation of renal cystic lesions with phase-contrast computed tomography (PCCT)

NASA Astrophysics Data System (ADS)

Noel, Peter B.; Willner, Marian; Fingerle, Alexander; Herzen, Julia; Münzel, Daniela; Hahn, Dieter; Rummeny, Ernst J.; Pfeiffer, Franz

2012-03-01

The diagnostic quality of phase-contrast computed tomography (PCCT) is one the unexplored areas in medical imaging; at the same time, it seems to offer the opportunity as a fast and highly sensitive diagnostic tool. Conventional computed tomography (CT) has had an enormous impact on medicine, while it is limited in soft-tissue contrast. One example that portrays this challenge is the differentiation between benign and malignant renal cysts. In this work we report on a feasibility study to determine the usefulness of PCCT in differentiation of renal cysts. A renal phantom was imaged with a grating-based PCCT system consisting of a standard rotating anode x-ray tube (40 kV, 70 mA) and a Pilatus II photoncounting detector (pixel size: 172 μm). The phantom is composed of a renal equivalent soft-tissue and cystic lesions grouped in non-enhancing cyst and hemorrhage series and an iodine enhancing series. The acquired projection images (absorption and phase-contrast) are reconstructed with a standard filtered backprojection algorithm. For evaluation both reconstructions are compared in respect to contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR), and subjective image quality. We found that with PCCT a significantly improved differentiation between hemorrhage renal cysts from contrast enhancing malignant cysts is possible. If comparing PCCT and CT with respect to CNR and SNR, PCCT shows significant improvements. In conclusion, PCCT has the potential to improve the diagnostics and characterization of renal cysts without using any contrast agents. These results in combination with a non-synchrotron setup indicate a future paradigm shift in diagnostic computed tomography.

Ensemble LUT classification for degraded document enhancement

NASA Astrophysics Data System (ADS)

Obafemi-Ajayi, Tayo; Agam, Gady; Frieder, Ophir

2008-01-01

The fast evolution of scanning and computing technologies have led to the creation of large collections of scanned paper documents. Examples of such collections include historical collections, legal depositories, medical archives, and business archives. Moreover, in many situations such as legal litigation and security investigations scanned collections are being used to facilitate systematic exploration of the data. It is almost always the case that scanned documents suffer from some form of degradation. Large degradations make documents hard to read and substantially deteriorate the performance of automated document processing systems. Enhancement of degraded document images is normally performed assuming global degradation models. When the degradation is large, global degradation models do not perform well. In contrast, we propose to estimate local degradation models and use them in enhancing degraded document images. Using a semi-automated enhancement system we have labeled a subset of the Frieder diaries collection.1 This labeled subset was then used to train an ensemble classifier. The component classifiers are based on lookup tables (LUT) in conjunction with the approximated nearest neighbor algorithm. The resulting algorithm is highly effcient. Experimental evaluation results are provided using the Frieder diaries collection.1

Peripheral zone lesions of intermediary risk in multiparametric prostate MRI: Frequency and validation of the PI-RADSv2 risk stratification algorithm based on focal contrast enhancement.

PubMed

Benndorf, Matthias; Waibel, Lorenz; Krönig, Malte; Jilg, Cordula Annette; Langer, Mathias; Krauss, Tobias

2018-02-01

To validate the risk stratification algorithm of the Prostate Imaging Reporting and Data System (PI-RADSv2) for intermediary risk lesions (PI-RADSv2 category 3) in the peripheral zone based on focal contrast enhancement and to compare cancer rates in category 3, upgraded category 4 and category 4 based on markedly low ADC value. We retrospectively analyze 172 consecutive patients undergoing prostate MRI with 315 histopathologically verified lesions. We select all lesions either assigned category 3 or category 4 in the peripheral zone for further analysis. We compare cancer rates with the two-sided chi-squared test. To determine inter-observer agreement about contrast enhancement two blinded radiologists evaluate the subset of category 3 lesions based on the diffusion weighted sequence. The frequency of peripheral PI-RADS 3, upgraded PI-RADS 4 and PI-RADS 4 lesions based on markedly low ADC value is 10.8%, 10.8% and 20.3%, respectively. Cancer rates (significant cancer only) in these subgroups are 8.8% (3/34), 23.5% (8/34) and 40.6% (26/64), P < 0.01. Inter-observer agreement is moderate for evaluation of contrast enhancement with kappa values between 0.46 and 0.5. We demonstrate a trend of increasing cancer rate from PI-RADSv2 category 3 to upgraded category 4 to category 4 based on markedly low ADC value. Peripheral lesions of intermediary risk in the diffusion weighted sequence account for 21.6% of all prostate lesions encountered. Since it is likely that patient management recommendations will be linked to assessment categories in future versions of PI-RADS, cancer rates in upgraded category 4 and category 4 based on markedly low ADC values should be in a similar range. We conclude that in future studies of PI-RADSv2 upgraded category 4 and category 4 based on markedly low ADC value should be reported separately to generate a database for meta-analysis of cancer rates. Copyright © 2017 Elsevier B.V. All rights reserved.

Gamma Knife surgery for arteriovenous malformations in the brain: integration of time-resolved contrast-enhanced magnetic resonance angiography into dosimetry planning. Technical note.

PubMed

Taschner, Christian A; Le Thuc, Vianney; Reyns, Nicolas; Gieseke, Juergen; Gauvrit, Jean-Yves; Pruvo, Jean-Pierre; Leclerc, Xavier

2007-10-01

The aim of this study was to develop an algorithm for the integration of time-resolved contrast-enhanced magnetic resonance (MR) angiography into dosimetry planning for Gamma Knife surgery (GKS) of arteriovenous malformations (AVMs) in the brain. Twelve patients harboring brain AVMs referred for GKS underwent intraarterial digital subtraction (DS) angiography and time-resolved MR angiography while wearing an externally applied cranial stereotactic frame. Time-resolved MR angiography was performed on a 1.5-tesla MR unit (Achieva, Philips Medical Systems) using contrast-enhanced 3D fast field echo sequencing with stochastic central k-space ordering. Postprocessing with interactive data language (Research Systems, Inc.) produced hybrid data sets containing dynamic angiographic information and the MR markers necessary for stereotactic transformation. Image files were sent to the Leksell GammaPlan system (Elekta) for dosimetry planning. Stereotactic transformation of the hybrid data sets containing the time-resolved MR angiography information with automatic detection of the MR markers was possible in all 12 cases. The stereotactic coordinates of vascular structures predefined from time-resolved MR angiography matched with DS angiography data in all cases. In 10 patients dosimetry planning could be performed based on time-resolved MR angiography data. In two patients, time-resolved MR angiography data alone were considered insufficient. The target volumes showed a notable shift of centers between modalities. Integration of time-resolved MR angiography data into the Leksell GammaPlan system for patients with brain AVMs is feasible. The proposed algorithm seems concise and sufficiently robust for clinical application. The quality of the time-resolved MR angiography sequencing needs further improvement.

SU-F-I-16: Short Breast MRI with High-Resolution T2-Weighted and Dynamic Contrast Enhanced T1-Weighted Images

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

Ma, J; Son, J; Arun, B

Purpose: To develop and demonstrate a short breast (sb) MRI protocol that acquires both T2-weighted and dynamic contrast-enhanced T1-weighted images in approximately ten minutes. Methods: The sb-MRI protocol consists of two novel pulse sequences. The first is a flexible fast spin-echo triple-echo Dixon (FTED) sequence for high-resolution fat-suppressed T2-weighted imaging, and the second is a 3D fast dual-echo spoiled gradient sequence (FLEX) for volumetric fat-suppressed T1-weighted imaging before and post contrast agent injection. The flexible FTED sequence replaces each single readout during every echo-spacing period of FSE with three fast-switching bipolar readouts to produce three raw images in a singlemore » acquisition. These three raw images are then post-processed using a Dixon algorithm to generate separate water-only and fat-only images. The FLEX sequence acquires two echoes using dual-echo readout after each RF excitation and the corresponding images are post-processed using a similar Dixon algorithm to yield water-only and fat-only images. The sb-MRI protocol was implemented on a 3T MRI scanner and used for patients who had undergone concurrent clinical MRI for breast cancer screening. Results: With the same scan parameters (eg, spatial coverage, field of view, spatial and temporal resolution) as the clinical protocol, the total scan-time of the sb-MRI protocol (including the localizer, bilateral T2-weighted, and dynamic contrast-enhanced T1-weighted images) was 11 minutes. In comparison, the clinical breast MRI protocol took 43 minutes. Uniform fat suppression and high image quality were consistently achieved by sb-MRI. Conclusion: We demonstrated a sb-MRI protocol comprising both T2-weighted and dynamic contrast-enhanced T1-weighted images can be performed in approximately ten minutes. The spatial and temporal resolution of the images easily satisfies the current breast MRI accreditation guidelines by the American College of Radiology. The protocol has the potential of making breast MRI more widely accessible to and more tolerable by the patients. JMA is the inventor of United States patents that are owned by the University of Texas Board of Regents and currently licensed to GE Healthcare and Siemens Gmbh.« less

The combination of a reduction in contrast agent dose with low tube voltage and an adaptive statistical iterative reconstruction algorithm in CT enterography: Effects on image quality and radiation dose.

PubMed

Feng, Cui; Zhu, Di; Zou, Xianlun; Li, Anqin; Hu, Xuemei; Li, Zhen; Hu, Daoyu

2018-03-01

To investigate the subjective and quantitative image quality and radiation exposure of CT enterography (CTE) examination performed at low tube voltage and low concentration of contrast agent with adaptive statistical iterative reconstruction (ASIR) algorithm, compared with conventional CTE.One hundred thirty-seven patients with suspected or proved gastrointestinal diseases underwent contrast enhanced CTE in a multidetector computed tomography (MDCT) scanner. All cases were assigned to 2 groups. Group A (n = 79) underwent CT with low tube voltage based on patient body mass index (BMI) (BMI < 23 kg/m, 80 kVp; BMI ≥ 23 kg/m, 100 kVp) and low concentration of contrast agent (270 mg I/mL), the images were reconstructed with standard filtered back projection (FBP) algorithm and 50% ASIR algorithm. Group B (n = 58) underwent conventional CTE with 120 kVp and 350 mg I/mL contrast agent, the images were reconstructed with FBP algorithm. The computed tomography dose index volume (CTDIvol), dose length product (DLP), effective dose (ED), and total iodine dosage were calculated and compared. The CT values, contrast-to-noise ratio (CNR), and signal-to-noise ratio (SNR) of the normal bowel wall, gastrointestinal lesions, and mesenteric vessels were assessed and compared. The subjective image quality was assessed independently and blindly by 2 radiologists using a 5-point Likert scale.The differences of values for CTDIvol (8.64 ± 2.72 vs 11.55 ± 3.95, P < .001), ED (6.34 ± 2.24 vs 8.52 ± 3.02, P < .001), and DLP (422.6 ± 149.40 vs 568.30 ± 213.90, P < .001) were significant between group A and group B, with a reduction of 25.2%, 25.7%, and 25.7% in group A, respectively. The total iodine dosage in group A was reduced by 26.1%. The subjective image quality did not differ between the 2 groups (P > .05) and all image quality scores were greater than or equal to 3 (moderate). Fifty percent ASIR-A group images provided lower image noise, but similar or higher quantitative image quality in comparison with FBP-B group images.Compared with the conventional protocol, CTE performed at low tube voltage, low concentration of contrast agent with 50% ASIR algorithm produce a diagnostically acceptable image quality with a mean ED of 6.34 mSv and a total iodine dose reduction of 26.1%.

Automated Critical Test Findings Identification and Online Notification System Using Artificial Intelligence in Imaging.

PubMed

Prevedello, Luciano M; Erdal, Barbaros S; Ryu, John L; Little, Kevin J; Demirer, Mutlu; Qian, Songyue; White, Richard D

2017-12-01

Purpose To evaluate the performance of an artificial intelligence (AI) tool using a deep learning algorithm for detecting hemorrhage, mass effect, or hydrocephalus (HMH) at non-contrast material-enhanced head computed tomographic (CT) examinations and to determine algorithm performance for detection of suspected acute infarct (SAI). Materials and Methods This HIPAA-compliant retrospective study was completed after institutional review board approval. A training and validation dataset of noncontrast-enhanced head CT examinations that comprised 100 examinations of HMH, 22 of SAI, and 124 of noncritical findings was obtained resulting in 2583 representative images. Examinations were processed by using a convolutional neural network (deep learning) using two different window and level configurations (brain window and stroke window). AI algorithm performance was tested on a separate dataset containing 50 examinations with HMH findings, 15 with SAI findings, and 35 with noncritical findings. Results Final algorithm performance for HMH showed 90% (45 of 50) sensitivity (95% confidence interval [CI]: 78%, 97%) and 85% (68 of 80) specificity (95% CI: 76%, 92%), with area under the receiver operating characteristic curve (AUC) of 0.91 with the brain window. For SAI, the best performance was achieved with the stroke window showing 62% (13 of 21) sensitivity (95% CI: 38%, 82%) and 96% (27 of 28) specificity (95% CI: 82%, 100%), with AUC of 0.81. Conclusion AI using deep learning demonstrates promise for detecting critical findings at noncontrast-enhanced head CT. A dedicated algorithm was required to detect SAI. Detection of SAI showed lower sensitivity in comparison to detection of HMH, but showed reasonable performance. Findings support further investigation of the algorithm in a controlled and prospective clinical setting to determine whether it can independently screen noncontrast-enhanced head CT examinations and notify the interpreting radiologist of critical findings. © RSNA, 2017 Online supplemental material is available for this article.

Intraocular scattering compensation in retinal imaging

PubMed Central

Christaras, Dimitrios; Ginis, Harilaos; Pennos, Alexandros; Artal, Pablo

2016-01-01

Intraocular scattering affects fundus imaging in a similar way that affects vision; it causes a decrease in contrast which depends on both the intrinsic scattering of the eye but also on the dynamic range of the image. Consequently, in cases where the absolute intensity in the fundus image is important, scattering can lead to a wrong estimation. In this paper, a setup capable of acquiring fundus images and estimating objectively intraocular scattering was built, and the acquired images were then used for scattering compensation in fundus imaging. The method consists of two parts: first, reconstruct the individual’s wide-angle Point Spread Function (PSF) at a specific wavelength to be used within an enhancement algorithm on an acquired fundus image to compensate for scattering. As a proof of concept, a single pass measurement with a scatter filter was carried out first and the complete algorithm of the PSF reconstruction and the scattering compensation was applied. The advantage of the single pass test is that one can compare the reconstructed image with the original one and see the validity, thus testing the efficiency of the method. Following the test, the algorithm was applied in actual fundus images in human eyes and the effect on the contrast of the image before and after the compensation was compared. The comparison showed that depending on the wavelength, contrast can be reduced by 8.6% under certain conditions. PMID:27867710

Semiautomated hybrid algorithm for estimation of three-dimensional liver surface in CT using dynamic cellular automata and level-sets.

PubMed

Dakua, Sarada Prasad; Abinahed, Julien; Al-Ansari, Abdulla

2015-04-01

Liver segmentation continues to remain a major challenge, largely due to its intense complexity with surrounding anatomical structures (stomach, kidney, and heart), high noise level and lack of contrast in pathological computed tomography (CT) data. We present an approach to reconstructing the liver surface in low contrast CT. The main contributions are: (1) a stochastic resonance-based methodology in discrete cosine transform domain is developed to enhance the contrast of pathological liver images, (2) a new formulation is proposed to prevent the object boundary, resulting from the cellular automata method, from leaking into the surrounding areas of similar intensity, and (3) a level-set method is suggested to generate intermediate segmentation contours from two segmented slices distantly located in a subject sequence. We have tested the algorithm on real datasets obtained from two sources, Hamad General Hospital and medical image computing and computer-assisted interventions grand challenge workshop. Various parameters in the algorithm, such as [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text], play imperative roles, thus their values are precisely selected. Both qualitative and quantitative evaluation performed on liver data show promising segmentation accuracy when compared with ground truth data reflecting the potential of the proposed method.

Proposal of a skin tests based approach for the prevention of recurrent hypersensitivity reactions to iodinated contrast media.

PubMed

Della-Torre, E; Berti, A; Yacoub, M R; Guglielmi, B; Tombetti, E; Sabbadini, M G; Voltolini, S; Colombo, G

2015-05-01

The purpose of the present work is to evaluate the efficacy of an approach that combines clinical history, skin tests results, and premedication, in preventing recurrent hypersensitivity reactions to iodinated contrast media (ICM). Skin Prick tests, Intradermal tests, and Patch tests were performed in 36 patients with a previous reaction to ICM. All patients underwent a second contrast enhanced radiological procedure with an alternative ICM selected on the basis of the proposed approach. After alternative ICM re-injection, only one patient presented a mild NIR. The proposed algorithm, validated in clinical settings where repeated radiological exams are needed, offers a safe and practical approach for protecting patients from recurrent hypersensitivity reactions to ICM.

Multi-Parametric MRI and Texture Analysis to Visualize Spatial Histologic Heterogeneity and Tumor Extent in Glioblastoma.

PubMed

Hu, Leland S; Ning, Shuluo; Eschbacher, Jennifer M; Gaw, Nathan; Dueck, Amylou C; Smith, Kris A; Nakaji, Peter; Plasencia, Jonathan; Ranjbar, Sara; Price, Stephen J; Tran, Nhan; Loftus, Joseph; Jenkins, Robert; O'Neill, Brian P; Elmquist, William; Baxter, Leslie C; Gao, Fei; Frakes, David; Karis, John P; Zwart, Christine; Swanson, Kristin R; Sarkaria, Jann; Wu, Teresa; Mitchell, J Ross; Li, Jing

2015-01-01

Genetic profiling represents the future of neuro-oncology but suffers from inadequate biopsies in heterogeneous tumors like Glioblastoma (GBM). Contrast-enhanced MRI (CE-MRI) targets enhancing core (ENH) but yields adequate tumor in only ~60% of cases. Further, CE-MRI poorly localizes infiltrative tumor within surrounding non-enhancing parenchyma, or brain-around-tumor (BAT), despite the importance of characterizing this tumor segment, which universally recurs. In this study, we use multiple texture analysis and machine learning (ML) algorithms to analyze multi-parametric MRI, and produce new images indicating tumor-rich targets in GBM. We recruited primary GBM patients undergoing image-guided biopsies and acquired pre-operative MRI: CE-MRI, Dynamic-Susceptibility-weighted-Contrast-enhanced-MRI, and Diffusion Tensor Imaging. Following image coregistration and region of interest placement at biopsy locations, we compared MRI metrics and regional texture with histologic diagnoses of high- vs low-tumor content (≥80% vs <80% tumor nuclei) for corresponding samples. In a training set, we used three texture analysis algorithms and three ML methods to identify MRI-texture features that optimized model accuracy to distinguish tumor content. We confirmed model accuracy in a separate validation set. We collected 82 biopsies from 18 GBMs throughout ENH and BAT. The MRI-based model achieved 85% cross-validated accuracy to diagnose high- vs low-tumor in the training set (60 biopsies, 11 patients). The model achieved 81.8% accuracy in the validation set (22 biopsies, 7 patients). Multi-parametric MRI and texture analysis can help characterize and visualize GBM's spatial histologic heterogeneity to identify regional tumor-rich biopsy targets.

Automatic segmentation of the optic nerve head for deformation measurements in video rate optical coherence tomography

NASA Astrophysics Data System (ADS)

Hidalgo-Aguirre, Maribel; Gitelman, Julian; Lesk, Mark Richard; Costantino, Santiago

2015-11-01

Optical coherence tomography (OCT) imaging has become a standard diagnostic tool in ophthalmology, providing essential information associated with various eye diseases. In order to investigate the dynamics of the ocular fundus, we present a simple and accurate automated algorithm to segment the inner limiting membrane in video-rate optic nerve head spectral domain (SD) OCT images. The method is based on morphological operations including a two-step contrast enhancement technique, proving to be very robust when dealing with low signal-to-noise ratio images and pathological eyes. An analysis algorithm was also developed to measure neuroretinal tissue deformation from the segmented retinal profiles. The performance of the algorithm is demonstrated, and deformation results are presented for healthy and glaucomatous eyes.

Differentiation of Enhancing Glioma and Primary Central Nervous System Lymphoma by Texture-Based Machine Learning.

PubMed

Alcaide-Leon, P; Dufort, P; Geraldo, A F; Alshafai, L; Maralani, P J; Spears, J; Bharatha, A

2017-06-01

Accurate preoperative differentiation of primary central nervous system lymphoma and enhancing glioma is essential to avoid unnecessary neurosurgical resection in patients with primary central nervous system lymphoma. The purpose of the study was to evaluate the diagnostic performance of a machine-learning algorithm by using texture analysis of contrast-enhanced T1-weighted images for differentiation of primary central nervous system lymphoma and enhancing glioma. Seventy-one adult patients with enhancing gliomas and 35 adult patients with primary central nervous system lymphomas were included. The tumors were manually contoured on contrast-enhanced T1WI, and the resulting volumes of interest were mined for textural features and subjected to a support vector machine-based machine-learning protocol. Three readers classified the tumors independently on contrast-enhanced T1WI. Areas under the receiver operating characteristic curves were estimated for each reader and for the support vector machine classifier. A noninferiority test for diagnostic accuracy based on paired areas under the receiver operating characteristic curve was performed with a noninferiority margin of 0.15. The mean areas under the receiver operating characteristic curve were 0.877 (95% CI, 0.798-0.955) for the support vector machine classifier; 0.878 (95% CI, 0.807-0.949) for reader 1; 0.899 (95% CI, 0.833-0.966) for reader 2; and 0.845 (95% CI, 0.757-0.933) for reader 3. The mean area under the receiver operating characteristic curve of the support vector machine classifier was significantly noninferior to the mean area under the curve of reader 1 ( P = .021), reader 2 ( P = .035), and reader 3 ( P = .007). Support vector machine classification based on textural features of contrast-enhanced T1WI is noninferior to expert human evaluation in the differentiation of primary central nervous system lymphoma and enhancing glioma. © 2017 by American Journal of Neuroradiology.

Overview of benefits, challenges, and requirements of wheeled-vehicle mounted infrared sensors

NASA Astrophysics Data System (ADS)

Miller, John Lester; Clayton, Paul; Olsson, Stefan F.

2013-06-01

Requirements for vehicle mounted infrared sensors, especially as imagers evolve to high definition (HD) format will be detailed and analyzed. Lessons learned from integrations of infrared sensors on armored vehicles, unarmored military vehicles and commercial automobiles will be discussed. Comparisons between sensors for driving and those for situation awareness, targeting and other functions will be presented. Conclusions will be drawn regarding future applications and installations. New business requirements for more advanced digital image processing algorithms in the sensor system will be discussed. Examples of these are smarter contrast/brightness adjustments algorithms, detail enhancement, intelligent blending (IR-Vis) modes, and augmented reality.

Reproducibility of dynamic contrast-enhanced MRI and dynamic susceptibility contrast MRI in the study of brain gliomas: a comparison of data obtained using different commercial software.

PubMed

Conte, Gian Marco; Castellano, Antonella; Altabella, Luisa; Iadanza, Antonella; Cadioli, Marcello; Falini, Andrea; Anzalone, Nicoletta

2017-04-01

Dynamic susceptibility contrast MRI (DSC) and dynamic contrast-enhanced MRI (DCE) are useful tools in the diagnosis and follow-up of brain gliomas; nevertheless, both techniques leave the open issue of data reproducibility. We evaluated the reproducibility of data obtained using two different commercial software for perfusion maps calculation and analysis, as one of the potential sources of variability can be the software itself. DSC and DCE analyses from 20 patients with gliomas were tested for both the intrasoftware (as intraobserver and interobserver reproducibility) and the intersoftware reproducibility, as well as the impact of different postprocessing choices [vascular input function (VIF) selection and deconvolution algorithms] on the quantification of perfusion biomarkers plasma volume (Vp), volume transfer constant (K trans ) and rCBV. Data reproducibility was evaluated with the intraclass correlation coefficient (ICC) and Bland-Altman analysis. For all the biomarkers, the intra- and interobserver reproducibility resulted in almost perfect agreement in each software, whereas for the intersoftware reproducibility the value ranged from 0.311 to 0.577, suggesting fair to moderate agreement; Bland-Altman analysis showed high dispersion of data, thus confirming these findings. Comparisons of different VIF estimation methods for DCE biomarkers resulted in ICC of 0.636 for K trans and 0.662 for Vp; comparison of two deconvolution algorithms in DSC resulted in an ICC of 0.999. The use of single software ensures very good intraobserver and interobservers reproducibility. Caution should be taken when comparing data obtained using different software or different postprocessing within the same software, as reproducibility is not guaranteed anymore.

Extraction of line properties based on direction fields.

PubMed

Kutka, R; Stier, S

1996-01-01

The authors present a new set of algorithms for segmenting lines, mainly blood vessels in X-ray images, and extracting properties such as their intensities, diameters, and center lines. The authors developed a tracking algorithm that checks rules taking the properties of vessels into account. The tools even detect veins, arteries, or catheters of two pixels in diameter and with poor contrast. Compared with other algorithms, such as the Canny line detector or anisotropic diffusion, the authors extract a smoother and connected vessel tree without artifacts in the image background. As the tools depend on common intermediate results, they are very fast when used together. The authors' results will support the 3-D reconstruction of the vessel tree from stereoscopic projections. Moreover, the authors make use of their line intensity measure for enhancing and improving the visibility of vessels in 3-D X-ray images. The processed images are intended to support radiologists in diagnosis, radiation therapy planning, and surgical planning. Radiologists verified the improved quality of the processed images and the enhanced visibility of relevant details, particularly fine blood vessels.

Enhanced visualization of abnormalities in digital-mammographic images

NASA Astrophysics Data System (ADS)

Young, Susan S.; Moore, William E.

2002-05-01

This paper describes two new presentation methods that are intended to improve the ability of radiologists to visualize abnormalities in mammograms by enhancing the appearance of the breast parenchyma pattern relative to the fatty-tissue surroundings. The first method, referred to as mountain- view, is obtained via multiscale edge decomposition through filter banks. The image is displayed in a multiscale edge domain that causes the image to have a topographic-like appearance. The second method displays the image in the intensity domain and is referred to as contrast-enhancement presentation. The input image is first passed through a decomposition filter bank to produce a filtered output (Id). The image at the lowest resolution is processed using a LUT (look-up table) to produce a tone scaled image (I'). The LUT is designed to optimally map the code value range corresponding to the parenchyma pattern in the mammographic image into the dynamic range of the output medium. The algorithm uses a contrast weight control mechanism to produce the desired weight factors to enhance the edge information corresponding to the parenchyma pattern. The output image is formed using a reconstruction filter bank through I' and enhanced Id.

Automatic spectral imaging protocol selection and iterative reconstruction in abdominal CT with reduced contrast agent dose: initial experience.

PubMed

Lv, Peijie; Liu, Jie; Chai, Yaru; Yan, Xiaopeng; Gao, Jianbo; Dong, Junqiang

2017-01-01

To evaluate the feasibility, image quality, and radiation dose of automatic spectral imaging protocol selection (ASIS) and adaptive statistical iterative reconstruction (ASIR) with reduced contrast agent dose in abdominal multiphase CT. One hundred and sixty patients were randomly divided into two scan protocols (n = 80 each; protocol A, 120 kVp/450 mgI/kg, filtered back projection algorithm (FBP); protocol B, spectral CT imaging with ASIS and 40 to 70 keV monochromatic images generated per 300 mgI/kg, ASIR algorithm. Quantitative parameters (image noise and contrast-to-noise ratios [CNRs]) and qualitative visual parameters (image noise, small structures, organ enhancement, and overall image quality) were compared. Monochromatic images at 50 keV and 60 keV provided similar or lower image noise, but higher contrast and overall image quality as compared with 120-kVp images. Despite the higher image noise, 40-keV images showed similar overall image quality compared to 120-kVp images. Radiation dose did not differ between the two protocols, while contrast agent dose in protocol B was reduced by 33 %. Application of ASIR and ASIS to monochromatic imaging from 40 to 60 keV allowed contrast agent dose reduction with adequate image quality and without increasing radiation dose compared to 120 kVp with FBP. • Automatic spectral imaging protocol selection provides appropriate scan protocols. • Abdominal CT is feasible using spectral imaging and 300 mgI/kg contrast agent. • 50-keV monochromatic images with 50 % ASIR provide optimal image quality.

A parallelizable real-time motion tracking algorithm with applications to ultrasonic strain imaging

NASA Astrophysics Data System (ADS)

Jiang, J.; Hall, T. J.

2007-07-01

Ultrasound-based mechanical strain imaging systems utilize signals from conventional diagnostic ultrasound systems to image tissue elasticity contrast that provides new diagnostically valuable information. Previous works (Hall et al 2003 Ultrasound Med. Biol. 29 427, Zhu and Hall 2002 Ultrason. Imaging 24 161) demonstrated that uniaxial deformation with minimal elevation motion is preferred for breast strain imaging and real-time strain image feedback to operators is important to accomplish this goal. The work reported here enhances the real-time speckle tracking algorithm with two significant modifications. One fundamental change is that the proposed algorithm is a column-based algorithm (a column is defined by a line of data parallel to the ultrasound beam direction, i.e. an A-line), as opposed to a row-based algorithm (a row is defined by a line of data perpendicular to the ultrasound beam direction). Then, displacement estimates from its adjacent columns provide good guidance for motion tracking in a significantly reduced search region to reduce computational cost. Consequently, the process of displacement estimation can be naturally split into at least two separated tasks, computed in parallel, propagating outward from the center of the region of interest (ROI). The proposed algorithm has been implemented and optimized in a Windows® system as a stand-alone ANSI C++ program. Results of preliminary tests, using numerical and tissue-mimicking phantoms, and in vivo tissue data, suggest that high contrast strain images can be consistently obtained with frame rates (10 frames s-1) that exceed our previous methods.

Enhanced electrocaloric cooling in ferroelectric single crystals by electric field reversal

NASA Astrophysics Data System (ADS)

Ma, Yang-Bin; Novak, Nikola; Koruza, Jurij; Yang, Tongqing; Albe, Karsten; Xu, Bai-Xiang

2016-09-01

An improved thermodynamic cycle is validated in ferroelectric single crystals, where the cooling effect of an electrocaloric refrigerant is enhanced by applying a reversed electric field. In contrast to the conventional adiabatic heating or cooling by on-off cycles of the external electric field, applying a reversed field is significantly improving the cooling efficiency, since the variation in configurational entropy is increased. By comparing results from computer simulations using Monte Carlo algorithms and experiments using direct electrocaloric measurements, we show that the electrocaloric cooling efficiency can be enhanced by more than 20% in standard ferroelectrics and also relaxor ferroelectrics, like Pb (Mg1 /3 /Nb2 /3)0.71Ti0.29O3 .

Automatic vasculature identification in coronary angiograms by adaptive geometrical tracking.

PubMed

Xiao, Ruoxiu; Yang, Jian; Goyal, Mahima; Liu, Yue; Wang, Yongtian

2013-01-01

As the uneven distribution of contrast agents and the perspective projection principle of X-ray, the vasculatures in angiographic image are with low contrast and are generally superposed with other organic tissues; therefore, it is very difficult to identify the vasculature and quantitatively estimate the blood flow directly from angiographic images. In this paper, we propose a fully automatic algorithm named adaptive geometrical vessel tracking (AGVT) for coronary artery identification in X-ray angiograms. Initially, the ridge enhancement (RE) image is obtained utilizing multiscale Hessian information. Then, automatic initialization procedures including seed points detection, and initial directions determination are performed on the RE image. The extracted ridge points can be adjusted to the geometrical centerline points adaptively through diameter estimation. Bifurcations are identified by discriminating connecting relationship of the tracked ridge points. Finally, all the tracked centerlines are merged and smoothed by classifying the connecting components on the vascular structures. Synthetic angiographic images and clinical angiograms are used to evaluate the performance of the proposed algorithm. The proposed algorithm is compared with other two vascular tracking techniques in terms of the efficiency and accuracy, which demonstrate successful applications of the proposed segmentation and extraction scheme in vasculature identification.

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.

Two-Dimensional Signal Processing and Storage and Theory and Applications of Electromagnetic Measurements.

DTIC Science & Technology

1983-06-01

system, provides a convenient, low- noise , fully parallel method of improving contrast and enhancing structural detail in an image prior to input to a...directed towards problems in deconvolution, reconstruction from projections, bandlimited extrapolation, and shift varying deblurring of images...deconvolution algorithm has been studied with promising 5 results [I] for simulated motion blurs. Future work will focus on noise effects and the extension

Exploring combined dark and bright field illumination to improve the detection of defects on specular surfaces

NASA Astrophysics Data System (ADS)

Forte, Paulo M. F.; Felgueiras, P. E. R.; Ferreira, Flávio P.; Sousa, M. A.; Nunes-Pereira, Eduardo J.; Bret, Boris P. J.; Belsley, Michael S.

2017-01-01

An automatic optical inspection system for detecting local defects on specular surfaces is presented. The system uses an image display to produce a sequence of structured diffuse illumination patterns and a digital camera to acquire the corresponding sequence of images. An image enhancement algorithm, which measures the local intensity variations between bright- and dark-field illumination conditions, yields a final image in which the defects are revealed with a high contrast. Subsequently, an image segmentation algorithm, which compares statistically the enhanced image of the inspected surface with the corresponding image for a defect-free template, allows separating defects from non-defects with an adjusting decision threshold. The method can be applied to shiny surfaces of any material including metal, plastic and glass. The described method was tested on the plastic surface of a car dashboard system. We were able to detect not only scratches but also dust and fingerprints. In our experiment we observed a detection contrast increase from about 40%, when using an extended light source, to more than 90% when using a structured light source. The presented method is simple, robust and can be carried out with short cycle times, making it appropriate for applications in industrial environments.

Diagnostic value of contrast-enhanced ultrasonography in the characterization of ovarian tumors☆

PubMed Central

Sconfienza, L.M.; Perrone, N.; Delnevo, A.; Lacelli, F.; Murolo, C.; Gandolfo, N.; Serafini, G.

2009-01-01

Introduction Vascularity influences the characteristics of gynecologic tumors observed with direct imaging techniques that reveal the macrovascular component of these lesions (color and power Doppler) and with indirect imaging involving the administration of contrast agents to examine the microcirculation and interstitial perfusion (contrast-enhanced computed tomography [CT] and magnetic resonance [MR] imaging). The purpose of this study was to determine whether contrast-enhanced ultrasonography (CEUS) of ovarian lesions provides useful information that cannot be obtained with conventional US. Materials and methods We used CEUS to assess 72 nonspecific adnexal lesions in 61 patients. CEUS was performed with a 4.8-ml bolus of a second-generation ultrasonographic contrast agent and dedicated imaging algorithms. For each lesion, B-mode morphology, CEUS morphology, and time/intensity curves were evaluated. Results In 8/61 cases (13.1%) CEUS offered no additional morphovascular information. In 38/61 cases (62.3%), it provided additional information that did not modify the management of the lesion, and in 15/61 cases (24.6%) it gave additional information that modified the management of the lesion. Malignant lesions were characterized by significantly shorter times to peak enhancement (11.9 ± 3.1 s vs 19.8 ± 4.0 s p < 0.01) and significantly higher peak intensity (24.7 ± 4.2 dB vs 17.8 ± 3.3 dB p < 0.01) compared with benign lesions. Conclusions CEUS improves diagnostic confidence in the characterization of liquid-corpuscular lesions where conventional US is inconclusive. CEUS can be proposed as a valid alternative to CT and MR. However, information obtained by CEUS influences the therapy in a limited percentage of cases (24.6%). PMID:23396092

Interactive lesion segmentation on dynamic contrast enhanced breast MRI using a Markov model

NASA Astrophysics Data System (ADS)

Wu, Qiu; Salganicoff, Marcos; Krishnan, Arun; Fussell, Donald S.; Markey, Mia K.

2006-03-01

The purpose of this study is to develop a method for segmenting lesions on Dynamic Contrast-Enhanced (DCE) breast MRI. DCE breast MRI, in which the breast is imaged before, during, and after the administration of a contrast agent, enables a truly 3D examination of breast tissues. This functional angiogenic imaging technique provides noninvasive assessment of microcirculatory characteristics of tissues in addition to traditional anatomical structure information. Since morphological features and kinetic curves from segmented lesions are to be used for diagnosis and treatment decisions, lesion segmentation is a key pre-processing step for classification. In our study, the ROI is defined by a bounding box containing the enhancement region in the subtraction image, which is generated by subtracting the pre-contrast image from 1st post-contrast image. A maximum a posteriori (MAP) estimate of the class membership (lesion vs. non-lesion) for each voxel is obtained using the Iterative Conditional Mode (ICM) method. The prior distribution of the class membership is modeled as a multi-level logistic model, a Markov Random Field model in which the class membership of each voxel is assumed to depend upon its nearest neighbors only. The likelihood distribution is assumed to be Gaussian. The parameters of each Gaussian distribution are estimated from a dozen voxels manually selected as representative of the class. The experimental segmentation results demonstrate anatomically plausible breast tissue segmentation and the predicted class membership of voxels from the interactive segmentation algorithm agrees with the manual classifications made by inspection of the kinetic enhancement curves. The proposed method is advantageous in that it is efficient, flexible, and robust.

Diagnostic value of contrast-enhanced ultrasonography in the characterization of ovarian tumors().

PubMed

Sconfienza, L M; Perrone, N; Delnevo, A; Lacelli, F; Murolo, C; Gandolfo, N; Serafini, G

2010-03-01

Vascularity influences the characteristics of gynecologic tumors observed with direct imaging techniques that reveal the macrovascular component of these lesions (color and power Doppler) and with indirect imaging involving the administration of contrast agents to examine the microcirculation and interstitial perfusion (contrast-enhanced computed tomography [CT] and magnetic resonance [MR] imaging). The purpose of this study was to determine whether contrast-enhanced ultrasonography (CEUS) of ovarian lesions provides useful information that cannot be obtained with conventional US. We used CEUS to assess 72 nonspecific adnexal lesions in 61 patients. CEUS was performed with a 4.8-ml bolus of a second-generation ultrasonographic contrast agent and dedicated imaging algorithms. For each lesion, B-mode morphology, CEUS morphology, and time/intensity curves were evaluated. In 8/61 cases (13.1%) CEUS offered no additional morphovascular information. In 38/61 cases (62.3%), it provided additional information that did not modify the management of the lesion, and in 15/61 cases (24.6%) it gave additional information that modified the management of the lesion. Malignant lesions were characterized by significantly shorter times to peak enhancement (11.9 ± 3.1 s vs 19.8 ± 4.0 s p < 0.01) and significantly higher peak intensity (24.7 ± 4.2 dB vs 17.8 ± 3.3 dB p < 0.01) compared with benign lesions. CEUS improves diagnostic confidence in the characterization of liquid-corpuscular lesions where conventional US is inconclusive. CEUS can be proposed as a valid alternative to CT and MR. However, information obtained by CEUS influences the therapy in a limited percentage of cases (24.6%).

SU-E-I-04: Improving CT Quality for Radiation Therapy of Patients with High Body Mass Index Using Iterative Reconstruction Algorithms

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

Noid, G; Tai, A; Li, X

2015-06-15

Purpose: Iterative reconstruction (IR) algorithms are developed to improve CT image quality (IQ) by reducing noise without diminishing spatial resolution or contrast. The CT IQ for patients with a high Body Mass Index (BMI) can suffer from increased noise due to photon starvation. The purpose of this study is to investigate and to quantify the IQ enhancement for high BMI patients through the application of IR algorithms. Methods: CT raw data collected for 6 radiotherapy (RT) patients with BMI, greater than or equal to 30 were retrospectively analyzed. All CT data were acquired using a CT scanner (Somaton Definition ASmore » Open, Siemens) installed in a linac room (CT-on-rails) using standard imaging protocols. The CT data were reconstructed using the Sinogram Affirmed Iterative Reconstruction (SAFIRE) and Filtered Back Projection (FBP) methods. IQ metrics of the obtained CTs were compared and correlated with patient depth and BMI. The patient depth was defined as the largest distance from anterior to posterior along the bilateral symmetry axis. Results: IR techniques are demonstrated to preserve contrast and reduce noise in comparison to traditional FBP. Driven by the reduction in noise, the contrast to noise ratio is roughly doubled by adopting the highest SAFIRE strength. A significant correlation was observed between patient depth and IR noise reduction through Pearson’s correlation test (R = 0.9429/P = 0.0167). The mean patient depth was 30.4 cm and the average relative noise reduction for the strongest iterative reconstruction was 55%. Conclusion: The IR techniques produce a measureable enhancement to CT IQ by reducing the noise. Dramatic noise reduction is evident for the high BMI patients. The improved CT IQ enables more accurate delineation of tumors and organs at risk and more accuarte dose calculations for RT planning and delivery guidance. Supported by Siemens.« less

A new full-field digital mammography system with and without the use of an advanced post-processing algorithm: comparison of image quality and diagnostic performance.

PubMed

Ahn, Hye Shin; Kim, Sun Mi; Jang, Mijung; Yun, Bo La; Kim, Bohyoung; Ko, Eun Sook; Han, Boo-Kyung; Chang, Jung Min; Yi, Ann; Cho, Nariya; Moon, Woo Kyung; Choi, Hye Young

2014-01-01

To compare new full-field digital mammography (FFDM) with and without use of an advanced post-processing algorithm to improve image quality, lesion detection, diagnostic performance, and priority rank. During a 22-month period, we prospectively enrolled 100 cases of specimen FFDM mammography (Brestige®), which was performed alone or in combination with a post-processing algorithm developed by the manufacturer: group A (SMA), specimen mammography without application of "Mammogram enhancement ver. 2.0"; group B (SMB), specimen mammography with application of "Mammogram enhancement ver. 2.0". Two sets of specimen mammographies were randomly reviewed by five experienced radiologists. Image quality, lesion detection, diagnostic performance, and priority rank with regard to image preference were evaluated. Three aspects of image quality (overall quality, contrast, and noise) of the SMB were significantly superior to those of SMA (p < 0.05). SMB was significantly superior to SMA for visualizing calcifications (p < 0.05). Diagnostic performance, as evaluated by cancer score, was similar between SMA and SMB. SMB was preferred to SMA by four of the five reviewers. The post-processing algorithm may improve image quality with better image preference in FFDM than without use of the software.

Decoding mobile-phone image sensor rolling shutter effect for visible light communications

NASA Astrophysics Data System (ADS)

Liu, Yang

2016-01-01

Optical wireless communication (OWC) using visible lights, also known as visible light communication (VLC), has attracted significant attention recently. As the traditional OWC and VLC receivers (Rxs) are based on PIN photo-diode or avalanche photo-diode, deploying the complementary metal-oxide-semiconductor (CMOS) image sensor as the VLC Rx is attractive since nowadays nearly every person has a smart phone with embedded CMOS image sensor. However, deploying the CMOS image sensor as the VLC Rx is challenging. In this work, we propose and demonstrate two simple contrast ratio (CR) enhancement schemes to improve the contrast of the rolling shutter pattern. Then we describe their processing algorithms one by one. The experimental results show that both the proposed CR enhancement schemes can significantly mitigate the high-intensity fluctuations of the rolling shutter pattern and improve the bit-error-rate performance.

Infrared dim and small target detecting and tracking method inspired by Human Visual System

NASA Astrophysics Data System (ADS)

Dong, Xiabin; Huang, Xinsheng; Zheng, Yongbin; Shen, Lurong; Bai, Shengjian

2014-01-01

Detecting and tracking dim and small target in infrared images and videos is one of the most important techniques in many computer vision applications, such as video surveillance and infrared imaging precise guidance. Recently, more and more algorithms based on Human Visual System (HVS) have been proposed to detect and track the infrared dim and small target. In general, HVS concerns at least three mechanisms including contrast mechanism, visual attention and eye movement. However, most of the existing algorithms simulate only a single one of the HVS mechanisms, resulting in many drawbacks of these algorithms. A novel method which combines the three mechanisms of HVS is proposed in this paper. First, a group of Difference of Gaussians (DOG) filters which simulate the contrast mechanism are used to filter the input image. Second, a visual attention, which is simulated by a Gaussian window, is added at a point near the target in order to further enhance the dim small target. This point is named as the attention point. Eventually, the Proportional-Integral-Derivative (PID) algorithm is first introduced to predict the attention point of the next frame of an image which simulates the eye movement of human being. Experimental results of infrared images with different types of backgrounds demonstrate the high efficiency and accuracy of the proposed method to detect and track the dim and small targets.

Archaeological Feature Detection from Archive Aerial Photography with a Sfm-Mvs and Image Enhancement Pipeline

NASA Astrophysics Data System (ADS)

Peppa, M. V.; Mills, J. P.; Fieber, K. D.; Haynes, I.; Turner, S.; Turner, A.; Douglas, M.; Bryan, P. G.

2018-05-01

Understanding and protecting cultural heritage involves the detection and long-term documentation of archaeological remains alongside the spatio-temporal analysis of their landscape evolution. Archive aerial photography can illuminate traces of ancient features which typically appear with different brightness values from their surrounding environment, but are not always well defined. This research investigates the implementation of the Structure-from-Motion - Multi-View Stereo image matching approach with an image enhancement algorithm to derive three epochs of orthomosaics and digital surface models from visible and near infrared historic aerial photography. The enhancement algorithm uses decorrelation stretching to improve the contrast of the orthomosaics so as archaeological features are better detected. Results include 2D / 3D locations of detected archaeological traces stored into a geodatabase for further archaeological interpretation and correlation with benchmark observations. The study also discusses the merits and difficulties of the process involved. This research is based on a European-wide project, entitled "Cultural Heritage Through Time", and the case study research was carried out as a component of the project in the UK.

Fast processing of microscopic images using object-based extended depth of field.

PubMed

Intarapanich, Apichart; Kaewkamnerd, Saowaluck; Pannarut, Montri; Shaw, Philip J; Tongsima, Sissades

2016-12-22

Microscopic analysis requires that foreground objects of interest, e.g. cells, are in focus. In a typical microscopic specimen, the foreground objects may lie on different depths of field necessitating capture of multiple images taken at different focal planes. The extended depth of field (EDoF) technique is a computational method for merging images from different depths of field into a composite image with all foreground objects in focus. Composite images generated by EDoF can be applied in automated image processing and pattern recognition systems. However, current algorithms for EDoF are computationally intensive and impractical, especially for applications such as medical diagnosis where rapid sample turnaround is important. Since foreground objects typically constitute a minor part of an image, the EDoF technique could be made to work much faster if only foreground regions are processed to make the composite image. We propose a novel algorithm called object-based extended depths of field (OEDoF) to address this issue. The OEDoF algorithm consists of four major modules: 1) color conversion, 2) object region identification, 3) good contrast pixel identification and 4) detail merging. First, the algorithm employs color conversion to enhance contrast followed by identification of foreground pixels. A composite image is constructed using only these foreground pixels, which dramatically reduces the computational time. We used 250 images obtained from 45 specimens of confirmed malaria infections to test our proposed algorithm. The resulting composite images with all in-focus objects were produced using the proposed OEDoF algorithm. We measured the performance of OEDoF in terms of image clarity (quality) and processing time. The features of interest selected by the OEDoF algorithm are comparable in quality with equivalent regions in images processed by the state-of-the-art complex wavelet EDoF algorithm; however, OEDoF required four times less processing time. This work presents a modification of the extended depth of field approach for efficiently enhancing microscopic images. This selective object processing scheme used in OEDoF can significantly reduce the overall processing time while maintaining the clarity of important image features. The empirical results from parasite-infected red cell images revealed that our proposed method efficiently and effectively produced in-focus composite images. With the speed improvement of OEDoF, this proposed algorithm is suitable for processing large numbers of microscope images, e.g., as required for medical diagnosis.

Low illumination color image enhancement based on improved Retinex

NASA Astrophysics Data System (ADS)

Liao, Shujing; Piao, Yan; Li, Bing

2017-11-01

Low illumination color image usually has the characteristics of low brightness, low contrast, detail blur and high salt and pepper noise, which greatly affected the later image recognition and information extraction. Therefore, in view of the degradation of night images, the improved algorithm of traditional Retinex. The specific approach is: First, the original RGB low illumination map is converted to the YUV color space (Y represents brightness, UV represents color), and the Y component is estimated by using the sampling acceleration guidance filter to estimate the background light; Then, the reflection component is calculated by the classical Retinex formula and the brightness enhancement ratio between original and enhanced is calculated. Finally, the color space conversion from YUV to RGB and the feedback enhancement of the UV color component are carried out.

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.

Optimization of Contrast Detection Power with Probabilistic Behavioral Information

PubMed Central

Cordes, Dietmar; Herzmann, Grit; Nandy, Rajesh; Curran, Tim

2012-01-01

Recent progress in the experimental design for event-related fMRI experiments made it possible to find the optimal stimulus sequence for maximum contrast detection power using a genetic algorithm. In this study, a novel algorithm is proposed for optimization of contrast detection power by including probabilistic behavioral information, based on pilot data, in the genetic algorithm. As a particular application, a recognition memory task is studied and the design matrix optimized for contrasts involving the familiarity of individual items (pictures of objects) and the recollection of qualitative information associated with the items (left/right orientation). Optimization of contrast efficiency is a complicated issue whenever subjects’ responses are not deterministic but probabilistic. Contrast efficiencies are not predictable unless behavioral responses are included in the design optimization. However, available software for design optimization does not include options for probabilistic behavioral constraints. If the anticipated behavioral responses are included in the optimization algorithm, the design is optimal for the assumed behavioral responses, and the resulting contrast efficiency is greater than what either a block design or a random design can achieve. Furthermore, improvements of contrast detection power depend strongly on the behavioral probabilities, the perceived randomness, and the contrast of interest. The present genetic algorithm can be applied to any case in which fMRI contrasts are dependent on probabilistic responses that can be estimated from pilot data. PMID:22326984

Update on the non-prewhitening model observer in computed tomography for the assessment of the adaptive statistical and model-based iterative reconstruction algorithms

NASA Astrophysics Data System (ADS)

Ott, Julien G.; Becce, Fabio; Monnin, Pascal; Schmidt, Sabine; Bochud, François O.; Verdun, Francis R.

2014-08-01

The state of the art to describe image quality in medical imaging is to assess the performance of an observer conducting a task of clinical interest. This can be done by using a model observer leading to a figure of merit such as the signal-to-noise ratio (SNR). Using the non-prewhitening (NPW) model observer, we objectively characterised the evolution of its figure of merit in various acquisition conditions. The NPW model observer usually requires the use of the modulation transfer function (MTF) as well as noise power spectra. However, although the computation of the MTF poses no problem when dealing with the traditional filtered back-projection (FBP) algorithm, this is not the case when using iterative reconstruction (IR) algorithms, such as adaptive statistical iterative reconstruction (ASIR) or model-based iterative reconstruction (MBIR). Given that the target transfer function (TTF) had already shown it could accurately express the system resolution even with non-linear algorithms, we decided to tune the NPW model observer, replacing the standard MTF by the TTF. It was estimated using a custom-made phantom containing cylindrical inserts surrounded by water. The contrast differences between the inserts and water were plotted for each acquisition condition. Then, mathematical transformations were performed leading to the TTF. As expected, the first results showed a dependency of the image contrast and noise levels on the TTF for both ASIR and MBIR. Moreover, FBP also proved to be dependent of the contrast and noise when using the lung kernel. Those results were then introduced in the NPW model observer. We observed an enhancement of SNR every time we switched from FBP to ASIR to MBIR. IR algorithms greatly improve image quality, especially in low-dose conditions. Based on our results, the use of MBIR could lead to further dose reduction in several clinical applications.

Active imaging with the aids of polarization retrieve in turbid media system

NASA Astrophysics Data System (ADS)

Tao, Qiangqiang; Sun, Yongxuan; Shen, Fei; Xu, Qiang; Gao, Jun; Guo, Zhongyi

2016-01-01

We propose a novel active imaging based on the polarization retrieve (PR) method in turbid media system. In our simulations, the Monte Carlo (MC) algorithm has been used to investigate the scattering process between the incident photons and the scattering particles, and the visually concordant object but with different polarization characteristics in different regions, has been selected as the original target that is placed in the turbid media. Under linearly and circularly polarized illuminations, the simulation results demonstrate that the corresponding polarization properties can provide additional information for the imaging, and the contrast of the polarization image can also be enhanced greatly compared to the simplex intensity image in the turbid media. Besides, the polarization image adjusted by the PR method can further enhance the visibility and contrast. In addition, by PR imaging method, with the increasing particles' size in Mie's scale, the visibility can be enhanced, because of the increased forward scattering effect. In general, in the same circumstance, the circular polarization images can offer a better contrast and visibility than that of linear ones. The results indicate that the PR imaging method is more applicable to the scattering media system with relatively larger particles such as aerosols, heavy fog, cumulus, and seawater, as well as to biological tissues and blood media.

Spectral contrast-enhanced optical coherence tomography for improved detection of tumor microvasculature and functional imaging of lymphatic drainage

NASA Astrophysics Data System (ADS)

SoRelle, Elliott D.; Liba, Orly; Sen, Debasish; de la Zerda, Adam

2017-03-01

Optical Coherence Tomography (OCT) is well-suited to study in vivo dynamics of blood circulation and lymphatic flow because of the technique's combination of rapid image acquisition, micron spatial resolution, and penetration depth in turbid tissues. However, OCT has been historically constrained by a dearth of contrast agents that are readily distinguished from the strong scattering intrinsic to biological tissues. In this study, we demonstrate large gold nanorods (LGNRs) as optimized contrast agents for OCT. LGNRs produce 32-fold greater backscattering than GNRs previously tested for contrast-enhanced OCT. Furthermore, LGNRs exhibit 110-fold stronger spectral signal than conventional GNRs when coupled with custom spectral detection algorithms. This signal enhancement enables picomolar OCT detection sensitivity in vivo and single-particle detection against optically-clear backgrounds. Moreover, the ability to synthesize LGNRs with tunable spectral peaks provides a viable platform for multiplexed imaging studies. To explore the advantages of LGNRs as OCT contrast agents, we implemented them for noninvasive 3D imaging of tumor blood supply and active lymphatic drainage in mice. Spectral detection of LGNRs enabled 100% improvement in imaging depth for detecting microvasculature (vessels 20 μm in diameter) in U87MG glioblastoma xenografts in mice pinnae. We also demonstrated our approach's ability to map the spatial dependence of lymph drainage and flow directionality within lymphatic capillaries. Using LGNRs with distinct spectra, we further identified the functional states of individual lymphatic valves in vivo. Thus, this approach provides a powerful new platform for functional imaging that may be extended for future molecular imaging studies with OCT.

A Methodology for the Hybridization Based in Active Components: The Case of cGA and Scatter Search.

PubMed

Villagra, Andrea; Alba, Enrique; Leguizamón, Guillermo

2016-01-01

This work presents the results of a new methodology for hybridizing metaheuristics. By first locating the active components (parts) of one algorithm and then inserting them into second one, we can build efficient and accurate optimization, search, and learning algorithms. This gives a concrete way of constructing new techniques that contrasts the spread ad hoc way of hybridizing. In this paper, the enhanced algorithm is a Cellular Genetic Algorithm (cGA) which has been successfully used in the past to find solutions to such hard optimization problems. In order to extend and corroborate the use of active components as an emerging hybridization methodology, we propose here the use of active components taken from Scatter Search (SS) to improve cGA. The results obtained over a varied set of benchmarks are highly satisfactory in efficacy and efficiency when compared with a standard cGA. Moreover, the proposed hybrid approach (i.e., cGA+SS) has shown encouraging results with regard to earlier applications of our methodology.

Text extraction via an edge-bounded averaging and a parametric character model

NASA Astrophysics Data System (ADS)

Fan, Jian

2003-01-01

We present a deterministic text extraction algorithm that relies on three basic assumptions: color/luminance uniformity of the interior region, closed boundaries of sharp edges and the consistency of local contrast. The algorithm is basically independent of the character alphabet, text layout, font size and orientation. The heart of this algorithm is an edge-bounded averaging for the classification of smooth regions that enhances robustness against noise without sacrificing boundary accuracy. We have also developed a verification process to clean up the residue of incoherent segmentation. Our framework provides a symmetric treatment for both regular and inverse text. We have proposed three heuristics for identifying the type of text from a cluster consisting of two types of pixel aggregates. Finally, we have demonstrated the advantages of the proposed algorithm over adaptive thresholding and block-based clustering methods in terms of boundary accuracy, segmentation coherency, and capability to identify inverse text and separate characters from background patches.

A combined reconstruction-classification method for diffuse optical tomography.

PubMed

Hiltunen, P; Prince, S J D; Arridge, S

2009-11-07

We present a combined classification and reconstruction algorithm for diffuse optical tomography (DOT). DOT is a nonlinear ill-posed inverse problem. Therefore, some regularization is needed. We present a mixture of Gaussians prior, which regularizes the DOT reconstruction step. During each iteration, the parameters of a mixture model are estimated. These associate each reconstructed pixel with one of several classes based on the current estimate of the optical parameters. This classification is exploited to form a new prior distribution to regularize the reconstruction step and update the optical parameters. The algorithm can be described as an iteration between an optimization scheme with zeroth-order variable mean and variance Tikhonov regularization and an expectation-maximization scheme for estimation of the model parameters. We describe the algorithm in a general Bayesian framework. Results from simulated test cases and phantom measurements show that the algorithm enhances the contrast of the reconstructed images with good spatial accuracy. The probabilistic classifications of each image contain only a few misclassified pixels.

A novel retinal vessel extraction algorithm based on matched filtering and gradient vector flow

NASA Astrophysics Data System (ADS)

Yu, Lei; Xia, Mingliang; Xuan, Li

2013-10-01

The microvasculature network of retina plays an important role in the study and diagnosis of retinal diseases (age-related macular degeneration and diabetic retinopathy for example). Although it is possible to noninvasively acquire high-resolution retinal images with modern retinal imaging technologies, non-uniform illumination, the low contrast of thin vessels and the background noises all make it difficult for diagnosis. In this paper, we introduce a novel retinal vessel extraction algorithm based on gradient vector flow and matched filtering to segment retinal vessels with different likelihood. Firstly, we use isotropic Gaussian kernel and adaptive histogram equalization to smooth and enhance the retinal images respectively. Secondly, a multi-scale matched filtering method is adopted to extract the retinal vessels. Then, the gradient vector flow algorithm is introduced to locate the edge of the retinal vessels. Finally, we combine the results of matched filtering method and gradient vector flow algorithm to extract the vessels at different likelihood levels. The experiments demonstrate that our algorithm is efficient and the intensities of vessel images exactly represent the likelihood of the vessels.

Contrast-enhanced ultrasound may distinguish gallbladder adenoma from cholesterol polyps: a prospective case-control study.

PubMed

Fei, Xiang; Lu, Wen-Ping; Luo, Yu-Kun; Xu, Jian-Hon; Li, Yan-Mi; Shi, Huai-Yin; Jiao, Zi-Yu; Li, Hong-tian

2015-10-01

The aim of this study was to find the independent risk factors related with gallbladder (GB) adenoma compared to cholesterol polyp by contrast-enhanced ultrasound (CEUS). Between January 2010 and September 2014, a total of 122 consecutive patients undergoing cholecystectomy for GB polypoid lesions were enrolled. Before cholecystectomy, each patient underwent conventional US and CEUS examination and all image features were documented. The patients were divided into adenoma group and cholesterol polyp group according to the pathological findings. All the image features between two groups were statistically compared. There were differences in patient age, lesion size, echogenicity, and vascularity of lesion between two groups (P < 0.05). There were differences in stalk width and enhancement intensity between the two groups (P < 0.05). Multiple logistic regression analysis proved that enhancement intensity, stalk of lesion, and vascularity were the independent risk factors related with GB adenoma (P < 0.05). CEUS could offer useful information to distinguish adenoma from cholesterol polyp. The treatment algorithm for gallbladder polyp lesions would likely benefit from CEUS as a routine imaging investigation, especially in cases where the polyp is larger than 1 cm.

MO-DE-207B-10: Impact of Morphologic Characteristics On Radiomics Features From Contast-Enhanced CT for Primary Lung Tumors

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

Fried, D; Zhang, L; Fave, X

Purpose: Determine the impact of morphologic characteristics (e.g. necrosis, vascular enhancement, and cavitation) on radiomic features from contrast enhanced CT (CE-CT) in primary lung tumors. Methods: We developed an auto-segmentation algorithm to separate lung tumors on contrast-enhanced CT into cavitation (air), necrosis, tissue, and enhancing vessels using a combination of thresholding and region-growing. An auto-segmentation algorithm was also designed to identify necrosis on FDG-PET scans. Wilcoxon rank-sum tests were used to determine if significant differences existed in radiomics features (histogram-uniformity and Laplacian-of-Gaussian average) from 249 patients, found to prognostic in previous work, based on the presence/absence of morphologic features. Featuremore » values were also compared between the original tumor contours and contours excluding a specific morphologic feature. Comparison of necrosis segmentation on CE-CT versus FDG-PET was performed in 78 patients to assess for agreement using the concordance correlation coefficient (CCC). Results: Tumors with cavitation and enhancing vasculature had lower uniformity values (p = 0.001 and p = 0.03, respectively). Tumors with enhancing vasculature and necrosis had higher Laplacian-of-Gaussian average values (measure of “edges” within the tumor) (p < 0.001). Removing these tissue types from regions-of-interest did not drastically alter either radiomic feature value (all scenarios had R{sup 2} > 0.8). This suggests there may be interactions between morphologic characteristics and the radiomic feature value of tumor tissue. Comparison of necrosis volume and percent necrosis volume of tumor were found to have CCC values of 0.85 and 0.76, respectively between CE-CT and FDG-PET segmentation methods. Conclusions: Tumors with enhancing vasculature, necrosis, and cavitation have higher radiomic feature values that are associated with poor prognosis than tumors without these features. Removing these tissue types from quantitative assessment did not drastically impact radiomic feature values. High reproducibility of CE-CT segmented necrosis compared to FDG-PET segmented necrosis provides a reasonable validation of segmentation accuracy on CE-CT.« less

Single image super resolution algorithm based on edge interpolation in NSCT domain

NASA Astrophysics Data System (ADS)

Zhang, Mengqun; Zhang, Wei; He, Xinyu

2017-11-01

In order to preserve the texture and edge information and to improve the space resolution of single frame, a superresolution algorithm based on Contourlet (NSCT) is proposed. The original low resolution image is transformed by NSCT, and the directional sub-band coefficients of the transform domain are obtained. According to the scale factor, the high frequency sub-band coefficients are amplified by the interpolation method based on the edge direction to the desired resolution. For high frequency sub-band coefficients with noise and weak targets, Bayesian shrinkage is used to calculate the threshold value. The coefficients below the threshold are determined by the correlation among the sub-bands of the same scale to determine whether it is noise and de-noising. The anisotropic diffusion filter is used to effectively enhance the weak target in the low contrast region of the target and background. Finally, the high-frequency sub-band is amplified by the bilinear interpolation method to the desired resolution, and then combined with the high-frequency subband coefficients after de-noising and small target enhancement, the NSCT inverse transform is used to obtain the desired resolution image. In order to verify the effectiveness of the proposed algorithm, the proposed algorithm and several common image reconstruction methods are used to test the synthetic image, motion blurred image and hyperspectral image, the experimental results show that compared with the traditional single resolution algorithm, the proposed algorithm can obtain smooth edges and good texture features, and the reconstructed image structure is well preserved and the noise is suppressed to some extent.

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.

Automatic Segmentation of Invasive Breast Carcinomas from DCE-MRI using Time Series Analysis

PubMed Central

Jayender, Jagadaeesan; Chikarmane, Sona; Jolesz, Ferenc A.; Gombos, Eva

2013-01-01

Purpose Quantitative segmentation methods based on black-box modeling and pharmacokinetic modeling are highly dependent on imaging pulse sequence, timing of bolus injection, arterial input function, imaging noise and fitting algorithms. To accurately segment invasive ductal carcinomas (IDCs) from dynamic contrast enhanced MRI (DCE-MRI) using time series analysis based on linear dynamic system (LDS) modeling. Methods We modeled the underlying dynamics of the tumor by a LDS and use the system parameters to segment the carcinoma on the DCE-MRI. Twenty-four patients with biopsy-proven IDCs were analyzed. The lesions segmented by the algorithm were compared with an expert radiologist’s segmentation and the output of a commercial software, CADstream. The results are quantified in terms of the accuracy and sensitivity of detecting the lesion and the amount of overlap, measured in terms of the Dice similarity coefficient (DSC). Results The segmentation algorithm detected the tumor with 90% accuracy and 100% sensitivity when compared to the radiologist’s segmentation and 82.1% accuracy and 100% sensitivity when compared to the CADstream output. The overlap of the algorithm output with the radiologist’s segmentation and CADstream output, computed in terms of the DSC was 0.77 and 0.72 respectively. The algorithm also shows robust stability to imaging noise. Simulated imaging noise with zero mean and standard deviation equal to 25% of the base signal intensity was added to the DCE-MRI series. The amount of overlap between the tumor maps generated by the LDS-based algorithm from the noisy and original DCE-MRI was DSC=0.95. Conclusion The time-series analysis based segmentation algorithm provides high accuracy and sensitivity in delineating the regions of enhanced perfusion corresponding to tumor from DCE-MRI. PMID:24115175

Synchronized multiartifact reduction with tomographic reconstruction (SMART-RECON): A statistical model based iterative image reconstruction method to eliminate limited-view artifacts and to mitigate the temporal-average artifacts in time-resolved CT.

PubMed

Chen, Guang-Hong; Li, Yinsheng

2015-08-01

In x-ray computed tomography (CT), a violation of the Tuy data sufficiency condition leads to limited-view artifacts. In some applications, it is desirable to use data corresponding to a narrow temporal window to reconstruct images with reduced temporal-average artifacts. However, the need to reduce temporal-average artifacts in practice may result in a violation of the Tuy condition and thus undesirable limited-view artifacts. In this paper, the authors present a new iterative reconstruction method, synchronized multiartifact reduction with tomographic reconstruction (SMART-RECON), to eliminate limited-view artifacts using data acquired within an ultranarrow temporal window that severely violates the Tuy condition. In time-resolved contrast enhanced CT acquisitions, image contrast dynamically changes during data acquisition. Each image reconstructed from data acquired in a given temporal window represents one time frame and can be denoted as an image vector. Conventionally, each individual time frame is reconstructed independently. In this paper, all image frames are grouped into a spatial-temporal image matrix and are reconstructed together. Rather than the spatial and/or temporal smoothing regularizers commonly used in iterative image reconstruction, the nuclear norm of the spatial-temporal image matrix is used in SMART-RECON to regularize the reconstruction of all image time frames. This regularizer exploits the low-dimensional structure of the spatial-temporal image matrix to mitigate limited-view artifacts when an ultranarrow temporal window is desired in some applications to reduce temporal-average artifacts. Both numerical simulations in two dimensional image slices with known ground truth and in vivo human subject data acquired in a contrast enhanced cone beam CT exam have been used to validate the proposed SMART-RECON algorithm and to demonstrate the initial performance of the algorithm. Reconstruction errors and temporal fidelity of the reconstructed images were quantified using the relative root mean square error (rRMSE) and the universal quality index (UQI) in numerical simulations. The performance of the SMART-RECON algorithm was compared with that of the prior image constrained compressed sensing (PICCS) reconstruction quantitatively in simulations and qualitatively in human subject exam. In numerical simulations, the 240(∘) short scan angular span was divided into four consecutive 60(∘) angular subsectors. SMART-RECON enables four high temporal fidelity images without limited-view artifacts. The average rRMSE is 16% and UQIs are 0.96 and 0.95 for the two local regions of interest, respectively. In contrast, the corresponding average rRMSE and UQIs are 25%, 0.78, and 0.81, respectively, for the PICCS reconstruction. Note that only one filtered backprojection image can be reconstructed from the same data set with an average rRMSE and UQIs are 45%, 0.71, and 0.79, respectively, to benchmark reconstruction accuracies. For in vivo contrast enhanced cone beam CT data acquired from a short scan angular span of 200(∘), three 66(∘) angular subsectors were used in SMART-RECON. The results demonstrated clear contrast difference in three SMART-RECON reconstructed image volumes without limited-view artifacts. In contrast, for the same angular sectors, PICCS cannot reconstruct images without limited-view artifacts and with clear contrast difference in three reconstructed image volumes. In time-resolved CT, the proposed SMART-RECON method provides a new method to eliminate limited-view artifacts using data acquired in an ultranarrow temporal window, which corresponds to approximately 60(∘) angular subsectors.

Model based LV-reconstruction in bi-plane x-ray angiography

NASA Astrophysics Data System (ADS)

Backfrieder, Werner; Carpella, Martin; Swoboda, Roland; Steinwender, Clemens; Gabriel, Christian; Leisch, Franz

2005-04-01

Interventional x-ray angiography is state of the art in diagnosis and therapy of severe diseases of the cardiovascular system. Diagnosis is based on contrast enhanced dynamic projection images of the left ventricle. A new model based algorithm for three dimensional reconstruction of the left ventricle from bi-planar angiograms was developed. Parametric super ellipses are deformed until their projection profiles optimally fit measured ventricular projections. Deformation is controlled by a simplex optimization procedure. A resulting optimized parameter set builds the initial guess for neighboring slices. A three dimensional surface model of the ventricle is built from stacked contours. The accuracy of the algorithm has been tested with mathematical phantom data and clinical data. Results show conformance with provided projection data and high convergence speed makes the algorithm useful for clinical application. Fully three dimensional reconstruction of the left ventricle has a high potential for improvements of clinical findings in interventional cardiology.

An efficient iteration strategy for the solution of the Euler equations

NASA Technical Reports Server (NTRS)

Walters, R. W.; Dwoyer, D. L.

1985-01-01

A line Gauss-Seidel (LGS) relaxation algorithm in conjunction with a one-parameter family of upwind discretizations of the Euler equations in two-dimensions is described. The basic algorithm has the property that convergence to the steady-state is quadratic for fully supersonic flows and linear otherwise. This is in contrast to the block ADI methods (either central or upwind differenced) and the upwind biased relaxation schemes, all of which converge linearly, independent of the flow regime. Moreover, the algorithm presented here is easily enhanced to detect regions of subsonic flow embedded in supersonic flow. This allows marching by lines in the supersonic regions, converging each line quadratically, and iterating in the subsonic regions, thus yielding a very efficient iteration strategy. Numerical results are presented for two-dimensional supersonic and transonic flows containing both oblique and normal shock waves which confirm the efficiency of the iteration strategy.

Robust Blind Learning Algorithm for Nonlinear Equalization Using Input Decision Information.

PubMed

Xu, Lu; Huang, Defeng David; Guo, Yingjie Jay

2015-12-01

In this paper, we propose a new blind learning algorithm, namely, the Benveniste-Goursat input-output decision (BG-IOD), to enhance the convergence performance of neural network-based equalizers for nonlinear channel equalization. In contrast to conventional blind learning algorithms, where only the output of the equalizer is employed for updating system parameters, the BG-IOD exploits a new type of extra information, the input decision information obtained from the input of the equalizer, to mitigate the influence of the nonlinear equalizer structure on parameters learning, thereby leading to improved convergence performance. We prove that, with the input decision information, a desirable convergence capability that the output symbol error rate (SER) is always less than the input SER if the input SER is below a threshold, can be achieved. Then, the BG soft-switching technique is employed to combine the merits of both input and output decision information, where the former is used to guarantee SER convergence and the latter is to improve SER performance. Simulation results show that the proposed algorithm outperforms conventional blind learning algorithms, such as stochastic quadratic distance and dual mode constant modulus algorithm, in terms of both convergence performance and SER performance, for nonlinear equalization.

GRAPE: a graphical pipeline environment for image analysis in adaptive magnetic resonance imaging.

PubMed

Gabr, Refaat E; Tefera, Getaneh B; Allen, William J; Pednekar, Amol S; Narayana, Ponnada A

2017-03-01

We present a platform, GRAphical Pipeline Environment (GRAPE), to facilitate the development of patient-adaptive magnetic resonance imaging (MRI) protocols. GRAPE is an open-source project implemented in the Qt C++ framework to enable graphical creation, execution, and debugging of real-time image analysis algorithms integrated with the MRI scanner. The platform provides the tools and infrastructure to design new algorithms, and build and execute an array of image analysis routines, and provides a mechanism to include existing analysis libraries, all within a graphical environment. The application of GRAPE is demonstrated in multiple MRI applications, and the software is described in detail for both the user and the developer. GRAPE was successfully used to implement and execute three applications in MRI of the brain, performed on a 3.0-T MRI scanner: (i) a multi-parametric pipeline for segmenting the brain tissue and detecting lesions in multiple sclerosis (MS), (ii) patient-specific optimization of the 3D fluid-attenuated inversion recovery MRI scan parameters to enhance the contrast of brain lesions in MS, and (iii) an algebraic image method for combining two MR images for improved lesion contrast. GRAPE allows graphical development and execution of image analysis algorithms for inline, real-time, and adaptive MRI applications.

Maximum Entropy Approach in Dynamic Contrast-Enhanced Magnetic Resonance Imaging.

PubMed

Farsani, Zahra Amini; Schmid, Volker J

2017-01-01

In the estimation of physiological kinetic parameters from Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) data, the determination of the arterial input function (AIF) plays a key role. This paper proposes a Bayesian method to estimate the physiological parameters of DCE-MRI along with the AIF in situations, where no measurement of the AIF is available. In the proposed algorithm, the maximum entropy method (MEM) is combined with the maximum a posterior approach (MAP). To this end, MEM is used to specify a prior probability distribution of the unknown AIF. The ability of this method to estimate the AIF is validated using the Kullback-Leibler divergence. Subsequently, the kinetic parameters can be estimated with MAP. The proposed algorithm is evaluated with a data set from a breast cancer MRI study. The application shows that the AIF can reliably be determined from the DCE-MRI data using MEM. Kinetic parameters can be estimated subsequently. The maximum entropy method is a powerful tool to reconstructing images from many types of data. This method is useful for generating the probability distribution based on given information. The proposed method gives an alternative way to assess the input function from the existing data. The proposed method allows a good fit of the data and therefore a better estimation of the kinetic parameters. In the end, this allows for a more reliable use of DCE-MRI. Schattauer GmbH.

Contrast-enhanced optical coherence tomography with picomolar sensitivity for functional in vivo imaging

NASA Astrophysics Data System (ADS)

Liba, Orly; Sorelle, Elliott D.; Sen, Debasish; de La Zerda, Adam

2016-03-01

Optical Coherence Tomography (OCT) enables real-time imaging of living tissues at cell-scale resolution over millimeters in three dimensions. Despite these advantages, functional biological studies with OCT have been limited by a lack of exogenous contrast agents that can be distinguished from tissue. Here we report an approach to functional OCT imaging that implements custom algorithms to spectrally identify unique contrast agents: large gold nanorods (LGNRs). LGNRs exhibit 110-fold greater spectral signal per particle than conventional GNRs, which enables detection of individual LGNRs in water and concentrations as low as 250 pM in the circulation of living mice. This translates to ~40 particles per imaging voxel in vivo. Unlike previous implementations of OCT spectral detection, the methods described herein adaptively compensate for depth and processing artifacts on a per sample basis. Collectively, these methods enable high-quality noninvasive contrast-enhanced imaging of OCT in living subjects, including detection of tumor microvasculature at twice the depth achievable with conventional OCT. Additionally, multiplexed detection of spectrally-distinct LGNRs was demonstrated to observe discrete patterns of lymphatic drainage and identify individual lymphangions and lymphatic valve functional states. These capabilities provide a powerful platform for molecular imaging and characterization of tissue noninvasively at cellular resolution, called MOZART.

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

PubMed

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

2017-01-01

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

Impact of Reconstruction Algorithms on CT Radiomic Features of Pulmonary Tumors: Analysis of Intra- and Inter-Reader Variability and Inter-Reconstruction Algorithm Variability.

PubMed

Kim, Hyungjin; Park, Chang Min; Lee, Myunghee; Park, Sang Joon; Song, Yong Sub; Lee, Jong Hyuk; Hwang, Eui Jin; Goo, Jin Mo

2016-01-01

To identify the impact of reconstruction algorithms on CT radiomic features of pulmonary tumors and to reveal and compare the intra- and inter-reader and inter-reconstruction algorithm variability of each feature. Forty-two patients (M:F = 19:23; mean age, 60.43±10.56 years) with 42 pulmonary tumors (22.56±8.51mm) underwent contrast-enhanced CT scans, which were reconstructed with filtered back projection and commercial iterative reconstruction algorithm (level 3 and 5). Two readers independently segmented the whole tumor volume. Fifteen radiomic features were extracted and compared among reconstruction algorithms. Intra- and inter-reader variability and inter-reconstruction algorithm variability were calculated using coefficients of variation (CVs) and then compared. Among the 15 features, 5 first-order tumor intensity features and 4 gray level co-occurrence matrix (GLCM)-based features showed significant differences (p<0.05) among reconstruction algorithms. As for the variability, effective diameter, sphericity, entropy, and GLCM entropy were the most robust features (CV≤5%). Inter-reader variability was larger than intra-reader or inter-reconstruction algorithm variability in 9 features. However, for entropy, homogeneity, and 4 GLCM-based features, inter-reconstruction algorithm variability was significantly greater than inter-reader variability (p<0.013). Most of the radiomic features were significantly affected by the reconstruction algorithms. Inter-reconstruction algorithm variability was greater than inter-reader variability for entropy, homogeneity, and GLCM-based features.

Should the parameters of a BCI translation algorithm be continually adapted?

PubMed

McFarland, Dennis J; Sarnacki, William A; Wolpaw, Jonathan R

2011-07-15

People with or without motor disabilities can learn to control sensorimotor rhythms (SMRs) recorded from the scalp to move a computer cursor in one or more dimensions or can use the P300 event-related potential as a control signal to make discrete selections. Data collected from individuals using an SMR-based or P300-based BCI were evaluated offline to estimate the impact on performance of continually adapting the parameters of the translation algorithm during BCI operation. The performance of the SMR-based BCI was enhanced by adaptive updating of the feature weights or adaptive normalization of the features. In contrast, P300 performance did not benefit from either of these procedures. Copyright © 2011 Elsevier B.V. All rights reserved.

New algorithm for detecting smaller retinal blood vessels in fundus images

NASA Astrophysics Data System (ADS)

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

2010-03-01

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

Assessing the scale of tumor heterogeneity by complete hierarchical segmentation of MRI.

PubMed

Gensheimer, Michael F; Hawkins, Douglas S; Ermoian, Ralph P; Trister, Andrew D

2015-02-07

In many cancers, intratumoral heterogeneity has been found in histology, genetic variation and vascular structure. We developed an algorithm to interrogate different scales of heterogeneity using clinical imaging. We hypothesize that heterogeneity of perfusion at coarse scale may correlate with treatment resistance and propensity for disease recurrence. The algorithm recursively segments the tumor image into increasingly smaller regions. Each dividing line is chosen so as to maximize signal intensity difference between the two regions. This process continues until the tumor has been divided into single voxels, resulting in segments at multiple scales. For each scale, heterogeneity is measured by comparing each segmented region to the adjacent region and calculating the difference in signal intensity histograms. Using digital phantom images, we showed that the algorithm is robust to image artifacts and various tumor shapes. We then measured the primary tumor scales of contrast enhancement heterogeneity in MRI of 18 rhabdomyosarcoma patients. Using Cox proportional hazards regression, we explored the influence of heterogeneity parameters on relapse-free survival. Coarser scale of maximum signal intensity heterogeneity was prognostic of shorter survival (p = 0.05). By contrast, two fractal parameters and three Haralick texture features were not prognostic. In summary, our algorithm produces a biologically motivated segmentation of tumor regions and reports the amount of heterogeneity at various distance scales. If validated on a larger dataset, this prognostic imaging biomarker could be useful to identify patients at higher risk for recurrence and candidates for alternative treatment.

Exploring silver as a contrast agent for contrast-enhanced dual-energy X-ray breast imaging

PubMed Central

Tsourkas, A; Maidment, A D A

2014-01-01

Objective: Through prior monoenergetic modelling, we have identified silver as a potential alternative to iodine in dual-energy (DE) X-ray breast imaging. The purpose of this study was to compare the performance of silver and iodine contrast agents in a commercially available DE imaging system through a quantitative analysis of signal difference-to-noise ratio (SDNR). Methods: A polyenergetic simulation algorithm was developed to model the signal intensity and noise. The model identified the influence of various technique parameters on SDNR. The model was also used to identify the optimal imaging techniques for silver and iodine, so that the two contrast materials could be objectively compared. Results: The major influences on the SDNR were the low-energy dose fraction and breast thickness. An increase in the value of either of these parameters resulted in a decrease in SDNR. The SDNR for silver was on average 43% higher than that for iodine when imaged at their respective optimal conditions, and 40% higher when both were imaged at the optimal conditions for iodine. Conclusion: A silver contrast agent should provide benefit over iodine, even when translated to the clinic without modification of imaging system or protocol. If the system were slightly modified to reflect the lower k-edge of silver, the difference in SDNR between the two materials would be increased. Advances in knowledge: These data are the first to demonstrate the suitability of silver as a contrast material in a clinical contrast-enhanced DE image acquisition system. PMID:24998157

Methods and algorithms for optical coherence tomography-based angiography: a review and comparison

NASA Astrophysics Data System (ADS)

Zhang, Anqi; Zhang, Qinqin; Chen, Chieh-Li; Wang, Ruikang K.

2015-10-01

Optical coherence tomography (OCT)-based angiography is increasingly becoming a clinically useful and important imaging technique due to its ability to provide volumetric microvascular networks innervating tissue beds in vivo without a need for exogenous contrast agent. Numerous OCT angiography algorithms have recently been proposed for the purpose of contrasting microvascular networks. A general literature review is provided on the recent progress of OCT angiography methods and algorithms. The basic physics and mathematics behind each method together with its contrast mechanism are described. Potential directions for future technical development of OCT based angiography is then briefly discussed. Finally, by the use of clinical data captured from normal and pathological subjects, the imaging performance of vascular networks delivered by the most recently reported algorithms is evaluated and compared, including optical microangiography, speckle variance, phase variance, split-spectrum amplitude decorrelation angiography, and correlation mapping. It is found that the method that utilizes complex OCT signal to contrast retinal blood flow delivers the best performance among all the algorithms in terms of image contrast and vessel connectivity. The purpose of this review is to help readers understand and select appropriate OCT angiography algorithm for use in specific applications.

An approach to integrate the human vision psychology and perception knowledge into image enhancement

NASA Astrophysics Data System (ADS)

Wang, Hui; Huang, Xifeng; Ping, Jiang

2009-07-01

Image enhancement is very important image preprocessing technology especially when the image is captured in the poor imaging condition or dealing with the high bits image. The benefactor of image enhancement either may be a human observer or a computer vision process performing some kind of higher-level image analysis, such as target detection or scene understanding. One of the main objects of the image enhancement is getting a high dynamic range image and a high contrast degree image for human perception or interpretation. So, it is very necessary to integrate either empirical or statistical human vision psychology and perception knowledge into image enhancement. The human vision psychology and perception claims that humans' perception and response to the intensity fluctuation δu of visual signals are weighted by the background stimulus u, instead of being plainly uniform. There are three main laws: Weber's law, Weber- Fechner's law and Stevens's Law that describe this phenomenon in the psychology and psychophysics. This paper will integrate these three laws of the human vision psychology and perception into a very popular image enhancement algorithm named Adaptive Plateau Equalization (APE). The experiments were done on the high bits star image captured in night scene and the infrared-red image both the static image and the video stream. For the jitter problem in the video stream, this algorithm reduces this problem using the difference between the current frame's plateau value and the previous frame's plateau value to correct the current frame's plateau value. Considering the random noise impacts, the pixel value mapping process is not only depending on the current pixel but the pixels in the window surround the current pixel. The window size is usually 3×3. The process results of this improved algorithms is evaluated by the entropy analysis and visual perception analysis. The experiments' result showed the improved APE algorithms improved the quality of the image, the target and the surrounding assistant targets could be identified easily, and the noise was not amplified much. For the low quality image, these improved algorithms augment the information entropy and improve the image and the video stream aesthetic quality, while for the high quality image they will not debase the quality of the image.

Modification of YAPE keypoint detection algorithm for wide local contrast range images

NASA Astrophysics Data System (ADS)

Lukoyanov, A.; Nikolaev, D.; Konovalenko, I.

2018-04-01

Keypoint detection is an important tool of image analysis, and among many contemporary keypoint detection algorithms YAPE is known for its computational performance, allowing its use in mobile and embedded systems. One of its shortcomings is high sensitivity to local contrast which leads to high detection density in high-contrast areas while missing detections in low-contrast ones. In this work we study the contrast sensitivity of YAPE and propose a modification which compensates for this property on images with wide local contrast range (Yet Another Contrast-Invariant Point Extractor, YACIPE). As a model example, we considered the traffic sign recognition problem, where some signs are well-lighted, whereas others are in shadows and thus have low contrast. We show that the number of traffic signs on the image of which has not been detected any keypoints is 40% less for the proposed modification compared to the original algorithm.

An algorithm for sensing venous oxygenation using ultrasound-modulated light enhanced by microbubbles

NASA Astrophysics Data System (ADS)

Honeysett, Jack E.; Stride, Eleanor; Deng, Jing; Leung, Terence S.

2012-02-01

Near-infrared spectroscopy (NIRS) can provide an estimate of the mean oxygen saturation in tissue. This technique is limited by optical scattering, which reduces the spatial resolution of the measurement, and by absorption, which makes the measurement insensitive to oxygenation changes in larger deep blood vessels relative to that in the superficial tissue. Acousto-optic (AO) techniques which combine focused ultrasound (US) with diffuse light have been shown to improve the spatial resolution as a result of US-modulation of the light signal, however this technique still suffers from low signal-to-noise when detecting a signal from regions of high optical absorption. Combining an US contrast agent with this hybrid technique has been proposed to amplify an AO signal. Microbubbles are a clinical contrast agent used in diagnostic US for their ability to resonate in a sound field: in this work we also make use of their optical scattering properties (modelled using Mie theory). A perturbation Monte Carlo (pMC) model of light transport in a highly absorbing blood vessel containing microbubbles surrounded by tissue is used to calculate the AO signal detected on the top surface of the tissue. An algorithm based on the modified Beer-Lambert law is derived which expresses intravenous oxygen saturation in terms of an AO signal. This is used to determine the oxygen saturation in the blood vessel from a dual wavelength microbubble-contrast AO measurement. Applying this algorithm to the simulation data shows that the venous oxygen saturation is accurately recovered, and this measurement is robust to changes in the oxygenation of the superficial tissue layer.

Calibration of Wide-Field Deconvolution Microscopy for Quantitative Fluorescence Imaging

PubMed Central

Lee, Ji-Sook; Wee, Tse-Luen (Erika); Brown, Claire M.

2014-01-01

Deconvolution enhances contrast in fluorescence microscopy images, especially in low-contrast, high-background wide-field microscope images, improving characterization of features within the sample. Deconvolution can also be combined with other imaging modalities, such as confocal microscopy, and most software programs seek to improve resolution as well as contrast. Quantitative image analyses require instrument calibration and with deconvolution, necessitate that this process itself preserves the relative quantitative relationships between fluorescence intensities. To ensure that the quantitative nature of the data remains unaltered, deconvolution algorithms need to be tested thoroughly. This study investigated whether the deconvolution algorithms in AutoQuant X3 preserve relative quantitative intensity data. InSpeck Green calibration microspheres were prepared for imaging, z-stacks were collected using a wide-field microscope, and the images were deconvolved using the iterative deconvolution algorithms with default settings. Afterwards, the mean intensities and volumes of microspheres in the original and the deconvolved images were measured. Deconvolved data sets showed higher average microsphere intensities and smaller volumes than the original wide-field data sets. In original and deconvolved data sets, intensity means showed linear relationships with the relative microsphere intensities given by the manufacturer. Importantly, upon normalization, the trend lines were found to have similar slopes. In original and deconvolved images, the volumes of the microspheres were quite uniform for all relative microsphere intensities. We were able to show that AutoQuant X3 deconvolution software data are quantitative. In general, the protocol presented can be used to calibrate any fluorescence microscope or image processing and analysis procedure. PMID:24688321

Performance of 12 DIR algorithms in low-contrast regions for mass and density conserving deformation

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

Yeo, U. J.; Supple, J. R.; Franich, R. D.

2013-10-15

Purpose: Deformable image registration (DIR) has become a key tool for adaptive radiotherapy to account for inter- and intrafraction organ deformation. Of contemporary interest, the application to deformable dose accumulation requires accurate deformation even in low contrast regions where dose gradients may exist within near-uniform tissues. One expects high-contrast features to generally be deformed more accurately by DIR algorithms. The authors systematically assess the accuracy of 12 DIR algorithms and quantitatively examine, in particular, low-contrast regions, where accuracy has not previously been established.Methods: This work investigates DIR algorithms in three dimensions using deformable gel (DEFGEL) [U. J. Yeo, M. L.more » Taylor, L. Dunn, R. L. Smith, T. Kron, and R. D. Franich, “A novel methodology for 3D deformable dosimetry,” Med. Phys. 39, 2203–2213 (2012)], for application to mass- and density-conserving deformations. CT images of DEFGEL phantoms with 16 fiducial markers (FMs) implanted were acquired in deformed and undeformed states for three different representative deformation geometries. Nonrigid image registration was performed using 12 common algorithms in the public domain. The optimum parameter setup was identified for each algorithm and each was tested for deformation accuracy in three scenarios: (I) original images of the DEFGEL with 16 FMs; (II) images with eight of the FMs mathematically erased; and (III) images with all FMs mathematically erased. The deformation vector fields obtained for scenarios II and III were then applied to the original images containing all 16 FMs. The locations of the FMs estimated by the algorithms were compared to actual locations determined by CT imaging. The accuracy of the algorithms was assessed by evaluation of three-dimensional vectors between true marker locations and predicted marker locations.Results: The mean magnitude of 16 error vectors per sample ranged from 0.3 to 3.7, 1.0 to 6.3, and 1.3 to 7.5 mm across algorithms for scenarios I to III, respectively. The greatest accuracy was exhibited by the original Horn and Schunck optical flow algorithm. In this case, for scenario III (erased FMs not contributing to driving the DIR calculation), the mean error was half that of the modified demons algorithm (which exhibited the greatest error), across all deformations. Some algorithms failed to reproduce the geometry at all, while others accurately deformed high contrast features but not low-contrast regions—indicating poor interpolation between landmarks.Conclusions: The accuracy of DIR algorithms was quantitatively evaluated using a tissue equivalent, mass, and density conserving DEFGEL phantom. For the model studied, optical flow algorithms performed better than demons algorithms, with the original Horn and Schunck performing best. The degree of error is influenced more by the magnitude of displacement than the geometric complexity of the deformation. As might be expected, deformation is estimated less accurately for low-contrast regions than for high-contrast features, and the method presented here allows quantitative analysis of the differences. The evaluation of registration accuracy through observation of the same high contrast features that drive the DIR calculation is shown to be circular and hence misleading.« less

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

Naseri, M; Rajabi, H; Wang, J

Purpose: Respiration causes lesion smearing, image blurring and quality degradation, affecting lesion contrast and the ability to define correct lesion size. The spatial resolution of current multi pinhole SPECT (MPHS) scanners is sub-millimeter. Therefore, the effect of motion is more noticeable in comparison to conventional SPECT scanner. Gated imaging aims to reduce motion artifacts. A major issue in gating is the lack of statistics and individual reconstructed frames are noisy. The increased noise in each frame, deteriorates the quantitative accuracy of the MPHS Images. The objective of this work, is to enhance the image quality in 4D-MPHS imaging, by 4Dmore » image reconstruction. Methods: The new algorithm requires deformation vector fields (DVFs) that are calculated by non-rigid Demons registration. The algorithm is based on the motion-incorporated version of ordered subset expectation maximization (OSEM) algorithm. This iterative algorithm is capable to make full use of all projections to reconstruct each individual frame. To evaluate the performance of the proposed algorithm a simulation study was conducted. A fast ray tracing method was used to generate MPHS projections of a 4D digital mouse phantom with a small tumor in liver in eight different respiratory phases. To evaluate the 4D-OSEM algorithm potential, tumor to liver activity ratio was compared with other image reconstruction methods including 3D-MPHS and post reconstruction registered with Demons-derived DVFs. Results: Image quality of 4D-MPHS is greatly improved by the 4D-OSEM algorithm. When all projections are used to reconstruct a 3D-MPHS, motion blurring artifacts are present, leading to overestimation of the tumor size and 24% tumor contrast underestimation. This error reduced to 16% and 10% for post reconstruction registration methods and 4D-OSEM respectively. Conclusion: 4D-OSEM method can be used for motion correction in 4D-MPHS. The statistics and quantification are improved since all projection data are combined together to update the image.« less

Tomographic image reconstruction using x-ray phase information

NASA Astrophysics Data System (ADS)

Momose, Atsushi; Takeda, Tohoru; Itai, Yuji; Hirano, Keiichi

1996-04-01

We have been developing phase-contrast x-ray computed tomography (CT) to make possible the observation of biological soft tissues without contrast enhancement. Phase-contrast x-ray CT requires for its input data the x-ray phase-shift distributions or phase-mapping images caused by an object. These were measured with newly developed fringe-scanning x-ray interferometry. Phase-mapping images at different projection directions were obtained by rotating the object in an x-ray interferometer, and were processed with a standard CT algorithm. A phase-contrast x-ray CT image of a nonstained cancerous tissue was obtained using 17.7 keV synchrotron x rays with 12 micrometer voxel size, although the size of the observation area was at most 5 mm. The cancerous lesions were readily distinguishable from normal tissues. Moreover, fine structures corresponding to cancerous degeneration and fibrous tissues were clearly depicted. It is estimated that the present system is sensitive down to a density deviation of 4 mg/cm3.

Diffusion MRI: literature review in salivary gland tumors.

PubMed

Attyé, A; Troprès, I; Rouchy, R-C; Righini, C; Espinoza, S; Kastler, A; Krainik, A

2017-07-01

Surgical resection is currently the best treatment for salivary gland tumors. A reliable magnetic resonance imaging mapping, encompassing tumor grade, location, and extension may assist safe and effective tumor resection and provide better information for patients regarding potential risks and morbidity after surgical intervention. However, direct examination of the tumor grade and extension using conventional morphological MRI remains difficult, often requiring contrast media injection and complex algorithms on perfusion imaging to estimate the degree of malignancy. In addition, contrast-enhanced MRI technique may be problematic due to the recently demonstrated gadolinium accumulation in the dentate nucleus of the cerebellum. Significant developments in magnetic resonance diffusion imaging, involving voxel-based quantitative analysis through the measurement of the apparent diffusion coefficient, have enhanced our knowledge on the different histopathological salivary tumor grades. Other diffusion imaging-derived techniques, including high-order tractography models, have recently demonstrated their usefulness in assessing the facial nerve location in parotid tumor context. All of these imaging techniques do not require contrast media injection. Our review starts by outlining the physical basis of diffusion imaging, before discussing findings from diagnostic studies testing its usefulness in assessing salivary glands tumors with diffusion MRI. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Heterogeneity wavelet kinetics from DCE-MRI for classifying gene expression based breast cancer recurrence risk.

PubMed

Mahrooghy, Majid; Ashraf, Ahmed B; Daye, Dania; Mies, Carolyn; Feldman, Michael; Rosen, Mark; Kontos, Despina

2013-01-01

Breast tumors are heterogeneous lesions. Intra-tumor heterogeneity presents a major challenge for cancer diagnosis and treatment. Few studies have worked on capturing tumor heterogeneity from imaging. Most studies to date consider aggregate measures for tumor characterization. In this work we capture tumor heterogeneity by partitioning tumor pixels into subregions and extracting heterogeneity wavelet kinetic (HetWave) features from breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to obtain the spatiotemporal patterns of the wavelet coefficients and contrast agent uptake from each partition. Using a genetic algorithm for feature selection, and a logistic regression classifier with leave one-out cross validation, we tested our proposed HetWave features for the task of classifying breast cancer recurrence risk. The classifier based on our features gave an ROC AUC of 0.78, outperforming previously proposed kinetic, texture, and spatial enhancement variance features which give AUCs of 0.69, 0.64, and 0.65, respectively.

Imaging Performance of a Handheld Ultrasound System With Real-Time Computer-Aided Detection of Lumbar Spine Anatomy: A Feasibility Study.

PubMed

Tiouririne, Mohamed; Dixon, Adam J; Mauldin, F William; Scalzo, David; Krishnaraj, Arun

2017-08-01

The aim of this study was to evaluate the imaging performance of a handheld ultrasound system and the accuracy of an automated lumbar spine computer-aided detection (CAD) algorithm in the spines of human subjects. This study was approved by the institutional review board of the University of Virginia. The authors designed a handheld ultrasound system with enhanced bone image quality and fully automated CAD of lumbar spine anatomy. The imaging performance was evaluated by imaging the lumbar spines of 68 volunteers with body mass index between 18.5 and 48 kg/m. The accuracy, sensitivity, and specificity of the lumbar spine CAD algorithm were assessed by comparing the algorithm's results to ground-truth segmentations of neuraxial anatomy provided by radiologists. The lumbar spine CAD algorithm detected the epidural space with a sensitivity of 94.2% (95% confidence interval [CI], 85.1%-98.1%) and a specificity of 85.5% (95% CI, 81.7%-88.6%) and measured its depth with an error of approximately ±0.5 cm compared with measurements obtained manually from the 2-dimensional ultrasound images. The spine midline was detected with a sensitivity of 93.9% (95% CI, 85.8%-97.7%) and specificity of 91.3% (95% CI, 83.6%-96.9%), and its lateral position within the ultrasound image was measured with an error of approximately ±0.3 cm. The bone enhancement imaging mode produced images with 5.1- to 10-fold enhanced bone contrast when compared with a comparable handheld ultrasound imaging system. The results of this study demonstrate the feasibility of CAD for assisting with real-time interpretation of ultrasound images of the lumbar spine at the bedside.

A Three-Stage Enhanced Reactive Power and Voltage Optimization Method for High Penetration of Solar

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

Ke, Xinda; Huang, Renke; Vallem, Mallikarjuna R.

This paper presents a three-stage enhanced volt/var optimization method to stabilize voltage fluctuations in transmission networks by optimizing the usage of reactive power control devices. In contrast with existing volt/var optimization algorithms, the proposed method optimizes the voltage profiles of the system, while keeping the voltage and real power output of the generators as close to the original scheduling values as possible. This allows the method to accommodate realistic power system operation and market scenarios, in which the original generation dispatch schedule will not be affected. The proposed method was tested and validated on a modified IEEE 118-bus system withmore » photovoltaic data.« less

Reconstruction of Thermographic Signals to Map Perforator Vessels in Humans

PubMed Central

Liu, Wei-Min; Maivelett, Jordan; Kato, Gregory J.; Taylor, James G.; Yang, Wen-Chin; Liu, Yun-Chung; Yang, You-Gang; Gorbach, Alexander M.

2013-01-01

Thermal representations on the surface of a human forearm of underlying perforator vessels have previously been mapped via recovery-enhanced infrared imaging, which is performed as skin blood flow recovers to baseline levels following cooling of the forearm. We noted that the same vessels could also be observed during reactive hyperaemia tests after complete 5-min occlusion of the forearm by an inflatable cuff. However, not all subjects showed vessels with acceptable contrast. Therefore, we applied a thermographic signal reconstruction algorithm to reactive hyperaemia testing, which substantially enhanced signal-to-noise ratios between perforator vessels and their surroundings, thereby enabling their mapping with higher accuracy and a shorter occlusion period. PMID:23667389

3D widefield light microscope image reconstruction without dyes

NASA Astrophysics Data System (ADS)

Larkin, S.; Larson, J.; Holmes, C.; Vaicik, M.; Turturro, M.; Jurkevich, A.; Sinha, S.; Ezashi, T.; Papavasiliou, G.; Brey, E.; Holmes, T.

2015-03-01

3D image reconstruction using light microscope modalities without exogenous contrast agents is proposed and investigated as an approach to produce 3D images of biological samples for live imaging applications. Multimodality and multispectral imaging, used in concert with this 3D optical sectioning approach is also proposed as a way to further produce contrast that could be specific to components in the sample. The methods avoid usage of contrast agents. Contrast agents, such as fluorescent or absorbing dyes, can be toxic to cells or alter cell behavior. Current modes of producing 3D image sets from a light microscope, such as 3D deconvolution algorithms and confocal microscopy generally require contrast agents. Zernike phase contrast (ZPC), transmitted light brightfield (TLB), darkfield microscopy and others can produce contrast without dyes. Some of these modalities have not previously benefitted from 3D image reconstruction algorithms, however. The 3D image reconstruction algorithm is based on an underlying physical model of scattering potential, expressed as the sample's 3D absorption and phase quantities. The algorithm is based upon optimizing an objective function - the I-divergence - while solving for the 3D absorption and phase quantities. Unlike typical deconvolution algorithms, each microscope modality, such as ZPC or TLB, produces two output image sets instead of one. Contrast in the displayed image and 3D renderings is further enabled by treating the multispectral/multimodal data as a feature set in a mathematical formulation that uses the principal component method of statistics.

Image enhancement based on in vivo hyperspectral gastroscopic images: a case study

NASA Astrophysics Data System (ADS)

Gu, Xiaozhou; Han, Zhimin; Yao, Liqing; Zhong, Yunshi; Shi, Qiang; Fu, Ye; Liu, Changsheng; Wang, Xiguang; Xie, Tianyu

2016-10-01

Hyperspectral imaging (HSI) has been recognized as a powerful tool for noninvasive disease detection in the gastrointestinal field. However, most of the studies on HSI in this field have involved ex vivo biopsies or resected tissues. We proposed an image enhancement method based on in vivo hyperspectral gastroscopic images. First, we developed a flexible gastroscopy system capable of obtaining in vivo hyperspectral images of different types of stomach disease mucosa. Then, depending on a specific object, an appropriate band selection algorithm based on dependence of information was employed to determine a subset of spectral bands that would yield useful spatial information. Finally, these bands were assigned to be the color components of an enhanced image of the object. A gastric ulcer case study demonstrated that our method yields higher color tone contrast, which enhanced the displays of the gastric ulcer regions, and that it will be valuable in clinical applications.

A Methodology for the Hybridization Based in Active Components: The Case of cGA and Scatter Search

PubMed Central

Alba, Enrique; Leguizamón, Guillermo

2016-01-01

This work presents the results of a new methodology for hybridizing metaheuristics. By first locating the active components (parts) of one algorithm and then inserting them into second one, we can build efficient and accurate optimization, search, and learning algorithms. This gives a concrete way of constructing new techniques that contrasts the spread ad hoc way of hybridizing. In this paper, the enhanced algorithm is a Cellular Genetic Algorithm (cGA) which has been successfully used in the past to find solutions to such hard optimization problems. In order to extend and corroborate the use of active components as an emerging hybridization methodology, we propose here the use of active components taken from Scatter Search (SS) to improve cGA. The results obtained over a varied set of benchmarks are highly satisfactory in efficacy and efficiency when compared with a standard cGA. Moreover, the proposed hybrid approach (i.e., cGA+SS) has shown encouraging results with regard to earlier applications of our methodology. PMID:27403153

Generalized sidelobe canceller beamforming method for ultrasound imaging.

PubMed

Wang, Ping; Li, Na; Luo, Han-Wu; Zhu, Yong-Kun; Cui, Shi-Gang

2017-03-01

A modified generalized sidelobe canceller (IGSC) algorithm is proposed to enhance the resolution and robustness against the noise of the traditional generalized sidelobe canceller (GSC) and coherence factor combined method (GSC-CF). In the GSC algorithm, weighting vector is divided into adaptive and non-adaptive parts, while the non-adaptive part does not block all the desired signal. A modified steer vector of the IGSC algorithm is generated by the projection of the non-adaptive vector on the signal space constructed by the covariance matrix of received data. The blocking matrix is generated based on the orthogonal complementary space of the modified steer vector and the weighting vector is updated subsequently. The performance of IGSC was investigated by simulations and experiments. Through simulations, IGSC outperformed GSC-CF in terms of spatial resolution by 0.1 mm regardless there is noise or not, as well as the contrast ratio respect. The proposed IGSC can be further improved by combining with CF. The experimental results also validated the effectiveness of the proposed algorithm with dataset provided by the University of Michigan.

Probabilistic fusion of stereo with color and contrast for bilayer segmentation.

PubMed

Kolmogorov, Vladimir; Criminisi, Antonio; Blake, Andrew; Cross, Geoffrey; Rother, Carsten

2006-09-01

This paper describes models and algorithms for the real-time segmentation of foreground from background layers in stereo video sequences. Automatic separation of layers from color/contrast or from stereo alone is known to be error-prone. Here, color, contrast, and stereo matching information are fused to infer layers accurately and efficiently. The first algorithm, Layered Dynamic Programming (LDP), solves stereo in an extended six-state space that represents both foreground/background layers and occluded regions. The stereo-match likelihood is then fused with a contrast-sensitive color model that is learned on-the-fly and stereo disparities are obtained by dynamic programming. The second algorithm, Layered Graph Cut (LGC), does not directly solve stereo. Instead, the stereo match likelihood is marginalized over disparities to evaluate foreground and background hypotheses and then fused with a contrast-sensitive color model like the one used in LDP. Segmentation is solved efficiently by ternary graph cut. Both algorithms are evaluated with respect to ground truth data and found to have similar performance, substantially better than either stereo or color/ contrast alone. However, their characteristics with respect to computational efficiency are rather different. The algorithms are demonstrated in the application of background substitution and shown to give good quality composite video output.

IMAGEP - A FORTRAN ALGORITHM FOR DIGITAL IMAGE PROCESSING

NASA Technical Reports Server (NTRS)

Roth, D. J.

1994-01-01

IMAGEP is a FORTRAN computer algorithm containing various image processing, analysis, and enhancement functions. It is a keyboard-driven program organized into nine subroutines. Within the subroutines are other routines, also, selected via keyboard. Some of the functions performed by IMAGEP include digitization, storage and retrieval of images; image enhancement by contrast expansion, addition and subtraction, magnification, inversion, and bit shifting; display and movement of cursor; display of grey level histogram of image; and display of the variation of grey level intensity as a function of image position. This algorithm has possible scientific, industrial, and biomedical applications in material flaw studies, steel and ore analysis, and pathology, respectively. IMAGEP is written in VAX FORTRAN for DEC VAX series computers running VMS. The program requires the use of a Grinnell 274 image processor which can be obtained from Mark McCloud Associates, Campbell, CA. An object library of the required GMR series software is included on the distribution media. IMAGEP requires 1Mb of RAM for execution. The standard distribution medium for this program is a 1600 BPI 9track magnetic tape in VAX FILES-11 format. It is also available on a TK50 tape cartridge in VAX FILES-11 format. This program was developed in 1991. DEC, VAX, VMS, and TK50 are trademarks of Digital Equipment Corporation.

Experimental characterization of a direct conversion amorphous selenium detector with thicker conversion layer for dual-energy contrast-enhanced breast imaging.

PubMed

Scaduto, David A; Tousignant, Olivier; Zhao, Wei

2017-08-01

Dual-energy contrast-enhanced imaging is being investigated as a tool to identify and localize angiogenesis in the breast, a possible indicator of malignant tumors. This imaging technique requires that x-ray images are acquired at energies above the k-shell binding energy of an appropriate radiocontrast agent. Iodinated contrast agents are commonly used for vascular imaging, and require x-ray energies greater than 33 keV. Conventional direct conversion amorphous selenium (a-Se) flat-panel imagers for digital mammography show suboptimal absorption efficiencies at these higher energies. We use spatial-frequency domain image quality metrics to evaluate the performance of a prototype direct conversion flat-panel imager with a thicker a-Se layer, specifically fabricated for dual-energy contrast-enhanced breast imaging. Imaging performance was evaluated in a prototype digital breast tomosynthesis (DBT) system. The spatial resolution, noise characteristics, detective quantum efficiency, and temporal performance of the detector were evaluated for dual-energy imaging for both conventional full-field digital mammography (FFDM) and DBT. The zero-frequency detective quantum efficiency of the prototype detector is improved by approximately 20% over the conventional detector for higher energy beams required for imaging with iodinated contrast agents. The effect of oblique entry of x-rays on spatial resolution does increase with increasing photoconductor thickness, specifically for the most oblique views of a DBT scan. Degradation of spatial resolution due to focal spot motion was also observed. Temporal performance was found to be comparable to conventional mammographic detectors. Increasing the a-Se thickness in direct conversion flat-panel imagers results in better performance for dual-energy contrast-enhanced breast imaging. The reduction in spatial resolution due to oblique entry of x-rays is appreciable in the most extreme clinically relevant cases, but may not profoundly affect reconstructed images due to the algorithms and filters employed. Degradation to projection domain spatial resolution is thus outweighed by the improvement in detective quantum efficiency for high-energy x-rays. © 2017 American Association of Physicists in Medicine.

Blood vessel segmentation algorithms - Review of methods, datasets and evaluation metrics.

PubMed

Moccia, Sara; De Momi, Elena; El Hadji, Sara; Mattos, Leonardo S

2018-05-01

Blood vessel segmentation is a topic of high interest in medical image analysis since the analysis of vessels is crucial for diagnosis, treatment planning and execution, and evaluation of clinical outcomes in different fields, including laryngology, neurosurgery and ophthalmology. Automatic or semi-automatic vessel segmentation can support clinicians in performing these tasks. Different medical imaging techniques are currently used in clinical practice and an appropriate choice of the segmentation algorithm is mandatory to deal with the adopted imaging technique characteristics (e.g. resolution, noise and vessel contrast). This paper aims at reviewing the most recent and innovative blood vessel segmentation algorithms. Among the algorithms and approaches considered, we deeply investigated the most novel blood vessel segmentation including machine learning, deformable model, and tracking-based approaches. This paper analyzes more than 100 articles focused on blood vessel segmentation methods. For each analyzed approach, summary tables are presented reporting imaging technique used, anatomical region and performance measures employed. Benefits and disadvantages of each method are highlighted. Despite the constant progress and efforts addressed in the field, several issues still need to be overcome. A relevant limitation consists in the segmentation of pathological vessels. Unfortunately, not consistent research effort has been addressed to this issue yet. Research is needed since some of the main assumptions made for healthy vessels (such as linearity and circular cross-section) do not hold in pathological tissues, which on the other hand require new vessel model formulations. Moreover, image intensity drops, noise and low contrast still represent an important obstacle for the achievement of a high-quality enhancement. This is particularly true for optical imaging, where the image quality is usually lower in terms of noise and contrast with respect to magnetic resonance and computer tomography angiography. No single segmentation approach is suitable for all the different anatomical region or imaging modalities, thus the primary goal of this review was to provide an up to date source of information about the state of the art of the vessel segmentation algorithms so that the most suitable methods can be chosen according to the specific task. Copyright © 2018 Elsevier B.V. All rights reserved.

Unattended real-time re-establishment of visibility in high dynamic range video and stills

NASA Astrophysics Data System (ADS)

Abidi, B.

2014-05-01

We describe a portable unattended persistent surveillance system that corrects for harsh illumination conditions, where bright sun light creates mixed contrast effects, i.e., heavy shadows and washouts. These effects result in high dynamic range scenes, where illuminance can vary from few luxes to a 6 figure value. When using regular monitors and cameras, such wide span of illuminations can only be visualized if the actual range of values is compressed, leading to the creation of saturated and/or dark noisy areas and a loss of information in these areas. Images containing extreme mixed contrast cannot be fully enhanced from a single exposure, simply because all information is not present in the original data. The active intervention in the acquisition process is required. A software package, capable of integrating multiple types of COTS and custom cameras, ranging from Unmanned Aerial Systems (UAS) data links to digital single-lens reflex cameras (DSLR), is described. Hardware and software are integrated via a novel smart data acquisition algorithm, which communicates to the camera the parameters that would maximize information content in the final processed scene. A fusion mechanism is then applied to the smartly acquired data, resulting in an enhanced scene where information in both dark and bright areas is revealed. Multi-threading and parallel processing are exploited to produce automatic real time full motion corrected video. A novel enhancement algorithm was also devised to process data from legacy and non-controllable cameras. The software accepts and processes pre-recorded sequences and stills, enhances visible, night vision, and Infrared data, and successfully applies to night time and dark scenes. Various user options are available, integrating custom functionalities of the application into intuitive and easy to use graphical interfaces. The ensuing increase in visibility in surveillance video and intelligence imagery will expand the performance and timely decision making of the human analyst, as well as that of unmanned systems performing automatic data exploitation, such as target detection and identification.

Closed Loop, DM Diversity-based, Wavefront Correction Algorithm for High Contrast Imaging Systems

NASA Technical Reports Server (NTRS)

Give'on, Amir; Belikov, Ruslan; Shaklan, Stuart; Kasdin, Jeremy

2007-01-01

High contrast imaging from space relies on coronagraphs to limit diffraction and a wavefront control systems to compensate for imperfections in both the telescope optics and the coronagraph. The extreme contrast required (up to 10(exp -10) for terrestrial planets) puts severe requirements on the wavefront control system, as the achievable contrast is limited by the quality of the wavefront. This paper presents a general closed loop correction algorithm for high contrast imaging coronagraphs by minimizing the energy in a predefined region in the image where terrestrial planets could be found. The estimation part of the algorithm reconstructs the complex field in the image plane using phase diversity caused by the deformable mirror. This method has been shown to achieve faster and better correction than classical speckle nulling.

Improvement of Speckle Contrast Image Processing by an Efficient Algorithm.

PubMed

Steimers, A; Farnung, W; Kohl-Bareis, M

2016-01-01

We demonstrate an efficient algorithm for the temporal and spatial based calculation of speckle contrast for the imaging of blood flow by laser speckle contrast analysis (LASCA). It reduces the numerical complexity of necessary calculations, facilitates a multi-core and many-core implementation of the speckle analysis and enables an independence of temporal or spatial resolution and SNR. The new algorithm was evaluated for both spatial and temporal based analysis of speckle patterns with different image sizes and amounts of recruited pixels as sequential, multi-core and many-core code.

Quantitative three-dimensional transrectal ultrasound (TRUS) for prostate imaging

NASA Astrophysics Data System (ADS)

Pathak, Sayan D.; Aarnink, Rene G.; de la Rosette, Jean J.; Chalana, Vikram; Wijkstra, Hessel; Haynor, David R.; Debruyne, Frans M. J.; Kim, Yongmin

1998-06-01

With the number of men seeking medical care for prostate diseases rising steadily, the need of a fast and accurate prostate boundary detection and volume estimation tool is being increasingly experienced by the clinicians. Currently, these measurements are made manually, which results in a large examination time. A possible solution is to improve the efficiency by automating the boundary detection and volume estimation process with minimal involvement from the human experts. In this paper, we present an algorithm based on SNAKES to detect the boundaries. Our approach is to selectively enhance the contrast along the edges using an algorithm called sticks and integrate it with a SNAKES model. This integrated algorithm requires an initial curve for each ultrasound image to initiate the boundary detection process. We have used different schemes to generate the curves with a varying degree of automation and evaluated its effects on the algorithm performance. After the boundaries are identified, the prostate volume is calculated using planimetric volumetry. We have tested our algorithm on 6 different prostate volumes and compared the performance against the volumes manually measured by 3 experts. With the increase in the user inputs, the algorithm performance improved as expected. The results demonstrate that given an initial contour reasonably close to the prostate boundaries, the algorithm successfully delineates the prostate boundaries in an image, and the resulting volume measurements are in close agreement with those made by the human experts.

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 Firefly Algorithm-based Approach for Pseudo-Relevance Feedback: Application to Medical Database.

PubMed

Khennak, Ilyes; Drias, Habiba

2016-11-01

The difficulty of disambiguating the sense of the incomplete and imprecise keywords that are extensively used in the search queries has caused the failure of search systems to retrieve the desired information. One of the most powerful and promising method to overcome this shortcoming and improve the performance of search engines is Query Expansion, whereby the user's original query is augmented by new keywords that best characterize the user's information needs and produce more useful query. In this paper, a new Firefly Algorithm-based approach is proposed to enhance the retrieval effectiveness of query expansion while maintaining low computational complexity. In contrast to the existing literature, the proposed approach uses a Firefly Algorithm to find the best expanded query among a set of expanded query candidates. Moreover, this new approach allows the determination of the length of the expanded query empirically. Experimental results on MEDLINE, the on-line medical information database, show that our proposed approach is more effective and efficient compared to the state-of-the-art.

Bilateral filtering using the full noise covariance matrix applied to x-ray phase-contrast computed tomography.

PubMed

Allner, S; Koehler, T; Fehringer, A; Birnbacher, L; Willner, M; Pfeiffer, F; Noël, P B

2016-05-21

The purpose of this work is to develop an image-based de-noising algorithm that exploits complementary information and noise statistics from multi-modal images, as they emerge in x-ray tomography techniques, for instance grating-based phase-contrast CT and spectral CT. Among the noise reduction methods, image-based de-noising is one popular approach and the so-called bilateral filter is a well known algorithm for edge-preserving filtering. We developed a generalization of the bilateral filter for the case where the imaging system provides two or more perfectly aligned images. The proposed generalization is statistically motivated and takes the full second order noise statistics of these images into account. In particular, it includes a noise correlation between the images and spatial noise correlation within the same image. The novel generalized three-dimensional bilateral filter is applied to the attenuation and phase images created with filtered backprojection reconstructions from grating-based phase-contrast tomography. In comparison to established bilateral filters, we obtain improved noise reduction and at the same time a better preservation of edges in the images on the examples of a simulated soft-tissue phantom, a human cerebellum and a human artery sample. The applied full noise covariance is determined via cross-correlation of the image noise. The filter results yield an improved feature recovery based on enhanced noise suppression and edge preservation as shown here on the example of attenuation and phase images captured with grating-based phase-contrast computed tomography. This is supported by quantitative image analysis. Without being bound to phase-contrast imaging, this generalized filter is applicable to any kind of noise-afflicted image data with or without noise correlation. Therefore, it can be utilized in various imaging applications and fields.

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

PubMed Central

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

2017-01-01

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

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.

TU-CD-BRA-08: Single-Energy Computed Tomography-Based Pulmonary Perfusion Imaging: Proof-Of-Principle in a Canine Model

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

Yamamoto, T; Boone, J; Kent, M

Purpose: Pulmonary perfusion imaging has provided significant insights into pulmonary diseases, and can be useful in radiotherapy. The purpose of this study was to prospectively establish proof-of-principle in a canine model for single-energy CT-based perfusion imaging, which has the potential for widespread clinical implementation. Methods: Single-energy CT perfusion imaging is based on: (1) acquisition of inspiratory breath-hold CT scans before and after intravenous injection of iodinated contrast medium, (2) deformable image registration (DIR) of the two CT image data sets, and (3) subtraction of the pre-contrast image from post-contrast image, yielding a map of Hounsfield unit (HU) enhancement. These subtractionmore » image data sets hypothetically represent perfused blood volume, a surrogate for perfusion. In an IACUC-approved clinical trial, we acquired pre- and post-contrast CT scans in the prone posture for six anesthetized, mechanically-ventilated dogs. The elastix algorithm was used for DIR. The registration accuracy was quantified using the target registration errors (TREs) for 50 pulmonary landmarks in each dog. The gradient of HU enhancement between gravity-dependent (ventral) and non-dependent (dorsal) regions was evaluated to quantify the known effect of gravity, i.e., greater perfusion in ventral regions. Results: The lung volume difference between the two scans was 4.3±3.5% on average (range 0.3%–10.1%). DIR demonstrated an average TRE of 0.7±1.0 mm. HU enhancement in lung parenchyma was 34±10 HU on average and varied considerably between individual dogs, indicating the need for improvement of the contrast injection protocol. HU enhancement in ventral (gravity-dependent) regions was found to be greater than in dorsal regions. A population average ventral-to-dorsal gradient of HU enhancement was strong (R{sup 2}=0.94) and statistically significant (p<0.01). Conclusion: This canine study demonstrated relatively accurate DIR and a strong ventral-to-dorsal gradient of HU enhancement, providing proof-of-principle for single-energy CT pulmonary perfusion imaging. This ongoing study will enroll more dogs and investigate the physiological significance. This study was supported by a Philips Healthcare/Radiological Society of North America (RSNA) Research Seed Grant (RSD1458)« less

Influence of Contrast Media on Bone Mineral Density (BMD) Measurements from Routine Contrast-Enhanced MDCT Datasets using a Phantom-less BMD Measurement Tool.

PubMed

Toelly, Andrea; Bardach, Constanze; Weber, Michael; Gong, Rui; Lai, Yanbo; Wang, Pei; Guo, Yulin; Kirschke, Jan; Baum, Thomas; Gruber, Michael

2017-06-01

Aim  To evaluate the differences in phantom-less bone mineral density (BMD) measurements in contrast-enhanced routine MDCT scans at different contrast phases, and to develop an algorithm for calculating a reliable BMD value. Materials and Methods  112 postmenopausal women from the age of 40 to 77 years (mean age: 57.31 years; SD 9.61) who underwent a clinically indicated MDCT scan, consisting of an unenhanced, an arterial, and a venous phase, were included. A retrospective analysis of the BMD values of the Th12 to L4 vertebrae in each phase was performed using a commercially available phantom-less measurement tool. Results  The mean BMD value in the unenhanced MDCT scans was 79.76 mg/cm³ (SD 31.20), in the arterial phase it was 85.09 mg/cm³ (SD 31.61), and in the venous phase it was 86.18 mg/cm³ (SD 31.30). A significant difference (p < 0.001) was found between BMD values on unenhanced and contrast-enhanced MDCT scans. There was no significant difference between BMD values in the arterial and venous phases (p = 0.228). The following conversion formulas were calculated using linear regression: unenhanced BMD = -2.287 + 0.964 * [arterial BMD value] and -4.517 + 0.978 * [venous BMD value]. The intrarater agreement of BMD measurements was calculated with an intraclass correlation (ICC) of 0.984 and the interobserver reliability was calculated with an ICC of 0.991. Conclusion  Phantom-less BMD measurements in contrast-enhanced MDCT scans result in increased mean BMD values, but, with the formulas applied in our study, a reliable BMD value can be calculated. However, the mean BMD values did not differ significantly between the arterial and venous phases. Key points   · BMD can be assessed on routine CT scans using a phantom-less tool.. · i. v. contrast agent significantly elevates BMD values measured on routine CT scans.. · BMD values measured in the arterial and venous phase did not differ significantly.. · Conversion formulas were defined for the calculation of a reliable BMD.. · The phantom-less tool showed good reliability and is a promising method.. Citation Format · Toelly A, Bardach C, Weber M et al. Influence of Contrast Media on Bone Mineral Density (BMD) Measurements from Routine Contrast-Enhanced MDCT Datasets using a Phantom-less BMD Measurement Tool. Fortschr Röntgenstr 2017; 189: 537 - 543. © Georg Thieme Verlag KG Stuttgart · New York.

Automated Registration of Sequential Breath-Hold Dynamic Contrast-Enhanced MRI Images: a Comparison of 3 Techniques

PubMed Central

Rajaraman, Sivaramakrishnan; Rodriguez, Jeffery J.; Graff, Christian; Altbach, Maria I.; Dragovich, Tomislav; Sirlin, Claude B.; Korn, Ronald L.; Raghunand, Natarajan

2011-01-01

Dynamic Contrast-Enhanced MRI (DCE-MRI) is increasingly in use as an investigational biomarker of response in cancer clinical studies. Proper registration of images acquired at different time-points is essential for deriving diagnostic information from quantitative pharmacokinetic analysis of these data. Motion artifacts in the presence of time-varying intensity due to contrast-enhancement make this registration problem challenging. DCE-MRI of chest and abdominal lesions is typically performed during sequential breath-holds, which introduces misregistration due to inconsistent diaphragm positions, and also places constraints on temporal resolution vis-à-vis free-breathing. In this work, we have employed a computer-generated DCE-MRI phantom to compare the performance of two published methods, Progressive Principal Component Registration and Pharmacokinetic Model-Driven Registration, with Sequential Elastic Registration (SER) to register adjacent time-sample images using a published general-purpose elastic registration algorithm. In all 3 methods, a 3-D rigid-body registration scheme with a mutual information similarity measure was used as a pre-processing step. The DCE-MRI phantom images were mathematically deformed to simulate misregistration which was corrected using the 3 schemes. All 3 schemes were comparably successful in registering large regions of interest (ROIs) such as muscle, liver, and spleen. SER was superior in retaining tumor volume and shape, and in registering smaller but important ROIs such as tumor core and tumor rim. The performance of SER on clinical DCE-MRI datasets is also presented. PMID:21531108

Simulation of optical signaling among nano-bio-sensors: enhancing of bioimaging contrast.

PubMed

SalmanOgli, A; Behzadi, S; Rostami, A

2014-09-01

In this article, the nanoparticle-dye systems is designed and simulated to illustrate the possibility of enhancement in optical imaging contrast. For this, the firefly optimization technique is used as an optical signaling mechanism among agents (nanoparticle-dye) because fireflies attract together due to their flashing light and optical signaling that is produced by a process of bioluminescence (also it has been investigated that other parameters such as neural response and brain function have essential role in attracting fireflies to each other). The first parameter is coincided with our work, because the nanoparticle-dye systems have ability to augment of received light and its amplification cause that the designed complex system act as a brightness particle. This induced behavior of nanoparticles can be considered as an optical communication and signaling. Indeed by functionalization of nanoparticles and then due to higher brightness of the tumor site because of active targeting, the other particles can be guided to reach toward the target point and the signaling among agents is done by optical relation similar to firefly nature. Moreover, the fundamental of this work is the use of surface plasmon resonance and plasmons hybridization, in which photonic signals can be manipulated on the nanoscale and can be used in biomedical applications such as electromagnetic field enhancement. Finally, it can be mentioned that by simultaneously using plasmon hybridization, near-field augmentation, and firefly algorithm, the optical imaging contrast can be impressively improved.

DOSIMETRIC CONSEQUENCES OF USING CONTRAST-ENHANCED COMPUTED TOMOGRAPHIC IMAGES FOR INTENSITY-MODULATED STEREOTACTIC BODY RADIOTHERAPY PLANNING.

PubMed

Yoshikawa, Hiroto; Roback, Donald M; Larue, Susan M; Nolan, Michael W

2015-01-01

Potential benefits of planning radiation therapy on a contrast-enhanced computed tomography scan (ceCT) should be weighed against the possibility that this practice may be associated with an inadvertent risk of overdosing nearby normal tissues. This study investigated the influence of ceCT on intensity-modulated stereotactic body radiotherapy (IM-SBRT) planning. Dogs with head and neck, pelvic, or appendicular tumors were included in this retrospective cross-sectional study. All IM-SBRT plans were constructed on a pre- or ceCT. Contours for tumor and organs at risk (OAR) were manually constructed and copied onto both CT's; IM-SBRT plans were calculated on each CT in a manner that resulted in equal radiation fluence. The maximum and mean doses for OAR, and minimum, maximum, and mean doses for targets were compared. Data were collected from 40 dogs per anatomic site (head and neck, pelvis, and limbs). The average dose difference between minimum, maximum, and mean doses as calculated on pre- and ceCT plans for the gross tumor volume was less than 1% for all anatomic sites. Similarly, the differences between mean and maximum doses for OAR were less than 1%. The difference in dose distribution between plans made on CTs with and without contrast enhancement was tolerable at all treatment sites. Therefore, although caution would be recommended when planning IM-SBRT for tumors near "reservoirs" for contrast media (such as the heart and urinary bladder), findings supported the use of ceCT with this dose calculation algorithm for both target delineation and IM-SBRT treatment planning. © 2015 American College of Veterinary Radiology.

Contrast-enhanced imaging of SPIO-labeled platelets using magnetomotive ultrasound

PubMed Central

Pope, Ava G.; Wu, Gongting; McWhorter, Frances Y.; Merricks, Elizabeth C.; Nichols, Timothy C.; Czernuszewicz, Tomasz J.; Gallippi, Caterina M.; Oldenburg, Amy L.

2013-01-01

The ability to image platelets in vivo can provide insight into blood clotting processes and coagulopathies, and aid in identifying sites of vascular endothelial damage related to trauma or cardiovascular disease. Toward this end, we have developed a magnetomotive ultrasound (MMUS) system that provides contrast-enhanced imaging of superparamagnetic iron oxide (SPIO) labeled platelets via magnetically-induced vibration. Platelets are a promising platform for functional imaging contrast because they readily take up SPIOs and are easily harvested from blood. Here we report a novel MMUS system that accommodates an arbitrarily thick sample while maintaining portability. We employed a frequency- and phase-locked motion detection algorithm based on bandpass filtering of the differential RF phase, which allows for the detection of sub-resolution vibration amplitudes on the order of several nanometers. We then demonstrated MMUS in homogenous tissue phantoms at SPIO concentrations as low as 0.09 mg/ml Fe (p < 0.0001, n = 6, t-test). Finally, we showed that our system is capable of 3-dimensional imaging of a 185 μL simulated clot containing SPIO-platelets. This highlights the potential utility for non-invasive imaging of platelet-rich clots, which would constitute a fundamental advance in technology for the study of hemostasis and detection of clinically relevant thrombi. PMID:24077004

Contrast-enhanced imaging of SPIO-labeled platelets using magnetomotive ultrasound

NASA Astrophysics Data System (ADS)

Pope, Ava G.; Wu, Gongting; McWhorter, Frances Y.; Merricks, Elizabeth P.; Nichols, Timothy C.; Czernuszewicz, Tomasz J.; Gallippi, Caterina M.; Oldenburg, Amy L.

2013-10-01

The ability to image platelets in vivo can provide insight into blood clotting processes and coagulopathies, and aid in identifying sites of vascular endothelial damage related to trauma or cardiovascular disease. Toward this end, we have developed a magnetomotive ultrasound (MMUS) system that provides contrast-enhanced imaging of superparamagnetic iron oxide (SPIO) labeled platelets via magnetically-induced vibration. Platelets are a promising platform for functional imaging contrast because they readily take up SPIOs and are easily harvested from blood. Here we report a novel MMUS system that accommodates an arbitrarily thick sample while maintaining portability. We employed a frequency- and phase-locked motion detection algorithm based on bandpass filtering of the differential RF phase, which allows for the detection of sub-resolution vibration amplitudes on the order of several nanometers. We then demonstrated MMUS in homogenous tissue phantoms at SPIO concentrations as low as 0.09 mg ml-1 Fe (p < 0.0001, n = 6, t-test). Finally, we showed that our system is capable of three-dimensional imaging of a 185 µL simulated clot containing SPIO-platelets. This highlights the potential utility for non-invasive imaging of platelet-rich clots, which would constitute a fundamental advance in technology for the study of hemostasis and detection of clinically relevant thrombi.

Contrast-enhanced imaging of SPIO-labeled platelets using magnetomotive ultrasound.

PubMed

Pope, Ava G; Wu, Gongting; McWhorter, Frances Y; Merricks, Elizabeth P; Nichols, Timothy C; Czernuszewicz, Tomasz J; Gallippi, Caterina M; Oldenburg, Amy L

2013-10-21

The ability to image platelets in vivo can provide insight into blood clotting processes and coagulopathies, and aid in identifying sites of vascular endothelial damage related to trauma or cardiovascular disease. Toward this end, we have developed a magnetomotive ultrasound (MMUS) system that provides contrast-enhanced imaging of superparamagnetic iron oxide (SPIO) labeled platelets via magnetically-induced vibration. Platelets are a promising platform for functional imaging contrast because they readily take up SPIOs and are easily harvested from blood. Here we report a novel MMUS system that accommodates an arbitrarily thick sample while maintaining portability. We employed a frequency- and phase-locked motion detection algorithm based on bandpass filtering of the differential RF phase, which allows for the detection of sub-resolution vibration amplitudes on the order of several nanometers. We then demonstrated MMUS in homogenous tissue phantoms at SPIO concentrations as low as 0.09 mg ml(-1) Fe (p < 0.0001, n = 6, t-test). Finally, we showed that our system is capable of three-dimensional imaging of a 185 µL simulated clot containing SPIO-platelets. This highlights the potential utility for non-invasive imaging of platelet-rich clots, which would constitute a fundamental advance in technology for the study of hemostasis and detection of clinically relevant thrombi.

Comparison of Oral Contrast-Enhanced Transabdominal Ultrasound Imaging With Transverse Contrast-Enhanced Computed Tomography in Preoperative Tumor Staging of Advanced Gastric Carcinoma.

PubMed

He, Xuemei; Sun, Jing; Huang, Xiaoling; Zeng, Chun; Ge, Yinggang; Zhang, Jun; Wu, Jingxian

2017-12-01

This study assessed the diagnostic performance of transabdominal oral contrast-enhanced ultrasound (US) imaging for preoperative tumor staging of advanced gastric carcinoma by comparing it with transverse contrast-enhanced computed tomography (CT). This retrospective study included 42 patients with advanced gastric cancer who underwent laparoscopy, radical surgery, or palliative surgery because of serious complications and had a body mass index of less than 25 kg/m 2 . A cereal-based oral contrast agent was used for transabdominal oral contrast-enhanced US. Retrospective analyses were conducted using preoperative tumor staging data acquired by either transabdominal oral contrast-enhanced US or transverse contrast-enhanced CT. Both contrast-enhanced US and contrast-enhanced CT examinations were reviewed by 2 experienced radiologists independently for preoperative tumor staging according to the seventh edition of the TNM classification. The accuracy, sensitivity, and specificity were calculated by comparing the results of contrast-enhanced US and contrast-enhanced CT with pathologic findings. The overall accuracies of the imaging modalities were compared by the McNemar test. No significant difference was noted in the overall accuracy of transabdominal oral contrast-enhanced US (86% [36 of 42]) and transverse contrast-enhanced CT (83% [35 of 42] P > .999). For stage T2 to T4 gastric cancer, the accuracies of transabdominal oral contrast-enhanced US were 88%, 86%, and 98%, respectively, and those of transverse contrast-enhanced CT were 93%, 83%, and 90%. The overall accuracy of transabdominal oral contrast-enhanced US was comparable with that of transverse contrast-enhanced CT for preoperative tumor staging of advanced gastric cancer. © 2017 by the American Institute of Ultrasound in Medicine.

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

NASA Astrophysics Data System (ADS)

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

2014-03-01

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

Understanding Physiological and Degenerative Natural Vision Mechanisms to Define Contrast and Contour Operators

PubMed Central

Demongeot, Jacques; Fouquet, Yannick; Tayyab, Muhammad; Vuillerme, Nicolas

2009-01-01

Background Dynamical systems like neural networks based on lateral inhibition have a large field of applications in image processing, robotics and morphogenesis modeling. In this paper, we will propose some examples of dynamical flows used in image contrasting and contouring. Methodology First we present the physiological basis of the retina function by showing the role of the lateral inhibition in the optical illusions and pathologic processes generation. Then, based on these biological considerations about the real vision mechanisms, we study an enhancement method for contrasting medical images, using either a discrete neural network approach, or its continuous version, i.e. a non-isotropic diffusion reaction partial differential system. Following this, we introduce other continuous operators based on similar biomimetic approaches: a chemotactic contrasting method, a viability contouring algorithm and an attentional focus operator. Then, we introduce the new notion of mixed potential Hamiltonian flows; we compare it with the watershed method and we use it for contouring. Conclusions We conclude by showing the utility of these biomimetic methods with some examples of application in medical imaging and computed assisted surgery. PMID:19547712

Synchronized multiartifact reduction with tomographic reconstruction (SMART-RECON): A statistical model based iterative image reconstruction method to eliminate limited-view artifacts and to mitigate the temporal-average artifacts in time-resolved CT

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

Chen, Guang-Hong, E-mail: gchen7@wisc.edu; Li, Yinsheng

Purpose: In x-ray computed tomography (CT), a violation of the Tuy data sufficiency condition leads to limited-view artifacts. In some applications, it is desirable to use data corresponding to a narrow temporal window to reconstruct images with reduced temporal-average artifacts. However, the need to reduce temporal-average artifacts in practice may result in a violation of the Tuy condition and thus undesirable limited-view artifacts. In this paper, the authors present a new iterative reconstruction method, synchronized multiartifact reduction with tomographic reconstruction (SMART-RECON), to eliminate limited-view artifacts using data acquired within an ultranarrow temporal window that severely violates the Tuy condition. Methods:more » In time-resolved contrast enhanced CT acquisitions, image contrast dynamically changes during data acquisition. Each image reconstructed from data acquired in a given temporal window represents one time frame and can be denoted as an image vector. Conventionally, each individual time frame is reconstructed independently. In this paper, all image frames are grouped into a spatial–temporal image matrix and are reconstructed together. Rather than the spatial and/or temporal smoothing regularizers commonly used in iterative image reconstruction, the nuclear norm of the spatial–temporal image matrix is used in SMART-RECON to regularize the reconstruction of all image time frames. This regularizer exploits the low-dimensional structure of the spatial–temporal image matrix to mitigate limited-view artifacts when an ultranarrow temporal window is desired in some applications to reduce temporal-average artifacts. Both numerical simulations in two dimensional image slices with known ground truth and in vivo human subject data acquired in a contrast enhanced cone beam CT exam have been used to validate the proposed SMART-RECON algorithm and to demonstrate the initial performance of the algorithm. Reconstruction errors and temporal fidelity of the reconstructed images were quantified using the relative root mean square error (rRMSE) and the universal quality index (UQI) in numerical simulations. The performance of the SMART-RECON algorithm was compared with that of the prior image constrained compressed sensing (PICCS) reconstruction quantitatively in simulations and qualitatively in human subject exam. Results: In numerical simulations, the 240{sup ∘} short scan angular span was divided into four consecutive 60{sup ∘} angular subsectors. SMART-RECON enables four high temporal fidelity images without limited-view artifacts. The average rRMSE is 16% and UQIs are 0.96 and 0.95 for the two local regions of interest, respectively. In contrast, the corresponding average rRMSE and UQIs are 25%, 0.78, and 0.81, respectively, for the PICCS reconstruction. Note that only one filtered backprojection image can be reconstructed from the same data set with an average rRMSE and UQIs are 45%, 0.71, and 0.79, respectively, to benchmark reconstruction accuracies. For in vivo contrast enhanced cone beam CT data acquired from a short scan angular span of 200{sup ∘}, three 66{sup ∘} angular subsectors were used in SMART-RECON. The results demonstrated clear contrast difference in three SMART-RECON reconstructed image volumes without limited-view artifacts. In contrast, for the same angular sectors, PICCS cannot reconstruct images without limited-view artifacts and with clear contrast difference in three reconstructed image volumes. Conclusions: In time-resolved CT, the proposed SMART-RECON method provides a new method to eliminate limited-view artifacts using data acquired in an ultranarrow temporal window, which corresponds to approximately 60{sup ∘} angular subsectors.« less

Infrared and visible image fusion with the target marked based on multi-resolution visual attention mechanisms

NASA Astrophysics Data System (ADS)

Huang, Yadong; Gao, Kun; Gong, Chen; Han, Lu; Guo, Yue

2016-03-01

During traditional multi-resolution infrared and visible image fusion processing, the low contrast ratio target may be weakened and become inconspicuous because of the opposite DN values in the source images. So a novel target pseudo-color enhanced image fusion algorithm based on the modified attention model and fast discrete curvelet transformation is proposed. The interesting target regions are extracted from source images by introducing the motion features gained from the modified attention model, and source images are performed the gray fusion via the rules based on physical characteristics of sensors in curvelet domain. The final fusion image is obtained by mapping extracted targets into the gray result with the proper pseudo-color instead. The experiments show that the algorithm can highlight dim targets effectively and improve SNR of fusion image.

Rest-Stress Limb Perfusion Imaging in Humans with Contrast Ultrasound Using Intermediate-Power Imaging and Microbubbles Resistant to Inertial Cavitation.

PubMed

Davidson, Brian P; Hodovan, James; Belcik, J Todd; Moccetti, Federico; Xie, Aris; Ammi, Azzdine Y; Lindner, Jonathan R

2017-05-01

Contrast-enhanced ultrasound (CEU) limb perfusion imaging is a promising approach for evaluating peripheral artery disease (PAD). However, low signal enhancement in skeletal muscle has necessitated high-power intermittent imaging algorithms, which are not clinically feasible. We hypothesized that CEU using a combination of intermediate power and a contrast agent resistant to inertial cavitation would allow real-time limb stress perfusion imaging. In normal volunteers, CEU of the calf skeletal muscle was performed on separate days with Sonazoid, Optison, or Definity. Progressive reduction in the ultrasound pulsing interval was used to assess the balance between signal enhancement and agent destruction at escalating mechanical indices (MI, 0.1-0.4). Real-time perfusion imaging at MI 0.1-0.4 using postdestructive replenishment kinetics was performed at rest and during 25 W plantar flexion contractile exercise. For Optison, limb perfusion imaging was unreliable at rest due to very low signal enhancement generated at all MIs and was possible during exercise-induced hyperemia only at MI 0.1 due to agent destruction at higher MIs. For Definity, signal intensity progressively increased with MI but was offset by microbubble destruction, which resulted in modest signal enhancement during CEU perfusion imaging and distortion of replenishment curves at MI ≥ 0.2. For Sonazoid, there strong signal enhancement at MI ≥ 0.2, with little destruction detected only at MI 0.4. Accordingly, high signal intensity and nondistorted perfusion imaging was possible at MI 0.2-0.3 and detected an 8.0- ± 5.7-fold flow reserve. Rest-stress limb perfusion imaging in humans with real-time CEU, which requires only seconds to perform, is possible using microbubbles with viscoelastic properties that produce strong nonlinear signal generation without destruction at intermediate acoustic pressures. Copyright © 2016 American Society of Echocardiography. All rights reserved.

Generalized PSF modeling for optimized quantitation in PET imaging.

PubMed

Ashrafinia, Saeed; Mohy-Ud-Din, Hassan; Karakatsanis, Nicolas A; Jha, Abhinav K; Casey, Michael E; Kadrmas, Dan J; Rahmim, Arman

2017-06-21

Point-spread function (PSF) modeling offers the ability to account for resolution degrading phenomena within the PET image generation framework. PSF modeling improves resolution and enhances contrast, but at the same time significantly alters image noise properties and induces edge overshoot effect. Thus, studying the effect of PSF modeling on quantitation task performance can be very important. Frameworks explored in the past involved a dichotomy of PSF versus no-PSF modeling. By contrast, the present work focuses on quantitative performance evaluation of standard uptake value (SUV) PET images, while incorporating a wide spectrum of PSF models, including those that under- and over-estimate the true PSF, for the potential of enhanced quantitation of SUVs. The developed framework first analytically models the true PSF, considering a range of resolution degradation phenomena (including photon non-collinearity, inter-crystal penetration and scattering) as present in data acquisitions with modern commercial PET systems. In the context of oncologic liver FDG PET imaging, we generated 200 noisy datasets per image-set (with clinically realistic noise levels) using an XCAT anthropomorphic phantom with liver tumours of varying sizes. These were subsequently reconstructed using the OS-EM algorithm with varying PSF modelled kernels. We focused on quantitation of both SUV mean and SUV max , including assessment of contrast recovery coefficients, as well as noise-bias characteristics (including both image roughness and coefficient of-variability), for different tumours/iterations/PSF kernels. It was observed that overestimated PSF yielded more accurate contrast recovery for a range of tumours, and typically improved quantitative performance. For a clinically reasonable number of iterations, edge enhancement due to PSF modeling (especially due to over-estimated PSF) was in fact seen to lower SUV mean bias in small tumours. Overall, the results indicate that exactly matched PSF modeling does not offer optimized PET quantitation, and that PSF overestimation may provide enhanced SUV quantitation. Furthermore, generalized PSF modeling may provide a valuable approach for quantitative tasks such as treatment-response assessment and prognostication.

X-ray phase contrast tomography by tracking near field speckle

PubMed Central

Wang, Hongchang; Berujon, Sebastien; Herzen, Julia; Atwood, Robert; Laundy, David; Hipp, Alexander; Sawhney, Kawal

2015-01-01

X-ray imaging techniques that capture variations in the x-ray phase can yield higher contrast images with lower x-ray dose than is possible with conventional absorption radiography. However, the extraction of phase information is often more difficult than the extraction of absorption information and requires a more sophisticated experimental arrangement. We here report a method for three-dimensional (3D) X-ray phase contrast computed tomography (CT) which gives quantitative volumetric information on the real part of the refractive index. The method is based on the recently developed X-ray speckle tracking technique in which the displacement of near field speckle is tracked using a digital image correlation algorithm. In addition to differential phase contrast projection images, the method allows the dark-field images to be simultaneously extracted. After reconstruction, compared to conventional absorption CT images, the 3D phase CT images show greatly enhanced contrast. This new imaging method has advantages compared to other X-ray imaging methods in simplicity of experimental arrangement, speed of measurement and relative insensitivity to beam movements. These features make the technique an attractive candidate for material imaging such as in-vivo imaging of biological systems containing soft tissue. PMID:25735237

Fast and automatic algorithm for optic disc extraction in retinal images using principle-component-analysis-based preprocessing and curvelet transform.

PubMed

Shahbeig, Saleh; Pourghassem, Hossein

2013-01-01

Optic disc or optic nerve (ON) head extraction in retinal images has widespread applications in retinal disease diagnosis and human identification in biometric systems. This paper introduces a fast and automatic algorithm for detecting and extracting the ON region accurately from the retinal images without the use of the blood-vessel information. In this algorithm, to compensate for the destructive changes of the illumination and also enhance the contrast of the retinal images, we estimate the illumination of background and apply an adaptive correction function on the curvelet transform coefficients of retinal images. In other words, we eliminate the fault factors and pave the way to extract the ON region exactly. Then, we detect the ON region from retinal images using the morphology operators based on geodesic conversions, by applying a proper adaptive correction function on the reconstructed image's curvelet transform coefficients and a novel powerful criterion. Finally, using a local thresholding on the detected area of the retinal images, we extract the ON region. The proposed algorithm is evaluated on available images of DRIVE and STARE databases. The experimental results indicate that the proposed algorithm obtains an accuracy rate of 100% and 97.53% for the ON extractions on DRIVE and STARE databases, respectively.

LETTER TO THE EDITOR: Free-response operator characteristic models for visual search

NASA Astrophysics Data System (ADS)

Hutchinson, T. P.

2007-05-01

Computed tomography of diffraction enhanced imaging (DEI-CT) is a novel x-ray phase-contrast computed tomography which is applied to inspect weakly absorbing low-Z samples. Refraction-angle images which are extracted from a series of raw DEI images measured in different positions of the rocking curve of the analyser can be regarded as projections of DEI-CT. Based on them, the distribution of refractive index decrement in the sample can be reconstructed according to the principles of CT. How to combine extraction methods and reconstruction algorithms to obtain the most accurate reconstructed results is investigated in detail in this paper. Two kinds of comparison, the comparison of different extraction methods and the comparison between 'two-step' algorithms and the Hilbert filtered backprojection (HFBP) algorithm, draw the conclusion that the HFBP algorithm based on the maximum refraction-angle (MRA) method may be the best combination at present. Though all current extraction methods including the MRA method are approximate methods and cannot calculate very large refraction-angle values, the HFBP algorithm based on the MRA method is able to provide quite acceptable estimations of the distribution of refractive index decrement of the sample. The conclusion is proved by the experimental results at the Beijing Synchrotron Radiation Facility.

[Non-palpable testicular tumors in adults: A management based on imaging? Issue from the French Urologic Association Genital Cancer committee's edit].

PubMed

Rocher, L; Feretti, L; Camparo, P; Savoie, P H; Morel-Journel, N; Murez, T; Sebe, P; Flechon, A; Méjean, A; Durand, X

2018-04-09

Help in management of non-palpable testicular tumors. French Urologic Association Genital cancer committee's Edit. To review their characterization at imaging findings of non-palpable testicular tumors. Literature review (PubMed, Medline) of urological and radiological studies dealing with testicular tumors using keywords: non-palpable/incidental testicular tumors; color Doppler ultrasound; US elastography; magnetic resonance imaging; contrast enhanced sonography; partial surgery. Color Doppler is the basic exam. The size, the presence of microlithts/microlithiasis/macrocalcifications, the vascular architecture are major semiological findings to suggest the benign or the malignant nature of the lesion. Other techniques like multiparametric MRI, contrast-enhanced sonography, sonographic elastography are still in evaluation. The frequency of benign tumors such as Leydig cell tumors lead to preservation management, through improved characterization, monitoring or tumorectomy. Non-randomized study - a very few prospective studies. The era of total orchiectomy for any uncertain testicular lesion is over. We try the challenge of characterization, and define management's algorithms based on the suspected nature of the tumors. Copyright © 2018 Elsevier Masson SAS. All rights reserved.

Accuracy and cost-effectiveness of dynamic contrast-enhanced CT in the characterisation of solitary pulmonary nodules-the SPUtNIk study.

PubMed

Qureshi, N R; Rintoul, R C; Miles, K A; George, S; Harris, S; Madden, J; Cozens, K; Little, L A; Eichhorst, K; Jones, J; Moate, P; McClement, C; Pike, L; Sinclair, D; Wong, W L; Shekhdar, J; Eaton, R; Shah, A; Brindle, L; Peebles, C; Banerjee, A; Dizdarevic, S; Han, S; Poon, F W; Groves, A M; Kurban, L; Frew, A J; Callister, M E; Crosbie, P; Gleeson, F V; Karunasaagarar, K; Kankam, O; Gilbert, F J

2016-01-01

Solitary pulmonary nodules (SPNs) are common on CT. The most cost-effective investigation algorithm is still to be determined. Dynamic contrast-enhanced CT (DCE-CT) is an established diagnostic test not widely available in the UK currently. The SPUtNIk study will assess the diagnostic accuracy, clinical utility and cost-effectiveness of DCE-CT, alongside the current CT and 18-flurodeoxyglucose-positron emission tomography) ( 18 FDG-PET)-CT nodule characterisation strategies in the National Health Service (NHS). Image acquisition and data analysis for 18 FDG-PET-CT and DCE-CT will follow a standardised protocol with central review of 10% to ensure quality assurance. Decision analytic modelling will assess the likely costs and health outcomes resulting from incorporation of DCE-CT into management strategies for patients with SPNs. Approval has been granted by the South West Research Ethics Committee. Ethics reference number 12/SW/0206. The results of the trial will be presented at national and international meetings and published in an Health Technology Assessment (HTA) Monograph and in peer-reviewed journals. ISRCTN30784948; Pre-results.

The cascaded moving k-means and fuzzy c-means clustering algorithms for unsupervised segmentation of malaria images

NASA Astrophysics Data System (ADS)

Abdul-Nasir, Aimi Salihah; Mashor, Mohd Yusoff; Halim, Nurul Hazwani Abd; Mohamed, Zeehaida

2015-05-01

Malaria is a life-threatening parasitic infectious disease that corresponds for nearly one million deaths each year. Due to the requirement of prompt and accurate diagnosis of malaria, the current study has proposed an unsupervised pixel segmentation based on clustering algorithm in order to obtain the fully segmented red blood cells (RBCs) infected with malaria parasites based on the thin blood smear images of P. vivax species. In order to obtain the segmented infected cell, the malaria images are first enhanced by using modified global contrast stretching technique. Then, an unsupervised segmentation technique based on clustering algorithm has been applied on the intensity component of malaria image in order to segment the infected cell from its blood cells background. In this study, cascaded moving k-means (MKM) and fuzzy c-means (FCM) clustering algorithms has been proposed for malaria slide image segmentation. After that, median filter algorithm has been applied to smooth the image as well as to remove any unwanted regions such as small background pixels from the image. Finally, seeded region growing area extraction algorithm has been applied in order to remove large unwanted regions that are still appeared on the image due to their size in which cannot be cleaned by using median filter. The effectiveness of the proposed cascaded MKM and FCM clustering algorithms has been analyzed qualitatively and quantitatively by comparing the proposed cascaded clustering algorithm with MKM and FCM clustering algorithms. Overall, the results indicate that segmentation using the proposed cascaded clustering algorithm has produced the best segmentation performances by achieving acceptable sensitivity as well as high specificity and accuracy values compared to the segmentation results provided by MKM and FCM algorithms.

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.

Automated metastatic brain lesion detection: a computer aided diagnostic and clinical research tool

NASA Astrophysics Data System (ADS)

Devine, Jeremy; Sahgal, Arjun; Karam, Irene; Martel, Anne L.

2016-03-01

The accurate localization of brain metastases in magnetic resonance (MR) images is crucial for patients undergoing stereotactic radiosurgery (SRS) to ensure that all neoplastic foci are targeted. Computer automated tumor localization and analysis can improve both of these tasks by eliminating inter and intra-observer variations during the MR image reading process. Lesion localization is accomplished using adaptive thresholding to extract enhancing objects. Each enhancing object is represented as a vector of features which includes information on object size, symmetry, position, shape, and context. These vectors are then used to train a random forest classifier. We trained and tested the image analysis pipeline on 3D axial contrast-enhanced MR images with the intention of localizing the brain metastases. In our cross validation study and at the most effective algorithm operating point, we were able to identify 90% of the lesions at a precision rate of 60%.

Arterial tree tracking from anatomical landmarks in magnetic resonance angiography scans

NASA Astrophysics Data System (ADS)

O'Neil, Alison; Beveridge, Erin; Houston, Graeme; McCormick, Lynne; Poole, Ian

2014-03-01

This paper reports on arterial tree tracking in fourteen Contrast Enhanced MRA volumetric scans, given the positions of a predefined set of vascular landmarks, by using the A* algorithm to find the optimal path for each vessel based on voxel intensity and a learnt vascular probability atlas. The algorithm is intended for use in conjunction with an automatic landmark detection step, to enable fully automatic arterial tree tracking. The scan is filtered to give two further images using the top-hat transform with 4mm and 8mm cubic structuring elements. Vessels are then tracked independently on the scan in which the vessel of interest is best enhanced, as determined from knowledge of typical vessel diameter and surrounding structures. A vascular probability atlas modelling expected vessel location and orientation is constructed by non-rigidly registering the training scans to the test scan using a 3D thin plate spline to match landmark correspondences, and employing kernel density estimation with the ground truth center line points to form a probability density distribution. Threshold estimation by histogram analysis is used to segment background from vessel intensities. The A* algorithm is run using a linear cost function constructed from the threshold and the vascular atlas prior. Tracking results are presented for all major arteries excluding those in the upper limbs. An improvement was observed when tracking was informed by contextual information, with particular benefit for peripheral vessels.

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

PubMed

Anderson, Jeffrey R; Barrett, Steven F

2009-01-01

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

Virtual unrolling and deciphering of Herculaneum papyri by X-ray phase-contrast tomography

PubMed Central

Bukreeva, I.; Mittone, A.; Bravin, A.; Festa, G.; Alessandrelli, M.; Coan, P.; Formoso, V.; Agostino, R. G.; Giocondo, M.; Ciuchi, F.; Fratini, M.; Massimi, L.; Lamarra, A.; Andreani, C.; Bartolino, R.; Gigli, G.; Ranocchia, G.; Cedola, A.

2016-01-01

A collection of more than 1800 carbonized papyri, discovered in the Roman ‘Villa dei Papiri’ at Herculaneum is the unique classical library survived from antiquity. These papyri were charred during 79 A.D. Vesuvius eruption, a circumstance which providentially preserved them until now. This magnificent collection contains an impressive amount of treatises by Greek philosophers and, especially, Philodemus of Gadara, an Epicurean thinker of 1st century BC. We read many portions of text hidden inside carbonized Herculaneum papyri using enhanced X-ray phase-contrast tomography non-destructive technique and a new set of numerical algorithms for ‘virtual-unrolling’. Our success lies in revealing the largest portion of Greek text ever detected so far inside unopened scrolls, with unprecedented spatial resolution and contrast, all without damaging these precious historical manuscripts. Parts of text have been decoded and the ‘voice’ of the Epicurean philosopher Philodemus is brought back again after 2000 years from Herculaneum papyri. PMID:27265417

An efficient method for automatic morphological abnormality detection from human sperm images.

PubMed

Ghasemian, Fatemeh; Mirroshandel, Seyed Abolghasem; Monji-Azad, Sara; Azarnia, Mahnaz; Zahiri, Ziba

2015-12-01

Sperm morphology analysis (SMA) is an important factor in the diagnosis of human male infertility. This study presents an automatic algorithm for sperm morphology analysis (to detect malformation) using images of human sperm cells. The SMA method was used to detect and analyze different parts of the human sperm. First of all, SMA removes the image noises and enhances the contrast of the image to a great extent. Then it recognizes the different parts of sperm (e.g., head, tail) and analyzes the size and shape of each part. Finally, the algorithm classifies each sperm as normal or abnormal. Malformations in the head, midpiece, and tail of a sperm, can be detected by the SMA method. In contrast to other similar methods, the SMA method can work with low resolution and non-stained images. Furthermore, an image collection created for the SMA, has also been described in this study. This benchmark consists of 1457 sperm images from 235 patients, and is known as human sperm morphology analysis dataset (HSMA-DS). The proposed algorithm was tested on HSMA-DS. The experimental results show the high ability of SMA to detect morphological deformities from sperm images. In this study, the SMA algorithm produced above 90% accuracy in sperm abnormality detection task. Another advantage of the proposed method is its low computation time (that is, less than 9s), as such, the expert can quickly decide to choose the analyzed sperm or select another one. Automatic and fast analysis of human sperm morphology can be useful during intracytoplasmic sperm injection for helping embryologists to select the best sperm in real time. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Quantitative evaluation of mucosal vascular contrast in narrow band imaging using Monte Carlo modeling

NASA Astrophysics Data System (ADS)

Le, Du; Wang, Quanzeng; Ramella-Roman, Jessica; Pfefer, Joshua

2012-06-01

Narrow-band imaging (NBI) is a spectrally-selective reflectance imaging technique for enhanced visualization of superficial vasculature. Prior clinical studies have indicated NBI's potential for detection of vasculature abnormalities associated with gastrointestinal mucosal neoplasia. While the basic mechanisms behind the increased vessel contrast - hemoglobin absorption and tissue scattering - are known, a quantitative understanding of the effect of tissue and device parameters has not been achieved. In this investigation, we developed and implemented a numerical model of light propagation that simulates NBI reflectance distributions. This was accomplished by incorporating mucosal tissue layers and vessel-like structures in a voxel-based Monte Carlo algorithm. Epithelial and mucosal layers as well as blood vessels were defined using wavelength-specific optical properties. The model was implemented to calculate reflectance distributions and vessel contrast values as a function of vessel depth (0.05 to 0.50 mm) and diameter (0.01 to 0.10 mm). These relationships were determined for NBI wavelengths of 410 nm and 540 nm, as well as broadband illumination common to standard endoscopic imaging. The effects of illumination bandwidth on vessel contrast were also simulated. Our results provide a quantitative analysis of the effect of absorption and scattering on vessel contrast. Additional insights and potential approaches for improving NBI system contrast are discussed.

Automatic detection of larynx cancer from contrast-enhanced magnetic resonance images

NASA Astrophysics Data System (ADS)

Doshi, Trushali; Soraghan, John; Grose, Derek; MacKenzie, Kenneth; Petropoulakis, Lykourgos

2015-03-01

Detection of larynx cancer from medical imaging is important for the quantification and for the definition of target volumes in radiotherapy treatment planning (RTP). Magnetic resonance imaging (MRI) is being increasingly used in RTP due to its high resolution and excellent soft tissue contrast. Manually detecting larynx cancer from sequential MRI is time consuming and subjective. The large diversity of cancer in terms of geometry, non-distinct boundaries combined with the presence of normal anatomical regions close to the cancer regions necessitates the development of automatic and robust algorithms for this task. A new automatic algorithm for the detection of larynx cancer from 2D gadoliniumenhanced T1-weighted (T1+Gd) MRI to assist clinicians in RTP is presented. The algorithm employs edge detection using spatial neighborhood information of pixels and incorporates this information in a fuzzy c-means clustering process to robustly separate different tissues types. Furthermore, it utilizes the information of the expected cancerous location for cancer regions labeling. Comparison of this automatic detection system with manual clinical detection on real T1+Gd axial MRI slices of 2 patients (24 MRI slices) with visible larynx cancer yields an average dice similarity coefficient of 0.78+/-0.04 and average root mean square error of 1.82+/-0.28 mm. Preliminary results show that this fully automatic system can assist clinicians in RTP by obtaining quantifiable and non-subjective repeatable detection results in a particular time-efficient and unbiased fashion.

Automatic rock detection for in situ spectroscopy applications on Mars

NASA Astrophysics Data System (ADS)

Mahapatra, Pooja; Foing, Bernard H.

A novel algorithm for rock detection has been developed for effectively utilising Mars rovers, and enabling autonomous selection of target rocks that require close-contact spectroscopic measurements. The algorithm demarcates small rocks in terrain images as seen by cameras on a Mars rover during traverse. This information may be used by the rover for selection of geologically relevant sample rocks, and (in conjunction with a rangefinder) to pick up target samples using a robotic arm for automatic in situ determination of rock composition and mineralogy using, for example, a Raman spectrometer. Determining rock samples within the region that are of specific interest without physically approaching them significantly reduces time, power and risk. Input images in colour are converted to greyscale for intensity analysis. Bilateral filtering is used for texture removal while preserving rock boundaries. Unsharp masking is used for contrast enhance-ment. Sharp contrasts in intensities are detected using Canny edge detection, with thresholds that are calculated from the image obtained after contrast-limited adaptive histogram equalisation of the unsharp masked image. Scale-space representations are then generated by convolving this image with a Gaussian kernel. A scale-invariant blob detector (Laplacian of the Gaussian, LoG) detects blobs independently of their sizes, and therefore requires a multi-scale approach with automatic scale se-lection. The scale-space blob detector consists of convolution of the Canny edge-detected image with a scale-normalised LoG at several scales, and finding the maxima of squared LoG response in scale-space. After the extraction of local intensity extrema, the intensity profiles along rays going out of the local extremum are investigated. An ellipse is fitted to the region determined by significant changes in the intensity profiles. The fitted ellipses are overlaid on the original Mars terrain image for a visual estimation of the rock detection accuracy, and the number of ellipses are counted. Since geometry and illumination have the least effect on small rocks, the proposed algorithm is effective in detecting small rocks (or bigger rocks at larger distances from the camera) that consist of a small fraction of image pixels. Acknowledgements: The first author would like to express her gratitude to the European Space Agency (ESA/ESTEC) and the International Lunar Exploration Working Group (ILEWG) for their support of this work.

SU-F-P-06: Moving From Computed Radiography to Digital Radiography: A Collaborative Approach to Improve Image Quality

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

Sandoval, D; Mlady, G; Selwyn, R

Purpose: To bring together radiologists, technologists, and physicists to utilize post-processing techniques in digital radiography (DR) in order to optimize image acquisition and improve image quality. Methods: Sub-optimal images acquired on a new General Electric (GE) DR system were flagged for follow-up by radiologists and reviewed by technologists and medical physicists. Various exam types from adult musculoskeletal (n=35), adult chest (n=4), and pediatric (n=7) were chosen for review. 673 total images were reviewed. These images were processed using five customized algorithms provided by GE. An image score sheet was created allowing the radiologist to assign a numeric score to eachmore » of the processed images, this allowed for objective comparison to the original images. Each image was scored based on seven properties: 1) overall image look, 2) soft tissue contrast, 3) high contrast, 4) latitude, 5) tissue equalization, 6) edge enhancement, 7) visualization of structures. Additional space allowed for additional comments not captured in scoring categories. Radiologists scored the images from 1 – 10 with 1 being non-diagnostic quality and 10 being superior diagnostic quality. Scores for each custom algorithm for each image set were summed. The algorithm with the highest score for each image set was then set as the default processing. Results: Images placed into the PACS “QC folder” for image processing reasons decreased. Feedback from radiologists was, overall, that image quality for these studies had improved. All default processing for these image types was changed to the new algorithm. Conclusion: This work is an example of the collaboration between radiologists, technologists, and physicists at the University of New Mexico to add value to the radiology department. The significant amount of work required to prepare the processing algorithms, reprocessing and scoring of the images was eagerly taken on by all team members in order to produce better quality images and improve patient care.« less

Infrared image enhancement based on the edge detection and mathematical morphology

NASA Astrophysics Data System (ADS)

Zhang, Linlin; Zhao, Yuejin; Dong, Liquan; Liu, Xiaohua; Yu, Xiaomei; Hui, Mei; Chu, Xuhong; Gong, Cheng

2010-11-01

The development of the un-cooled infrared imaging technology from military necessity. At present, It is widely applied in industrial, medicine, scientific and technological research and so on. The infrared radiation temperature distribution of the measured object's surface can be observed visually. The collection of infrared images from our laboratory has following characteristics: Strong spatial correlation, Low contrast , Poor visual effect; Without color or shadows because of gray image , and has low resolution; Low definition compare to the visible light image; Many kinds of noise are brought by the random disturbances of the external environment. Digital image processing are widely applied in many areas, it can now be studied up close and in detail in many research field. It has become one kind of important means of the human visual continuation. Traditional methods for image enhancement cannot capture the geometric information of images and tend to amplify noise. In order to remove noise and improve visual effect. Meanwhile, To overcome the above enhancement issues. The mathematical model of FPA unit was constructed based on matrix transformation theory. According to characteristics of FPA, Image enhancement algorithm which combined with mathematical morphology and edge detection are established. First of all, Image profile is obtained by using the edge detection combine with mathematical morphological operators. And then, through filling the template profile by original image to get the ideal background image, The image noise can be removed on the base of the above method. The experiments show that utilizing the proposed algorithm can enhance image detail and the signal to noise ratio.

Low dose reconstruction algorithm for differential phase contrast imaging.

PubMed

Wang, Zhentian; Huang, Zhifeng; Zhang, Li; Chen, Zhiqiang; Kang, Kejun; Yin, Hongxia; Wang, Zhenchang; Marco, Stampanoni

2011-01-01

Differential phase contrast imaging computed tomography (DPCI-CT) is a novel x-ray inspection method to reconstruct the distribution of refraction index rather than the attenuation coefficient in weakly absorbing samples. In this paper, we propose an iterative reconstruction algorithm for DPCI-CT which benefits from the new compressed sensing theory. We first realize a differential algebraic reconstruction technique (DART) by discretizing the projection process of the differential phase contrast imaging into a linear partial derivative matrix. In this way the compressed sensing reconstruction problem of DPCI reconstruction can be transformed to a resolved problem in the transmission imaging CT. Our algorithm has the potential to reconstruct the refraction index distribution of the sample from highly undersampled projection data. Thus it can significantly reduce the dose and inspection time. The proposed algorithm has been validated by numerical simulations and actual experiments.

Influence of adaptive statistical iterative reconstruction algorithm on image quality in coronary computed tomography angiography.

PubMed

Precht, Helle; Thygesen, Jesper; Gerke, Oke; Egstrup, Kenneth; Waaler, Dag; Lambrechtsen, Jess

2016-12-01

Coronary computed tomography angiography (CCTA) requires high spatial and temporal resolution, increased low contrast resolution for the assessment of coronary artery stenosis, plaque detection, and/or non-coronary pathology. Therefore, new reconstruction algorithms, particularly iterative reconstruction (IR) techniques, have been developed in an attempt to improve image quality with no cost in radiation exposure. To evaluate whether adaptive statistical iterative reconstruction (ASIR) enhances perceived image quality in CCTA compared to filtered back projection (FBP). Thirty patients underwent CCTA due to suspected coronary artery disease. Images were reconstructed using FBP, 30% ASIR, and 60% ASIR. Ninety image sets were evaluated by five observers using the subjective visual grading analysis (VGA) and assessed by proportional odds modeling. Objective quality assessment (contrast, noise, and the contrast-to-noise ratio [CNR]) was analyzed with linear mixed effects modeling on log-transformed data. The need for ethical approval was waived by the local ethics committee as the study only involved anonymously collected clinical data. VGA showed significant improvements in sharpness by comparing FBP with ASIR, resulting in odds ratios of 1.54 for 30% ASIR and 1.89 for 60% ASIR ( P  = 0.004). The objective measures showed significant differences between FBP and 60% ASIR ( P  < 0.0001) for noise, with an estimated ratio of 0.82, and for CNR, with an estimated ratio of 1.26. ASIR improved the subjective image quality of parameter sharpness and, objectively, reduced noise and increased CNR.

Enhancement and restoration of non-uniform illuminated Fundus Image of Retina obtained through thin layer of cataract.

PubMed

Mitra, Anirban; Roy, Sudipta; Roy, Somais; Setua, Sanjit Kumar

2018-03-01

Retinal fundus images are extensively used in manually or without human intervention to identify and analyze various diseases. Due to the comprehensive imaging arrangement, there is a large radiance, reflectance and contrast inconsistency within and across images. A novel method is proposed based on the cataract physical model to reduce the generated blurriness of the fundus image at the time of image acquisition through the thin layer of cataract by the fundus camera. After the blurriness reduction the method is proposed the enhancement procedure of the images with an objective on contrast perfection with no preamble of artifacts. Due to the uneven distribution of thickness of the cataract, the cataract surroundings are first predicted in the domain of frequency. Second, the resultant image of first step enhanced by the intensity histogram equalization in the adapted Hue Saturation Intensity (HSI) color image space such as the gamut problem can be avoided. The concluding image with suitable color and disparity is acquired by using the proposed max-min color correction approach. The result indicates that not only the proposed method can more effectively enhanced the non-uniform image of retina obtain through thin layer of cataract, but also the resulting image show appropriate brightness and saturation and maintain complete color space information. The projected enhancement method has been tested on the openly available datasets and the result evaluated with the standard used image enhancement algorithms and the cataract removal method. Results show noticeable development over existing methods. Cataract often prevents the clinician from objectively evaluating fundus feature. Cataract also affect subjective test. Enhancement and restoration of non-uniform illuminated Fundus Image of Retina obtained through thin layer of Cataract has shown here to be potentially beneficial. Copyright © 2018 Elsevier B.V. All rights reserved.

Clinical utility of three-dimensional contrast-enhanced ultrasound in the differentiation between noninvasive and invasive neoplasms of urinary bladder.

PubMed

Li, Qiu-yang; Tang, Jie; He, En-hui; Li, Yan-mi; Zhou, Yun; Zhang, Xu; Chen, Guangfu

2012-11-01

The purpose of this study was to evaluate the effectiveness of three-dimensional contrast-enhanced ultrasound in differentiating invasive and noninvasive neoplasms of urinary bladder. A total of 60 lesions in 60 consecutive patients with bladder tumors received three dimensional ultrasonography, low acoustic power contrast enhanced ultrasonography and low acoustic power three-dimensional contrast-enhanced ultrasound examination. The IU22 ultrasound scanner and a volume transducer were used and the ultrasound contrast agent was SonoVue. The contrast-specific sonographic imaging modes were PI (pulse inversion) and PM (power modulation). The three dimensional ultrasonography, contrast enhanced ultrasonography, and three-dimensional contrast-enhanced ultrasound images were independently reviewed by two readers who were not in the images acquisition. Images were analyzed off-site. A level of confidence in the diagnosis of tumor invasion of the muscle layer was assigned on a 5° scale. Receiver operating characteristic analysis was used to assess overall confidence in the diagnosis of muscle invasion by tumor. Kappa values were used to assess inter-readers agreement. Histologic diagnosis was obtained for all patients. Final pathologic staging revealed 44 noninvasive tumors and 16 invasive tumors. Three-dimensional contrast-enhanced ultrasound depicted all 16 muscle-invasive tumors. The diagnostic performance of three-dimensional contrast-enhanced ultrasound was better than those of three dimensional ultrasonography and contrast enhanced ultrasonography. The receiver operating characteristic curves were 0.976 and 0.967 for three-dimensional contrast-enhanced ultrasound, those for three dimensional ultrasonography were 0.881 and 0.869, those for contrast enhanced ultrasonography were 0.927 and 0.929. The kappa values in the three dimensional ultrasonography, contrast enhanced ultrasonography and three-dimensional contrast-enhanced ultrasound for inter-reader agreements were 0.717, 0.794 and 0.914. Three-dimensional contrast-enhanced ultrasound imaging, with contrast-enhanced spatial visualization is clinical useful for differentiating invasive and noninvasive neoplasms of urinary bladder objectively. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

An enhanced TIMESAT algorithm for estimating vegetation phenology metrics from MODIS data

USGS Publications Warehouse

Tan, B.; Morisette, J.T.; Wolfe, R.E.; Gao, F.; Ederer, G.A.; Nightingale, J.; Pedelty, J.A.

2011-01-01

An enhanced TIMESAT algorithm was developed for retrieving vegetation phenology metrics from 250 m and 500 m spatial resolution Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indexes (VI) over North America. MODIS VI data were pre-processed using snow-cover and land surface temperature data, and temporally smoothed with the enhanced TIMESAT algorithm. An objective third derivative test was applied to define key phenology dates and retrieve a set of phenology metrics. This algorithm has been applied to two MODIS VIs: Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). In this paper, we describe the algorithm and use EVI as an example to compare three sets of TIMESAT algorithm/MODIS VI combinations: a) original TIMESAT algorithm with original MODIS VI, b) original TIMESAT algorithm with pre-processed MODIS VI, and c) enhanced TIMESAT and pre-processed MODIS VI. All retrievals were compared with ground phenology observations, some made available through the National Phenology Network. Our results show that for MODIS data in middle to high latitude regions, snow and land surface temperature information is critical in retrieving phenology metrics from satellite observations. The results also show that the enhanced TIMESAT algorithm can better accommodate growing season start and end dates that vary significantly from year to year. The TIMESAT algorithm improvements contribute to more spatial coverage and more accurate retrievals of the phenology metrics. Among three sets of TIMESAT/MODIS VI combinations, the start of the growing season metric predicted by the enhanced TIMESAT algorithm using pre-processed MODIS VIs has the best associations with ground observed vegetation greenup dates. ?? 2010 IEEE.

An Enhanced TIMESAT Algorithm for Estimating Vegetation Phenology Metrics from MODIS Data

NASA Technical Reports Server (NTRS)

Tan, Bin; Morisette, Jeffrey T.; Wolfe, Robert E.; Gao, Feng; Ederer, Gregory A.; Nightingale, Joanne; Pedelty, Jeffrey A.

2012-01-01

An enhanced TIMESAT algorithm was developed for retrieving vegetation phenology metrics from 250 m and 500 m spatial resolution Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indexes (VI) over North America. MODIS VI data were pre-processed using snow-cover and land surface temperature data, and temporally smoothed with the enhanced TIMESAT algorithm. An objective third derivative test was applied to define key phenology dates and retrieve a set of phenology metrics. This algorithm has been applied to two MODIS VIs: Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). In this paper, we describe the algorithm and use EVI as an example to compare three sets of TIMESAT algorithm/MODIS VI combinations: a) original TIMESAT algorithm with original MODIS VI, b) original TIMESAT algorithm with pre-processed MODIS VI, and c) enhanced TIMESAT and pre-processed MODIS VI. All retrievals were compared with ground phenology observations, some made available through the National Phenology Network. Our results show that for MODIS data in middle to high latitude regions, snow and land surface temperature information is critical in retrieving phenology metrics from satellite observations. The results also show that the enhanced TIMESAT algorithm can better accommodate growing season start and end dates that vary significantly from year to year. The TIMESAT algorithm improvements contribute to more spatial coverage and more accurate retrievals of the phenology metrics. Among three sets of TIMESAT/MODIS VI combinations, the start of the growing season metric predicted by the enhanced TIMESAT algorithm using pre-processed MODIS VIs has the best associations with ground observed vegetation greenup dates.

Cryotherapy of Renal Lesions: Enhancement on Contrast-Enhanced Sonography on Postoperative Day 1 Does Not Imply Viable Tissue Persistence.

PubMed

Bertolotto, Michele; Siracusano, Salvatore; Cicero, Calogero; Iannelli, Mariano; Silvestri, Tommaso; Celia, Antonio; Guarise, Alessandro; Stacul, Fulvio

2017-02-01

To investigate whether persistent enhancement detected on contrast-enhanced sonography at postoperative day 1 (early contrast-enhanced sonography) after cryoablation of renal tumors implies the presence of residual viable tumor tissue, defined as residual enhancing tissue on reference imaging (computed tomography or magnetic resonance imaging) performed 6 months after the procedure. Seventy-four patients with percutaneous cryoablation of renal tumors had early contrast-enhanced sonography from November 2011 to August 2015. Two independent readers evaluated early contrast-enhanced sonographic findings and contrast-enhanced sonographic investigations performed 1 month after cryoablation of lesions that displayed enhancement on early contrast-enhanced sonography. They scored intralesional enhancement in 4 groups: no enhancement, few intralesional vessels, focal enhancing areas, and diffuse enhancement. Inter-reader agreement in evaluating lesion vascularity on early contrast-enhanced sonography was assessed with weighted κ statistics. Computed tomography or magnetic resonance imaging performed 6 months after the treatment was the reference procedure for assessing the absence or presence of residual disease. Inter-reader agreement in assessing intratumoral vascularization on early contrast-enhanced sonography was very good (κ = 0.90). Enhancement was absent for both readers in 33 of 74 cases; only a few intralesional vessels were visible in 21; whereas diffuse or focal enhancement was present in 13. In the remaining 7 patients, there were differences. Four lesions with focal enhancement on early contrast-enhanced sonography and 1 that was considered avascular had residual tumors on reference imaging. Ablation was successful in the remaining 69 of 74 patients (93%). After cryoablation, intratumoral enhancement on early contrast-enhanced sonography does not imply tumor cell viability. © 2016 by the American Institute of Ultrasound in Medicine.

Processing of Cryo-EM Movie Data.

PubMed

Ripstein, Z A; Rubinstein, J L

2016-01-01

Direct detector device (DDD) cameras dramatically enhance the capabilities of electron cryomicroscopy (cryo-EM) due to their improved detective quantum efficiency (DQE) relative to other detectors. DDDs use semiconductor technology that allows micrographs to be recorded as movies rather than integrated individual exposures. Movies from DDDs improve cryo-EM in another, more surprising, way. DDD movies revealed beam-induced specimen movement as a major source of image degradation and provide a way to partially correct the problem by aligning frames or regions of frames to account for this specimen movement. In this chapter, we use a self-consistent mathematical notation to explain, compare, and contrast several of the most popular existing algorithms for computationally correcting specimen movement in DDD movies. We conclude by discussing future developments in algorithms for processing DDD movies that would extend the capabilities of cryo-EM even further. © 2016 Elsevier Inc. All rights reserved.

Integrating instance selection, instance weighting, and feature weighting for nearest neighbor classifiers by coevolutionary algorithms.

PubMed

Derrac, Joaquín; Triguero, Isaac; Garcia, Salvador; Herrera, Francisco

2012-10-01

Cooperative coevolution is a successful trend of evolutionary computation which allows us to define partitions of the domain of a given problem, or to integrate several related techniques into one, by the use of evolutionary algorithms. It is possible to apply it to the development of advanced classification methods, which integrate several machine learning techniques into a single proposal. A novel approach integrating instance selection, instance weighting, and feature weighting into the framework of a coevolutionary model is presented in this paper. We compare it with a wide range of evolutionary and nonevolutionary related methods, in order to show the benefits of the employment of coevolution to apply the techniques considered simultaneously. The results obtained, contrasted through nonparametric statistical tests, show that our proposal outperforms other methods in the comparison, thus becoming a suitable tool in the task of enhancing the nearest neighbor classifier.

A hybrid continuous-wave terahertz imaging system

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

Dolganova, Irina N., E-mail: in.dolganova@gmail.com; Zaytsev, Kirill I., E-mail: kirzay@gmail.ru; Metelkina, Anna A.

2015-11-15

A hybrid (active-passive mode) terahertz (THz) imaging system and an algorithm for imaging synthesis are proposed to enhance the THz image quality. The concept of image contrast is used to compare active and passive THz imaging. Combining the measurement of the self-emitted radiation of the object with the back-scattered source radiation measurement, it becomes possible to use the THz image to retrieve maximum information about the object. The experimental results confirm the advantages of hybrid THz imaging systems, which can be generalized for a wide range of applications in the material sciences, chemical physics, bio-systems, etc.

A novel false color mapping model-based fusion method of visual and infrared images

NASA Astrophysics Data System (ADS)

Qi, Bin; Kun, Gao; Tian, Yue-xin; Zhu, Zhen-yu

2013-12-01

A fast and efficient image fusion method is presented to generate near-natural colors from panchromatic visual and thermal imaging sensors. Firstly, a set of daytime color reference images are analyzed and the false color mapping principle is proposed according to human's visual and emotional habits. That is, object colors should remain invariant after color mapping operations, differences between infrared and visual images should be enhanced and the background color should be consistent with the main scene content. Then a novel nonlinear color mapping model is given by introducing the geometric average value of the input visual and infrared image gray and the weighted average algorithm. To determine the control parameters in the mapping model, the boundary conditions are listed according to the mapping principle above. Fusion experiments show that the new fusion method can achieve the near-natural appearance of the fused image, and has the features of enhancing color contrasts and highlighting the infrared brilliant objects when comparing with the traditional TNO algorithm. Moreover, it owns the low complexity and is easy to realize real-time processing. So it is quite suitable for the nighttime imaging apparatus.

Duality quantum algorithm efficiently simulates open quantum systems

PubMed Central

Wei, Shi-Jie; Ruan, Dong; Long, Gui-Lu

2016-01-01

Because of inevitable coupling with the environment, nearly all practical quantum systems are open system, where the evolution is not necessarily unitary. In this paper, we propose a duality quantum algorithm for simulating Hamiltonian evolution of an open quantum system. In contrast to unitary evolution in a usual quantum computer, the evolution operator in a duality quantum computer is a linear combination of unitary operators. In this duality quantum algorithm, the time evolution of the open quantum system is realized by using Kraus operators which is naturally implemented in duality quantum computer. This duality quantum algorithm has two distinct advantages compared to existing quantum simulation algorithms with unitary evolution operations. Firstly, the query complexity of the algorithm is O(d3) in contrast to O(d4) in existing unitary simulation algorithm, where d is the dimension of the open quantum system. Secondly, By using a truncated Taylor series of the evolution operators, this duality quantum algorithm provides an exponential improvement in precision compared with previous unitary simulation algorithm. PMID:27464855

Enhancement of dynamic myocardial perfusion PET images based on low-rank plus sparse decomposition.

PubMed

Lu, Lijun; Ma, Xiaomian; Mohy-Ud-Din, Hassan; Ma, Jianhua; Feng, Qianjin; Rahmim, Arman; Chen, Wufan

2018-02-01

The absolute quantification of dynamic myocardial perfusion (MP) PET imaging is challenged by the limited spatial resolution of individual frame images due to division of the data into shorter frames. This study aims to develop a method for restoration and enhancement of dynamic PET images. We propose that the image restoration model should be based on multiple constraints rather than a single constraint, given the fact that the image characteristic is hardly described by a single constraint alone. At the same time, it may be possible, but not optimal, to regularize the image with multiple constraints simultaneously. Fortunately, MP PET images can be decomposed into a superposition of background vs. dynamic components via low-rank plus sparse (L + S) decomposition. Thus, we propose an L + S decomposition based MP PET image restoration model and express it as a convex optimization problem. An iterative soft thresholding algorithm was developed to solve the problem. Using realistic dynamic 82 Rb MP PET scan data, we optimized and compared its performance with other restoration methods. The proposed method resulted in substantial visual as well as quantitative accuracy improvements in terms of noise versus bias performance, as demonstrated in extensive 82 Rb MP PET simulations. In particular, the myocardium defect in the MP PET images had improved visual as well as contrast versus noise tradeoff. The proposed algorithm was also applied on an 8-min clinical cardiac 82 Rb MP PET study performed on the GE Discovery PET/CT, and demonstrated improved quantitative accuracy (CNR and SNR) compared to other algorithms. The proposed method is effective for restoration and enhancement of dynamic PET images. Copyright © 2017 Elsevier B.V. All rights reserved.

LED light design method for high contrast and uniform illumination imaging in machine vision.

PubMed

Wu, Xiaojun; Gao, Guangming

2018-03-01

In machine vision, illumination is very critical to determine the complexity of the inspection algorithms. Proper lights can obtain clear and sharp images with the highest contrast and low noise between the interested object and the background, which is conducive to the target being located, measured, or inspected. Contrary to the empirically based trial-and-error convention to select the off-the-shelf LED light in machine vision, an optimization algorithm for LED light design is proposed in this paper. It is composed of the contrast optimization modeling and the uniform illumination technology for non-normal incidence (UINI). The contrast optimization model is built based on the surface reflection characteristics, e.g., the roughness, the reflective index, and light direction, etc., to maximize the contrast between the features of interest and the background. The UINI can keep the uniformity of the optimized lighting by the contrast optimization model. The simulation and experimental results demonstrate that the optimization algorithm is effective and suitable to produce images with the highest contrast and uniformity, which is very inspirational to the design of LED illumination systems in machine vision.

A reconstruction method for cone-beam differential x-ray phase-contrast computed tomography.

PubMed

Fu, Jian; Velroyen, Astrid; Tan, Renbo; Zhang, Junwei; Chen, Liyuan; Tapfer, Arne; Bech, Martin; Pfeiffer, Franz

2012-09-10

Most existing differential phase-contrast computed tomography (DPC-CT) approaches are based on three kinds of scanning geometries, described by parallel-beam, fan-beam and cone-beam. Due to the potential of compact imaging systems with magnified spatial resolution, cone-beam DPC-CT has attracted significant interest. In this paper, we report a reconstruction method based on a back-projection filtration (BPF) algorithm for cone-beam DPC-CT. Due to the differential nature of phase contrast projections, the algorithm restrains from differentiation of the projection data prior to back-projection, unlike BPF algorithms commonly used for absorption-based CT data. This work comprises a numerical study of the algorithm and its experimental verification using a dataset measured with a three-grating interferometer and a micro-focus x-ray tube source. Moreover, the numerical simulation and experimental results demonstrate that the proposed method can deal with several classes of truncated cone-beam datasets. We believe that this feature is of particular interest for future medical cone-beam phase-contrast CT imaging applications.

Infrared image enhancement using H(infinity) bounds for surveillance applications.

PubMed

Qidwai, Uvais

2008-08-01

In this paper, two algorithms have been presented to enhance the infrared (IR) images. Using the autoregressive moving average model structure and H(infinity) optimal bounds, the image pixels are mapped from the IR pixel space into normal optical image space, thus enhancing the IR image for improved visual quality. Although H(infinity)-based system identification algorithms are very common now, they are not quite suitable for real-time applications owing to their complexity. However, many variants of such algorithms are possible that can overcome this constraint. Two such algorithms have been developed and implemented in this paper. Theoretical and algorithmic results show remarkable enhancement in the acquired images. This will help in enhancing the visual quality of IR images for surveillance applications.

Diagnostic accuracy of stress perfusion CMR in comparison with quantitative coronary angiography: fully quantitative, semiquantitative, and qualitative assessment.

PubMed

Mordini, Federico E; Haddad, Tariq; Hsu, Li-Yueh; Kellman, Peter; Lowrey, Tracy B; Aletras, Anthony H; Bandettini, W Patricia; Arai, Andrew E

2014-01-01

This study's primary objective was to determine the sensitivity, specificity, and accuracy of fully quantitative stress perfusion cardiac magnetic resonance (CMR) versus a reference standard of quantitative coronary angiography. We hypothesized that fully quantitative analysis of stress perfusion CMR would have high diagnostic accuracy for identifying significant coronary artery stenosis and exceed the accuracy of semiquantitative measures of perfusion and qualitative interpretation. Relatively few studies apply fully quantitative CMR perfusion measures to patients with coronary disease and comparisons to semiquantitative and qualitative methods are limited. Dual bolus dipyridamole stress perfusion CMR exams were performed in 67 patients with clinical indications for assessment of myocardial ischemia. Stress perfusion images alone were analyzed with a fully quantitative perfusion (QP) method and 3 semiquantitative methods including contrast enhancement ratio, upslope index, and upslope integral. Comprehensive exams (cine imaging, stress/rest perfusion, late gadolinium enhancement) were analyzed qualitatively with 2 methods including the Duke algorithm and standard clinical interpretation. A 70% or greater stenosis by quantitative coronary angiography was considered abnormal. The optimum diagnostic threshold for QP determined by receiver-operating characteristic curve occurred when endocardial flow decreased to <50% of mean epicardial flow, which yielded a sensitivity of 87% and specificity of 93%. The area under the curve for QP was 92%, which was superior to semiquantitative methods: contrast enhancement ratio: 78%; upslope index: 82%; and upslope integral: 75% (p = 0.011, p = 0.019, p = 0.004 vs. QP, respectively). Area under the curve for QP was also superior to qualitative methods: Duke algorithm: 70%; and clinical interpretation: 78% (p < 0.001 and p < 0.001 vs. QP, respectively). Fully quantitative stress perfusion CMR has high diagnostic accuracy for detecting obstructive coronary artery disease. QP outperforms semiquantitative measures of perfusion and qualitative methods that incorporate a combination of cine, perfusion, and late gadolinium enhancement imaging. These findings suggest a potential clinical role for quantitative stress perfusion CMR. Copyright © 2014 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

Speech enhancement on smartphone voice recording

NASA Astrophysics Data System (ADS)

Tris Atmaja, Bagus; Nur Farid, Mifta; Arifianto, Dhany

2016-11-01

Speech enhancement is challenging task in audio signal processing to enhance the quality of targeted speech signal while suppress other noises. In the beginning, the speech enhancement algorithm growth rapidly from spectral subtraction, Wiener filtering, spectral amplitude MMSE estimator to Non-negative Matrix Factorization (NMF). Smartphone as revolutionary device now is being used in all aspect of life including journalism; personally and professionally. Although many smartphones have two microphones (main and rear) the only main microphone is widely used for voice recording. This is why the NMF algorithm widely used for this purpose of speech enhancement. This paper evaluate speech enhancement on smartphone voice recording by using some algorithms mentioned previously. We also extend the NMF algorithm to Kulback-Leibler NMF with supervised separation. The last algorithm shows improved result compared to others by spectrogram and PESQ score evaluation.

Adaptive gamma correction-based expert system for nonuniform illumination face enhancement

NASA Astrophysics Data System (ADS)

Abdelhamid, Iratni; Mustapha, Aouache; Adel, Oulefki

2018-03-01

The image quality of a face recognition system suffers under severe lighting conditions. Thus, this study aims to develop an approach for nonuniform illumination adjustment based on an adaptive gamma correction (AdaptGC) filter that can solve the aforementioned issue. An approach for adaptive gain factor prediction was developed via neural network model-based cross-validation (NN-CV). To achieve this objective, a gamma correction function and its effects on the face image quality with different gain values were examined first. Second, an orientation histogram (OH) algorithm was assessed as a face's feature descriptor. Subsequently, a density histogram module was developed for face label generation. During the NN-CV construction, the model was assessed to recognize the OH descriptor and predict the face label. The performance of the NN-CV model was evaluated by examining the statistical measures of root mean square error and coefficient of efficiency. Third, to evaluate the AdaptGC enhancement approach, an image quality metric was adopted using enhancement by entropy, contrast per pixel, second-derivative-like measure of enhancement, and sharpness, then supported by visual inspection. The experiment results were examined using five face's databases, namely, extended Yale-B, Carnegie Mellon University-Pose, Illumination, and Expression, Mobio, FERET, and Oulu-CASIA-NIR-VIS. The final results prove that AdaptGC filter implementation compared with state-of-the-art methods is the best choice in terms of contrast and nonuniform illumination adjustment. In summary, the benefits attained prove that AdaptGC is driven by a profitable enhancement rate, which provides satisfying features for high rate face recognition systems.

Development of a stained cell nuclei counting system

NASA Astrophysics Data System (ADS)

Timilsina, Niranjan; Moffatt, Christopher; Okada, Kazunori

2011-03-01

This paper presents a novel cell counting system which exploits the Fast Radial Symmetry Transformation (FRST) algorithm [1]. The driving force behind our system is a research on neurogenesis in the intact nervous system of Manduca Sexta or the Tobacco Hornworm, which was being studied to assess the impact of age, food and environment on neurogenesis. The varying thickness of the intact nervous system in this species often yields images with inhomogeneous background and inconsistencies such as varying illumination, variable contrast, and irregular cell size. For automated counting, such inhomogeneity and inconsistencies must be addressed, which no existing work has done successfully. Thus, our goal is to devise a new cell counting algorithm for the images with non-uniform background. Our solution adapts FRST: a computer vision algorithm which is designed to detect points of interest on circular regions such as human eyes. This algorithm enhances the occurrences of the stained-cell nuclei in 2D digital images and negates the problems caused by their inhomogeneity. Besides FRST, our algorithm employs standard image processing methods, such as mathematical morphology and connected component analysis. We have evaluated the developed cell counting system with fourteen digital images of Tobacco Hornworm's nervous system collected for this study with ground-truth cell counts by biology experts. Experimental results show that our system has a minimum error of 1.41% and mean error of 16.68% which is at least forty-four percent better than the algorithm without FRST.

Robust perception algorithms for road and track autonomous following

NASA Astrophysics Data System (ADS)

Marion, Vincent; Lecointe, Olivier; Lewandowski, Cecile; Morillon, Joel G.; Aufrere, Romuald; Marcotegui, Beatrix; Chapuis, Roland; Beucher, Serge

2004-09-01

The French Military Robotic Study Program (introduced in Aerosense 2003), sponsored by the French Defense Procurement Agency and managed by Thales Airborne Systems as the prime contractor, focuses on about 15 robotic themes, which can provide an immediate "operational add-on value." The paper details the "road and track following" theme (named AUT2), which main purpose was to develop a vision based sub-system to automatically detect roadsides of an extended range of roads and tracks suitable to military missions. To achieve the goal, efforts focused on three main areas: (1) Improvement of images quality at algorithms inputs, thanks to the selection of adapted video cameras, and the development of a THALES patented algorithm: it removes in real time most of the disturbing shadows in images taken in natural environments, enhances contrast and lowers reflection effect due to films of water. (2) Selection and improvement of two complementary algorithms (one is segment oriented, the other region based) (3) Development of a fusion process between both algorithms, which feeds in real time a road model with the best available data. Each previous step has been developed so that the global perception process is reliable and safe: as an example, the process continuously evaluates itself and outputs confidence criteria qualifying roadside detection. The paper presents the processes in details, and the results got from passed military acceptance tests, which trigger the next step: autonomous track following (named AUT3).

[Digital breast tomosynthesis : technical principles, current clinical relevance and future perspectives].

PubMed

Hellerhoff, K

2010-11-01

In recent years digital full field mammography has increasingly replaced conventional film mammography. High quality imaging is guaranteed by high quantum efficiency and very good contrast resolution with optimized dosing even for women with dense glandular tissue. However, digital mammography remains a projection procedure by which overlapping tissue limits the detectability of subtle alterations. Tomosynthesis is a procedure developed from digital mammography for slice examination of breasts which eliminates the effects of overlapping tissue and allows 3D imaging of breasts. A curved movement of the X-ray tube during scanning allows the acquisition of many 2D images from different angles. Subseqently, reconstruction algorithms employing a shift and add method improve the recognition of details at a defined level and at the same time eliminate smear artefacts due to overlapping structures. The total dose corresponds to that of conventional mammography imaging. The technical procedure, including the number of levels, suitable anodes/filter combinations, angle regions of images and selection of reconstruction algorithms, is presently undergoing optimization. Previous studies on the clinical value of tomosynthesis have examined screening parameters, such as recall rate and detection rate as well as information on tumor extent for histologically proven breast tumors. More advanced techniques, such as contrast medium-enhanced tomosynthesis, are presently under development and dual-energy imaging is of particular importance.

Enhanced linear-array photoacoustic beamforming using modified coherence factor.

PubMed

Mozaffarzadeh, Moein; Yan, Yan; Mehrmohammadi, Mohammad; Makkiabadi, Bahador

2018-02-01

Photoacoustic imaging (PAI) is a promising medical imaging modality providing the spatial resolution of ultrasound imaging and the contrast of optical imaging. For linear-array PAI, a beamformer can be used as the reconstruction algorithm. Delay-and-sum (DAS) is the most prevalent beamforming algorithm in PAI. However, using DAS beamformer leads to low-resolution images as well as high sidelobes due to nondesired contribution of off-axis signals. Coherence factor (CF) is a weighting method in which each pixel of the reconstructed image is weighted, based on the spatial spectrum of the aperture, to mainly improve the contrast. We demonstrate that the numerator of the formula of CF contains a DAS algebra and propose the use of a delay-multiply-and-sum beamformer instead of the available DAS on the numerator. The proposed weighting technique, modified CF (MCF), has been evaluated numerically and experimentally compared to CF. It was shown that MCF leads to lower sidelobes and better detectable targets. The quantitative results of the experiment (using wire targets) show that MCF leads to for about 45% and 40% improvement, in comparison with CF, in the terms of signal-to-noise ratio and full-width-half-maximum, respectively. (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).

A Coherent VLSI Environment

DTIC Science & Technology

1987-03-31

processors . The symmetry-breaking algorithms give efficient ways to convert probabilistic algorithms to deterministic algorithms. Some of the...techniques have been applied to construct several efficient linear- processor algorithms for graph problems, including an O(lg* n)-time algorithm for (A + 1...On n-node graphs, the algorithm works in O(log 2 n) time using only n processors , in contrast to the previous best algorithm which used about n3

Enhanced round robin CPU scheduling with burst time based time quantum

NASA Astrophysics Data System (ADS)

Indusree, J. R.; Prabadevi, B.

2017-11-01

Process scheduling is a very important functionality of Operating system. The main-known process-scheduling algorithms are First Come First Serve (FCFS) algorithm, Round Robin (RR) algorithm, Priority scheduling algorithm and Shortest Job First (SJF) algorithm. Compared to its peers, Round Robin (RR) algorithm has the advantage that it gives fair share of CPU to the processes which are already in the ready-queue. The effectiveness of the RR algorithm greatly depends on chosen time quantum value. Through this research paper, we are proposing an enhanced algorithm called Enhanced Round Robin with Burst-time based Time Quantum (ERRBTQ) process scheduling algorithm which calculates time quantum as per the burst-time of processes already in ready queue. The experimental results and analysis of ERRBTQ algorithm clearly indicates the improved performance when compared with conventional RR and its variants.

Characterization of Intraventricular and Intracerebral Hematomas in Non-Contrast CT

PubMed Central

Nowinski, Wieslaw L; Gomolka, Ryszard S; Qian, Guoyu; Gupta, Varsha; Ullman, Natalie L; Hanley, Daniel F

2014-01-01

Summary Characterization of hematomas is essential in scan reading, manual delineation, and designing automatic segmentation algorithms. Our purpose is to characterize the distribution of intraventricular (IVH) and intracerebral hematomas (ICH) in NCCT scans, study their relationship to gray matter (GM), and to introduce a new tool for quantitative hematoma delineation. We used 289 serial retrospective scans of 51 patients. Hematomas were manually delineated in a two-stage process. Hematoma contours generated in the first stage were quantified and enhanced in the second stage. Delineation was based on new quantitative rules and hematoma profiling, and assisted by a dedicated tool superimposing quantitative information on scans with 3D hematoma display. The tool provides: density maps (40-85HU), contrast maps (8/15HU), mean horizontal/vertical contrasts for hematoma contours, and hematoma contours below a specified mean contrast (8HU). White matter (WM) and GM were segmented automatically. IVH/ICH on serial NCCT is characterized by 59.0HU mean, 60.0HU median, 11.6HU standard deviation, 23.9HU mean contrast, –0.99HU/day slope, and –0.24 skewness (changing over time from negative to positive). Its 0.1st-99.9th percentile range corresponds to 25-88HU range. WM and GM are highly correlated (R 2=0.88; p<10–10) whereas the GM-GS correlation is weak (R 2=0.14; p<10–10). The intersection point of mean GM-hematoma density distributions is at 55.6±5.8HU with the corresponding GM/hematoma percentiles of 88th/40th. Objective characterization of IVH/ICH and stating the rules quantitatively will aid raters to delineate hematomas more robustly and facilitate designing algorithms for automatic hematoma segmentation. Our two-stage process is general and potentially applicable to delineate other pathologies on various modalities more robustly and quantitatively. PMID:24976197

Characterization of intraventricular and intracerebral hematomas in non-contrast CT.

PubMed

Nowinski, Wieslaw L; Gomolka, Ryszard S; Qian, Guoyu; Gupta, Varsha; Ullman, Natalie L; Hanley, Daniel F

2014-06-01

Characterization of hematomas is essential in scan reading, manual delineation, and designing automatic segmentation algorithms. Our purpose is to characterize the distribution of intraventricular (IVH) and intracerebral hematomas (ICH) in NCCT scans, study their relationship to gray matter (GM), and to introduce a new tool for quantitative hematoma delineation. We used 289 serial retrospective scans of 51 patients. Hematomas were manually delineated in a two-stage process. Hematoma contours generated in the first stage were quantified and enhanced in the second stage. Delineation was based on new quantitative rules and hematoma profiling, and assisted by a dedicated tool superimposing quantitative information on scans with 3D hematoma display. The tool provides: density maps (40-85HU), contrast maps (8/15HU), mean horizontal/vertical contrasts for hematoma contours, and hematoma contours below a specified mean contrast (8HU). White matter (WM) and GM were segmented automatically. IVH/ICH on serial NCCT is characterized by 59.0HU mean, 60.0HU median, 11.6HU standard deviation, 23.9HU mean contrast, -0.99HU/day slope, and -0.24 skewness (changing over time from negative to positive). Its 0.1(st)-99.9(th) percentile range corresponds to 25-88HU range. WM and GM are highly correlated (R (2)=0.88; p<10(-10)) whereas the GM-GS correlation is weak (R (2)=0.14; p<10(-10)). The intersection point of mean GM-hematoma density distributions is at 55.6±5.8HU with the corresponding GM/hematoma percentiles of 88(th)/40(th). Objective characterization of IVH/ICH and stating the rules quantitatively will aid raters to delineate hematomas more robustly and facilitate designing algorithms for automatic hematoma segmentation. Our two-stage process is general and potentially applicable to delineate other pathologies on various modalities more robustly and quantitatively.

Journal Club: Comparison of assessment of preoperative pulmonary vasculature in patients with non-small cell lung cancer by non-contrast- and 4D contrast-enhanced 3-T MR angiography and contrast-enhanced 64-MDCT.

PubMed

Ohno, Yoshiharu; Nishio, Mizuho; Koyama, Hisanobu; Yoshikawa, Takeshi; Matsumoto, Sumiaki; Seki, Shinichiro; Sugimura, Kazuro

2014-03-01

The purpose of this article is to prospectively and directly compare the capabilities of non-contrast-enhanced MR angiography (MRA), 4D contrast-enhanced MRA, and contrast-enhanced MDCT for assessing pulmonary vasculature in patients with non-small cell lung cancer (NSCLC) before surgical treatment. A total of 77 consecutive patients (41 men and 36 women; mean age, 71 years) with pathologically proven and clinically assessed stage I NSCLC underwent thin-section contrast-enhanced MDCT, non-contrast-enhanced and contrast-enhanced MRA, and surgical treatment. The capability for anomaly assessment of the three methods was independently evaluated by two reviewers using a 5-point visual scoring system, and final assessment for each patient was made by consensus of the two readers. Interobserver agreement for pulmonary arterial and venous assessment was evaluated with the kappa statistic. Then, sensitivity, specificity, and accuracy for the detection of anomalies were directly compared among the three methods by use of the McNemar test. Interobserver agreement for pulmonary artery and vein assessment was substantial or almost perfect (κ=0.72-0.86). For pulmonary arterial and venous variation assessment, there were no significant differences in sensitivity, specificity, and accuracy among non-contrast-enhanced MRA (pulmonary arteries: sensitivity, 77.1%; specificity, 97.4%; accuracy, 87.7%; pulmonary veins: sensitivity, 50%; specificity, 98.5%; accuracy, 93.2%), 4D contrast-enhanced MRA (pulmonary arteries: sensitivity, 77.1%; specificity, 97.4%; accuracy, 87.7%; pulmonary veins: sensitivity, 62.5%; specificity, 100.0%; accuracy, 95.9%), and thin-section contrast-enhanced MDCT (pulmonary arteries: sensitivity, 91.4%; specificity, 89.5%; accuracy, 90.4%; pulmonary veins: sensitivity, 50%; specificity, 100.0%; accuracy, 95.9%) (p>0.05). Pulmonary vascular assessment of patients with NSCLC before surgical resection by non-contrast-enhanced MRA can be considered equivalent to that by 4D contrast-enhanced MRA and contrast-enhanced MDCT.

Fluorescence and diffusive wave diffraction tomographic probes in turbid media

NASA Astrophysics Data System (ADS)

Li, Xingde

1998-10-01

Light transport over long distances in tissue-like highly scattering media is well approximated as a diffusive process. Diffusing photons can be used to detect, localize and characterize non-invasively optical inhomogeneities such as tumors and hematomas embedded in thick biological tissue. Most of the contrast relies on the endogenous optical property differences between the inhomogeneities and the surrounding media. Recently exogenous fluorescent contrast agents have been considered as a means to enhance the sensitivity and specificity for tumor detection. In the first part of the thesis (Chapter 2 and 3), a theoretical basis is established for modeling the transport, of fluorescent photons in highly scattering media. Fluorescent Diffuse Photon Density Waves (FDPDW) are used to describe the transport of fluorescent photons. A detailed analysis based upon a practical signal-to-noise model was used to access the utility of the fluorescent method. The analysis reveals that a small heterogeneity, embedded in deep tissue-like turbid media with biologically relevant parameters, and with a practically achievable 5-fold fluorophore concentration contrast, can be detected and localized when its radius is greater than 0.2 cm, and can be characterized when its radius is greater than 0.7 cm. In vivo and preliminary clinical studies demonstrate the feasibility of using FDPDW's for tumor diagnosis. Optical imaging with diffusing photons is challenging. Many of the imaging algorithms developed so far are either fundamentally incorrect as in the case of back- projection approach, or require a huge amount of computational resources and CPU time. In the second part of the thesis (Chapter 4), a fast, K-space diffraction tomographic imaging algorithm based upon spatial angular spectrum analysis is derived and applied. Absolute optical properties of thin inhomogeneities and relative optical properties of spatially extended inhomogeneities are reconstructed within a sub-second time scale. Phantom experiments have demonstrated the power of the K-space algorithm and preliminary clinical investigations have exhibited its potential for real time optical diagnosis and imaging of breast cancer.

Noninvasive identification of the total peripheral resistance baroreflex

NASA Technical Reports Server (NTRS)

Mukkamala, Ramakrishna; Toska, Karin; Cohen, Richard J.

2003-01-01

We propose two identification algorithms for quantitating the total peripheral resistance (TPR) baroreflex, an important contributor to short-term arterial blood pressure (ABP) regulation. Each algorithm analyzes beat-to-beat fluctuations in ABP and cardiac output, which may both be obtained noninvasively in humans. For a theoretical evaluation, we applied both algorithms to a realistic cardiovascular model. The results contrasted with only one of the algorithms proving to be reliable. This algorithm was able to track changes in the static gains of both the arterial and cardiopulmonary TPR baroreflex. We then applied both algorithms to a preliminary set of human data and obtained contrasting results much like those obtained from the cardiovascular model, thereby making the theoretical evaluation results more meaningful. This study suggests that, with experimental testing, the reliable identification algorithm may provide a powerful, noninvasive means for quantitating the TPR baroreflex. This study also provides an example of the role that models can play in the development and initial evaluation of algorithms aimed at quantitating important physiological mechanisms.

A study on mastectomy samples to evaluate breast imaging quality and potential clinical relevance of differential phase contrast mammography.

PubMed

Hauser, Nik; Wang, Zhentian; Kubik-Huch, Rahel A; Trippel, Mafalda; Singer, Gad; Hohl, Michael K; Roessl, Ewald; Köhler, Thomas; van Stevendaal, Udo; Wieberneit, Nataly; Stampanoni, Marco

2014-03-01

Differential phase contrast and scattering-based x-ray mammography has the potential to provide additional and complementary clinically relevant information compared with absorption-based mammography. The purpose of our study was to provide a first statistical evaluation of the imaging capabilities of the new technique compared with digital absorption mammography. We investigated non-fixed mastectomy samples of 33 patients with invasive breast cancer, using grating-based differential phase contrast mammography (mammoDPC) with a conventional, low-brilliance x-ray tube. We simultaneously recorded absorption, differential phase contrast, and small-angle scattering signals that were combined into novel high-frequency-enhanced images with a dedicated image fusion algorithm. Six international, expert breast radiologists evaluated clinical digital and experimental mammograms in a 2-part blinded, prospective independent reader study. The results were statistically analyzed in terms of image quality and clinical relevance. The results of the comparison of mammoDPC with clinical digital mammography revealed the general quality of the images to be significantly superior (P < 0.001); sharpness, lesion delineation, as well as the general visibility of calcifications to be significantly more assessable (P < 0.001); and delineation of anatomic components of the specimens (surface structures) to be significantly sharper (P < 0.001). Spiculations were significantly better identified, and the overall clinically relevant information provided by mammoDPC was judged to be superior (P < 0.001). Our results demonstrate that complementary information provided by phase and scattering enhanced mammograms obtained with the mammoDPC approach deliver images of generally superior quality. This technique has the potential to improve radiological breast diagnostics.

Breast mass segmentation in mammograms combining fuzzy c-means and active contours

NASA Astrophysics Data System (ADS)

Hmida, Marwa; Hamrouni, Kamel; Solaiman, Basel; Boussetta, Sana

2018-04-01

Segmentation of breast masses in mammograms is a challenging issue due to the nature of mammography and the characteristics of masses. In fact, mammographic images are poor in contrast and breast masses have various shapes and densities with fuzzy and ill-defined borders. In this paper, we propose a method based on a modified Chan-Vese active contour model for mass segmentation in mammograms. We conduct the experiment on mass Regions of Interest (ROI) extracted from the MIAS database. The proposed method consists of mainly three stages: Firstly, the ROI is preprocessed to enhance the contrast. Next, two fuzzy membership maps are generated from the preprocessed ROI based on fuzzy C-Means algorithm. These fuzzy membership maps are finally used to modify the energy of the Chan-Vese model and to perform the final segmentation. Experimental results indicate that the proposed method yields good mass segmentation results.

A resolution-enhancing image reconstruction method for few-view differential phase-contrast tomography

NASA Astrophysics Data System (ADS)

Guan, Huifeng; Anastasio, Mark A.

2017-03-01

It is well-known that properly designed image reconstruction methods can facilitate reductions in imaging doses and data-acquisition times in tomographic imaging. The ability to do so is particularly important for emerging modalities such as differential X-ray phase-contrast tomography (D-XPCT), which are currently limited by these factors. An important application of D-XPCT is high-resolution imaging of biomedical samples. However, reconstructing high-resolution images from few-view tomographic measurements remains a challenging task. In this work, a two-step sub-space reconstruction strategy is proposed and investigated for use in few-view D-XPCT image reconstruction. It is demonstrated that the resulting iterative algorithm can mitigate the high-frequency information loss caused by data incompleteness and produce images that have better preserved high spatial frequency content than those produced by use of a conventional penalized least squares (PLS) estimator.

Contrast cancellation technique applied to digital x-ray imaging using silicon strip detectors

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

Avila, C.; Lopez, J.; Sanabria, J. C.

2005-12-15

Dual-energy mammographic imaging experimental tests have been performed using a compact dichromatic imaging system based on a conventional x-ray tube, a mosaic crystal, and a 384-strip silicon detector equipped with full-custom electronics with single photon counting capability. For simulating mammal tissue, a three-component phantom, made of Plexiglass, polyethylene, and water, has been used. Images have been collected with three different pairs of x-ray energies: 16-32 keV, 18-36 keV, and 20-40 keV. A Monte Carlo simulation of the experiment has also been carried out using the MCNP-4C transport code. The Alvarez-Macovski algorithm has been applied both to experimental and simulated datamore » to remove the contrast between two of the phantom materials so as to enhance the visibility of the third one.« less

Adaptive Intuitionistic Fuzzy Enhancement of Brain Tumor MR Images

NASA Astrophysics Data System (ADS)

Deng, He; Deng, Wankai; Sun, Xianping; Ye, Chaohui; Zhou, Xin

2016-10-01

Image enhancement techniques are able to improve the contrast and visual quality of magnetic resonance (MR) images. However, conventional methods cannot make up some deficiencies encountered by respective brain tumor MR imaging modes. In this paper, we propose an adaptive intuitionistic fuzzy sets-based scheme, called as AIFE, which takes information provided from different MR acquisitions and tries to enhance the normal and abnormal structural regions of the brain while displaying the enhanced results as a single image. The AIFE scheme firstly separates an input image into several sub images, then divides each sub image into object and background areas. After that, different novel fuzzification, hyperbolization and defuzzification operations are implemented on each object/background area, and finally an enhanced result is achieved via nonlinear fusion operators. The fuzzy implementations can be processed in parallel. Real data experiments demonstrate that the AIFE scheme is not only effectively useful to have information from images acquired with different MR sequences fused in a single image, but also has better enhancement performance when compared to conventional baseline algorithms. This indicates that the proposed AIFE scheme has potential for improving the detection and diagnosis of brain tumors.

High Resolution X-ray Phase Contrast Imaging with Acoustic Tissue-Selective Contrast Enhancement

DTIC Science & Technology

2008-06-01

Imaging with Acoustic Tissue-Selective Contrast Enhancement PRINCIPAL INVESTIGATOR: Gerald J. Diebold, Ph.D. CONTRACTING... Contrast Imaging with Acoustic Tissue-Selective Contrast Enhancement 5b. GRANT NUMBER W81XWH-04-1-0481 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S...additional phase contrast features are visible at the interfaces of soft tissues as slight contrast enhancements . The image sequence in Fig. 2 shows an image

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.

Automatic liver tumor segmentation on computed tomography for patient treatment planning and monitoring

PubMed Central

Moghbel, Mehrdad; Mashohor, Syamsiah; Mahmud, Rozi; Saripan, M. Iqbal Bin

2016-01-01

Segmentation of liver tumors from Computed Tomography (CT) and tumor burden analysis play an important role in the choice of therapeutic strategies for liver diseases and treatment monitoring. In this paper, a new segmentation method for liver tumors from contrast-enhanced CT imaging is proposed. As manual segmentation of tumors for liver treatment planning is both labor intensive and time-consuming, a highly accurate automatic tumor segmentation is desired. The proposed framework is fully automatic requiring no user interaction. The proposed segmentation evaluated on real-world clinical data from patients is based on a hybrid method integrating cuckoo optimization and fuzzy c-means algorithm with random walkers algorithm. The accuracy of the proposed method was validated using a clinical liver dataset containing one of the highest numbers of tumors utilized for liver tumor segmentation containing 127 tumors in total with further validation of the results by a consultant radiologist. The proposed method was able to achieve one of the highest accuracies reported in the literature for liver tumor segmentation compared to other segmentation methods with a mean overlap error of 22.78 % and dice similarity coefficient of 0.75 in 3Dircadb dataset and a mean overlap error of 15.61 % and dice similarity coefficient of 0.81 in MIDAS dataset. The proposed method was able to outperform most other tumor segmentation methods reported in the literature while representing an overlap error improvement of 6 % compared to one of the best performing automatic methods in the literature. The proposed framework was able to provide consistently accurate results considering the number of tumors and the variations in tumor contrast enhancements and tumor appearances while the tumor burden was estimated with a mean error of 0.84 % in 3Dircadb dataset. PMID:27540353

The introduction of capillary structures in 4D simulated vascular tree for ART 3.5D algorithm further validation

NASA Astrophysics Data System (ADS)

Barra, Beatrice; El Hadji, Sara; De Momi, Elena; Ferrigno, Giancarlo; Cardinale, Francesco; Baselli, Giuseppe

2017-03-01

Several neurosurgical procedures, such as Artero Venous Malformations (AVMs), aneurysm embolizations and StereoElectroEncephaloGraphy (SEEG) require accurate reconstruction of the cerebral vascular tree, as well as the classification of arteries and veins, in order to increase the safety of the intervention. Segmentation of arteries and veins from 4D CT perfusion scans has already been proposed in different studies. Nonetheless, such procedures require long acquisition protocols and the radiation dose given to the patient is not negligible. Hence, space is open to approaches attempting to recover the dynamic information from standard Contrast Enhanced Cone Beam Computed Tomography (CE-CBCT) scans. The algorithm proposed by our team is called ART 3.5 D. It is a novel algorithm based on the postprocessing of both the angiogram and the raw data of a standard Digital Subtraction Angiography from a CBCT (DSACBCT) allowing arteries and veins segmentation and labeling without requiring any additional radiation exposure for the patient and neither lowering the resolution. In addition, while in previous versions of the algorithm just the distinction of arteries and veins was considered, here the capillary phase simulation and identification is introduced, in order to increase further information useful for more precise vasculature segmentation.

Development of a hybrid image processing algorithm for automatic evaluation of intramuscular fat content in beef M. longissimus dorsi.

PubMed

Du, Cheng-Jin; Sun, Da-Wen; Jackman, Patrick; Allen, Paul

2008-12-01

An automatic method for estimating the content of intramuscular fat (IMF) in beef M. longissimus dorsi (LD) was developed using a sequence of image processing algorithm. To extract IMF particles within the LD muscle from structural features of intermuscular fat surrounding the muscle, three steps of image processing algorithm were developed, i.e. bilateral filter for noise removal, kernel fuzzy c-means clustering (KFCM) for segmentation, and vector confidence connected and flood fill for IMF extraction. The technique of bilateral filtering was firstly applied to reduce the noise and enhance the contrast of the beef image. KFCM was then used to segment the filtered beef image into lean, fat, and background. The IMF was finally extracted from the original beef image by using the techniques of vector confidence connected and flood filling. The performance of the algorithm developed was verified by correlation analysis between the IMF characteristics and the percentage of chemically extractable IMF content (P<0.05). Five IMF features are very significantly correlated with the fat content (P<0.001), including count densities of middle (CDMiddle) and large (CDLarge) fat particles, area densities of middle and large fat particles, and total fat area per unit LD area. The highest coefficient is 0.852 for CDLarge.

Curvature correction of retinal OCTs using graph-based geometry detection

NASA Astrophysics Data System (ADS)

Kafieh, Raheleh; Rabbani, Hossein; Abramoff, Michael D.; Sonka, Milan

2013-05-01

In this paper, we present a new algorithm as an enhancement and preprocessing step for acquired optical coherence tomography (OCT) images of the retina. The proposed method is composed of two steps, first of which is a denoising algorithm with wavelet diffusion based on a circular symmetric Laplacian model, and the second part can be described in terms of graph-based geometry detection and curvature correction according to the hyper-reflective complex layer in the retina. The proposed denoising algorithm showed an improvement of contrast-to-noise ratio from 0.89 to 1.49 and an increase of signal-to-noise ratio (OCT image SNR) from 18.27 to 30.43 dB. By applying the proposed method for estimation of the interpolated curve using a full automatic method, the mean ± SD unsigned border positioning error was calculated for normal and abnormal cases. The error values of 2.19 ± 1.25 and 8.53 ± 3.76 µm were detected for 200 randomly selected slices without pathological curvature and 50 randomly selected slices with pathological curvature, respectively. The important aspect of this algorithm is its ability in detection of curvature in strongly pathological images that surpasses previously introduced methods; the method is also fast, compared to the relatively low speed of similar methods.

Limitations of contrast enhancement for infrared target identification

NASA Astrophysics Data System (ADS)

Du Bosq, Todd W.; Fanning, Jonathan D.

2009-05-01

Contrast enhancement and dynamic range compression are currently being used to improve the performance of infrared imagers by increasing the contrast between the target and the scene content. Automatic contrast enhancement techniques do not always achieve this improvement. In some cases, the contrast can increase to a level of target saturation. This paper assesses the range-performance effects of contrast enhancement for target identification as a function of image saturation. Human perception experiments were performed to determine field performance using contrast enhancement on the U.S. Army RDECOM CERDEC NVESD standard military eight target set using an un-cooled LWIR camera. The experiments compare the identification performance of observers viewing contrast enhancement processed images at various levels of saturation. Contrast enhancement is modeled in the U.S. Army thermal target acquisition model (NVThermIP) by changing the scene contrast temperature. The model predicts improved performance based on any improved target contrast, regardless of specific feature saturation or enhancement. The measured results follow the predicted performance based on the target task difficulty metric used in NVThermIP for the non-saturated cases. The saturated images reduce the information contained in the target and performance suffers. The model treats the contrast of the target as uniform over spatial frequency. As the contrast is enhanced, the model assumes that the contrast is enhanced uniformly over the spatial frequencies. After saturation, the spatial cues that differentiate one tank from another are located in a limited band of spatial frequencies. A frequency dependent treatment of target contrast is needed to predict performance of over-processed images.

New frontier in hypericin-mediated diagnosis of cancer with current optical technologies.

PubMed

Olivo, Malini; Fu, Chit Yaw; Raghavan, Vijaya; Lau, Weber Kam On

2012-02-01

Photosensitizers (PSs) have shown great potentials as molecular contrast agents in photodynamic diagnosis (PDD) of cancer. While the diagnostic values of PSs have been proven previously, little efforts have been put into developing optical imaging and diagnostic algorithms. In this article, we review the recent development of optical probes that have been used in conjunction with a potent PS, hypericin (HY). Various fluorescence techniques such as laser confocal microscopy, fluorescence urine cytology, endoscopy and endomicroscopy are covered. We will also discuss about image processing and classification approaches employed for accurate PDD. We anticipate that continual efforts in these developments could lead to an objective PDD and complete surgical clearance of tumors. Recent advancements in nanotechnology have also opened new horizons for PSs. The use of biocompatible gold nanoparticles as carrier for enhanced targeted delivery of HY has been attained. In addition, plasmonic properties of nanoparticles were harnessed to induce localized hyperthermia and to manage the release of PS molecules, enabling a better therapeutic outcome of a combined photodynamic and photothermal therapy. Finally, we discuss how nanoparticles can be used as contrast agents for other optical techniques such as optical coherence tomography and surface-enhanced Raman scattering imaging.

Accuracy and cost-effectiveness of dynamic contrast-enhanced CT in the characterisation of solitary pulmonary nodules—the SPUtNIk study

PubMed Central

Qureshi, N R; Rintoul, R C; Miles, K A; George, S; Harris, S; Madden, J; Cozens, K; Little, L A; Eichhorst, K; Jones, J; Moate, P; McClement, C; Pike, L; Sinclair, D; Wong, W L; Shekhdar, J; Eaton, R; Shah, A; Brindle, L; Peebles, C; Banerjee, A; Dizdarevic, S; Han, S; Poon, F W; Groves, A M; Kurban, L; Frew, A J; Callister, M E; Crosbie, P; Gleeson, F V; Karunasaagarar, K; Kankam, O; Gilbert, F J

2016-01-01

Introduction Solitary pulmonary nodules (SPNs) are common on CT. The most cost-effective investigation algorithm is still to be determined. Dynamic contrast-enhanced CT (DCE-CT) is an established diagnostic test not widely available in the UK currently. Methods and analysis The SPUtNIk study will assess the diagnostic accuracy, clinical utility and cost-effectiveness of DCE-CT, alongside the current CT and 18-flurodeoxyglucose-positron emission tomography) (18FDG-PET)-CT nodule characterisation strategies in the National Health Service (NHS). Image acquisition and data analysis for 18FDG-PET-CT and DCE-CT will follow a standardised protocol with central review of 10% to ensure quality assurance. Decision analytic modelling will assess the likely costs and health outcomes resulting from incorporation of DCE-CT into management strategies for patients with SPNs. Ethics and dissemination Approval has been granted by the South West Research Ethics Committee. Ethics reference number 12/SW/0206. The results of the trial will be presented at national and international meetings and published in an Health Technology Assessment (HTA) Monograph and in peer-reviewed journals. Trial registration number ISRCTN30784948; Pre-results. PMID:27843550

Improving iris recognition performance using segmentation, quality enhancement, match score fusion, and indexing.

PubMed

Vatsa, Mayank; Singh, Richa; Noore, Afzel

2008-08-01

This paper proposes algorithms for iris segmentation, quality enhancement, match score fusion, and indexing to improve both the accuracy and the speed of iris recognition. A curve evolution approach is proposed to effectively segment a nonideal iris image using the modified Mumford-Shah functional. Different enhancement algorithms are concurrently applied on the segmented iris image to produce multiple enhanced versions of the iris image. A support-vector-machine-based learning algorithm selects locally enhanced regions from each globally enhanced image and combines these good-quality regions to create a single high-quality iris image. Two distinct features are extracted from the high-quality iris image. The global textural feature is extracted using the 1-D log polar Gabor transform, and the local topological feature is extracted using Euler numbers. An intelligent fusion algorithm combines the textural and topological matching scores to further improve the iris recognition performance and reduce the false rejection rate, whereas an indexing algorithm enables fast and accurate iris identification. The verification and identification performance of the proposed algorithms is validated and compared with other algorithms using the CASIA Version 3, ICE 2005, and UBIRIS iris databases.

Contrast-enhanced fluid-attenuated inversion recovery vs. contrast-enhanced spin echo T1-weighted brain imaging.

PubMed

Falzone, Cristian; Rossi, Federica; Calistri, Maurizio; Tranquillo, Massimo; Baroni, Massimo

2008-01-01

In humans, contrast-enhanced fluid-attenuated inversion recovery (FLAIR) imaging plays an important role in detecting brain disease. The aim of this study was to define the clinical utility of contrast-enhanced FLAIR imaging by comparing the results with those with contrast-enhanced spin echo T1-weighted images (SE T1WI) in animals with different brain disorders. Forty-one dogs and five cats with a clinical suspicion of brain disease and 30 normal animals (25 dogs and five cats) were evaluated using a 0.2 T permanent magnet. Before contrast medium injection, spin echo T1-weighted, SE T1WI, and FLAIR sequences were acquired in three planes. SE T1WI and FLAIR images were also acquired after gadolinium injection. Sensitivity in detecting the number, location, margin, and enhancement pattern and rate were evaluated. No lesions were found in a normal animal. In affected animals, 48 lesions in 34 patients were detected in contrast-enhanced SE T1WI whereas 81 lesions in 44 patients were detected in contrast-enhanced FLAIR images. There was no difference in the characteristics of the margins or enhancement pattern of the detected lesions. The objective enhancement rate, the mean value between lesion-to-white matter ratio and lesion-to-gray matter ratio, although representing an overlap of T1 and T2 effects and not pure contrast medium shortening of T1 relaxation, was better in contrast-enhanced FLAIR images. These results suggest a superiority of contrast-enhanced FLAIR images as compared with contrast-enhanced SE T1WI in detecting enhancing brain lesions.

Contrast enhancement for in vivo visible reflectance imaging of tissue oxygenation.

PubMed

Crane, Nicole J; Schultz, Zachary D; Levin, Ira W

2007-08-01

Results are presented illustrating a straightforward algorithm to be used for real-time monitoring of oxygenation levels in blood cells and tissue based on the visible spectrum of hemoglobin. Absorbance images obtained from the visible reflection of white light through separate red and blue bandpass filters recorded by monochrome charge-coupled devices (CCDs) are combined to create enhanced images that suggest a quantitative correlation between the degree of oxygenated and deoxygenated hemoglobin in red blood cells. The filter bandpass regions are chosen specifically to mimic the color response of commercial 3-CCD cameras, representative of detectors with which the operating room laparoscopic tower systems are equipped. Adaptation of this filter approach is demonstrated for laparoscopic donor nephrectomies in which images are analyzed in terms of real-time in vivo monitoring of tissue oxygenation.

Neutron Radiography of Fluid Flow for Geothermal Energy Research

NASA Astrophysics Data System (ADS)

Bingham, P.; Polsky, Y.; Anovitz, L.; Carmichael, J.; Bilheux, H.; Jacobsen, D.; Hussey, D.

Enhanced geothermal systems seek to expand the potential for geothermal energy by engineering heat exchange systems within the earth. A neutron radiography imaging method has been developed for the study of fluid flow through rock under environmental conditions found in enhanced geothermal energy systems. For this method, a pressure vessel suitable for neutron radiography was designed and fabricated, modifications to imaging instrument setups were tested, multiple contrast agents were tested, and algorithms developed for tracking of flow. The method has shown success for tracking of single phase flow through a manufactured crack in a 3.81 cm (1.5 inch) diameter core within a pressure vessel capable of confinement up to 69 MPa (10,000 psi) using a particle tracking approach with bubbles of fluorocarbon-based fluid as the ;particles; and imaging with 10 ms exposures.

Cell Motility Dynamics: A Novel Segmentation Algorithm to Quantify Multi-Cellular Bright Field Microscopy Images

PubMed Central

Zaritsky, Assaf; Natan, Sari; Horev, Judith; Hecht, Inbal; Wolf, Lior; Ben-Jacob, Eshel; Tsarfaty, Ilan

2011-01-01

Confocal microscopy analysis of fluorescence and morphology is becoming the standard tool in cell biology and molecular imaging. Accurate quantification algorithms are required to enhance the understanding of different biological phenomena. We present a novel approach based on image-segmentation of multi-cellular regions in bright field images demonstrating enhanced quantitative analyses and better understanding of cell motility. We present MultiCellSeg, a segmentation algorithm to separate between multi-cellular and background regions for bright field images, which is based on classification of local patches within an image: a cascade of Support Vector Machines (SVMs) is applied using basic image features. Post processing includes additional classification and graph-cut segmentation to reclassify erroneous regions and refine the segmentation. This approach leads to a parameter-free and robust algorithm. Comparison to an alternative algorithm on wound healing assay images demonstrates its superiority. The proposed approach was used to evaluate common cell migration models such as wound healing and scatter assay. It was applied to quantify the acceleration effect of Hepatocyte growth factor/scatter factor (HGF/SF) on healing rate in a time lapse confocal microscopy wound healing assay and demonstrated that the healing rate is linear in both treated and untreated cells, and that HGF/SF accelerates the healing rate by approximately two-fold. A novel fully automated, accurate, zero-parameters method to classify and score scatter-assay images was developed and demonstrated that multi-cellular texture is an excellent descriptor to measure HGF/SF-induced cell scattering. We show that exploitation of textural information from differential interference contrast (DIC) images on the multi-cellular level can prove beneficial for the analyses of wound healing and scatter assays. The proposed approach is generic and can be used alone or alongside traditional fluorescence single-cell processing to perform objective, accurate quantitative analyses for various biological applications. PMID:22096600

Cell motility dynamics: a novel segmentation algorithm to quantify multi-cellular bright field microscopy images.

PubMed

Zaritsky, Assaf; Natan, Sari; Horev, Judith; Hecht, Inbal; Wolf, Lior; Ben-Jacob, Eshel; Tsarfaty, Ilan

2011-01-01

Confocal microscopy analysis of fluorescence and morphology is becoming the standard tool in cell biology and molecular imaging. Accurate quantification algorithms are required to enhance the understanding of different biological phenomena. We present a novel approach based on image-segmentation of multi-cellular regions in bright field images demonstrating enhanced quantitative analyses and better understanding of cell motility. We present MultiCellSeg, a segmentation algorithm to separate between multi-cellular and background regions for bright field images, which is based on classification of local patches within an image: a cascade of Support Vector Machines (SVMs) is applied using basic image features. Post processing includes additional classification and graph-cut segmentation to reclassify erroneous regions and refine the segmentation. This approach leads to a parameter-free and robust algorithm. Comparison to an alternative algorithm on wound healing assay images demonstrates its superiority. The proposed approach was used to evaluate common cell migration models such as wound healing and scatter assay. It was applied to quantify the acceleration effect of Hepatocyte growth factor/scatter factor (HGF/SF) on healing rate in a time lapse confocal microscopy wound healing assay and demonstrated that the healing rate is linear in both treated and untreated cells, and that HGF/SF accelerates the healing rate by approximately two-fold. A novel fully automated, accurate, zero-parameters method to classify and score scatter-assay images was developed and demonstrated that multi-cellular texture is an excellent descriptor to measure HGF/SF-induced cell scattering. We show that exploitation of textural information from differential interference contrast (DIC) images on the multi-cellular level can prove beneficial for the analyses of wound healing and scatter assays. The proposed approach is generic and can be used alone or alongside traditional fluorescence single-cell processing to perform objective, accurate quantitative analyses for various biological applications.

The Effectiveness of Neurofeedback Training in Algorithmic Thinking Skills Enhancement.

PubMed

Plerou, Antonia; Vlamos, Panayiotis; Triantafillidis, Chris

2017-01-01

Although research on learning difficulties are overall in an advanced stage, studies related to algorithmic thinking difficulties are limited, since interest in this field has been recently raised. In this paper, an interactive evaluation screener enhanced with neurofeedback elements, referring to algorithmic tasks solving evaluation, is proposed. The effect of HCI, color, narration and neurofeedback elements effect was evaluated in the case of algorithmic tasks assessment. Results suggest the enhanced performance in the case of neurofeedback trained group in terms of total correct and optimal algorithmic tasks solution. Furthermore, findings suggest that skills, concerning the way that an algorithm is conceived, designed, applied and evaluated are essentially improved.

High-Contrast Coronagraph Performance in the Presence of DM Actuator Defects

NASA Technical Reports Server (NTRS)

Sidick, Erkin; Shaklan, Stuart; Cady, Eric

2015-01-01

Deformable Mirrors (DMs) are critical elements in high contrast coronagraphs, requiring precision and stability measured in picometers to enable detection of Earth-like exoplanets. Occasionally DM actuators or their associated cables or electronics fail, requiring a wavefront control algorithm to compensate for actuators that may be displaced from their neighbors by hundreds of nanometers. We have carried out experiments on our High-Contrast Imaging Testbed (HCIT) to study the impact of failed actuators in partial fulfillment of the Terrestrial Planet Finder Coronagraph optical model validation milestone. We show that the wavefront control algorithm adapts to several broken actuators and maintains dark-hole contrast in broadband light.

High-contrast coronagraph performance in the presence of DM actuator defects

NASA Astrophysics Data System (ADS)

Sidick, Erkin; Shaklan, Stuart; Cady, Eric

2015-09-01

Deformable Mirrors (DMs) are critical elements in high contrast coronagraphs, requiring precision and stability measured in picometers to enable detection of Earth-like exoplanets. Occasionally DM actuators or their associated cables or electronics fail, requiring a wavefront control algorithm to compensate for actuators that may be displaced from their neighbors by hundreds of nanometers. We have carried out experiments on our High-Contrast Imaging Testbed (HCIT) to study the impact of failed actuators in partial fulfilment of the Terrestrial Planet Finder Coronagraph optical model validation milestone. We show that the wavefront control algorithm adapts to several broken actuators and maintains dark-hole contrast in broadband light.

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

NASA Astrophysics Data System (ADS)

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

2017-07-01

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

Complete penile corporeal septation: evaluation with contrast enhanced US.

PubMed

Bertolotto, Michele; Bucci, Stefano; Quaia, Emilio; Coss, Matteo; Liguori, Giovanni

2008-01-01

Complete penile corporeal septation is a rare malformation in which the corpora cavernosa are completely isolated. We describe a new method to reach the diagnosis of this malformation using contrast enhanced US. Two patients with complete penile corporeal septation underwent color Doppler and contrast enhanced US after bilateral cavernosal injection of 10 microg prostaglandin E1. Contrast enhanced US was performed using a contrast specific software (Contrast-Tuned imaging, EsaOte, Genoa, Italy) and a linear transducer designed to evaluate superficial structures. Microbubbles of SonoVue (Bracco, Milan, Italy) were injected in one corpus cavernosum. After cavernosal injection of microbubbles no adverse events were observed. Contrast enhanced US showed unilateral enhancement of the corpus cavernosum in which microbubbles were injected. Cavernosography confirmed unilateral corporeal opacification. Contrast enhanced US can be used effectively to diagnose complete penile corporeal septation.

A combined approach of self-referencing and Principle Component Thermography for transient, steady, and selective heating scenarios

NASA Astrophysics Data System (ADS)

Omar, M. A.; Parvataneni, R.; Zhou, Y.

2010-09-01

Proposed manuscript describes the implementation of a two step processing procedure, composed of the self-referencing and the Principle Component Thermography (PCT). The combined approach enables the processing of thermograms from transient (flash), steady (halogen) and selective (induction) thermal perturbations. Firstly, the research discusses the three basic processing schemes typically applied for thermography; namely mathematical transformation based processing, curve-fitting processing, and direct contrast based calculations. Proposed algorithm utilizes the self-referencing scheme to create a sub-sequence that contains the maximum contrast information and also compute the anomalies' depth values. While, the Principle Component Thermography operates on the sub-sequence frames by re-arranging its data content (pixel values) spatially and temporally then it highlights the data variance. The PCT is mainly used as a mathematical mean to enhance the defects' contrast thus enabling its shape and size retrieval. The results show that the proposed combined scheme is effective in processing multiple size defects in sandwich steel structure in real-time (<30 Hz) and with full spatial coverage, without the need for a priori defect-free area.

Respiratory Motion-Resolved Compressed Sensing Reconstruction of Free-Breathing Radial Acquisition for Dynamic Liver MRI

PubMed Central

Chandarana, Hersh; Feng, Li; Ream, Justin; Wang, Annie; Babb, James S; Block, Kai Tobias; Sodickson, Daniel K; Otazo, Ricardo

2015-01-01

Purpose Demonstrate feasibility of free-breathing radial acquisition with respiratory motion-resolved compressed sensing (CS) reconstruction (XD-GRASP) for multiphase dynamic Gd-EOB-DTPA enhanced liver imaging, and compare image quality to CS reconstruction with respiratory motion-averaging (GRASP) and prior conventional breath-held Cartesian-sampled datasets (BH-VIBE) in same patients. Subjects and Methods In this HIPAA-compliant prospective study, 16 subjects underwent free-breathing continuous radial acquisition during Gd-EOB-DTPA injection, and had prior BH-VIBE exam available. Acquired data were reconstructed using motion-averaging GRASP approach, in which consecutive 84-spokes were grouped in each contrast-enhanced phase for a temporal resolution of ~14 seconds. Additionally, respiratory motion-resolved reconstruction was performed from the same k-space data, by sorting each contrast-enhanced phase into multiple respiratory motion states using compressed sensing algorithm named XD-GRASP, which exploits sparsity along both the contrast-enhancement and respiratory-state dimensions. Contrast-enhanced dynamic multi-phase XD-GRASP, GRASP, and BH-VIBE images were anonymized, pooled together in a random order and presented to two board-certified radiologists for independent evaluation of image quality, with higher score indicating more optimal exam. Results XD-GRASP reconstructions had significantly (all p<0.05) higher overall image quality scores compared to GRASP for early arterial (Reader 1: 4.3 ± 0.6 vs. 3.31 ± 0.6 ; Reader 2: 3.81 ± 0.8 vs. 3.38 ± 0.9) and late arterial (Reader 1: 4.5 ± 0.6 vs. 3.63 ± 0.6; Reader 2: 3.56 ± 0.5 vs. 2.88 ± 0.7) phases of enhancement for both readers. XD-GRASP also had higher overall image quality score in portal venous phase which was significant for Reader 1 (4.44 ± 0.5 vs. 3.75 ± 0.8; p=0.002). In addition, XD-GRASP had higher overall image quality score compared to BH-VIBE for early (Reader 1: 4.3±0.6 vs. 3.88±0.6; Reader 2: 3.81±0.8 vs. 3.50±1.0) and late (Reader 1: 4.5±0.6 vs. 3.44±0.6; Reader 2: 3.56±0.5 vs. 2.94±0.9) arterial phases. Conclusion Free-breathing motion-resolved XD-GRASP reconstructions provide diagnostic high-quality multiphase images in patients undergoing Gd-EOB-DTPA-enhanced liver exam. PMID:26146869

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

Denni Algorithm An Enhanced Of SMS (Scan, Move and Sort) Algorithm

NASA Astrophysics Data System (ADS)

Aprilsyah Lubis, Denni; Salim Sitompul, Opim; Marwan; Tulus; Andri Budiman, M.

2017-12-01

Sorting has been a profound area for the algorithmic researchers, and many resources are invested to suggest a more working sorting algorithm. For this purpose many existing sorting algorithms were observed in terms of the efficiency of the algorithmic complexity. Efficient sorting is important to optimize the use of other algorithms that require sorted lists to work correctly. Sorting has been considered as a fundamental problem in the study of algorithms that due to many reasons namely, the necessary to sort information is inherent in many applications, algorithms often use sorting as a key subroutine, in algorithm design there are many essential techniques represented in the body of sorting algorithms, and many engineering issues come to the fore when implementing sorting algorithms., Many algorithms are very well known for sorting the unordered lists, and one of the well-known algorithms that make the process of sorting to be more economical and efficient is SMS (Scan, Move and Sort) algorithm, an enhancement of Quicksort invented Rami Mansi in 2010. This paper presents a new sorting algorithm called Denni-algorithm. The Denni algorithm is considered as an enhancement on the SMS algorithm in average, and worst cases. The Denni algorithm is compared with the SMS algorithm and the results were promising.

Influence of adaptive statistical iterative reconstruction algorithm on image quality in coronary computed tomography angiography

PubMed Central

Thygesen, Jesper; Gerke, Oke; Egstrup, Kenneth; Waaler, Dag; Lambrechtsen, Jess

2016-01-01

Background Coronary computed tomography angiography (CCTA) requires high spatial and temporal resolution, increased low contrast resolution for the assessment of coronary artery stenosis, plaque detection, and/or non-coronary pathology. Therefore, new reconstruction algorithms, particularly iterative reconstruction (IR) techniques, have been developed in an attempt to improve image quality with no cost in radiation exposure. Purpose To evaluate whether adaptive statistical iterative reconstruction (ASIR) enhances perceived image quality in CCTA compared to filtered back projection (FBP). Material and Methods Thirty patients underwent CCTA due to suspected coronary artery disease. Images were reconstructed using FBP, 30% ASIR, and 60% ASIR. Ninety image sets were evaluated by five observers using the subjective visual grading analysis (VGA) and assessed by proportional odds modeling. Objective quality assessment (contrast, noise, and the contrast-to-noise ratio [CNR]) was analyzed with linear mixed effects modeling on log-transformed data. The need for ethical approval was waived by the local ethics committee as the study only involved anonymously collected clinical data. Results VGA showed significant improvements in sharpness by comparing FBP with ASIR, resulting in odds ratios of 1.54 for 30% ASIR and 1.89 for 60% ASIR (P = 0.004). The objective measures showed significant differences between FBP and 60% ASIR (P < 0.0001) for noise, with an estimated ratio of 0.82, and for CNR, with an estimated ratio of 1.26. Conclusion ASIR improved the subjective image quality of parameter sharpness and, objectively, reduced noise and increased CNR. PMID:28405477

What makes a cell face-selective: the importance of contrast

PubMed Central

Ohayon, Shay; Freiwald, Winrich A; Tsao, Doris Y

2012-01-01

Summary Faces are robustly detected by computer vision algorithms that search for characteristic coarse contrast features. Here, we investigated whether face-selective cells in the primate brain exploit contrast features as well. We recorded from face-selective neurons in macaque inferotemporal cortex, while presenting a face-like collage of regions whose luminances were changed randomly. Modulating contrast combinations between regions induced activity changes ranging from no response to a response greater than that to a real face in 50% of cells. The critical stimulus factor determining response magnitude was contrast polarity, e.g., nose region brighter than left eye. Contrast polarity preferences were consistent across cells, suggesting a common computational strategy across the population, and matched features used by computer vision algorithms for face detection. Furthermore, most cells were tuned both for contrast polarity and for the geometry of facial features, suggesting cells encode information useful both for detection and recognition. PMID:22578507

WE-D-18A-04: How Iterative Reconstruction Algorithms Affect the MTFs of Variable-Contrast Targets in CT Images

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

Dodge, C.T.; Rong, J.; Dodge, C.W.

2014-06-15

Purpose: To determine how filtered back-projection (FBP), adaptive statistical (ASiR), and model based (MBIR) iterative reconstruction algorithms affect the measured modulation transfer functions (MTFs) of variable-contrast targets over a wide range of clinically applicable dose levels. Methods: The Catphan 600 CTP401 module, surrounded by an oval, fat-equivalent ring to mimic patient size/shape, was scanned on a GE HD750 CT scanner at 1, 2, 3, 6, 12 and 24 mGy CTDIvol levels with typical patient scan parameters: 120kVp, 0.8s, 40mm beam width, large SFOV, 2.5mm thickness, 0.984 pitch. The images were reconstructed using GE's Standard kernel with FBP; 20%, 40% andmore » 70% ASiR; and MBIR. A task-based MTF (MTFtask) was computed for six cylindrical targets: 2 low-contrast (Polystyrene, LDPE), 2 medium-contrast (Delrin, PMP), and 2 high-contrast (Teflon, air). MTFtask was used to compare the performance of reconstruction algorithms with decreasing CTDIvol from 24mGy, which is currently used in the clinic. Results: For the air target and 75% dose savings (6 mGy), MBIR MTFtask at 5 lp/cm measured 0.24, compared to 0.20 for 70% ASiR and 0.11 for FBP. Overall, for both high-contrast targets, MBIR MTFtask improved with increasing CTDIvol and consistently outperformed ASiR and FBP near the system's Nyquist frequency. Conversely, for Polystyrene at 6 mGy, MBIR (0.10) and 70% ASiR (0.07) MTFtask was lower than for FBP (0.18). For medium and low-contrast targets, FBP remains the best overall algorithm for improved resolution at low CTDIvol (1–6 mGy) levels, whereas MBIR is comparable at higher dose levels (12–24 mGy). Conclusion: MBIR improved the MTF of small, high-contrast targets compared to FBP and ASiR at doses of 50%–12.5% of those currently used in the clinic. However, for imaging low- and mediumcontrast targets, FBP performed the best across all dose levels. For assessing MTF from different reconstruction algorithms, task-based MTF measurements are necessary.« less

Ptychography: use of quantitative phase information for high-contrast label free time-lapse imaging of living cells

NASA Astrophysics Data System (ADS)

Suman, Rakesh; O'Toole, Peter

2014-03-01

Here we report a novel label free, high contrast and quantitative method for imaging live cells. The technique reconstructs an image from overlapping diffraction patterns using a ptychographical algorithm. The algorithm utilises both amplitude and phase data from the sample to report on quantitative changes related to the refractive index (RI) and thickness of the specimen. We report the ability of this technique to generate high contrast images, to visualise neurite elongation in neuronal cells, and to provide measure of cell proliferation.

Image enhancement and advanced information extraction techniques for ERTS-1 data

NASA Technical Reports Server (NTRS)

Malila, W. A. (Principal Investigator); Nalepka, R. F.; Sarno, J. E.

1975-01-01

The author has identified the following significant results. It was demonstrated and concluded that: (1) the atmosphere has significant effects on ERTS MSS data which can seriously degrade recognition performance; (2) the application of selected signature extension techniques serve to reduce the deleterious effects of both the atmosphere and changing ground conditions on recognition performance; and (3) a proportion estimation algorithm for overcoming problems in acreage estimation accuracy resulting from the coarse spatial resolution of the ERTS MSS, was able to significantly improve acreage estimation accuracy over that achievable by conventional techniques, especially for high contrast targets such as lakes and ponds.

An enhanced DWBA algorithm in hybrid WDM/TDM EPON networks with heterogeneous propagation delays

NASA Astrophysics Data System (ADS)

Li, Chengjun; Guo, Wei; Jin, Yaohui; Sun, Weiqiang; Hu, Weisheng

2011-12-01

An enhanced dynamic wavelength and bandwidth allocation (DWBA) algorithm in hybrid WDM/TDM PON is proposed and experimentally demonstrated. In addition to the fairness of bandwidth allocation, this algorithm also considers the varying propagation delays between ONUs and OLT. The simulation based on MATLAB indicates that the improved algorithm has a better performance compared with some other algorithms.

Detection of the ice assertion on aircraft using empirical mode decomposition enhanced by multi-objective optimization

NASA Astrophysics Data System (ADS)

Bagherzadeh, Seyed Amin; Asadi, Davood

2017-05-01

In search of a precise method for analyzing nonlinear and non-stationary flight data of an aircraft in the icing condition, an Empirical Mode Decomposition (EMD) algorithm enhanced by multi-objective optimization is introduced. In the proposed method, dissimilar IMF definitions are considered by the Genetic Algorithm (GA) in order to find the best decision parameters of the signal trend. To resolve disadvantages of the classical algorithm caused by the envelope concept, the signal trend is estimated directly in the proposed method. Furthermore, in order to simplify the performance and understanding of the EMD algorithm, the proposed method obviates the need for a repeated sifting process. The proposed enhanced EMD algorithm is verified by some benchmark signals. Afterwards, the enhanced algorithm is applied to simulated flight data in the icing condition in order to detect the ice assertion on the aircraft. The results demonstrate the effectiveness of the proposed EMD algorithm in aircraft ice detection by providing a figure of merit for the icing severity.

Using Anatomic Magnetic Resonance Image Information to Enhance Visualization and Interpretation of Functional Images: A Comparison of Methods Applied to Clinical Arterial Spin Labeling Images

PubMed Central

Dai, Weiying; Soman, Salil; Hackney, David B.; Wong, Eric T.; Robson, Philip M.; Alsop, David C.

2017-01-01

Functional imaging provides hemodynamic and metabolic information and is increasingly being incorporated into clinical diagnostic and research studies. Typically functional images have reduced signal-to-noise ratio and spatial resolution compared to other non-functional cross sectional images obtained as part of a routine clinical protocol. We hypothesized that enhancing visualization and interpretation of functional images with anatomic information could provide preferable quality and superior diagnostic value. In this work, we implemented five methods (frequency addition, frequency multiplication, wavelet transform, non-subsampled contourlet transform and intensity-hue-saturation) and a newly proposed ShArpening by Local Similarity with Anatomic images (SALSA) method to enhance the visualization of functional images, while preserving the original functional contrast and quantitative signal intensity characteristics over larger spatial scales. Arterial spin labeling blood flow MR images of the brain were visualization enhanced using anatomic images with multiple contrasts. The algorithms were validated on a numerical phantom and their performance on images of brain tumor patients were assessed by quantitative metrics and neuroradiologist subjective ratings. The frequency multiplication method had the lowest residual error for preserving the original functional image contrast at larger spatial scales (55%–98% of the other methods with simulated data and 64%–86% with experimental data). It was also significantly more highly graded by the radiologists (p<0.005 for clear brain anatomy around the tumor). Compared to other methods, the SALSA provided 11%–133% higher similarity with ground truth images in the simulation and showed just slightly lower neuroradiologist grading score. Most of these monochrome methods do not require any prior knowledge about the functional and anatomic image characteristics, except the acquired resolution. Hence, automatic implementation on clinical images should be readily feasible. PMID:27723582

Small-bowel MRI in children and young adults with Crohn disease: retrospective head-to-head comparison of contrast-enhanced and diffusion-weighted MRI.

PubMed

Neubauer, Henning; Pabst, Thomas; Dick, Anke; Machann, Wolfram; Evangelista, Laura; Wirth, Clemens; Köstler, Herbert; Hahn, Dietbert; Beer, Meinrad

2013-01-01

Small-bowel MRI based on contrast-enhanced T1-weighted sequences has been challenged by diffusion-weighted imaging (DWI) for detection of inflammatory bowel lesions and complications in patients with Crohn disease. To evaluate free-breathing DWI, as compared to contrast-enhanced MRI, in children, adolescents and young adults with Crohn disease. This retrospective study included 33 children and young adults with Crohn disease ages 17 ± 3 years (mean ± standard deviation) and 27 matched controls who underwent small-bowel MRI with contrast-enhanced T1-weighted sequences and DWI at 1.5 T. The detectability of Crohn manifestations was determined. Concurrent colonoscopy as reference was available in two-thirds of the children with Crohn disease. DWI and contrast-enhanced MRI correctly identified 32 and 31 patients, respectively. All 22 small-bowel lesions and all Crohn complications were detected. False-positive findings (two on DWI, one on contrast-enhanced MRI), compared to colonoscopy, were a result of large-bowel lumen collapse. Inflammatory wall thickening was comparable on DWI and contrast-enhanced MRI. DWI was superior to contrast-enhanced MRI for detection of lesions in 27% of the assessed bowel segments and equal to contrast-enhanced MRI in 71% of segments. DWI facilitates fast, accurate and comprehensive workup in Crohn disease without the need for intravenous administration of contrast medium. Contrast-enhanced MRI is superior in terms of spatial resolution and multiplanar acquisition.

Stochastic parallel gradient descent based adaptive optics used for a high contrast imaging coronagraph

NASA Astrophysics Data System (ADS)

Dong, Bing; Ren, De-Qing; Zhang, Xi

2011-08-01

An adaptive optics (AO) system based on a stochastic parallel gradient descent (SPGD) algorithm is proposed to reduce the speckle noises in the optical system of a stellar coronagraph in order to further improve the contrast. The principle of the SPGD algorithm is described briefly and a metric suitable for point source imaging optimization is given. The feasibility and good performance of the SPGD algorithm is demonstrated by an experimental system featured with a 140-actuator deformable mirror and a Hartmann-Shark wavefront sensor. Then the SPGD based AO is applied to a liquid crystal array (LCA) based coronagraph to improve the contrast. The LCA can modulate the incoming light to generate a pupil apodization mask of any pattern. A circular stepped pattern is used in our preliminary experiment and the image contrast shows improvement from 10-3 to 10-4.5 at an angular distance of 2λ/D after being corrected by SPGD based AO.

An effective one-dimensional anisotropic fingerprint enhancement algorithm

NASA Astrophysics Data System (ADS)

Ye, Zhendong; Xie, Mei

2012-01-01

Fingerprint identification is one of the most important biometric technologies. The performance of the minutiae extraction and the speed of the fingerprint verification system rely heavily on the quality of the input fingerprint images, so the enhancement of the low fingerprint is a critical and difficult step in a fingerprint verification system. In this paper we proposed an effective algorithm for fingerprint enhancement. Firstly we use normalization algorithm to reduce the variations in gray level values along ridges and valleys. Then we utilize the structure tensor approach to estimate each pixel of the fingerprint orientations. At last we propose a novel algorithm which combines the advantages of onedimensional Gabor filtering method and anisotropic method to enhance the fingerprint in recoverable region. The proposed algorithm has been evaluated on the database of Fingerprint Verification Competition 2004, and the results show that our algorithm performs within less time.

An effective one-dimensional anisotropic fingerprint enhancement algorithm

NASA Astrophysics Data System (ADS)

Ye, Zhendong; Xie, Mei

2011-12-01

Fingerprint identification is one of the most important biometric technologies. The performance of the minutiae extraction and the speed of the fingerprint verification system rely heavily on the quality of the input fingerprint images, so the enhancement of the low fingerprint is a critical and difficult step in a fingerprint verification system. In this paper we proposed an effective algorithm for fingerprint enhancement. Firstly we use normalization algorithm to reduce the variations in gray level values along ridges and valleys. Then we utilize the structure tensor approach to estimate each pixel of the fingerprint orientations. At last we propose a novel algorithm which combines the advantages of onedimensional Gabor filtering method and anisotropic method to enhance the fingerprint in recoverable region. The proposed algorithm has been evaluated on the database of Fingerprint Verification Competition 2004, and the results show that our algorithm performs within less time.

Toward an Objective Enhanced-V Detection Algorithm

NASA Technical Reports Server (NTRS)

Moses, John F.; Brunner,Jason C.; Feltz, Wayne F.; Ackerman, Steven A.; Moses, John F.; Rabin, Robert M.

2007-01-01

The area of coldest cloud tops above thunderstorms sometimes has a distinct V or U shape. This pattern, often referred to as an "enhanced-V signature, has been observed to occur during and preceding severe weather. This study describes an algorithmic approach to objectively detect overshooting tops, temperature couplets, and enhanced-V features with observations from the Geostationary Operational Environmental Satellite and Low Earth Orbit data. The methodology consists of temperature, temperature difference, and distance thresholds for the overshooting top and temperature couplet detection parts of the algorithm and consists of cross correlation statistics of pixels for the enhanced-V detection part of the algorithm. The effectiveness of the overshooting top and temperature couplet detection components of the algorithm is examined using GOES and MODIS image data for case studies in the 2003-2006 seasons. The main goal is for the algorithm to be useful for operations with future sensors, such as GOES-R.

X-ray computed tomography of wood-adhesive bondlines: Attenuation and phase-contrast effects

DOE PAGES

Paris, Jesse L.; Kamke, Frederick A.; Xiao, Xianghui

2015-07-29

Microscale X-ray computed tomography (XCT) is discussed as a technique for identifying 3D adhesive distribution in wood-adhesive bondlines. Visualization and material segmentation of the adhesives from the surrounding cellular structures require sufficient gray-scale contrast in the reconstructed XCT data. Commercial wood-adhesive polymers have similar chemical characteristics and density to wood cell wall polymers and therefore do not provide good XCT attenuation contrast in their native form. Here, three different adhesive types, namely phenol formaldehyde, polymeric diphenylmethane diisocyanate, and a hybrid polyvinyl acetate, are tagged with iodine such that they yield sufficient X-ray attenuation contrast. However, phase-contrast effects at material edgesmore » complicate image quality and segmentation in XCT data reconstructed with conventional filtered backprojection absorption contrast algorithms. A quantitative phase retrieval algorithm, which isolates and removes the phase-contrast effect, was demonstrated. The paper discusses and illustrates the balance between material X-ray attenuation and phase-contrast effects in all quantitative XCT analyses of wood-adhesive bondlines.« less

X-ray computed tomography of wood-adhesive bondlines: Attenuation and phase-contrast effects

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

Paris, Jesse L.; Kamke, Frederick A.; Xiao, Xianghui

Microscale X-ray computed tomography (XCT) is discussed as a technique for identifying 3D adhesive distribution in wood-adhesive bondlines. Visualization and material segmentation of the adhesives from the surrounding cellular structures require sufficient gray-scale contrast in the reconstructed XCT data. Commercial wood-adhesive polymers have similar chemical characteristics and density to wood cell wall polymers and therefore do not provide good XCT attenuation contrast in their native form. Here, three different adhesive types, namely phenol formaldehyde, polymeric diphenylmethane diisocyanate, and a hybrid polyvinyl acetate, are tagged with iodine such that they yield sufficient X-ray attenuation contrast. However, phase-contrast effects at material edgesmore » complicate image quality and segmentation in XCT data reconstructed with conventional filtered backprojection absorption contrast algorithms. A quantitative phase retrieval algorithm, which isolates and removes the phase-contrast effect, was demonstrated. The paper discusses and illustrates the balance between material X-ray attenuation and phase-contrast effects in all quantitative XCT analyses of wood-adhesive bondlines.« less

Application of Fourier-wavelet regularized deconvolution for improving image quality of free space propagation x-ray phase contrast imaging.

PubMed

Zhou, Zhongxing; Gao, Feng; Zhao, Huijuan; Zhang, Lixin

2012-11-21

New x-ray phase contrast imaging techniques without using synchrotron radiation confront a common problem from the negative effects of finite source size and limited spatial resolution. These negative effects swamp the fine phase contrast fringes and make them almost undetectable. In order to alleviate this problem, deconvolution procedures should be applied to the blurred x-ray phase contrast images. In this study, three different deconvolution techniques, including Wiener filtering, Tikhonov regularization and Fourier-wavelet regularized deconvolution (ForWaRD), were applied to the simulated and experimental free space propagation x-ray phase contrast images of simple geometric phantoms. These algorithms were evaluated in terms of phase contrast improvement and signal-to-noise ratio. The results demonstrate that the ForWaRD algorithm is most appropriate for phase contrast image restoration among above-mentioned methods; it can effectively restore the lost information of phase contrast fringes while reduce the amplified noise during Fourier regularization.

Effects of speckle/pixel size ratio on temporal and spatial speckle-contrast analysis of dynamic scattering systems: Implications for measurements of blood-flow dynamics.

PubMed

Ramirez-San-Juan, J C; Mendez-Aguilar, E; Salazar-Hermenegildo, N; Fuentes-Garcia, A; Ramos-Garcia, R; Choi, B

2013-01-01

Laser Speckle Contrast Imaging (LSCI) is an optical technique used to generate blood flow maps with high spatial and temporal resolution. It is well known that in LSCI, the speckle size must exceed the Nyquist criterion to maximize the speckle's pattern contrast. In this work, we study experimentally the effect of speckle-pixel size ratio not only in dynamic speckle contrast, but also on the calculation of the relative flow speed for temporal and spatial analysis. Our data suggest that the temporal LSCI algorithm is more accurate at assessing the relative changes in flow speed than the spatial algorithm.

Methyl green and nitrotetrazolium blue chloride co-expression in colon tissue: A hyperspectral microscopic imaging analysis

NASA Astrophysics Data System (ADS)

Li, Qingli; Liu, Hongying; Wang, Yiting; Sun, Zhen; Guo, Fangmin; Zhu, Jianzhong

2014-12-01

Histological observation of dual-stained colon sections is usually performed by visual observation under a light microscope, or by viewing on a computer screen with the assistance of image processing software in both research and clinical settings. These traditional methods are usually not sufficient to reliably differentiate spatially overlapping chromogens generated by different dyes. Hyperspectral microscopic imaging technology offers a solution for these constraints as the hyperspectral microscopic images contain information that allows differentiation between spatially co-located chromogens with similar but different spectra. In this paper, a hyperspectral microscopic imaging (HMI) system is used to identify methyl green and nitrotetrazolium blue chloride in dual-stained colon sections. Hyperspectral microscopic images are captured and the normalized score algorithm is proposed to identify the stains and generate the co-expression results. Experimental results show that the proposed normalized score algorithm can generate more accurate co-localization results than the spectral angle mapper algorithm. The hyperspectral microscopic imaging technology can enhance the visualization of dual-stained colon sections, improve the contrast and legibility of each stain using their spectral signatures, which is helpful for pathologist performing histological analyses.

Brain tumor locating in 3D MR volume using symmetry

NASA Astrophysics Data System (ADS)

Dvorak, Pavel; Bartusek, Karel

2014-03-01

This work deals with the automatic determination of a brain tumor location in 3D magnetic resonance volumes. The aim of this work is not the precise segmentation of the tumor and its parts but only the detection of its location. This work is the first step in the tumor segmentation process, an important topic in neuro-image processing. The algorithm expects 3D magnetic resonance volumes of brain containing a tumor. The detection is based on locating the area that breaks the left-right symmetry of the brain. This is done by multi-resolution comparing of corresponding regions in left and right hemisphere. The output of the computation is the probabilistic map of the tumor location. The created algorithm was tested on 80 volumes from publicly available BRATS databases containing 3D brain volumes afflicted by a brain tumor. These pathological structures had various sizes and shapes and were located in various parts of the brain. The locating performance of the algorithm was 85% for T1-weighted volumes, 91% for T1-weighted contrast enhanced volumes, 96% for FLAIR and T2-wieghted volumes and 95% for their combinations.

Two-dimensional statistical linear discriminant analysis for real-time robust vehicle-type recognition

NASA Astrophysics Data System (ADS)

Zafar, I.; Edirisinghe, E. A.; Acar, S.; Bez, H. E.

2007-02-01

Automatic vehicle Make and Model Recognition (MMR) systems provide useful performance enhancements to vehicle recognitions systems that are solely based on Automatic License Plate Recognition (ALPR) systems. Several car MMR systems have been proposed in literature. However these approaches are based on feature detection algorithms that can perform sub-optimally under adverse lighting and/or occlusion conditions. In this paper we propose a real time, appearance based, car MMR approach using Two Dimensional Linear Discriminant Analysis that is capable of addressing this limitation. We provide experimental results to analyse the proposed algorithm's robustness under varying illumination and occlusions conditions. We have shown that the best performance with the proposed 2D-LDA based car MMR approach is obtained when the eigenvectors of lower significance are ignored. For the given database of 200 car images of 25 different make-model classifications, a best accuracy of 91% was obtained with the 2D-LDA approach. We use a direct Principle Component Analysis (PCA) based approach as a benchmark to compare and contrast the performance of the proposed 2D-LDA approach to car MMR. We conclude that in general the 2D-LDA based algorithm supersedes the performance of the PCA based approach.

Geological mapping of the Schuppen belt of north-east India using geospatial technology

NASA Astrophysics Data System (ADS)

Ghosh, Tanaya; Basu, Surajit; Hazra, Sugata

2014-01-01

A revised geologic map of the Schuppen belt of northeast India has been prepared based on interpretation of digitally enhanced satellite images. The satellite image interpretation is supported by limited field work and existing geologic maps. Available geological maps of this fold thrust belt are discontinuous and multi-scaled. The authors are of multiple opinions regarding the trajectory of formation boundaries and fault contacts. Digital image processing of satellite images and limited field surveys have been used to reinterpret and modify the existing geological maps of this fold thrust belt. Optical data of Landsat Thematic Mapper, Enhanced Thematic Mapper and elevation data of ASTER have been used to prepare this revised geological map. The study area extends from Hajadisa in south to Digboi oilfield in north, bounded by Naga thrust in the west and Disang thrust in the east. PCA, Image fusion, Linear Contrast stretch, Histogram Equalization and Painted relief algorithms have been used for the delineation of major geological lineaments like lithological boundary, thrust and strike slip faults. Digital elevation maps have enabled in the discrimination between thrust contacts and lithological boundaries, with the former being located mostly in the valleys. Textural enhancements of PCA, colour composites and Painted relief algorithm have been used to discriminate between different rock types. Few geological concepts about the terrain have been revisited and modified. It is assumed that this revised map should be of practical use as this terrain promises unexploited hydrocarbon reserves.

Enhanced visualization of inner ear structures

NASA Astrophysics Data System (ADS)

Niemczyk, Kazimierz; Kucharski, Tomasz; Kujawinska, Malgorzata; Bruzgielewicz, Antoni

2004-07-01

Recently surgery requires extensive support from imaging technologies in order to increase effectiveness and safety of operations. One of important tasks is to enhance visualisation of quasi-phase (transparent) 3d structures. Those structures are characterized by very low contrast. It makes differentiation of tissues in field of view very difficult. For that reason the surgeon may be extremly uncertain during operation. This problem is connected with supporting operations of inner ear during which physician has to perform cuts at specific places of quasi-transparent velums. Conventionally during such operations medical doctor views the operating field through stereoscopic microscope. In the paper we propose a 3D visualisation system based on Helmet Mounted Display. Two CCD cameras placed at the output of microscope perform acquisition of stereo pairs of images. The images are processed in real-time with the goal of enhancement of quasi-phased structures. The main task is to create algorithm that is not sensitive to changes in intensity distribution. The disadvantages of existing algorithms is their lack of adaptation to occuring reflexes and shadows in field of view. The processed images from both left and right channels are overlaid on the actual images exported and displayed at LCD's of Helmet Mounted Display. A physician observes by HMD (Helmet Mounted Display) a stereoscopic operating scene with indication of the places of special interest. The authors present the hardware ,procedures applied and initial results of inner ear structure visualisation. Several problems connected with processing of stereo-pair images are discussed.

Region-of-interest image reconstruction in circular cone-beam microCT

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

Cho, Seungryong; Bian, Junguo; Pelizzari, Charles A.

2007-12-15

Cone-beam microcomputed tomography (microCT) is one of the most popular choices for small animal imaging which is becoming an important tool for studying animal models with transplanted diseases. Region-of-interest (ROI) imaging techniques in CT, which can reconstruct an ROI image from the projection data set of the ROI, can be used not only for reducing imaging-radiation exposure to the subject and scatters to the detector but also for potentially increasing spatial resolution of the reconstructed images. Increasing spatial resolution in microCT images can facilitate improved accuracy in many assessment tasks. A method proposed previously for increasing CT image spatial resolutionmore » entails the exploitation of the geometric magnification in cone-beam CT. Due to finite detector size, however, this method can lead to data truncation for a large geometric magnification. The Feldkamp-Davis-Kress (FDK) algorithm yields images with artifacts when truncated data are used, whereas the recently developed backprojection filtration (BPF) algorithm is capable of reconstructing ROI images without truncation artifacts from truncated cone-beam data. We apply the BPF algorithm to reconstructing ROI images from truncated data of three different objects acquired by our circular cone-beam microCT system. Reconstructed images by use of the FDK and BPF algorithms from both truncated and nontruncated cone-beam data are compared. The results of the experimental studies demonstrate that, from certain truncated data, the BPF algorithm can reconstruct ROI images with quality comparable to that reconstructed from nontruncated data. In contrast, the FDK algorithm yields ROI images with truncation artifacts. Therefore, an implication of the studies is that, when truncated data are acquired with a configuration of a large geometric magnification, the BPF algorithm can be used for effective enhancement of the spatial resolution of a ROI image.« less

Cardinality enhancement utilizing Sequential Algorithm (SeQ) code in OCDMA system

NASA Astrophysics Data System (ADS)

Fazlina, C. A. S.; Rashidi, C. B. M.; Rahman, A. K.; Aljunid, S. A.

2017-11-01

Optical Code Division Multiple Access (OCDMA) has been important with increasing demand for high capacity and speed for communication in optical networks because of OCDMA technique high efficiency that can be achieved, hence fibre bandwidth is fully used. In this paper we will focus on Sequential Algorithm (SeQ) code with AND detection technique using Optisystem design tool. The result revealed SeQ code capable to eliminate Multiple Access Interference (MAI) and improve Bit Error Rate (BER), Phase Induced Intensity Noise (PIIN) and orthogonally between users in the system. From the results, SeQ shows good performance of BER and capable to accommodate 190 numbers of simultaneous users contrast with existing code. Thus, SeQ code have enhanced the system about 36% and 111% of FCC and DCS code. In addition, SeQ have good BER performance 10-25 at 155 Mbps in comparison with 622 Mbps, 1 Gbps and 2 Gbps bit rate. From the plot graph, 155 Mbps bit rate is suitable enough speed for FTTH and LAN networks. Resolution can be made based on the superior performance of SeQ code. Thus, these codes will give an opportunity in OCDMA system for better quality of service in an optical access network for future generation's usage

Segmentation of vessels: the corkscrew algorithm

NASA Astrophysics Data System (ADS)

Wesarg, Stefan; Firle, Evelyn A.

2004-05-01

Medical imaging is nowadays much more than only providing data for diagnosis. It also links 'classical' diagnosis to modern forms of treatment such as image guided surgery. Those systems require the identification of organs, anatomical regions of the human body etc., i. e. the segmentation of structures from medical data sets. The algorithms used for these segmentation tasks strongly depend on the object to be segmented. One structure which plays an important role in surgery planning are vessels that are found everywhere in the human body. Several approaches for their extraction already exist. However, there is no general one which is suitable for all types of data or all sorts of vascular structures. This work presents a new algorithm for the segmentation of vessels. It can be classified as a skeleton-based approach working on 3D data sets, and has been designed for a reliable segmentation of coronary arteries. The algorithm is a semi-automatic extraction technique requiring the definition of the start and end the point of the (centerline) path to be found. A first estimation of the vessel's centerline is calculated and then corrected iteratively by detecting the vessel's border perpendicular to the centerline. We used contrast enhanced CT data sets of the thorax for testing our approach. Coronary arteries have been extracted from the data sets using the 'corkscrew algorithm' presented in this work. The segmentation turned out to be robust even if moderate breathing artifacts were present in the data sets.

Subjective comparison and evaluation of speech enhancement algorithms

PubMed Central

Hu, Yi; Loizou, Philipos C.

2007-01-01

Making meaningful comparisons between the performance of the various speech enhancement algorithms proposed over the years, has been elusive due to lack of a common speech database, differences in the types of noise used and differences in the testing methodology. To facilitate such comparisons, we report on the development of a noisy speech corpus suitable for evaluation of speech enhancement algorithms. This corpus is subsequently used for the subjective evaluation of 13 speech enhancement methods encompassing four classes of algorithms: spectral subtractive, subspace, statistical-model based and Wiener-type algorithms. The subjective evaluation was performed by Dynastat, Inc. using the ITU-T P.835 methodology designed to evaluate the speech quality along three dimensions: signal distortion, noise distortion and overall quality. This paper reports the results of the subjective tests. PMID:18046463

Smart Image Enhancement Process

NASA Technical Reports Server (NTRS)

Jobson, Daniel J. (Inventor); Rahman, Zia-ur (Inventor); Woodell, Glenn A. (Inventor)

2012-01-01

Contrast and lightness measures are used to first classify the image as being one of non-turbid and turbid. If turbid, the original image is enhanced to generate a first enhanced image. If non-turbid, the original image is classified in terms of a merged contrast/lightness score based on the contrast and lightness measures. The non-turbid image is enhanced to generate a second enhanced image when a poor contrast/lightness score is associated therewith. When the second enhanced image has a poor contrast/lightness score associated therewith, this image is enhanced to generate a third enhanced image. A sharpness measure is computed for one image that is selected from (i) the non-turbid image, (ii) the first enhanced image, (iii) the second enhanced image when a good contrast/lightness score is associated therewith, and (iv) the third enhanced image. If the selected image is not-sharp, it is sharpened to generate a sharpened image. The final image is selected from the selected image and the sharpened image.

Detection of low-contrast images in film-grain noise.

PubMed

Naderi, F; Sawchuk, A A

1978-09-15

When low contrast photographic images are digitized by a very small aperture, extreme film-grain noise almost completely obliterates the image information. Using a large aperture to average out the noise destroys the fine details of the image. In these situations conventional statistical restoration techniques have little effect, and well chosen heuristic algorithms have yielded better results. In this paper we analyze the noisecheating algorithm of Zweig et al. [J. Opt. Soc. Am. 65, 1347 (1975)] and show that it can be justified by classical maximum-likelihood detection theory. A more general algorithm applicable to a broader class of images is then developed by considering the signal-dependent nature of film-grain noise. Finally, a Bayesian detection algorithm with improved performance is presented.

K-edge ratio method for identification of multiple nanoparticulate contrast agents by spectral CT imaging

PubMed Central

Ghadiri, H; Ay, M R; Shiran, M B; Soltanian-Zadeh, H

2013-01-01

Objective: Recently introduced energy-sensitive X-ray CT makes it feasible to discriminate different nanoparticulate contrast materials. The purpose of this work is to present a K-edge ratio method for differentiating multiple simultaneous contrast agents using spectral CT. Methods: The ratio of two images relevant to energy bins straddling the K-edge of the materials is calculated using an analytic CT simulator. In the resulting parametric map, the selected contrast agent regions can be identified using a thresholding algorithm. The K-edge ratio algorithm is applied to spectral images of simulated phantoms to identify and differentiate up to four simultaneous and targeted CT contrast agents. Results: We show that different combinations of simultaneous CT contrast agents can be identified by the proposed K-edge ratio method when energy-sensitive CT is used. In the K-edge parametric maps, the pixel values for biological tissues and contrast agents reach a maximum of 0.95, whereas for the selected contrast agents, the pixel values are larger than 1.10. The number of contrast agents that can be discriminated is limited owing to photon starvation. For reliable material discrimination, minimum photon counts corresponding to 140 kVp, 100 mAs and 5-mm slice thickness must be used. Conclusion: The proposed K-edge ratio method is a straightforward and fast method for identification and discrimination of multiple simultaneous CT contrast agents. Advances in knowledge: A new spectral CT-based algorithm is proposed which provides a new concept of molecular CT imaging by non-iteratively identifying multiple contrast agents when they are simultaneously targeting different organs. PMID:23934964

Assessment of contrast enhanced respiration managed cone-beam CT for image guided radiotherapy of intrahepatic tumors

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

Jensen, Nikolaj K. G., E-mail: nkyj@regionsjaelland.dk; Stewart, Errol; Imaging Research Lab, Robarts Research Institute, London, Ontario N6A 5B7

2014-05-15

Purpose: Contrast enhancement and respiration management are widely used during image acquisition for radiotherapy treatment planning of liver tumors along with respiration management at the treatment unit. However, neither respiration management nor intravenous contrast is commonly used during cone-beam CT (CBCT) image acquisition for alignment prior to radiotherapy. In this study, the authors investigate the potential gains of injecting an iodinated contrast agent in combination with respiration management during CBCT acquisition for liver tumor radiotherapy. Methods: Five rabbits with implanted liver tumors were subjected to CBCT with and without motion management and contrast injection. The acquired CBCT images were registeredmore » to the planning CT to determine alignment accuracy and dosimetric impact. The authors developed a simulation tool for simulating contrast-enhanced CBCT images from dynamic contrast enhanced CT imaging (DCE-CT) to determine optimal contrast injection protocols. The tool was validated against contrast-enhanced CBCT of the rabbit subjects and was used for five human patients diagnosed with hepatocellular carcinoma. Results: In the rabbit experiment, when neither motion management nor contrast was used, tumor centroid misalignment between planning image and CBCT was 9.2 mm. This was reduced to 2.8 mm when both techniques were employed. Tumors were not visualized in clinical CBCT images of human subjects. Simulated contrast-enhanced CBCT was found to improve tumor contrast in all subjects. Different patients were found to require different contrast injections to maximize tumor contrast. Conclusions: Based on the authors’ animal study, respiration managed contrast enhanced CBCT improves IGRT significantly. Contrast enhanced CBCT benefits from patient specific tracer kinetics determined from DCE-CT.« less

Improved sensitivity of computed tomography towards iodine and gold nanoparticle contrast agents via iterative reconstruction methods

PubMed Central

Bernstein, Ally Leigh; Dhanantwari, Amar; Jurcova, Martina; Cheheltani, Rabee; Naha, Pratap Chandra; Ivanc, Thomas; Shefer, Efrat; Cormode, David Peter

2016-01-01

Computed tomography is a widely used medical imaging technique that has high spatial and temporal resolution. Its weakness is its low sensitivity towards contrast media. Iterative reconstruction techniques (ITER) have recently become available, which provide reduced image noise compared with traditional filtered back-projection methods (FBP), which may allow the sensitivity of CT to be improved, however this effect has not been studied in detail. We scanned phantoms containing either an iodine contrast agent or gold nanoparticles. We used a range of tube voltages and currents. We performed reconstruction with FBP, ITER and a novel, iterative, modal-based reconstruction (IMR) algorithm. We found that noise decreased in an algorithm dependent manner (FBP > ITER > IMR) for every scan and that no differences were observed in attenuation rates of the agents. The contrast to noise ratio (CNR) of iodine was highest at 80 kV, whilst the CNR for gold was highest at 140 kV. The CNR of IMR images was almost tenfold higher than that of FBP images. Similar trends were found in dual energy images formed using these algorithms. In conclusion, IMR-based reconstruction techniques will allow contrast agents to be detected with greater sensitivity, and may allow lower contrast agent doses to be used. PMID:27185492

Window classification of brain CT images in biomedical articles.

PubMed

Xue, Zhiyun; Antani, Sameer; Long, L Rodney; Demner-Fushman, Dina; Thoma, George R

2012-01-01

Effective capability to search biomedical articles based on visual properties of article images may significantly augment information retrieval in the future. In this paper, we present a new method to classify the window setting types of brain CT images. Windowing is a technique frequently used in the evaluation of CT scans, and is used to enhance contrast for the particular tissue or abnormality type being evaluated. In particular, it provides radiologists with an enhanced view of certain types of cranial abnormalities, such as the skull lesions and bone dysplasia which are usually examined using the " bone window" setting and illustrated in biomedical articles using "bone window images". Due to the inherent large variations of images among articles, it is important that the proposed method is robust. Our algorithm attained 90% accuracy in classifying images as bone window or non-bone window in a 210 image data set.

Imaging of cerebellopontine angle lesions: an update. Part 1: enhancing extra-axial lesions.

PubMed

Bonneville, Fabrice; Savatovsky, Julien; Chiras, Jacques

2007-10-01

Computed tomography (CT) and magnetic resonance (MR) imaging reliably demonstrate typical features of vestibular schwannomas or meningiomas in the vast majority of mass lesions in the cerebellopontine angle (CPA). However, a large variety of unusual lesions can also be encountered in the CPA. Covering the entire spectrum of lesions potentially found in the CPA, these articles explain the pertinent neuroimaging features that radiologists need to know to make clinically relevant diagnoses in these cases, including data from diffusion and perfusion-weighted imaging or MR spectroscopy, when available. A diagnostic algorithm based on the lesion's site of origin, shape and margins, density, signal intensity and contrast material uptake is also proposed. Part 1 describes the different enhancing extra-axial CPA masses primarily arising from the cerebellopontine cistern and its contents, including vestibular and non-vestibular schwannomas, meningioma, metastasis, aneurysm, tuberculosis and other miscellaneous meningeal lesions.

Fault-tolerant clock synchronization in distributed systems

NASA Technical Reports Server (NTRS)

Ramanathan, Parameswaran; Shin, Kang G.; Butler, Ricky W.

1990-01-01

Existing fault-tolerant clock synchronization algorithms are compared and contrasted. These include the following: software synchronization algorithms, such as convergence-averaging, convergence-nonaveraging, and consistency algorithms, as well as probabilistic synchronization; hardware synchronization algorithms; and hybrid synchronization. The worst-case clock skews guaranteed by representative algorithms are compared, along with other important aspects such as time, message, and cost overhead imposed by the algorithms. More recent developments such as hardware-assisted software synchronization and algorithms for synchronizing large, partially connected distributed systems are especially emphasized.

Comparison of statistical algorithms for detecting homogeneous river reaches along a longitudinal continuum

NASA Astrophysics Data System (ADS)

Leviandier, Thierry; Alber, A.; Le Ber, F.; Piégay, H.

2012-02-01

Seven methods designed to delineate homogeneous river segments, belonging to four families, namely — tests of homogeneity, contrast enhancing, spatially constrained classification, and hidden Markov models — are compared, firstly on their principles, then on a case study, and on theoretical templates. These templates contain patterns found in the case study but not considered in the standard assumptions of statistical methods, such as gradients and curvilinear structures. The influence of data resolution, noise and weak satisfaction of the assumptions underlying the methods is investigated. The control of the number of reaches obtained in order to achieve meaningful comparisons is discussed. No method is found that outperforms all the others on all trials. However, the methods with sequential algorithms (keeping at order n + 1 all breakpoints found at order n) fail more often than those running complete optimisation at any order. The Hubert-Kehagias method and Hidden Markov Models are the most successful at identifying subpatterns encapsulated within the templates. Ergodic Hidden Markov Models are, moreover, liable to exhibit transition areas.

Unsupervised texture image segmentation by improved neural network ART2

NASA Technical Reports Server (NTRS)

Wang, Zhiling; Labini, G. Sylos; Mugnuolo, R.; Desario, Marco

1994-01-01

We here propose a segmentation algorithm of texture image for a computer vision system on a space robot. An improved adaptive resonance theory (ART2) for analog input patterns is adapted to classify the image based on a set of texture image features extracted by a fast spatial gray level dependence method (SGLDM). The nonlinear thresholding functions in input layer of the neural network have been constructed by two parts: firstly, to reduce the effects of image noises on the features, a set of sigmoid functions is chosen depending on the types of the feature; secondly, to enhance the contrast of the features, we adopt fuzzy mapping functions. The cluster number in output layer can be increased by an autogrowing mechanism constantly when a new pattern happens. Experimental results and original or segmented pictures are shown, including the comparison between this approach and K-means algorithm. The system written in C language is performed on a SUN-4/330 sparc-station with an image board IT-150 and a CCD camera.

Automated quantification of myocardial infarction from MR images by accounting for partial volume effects: animal, phantom, and human study.

PubMed

Heiberg, Einar; Ugander, Martin; Engblom, Henrik; Götberg, Matthias; Olivecrona, Göran K; Erlinge, David; Arheden, Håkan

2008-02-01

Ethics committees approved human and animal study components; informed written consent was provided (prospective human study [20 men; mean age, 62 years]) or waived (retrospective human study [16 men, four women; mean age, 59 years]). The purpose of this study was to prospectively evaluate a clinically applicable method, accounting for the partial volume effect, to automatically quantify myocardial infarction from delayed contrast material-enhanced magnetic resonance images. Pixels were weighted according to signal intensity to calculate infarct fraction for each pixel. Mean bias +/- variability (or standard deviation), expressed as percentage left ventricular myocardium (%LVM), were -0.3 +/- 1.3 (animals), -1.2 +/- 1.7 (phantoms), and 0.3 +/- 2.7 (patients), respectively. Algorithm had lower variability than dichotomous approach (2.7 vs 7.7 %LVM, P < .01) and did not differ from interobserver variability for bias (P = .31) or variability (P = .38). The weighted approach provides automatic quantification of myocardial infarction with higher accuracy and lower variability than a dichotomous algorithm. (c) RSNA, 2007.

Parameter-tolerant design of high contrast gratings

NASA Astrophysics Data System (ADS)

Chevallier, Christyves; Fressengeas, Nicolas; Jacquet, Joel; Almuneau, Guilhem; Laaroussi, Youness; Gauthier-Lafaye, Olivier; Cerutti, Laurent; Genty, Frédéric

2015-02-01

This work is devoted to the design of high contrast grating mirrors taking into account the technological constraints and tolerance of fabrication. First, a global optimization algorithm has been combined to a numerical analysis of grating structures (RCWA) to automatically design HCG mirrors. Then, the tolerances of the grating dimensions have been precisely studied to develop a robust optimization algorithm with which high contrast gratings, exhibiting not only a high efficiency but also large tolerance values, could be designed. Finally, several structures integrating previously designed HCGs has been simulated to validate and illustrate the interest of such gratings.

Structural damage identification using an enhanced thermal exchange optimization algorithm

NASA Astrophysics Data System (ADS)

Kaveh, A.; Dadras, A.

2018-03-01

The recently developed optimization algorithm-the so-called thermal exchange optimization (TEO) algorithm-is enhanced and applied to a damage detection problem. An offline parameter tuning approach is utilized to set the internal parameters of the TEO, resulting in the enhanced heat transfer optimization (ETEO) algorithm. The damage detection problem is defined as an inverse problem, and ETEO is applied to a wide range of structures. Several scenarios with noise and noise-free modal data are tested and the locations and extents of damages are identified with good accuracy.

Results of vardenafil mediated power Doppler ultrasound, contrast enhanced ultrasound and systematic random biopsies to detect prostate cancer.

PubMed

Morelli, Girolamo; Pagni, Riccardo; Mariani, Chiara; Minervini, Riccardo; Morelli, Andrea; Gori, Francesco; Ferdeghini, Ezio Maria; Paterni, Marco; Mauro, Eva; Guidi, Elisa; Armillotta, Nicola; Canale, Domenico; Vitti, Paolo; Caramella, Davide; Minervini, Andrea

2011-06-01

We evaluated the ability of the phosphodiesterase-5 inhibitor vardenafil to increase prostate microcirculation during power Doppler ultrasound. We also evaluated the results of contrast and vardenafil enhanced targeted biopsies compared to those of standard 12-core random biopsies to detect cancer. Between May 2008 and January 2010, 150 consecutive patients with prostate specific antigen more than 4 ng/ml at first diagnosis with negative digital rectal examination and transrectal ultrasound, and no clinical history of prostatitis underwent contrast enhanced power Doppler ultrasound (bolus injection of 2.4 ml SonoVue® contrast agent), followed by vardenafil enhanced power Doppler ultrasound (1 hour after oral administration of vardenafil 20 mg). All patients underwent standard 12-core transrectal ultrasound guided random prostate biopsy plus 1 further sampling from each suspected hypervascular lesion detected by contrast and vardenafil enhanced power Doppler ultrasound. Prostate cancer was detected in 44 patients (29.3%). Contrast and vardenafil enhanced power Doppler ultrasound detected suspicious, contrast enhanced and vardenafil enhanced areas in 112 (74.6%) and 110 patients (73.3%), and was diagnostic for cancer in 32 (28.5%) and 42 (38%), respectively. Analysis of standard technique, and contrast and vardenafil enhanced power Doppler ultrasound findings by biopsy core showed significantly higher detection using vardenafil vs contrast enhanced power Doppler ultrasound and standard technique (41.2% vs 22.7% and 8.1%, p <0.005 and <0.001, respectively). The detection rate of standard plus contrast or vardenafil enhanced power Doppler ultrasound was 10% and 11.7% (p not significant). Vardenafil enhanced power Doppler ultrasound enables excellent visualization of the microvasculature associated with cancer and can improve the detection rate compared to contrast enhanced power Doppler ultrasound and the random technique. Copyright © 2011 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

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

PubMed

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

2017-11-01

Insufficient image contrast associated with radiation therapy daily setup x-ray images could negatively affect accurate patient treatment setup. We developed a method to perform automatic and user-independent contrast enhancement on 2D kilo voltage (kV) and megavoltage (MV) x-ray images. The goal was to provide tissue contrast optimized for each treatment site in order to support accurate patient daily treatment setup and the subsequent offline review. The proposed method processes the 2D x-ray images with an optimized image processing filter chain, which consists of a noise reduction filter and a high-pass filter followed by a contrast limited adaptive histogram equalization (CLAHE) filter. The most important innovation is to optimize the image processing parameters automatically to determine the required image contrast settings per disease site and imaging modality. Three major parameters controlling the image processing chain, i.e., the Gaussian smoothing weighting factor for the high-pass filter, the block size, and the clip limiting parameter for the CLAHE filter, were determined automatically using an interior-point constrained optimization algorithm. Fifty-two kV and MV x-ray images were included in this study. The results were manually evaluated and ranked with scores from 1 (worst, unacceptable) to 5 (significantly better than adequate and visually praise worthy) by physicians and physicists. The average scores for the images processed by the proposed method, the CLAHE, and the best window-level adjustment were 3.92, 2.83, and 2.27, respectively. The percentage of the processed images received a score of 5 were 48, 29, and 18%, respectively. The proposed method is able to outperform the standard image contrast adjustment procedures that are currently used in the commercial clinical systems. When the proposed method is implemented in the clinical systems as an automatic image processing filter, it could be useful for allowing quicker and potentially more accurate treatment setup and facilitating the subsequent offline review and verification. © 2017 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.

Single-Scale Retinex Using Digital Signal Processors

NASA Technical Reports Server (NTRS)

Hines, Glenn; Rahman, Zia-Ur; Jobson, Daniel; Woodell, Glenn

2005-01-01

The Retinex is an image enhancement algorithm that improves the brightness, contrast and sharpness of an image. It performs a non-linear spatial/spectral transform that provides simultaneous dynamic range compression and color constancy. It has been used for a wide variety of applications ranging from aviation safety to general purpose photography. Many potential applications require the use of Retinex processing at video frame rates. This is difficult to achieve with general purpose processors because the algorithm contains a large number of complex computations and data transfers. In addition, many of these applications also constrain the potential architectures to embedded processors to save power, weight and cost. Thus we have focused on digital signal processors (DSPs) and field programmable gate arrays (FPGAs) as potential solutions for real-time Retinex processing. In previous efforts we attained a 21 (full) frame per second (fps) processing rate for the single-scale monochromatic Retinex with a TMS320C6711 DSP operating at 150 MHz. This was achieved after several significant code improvements and optimizations. Since then we have migrated our design to the slightly more powerful TMS320C6713 DSP and the fixed point TMS320DM642 DSP. In this paper we briefly discuss the Retinex algorithm, the performance of the algorithm executing on the TMS320C6713 and the TMS320DM642, and compare the results with the TMS320C6711.

Comparative analysis of semantic localization accuracies between adult and pediatric DICOM CT images

NASA Astrophysics Data System (ADS)

Robertson, Duncan; Pathak, Sayan D.; Criminisi, Antonio; White, Steve; Haynor, David; Chen, Oliver; Siddiqui, Khan

2012-02-01

Existing literature describes a variety of techniques for semantic annotation of DICOM CT images, i.e. the automatic detection and localization of anatomical structures. Semantic annotation facilitates enhanced image navigation, linkage of DICOM image content and non-image clinical data, content-based image retrieval, and image registration. A key challenge for semantic annotation algorithms is inter-patient variability. However, while the algorithms described in published literature have been shown to cope adequately with the variability in test sets comprising adult CT scans, the problem presented by the even greater variability in pediatric anatomy has received very little attention. Most existing semantic annotation algorithms can only be extended to work on scans of both adult and pediatric patients by adapting parameters heuristically in light of patient size. In contrast, our approach, which uses random regression forests ('RRF'), learns an implicit model of scale variation automatically using training data. In consequence, anatomical structures can be localized accurately in both adult and pediatric CT studies without the need for parameter adaptation or additional information about patient scale. We show how the RRF algorithm is able to learn scale invariance from a combined training set containing a mixture of pediatric and adult scans. Resulting localization accuracy for both adult and pediatric data remains comparable with that obtained using RRFs trained and tested using only adult data.

Object-oriented feature-tracking algorithms for SAR images of the marginal ice zone

NASA Technical Reports Server (NTRS)

Daida, Jason; Samadani, Ramin; Vesecky, John F.

1990-01-01

An unsupervised method that chooses and applies the most appropriate tracking algorithm from among different sea-ice tracking algorithms is reported. In contrast to current unsupervised methods, this method chooses and applies an algorithm by partially examining a sequential image pair to draw inferences about what was examined. Based on these inferences the reported method subsequently chooses which algorithm to apply to specific areas of the image pair where that algorithm should work best.

Optimal Battery Sizing in Photovoltaic Based Distributed Generation Using Enhanced Opposition-Based Firefly Algorithm for Voltage Rise Mitigation

PubMed Central

Wong, Ling Ai; Shareef, Hussain; Mohamed, Azah; Ibrahim, Ahmad Asrul

2014-01-01

This paper presents the application of enhanced opposition-based firefly algorithm in obtaining the optimal battery energy storage systems (BESS) sizing in photovoltaic generation integrated radial distribution network in order to mitigate the voltage rise problem. Initially, the performance of the original firefly algorithm is enhanced by utilizing the opposition-based learning and introducing inertia weight. After evaluating the performance of the enhanced opposition-based firefly algorithm (EOFA) with fifteen benchmark functions, it is then adopted to determine the optimal size for BESS. Two optimization processes are conducted where the first optimization aims to obtain the optimal battery output power on hourly basis and the second optimization aims to obtain the optimal BESS capacity by considering the state of charge constraint of BESS. The effectiveness of the proposed method is validated by applying the algorithm to the 69-bus distribution system and by comparing the performance of EOFA with conventional firefly algorithm and gravitational search algorithm. Results show that EOFA has the best performance comparatively in terms of mitigating the voltage rise problem. PMID:25054184

Optimal battery sizing in photovoltaic based distributed generation using enhanced opposition-based firefly algorithm for voltage rise mitigation.

PubMed

Wong, Ling Ai; Shareef, Hussain; Mohamed, Azah; Ibrahim, Ahmad Asrul

2014-01-01

This paper presents the application of enhanced opposition-based firefly algorithm in obtaining the optimal battery energy storage systems (BESS) sizing in photovoltaic generation integrated radial distribution network in order to mitigate the voltage rise problem. Initially, the performance of the original firefly algorithm is enhanced by utilizing the opposition-based learning and introducing inertia weight. After evaluating the performance of the enhanced opposition-based firefly algorithm (EOFA) with fifteen benchmark functions, it is then adopted to determine the optimal size for BESS. Two optimization processes are conducted where the first optimization aims to obtain the optimal battery output power on hourly basis and the second optimization aims to obtain the optimal BESS capacity by considering the state of charge constraint of BESS. The effectiveness of the proposed method is validated by applying the algorithm to the 69-bus distribution system and by comparing the performance of EOFA with conventional firefly algorithm and gravitational search algorithm. Results show that EOFA has the best performance comparatively in terms of mitigating the voltage rise problem.

Using flow information to support 3D vessel reconstruction from rotational angiography

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

Waechter, Irina; Bredno, Joerg; Weese, Juergen

2008-07-15

For the assessment of cerebrovascular diseases, it is beneficial to obtain three-dimensional (3D) morphologic and hemodynamic information about the vessel system. Rotational angiography is routinely used to image the 3D vascular geometry and we have shown previously that rotational subtraction angiography has the potential to also give quantitative information about blood flow. Flow information can be determined when the angiographic sequence shows inflow and possibly outflow of contrast agent. However, a standard volume reconstruction assumes that the vessel tree is uniformly filled with contrast agent during the whole acquisition. If this is not the case, the reconstruction exhibits artifacts. Here,more » we show how flow information can be used to support the reconstruction of the 3D vessel centerline and radii in this case. Our method uses the fast marching algorithm to determine the order in which voxels are analyzed. For every voxel, the rotational time intensity curve (R-TIC) is determined from the image intensities at the projection points of the current voxel. Next, the bolus arrival time of the contrast agent at the voxel is estimated from the R-TIC. Then, a measure of the intensity and duration of the enhancement is determined, from which a speed value is calculated that steers the propagation of the fast marching algorithm. The results of the fast marching algorithm are used to determine the 3D centerline by backtracking. The 3D radius is reconstructed from 2D radius estimates on the projection images. The proposed method was tested on computer simulated rotational angiography sequences with systematically varied x-ray acquisition, blood flow, and contrast agent injection parameters and on datasets from an experimental setup using an anthropomorphic cerebrovascular phantom. For the computer simulation, the mean absolute error of the 3D centerline and 3D radius estimation was 0.42 and 0.25 mm, respectively. For the experimental datasets, the mean absolute error of the 3D centerline was 0.45 mm. Under pulsatile and nonpulsatile conditions, flow information can be used to enable a 3D vessel reconstruction from rotational angiography with inflow and possibly outflow of contrast agent. We found that the most important parameter for the quality of the reconstruction of centerline and radii is the range through which the x-ray system rotates in the time span of the injection. Good results were obtained if this range was at least 135 deg. . As a standard c-arm can rotate 205 deg., typically one third of the acquisition can show inflow or outflow of contrast agent, which is required for the quantification of blood flow from rotational angiography.« less

Hyperspectral Imager-Tracker

NASA Technical Reports Server (NTRS)

Agurok, Llya

2013-01-01

The Hyperspectral Imager-Tracker (HIT) is a technique for visualization and tracking of low-contrast, fast-moving objects. The HIT architecture is based on an innovative and only recently developed concept in imaging optics. This innovative architecture will give the Light Prescriptions Innovators (LPI) HIT the possibility of simultaneously collecting the spectral band images (hyperspectral cube), IR images, and to operate with high-light-gathering power and high magnification for multiple fast- moving objects. Adaptive Spectral Filtering algorithms will efficiently increase the contrast of low-contrast scenes. The most hazardous parts of a space mission are the first stage of a launch and the last 10 kilometers of the landing trajectory. In general, a close watch on spacecraft operation is required at distances up to 70 km. Tracking at such distances is usually associated with the use of radar, but its milliradian angular resolution translates to 100- m spatial resolution at 70-km distance. With sufficient power, radar can track a spacecraft as a whole object, but will not provide detail in the case of an accident, particularly for small debris in the onemeter range, which can only be achieved optically. It will be important to track the debris, which could disintegrate further into more debris, all the way to the ground. Such fragmentation could cause ballistic predictions, based on observations using high-resolution but narrow-field optics for only the first few seconds of the event, to be inaccurate. No optical imager architecture exists to satisfy NASA requirements. The HIT was developed for space vehicle tracking, in-flight inspection, and in the case of an accident, a detailed recording of the event. The system is a combination of five subsystems: (1) a roving fovea telescope with a wide 30 field of regard; (2) narrow, high-resolution fovea field optics; (3) a Coude optics system for telescope output beam stabilization; (4) a hyperspectral-mutispectral imaging assembly; and (5) image analysis software with effective adaptive spectral filtering algorithm for real-time contrast enhancement.

Dual-energy CT for detection of contrast enhancement or leakage within high-density haematomas in patients with intracranial haemorrhage.

PubMed

Watanabe, Yoshiyuki; Tsukabe, Akio; Kunitomi, Yuki; Nishizawa, Mitsuo; Arisawa, Atsuko; Tanaka, Hisashi; Yoshiya, Kazuhisa; Shimazu, Takeshi; Tomiyama, Noriyuki

2014-04-01

Our study aimed to elucidate the diagnostic performance of dual-energy CT (DECT) in the detection of contrast enhancement in intracranial haematomas (ICrH) with early phase dual-energy computed tomography angiography (CTA) and compare the results with those obtained by delayed CT enhancement. Thirty-six patients with ICrH were retrospectively included in this study. All patients had undergone single-energy non-contrast CT and contrast-enhanced dual-source DECT. DECT images were post-processed with commercial software, followed by obtaining iodine images and virtual non-contrast images and generating combined images that created the impression of 120-kVp images. Two neuroradiologists, blinded to the patients' data, reviewed two reading sessions: session A (non-contrast CT and combined CT) and session B (non-contrast CT, combined CT, and iodine images) for detection of contrast enhancement in the haematomas. Contrast leakage or enhancement was detected in 23 (57.5 %) out of 40 haemorrhagic lesions in 36 patients on delayed CT. Three enhanced lesions were depicted only in the DECT iodine images. The sensitivity, specificity, positive predictive value, and negative predictive value of session A were 82.6, 94.1, 95.0, and 80.0 %, respectively, and those of session B were 95.7, 94.1, 95.7, and 94.1 %, respectively. DECT emphasised the iodine enhancement and facilitated the detection of contrast enhancement or leakage.

A comparative study of automatic image segmentation algorithms for target tracking in MR-IGRT.

PubMed

Feng, Yuan; Kawrakow, Iwan; Olsen, Jeff; Parikh, Parag J; Noel, Camille; Wooten, Omar; Du, Dongsu; Mutic, Sasa; Hu, Yanle

2016-03-08

On-board magnetic resonance (MR) image guidance during radiation therapy offers the potential for more accurate treatment delivery. To utilize the real-time image information, a crucial prerequisite is the ability to successfully segment and track regions of interest (ROI). The purpose of this work is to evaluate the performance of different segmentation algorithms using motion images (4 frames per second) acquired using a MR image-guided radiotherapy (MR-IGRT) system. Manual con-tours of the kidney, bladder, duodenum, and a liver tumor by an experienced radiation oncologist were used as the ground truth for performance evaluation. Besides the manual segmentation, images were automatically segmented using thresholding, fuzzy k-means (FKM), k-harmonic means (KHM), and reaction-diffusion level set evolution (RD-LSE) algorithms, as well as the tissue tracking algorithm provided by the ViewRay treatment planning and delivery system (VR-TPDS). The performance of the five algorithms was evaluated quantitatively by comparing with the manual segmentation using the Dice coefficient and target registration error (TRE) measured as the distance between the centroid of the manual ROI and the centroid of the automatically segmented ROI. All methods were able to successfully segment the bladder and the kidney, but only FKM, KHM, and VR-TPDS were able to segment the liver tumor and the duodenum. The performance of the thresholding, FKM, KHM, and RD-LSE algorithms degraded as the local image contrast decreased, whereas the performance of the VP-TPDS method was nearly independent of local image contrast due to the reference registration algorithm. For segmenting high-contrast images (i.e., kidney), the thresholding method provided the best speed (< 1 ms) with a satisfying accuracy (Dice = 0.95). When the image contrast was low, the VR-TPDS method had the best automatic contour. Results suggest an image quality determination procedure before segmentation and a combination of different methods for optimal segmentation with the on-board MR-IGRT system.

New false color mapping for image fusion

NASA Astrophysics Data System (ADS)

Toet, Alexander; Walraven, Jan

1996-03-01

A pixel-based color-mapping algorithm is presented that produces a fused false color rendering of two gray-level images representing different sensor modalities. The resulting images have a higher information content than each of the original images and retain sensor-specific image information. The unique component of each image modality is enhanced in the resulting fused color image representation. First, the common component of the two original input images is determined. Second, the common component is subtracted from the original images to obtain the unique component of each image. Third, the unique component of each image modality is subtracted from the image of the other modality. This step serves to enhance the representation of sensor-specific details in the final fused result. Finally, a fused color image is produced by displaying the images resulting from the last step through, respectively, the red and green channels of a color display. The method is applied to fuse thermal and visual images. The results show that the color mapping enhances the visibility of certain details and preserves the specificity of the sensor information. The fused images also have a fairly natural appearance. The fusion scheme involves only operations on corresponding pixels. The resolution of a fused image is therefore directly related to the resolution of the input images. Before fusing, the contrast of the images can be enhanced and their noise can be reduced by standard image- processing techniques. The color mapping algorithm is computationally simple. This implies that the investigated approaches can eventually be applied in real time and that the hardware needed is not too complicated or too voluminous (an important consideration when it has to fit in an airplane, for instance).

Introduction to the local enhancement of underwater imagery

NASA Astrophysics Data System (ADS)

Schmalz, Mark S.

1995-06-01

Image-based detection of submerged objects is frequently confounded by optical distortions in the aqueous medium. For example, scattering can severly degrade contrast and resolution in underwater (UW) images when illumination systems and cameras are not range-gated. Prior to the development of range-gated imaging, much research emphasis was placed upon the analysis of greyscale imagery acquired under incoherent illumination. Primarily as a result of current emphasis on coherent optical technologies, the progress of image processing (IP) research that pertains to UW imagery has lagged IP hardware and software development. In this paper, we summarize methods for the digital clarification of images that portray actively illuminated UW scenes, i.e., images of floodlit objects. We model the primary UW image components as: a) contrast degradation resulting from illuminant backscattering from the water column, b) a return signal that results from backscattering of the illuminant from the object of regard, and c) resolution loss, due to forward scattering of the return signal. Letting items a) and c) consititute error sources, one can locally apply the appropriate filters to reduce the contribution of such errors. Our technique emphasized local enhancement, as opposed to the global methods used in previous imaging practice. Our enhancement filters are based upon image-algebraic templates that are designed to compensate for the effects of single and multiple scattering as well as absorption within the water column. Discussion is based upon image clarity, algorithmic complexity, and computational efficiency.

[Contrast medium enhanced magnetic resonance tomography of liver metastases: positive versus negative contrast media].

PubMed

Hammerstingl, R M; Schwarz, W; Hochmuth, K; Staib-Sebler, E; Lorenz, M; Vogl, T J

2001-01-01

The development in oncologic liver surgery as well as modified interventional therapy strategies of the liver have resulted in improved diagnostic imaging. The evolution of contrast agents for MR imaging of the liver has proceeded along several different paths with the common goal of improving liver-lesion contrast. In MRI contrast agents act indirectly by their effects on relaxation times. Contrast agents used for hepatic MR imaging can be categorized in those that target the extracellular space, the hepatobiliary system, and the reticuloendothelial system. The first two result in a positive enhancement, the last one in a negative enhancement. Positive enhancers allow a better characterization of liver metastases using dynamic sequence protocols. Detection rate of liver metastases is increased using hepatobiliary contrast-enhanced MRI compared to unenhanced MRI. Negative enhancers, iron oxide particles, significantly increase tumor-to-liver contrast and allow detection of more lesions than other diagnostic methods. Iron-oxide enhanced MRI enables differential diagnosis of liver metastases comparing morphologic features using T2 and T1-weighted sequences.

Contrast Enhancement for Thermal Acoustic Breast Cancer Imaging via Resonant Stimulation

DTIC Science & Technology

2008-03-01

AD_________________ Award Number: W81XWH-06-1-0389 TITLE: Contrast Enhancement for Thermal...5a. CONTRACT NUMBER Contrast Enhancement for Thermal Acoustic Breast Cancer Imaging via Resonant Stimulation 5b. GRANT NUMBER W81XWH-06-1-0389...13. SUPPLEMENTARY NOTES 14. ABSTRACT This research plans to develop enhanced contrast thermal acoustic imaging (TAI) technology for the

ART 3.5D: an algorithm to label arteries and veins from three-dimensional angiography.

PubMed

Barra, Beatrice; De Momi, Elena; Ferrigno, Giancarlo; Pero, Guglielmo; Cardinale, Francesco; Baselli, Giuseppe

2016-10-01

Preoperative three-dimensional (3-D) visualization of brain vasculature by digital subtraction angiography from computerized tomography (CT) in neurosurgery is gaining more and more importance, since vessels are the primary landmarks both for organs at risk and for navigation. Surgical embolization of cerebral aneurysms and arteriovenous malformations, epilepsy surgery, and stereoelectroencephalography are a few examples. Contrast-enhanced cone-beam computed tomography (CE-CBCT) represents a powerful facility, since it is capable of acquiring images in the operation room, shortly before surgery. However, standard 3-D reconstructions do not provide a direct distinction between arteries and veins, which is of utmost importance and is left to the surgeon's inference so far. Pioneering attempts by true four-dimensional (4-D) CT perfusion scans were already described, though at the expense of longer acquisition protocols, higher dosages, and sensible resolution losses. Hence, space is open to approaches attempting to recover the contrast dynamics from standard CE-CBCT, on the basis of anomalies overlooked in the standard 3-D approach. This paper aims at presenting algebraic reconstruction technique (ART) 3.5D, a method that overcomes the clinical limitations of 4-D CT, from standard 3-D CE-CBCT scans. The strategy works on the 3-D angiography, previously segmented in the standard way, and reprocesses the dynamics hidden in the raw data to recover an approximate dynamics in each segmented voxel. Next, a classification algorithm labels the angiographic voxels and artery or vein. Numerical simulations were performed on a digital phantom of a simplified 3-D vasculature with contrast transit. CE-CBCT projections were simulated and used for ART 3.5D testing. We achieved up to 90% classification accuracy in simulations, proving the feasibility of the presented approach for dynamic information recovery for arteries and veins segmentation.

Simulations For Investigating the Contrast Mechanism of Biological Cells with High Frequency Scanning Acoustic Microscopy

NASA Astrophysics Data System (ADS)

Juntarapaso, Yada

Scanning Acoustic Microscopy (SAM) is one of the most powerful techniques for nondestructive evaluation and it is a promising tool for characterizing the elastic properties of biological tissues/cells. Exploring a single cell is important since there is a connection between single cell biomechanics and human cancer. Scanning acoustic microscopy (SAM) has been accepted and extensively utilized for acoustical cellular and tissue imaging including measurements of the mechanical and elastic properties of biological specimens. SAM provides superb advantages in that it is non-invasive, can measure mechanical properties of biological cells or tissues, and fixation/chemical staining is not necessary. The first objective of this research is to develop a program for simulating the images and contrast mechanism obtained by high-frequency SAM. Computer simulation algorithms based on MatlabRTM were built for simulating the images and contrast mechanisms. The mechanical properties of HeLa and MCF-7 cells were computed from the measurement data of the output signal amplitude as a function of distance from the focal planes of the acoustics lens which is known as V(z) . Algorithms for simulating V(z) responses involved the calculation of the reflectance function and were created based on ray theory and wave theory. The second objective is to design transducer arrays for SAM. Theoretical simulations based on Field II(c) programs of the high frequency ultrasound array designs were performed to enhance image resolution and volumetric imaging capabilities. Phased array beam forming and dynamic apodization and focusing were employed in the simulations. The new transducer array design will be state-of-the-art in improving the performance of SAM by electronic scanning and potentially providing a 4-D image of the specimen.

Multiphase CT scanning and different intravenous contrast media concentrations in combined F-18-FDG PET/CT: Effect on quantitative and clinical assessment.

PubMed

Rebière, Marilou; Verburg, Frederik A; Palmowski, Moritz; Krohn, Thomas; Pietsch, Hubertus; Kuhl, Christiane K; Mottaghy, Felix M; Behrendt, Florian F

2012-08-01

To evaluate the influence of multiphase CT scanning and different intravenous contrast media on contrast enhancement, attenuation correction and image quality in combined PET/CT. 140 patients were prospectively enrolled for F-18-FDG-PET/CT including a low-dose unenhanced, arterial and venous contrast enhanced CT. The first (second) 70 patients, received contrast medium with 370 (300) mg iodine/ml. The iodine delivery rate (1.3mg/s) and total iodine load (44.4g) were identical for both groups. Contrast enhancement and maximum and mean standardized FDG uptake values (SUVmax and SUVmean) were determined for the un-enhanced, arterial and venous PET/CT at multiple anatomic sites and PET reconstructions were visually evaluated. Arterial contrast enhancement was significantly higher for the 300mg/ml contrast medium compared to 370mgI/ml at all anatomic sites. Venous enhancement was not different between the two contrast media. SUVmean and SUVmax were significantly higher for the contrast enhanced compared to the non-enhanced PET/CT at all anatomic sites (all P<0.001). Tracer uptake was significantly higher in the arterial than in the venous PET/CT in the arteries using both contrast media (all P<0.001). No differences in tracer uptake were found between the contrast media (all P>0.05). Visual assessment revealed no relevant differences between the different PET reconstructions. There is no relevant qualitative influence on the PET scan from the use of different intravenous contrast media in its various phases in combined multiphase PET/CT. For quantitative analysis of tracer uptake it is required to use an identical PET/CT protocol. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

New Techniques for High-Contrast Imaging with ADI: The ACORNS-ADI SEEDS Data Reduction Pipeline

NASA Technical Reports Server (NTRS)

Brandt, Timothy D.; McElwain, Michael W.; Turner, Edwin L.; Abe, L.; Brandner, W.; Carson, J.; Egner, S.; Feldt, M.; Golota, T.; Grady, C. A.;

Experimental Verification of Bayesian Planet Detection Algorithms with a Shaped Pupil Coronagraph

NASA Astrophysics Data System (ADS)

Savransky, D.; Groff, T. D.; Kasdin, N. J.

2010-10-01

We evaluate the feasibility of applying Bayesian detection techniques to discovering exoplanets using high contrast laboratory data with simulated planetary signals. Background images are generated at the Princeton High Contrast Imaging Lab (HCIL), with a coronagraphic system utilizing a shaped pupil and two deformable mirrors (DMs) in series. Estimates of the electric field at the science camera are used to correct for quasi-static speckle and produce symmetric high contrast dark regions in the image plane. Planetary signals are added in software, or via a physical star-planet simulator which adds a second off-axis point source before the coronagraph with a beam recombiner, calibrated to a fixed contrast level relative to the source. We produce a variety of images, with varying integration times and simulated planetary brightness. We then apply automated detection algorithms such as matched filtering to attempt to extract the planetary signals. This allows us to evaluate the efficiency of these techniques in detecting planets in a high noise regime and eliminating false positives, as well as to test existing algorithms for calculating the required integration times for these techniques to be applicable.

Optimal wavefront estimation of incoherent sources

NASA Astrophysics Data System (ADS)

Riggs, A. J. Eldorado; Kasdin, N. Jeremy; Groff, Tyler

2014-08-01

Direct imaging is in general necessary to characterize exoplanets and disks. A coronagraph is an instrument used to create a dim (high-contrast) region in a star's PSF where faint companions can be detected. All coronagraphic high-contrast imaging systems use one or more deformable mirrors (DMs) to correct quasi-static aberrations and recover contrast in the focal plane. Simulations show that existing wavefront control algorithms can correct for diffracted starlight in just a few iterations, but in practice tens or hundreds of control iterations are needed to achieve high contrast. The discrepancy largely arises from the fact that simulations have perfect knowledge of the wavefront and DM actuation. Thus, wavefront correction algorithms are currently limited by the quality and speed of wavefront estimates. Exposures in space will take orders of magnitude more time than any calculations, so a nonlinear estimation method that needs fewer images but more computational time would be advantageous. In addition, current wavefront correction routines seek only to reduce diffracted starlight. Here we present nonlinear estimation algorithms that include optimal estimation of sources incoherent with a star such as exoplanets and debris disks.

Temporal contrast enhancement of a femtosecond fiber CPA system by filtering of SPM broadened spectra

NASA Astrophysics Data System (ADS)

Buldt, J.; Müller, M.; Klas, R.; Eidam, T.; Limpert, J.; Tünnermann, A.

2018-02-01

We present a novel approach for temporal contrast enhancement of energetic laser pulses by filtered SPM broadened spectra. A measured temporal contrast enhancement by at least 7 orders of magnitude in a simple setup has been achieved. This technique is applicable to a wide range of laser parameters and poses a highly efficient alternative to existing contrast-enhancement methods.

Research on a Queue Scheduling Algorithm in Wireless Communications Network

NASA Astrophysics Data System (ADS)

Yang, Wenchuan; Hu, Yuanmei; Zhou, Qiancai

This paper proposes a protocol QS-CT, Queue Scheduling Mechanism based on Multiple Access in Ad hoc net work, which adds queue scheduling mechanism to RTS-CTS-DATA using multiple access protocol. By endowing different queues different scheduling mechanisms, it makes networks access to the channel much more fairly and effectively, and greatly enhances the performance. In order to observe the final performance of the network with QS-CT protocol, we simulate it and compare it with MACA/C-T without QS-CT protocol. Contrast to MACA/C-T, the simulation result shows that QS-CT has greatly improved the throughput, delay, rate of packets' loss and other key indicators.

Guided filtering for solar image/video processing

NASA Astrophysics Data System (ADS)

Xu, Long; Yan, Yihua; Cheng, Jun

2017-06-01

A new image enhancement algorithm employing guided filtering is proposed in this work for the enhancement of solar images and videos so that users can easily figure out important fine structures embedded in the recorded images/movies for solar observation. The proposed algorithm can efficiently remove image noises, including Gaussian and impulse noises. Meanwhile, it can further highlight fibrous structures on/beyond the solar disk. These fibrous structures can clearly demonstrate the progress of solar flare, prominence coronal mass emission, magnetic field, and so on. The experimental results prove that the proposed algorithm gives significant enhancement of visual quality of solar images beyond original input and several classical image enhancement algorithms, thus facilitating easier determination of interesting solar burst activities from recorded images/movies.

Numerical investigation of field enhancement by metal nano-particles using a hybrid FDTD-PSTD algorithm.

PubMed

Pernice, W H; Payne, F P; Gallagher, D F

2007-09-03

We present a novel numerical scheme for the simulation of the field enhancement by metal nano-particles in the time domain. The algorithm is based on a combination of the finite-difference time-domain method and the pseudo-spectral time-domain method for dispersive materials. The hybrid solver leads to an efficient subgridding algorithm that does not suffer from spurious field spikes as do FDTD schemes. Simulation of the field enhancement by gold particles shows the expected exponential field profile. The enhancement factors are computed for single particles and particle arrays. Due to the geometry conforming mesh the algorithm is stable for long integration times and thus suitable for the simulation of resonance phenomena in coupled nano-particle structures.

Fourier domain image fusion for differential X-ray phase-contrast breast imaging.

PubMed

Coello, Eduardo; Sperl, Jonathan I; Bequé, Dirk; Benz, Tobias; Scherer, Kai; Herzen, Julia; Sztrókay-Gaul, Anikó; Hellerhoff, Karin; Pfeiffer, Franz; Cozzini, Cristina; Grandl, Susanne

2017-04-01

X-Ray Phase-Contrast (XPC) imaging is a novel technology with a great potential for applications in clinical practice, with breast imaging being of special interest. This work introduces an intuitive methodology to combine and visualize relevant diagnostic features, present in the X-ray attenuation, phase shift and scattering information retrieved in XPC imaging, using a Fourier domain fusion algorithm. The method allows to present complementary information from the three acquired signals in one single image, minimizing the noise component and maintaining visual similarity to a conventional X-ray image, but with noticeable enhancement in diagnostic features, details and resolution. Radiologists experienced in mammography applied the image fusion method to XPC measurements of mastectomy samples and evaluated the feature content of each input and the fused image. This assessment validated that the combination of all the relevant diagnostic features, contained in the XPC images, was present in the fused image as well. Copyright © 2017 Elsevier B.V. All rights reserved.

Respiratory Motion-Resolved Compressed Sensing Reconstruction of Free-Breathing Radial Acquisition for Dynamic Liver Magnetic Resonance Imaging.

PubMed

Chandarana, Hersh; Feng, Li; Ream, Justin; Wang, Annie; Babb, James S; Block, Kai Tobias; Sodickson, Daniel K; Otazo, Ricardo

2015-11-01

This study aimed to demonstrate feasibility of free-breathing radial acquisition with respiratory motion-resolved compressed sensing reconstruction [extra-dimensional golden-angle radial sparse parallel imaging (XD-GRASP)] for multiphase dynamic gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced liver imaging, and to compare image quality to compressed sensing reconstruction with respiratory motion-averaging (GRASP) and prior conventional breath-held Cartesian-sampled data sets [BH volume interpolated breath-hold examination (VIBE)] in same patients. In this Health Insurance Portability and Accountability Act-compliant prospective study, 16 subjects underwent free-breathing continuous radial acquisition during Gd-EOB-DTPA injection and had prior BH-VIBE available. Acquired data were reconstructed using motion-averaging GRASP approach in which consecutive 84 spokes were grouped in each contrast-enhanced phase for a temporal resolution of approximately 14 seconds. Additionally, respiratory motion-resolved reconstruction was performed from the same k-space data by sorting each contrast-enhanced phase into multiple respiratory motion states using compressed sensing algorithm named XD-GRASP, which exploits sparsity along both the contrast-enhancement and respiratory-state dimensions.Contrast-enhanced dynamic multiphase XD-GRASP, GRASP, and BH-VIBE images were anonymized, pooled together in a random order, and presented to 2 board-certified radiologists for independent evaluation of image quality, with higher score indicating more optimal examination. The XD-GRASP reconstructions had significantly (all P < 0.05) higher overall image quality scores compared to GRASP for early arterial (reader 1: 4.3 ± 0.6 vs 3.31 ± 0.6; reader 2: 3.81 ± 0.8 vs 3.38 ± 0.9) and late arterial (reader 1: 4.5 ± 0.6 vs 3.63 ± 0.6; reader 2: 3.56 ± 0.5 vs 2.88 ± 0.7) phases of enhancement for both readers. The XD-GRASP also had higher overall image quality score in portal venous phase, which was significant for reader 1 (4.44 ± 0.5 vs 3.75 ± 0.8; P = 0.002). In addition, the XD-GRASP had higher overall image quality score compared to BH-VIBE for early (reader 1: 4.3 ± 0.6 vs 3.88 ± 0.6; reader 2: 3.81 ± 0.8 vs 3.50 ± 1.0) and late (reader 1: 4.5 ± 0.6 vs 3.44 ± 0.6; reader 2: 3.56 ± 0.5 vs 2.94 ± 0.9) arterial phases. Free-breathing motion-resolved XD-GRASP reconstructions provide diagnostic high-quality multiphase images in patients undergoing Gd-EOB-DTPA-enhanced liver examination.

Automatic luminous reflections detector using global threshold with increased luminosity contrast in images

NASA Astrophysics Data System (ADS)

Silva, Ricardo Petri; Naozuka, Gustavo Taiji; Mastelini, Saulo Martiello; Felinto, Alan Salvany

2018-01-01

The incidence of luminous reflections (LR) in captured images can interfere with the color of the affected regions. These regions tend to oversaturate, becoming whitish and, consequently, losing the original color information of the scene. Decision processes that employ images acquired from digital cameras can be impaired by the LR incidence. Such applications include real-time video surgeries, facial, and ocular recognition. This work proposes an algorithm called contrast enhancement of potential LR regions, which is a preprocessing to increase the contrast of potential LR regions, in order to improve the performance of automatic LR detectors. In addition, three automatic detectors were compared with and without the employment of our preprocessing method. The first one is a technique already consolidated in the literature called the Chang-Tseng threshold. We propose two automatic detectors called adapted histogram peak and global threshold. We employed four performance metrics to evaluate the detectors, namely, accuracy, precision, exactitude, and root mean square error. The exactitude metric is developed by this work. Thus, a manually defined reference model was created. The global threshold detector combined with our preprocessing method presented the best results, with an average exactitude rate of 82.47%.

Laboratory test of a polarimetry imaging subtraction system for the high-contrast imaging

NASA Astrophysics Data System (ADS)

Dou, Jiangpei; Ren, Deqing; Zhu, Yongtian; Zhang, Xi; Li, Rong

2012-09-01

We propose a polarimetry imaging subtraction test system that can be used for the direct imaging of the reflected light from exoplanets. Such a system will be able to remove the speckle noise scattered by the wave-front error and thus can enhance the high-contrast imaging. In this system, we use a Wollaston Prism (WP) to divide the incoming light into two simultaneous images with perpendicular linear polarizations. One of the images is used as the reference image. Then both the phase and geometric distortion corrections have been performed on the other image. The corrected image is subtracted with the reference image to remove the speckles. The whole procedure is based on an optimization algorithm and the target function is to minimize the residual speckles after subtraction. For demonstration purpose, here we only use a circular pupil in the test without integrating of our apodized-pupil coronagraph. It is shown that best result can be gained by inducing both phase and distortion corrections. Finally, it has reached an extra contrast gain of 50-times improvement in average, which is promising to be used for the direct imaging of exoplanets.

Diagnostic accuracy of contrast-enhanced computed tomography and contrast-enhanced magnetic resonance imaging of small renal masses in real practice: sensitivity and specificity according to subjective radiologic interpretation.

PubMed

Kim, Jae Heon; Sun, Hwa Yeon; Hwang, Jiyoung; Hong, Seong Sook; Cho, Yong Jin; Doo, Seung Whan; Yang, Won Jae; Song, Yun Seob

2016-10-12

The aim of this study was to investigate the diagnostic accuracy of contrast-enhanced computed tomography (CT) and contrast-enhanced magnetic resonance imaging (MRI) of small renal masses in real practice. Contrast-enhanced CT and MRI were performed between February 2008 and February 2013 on 68 patients who had suspected small (≤4 cm) renal cell carcinoma (RCC) based on ultrasonographic measurements. CT and MRI radiographs were reviewed, and the findings of small renal masses were re-categorized into five dichotomized scales by the same two radiologists who had interpreted the original images. Receiver operating characteristics curve analysis was performed, and sensitivity and specificity were determined. Among the 68 patients, 60 (88.2 %) had RCC and eight had benign disease. The diagnostic accuracy rates of contrast-enhanced CT and MRI were 79.41 and 88.23 %, respectively. Diagnostic accuracy was greater when using contrast-enhanced MRI because too many masses (67.6 %) were characterized as "4 (probably solid cancer) or 5 (definitely solid cancer)." The sensitivity of contrast-enhanced CT and MRI for predicting RCC were 79.7 and 88.1 %, respectively. The specificities of contrast-enhanced CT and MRI for predicting RCC were 44.4 and 33.3 %, respectively. Fourteen diagnoses (20.5 %) were missed or inconsistent compared with the final pathological diagnoses. One appropriate nephroureterectomy and five unnecessary percutaneous biopsies were performed for RCC. Seven unnecessary partial nephrectomies were performed for benign disease. Although contrast-enhanced CT and MRI showed high sensitivity for detecting small renal masses, specificity remained low.

Use of a quality improvement tool, the prioritization matrix, to identify and prioritize triage software algorithm enhancement.

PubMed

North, Frederick; Varkey, Prathiba; Caraballo, Pedro; Vsetecka, Darlene; Bartel, Greg

2007-10-11

Complex decision support software can require significant effort in maintenance and enhancement. A quality improvement tool, the prioritization matrix, was successfully used to guide software enhancement of algorithms in a symptom assessment call center.

Contrast-enhanced optical coherence microangiography with acoustic-actuated microbubbles

NASA Astrophysics Data System (ADS)

Liu, Yu-Hsuan; Zhang, Jia-Wei; Yeh, Chih-Kuang; Wei, Kuo-Chen; Liu, Hao-Li; Tsai, Meng-Tsan

2017-04-01

In this study, we propose to use gas-filled microbubbles (MBs) simultaneously actuated by the acoustic wave to enhance the imaging contrast of optical coherence tomography (OCT)-based angiography. In the phantom experiments, MBs can result in stronger backscattered intensity, enabling to enhance the contrast of OCT intensity image. Moreover, simultaneous application of low-intensity acoustic wave enables to temporally induce local vibration of particles and MBs in the vessels, resulting in time-variant OCT intensity which can be used for enhancing the contrast of OCT intensitybased angiography. Additionally, different acoustic modes and different acoustic powers to actuate MBs are performed and compared to investigate the feasibility of contrast enhancement. Finally, animal experiments are performed. The findings suggest that acoustic-actuated MBs can effectively enhance the imaging contrast of OCT-based angiography and the imaging depth of OCT angiography is also extended.

Speech enhancement based on modified phase-opponency detectors

NASA Astrophysics Data System (ADS)

Deshmukh, Om D.; Espy-Wilson, Carol Y.

2005-09-01

A speech enhancement algorithm based on a neural model was presented by Deshmukh et al., [149th meeting of the Acoustical Society America, 2005]. The algorithm consists of a bank of Modified Phase Opponency (MPO) filter pairs tuned to different center frequencies. This algorithm is able to enhance salient spectral features in speech signals even at low signal-to-noise ratios. However, the algorithm introduces musical noise and sometimes misses a spectral peak that is close in frequency to a stronger spectral peak. Refinement in the design of the MPO filters was recently made that takes advantage of the falling spectrum of the speech signal in sonorant regions. The modified set of filters leads to better separation of the noise and speech signals, and more accurate enhancement of spectral peaks. The improvements also lead to a significant reduction in musical noise. Continuity algorithms based on the properties of speech signals are used to further reduce the musical noise effect. The efficiency of the proposed method in enhancing the speech signal when the level of the background noise is fluctuating will be demonstrated. The performance of the improved speech enhancement method will be compared with various spectral subtraction-based methods. [Work supported by NSF BCS0236707.

Are judgments a form of data clustering? Reexamining contrast effects with the k-means algorithm.

PubMed

Boillaud, Eric; Molina, Guylaine

2015-04-01

A number of theories have been proposed to explain in precise mathematical terms how statistical parameters and sequential properties of stimulus distributions affect category ratings. Various contextual factors such as the mean, the midrange, and the median of the stimuli; the stimulus range; the percentile rank of each stimulus; and the order of appearance have been assumed to influence judgmental contrast. A data clustering reinterpretation of judgmental relativity is offered wherein the influence of the initial choice of centroids on judgmental contrast involves 2 combined frequency and consistency tendencies. Accounts of the k-means algorithm are provided, showing good agreement with effects observed on multiple distribution shapes and with a variety of interaction effects relating to the number of stimuli, the number of response categories, and the method of skewing. Experiment 1 demonstrates that centroid initialization accounts for contrast effects obtained with stretched distributions. Experiment 2 demonstrates that the iterative convergence inherent to the k-means algorithm accounts for the contrast reduction observed across repeated blocks of trials. The concept of within-cluster variance minimization is discussed, as is the applicability of a backward k-means calculation method for inferring, from empirical data, the values of the centroids that would serve as a representation of the judgmental context. (c) 2015 APA, all rights reserved.

Extracting paleo-climate signals from sediment laminae: A new, automated image processing method

NASA Astrophysics Data System (ADS)

Gan, S. Q.; Scholz, C. A.

2010-12-01

Lake sediment laminations commonly represent depositional seasonality in lacustrine environments. Their occurrence and quantitative attributes contain various signals of their depositional environment, limnological conditions and climate. However, the identification and measurement of laminae remains a mainly manual process that is not only tedious and labor intensive, but also subjective and error prone. We present a batch method to identify laminae and extract lamina properties automatically and accurately from sediment core images. Our algorithm is focused on image enhancement that improves the signal-to-noise ratio and maximizes and normalizes image contrast. The unique feature of these algorithms is that they are all direction-sensitive, i.e., the algorithms treat images in the horizontal direction and vertical direction differently and independently. The core process of lamina identification is to use a one-dimensional (1-D) lamina identification algorithm to produce a lamina map, and to use image blob analyses and lamina connectivity analyses to aggregate and smash two-dimensional (2-D) lamina data for the best representation of fine-scale stratigraphy in the sediment profile. The primary output datasets of the system are definitions of laminae and primary color values for each pixel and each lamina in the depth direction; other derived datasets can be retrieved at users’ discretion. Sediment core images from Lake Hitchcock , USA and Lake Bosumtwi, Ghana, were used for algorithm development and testing. As a demonstration of the utility of the software, we processed sediment core images from the top of 50 meters of drill core (representing the past ~100 ky) from Lake Bosumtwi, Ghana.

Low-dimensional recurrent neural network-based Kalman filter for speech enhancement.

PubMed

Xia, Youshen; Wang, Jun

2015-07-01

This paper proposes a new recurrent neural network-based Kalman filter for speech enhancement, based on a noise-constrained least squares estimate. The parameters of speech signal modeled as autoregressive process are first estimated by using the proposed recurrent neural network and the speech signal is then recovered from Kalman filtering. The proposed recurrent neural network is globally asymptomatically stable to the noise-constrained estimate. Because the noise-constrained estimate has a robust performance against non-Gaussian noise, the proposed recurrent neural network-based speech enhancement algorithm can minimize the estimation error of Kalman filter parameters in non-Gaussian noise. Furthermore, having a low-dimensional model feature, the proposed neural network-based speech enhancement algorithm has a much faster speed than two existing recurrent neural networks-based speech enhancement algorithms. Simulation results show that the proposed recurrent neural network-based speech enhancement algorithm can produce a good performance with fast computation and noise reduction. Copyright © 2015 Elsevier Ltd. All rights reserved.

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

NASA Astrophysics Data System (ADS)

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

2016-03-01

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

Radiofrequency ablation for treatment of sporadic angiomyolipoma.

PubMed

Prevoo, Warner; van den Bosch, Maurice A A J; Horenblas, Simon

2008-07-01

Symptomatic angiomyolipoma (AML) and asymptomatic AML larger than 4 cm in size are usually treated with nephron-sparing surgery or arterial embolization. We used another technique, that is, radiofrequency ablation (RFA), for treatment of a sporadic AML in a patient with a solitary kidney, in whom maximal sparing of normal renal tissue was required. Contrast-enhanced computed tomography (CT) showed an enhancing well-defined mainly lipomatous tumor, with a maximum diameter of 4.5 cm in the upper pole of the left kidney. Diagnosis of AML was confirmed with fine-needle aspiration biopsy. RFA was performed with a RF 3000 system, consisting of a generator that supplied up to 200W of power, connected to a 15-gauge LeVeen multipolar array electrode that was placed under CT-guidance centrally in the AML. Initial power was set at low power and increased with increments of 10W, according to the algorithm provided by the manufacturer, resulting in a final tumor end temperature above 65 degrees C. No complications occurred and the patient was discharged home the day after. During follow-up (12 months) function of the solitary kidney of the patient was preserved and patient did not have any AML-related symptoms develop. Contrast-enhanced CT scan showed complete (100%) tumor ablation with absence of enhancement in the tumor and decreased tumor size from 4.5 cm to 2.9 cm at 12 months. CT-guided RFA is a minimally invasive ablation procedure that allowed successful treatment of a sporadic AML in a patient with a solitary kidney. No complications occurred and no AML recurrence was observed during the 12-month follow-up.

The influence of different signal-to-background ratios on spatial resolution and F18-FDG-PET quantification using point spread function and time-of-flight reconstruction.

PubMed

Rogasch, Julian Mm; Hofheinz, Frank; Lougovski, Alexandr; Furth, Christian; Ruf, Juri; Großer, Oliver S; Mohnike, Konrad; Hass, Peter; Walke, Mathias; Amthauer, Holger; Steffen, Ingo G

2014-12-01

F18-fluorodeoxyglucose positron-emission tomography (FDG-PET) reconstruction algorithms can have substantial influence on quantitative image data used, e.g., for therapy planning or monitoring in oncology. We analyzed radial activity concentration profiles of differently reconstructed FDG-PET images to determine the influence of varying signal-to-background ratios (SBRs) on the respective spatial resolution, activity concentration distribution, and quantification (standardized uptake value [SUV], metabolic tumor volume [MTV]). Measurements were performed on a Siemens Biograph mCT 64 using a cylindrical phantom containing four spheres (diameter, 30 to 70 mm) filled with F18-FDG applying three SBRs (SBR1, 16:1; SBR2, 6:1; SBR3, 2:1). Images were reconstructed employing six algorithms (filtered backprojection [FBP], FBP + time-of-flight analysis [FBP + TOF], 3D-ordered subset expectation maximization [3D-OSEM], 3D-OSEM + TOF, point spread function [PSF], PSF + TOF). Spatial resolution was determined by fitting the convolution of the object geometry with a Gaussian point spread function to radial activity concentration profiles. MTV delineation was performed using fixed thresholds and semiautomatic background-adapted thresholding (ROVER, ABX, Radeberg, Germany). The pairwise Wilcoxon test revealed significantly higher spatial resolutions for PSF + TOF (up to 4.0 mm) compared to PSF, FBP, FBP + TOF, 3D-OSEM, and 3D-OSEM + TOF at all SBRs (each P < 0.05) with the highest differences for SBR1 decreasing to the lowest for SBR3. Edge elevations in radial activity profiles (Gibbs artifacts) were highest for PSF and PSF + TOF declining with decreasing SBR (PSF + TOF largest sphere; SBR1, 6.3%; SBR3, 2.7%). These artifacts induce substantial SUVmax overestimation compared to the reference SUV for PSF algorithms at SBR1 and SBR2 leading to substantial MTV underestimation in threshold-based segmentation. In contrast, both PSF algorithms provided the lowest deviation of SUVmean from reference SUV at SBR1 and SBR2. At high contrast, the PSF algorithms provided the highest spatial resolution and lowest SUVmean deviation from the reference SUV. In contrast, both algorithms showed the highest deviations in SUVmax and threshold-based MTV definition. At low contrast, all investigated reconstruction algorithms performed approximately equally. The use of PSF algorithms for quantitative PET data, e.g., for target volume definition or in serial PET studies, should be performed with caution - especially if comparing SUV of lesions with high and low contrasts.

77 FR 76318 - Self-Regulatory Organizations; ICE Clear Europe Limited; Notice of Filing and Immediate...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-12-27

... Methodology is an enhancement to the SPAN for the ICE Margining algorithm employed to calculate Original... Margining algorithm employed to calculate Original Margin and was designed to optimize and improve margin... framework algorithm. The enhancement will be additionally applied to: GOA: Gas Oil 1-Month CSO; BRZ: Brent...

Self-adaptive Solution Strategies

NASA Technical Reports Server (NTRS)

Padovan, J.

1984-01-01

The development of enhancements to current generation nonlinear finite element algorithms of the incremental Newton-Raphson type was overviewed. Work was introduced on alternative formulations which lead to improve algorithms that avoid the need for global level updating and inversion. To quantify the enhanced Newton-Raphson scheme and the new alternative algorithm, the results of several benchmarks are presented.

Tolerance of image enhancement brightness and contrast in lateral cephalometric digital radiography for Steiner analysis

NASA Astrophysics Data System (ADS)

Rianti, R. A.; Priaminiarti, M.; Syahraini, S. I.

2017-08-01

Image enhancement brightness and contrast can be adjusted on lateral cephalometric digital radiographs to improve image quality and anatomic landmarks for measurement by Steiner analysis. To determine the limit value for adjustments of image enhancement brightness and contrast in lateral cephalometric digital radiography for Steiner analysis. Image enhancement brightness and contrast were adjusted on 100 lateral cephalometric radiography in 10-point increments (-30, -20, -10, 0, +10, +20, +30). Steiner analysis measurements were then performed by two observers. Reliabilities were tested by the Interclass Correlation Coefficient (ICC) and significance tested by ANOVA or the Kruskal Wallis test. No significant differences were detected in lateral cephalometric analysis measurements following adjustment of the image enhancement brightness and contrast. The limit value of adjustments of the image enhancement brightness and contrast associated with incremental 10-point changes (-30, -20, -10, 0, +10, +20, +30) does not affect the results of Steiner analysis.

Time-frequency analysis-based time-windowing algorithm for the inverse synthetic aperture radar imaging of ships

NASA Astrophysics Data System (ADS)

Zhou, Peng; Zhang, Xi; Sun, Weifeng; Dai, Yongshou; Wan, Yong

2018-01-01

An algorithm based on time-frequency analysis is proposed to select an imaging time window for the inverse synthetic aperture radar imaging of ships. An appropriate range bin is selected to perform the time-frequency analysis after radial motion compensation. The selected range bin is that with the maximum mean amplitude among the range bins whose echoes are confirmed to be contributed by a dominant scatter. The criterion for judging whether the echoes of a range bin are contributed by a dominant scatter is key to the proposed algorithm and is therefore described in detail. When the first range bin that satisfies the judgment criterion is found, a sequence composed of the frequencies that have the largest amplitudes in every moment's time-frequency spectrum corresponding to this range bin is employed to calculate the length and the center moment of the optimal imaging time window. Experiments performed with simulation data and real data show the effectiveness of the proposed algorithm, and comparisons between the proposed algorithm and the image contrast-based algorithm (ICBA) are provided. Similar image contrast and lower entropy are acquired using the proposed algorithm as compared with those values when using the ICBA.

Enhanced MHT encryption scheme for chosen plaintext attack

NASA Astrophysics Data System (ADS)

Xie, Dahua; Kuo, C. C. J.

2003-11-01

Efficient multimedia encryption algorithms play a key role in multimedia security protection. One multimedia encryption algorithm known as the MHT (Multiple Huffman Tables) method was recently developed by Wu and Kuo. Even though MHT has many desirable properties, it is vulnerable to the chosen-plaintext attack (CPA). An enhanced MHT algorithm is proposed in this work to overcome this drawback. It is proved mathematically that the proposed algorithm is secure against the chosen plaintext attack.

New Enhanced Artificial Bee Colony (JA-ABC5) Algorithm with Application for Reactive Power Optimization

PubMed Central

2015-01-01

The standard artificial bee colony (ABC) algorithm involves exploration and exploitation processes which need to be balanced for enhanced performance. This paper proposes a new modified ABC algorithm named JA-ABC5 to enhance convergence speed and improve the ability to reach the global optimum by balancing exploration and exploitation processes. New stages have been proposed at the earlier stages of the algorithm to increase the exploitation process. Besides that, modified mutation equations have also been introduced in the employed and onlooker-bees phases to balance the two processes. The performance of JA-ABC5 has been analyzed on 27 commonly used benchmark functions and tested to optimize the reactive power optimization problem. The performance results have clearly shown that the newly proposed algorithm has outperformed other compared algorithms in terms of convergence speed and global optimum achievement. PMID:25879054

New enhanced artificial bee colony (JA-ABC5) algorithm with application for reactive power optimization.

PubMed

Sulaiman, Noorazliza; Mohamad-Saleh, Junita; Abro, Abdul Ghani

2015-01-01

The standard artificial bee colony (ABC) algorithm involves exploration and exploitation processes which need to be balanced for enhanced performance. This paper proposes a new modified ABC algorithm named JA-ABC5 to enhance convergence speed and improve the ability to reach the global optimum by balancing exploration and exploitation processes. New stages have been proposed at the earlier stages of the algorithm to increase the exploitation process. Besides that, modified mutation equations have also been introduced in the employed and onlooker-bees phases to balance the two processes. The performance of JA-ABC5 has been analyzed on 27 commonly used benchmark functions and tested to optimize the reactive power optimization problem. The performance results have clearly shown that the newly proposed algorithm has outperformed other compared algorithms in terms of convergence speed and global optimum achievement.

Preoperative prognostic value of dynamic contrast-enhanced MRI-derived contrast transfer coefficient and plasma volume in patients with cerebral gliomas.

PubMed

Nguyen, T B; Cron, G O; Mercier, J F; Foottit, C; Torres, C H; Chakraborty, S; Woulfe, J; Jansen, G H; Caudrelier, J M; Sinclair, J; Hogan, M J; Thornhill, R E; Cameron, I G

2015-01-01

The prognostic value of dynamic contrast-enhanced MR imaging-derived plasma volume obtained in tumor and the contrast transfer coefficient has not been well-established in patients with gliomas. We determined whether plasma volume and contrast transfer coefficient in tumor correlated with survival in patients with gliomas in addition to other factors such as age, type of surgery, preoperative Karnofsky score, contrast enhancement, and histopathologic grade. This prospective study included 46 patients with a new pathologically confirmed diagnosis of glioma. The contrast transfer coefficient and plasma volume obtained in tumor maps were calculated directly from the signal-intensity curve without T1 measurements, and values were obtained from multiple small ROIs placed within tumors. Survival curve analysis was performed by dichotomizing patients into groups of high and low contrast transfer coefficient and plasma volume. Univariate analysis was performed by using dynamic contrast-enhanced parameters and clinical factors. Factors that were significant on univariate analysis were entered into multivariate analysis. For all patients with gliomas, survival was worse for groups of patients with high contrast transfer coefficient and plasma volume obtained in tumor (P < .05). In subgroups of high- and low-grade gliomas, survival was worse for groups of patients with high contrast transfer coefficient and plasma volume obtained in tumor (P < .05). Univariate analysis showed that factors associated with lower survival were age older than 50 years, low Karnofsky score, biopsy-only versus resection, marked contrast enhancement versus no/mild enhancement, high contrast transfer coefficient, and high plasma volume obtained in tumor (P < .05). In multivariate analysis, a low Karnofsky score, biopsy versus resection in combination with marked contrast enhancement, and a high contrast transfer coefficient were associated with lower survival rates (P < .05). In patients with glioma, those with a high contrast transfer coefficient have lower survival than those with low parameters. © 2015 by American Journal of Neuroradiology.

Myocardial scar segmentation from magnetic resonance images using convolutional neural network

NASA Astrophysics Data System (ADS)

Zabihollahy, Fatemeh; White, James A.; Ukwatta, Eranga

2018-02-01

Accurate segmentation of the myocardial fibrosis or scar may provide important advancements for the prediction and management of malignant ventricular arrhythmias in patients with cardiovascular disease. In this paper, we propose a semi-automated method for segmentation of myocardial scar from late gadolinium enhancement magnetic resonance image (LGE-MRI) using a convolutional neural network (CNN). In contrast to image intensitybased methods, CNN-based algorithms have the potential to improve the accuracy of scar segmentation through the creation of high-level features from a combination of convolutional, detection and pooling layers. Our developed algorithm was trained using 2,336,703 image patches extracted from 420 slices of five 3D LGE-MR datasets, then validated on 2,204,178 patches from a testing dataset of seven 3D LGE-MR images including 624 slices, all obtained from patients with chronic myocardial infarction. For evaluation of the algorithm, we compared the algorithmgenerated segmentations to manual delineations by experts. Our CNN-based method reported an average Dice similarity coefficient (DSC), precision, and recall of 94.50 +/- 3.62%, 96.08 +/- 3.10%, and 93.96 +/- 3.75% as the accuracy of segmentation, respectively. As compared to several intensity threshold-based methods for scar segmentation, the results of our developed method have a greater agreement with manual expert segmentation.

CO2 microbubble contrast enhancement in x-ray angiography.

PubMed

Kariya, S; Komemushi, A; Nakatani, M; Yoshida, R; Sawada, S; Tanigawa, N

2013-04-01

To demonstrate that carbon dioxide (CO2) microbubble contrast enhancement depicts blood vessels when used for x-ray examinations. Microbubbles were generated by cavitation of physiological saline to which CO2 gas had been added using an ejector-type microbubble generator. The input pressure values for CO2 gas and physiological saline that produced a large quantity of CO2 microbubbles were obtained in a phantom. In an animal study, angiography was performed in three swine using three types of contrast: CO2 microbubbles, conventional CO2 gas, and iodinated contrast medium. For CO2 microbubble contrast enhancement, physiological saline, and CO2 gas were supplied at the input pressures calculated in the phantom experiment. Regions of interest were set in the abdominal aorta, external iliac arteries, and background. The difference in digital values between each artery and the background was calculated. The input pressures obtained in the phantom experiment were 0.16 MPa for physiological saline and 0.5 MPa for CO2 gas, with physiological saline input volume being 8.1 ml/s. Three interventional radiologists all evaluated the depictions of all arteries as "present" in the CO2 microbubble contrast enhancement, conventional CO2 contrast enhancement, and iodinated contrast enhancement performed in three swine. Digital values for all vessels with microbubble CO2 contrast enhancement were higher than background values. In x-ray angiography, blood vessels can be depicted by CO2 microbubble contrast enhancement, in which a large quantity of CO2 microbubbles is generated within blood vessels. Copyright © 2012 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

A Comparison of Four-Image Reconstruction Algorithms for 3-D PET Imaging of MDAPET Camera Using Phantom Data

NASA Astrophysics Data System (ADS)

Baghaei, H.; Wong, Wai-Hoi; Uribe, J.; Li, Hongdi; Wang, Yu; Liu, Yaqiang; Xing, Tao; Ramirez, R.; Xie, Shuping; Kim, Soonseok

2004-10-01

We compared two fully three-dimensional (3-D) image reconstruction algorithms and two 3-D rebinning algorithms followed by reconstruction with a two-dimensional (2-D) filtered-backprojection algorithm for 3-D positron emission tomography (PET) imaging. The two 3-D image reconstruction algorithms were ordered-subsets expectation-maximization (3D-OSEM) and 3-D reprojection (3DRP) algorithms. The two rebinning algorithms were Fourier rebinning (FORE) and single slice rebinning (SSRB). The 3-D projection data used for this work were acquired with a high-resolution PET scanner (MDAPET) with an intrinsic transaxial resolution of 2.8 mm. The scanner has 14 detector rings covering an axial field-of-view of 38.5 mm. We scanned three phantoms: 1) a uniform cylindrical phantom with inner diameter of 21.5 cm; 2) a uniform 11.5-cm cylindrical phantom with four embedded small hot lesions with diameters of 3, 4, 5, and 6 mm; and 3) the 3-D Hoffman brain phantom with three embedded small hot lesion phantoms with diameters of 3, 5, and 8.6 mm in a warm background. Lesions were placed at different radial and axial distances. We evaluated the different reconstruction methods for MDAPET camera by comparing the noise level of images, contrast recovery, and hot lesion detection, and visually compared images. We found that overall the 3D-OSEM algorithm, especially when images post filtered with the Metz filter, produced the best results in terms of contrast-noise tradeoff, and detection of hot spots, and reproduction of brain phantom structures. Even though the MDAPET camera has a relatively small maximum axial acceptance (/spl plusmn/5 deg), images produced with the 3DRP algorithm had slightly better contrast recovery and reproduced the structures of the brain phantom slightly better than the faster 2-D rebinning methods.

Denoised and texture enhanced MVCT to improve soft tissue conspicuity

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

Sheng, Ke, E-mail: ksheng@mednet.ucla.edu; Qi, Sharon X.; Gou, Shuiping

Purpose: MVCT images have been used in TomoTherapy treatment to align patients based on bony anatomies but its usefulness for soft tissue registration, delineation, and adaptive radiation therapy is limited due to insignificant photoelectric interaction components and the presence of noise resulting from low detector quantum efficiency of megavoltage x-rays. Algebraic reconstruction with sparsity regularizers as well as local denoising methods has not significantly improved the soft tissue conspicuity. The authors aim to utilize a nonlocal means denoising method and texture enhancement to recover the soft tissue information in MVCT (DeTECT). Methods: A block matching 3D (BM3D) algorithm was adaptedmore » to reduce the noise while keeping the texture information of the MVCT images. Following imaging denoising, a saliency map was created to further enhance visual conspicuity of low contrast structures. In this study, BM3D and saliency maps were applied to MVCT images of a CT imaging quality phantom, a head and neck, and four prostate patients. Following these steps, the contrast-to-noise ratios (CNRs) were quantified. Results: By applying BM3D denoising and saliency map, postprocessed MVCT images show remarkable improvements in imaging contrast without compromising resolution. For the head and neck patient, the difficult-to-see lymph nodes and vein in the carotid space in the original MVCT image became conspicuous in DeTECT. For the prostate patients, the ambiguous boundary between the bladder and the prostate in the original MVCT was clarified. The CNRs of phantom low contrast inserts were improved from 1.48 and 3.8 to 13.67 and 16.17, respectively. The CNRs of two regions-of-interest were improved from 1.5 and 3.17 to 3.14 and 15.76, respectively, for the head and neck patient. DeTECT also increased the CNR of prostate from 0.13 to 1.46 for the four prostate patients. The results are substantially better than a local denoising method using anisotropic diffusion. Conclusions: The authors showed that it is feasible to extract more soft tissue contrast information from the noisy MVCT images using a nonlocal means 3D block matching method in combination with saliency maps, revealing information that was originally unperceivable to human observers.« less

Nanodiamond-Manganese dual mode MRI contrast agents for enhanced liver tumor detection.

PubMed

Hou, Weixin; Toh, Tan Boon; Abdullah, Lissa Nurrul; Yvonne, Tay Wei Zheng; Lee, Kuan J; Guenther, Ilonka; Chow, Edward Kai-Hua

2017-04-01

Contrast agent-enhanced magnetic resonance (MR) imaging is critical for the diagnosis and monitoring of a number of diseases, including cancer. Certain clinical applications, including the detection of liver tumors, rely on both T1 and T2-weighted images even though contrast agent-enhanced MR imaging is not always reliable. Thus, there is a need for improved dual mode contrast agents with enhanced sensitivity. We report the development of a nanodiamond-manganese dual mode contrast agent that enhanced both T1 and T2-weighted MR imaging. Conjugation of manganese to nanodiamonds resulted in improved longitudinal and transverse relaxivity efficacy over unmodified MnCl 2 as well as clinical contrast agents. Following intravenous administration, nanodiamond-manganese complexes outperformed current clinical contrast agents in an orthotopic liver cancer mouse model while also reducing blood serum concentration of toxic free Mn 2+ ions. Thus, nanodiamond-manganese complexes may serve as more effective dual mode MRI contrast agent, particularly in cancer. Copyright © 2016 Elsevier Inc. All rights reserved.

Contrast medium usage reduction in abdominal computed tomography by using high-iodinated concentration contrast medium

NASA Astrophysics Data System (ADS)

Suwannasri, A.; Kaewlai, R.; Asavaphatiboon, S.

2016-03-01

This study was to determine if administration of a low volume high-concentration iodinated contrast medium can preserve image quality in comparison with regular-concentration intravenous contrast medium in patient undergoing contrast-enhancement abdominal computed tomography (CT). Eighty-four patients were randomly divided into 3 groups of similar iodine delivery rate; A: 1.2 cc/kg of iomeprol-400, B: 1.0 cc/kg of iomeprol-400 and C: 1.5 cc/kg of ioversol-350. Contrast enhancement of the liver parenchyma, pancreas and aorta was quantitatively measured in Hounsfield units and qualitative assessed by a radiologist. T-test was used to evaluate contrast enhancement, and Chi-square test was used to evaluate qualitative image assessment, at significance level of 0.05 with 95% confidence intervals. There were no statistically significant differences in contrast enhancement of liver parenchyma and pancreas between group A and group C in both quantitative and qualitative analyses. Group C showed superior vascular enhancement to group A and B on quantitative analysis.

Segmentation of the Clustered Cells with Optimized Boundary Detection in Negative Phase Contrast Images

PubMed Central

Wang, Yuliang; Zhang, Zaicheng; Wang, Huimin; Bi, Shusheng

2015-01-01

Cell image segmentation plays a central role in numerous biology studies and clinical applications. As a result, the development of cell image segmentation algorithms with high robustness and accuracy is attracting more and more attention. In this study, an automated cell image segmentation algorithm is developed to get improved cell image segmentation with respect to cell boundary detection and segmentation of the clustered cells for all cells in the field of view in negative phase contrast images. A new method which combines the thresholding method and edge based active contour method was proposed to optimize cell boundary detection. In order to segment clustered cells, the geographic peaks of cell light intensity were utilized to detect numbers and locations of the clustered cells. In this paper, the working principles of the algorithms are described. The influence of parameters in cell boundary detection and the selection of the threshold value on the final segmentation results are investigated. At last, the proposed algorithm is applied to the negative phase contrast images from different experiments. The performance of the proposed method is evaluated. Results show that the proposed method can achieve optimized cell boundary detection and highly accurate segmentation for clustered cells. PMID:26066315

Real-time spectrum estimation–based dual-channel speech-enhancement algorithm for cochlear implant

PubMed Central

2012-01-01

Background Improvement of the cochlear implant (CI) front-end signal acquisition is needed to increase speech recognition in noisy environments. To suppress the directional noise, we introduce a speech-enhancement algorithm based on microphone array beamforming and spectral estimation. The experimental results indicate that this method is robust to directional mobile noise and strongly enhances the desired speech, thereby improving the performance of CI devices in a noisy environment. Methods The spectrum estimation and the array beamforming methods were combined to suppress the ambient noise. The directivity coefficient was estimated in the noise-only intervals, and was updated to fit for the mobile noise. Results The proposed algorithm was realized in the CI speech strategy. For actual parameters, we use Maxflat filter to obtain fractional sampling points and cepstrum method to differentiate the desired speech frame and the noise frame. The broadband adjustment coefficients were added to compensate the energy loss in the low frequency band. Discussions The approximation of the directivity coefficient is tested and the errors are discussed. We also analyze the algorithm constraint for noise estimation and distortion in CI processing. The performance of the proposed algorithm is analyzed and further be compared with other prevalent methods. Conclusions The hardware platform was constructed for the experiments. The speech-enhancement results showed that our algorithm can suppresses the non-stationary noise with high SNR. Excellent performance of the proposed algorithm was obtained in the speech enhancement experiments and mobile testing. And signal distortion results indicate that this algorithm is robust with high SNR improvement and low speech distortion. PMID:23006896

Tracking the Evolution of Cerebral Gadolinium-Enhancing Lesions to Persistent T1 Black Holes in Multiple Sclerosis: Validation of a Semiautomated Pipeline.

PubMed

Andermatt, Simon; Papadopoulou, Athina; Radue, Ernst-Wilhelm; Sprenger, Till; Cattin, Philippe

2017-09-01

Some gadolinium-enhancing multiple sclerosis (MS) lesions remain T1-hypointense over months ("persistent black holes, BHs") and represent areas of pronounced tissue loss. A reduced conversion of enhancing lesions to persistent BHs could suggest a favorable effect of a medication on tissue repair. However, the individual tracking of enhancing lesions can be very time-consuming in large clinical trials. We created a semiautomated workflow for tracking the evolution of individual MS lesions, to calculate the proportion of enhancing lesions becoming persistent BHs at follow-up. Our workflow automatically coregisters, compares, and detects overlaps between lesion masks at different time points. We tested the algorithm in a data set of Magnetic Resonance images (1.5 and 3T; spin-echo T1-sequences) from a phase 3 clinical trial (n = 1,272), in which all enhancing lesions and all BHs had been previously segmented at baseline and year 2. The algorithm analyzed the segmentation masks in a longitudinal fashion to determine which enhancing lesions at baseline turned into BHs at year 2. Images of 50 patients (192 enhancing lesions) were also reviewed by an experienced MRI rater, blinded to the algorithm results. In this MRI data set, there were no cases that could not be processed by the algorithm. At year 2, 417 lesions were classified as persistent BHs (417/1,613 = 25.9%). The agreement between the rater and the algorithm was > 98%. Due to the semiautomated procedure, this algorithm can be of great value in the analysis of large clinical trials, when a rater-based analysis would be time-consuming. Copyright © 2017 by the American Society of Neuroimaging.

Development of a Tumor Histologic-Specific, Nano-Encapsulated Contrast for Enhancing Magnetic Resonance Imaging of Prostate Cancer

DTIC Science & Technology

2008-04-01

Nano-Encapsulated Contrast for Enhancing Magnetic Resonance Imaging of Prostate Cancer PRINCIPAL INVESTIGATOR: Joel W. Slaton, M.D...2008 4. TITLE AND SUBTITLE 5a. CONTRACT NUMBER Development of a Tumor Histologic-Specific, Nano-Encapsulated Contrast for Enhancing Magnetic...carry a contrast agent to human CaP cells growing in mice to enhance MR detection of cancer. Our work in the first year has focused on in vitro

77 FR 76316 - Self-Regulatory Organizations; ICE Clear Europe Limited; Notice of Filing and Immediate...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-12-27

... enhancement to the SPAN for the ICE Margining algorithm employed to calculate Original Margin. All capitalized... Allocation Methodology is an enhancement to the SPAN[supreg] \\6\\ for the ICE Margining algorithm employed to... the SPAN margin calculation algorithm itself has not been changed. As of August 30, 2011, Position...

Dose reduction in whole-body computed tomography of multiple injuries (DoReMI): protocol for a prospective cohort study

PubMed Central

2014-01-01

Background Single-pass, contrast-enhanced whole body multidetector computed tomography (MDCT) emerged as the diagnostic standard for evaluating patients with major trauma. Modern iterative image algorithms showed high image quality at a much lower radiation dose in the non-trauma setting. This study aims at investigating whether the radiation dose can safely be reduced in trauma patients without compromising the diagnostic accuracy and image quality. Methods/Design Prospective observational study with two consecutive cohorts of patients. Setting: A high-volume, academic, supra-regional trauma centre in Germany. Study population: Consecutive male and female patients who 1. had been exposed to a high-velocity trauma mechanism, 2. present with clinical evidence or high suspicion of multiple trauma (predicted Injury Severity Score [ISS] ≥16) and 3. are scheduled for primary MDCT based on the decision of the trauma leader on call. Imaging protocols: In a before/after design, a consecutive series of 500 patients will undergo single-pass, whole-body 128-row multi-detector computed tomography (MDCT) with a standard, as low as possible radiation dose. This will be followed by a consecutive series of 500 patients undergoing an approved ultra-low dose MDCT protocol using an image processing algorithm. Data: Routine administrative data and electronic patient records, as well as digital images stored in a picture archiving and communications system will serve as the primary data source. The protocol was approved by the institutional review board. Main outcomes: (1) incidence of delayed diagnoses, (2) diagnostic accuracy, as correlated to the reference standard of a synopsis of all subsequent clinical, imaging, surgical and autopsy findings, (3) patients’ safety, (4) radiation exposure (e.g. effective dose), (5) subjective image quality (assessed independently radiologists and trauma surgeons on a 100-mm visual analogue scale), (6) objective image quality (e.g., contrast-to-noise ratio). Analysis: Multivariate regression will be employed to adjust and correct the findings for time and cohort effects. An exploratory interim analysis halfway after introduction of low-dose MDCT will be conducted to assess whether this protocol is clearly inferior or superior to the current standard. Discussion Although non-experimental, this study will generate first large-scale data on the utility of imaging-enhancing algorithms in whole-body MDCT for major blunt trauma. Trial registration Current Controlled Trials ISRCTN74557102. PMID:24589310

Segmentation Approach Towards Phase-Contrast Microscopic Images of Activated Sludge to Monitor the Wastewater Treatment.

PubMed

Khan, Muhammad Burhan; Nisar, Humaira; Ng, Choon Aun; Yeap, Kim Ho; Lai, Koon Chun

2017-12-01

Image processing and analysis is an effective tool for monitoring and fault diagnosis of activated sludge (AS) wastewater treatment plants. The AS image comprise of flocs (microbial aggregates) and filamentous bacteria. In this paper, nine different approaches are proposed for image segmentation of phase-contrast microscopic (PCM) images of AS samples. The proposed strategies are assessed for their effectiveness from the perspective of microscopic artifacts associated with PCM. The first approach uses an algorithm that is based on the idea that different color space representation of images other than red-green-blue may have better contrast. The second uses an edge detection approach. The third strategy, employs a clustering algorithm for the segmentation and the fourth applies local adaptive thresholding. The fifth technique is based on texture-based segmentation and the sixth uses watershed algorithm. The seventh adopts a split-and-merge approach. The eighth employs Kittler's thresholding. Finally, the ninth uses a top-hat and bottom-hat filtering-based technique. The approaches are assessed, and analyzed critically with reference to the artifacts of PCM. Gold approximations of ground truth images are prepared to assess the segmentations. Overall, the edge detection-based approach exhibits the best results in terms of accuracy, and the texture-based algorithm in terms of false negative ratio. The respective scenarios are explained for suitability of edge detection and texture-based algorithms.

A knowledge-based framework for image enhancement in aviation security.

PubMed

Singh, Maneesha; Singh, Sameer; Partridge, Derek

2004-12-01

The main aim of this paper is to present a knowledge-based framework for automatically selecting the best image enhancement algorithm from several available on a per image basis in the context of X-ray images of airport luggage. The approach detailed involves a system that learns to map image features that represent its viewability to one or more chosen enhancement algorithms. Viewability measures have been developed to provide an automatic check on the quality of the enhanced image, i.e., is it really enhanced? The choice is based on ground-truth information generated by human X-ray screening experts. Such a system, for a new image, predicts the best-suited enhancement algorithm. Our research details the various characteristics of the knowledge-based system and shows extensive results on real images.

Contrast-enhanced sonography in pediatrics.

PubMed

McCarville, M Beth

2011-05-01

Microbubble US contrast agents are composed of an outer shell of protein, phospholipid or polymer that encase air or perfluorocarbon gas. These contrast agents have been widely used in adult cardiology patients to improve endocardial border delineation and have been proved safe and well tolerated in this patient population. There is also a growing body of literature elucidating the value of contrast-enhanced sonography to distinguish benign from malignant liver lesions in adults and to characterize non-hepatic adult malignancies. Because these agents have not been approved for pediatric use in many countries, less is known of the value of contrast-enhanced sonography in children. In this review I will discuss several proven and potential pediatric applications of contrast-enhanced sonography.

Semi-automated algorithm for localization of dermal/epidermal junction in reflectance confocal microscopy images of human skin

NASA Astrophysics Data System (ADS)

Kurugol, Sila; Dy, Jennifer G.; Rajadhyaksha, Milind; Gossage, Kirk W.; Weissmann, Jesse; Brooks, Dana H.

2011-03-01

The examination of the dermis/epidermis junction (DEJ) is clinically important for skin cancer diagnosis. Reflectance confocal microscopy (RCM) is an emerging tool for detection of skin cancers in vivo. However, visual localization of the DEJ in RCM images, with high accuracy and repeatability, is challenging, especially in fair skin, due to low contrast, heterogeneous structure and high inter- and intra-subject variability. We recently proposed a semi-automated algorithm to localize the DEJ in z-stacks of RCM images of fair skin, based on feature segmentation and classification. Here we extend the algorithm to dark skin. The extended algorithm first decides the skin type and then applies the appropriate DEJ localization method. In dark skin, strong backscatter from the pigment melanin causes the basal cells above the DEJ to appear with high contrast. To locate those high contrast regions, the algorithm operates on small tiles (regions) and finds the peaks of the smoothed average intensity depth profile of each tile. However, for some tiles, due to heterogeneity, multiple peaks in the depth profile exist and the strongest peak might not be the basal layer peak. To select the correct peak, basal cells are represented with a vector of texture features. The peak with most similar features to this feature vector is selected. The results show that the algorithm detected the skin types correctly for all 17 stacks tested (8 fair, 9 dark). The DEJ detection algorithm achieved an average distance from the ground truth DEJ surface of around 4.7μm for dark skin and around 7-14μm for fair skin.

Improving image quality in laboratory x-ray phase-contrast imaging

NASA Astrophysics Data System (ADS)

De Marco, F.; Marschner, M.; Birnbacher, L.; Viermetz, M.; Noël, P.; Herzen, J.; Pfeiffer, F.

2017-03-01

Grating-based X-ray phase-contrast (gbPC) is known to provide significant benefits for biomedical imaging. To investigate these benefits, a high-sensitivity gbPC micro-CT setup for small (≍ 5 cm) biological samples has been constructed. Unfortunately, high differential-phase sensitivity leads to an increased magnitude of data processing artifacts, limiting the quality of tomographic reconstructions. Most importantly, processing of phase-stepping data with incorrect stepping positions can introduce artifacts resembling Moiré fringes to the projections. Additionally, the focal spot size of the X-ray source limits resolution of tomograms. Here we present a set of algorithms to minimize artifacts, increase resolution and improve visual impression of projections and tomograms from the examined setup. We assessed two algorithms for artifact reduction: Firstly, a correction algorithm exploiting correlations of the artifacts and differential-phase data was developed and tested. Artifacts were reliably removed without compromising image data. Secondly, we implemented a new algorithm for flatfield selection, which was shown to exclude flat-fields with strong artifacts. Both procedures successfully improved image quality of projections and tomograms. Deconvolution of all projections of a CT scan can minimize blurring introduced by the finite size of the X-ray source focal spot. Application of the Richardson-Lucy deconvolution algorithm to gbPC-CT projections resulted in an improved resolution of phase-contrast tomograms. Additionally, we found that nearest-neighbor interpolation of projections can improve the visual impression of very small features in phase-contrast tomograms. In conclusion, we achieved an increase in image resolution and quality for the investigated setup, which may lead to an improved detection of very small sample features, thereby maximizing the setup's utility.

Quantitative breast MRI radiomics for cancer risk assessment and the monitoring of high-risk populations

NASA Astrophysics Data System (ADS)

Mendel, Kayla R.; Li, Hui; Giger, Maryellen L.

2016-03-01

Breast density is routinely assessed qualitatively in screening mammography. However, it is challenging to quantitatively determine a 3D density from a 2D image such as a mammogram. Furthermore, dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) is used more frequently in the screening of high-risk populations. The purpose of our study is to segment parenchyma and to quantitatively determine volumetric breast density on pre-contrast axial DCE-MRI images (i.e., non-contrast) using a semi-automated quantitative approach. In this study, we retroactively examined 3D DCE-MRI images taken for breast cancer screening of a high-risk population. We analyzed 66 cases with ages between 28 and 76 (mean 48.8, standard deviation 10.8). DCE-MRIs were obtained on a Philips 3.0 T scanner. Our semi-automated DCE-MRI algorithm includes: (a) segmentation of breast tissue from non-breast tissue using fuzzy cmeans clustering (b) separation of dense and fatty tissues using Otsu's method, and (c) calculation of volumetric density as the ratio of dense voxels to total breast voxels. We examined the relationship between pre-contrast DCE-MRI density and clinical BI-RADS density obtained from radiology reports, and obtained a statistically significant correlation [Spearman ρ-value of 0.66 (p < 0.0001)]. Our method within precision medicine may be useful for monitoring high-risk populations.

A numerical study of sensory-guided multiple views for improved object identification

NASA Astrophysics Data System (ADS)

Blakeslee, B. A.; Zelnio, E. G.; Koditschek, D. E.

2014-06-01

We explore the potential on-line adjustment of sensory controls for improved object identification and discrimination in the context of a simulated high resolution camera system carried onboard a maneuverable robotic platform that can actively choose its observational position and pose. Our early numerical studies suggest the significant efficacy and enhanced performance achieved by even very simple feedback-driven iteration of the view in contrast to identification from a fixed pose, uninformed by any active adaptation. Specifically, we contrast the discriminative performance of the same conventional classification system when informed by: a random glance at a vehicle; two random glances at a vehicle; or a random glance followed by a guided second look. After each glance, edge detection algorithms isolate the most salient features of the image and template matching is performed through the use of the Hausdor↵ distance, comparing the simulated sensed images with reference images of the vehicles. We present initial simulation statistics that overwhelmingly favor the third scenario. We conclude with a sketch of our near-future steps in this study that will entail: the incorporation of more sophisticated image processing and template matching algorithms; more complex discrimination tasks such as distinguishing between two similar vehicles or vehicles in motion; more realistic models of the observers mobility including platform dynamics and eventually environmental constraints; and expanding the sensing task beyond the identification of a specified object selected from a pre-defined library of alternatives.

Multi-level tree analysis of pulmonary artery/vein trees in non-contrast CT images

NASA Astrophysics Data System (ADS)

Gao, Zhiyun; Grout, Randall W.; Hoffman, Eric A.; Saha, Punam K.

2012-02-01

Diseases like pulmonary embolism and pulmonary hypertension are associated with vascular dystrophy. Identifying such pulmonary artery/vein (A/V) tree dystrophy in terms of quantitative measures via CT imaging significantly facilitates early detection of disease or a treatment monitoring process. A tree structure, consisting of nodes and connected arcs, linked to the volumetric representation allows multi-level geometric and volumetric analysis of A/V trees. Here, a new theory and method is presented to generate multi-level A/V tree representation of volumetric data and to compute quantitative measures of A/V tree geometry and topology at various tree hierarchies. The new method is primarily designed on arc skeleton computation followed by a tree construction based topologic and geometric analysis of the skeleton. The method starts with a volumetric A/V representation as input and generates its topologic and multi-level volumetric tree representations long with different multi-level morphometric measures. A new recursive merging and pruning algorithms are introduced to detect bad junctions and noisy branches often associated with digital geometric and topologic analysis. Also, a new notion of shortest axial path is introduced to improve the skeletal arc joining two junctions. The accuracy of the multi-level tree analysis algorithm has been evaluated using computer generated phantoms and pulmonary CT images of a pig vessel cast phantom while the reproducibility of method is evaluated using multi-user A/V separation of in vivo contrast-enhanced CT images of a pig lung at different respiratory volumes.

High dynamic range vision sensor for automotive applications

NASA Astrophysics Data System (ADS)

Grenet, Eric; Gyger, Steve; Heim, Pascal; Heitger, Friedrich; Kaess, Francois; Nussbaum, Pascal; Ruedi, Pierre-Francois

2005-02-01

A 128 x 128 pixels, 120 dB vision sensor extracting at the pixel level the contrast magnitude and direction of local image features is used to implement a lane tracking system. The contrast representation (relative change of illumination) delivered by the sensor is independent of the illumination level. Together with the high dynamic range of the sensor, it ensures a very stable image feature representation even with high spatial and temporal inhomogeneities of the illumination. Dispatching off chip image feature is done according to the contrast magnitude, prioritizing features with high contrast magnitude. This allows to reduce drastically the amount of data transmitted out of the chip, hence the processing power required for subsequent processing stages. To compensate for the low fill factor (9%) of the sensor, micro-lenses have been deposited which increase the sensitivity by a factor of 5, corresponding to an equivalent of 2000 ASA. An algorithm exploiting the contrast representation output by the vision sensor has been developed to estimate the position of a vehicle relative to the road markings. The algorithm first detects the road markings based on the contrast direction map. Then, it performs quadratic fits on selected kernel of 3 by 3 pixels to achieve sub-pixel accuracy on the estimation of the lane marking positions. The resulting precision on the estimation of the vehicle lateral position is 1 cm. The algorithm performs efficiently under a wide variety of environmental conditions, including night and rainy conditions.

Comparison of vessel enhancement algorithms applied to time-of-flight MRA images for cerebrovascular segmentation.

PubMed

Phellan, Renzo; Forkert, Nils D

2017-11-01

Vessel enhancement algorithms are often used as a preprocessing step for vessel segmentation in medical images to improve the overall segmentation accuracy. Each algorithm uses different characteristics to enhance vessels, such that the most suitable algorithm may vary for different applications. This paper presents a comparative analysis of the accuracy gains in vessel segmentation generated by the use of nine vessel enhancement algorithms: Multiscale vesselness using the formulas described by Erdt (MSE), Frangi (MSF), and Sato (MSS), optimally oriented flux (OOF), ranking orientations responses path operator (RORPO), the regularized Perona-Malik approach (RPM), vessel enhanced diffusion (VED), hybrid diffusion with continuous switch (HDCS), and the white top hat algorithm (WTH). The filters were evaluated and compared based on time-of-flight MRA datasets and corresponding manual segmentations from 5 healthy subjects and 10 patients with an arteriovenous malformation. Additionally, five synthetic angiographic datasets with corresponding ground truth segmentation were generated with three different noise levels (low, medium, and high) and also used for comparison. The parameters for each algorithm and subsequent segmentation were optimized using leave-one-out cross evaluation. The Dice coefficient, Matthews correlation coefficient, area under the ROC curve, number of connected components, and true positives were used for comparison. The results of this study suggest that vessel enhancement algorithms do not always lead to more accurate segmentation results compared to segmenting nonenhanced images directly. Multiscale vesselness algorithms, such as MSE, MSF, and MSS proved to be robust to noise, while diffusion-based filters, such as RPM, VED, and HDCS ranked in the top of the list in scenarios with medium or no noise. Filters that assume tubular-shapes, such as MSE, MSF, MSS, OOF, RORPO, and VED show a decrease in accuracy when considering patients with an AVM, because vessels may vary from its tubular-shape in this case. Vessel enhancement algorithms can help to improve the accuracy of the segmentation of the vascular system. However, their contribution to accuracy has to be evaluated as it depends on the specific applications, and in some cases it can lead to a reduction of the overall accuracy. No specific filter was suitable for all tested scenarios. © 2017 American Association of Physicists in Medicine.

Newly Diagnosed Breast Cancer: Comparison of Contrast-enhanced Spectral Mammography and Breast MR Imaging in the Evaluation of Extent of Disease.

PubMed

Lee-Felker, Stephanie A; Tekchandani, Leena; Thomas, Mariam; Gupta, Esha; Andrews-Tang, Denise; Roth, Antoinette; Sayre, James; Rahbar, Guita

2017-11-01

Purpose To compare the diagnostic performances of contrast material-enhanced spectral mammography and breast magnetic resonance (MR) imaging in the detection of index and secondary cancers in women with newly diagnosed breast cancer by using histologic or imaging follow-up as the standard of reference. Materials and Methods This institutional review board-approved, HIPAA-compliant, retrospective study included 52 women who underwent breast MR imaging and contrast-enhanced spectral mammography for newly diagnosed unilateral breast cancer between March 2014 and October 2015. Of those 52 patients, 46 were referred for contrast-enhanced spectral mammography and targeted ultrasonography because they had additional suspicious lesions at MR imaging. In six of the 52 patients, breast cancer had been diagnosed at an outside institution. These patients were referred for contrast-enhanced spectral mammography and targeted US as part of diagnostic imaging. Images from contrast-enhanced spectral mammography were analyzed by two fellowship-trained breast imagers with 2.5 years of experience with contrast-enhanced spectral mammography. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value were calculated for both imaging modalities and compared by using the Bennett statistic. Results Fifty-two women with 120 breast lesions were included for analysis (mean age, 50 years; range, 29-73 years). Contrast-enhanced spectral mammography had similar sensitivity to MR imaging (94% [66 of 70 lesions] vs 99% [69 of 70 lesions]), a significantly higher PPV than MR imaging (93% [66 of 71 lesions] vs 60% [69 of 115 lesions]), and fewer false-positive findings than MR imaging (five vs 45) (P < .001 for all results). In addition, contrast-enhanced spectral mammography depicted 11 of the 11 secondary cancers (100%) and MR imaging depicted 10 (91%). Conclusion Contrast-enhanced spectral mammography is potentially as sensitive as MR imaging in the evaluation of extent of disease in newly diagnosed breast cancer, with a higher PPV. © RSNA, 2017.

Differences between Drug-Induced and Contrast Media-Induced Adverse Reactions Based on Spontaneously Reported Adverse Drug Reactions.

PubMed

Ryu, JiHyeon; Lee, HeeYoung; Suh, JinUk; Yang, MyungSuk; Kang, WonKu; Kim, EunYoung

2015-01-01

We analyzed differences between spontaneously reported drug-induced (not including contrast media) and contrast media-induced adverse reactions. Adverse drug reactions reported by an in-hospital pharmacovigilance center (St. Mary's teaching hospital, Daejeon, Korea) from 2010-2012 were classified as drug-induced or contrast media-induced. Clinical patterns, frequency, causality, severity, Schumock and Thornton's preventability, and type A/B reactions were recorded. The trends among causality tools measuring drug and contrast-induced adverse reactions were analyzed. Of 1,335 reports, 636 drug-induced and contrast media-induced adverse reactions were identified. The prevalence of spontaneously reported adverse drug reaction-related admissions revealed a suspected adverse drug reaction-reporting rate of 20.9/100,000 (inpatient, 0.021%) and 3.9/100,000 (outpatients, 0.004%). The most common adverse drug reaction-associated drug classes included nervous system agents and anti-infectives. Dermatological and gastrointestinal adverse drug reactions were most frequently and similarly reported between drug and contrast media-induced adverse reactions. Compared to contrast media-induced adverse reactions, drug-induced adverse reactions were milder, more likely to be preventable (9.8% vs. 1.1%, p < 0.001), and more likely to be type A reactions (73.5% vs. 18.8%, p < 0.001). Females were over-represented among drug-induced adverse reactions (68.1%, p < 0.001) but not among contrast media-induced adverse reactions (56.6%, p = 0.066). Causality patterns differed between the two adverse reaction classes. The World Health Organization-Uppsala Monitoring Centre causality evaluation and Naranjo algorithm results significantly differed from those of the Korean algorithm version II (p < 0.001). We found differences in sex, preventability, severity, and type A/B reactions between spontaneously reported drug and contrast media-induced adverse reactions. The World Health Organization-Uppsala Monitoring Centre and Naranjo algorithm causality evaluation afforded similar results.

Differences between Drug-Induced and Contrast Media-Induced Adverse Reactions Based on Spontaneously Reported Adverse Drug Reactions

PubMed Central

Suh, JinUk; Yang, MyungSuk; Kang, WonKu; Kim, EunYoung

2015-01-01

Objective We analyzed differences between spontaneously reported drug-induced (not including contrast media) and contrast media-induced adverse reactions. Methods Adverse drug reactions reported by an in-hospital pharmacovigilance center (St. Mary’s teaching hospital, Daejeon, Korea) from 2010–2012 were classified as drug-induced or contrast media-induced. Clinical patterns, frequency, causality, severity, Schumock and Thornton’s preventability, and type A/B reactions were recorded. The trends among causality tools measuring drug and contrast-induced adverse reactions were analyzed. Results Of 1,335 reports, 636 drug-induced and contrast media-induced adverse reactions were identified. The prevalence of spontaneously reported adverse drug reaction-related admissions revealed a suspected adverse drug reaction-reporting rate of 20.9/100,000 (inpatient, 0.021%) and 3.9/100,000 (outpatients, 0.004%). The most common adverse drug reaction-associated drug classes included nervous system agents and anti-infectives. Dermatological and gastrointestinal adverse drug reactions were most frequently and similarly reported between drug and contrast media-induced adverse reactions. Compared to contrast media-induced adverse reactions, drug-induced adverse reactions were milder, more likely to be preventable (9.8% vs. 1.1%, p < 0.001), and more likely to be type A reactions (73.5% vs. 18.8%, p < 0.001). Females were over-represented among drug-induced adverse reactions (68.1%, p < 0.001) but not among contrast media-induced adverse reactions (56.6%, p = 0.066). Causality patterns differed between the two adverse reaction classes. The World Health Organization–Uppsala Monitoring Centre causality evaluation and Naranjo algorithm results significantly differed from those of the Korean algorithm version II (p < 0.001). Conclusions We found differences in sex, preventability, severity, and type A/B reactions between spontaneously reported drug and contrast media-induced adverse reactions. The World Health Organization–Uppsala Monitoring Centre and Naranjo algorithm causality evaluation afforded similar results. PMID:26544039

Analyser-based mammography using single-image reconstruction.

PubMed

Briedis, Dahliyani; Siu, Karen K W; Paganin, David M; Pavlov, Konstantin M; Lewis, Rob A

2005-08-07

We implement an algorithm that is able to decode a single analyser-based x-ray phase-contrast image of a sample, converting it into an equivalent conventional absorption-contrast radiograph. The algorithm assumes the projection approximation for x-ray propagation in a single-material object embedded in a substrate of approximately uniform thickness. Unlike the phase-contrast images, which have both directional bias and a bias towards edges present in the sample, the reconstructed images are directly interpretable in terms of the projected absorption coefficient of the sample. The technique was applied to a Leeds TOR[MAM] phantom, which is designed to test mammogram quality by the inclusion of simulated microcalcifications, filaments and circular discs. This phantom was imaged at varying doses using three modalities: analyser-based synchrotron phase-contrast images converted to equivalent absorption radiographs using our algorithm, slot-scanned synchrotron imaging and imaging using a conventional mammography unit. Features in the resulting images were then assigned a quality score by volunteers. The single-image reconstruction method achieved higher scores at equivalent and lower doses than the conventional mammography images, but no improvement of visualization of the simulated microcalcifications, and some degradation in image quality at reduced doses for filament features.

A Partitioning and Bounded Variable Algorithm for Linear Programming

ERIC Educational Resources Information Center

Sheskin, Theodore J.

2006-01-01

An interesting new partitioning and bounded variable algorithm (PBVA) is proposed for solving linear programming problems. The PBVA is a variant of the simplex algorithm which uses a modified form of the simplex method followed by the dual simplex method for bounded variables. In contrast to the two-phase method and the big M method, the PBVA does…

A Fusion Algorithm for GFP Image and Phase Contrast Image of Arabidopsis Cell Based on SFL-Contourlet Transform

PubMed Central

Feng, Peng; Wang, Jing; Wei, Biao; Mi, Deling

2013-01-01

A hybrid multiscale and multilevel image fusion algorithm for green fluorescent protein (GFP) image and phase contrast image of Arabidopsis cell is proposed in this paper. Combining intensity-hue-saturation (IHS) transform and sharp frequency localization Contourlet transform (SFL-CT), this algorithm uses different fusion strategies for different detailed subbands, which include neighborhood consistency measurement (NCM) that can adaptively find balance between color background and gray structure. Also two kinds of neighborhood classes based on empirical model are taken into consideration. Visual information fidelity (VIF) as an objective criterion is introduced to evaluate the fusion image. The experimental results of 117 groups of Arabidopsis cell image from John Innes Center show that the new algorithm cannot only make the details of original images well preserved but also improve the visibility of the fusion image, which shows the superiority of the novel method to traditional ones. PMID:23476716

Real-time Enhanced Vision System

NASA Technical Reports Server (NTRS)

Hines, Glenn D.; Rahman, Zia-Ur; Jobson, Daniel J.; Woodell, Glenn A.; Harrah, Steven D.

2005-01-01

Flying in poor visibility conditions, such as rain, snow, fog or haze, is inherently dangerous. However these conditions can occur at nearly any location, so inevitably pilots must successfully navigate through them. At NASA Langley Research Center (LaRC), under support of the Aviation Safety and Security Program Office and the Systems Engineering Directorate, we are developing an Enhanced Vision System (EVS) that combines image enhancement and synthetic vision elements to assist pilots flying through adverse weather conditions. This system uses a combination of forward-looking infrared and visible sensors for data acquisition. A core function of the system is to enhance and fuse the sensor data in order to increase the information content and quality of the captured imagery. These operations must be performed in real-time for the pilot to use while flying. For image enhancement, we are using the LaRC patented Retinex algorithm since it performs exceptionally well for improving low-contrast range imagery typically seen during poor visibility conditions. In general, real-time operation of the Retinex requires specialized hardware. To date, we have successfully implemented a single-sensor real-time version of the Retinex on several different Digital Signal Processor (DSP) platforms. In this paper we give an overview of the EVS and its performance requirements for real-time enhancement and fusion and we discuss our current real-time Retinex implementations on DSPs.

Real-time enhanced vision system

NASA Astrophysics Data System (ADS)

Hines, Glenn D.; Rahman, Zia-ur; Jobson, Daniel J.; Woodell, Glenn A.; Harrah, Steven D.

2005-05-01

Flying in poor visibility conditions, such as rain, snow, fog or haze, is inherently dangerous. However these conditions can occur at nearly any location, so inevitably pilots must successfully navigate through them. At NASA Langley Research Center (LaRC), under support of the Aviation Safety and Security Program Office and the Systems Engineering Directorate, we are developing an Enhanced Vision System (EVS) that combines image enhancement and synthetic vision elements to assist pilots flying through adverse weather conditions. This system uses a combination of forward-looking infrared and visible sensors for data acquisition. A core function of the system is to enhance and fuse the sensor data in order to increase the information content and quality of the captured imagery. These operations must be performed in real-time for the pilot to use while flying. For image enhancement, we are using the LaRC patented Retinex algorithm since it performs exceptionally well for improving low-contrast range imagery typically seen during poor visibility poor visibility conditions. In general, real-time operation of the Retinex requires specialized hardware. To date, we have successfully implemented a single-sensor real-time version of the Retinex on several different Digital Signal Processor (DSP) platforms. In this paper we give an overview of the EVS and its performance requirements for real-time enhancement and fusion and we discuss our current real-time Retinex implementations on DSPs.

A preliminary evaluation of self-made nanobubble in contrast-enhanced ultrasound imaging

NASA Astrophysics Data System (ADS)

Li, Chunfang; Wu, Kaizhi; Li, Jing; Liu, Haijuan; Zhou, Qibing; Ding, Mingyue

2014-03-01

Nanoscale bubbles (nanobubbles) have been reported to improve contrast in tumor-targeted ultrasound imaging due to the enhanced permeation and retention effects at tumor vascular leaks. In this work, a self-made nanobubble ultrasound contrast agent was preliminarily characterized and evaluated in-vitro and in-vivo. Fundamental properties such as morphology appearance, size distribution, zeta potential, bubble concentration (bubble numbers per milliliter contrast agent suspension) and the stability of nanobubbles were assessed by light microscope and particle sizing analysis. Then the concentration intensity curve and time intensity curves (TICs) were acquired by ultrasound imaging experiment in-vitro. Finally, the contrast-enhanced ultrasonography was performed on rat to investigate the procedure of liver perfusion. The results showed that the nanobubbles had good shape and uniform distribution with the average diameter of 507.9 nm, polydispersity index (PDI) of 0.527, and zeta potential of -19.17 mV. Significant contrast enhancement was observed in in-vitro ultrasound imaging, demonstrating that the self-made nanobubbles can enhance the contrast effect of ultrasound imaging efficiently in-vitro. Slightly contrast enhancement was observed in in-vivo ultrasound imaging, indicating that the nanobubbles are not stable enough in-vivo. Future work will be focused on improving the ultrasonic imaging performance, stability, and antibody binding of the nanoscale ultrasound contrast agent.

Suppression of vegetation in LANDSAT ETM+ remote sensing images

NASA Astrophysics Data System (ADS)

Yu, Le; Porwal, Alok; Holden, Eun-Jung; Dentith, Michael

2010-05-01

Vegetation cover is an impediment to the interpretation of multispectral remote sensing images for geological applications, especially in densely vegetated terrains. In order to enhance the underlying geological information in such terrains, it is desirable to suppress the reflectance component of vegetation. One form of spectral unmixing that has been successfully used for vegetation reflectance suppression in multispectral images is called "forced invariance". It is based on segregating components of the reflectance spectrum that are invariant with respect to a specific spectral index such as the NDVI. The forced invariance method uses algorithms such as software defoliation. However, the outputs of software defoliation are single channel data, which are not amenable to geological interpretations. Crippen and Blom (2001) proposed a new forced invariance algorithm that utilizes band statistics, rather than band ratios. The authors demonstrated the effectiveness of their algorithms on a LANDSAT TM scene from Nevada, USA, especially in open canopy areas in mixed and semi-arid terrains. In this presentation, we report the results of our experimentation with this algorithm on a densely to sparsely vegetated Landsat ETM+ scene. We selected a scene (Path 119, Row 39) acquired on 18th July, 2004. Two study areas located around the city of Hangzhou, eastern China were tested. One of them covers uninhabited hilly terrain characterized by low rugged topography, parts of the hills are densely vegetated; another one covers both inhabited urban areas and uninhabited hilly terrain, which is densely vegetated. Crippen and Blom's algorithm is implemented in the following sequential steps: (1) dark pixel correction; (2) vegetation index calculation; (3) estimation of statistical relationship between vegetation index and digital number (DN) values for each band; (4) calculation of a smooth best-fit curve for the above relationships; and finally, (5) selection of a target average DN value and scaling all pixels at each vegetation index level by an amount that shifts the curve to the target digital number (DN). The main drawback of their algorithm is severe distortions of the DN values of non-vegetated areas, a suggested solution is masking outliers such as cloud, water, etc. We therefore extend this algorithm by masking non-vegetated areas. Our algorithm comprises the following three steps: (1) masking of barren or sparsely vegetated areas using a threshold based on a vegetation index that is calculated after atmosphere correction (dark pixel correction and ACTOR were compared) in order to conserve their original spectral information through the subsequent processing; (2) applying Crippen and Blom's forced invariance algorithm to suppress the spectral response of vegetation only in vegetated areas; and (3) combining the processed vegetated areas with the masked barren or sparsely vegetated areas followed by histogram equalization to eliminate the differences in color-scales between these two types of areas, and enhance the integrated image. The output images of both study areas showed significant improvement over the original images in terms of suppression of vegetation reflectance and enhancement of the underlying geological information. The processed images show clear banding, probably associated with lithological variations in the underlying rock formations. The colors of non-vegetated pixels are distorted in the unmasked results but in the same location the pixels in the masked results show regions of higher contrast. We conclude that the algorithm offers an effective way to enhance geological information in LANDSAT TM/ETM+ images of terrains with significant vegetation cover. It is also suitable to other multispectral satellite data have bands in similar wavelength regions. In addition, an application of this method to hyperspectral data may be possible as long as it can provide the vegetation band ratios.

A comparative study of automatic image segmentation algorithms for target tracking in MR-IGRT.

PubMed

Feng, Yuan; Kawrakow, Iwan; Olsen, Jeff; Parikh, Parag J; Noel, Camille; Wooten, Omar; Du, Dongsu; Mutic, Sasa; Hu, Yanle

2016-03-01

On-board magnetic resonance (MR) image guidance during radiation therapy offers the potential for more accurate treatment delivery. To utilize the real-time image information, a crucial prerequisite is the ability to successfully segment and track regions of interest (ROI). The purpose of this work is to evaluate the performance of different segmentation algorithms using motion images (4 frames per second) acquired using a MR image-guided radiotherapy (MR-IGRT) system. Manual contours of the kidney, bladder, duodenum, and a liver tumor by an experienced radiation oncologist were used as the ground truth for performance evaluation. Besides the manual segmentation, images were automatically segmented using thresholding, fuzzy k-means (FKM), k-harmonic means (KHM), and reaction-diffusion level set evolution (RD-LSE) algorithms, as well as the tissue tracking algorithm provided by the ViewRay treatment planning and delivery system (VR-TPDS). The performance of the five algorithms was evaluated quantitatively by comparing with the manual segmentation using the Dice coefficient and target registration error (TRE) measured as the distance between the centroid of the manual ROI and the centroid of the automatically segmented ROI. All methods were able to successfully segment the bladder and the kidney, but only FKM, KHM, and VR-TPDS were able to segment the liver tumor and the duodenum. The performance of the thresholding, FKM, KHM, and RD-LSE algorithms degraded as the local image contrast decreased, whereas the performance of the VP-TPDS method was nearly independent of local image contrast due to the reference registration algorithm. For segmenting high-contrast images (i.e., kidney), the thresholding method provided the best speed (<1 ms) with a satisfying accuracy (Dice=0.95). When the image contrast was low, the VR-TPDS method had the best automatic contour. Results suggest an image quality determination procedure before segmentation and a combination of different methods for optimal segmentation with the on-board MR-IGRT system. PACS number(s): 87.57.nm, 87.57.N-, 87.61.Tg. © 2016 The Authors.

A comparative study of automatic image segmentation algorithms for target tracking in MR‐IGRT

PubMed Central

Feng, Yuan; Kawrakow, Iwan; Olsen, Jeff; Parikh, Parag J.; Noel, Camille; Wooten, Omar; Du, Dongsu; Mutic, Sasa

2016-01-01

On‐board magnetic resonance (MR) image guidance during radiation therapy offers the potential for more accurate treatment delivery. To utilize the real‐time image information, a crucial prerequisite is the ability to successfully segment and track regions of interest (ROI). The purpose of this work is to evaluate the performance of different segmentation algorithms using motion images (4 frames per second) acquired using a MR image‐guided radiotherapy (MR‐IGRT) system. Manual contours of the kidney, bladder, duodenum, and a liver tumor by an experienced radiation oncologist were used as the ground truth for performance evaluation. Besides the manual segmentation, images were automatically segmented using thresholding, fuzzy k‐means (FKM), k‐harmonic means (KHM), and reaction‐diffusion level set evolution (RD‐LSE) algorithms, as well as the tissue tracking algorithm provided by the ViewRay treatment planning and delivery system (VR‐TPDS). The performance of the five algorithms was evaluated quantitatively by comparing with the manual segmentation using the Dice coefficient and target registration error (TRE) measured as the distance between the centroid of the manual ROI and the centroid of the automatically segmented ROI. All methods were able to successfully segment the bladder and the kidney, but only FKM, KHM, and VR‐TPDS were able to segment the liver tumor and the duodenum. The performance of the thresholding, FKM, KHM, and RD‐LSE algorithms degraded as the local image contrast decreased, whereas the performance of the VP‐TPDS method was nearly independent of local image contrast due to the reference registration algorithm. For segmenting high‐contrast images (i.e., kidney), the thresholding method provided the best speed (<1 ms) with a satisfying accuracy (Dice=0.95). When the image contrast was low, the VR‐TPDS method had the best automatic contour. Results suggest an image quality determination procedure before segmentation and a combination of different methods for optimal segmentation with the on‐board MR‐IGRT system. PACS number(s): 87.57.nm, 87.57.N‐, 87.61.Tg

Development and validation of a dual sensing scheme to improve accuracy of bradycardia and pause detection in an insertable cardiac monitor.

PubMed

Passman, Rod S; Rogers, John D; Sarkar, Shantanu; Reiland, Jerry; Reisfeld, Erin; Koehler, Jodi; Mittal, Suneet

2017-07-01

Undersensing of premature ventricular beats and low-amplitude R waves are primary causes for inappropriate bradycardia and pause detections in insertable cardiac monitors (ICMs). The purpose of this study was to develop and validate an enhanced algorithm to reduce inappropriately detected bradycardia and pause episodes. Independent data sets to develop and validate the enhanced algorithm were derived from a database of ICM-detected bradycardia and pause episodes in de-identified patients monitored for unexplained syncope. The original algorithm uses an auto-adjusting sensitivity threshold for R-wave sensing to detect tachycardia and avoid T-wave oversensing. In the enhanced algorithm, a second sensing threshold is used with a long blanking and fixed lower sensitivity threshold, looking for evidence of undersensed signals. Data reported includes percent change in appropriate and inappropriate bradycardia and pause detections as well as changes in episode detection sensitivity and positive predictive value with the enhanced algorithm. The validation data set, from 663 consecutive patients, consisted of 4904 (161 patients) bradycardia and 2582 (133 patients) pause episodes, of which 2976 (61%) and 996 (39%) were appropriately detected bradycardia and pause episodes. The enhanced algorithm reduced inappropriate bradycardia and pause episodes by 95% and 47%, respectively, with 1.7% and 0.6% reduction in appropriate episodes, respectively. The average episode positive predictive value improved by 62% (P < .001) for bradycardia detection and by 26% (P < .001) for pause detection, with an average relative sensitivity of 95% (P < .001) and 99% (P = .5), respectively. The enhanced dual sense algorithm for bradycardia and pause detection in ICMs substantially reduced inappropriate episode detection with a minimal reduction in true episode detection. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

VIP: Vortex Image Processing Package for High-contrast Direct Imaging

NASA Astrophysics Data System (ADS)

Gomez Gonzalez, Carlos Alberto; Wertz, Olivier; Absil, Olivier; Christiaens, Valentin; Defrère, Denis; Mawet, Dimitri; Milli, Julien; Absil, Pierre-Antoine; Van Droogenbroeck, Marc; Cantalloube, Faustine; Hinz, Philip M.; Skemer, Andrew J.; Karlsson, Mikael; Surdej, Jean

2017-07-01

We present the Vortex Image Processing (VIP) library, a python package dedicated to astronomical high-contrast imaging. Our package relies on the extensive python stack of scientific libraries and aims to provide a flexible framework for high-contrast data and image processing. In this paper, we describe the capabilities of VIP related to processing image sequences acquired using the angular differential imaging (ADI) observing technique. VIP implements functionalities for building high-contrast data processing pipelines, encompassing pre- and post-processing algorithms, potential source position and flux estimation, and sensitivity curve generation. Among the reference point-spread function subtraction techniques for ADI post-processing, VIP includes several flavors of principal component analysis (PCA) based algorithms, such as annular PCA and incremental PCA algorithms capable of processing big datacubes (of several gigabytes) on a computer with limited memory. Also, we present a novel ADI algorithm based on non-negative matrix factorization, which comes from the same family of low-rank matrix approximations as PCA and provides fairly similar results. We showcase the ADI capabilities of the VIP library using a deep sequence on HR 8799 taken with the LBTI/LMIRCam and its recently commissioned L-band vortex coronagraph. Using VIP, we investigated the presence of additional companions around HR 8799 and did not find any significant additional point source beyond the four known planets. VIP is available at http://github.com/vortex-exoplanet/VIP and is accompanied with Jupyter notebook tutorials illustrating the main functionalities of the library.

Sequential contrast-enhanced MR imaging of the penis.

PubMed

Kaneko, K; De Mouy, E H; Lee, B E

1994-04-01

To determine the enhancement patterns of the penis at magnetic resonance (MR) imaging. Sequential contrast material-enhanced MR images of the penis in a flaccid state were obtained in 16 volunteers (12 with normal penile function and four with erectile dysfunction). Subjects with normal erectile function showed gradual and centrifugal enhancement of the corpora cavernosa, while those with erectile dysfunction showed poor enhancement with abnormal progression. Sequential contrast-enhanced MR imaging provides additional morphologic information for the evaluation of erectile dysfunction.

Comparison of low-contrast detectability between two CT reconstruction algorithms using voxel-based 3D printed textured phantoms.

PubMed

Solomon, Justin; Ba, Alexandre; Bochud, François; Samei, Ehsan

2016-12-01

To use novel voxel-based 3D printed textured phantoms in order to compare low-contrast detectability between two reconstruction algorithms, FBP (filtered-backprojection) and SAFIRE (sinogram affirmed iterative reconstruction) and determine what impact background texture (i.e., anatomical noise) has on estimating the dose reduction potential of SAFIRE. Liver volumes were segmented from 23 abdominal CT cases. The volumes were characterized in terms of texture features from gray-level co-occurrence and run-length matrices. Using a 3D clustered lumpy background (CLB) model, a fitting technique based on a genetic optimization algorithm was used to find CLB textures that were reflective of the liver textures, accounting for CT system factors of spatial blurring and noise. With the modeled background texture as a guide, four cylindrical phantoms (Textures A-C and uniform, 165 mm in diameter, and 30 mm height) were designed, each containing 20 low-contrast spherical signals (6 mm diameter at nominal contrast levels of ∼3.2, 5.2, 7.2, 10, and 14 HU with four repeats per signal). The phantoms were voxelized and input into a commercial multimaterial 3D printer (Object Connex 350), with custom software for voxel-based printing (using principles of digital dithering). Images of the textured phantoms and a corresponding uniform phantom were acquired at six radiation dose levels (SOMATOM Flash, Siemens Healthcare) and observer model detection performance (detectability index of a multislice channelized Hotelling observer) was estimated for each condition (5 contrasts × 6 doses × 2 reconstructions × 4 backgrounds = 240 total conditions). A multivariate generalized regression analysis was performed (linear terms, no interactions, random error term, log link function) to assess whether dose, reconstruction algorithm, signal contrast, and background type have statistically significant effects on detectability. Also, fitted curves of detectability (averaged across contrast levels) as a function of dose were constructed for each reconstruction algorithm and background texture. FBP and SAFIRE were compared for each background type to determine the improvement in detectability at a given dose, and the reduced dose at which SAFIRE had equivalent performance compared to FBP at 100% dose. Detectability increased with increasing radiation dose (P = 2.7 × 10 -59 ) and contrast level (P = 2.2 × 10 -86 ) and was higher in the uniform phantom compared to the textured phantoms (P = 6.9 × 10 -51 ). Overall, SAFIRE had higher d' compared to FBP (P = 0.02). The estimated dose reduction potential of SAFIRE was found to be 8%, 10%, 27%, and 8% for Texture-A, Texture-B, Texture-C and uniform phantoms. In all background types, detectability was higher with SAFIRE compared to FBP. However, the relative improvement observed from SAFIRE was highly dependent on the complexity of the background texture. Iterative algorithms such as SAFIRE should be assessed in the most realistic context possible.

Dual energy computed tomography for the head.

PubMed

Naruto, Norihito; Itoh, Toshihide; Noguchi, Kyo

2018-02-01

Dual energy CT (DECT) is a promising technology that provides better diagnostic accuracy in several brain diseases. DECT can generate various types of CT images from a single acquisition data set at high kV and low kV based on material decomposition algorithms. The two-material decomposition algorithm can separate bone/calcification from iodine accurately. The three-material decomposition algorithm can generate a virtual non-contrast image, which helps to identify conditions such as brain hemorrhage. A virtual monochromatic image has the potential to eliminate metal artifacts by reducing beam-hardening effects. DECT also enables exploration of advanced imaging to make diagnosis easier. One such novel application of DECT is the X-Map, which helps to visualize ischemic stroke in the brain without using iodine contrast medium.

Analytic image reconstruction from partial data for a single-scan cone-beam CT with scatter correction

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

Min, Jonghwan; Pua, Rizza; Cho, Seungryong, E-mail: scho@kaist.ac.kr

Purpose: A beam-blocker composed of multiple strips is a useful gadget for scatter correction and/or for dose reduction in cone-beam CT (CBCT). However, the use of such a beam-blocker would yield cone-beam data that can be challenging for accurate image reconstruction from a single scan in the filtered-backprojection framework. The focus of the work was to develop an analytic image reconstruction method for CBCT that can be directly applied to partially blocked cone-beam data in conjunction with the scatter correction. Methods: The authors developed a rebinned backprojection-filteration (BPF) algorithm for reconstructing images from the partially blocked cone-beam data in amore » circular scan. The authors also proposed a beam-blocking geometry considering data redundancy such that an efficient scatter estimate can be acquired and sufficient data for BPF image reconstruction can be secured at the same time from a single scan without using any blocker motion. Additionally, scatter correction method and noise reduction scheme have been developed. The authors have performed both simulation and experimental studies to validate the rebinned BPF algorithm for image reconstruction from partially blocked cone-beam data. Quantitative evaluations of the reconstructed image quality were performed in the experimental studies. Results: The simulation study revealed that the developed reconstruction algorithm successfully reconstructs the images from the partial cone-beam data. In the experimental study, the proposed method effectively corrected for the scatter in each projection and reconstructed scatter-corrected images from a single scan. Reduction of cupping artifacts and an enhancement of the image contrast have been demonstrated. The image contrast has increased by a factor of about 2, and the image accuracy in terms of root-mean-square-error with respect to the fan-beam CT image has increased by more than 30%. Conclusions: The authors have successfully demonstrated that the proposed scanning method and image reconstruction algorithm can effectively estimate the scatter in cone-beam projections and produce tomographic images of nearly scatter-free quality. The authors believe that the proposed method would provide a fast and efficient CBCT scanning option to various applications particularly including head-and-neck scan.« less

Image analysis for skeletal evaluation of carpal bones

NASA Astrophysics Data System (ADS)

Ko, Chien-Chuan; Mao, Chi-Wu; Lin, Chi-Jen; Sun, Yung-Nien

1995-04-01

The assessment of bone age is an important field to the pediatric radiology. It provides very important information for treatment and prediction of skeletal growth in a developing child. So far, various computerized algorithms for automatically assessing the skeletal growth have been reported. Most of these methods made attempt to analyze the phalangeal growth. The most fundamental step in these automatic measurement methods is the image segmentation that extracts bones from soft-tissue and background. These automatic segmentation methods of hand radiographs can roughly be categorized into two main approaches that are edge and region based methods. This paper presents a region-based carpal-bone segmentation approach. It is organized into four stages: contrast enhancement, moment-preserving thresholding, morphological processing, and region-growing labeling.

Visualizing dispersive features in 2D image via minimum gradient method

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

He, Yu; Wang, Yan; Shen, Zhi -Xun

Here, we developed a minimum gradient based method to track ridge features in a 2D image plot, which is a typical data representation in many momentum resolved spectroscopy experiments. Through both analytic formulation and numerical simulation, we compare this new method with existing DC (distribution curve) based and higher order derivative based analyses. We find that the new method has good noise resilience and enhanced contrast especially for weak intensity features and meanwhile preserves the quantitative local maxima information from the raw image. An algorithm is proposed to extract 1D ridge dispersion from the 2D image plot, whose quantitative applicationmore » to angle-resolved photoemission spectroscopy measurements on high temperature superconductors is demonstrated.« less

Visualizing dispersive features in 2D image via minimum gradient method

DOE PAGES

He, Yu; Wang, Yan; Shen, Zhi -Xun

2017-07-24

Here, we developed a minimum gradient based method to track ridge features in a 2D image plot, which is a typical data representation in many momentum resolved spectroscopy experiments. Through both analytic formulation and numerical simulation, we compare this new method with existing DC (distribution curve) based and higher order derivative based analyses. We find that the new method has good noise resilience and enhanced contrast especially for weak intensity features and meanwhile preserves the quantitative local maxima information from the raw image. An algorithm is proposed to extract 1D ridge dispersion from the 2D image plot, whose quantitative applicationmore » to angle-resolved photoemission spectroscopy measurements on high temperature superconductors is demonstrated.« less

Enhanced visualization of peripheral retinal vasculature with wavefront sensorless adaptive optics OCT angiography in diabetic patients

PubMed Central

Polans, James; Cunefare, David; Cole, Eli; Keller, Brenton; Mettu, Priyatham S.; Cousins, Scott W.; Allingham, Michael J.; Izatt, Joseph A.; Farsiu, Sina

2017-01-01

Optical coherence tomography angiography (OCTA) is a promising technique for non-invasive visualization of vessel networks in the human eye. We debut a system capable of acquiring wide field-of-view (>70°) OCT angiograms without mosaicking. Additionally, we report on enhancing the visualization of peripheral microvasculature using wavefront sensorless adaptive optics (WSAO). We employed a fast WSAO algorithm that enabled wavefront correction in <2 seconds by iterating the mirror shape at the speed of OCT B-scans rather than volumes. Also, we contrasted ~7° field-of-view OCTA angiograms acquired in the periphery with and without WSAO correction. On average, WSAO improved the sharpness of microvasculature by 65% in healthy and 38% in diseased eyes. Preliminary observations demonstrated that the location of 7° images could be identified directly from the wide field-of-view angiogram. A pilot study on a normal subject and patients with diabetic retinopathy showed the impact of utilizing WSAO for OCTA when visualizing peripheral vasculature pathologies. PMID:28059209

A novel encryption scheme for high-contrast image data in the Fresnelet domain

PubMed Central

Bibi, Nargis; Farwa, Shabieh; Jahngir, Adnan; Usman, Muhammad

2018-01-01

In this paper, a unique and more distinctive encryption algorithm is proposed. This is based on the complexity of highly nonlinear S box in Flesnelet domain. The nonlinear pattern is transformed further to enhance the confusion in the dummy data using Fresnelet technique. The security level of the encrypted image boosts using the algebra of Galois field in Fresnelet domain. At first level, the Fresnelet transform is used to propagate the given information with desired wavelength at specified distance. It decomposes given secret data into four complex subbands. These complex sub-bands are separated into two components of real subband data and imaginary subband data. At second level, the net subband data, produced at the first level, is deteriorated to non-linear diffused pattern using the unique S-box defined on the Galois field F28. In the diffusion process, the permuted image is substituted via dynamic algebraic S-box substitution. We prove through various analysis techniques that the proposed scheme enhances the cipher security level, extensively. PMID:29608609

Respiratory motion correction in dynamic MRI using robust data decomposition registration - application to DCE-MRI.

PubMed

Hamy, Valentin; Dikaios, Nikolaos; Punwani, Shonit; Melbourne, Andrew; Latifoltojar, Arash; Makanyanga, Jesica; Chouhan, Manil; Helbren, Emma; Menys, Alex; Taylor, Stuart; Atkinson, David

2014-02-01

Motion correction in Dynamic Contrast Enhanced (DCE-) MRI is challenging because rapid intensity changes can compromise common (intensity based) registration algorithms. In this study we introduce a novel registration technique based on robust principal component analysis (RPCA) to decompose a given time-series into a low rank and a sparse component. This allows robust separation of motion components that can be registered, from intensity variations that are left unchanged. This Robust Data Decomposition Registration (RDDR) is demonstrated on both simulated and a wide range of clinical data. Robustness to different types of motion and breathing choices during acquisition is demonstrated for a variety of imaged organs including liver, small bowel and prostate. The analysis of clinically relevant regions of interest showed both a decrease of error (15-62% reduction following registration) in tissue time-intensity curves and improved areas under the curve (AUC60) at early enhancement. Copyright © 2013 The Authors. Published by Elsevier B.V. All rights reserved.

Attention enhances contrast appearance via increased input baseline of neural responses

PubMed Central

Cutrone, Elizabeth K.; Heeger, David J.; Carrasco, Marisa

2014-01-01

Covert spatial attention increases the perceived contrast of stimuli at attended locations, presumably via enhancement of visual neural responses. However, the relation between perceived contrast and the underlying neural responses has not been characterized. In this study, we systematically varied stimulus contrast, using a two-alternative, forced-choice comparison task to probe the effect of attention on appearance across the contrast range. We modeled performance in the task as a function of underlying neural contrast-response functions. Fitting this model to the observed data revealed that an increased input baseline in the neural responses accounted for the enhancement of apparent contrast with spatial attention. PMID:25549920

Toward an Objective Enhanced-V Detection Algorithm

NASA Technical Reports Server (NTRS)

Brunner, Jason; Feltz, Wayne; Moses, John; Rabin, Robert; Ackerman, Steven

2007-01-01

The area of coldest cloud tops above thunderstorms sometimes has a distinct V or U shape. This pattern, often referred to as an "enhanced-V' signature, has been observed to occur during and preceding severe weather in previous studies. This study describes an algorithmic approach to objectively detect enhanced-V features with observations from the Geostationary Operational Environmental Satellite and Low Earth Orbit data. The methodology consists of cross correlation statistics of pixels and thresholds of enhanced-V quantitative parameters. The effectiveness of the enhanced-V detection method will be examined using Geostationary Operational Environmental Satellite, MODerate-resolution Imaging Spectroradiometer, and Advanced Very High Resolution Radiometer image data from case studies in the 2003-2006 seasons. The main goal of this study is to develop an objective enhanced-V detection algorithm for future implementation into operations with future sensors, such as GOES-R.

Indices of polarimetric purity for biological tissues inspection

NASA Astrophysics Data System (ADS)

Van Eeckhout, Albert; Lizana, Angel; Garcia-Caurel, Enric; Gil, José J.; Sansa, Adrià; Rodríguez, Carla; Estévez, Irene; González, Emilio; Escalera, Juan C.; Moreno, Ignacio; Campos, Juan

2018-02-01

We highlight the interest of using the Indices of Polarimetric Purity (IPPs) for the biological tissue inspection. These are three polarimetric metrics focused on the study of the depolarizing behaviour of the sample. The IPPs have been recently proposed in the literature and provide different and synthetized information than the commonly used depolarizing indices, as depolarization index (PΔ) or depolarization power (Δ). Compared with the standard polarimetric images of biological samples, IPPs enhance the contrast between different tissues of the sample and show differences between similar tissues which are not observed using the other standard techniques. Moreover, they present further physical information related to the depolarization mechanisms inherent to different tissues. In addition, the algorithm does not require advanced calculations (as in the case of polar decompositions), being the indices of polarimetric purity fast and easy to implement. We also propose a pseudo-coloured image method which encodes the sample information as a function of the different indices weights. These images allow us to customize the visualization of samples and to highlight certain of their constitutive structures. The interest and potential of the IPP approach are experimentally illustrated throughout the manuscript by comparing polarimetric images of different ex-vivo samples obtained with standard polarimetric methods with those obtained from the IPPs analysis. Enhanced contrast and retrieval of new information are experimentally obtained from the different IPP based images.

A system for automatic aorta sections measurements on chest CT

NASA Astrophysics Data System (ADS)

Pfeffer, Yitzchak; Mayer, Arnaldo; Zholkover, Adi; Konen, Eli

2016-03-01

A new method is proposed for caliber measurement of the ascending aorta (AA) and descending aorta (DA). A key component of the method is the automatic detection of the carina, as an anatomical landmark around which an axial volume of interest (VOI) can be defined to observe the aortic caliber. For each slice in the VOI, a linear profile line connecting the AA with the DA is found by pattern matching on the underlying intensity profile. Next, the aortic center position is found using Hough transform on the best linear segment candidate. Finally, region growing around the center provides an accurate segmentation and caliber measurement. We evaluated the algorithm on 113 sequential chest CT scans, slice thickness of 0.75 - 3.75mm, 90 with contrast agent injected. The algorithm success rates were computed as the percentage of scans in which the center of the AA was found. Automated measurements of AA caliber were compared with independent measurements of two experienced chest radiologists, comparing the absolute difference between the two radiologists with the absolute difference between the algorithm and each of the radiologists. The measurement stability was demonstrated by computing the STD of the absolute difference between the radiologists, and between the algorithm and the radiologists. Results: Success rates of 93% and 74% were achieved, for contrast injected cases and non-contrast cases, respectively. These results indicate that the algorithm can be robust in large variability of image quality, such as the cases in a realworld clinical setting. The average absolute difference between the algorithm and the radiologists was 1.85mm, lower than the average absolute difference between the radiologists, which was 2.1mm. The STD of the absolute difference between the algorithm and the radiologists was 1.5mm vs 1.6mm between the two radiologists. These results demonstrate the clinical relevance of the algorithm measurements.

An Extended Spectral-Spatial Classification Approach for Hyperspectral Data

NASA Astrophysics Data System (ADS)

Akbari, D.

2017-11-01

In this paper an extended classification approach for hyperspectral imagery based on both spectral and spatial information is proposed. The spatial information is obtained by an enhanced marker-based minimum spanning forest (MSF) algorithm. Three different methods of dimension reduction are first used to obtain the subspace of hyperspectral data: (1) unsupervised feature extraction methods including principal component analysis (PCA), independent component analysis (ICA), and minimum noise fraction (MNF); (2) supervised feature extraction including decision boundary feature extraction (DBFE), discriminate analysis feature extraction (DAFE), and nonparametric weighted feature extraction (NWFE); (3) genetic algorithm (GA). The spectral features obtained are then fed into the enhanced marker-based MSF classification algorithm. In the enhanced MSF algorithm, the markers are extracted from the classification maps obtained by both SVM and watershed segmentation algorithm. To evaluate the proposed approach, the Pavia University hyperspectral data is tested. Experimental results show that the proposed approach using GA achieves an approximately 8 % overall accuracy higher than the original MSF-based algorithm.

Multi-sparse dictionary colorization algorithm based on the feature classification and detail enhancement

NASA Astrophysics Data System (ADS)

Yan, Dan; Bai, Lianfa; Zhang, Yi; Han, Jing

2018-02-01

For the problems of missing details and performance of the colorization based on sparse representation, we propose a conceptual model framework for colorizing gray-scale images, and then a multi-sparse dictionary colorization algorithm based on the feature classification and detail enhancement (CEMDC) is proposed based on this framework. The algorithm can achieve a natural colorized effect for a gray-scale image, and it is consistent with the human vision. First, the algorithm establishes a multi-sparse dictionary classification colorization model. Then, to improve the accuracy rate of the classification, the corresponding local constraint algorithm is proposed. Finally, we propose a detail enhancement based on Laplacian Pyramid, which is effective in solving the problem of missing details and improving the speed of image colorization. In addition, the algorithm not only realizes the colorization of the visual gray-scale image, but also can be applied to the other areas, such as color transfer between color images, colorizing gray fusion images, and infrared images.

Non-parametric diffeomorphic image registration with the demons algorithm.

PubMed

Vercauteren, Tom; Pennec, Xavier; Perchant, Aymeric; Ayache, Nicholas

2007-01-01

We propose a non-parametric diffeomorphic image registration algorithm based on Thirion's demons algorithm. The demons algorithm can be seen as an optimization procedure on the entire space of displacement fields. The main idea of our algorithm is to adapt this procedure to a space of diffeomorphic transformations. In contrast to many diffeomorphic registration algorithms, our solution is computationally efficient since in practice it only replaces an addition of free form deformations by a few compositions. Our experiments show that in addition to being diffeomorphic, our algorithm provides results that are similar to the ones from the demons algorithm but with transformations that are much smoother and closer to the true ones in terms of Jacobians.

Identifying the association between contrast enhancement pattern, surgical resection, and prognosis in anaplastic glioma patients.

PubMed

Wang, Yinyan; Wang, Kai; Wang, Jiangfei; Li, Shaowu; Ma, Jun; Dai, Jianping; Jiang, Tao

2016-04-01

Contrast enhancement observable on magnetic resonance (MR) images reflects the destructive features of malignant gliomas. This study aimed to investigate the relationship between radiologic patterns of tumor enhancement, extent of resection, and prognosis in patients with anaplastic gliomas (AGs). Clinical data from 268 patients with histologically confirmed AGs were retrospectively analyzed. Contrast enhancement patterns were classified based on preoperative T1-contrast MR images. Univariate and multivariate analyses were performed to evaluate the prognostic value of MR enhancement patterns on progression-free survival (PFS) and overall survival (OS). The pattern of tumor contrast enhancement was associated with the extent of surgical resection in AGs. A gross total resection was more likely to be achieved for AGs with focal enhancement than those with diffuse (p = 0.001) or ring-like (p = 0.024) enhancement. Additionally, patients with focal-enhanced AGs had a significantly longer PFS and OS than those with diffuse (log-rank, p = 0.025 and p = 0.031, respectively) or ring-like (log-rank, p = 0.008 and p = 0.011, respectively) enhanced AGs. Furthermore, multivariate analysis identified the pattern of tumor enhancement as a significant predictor of PFS (p = 0.016, hazard ratio [HR] = 1.485) and OS (p = 0.030, HR = 1.446). Our results suggested that the contrast enhancement pattern on preoperative MR images was associated with the extent of resection and predictive of survival outcomes in AG patients.

Advances in Focal Plane Wavefront Estimation for Directly Imaging Exoplanets

NASA Astrophysics Data System (ADS)

Eldorado Riggs, A. J.; Kasdin, N. Jeremy; Groff, Tyler Dean

2015-01-01

To image cold exoplanets directly in visible light, an instrument on a telescope needs to suppress starlight by about 9 orders of magnitude at small separations from the star. A coronagraph changes the point spread function to create regions of high contrast where exoplanets or disks can be seen. Aberrations on the optics degrade the contrast by several orders of magnitude, so all high-contrast imaging systems incorporate one or more deformable mirrors (DMs) to recover regions of high contrast. With a coronagraphic instrument planned for the WFIRST-AFTA space telescope, there is a pressing need for faster, more robust estimation and control schemes for the DMs. Non-common path aberrations limit conventional phase conjugation schemes to medium star-to-planet contrast ratios of about 1e-6. High-contrast imaging requires estimation and control of both phase and amplitude in the same beam path as the science camera. Field estimation is a challenge since only intensity is measured; the most common approach, including that planned for WFIRST-AFTA, is to use DMs to create diversity, via pairs of small probe shapes, thereby allowing disambiguation of the electric field. Most implementations of DM Diversity require at least five images per electric field estimate and require narrowband measurements. This paper describes our new estimation algorithms that improve the speed (by using fewer images) and bandwidth of focal plane wavefront estimation. For narrowband estimation, we are testing nonlinear, recursive algorithms such as an iterative extended Kalman filter (IEKF) to use three images each iteration and build better, more robust estimates. We are also exploring the use of broadband estimation without the need for narrowband sub-filters and measurements. Here we present simulations of these algorithms with realistic noise and small signals to show how they might perform for WFIRST-AFTA. Once validated in simulations, we will test these algorithms experimentally in Princeton's HCIL and in the Jet Propulsion Laboratory's (JPL's) High Contrast Imaging Testbed (HCIT). Developing these faster, more robust wavefront estimators is a crucial for increasing the science yield of the WFIRST-AFTA coronagraphic instrument.

Enhancing Brain Lesions during Acute Optic Neuritis and/or Longitudinally Extensive Transverse Myelitis May Portend a Higher Relapse Rate in Neuromyelitis Optica Spectrum Disorders.

PubMed

Orman, G; Wang, K Y; Pekcevik, Y; Thompson, C B; Mealy, M; Levy, M; Izbudak, I

2017-05-01

Neuromyelitis optica spectrum disorders are inflammatory demyelinating disorders with optic neuritis and/or longitudinally extensive transverse myelitis episodes. We now know that neuromyelitis optica spectrum disorders are associated with antibodies to aquaporin-4, which are highly concentrated on astrocytic end-feet at the blood-brain barrier. Immune-mediated disruption of the blood-brain barrier may manifest as contrast enhancement on brain MR imaging. We aimed to delineate the extent and frequency of contrast enhancement on brain MR imaging within 1 month of optic neuritis and/or longitudinally extensive transverse myelitis attacks and to correlate contrast enhancement with outcome measures. Brain MRIs of patients with neuromyelitis optica spectrum disorders were evaluated for patterns of contrast enhancement (periependymal, cloudlike, leptomeningeal, and so forth). The Fisher exact test was used to evaluate differences between the proportion of contrast enhancement in patients who were seropositive and seronegative for aquaporin-4 antibodies. The Mann-Whitney test was used to compare the annualized relapse rate and disease duration between patients with and without contrast enhancement and with and without seropositivity. Brain MRIs of 77 patients were evaluated; 59 patients (10 males, 49 females) were scanned within 1 month of optic neuritis and/or longitudinally extensive transverse myelitis attacks and were included in the analysis. Forty-eight patients were seropositive, 9 were seronegative, and 2 were not tested for aquaporin-4 antibodies. Having brain contrast enhancement of any type during an acute attack was significantly associated with higher annualized relapse rates ( P = .03) and marginally associated with shorter disease duration ( P = .05). Having periependymal contrast enhancement was significantly associated with higher annualized relapse rates ( P = .03). Brain MRIs of patients with neuromyelitis optica spectrum disorders with contrast enhancement during an acute relapse of optic neuritis and/or longitudinally extensive transverse myelitis are associated with increased annual relapse rates. © 2017 by American Journal of Neuroradiology.

Long Distance Endovascular Growth of Jugulotympanic Paraganglioma Evident in 68Ga-DOTATATE PET but Concealed on CT.

PubMed

Avramovic, Nemanja; Weckesser, Matthias; Velasco, Aglaé; Stenner, Markus; Noto, Benjamin

2017-02-01

A 60-year-old woman was referred to contrast-enhanced CT for evaluation of jugular vein thrombosis incidentally detected by ultrasound. Contrast-enhanced CT showed an enhanced tumor of the right skull base highly suspicious of jugulotympanic paraganglioma. However, the jugular veins showed a nearly symmetric contrast enhancement without clear evidence of thrombosis. Consecutive Ga-DOTATATE PET/CT depicted high tumor uptake, which comprised the entire internal jugular vein. Endovascular growth of paraganglioma might be missed on contrast-enhanced CT because of high vascularization of the lesion. Ga-DOTATATE PET is suited for accurate determination of tumor extent.

Real-time 3-dimensional contrast-enhanced ultrasound in detecting hemorrhage of blunt renal trauma.

PubMed

Xu, Rui-Xue; Li, Ye-Kuo; Li, Ting; Wang, Sha-Sha; Yuan, Gui-Zhong; Zhou, Qun-Fang; Zheng, Hai-Rong; Yan, Fei

2013-10-01

The objective of this study is to evaluate the diagnostic value of real-time 3-dimensional contrast-enhanced ultrasound in the hemorrhage of blunt renal trauma. Eighteen healthy New Zealand white rabbits were randomly divided into 3 groups. Blunt renal trauma was performed on each group by using minitype striker. Ultrasonography, color Doppler flow imaging, and contrast-enhanced 2-dimensional and real-time 3-dimensional ultrasound were applied before and after the strike. The time to shock and blood pressure were subjected to statistical analysis. Then, a comparative study of ultrasound and pathology was carried out. All the struck kidneys were traumatic. In the ultrasonography, free fluid was found under the renal capsule. In the color Doppler flow imaging, active hemorrhage was not identified. In 2-dimensional contrast-enhanced ultrasound, active hemorrhage of the damaged kidney was characterized. Real-time 3-dimensional contrast-enhanced ultrasound showed a real-time and stereoscopic ongoing bleeding of the injured kidney. The wider the hemorrhage area in 4-dimensional contrast-enhanced ultrasound was, the faster the blood pressure decreased. Real-time 3-dimensional contrast-enhanced ultrasound is a promising noninvasive tool for stereoscopically and vividly detecting ongoing hemorrhage of blunt renal trauma in real time. © 2013.

Lip colour affects perceived sex typicality and attractiveness of human faces.

PubMed

Stephen, Ian D; McKeegan, Angela M

2010-01-01

The luminance contrast between facial features and facial skin is greater in women than in men, and women's use of make-up enhances this contrast. In black-and-white photographs, increased luminance contrast enhances femininity and attractiveness in women's faces, but reduces masculinity and attractiveness in men's faces. In Caucasians, much of the contrast between the lips and facial skin is in redness. Red lips have been considered attractive in women in geographically and temporally diverse cultures, possibly because they mimic vasodilation associated with sexual arousal. Here, we investigate the effects of lip luminance and colour contrast on the attractiveness and sex typicality (masculinity/femininity) of human faces. In a Caucasian sample, we allowed participants to manipulate the colour of the lips in colour-calibrated face photographs along CIELab L* (light--dark), a* (red--green), and b* (yellow--blue) axes to enhance apparent attractiveness and sex typicality. Participants increased redness contrast to enhance femininity and attractiveness of female faces, but reduced redness contrast to enhance masculinity of men's faces. Lip blueness was reduced more in female than male faces. Increased lightness contrast enhanced the attractiveness of both sexes, and had little effect on perceptions of sex typicality. The association between lip colour contrast and attractiveness in women's faces may be attributable to its association with oxygenated blood perfusion indicating oestrogen levels, sexual arousal, and cardiac and respiratory health.

GAMBIT: A Parameterless Model-Based Evolutionary Algorithm for Mixed-Integer Problems.

PubMed

Sadowski, Krzysztof L; Thierens, Dirk; Bosman, Peter A N

2018-01-01

Learning and exploiting problem structure is one of the key challenges in optimization. This is especially important for black-box optimization (BBO) where prior structural knowledge of a problem is not available. Existing model-based Evolutionary Algorithms (EAs) are very efficient at learning structure in both the discrete, and in the continuous domain. In this article, discrete and continuous model-building mechanisms are integrated for the Mixed-Integer (MI) domain, comprising discrete and continuous variables. We revisit a recently introduced model-based evolutionary algorithm for the MI domain, the Genetic Algorithm for Model-Based mixed-Integer opTimization (GAMBIT). We extend GAMBIT with a parameterless scheme that allows for practical use of the algorithm without the need to explicitly specify any parameters. We furthermore contrast GAMBIT with other model-based alternatives. The ultimate goal of processing mixed dependences explicitly in GAMBIT is also addressed by introducing a new mechanism for the explicit exploitation of mixed dependences. We find that processing mixed dependences with this novel mechanism allows for more efficient optimization. We further contrast the parameterless GAMBIT with Mixed-Integer Evolution Strategies (MIES) and other state-of-the-art MI optimization algorithms from the General Algebraic Modeling System (GAMS) commercial algorithm suite on problems with and without constraints, and show that GAMBIT is capable of solving problems where variable dependences prevent many algorithms from successfully optimizing them.

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.

Detection of convective initiation using Meteosat SEVIRI: implementation in and verification with the tracking and nowcasting algorithm Cb-TRAM

NASA Astrophysics Data System (ADS)

Merk, D.; Zinner, T.

2013-02-01

In this paper a new detection scheme for Convective Initation (CI) under day and night conditions is presented. The new algorithm combines the strengths of two existing methods for detecting Convective Initation with geostationary satellite data and uses the channels of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard Meteosat Second Generation (MSG). For the new algorithm five infrared criteria from the Satellite Convection Analysis and Tracking algorithm (SATCAST) and one High Resolution Visible channel (HRV) criteria from Cb-TRAM were adapted. This set of criteria aims for identifying the typical development of quickly developing convective cells in an early stage. The different criteria include timetrends of the 10.8 IR channel and IR channel differences as well as their timetrends. To provide the trend fields an optical flow based method is used, the Pyramidal Matching algorithm which is part of Cb-TRAM. The new detection scheme is implemented in Cb-TRAM and is verified for seven days which comprise different weather situations in Central Europe. Contrasted with the original early stage detection scheme of Cb-TRAM skill scores are provided. From the comparison against detections of later thunderstorm stages, which are also provided by Cb-TRAM, a decrease in false prior warnings (false alarm ratio) from 91 to 81% is presented, an increase of the critical success index from 7.4 to 12.7%, and a decrease of the BIAS from 320 to 146% for normal scan mode. Similar trends are found for rapid scan mode. Most obvious is the decline of false alarms found for synoptic conditions with upper cold air masses triggering convection.

Detection of convective initiation using Meteosat SEVIRI: implementation in and verification with the tracking and nowcasting algorithm Cb-TRAM

NASA Astrophysics Data System (ADS)

Merk, D.; Zinner, T.

2013-08-01

In this paper a new detection scheme for convective initiation (CI) under day and night conditions is presented. The new algorithm combines the strengths of two existing methods for detecting CI with geostationary satellite data. It uses the channels of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard Meteosat Second Generation (MSG). For the new algorithm five infrared (IR) criteria from the Satellite Convection Analysis and Tracking algorithm (SATCAST) and one high-resolution visible channel (HRV) criteria from Cb-TRAM were adapted. This set of criteria aims to identify the typical development of quickly developing convective cells in an early stage. The different criteria include time trends of the 10.8 IR channel, and IR channel differences, as well as their time trends. To provide the trend fields an optical-flow-based method is used: the pyramidal matching algorithm, which is part of Cb-TRAM. The new detection scheme is implemented in Cb-TRAM, and is verified for seven days which comprise different weather situations in central Europe. Contrasted with the original early-stage detection scheme of Cb-TRAM, skill scores are provided. From the comparison against detections of later thunderstorm stages, which are also provided by Cb-TRAM, a decrease in false prior warnings (false alarm ratio) from 91 to 81% is presented, an increase of the critical success index from 7.4 to 12.7%, and a decrease of the BIAS from 320 to 146% for normal scan mode. Similar trends are found for rapid scan mode. Most obvious is the decline of false alarms found for the synoptic class "cold air" masses.

Version 2 of the IASI NH3 neural network retrieval algorithm: near-real-time and reanalysed datasets

NASA Astrophysics Data System (ADS)

Van Damme, Martin; Whitburn, Simon; Clarisse, Lieven; Clerbaux, Cathy; Hurtmans, Daniel; Coheur, Pierre-François

2017-12-01

Recently, Whitburn et al.(2016) presented a neural-network-based algorithm for retrieving atmospheric ammonia (NH3) columns from Infrared Atmospheric Sounding Interferometer (IASI) satellite observations. In the past year, several improvements have been introduced, and the resulting new baseline version, Artificial Neural Network for IASI (ANNI)-NH3-v2.1, is documented here. One of the main changes to the algorithm is that separate neural networks were trained for land and sea observations, resulting in a better training performance for both groups. By reducing and transforming the input parameter space, performance is now also better for observations associated with favourable sounding conditions (i.e. enhanced thermal contrasts). Other changes relate to the introduction of a bias correction over land and sea and the treatment of the satellite zenith angle. In addition to these algorithmic changes, new recommendations for post-filtering the data and for averaging data in time or space are formulated. We also introduce a second dataset (ANNI-NH3-v2.1R-I) which relies on ERA-Interim ECMWF meteorological input data, along with surface temperature retrieved from a dedicated network, rather than the operationally provided Eumetsat IASI Level 2 (L2) data used for the standard near-real-time version. The need for such a dataset emerged after a series of sharp discontinuities were identified in the NH3 time series, which could be traced back to incremental changes in the IASI L2 algorithms for temperature and clouds. The reanalysed dataset is coherent in time and can therefore be used to study trends. Furthermore, both datasets agree reasonably well in the mean on recent data, after the date when the IASI meteorological L2 version 6 became operational (30 September 2014).

Low-Light Image Enhancement Using Adaptive Digital Pixel Binning

PubMed Central

Yoo, Yoonjong; Im, Jaehyun; Paik, Joonki

2015-01-01

This paper presents an image enhancement algorithm for low-light scenes in an environment with insufficient illumination. Simple amplification of intensity exhibits various undesired artifacts: noise amplification, intensity saturation, and loss of resolution. In order to enhance low-light images without undesired artifacts, a novel digital binning algorithm is proposed that considers brightness, context, noise level, and anti-saturation of a local region in the image. The proposed algorithm does not require any modification of the image sensor or additional frame-memory; it needs only two line-memories in the image signal processor (ISP). Since the proposed algorithm does not use an iterative computation, it can be easily embedded in an existing digital camera ISP pipeline containing a high-resolution image sensor. PMID:26121609

Contrast-enhanced time-resolved MRA for follow-up of intracranial aneurysms treated with the pipeline embolization device.

PubMed

Boddu, S R; Tong, F C; Dehkharghani, S; Dion, J E; Saindane, A M

2014-01-01

Endovascular reconstruction and flow diversion by using the Pipeline Embolization Device is an effective treatment for complex cerebral aneurysms. Accurate noninvasive alternatives to DSA for follow-up after Pipeline Embolization Device treatment are desirable. This study evaluated the accuracy of contrast-enhanced time-resolved MRA for this purpose, hypothesizing that contrast-enhanced time-resolved MRA will be comparable with DSA and superior to 3D-TOF MRA. During a 24-month period, 37 Pipeline Embolization Device-treated intracranial aneurysms in 26 patients underwent initial follow-up by using 3D-TOF MRA, contrast-enhanced time-resolved MRA, and DSA. MRA was performed on a 1.5T unit by using 3D-TOF and time-resolved imaging of contrast kinetics. All patients underwent DSA a median of 0 days (range, 0-68) after MRA. Studies were evaluated for aneurysm occlusion, quality of visualization of the reconstructed artery, and measurable luminal diameter of the Pipeline Embolization Device, with DSA used as the reference standard. The sensitivity, specificity, and positive and negative predictive values of contrast-enhanced time-resolved MRA relative to DSA for posttreatment aneurysm occlusion were 96%, 85%, 92%, and 92%. Contrast-enhanced time-resolved MRA demonstrated superior quality of visualization (P = .0001) and a higher measurable luminal diameter (P = .0001) of the reconstructed artery compared with 3D-TOF MRA but no significant difference compared with DSA. Contrast-enhanced time-resolved MRA underestimated the luminal diameter of the reconstructed artery by 0.965 ± 0.497 mm (27% ± 13%) relative to DSA. Contrast-enhanced time-resolved MRA is a reliable noninvasive method for monitoring intracranial aneurysms following flow diversion and vessel reconstruction by using the Pipeline Embolization Device. © 2014 by American Journal of Neuroradiology.

Multi-frame knowledge based text enhancement for mobile phone captured videos

NASA Astrophysics Data System (ADS)

Ozarslan, Suleyman; Eren, P. Erhan

2014-02-01

In this study, we explore automated text recognition and enhancement using mobile phone captured videos of store receipts. We propose a method which includes Optical Character Resolution (OCR) enhanced by our proposed Row Based Multiple Frame Integration (RB-MFI), and Knowledge Based Correction (KBC) algorithms. In this method, first, the trained OCR engine is used for recognition; then, the RB-MFI algorithm is applied to the output of the OCR. The RB-MFI algorithm determines and combines the most accurate rows of the text outputs extracted by using OCR from multiple frames of the video. After RB-MFI, KBC algorithm is applied to these rows to correct erroneous characters. Results of the experiments show that the proposed video-based approach which includes the RB-MFI and the KBC algorithm increases the word character recognition rate to 95%, and the character recognition rate to 98%.

Evaluation of Eu(II) -based positive contrast enhancement after intravenous, intraperitoneal, and subcutaneous injections.

PubMed

Ekanger, Levi A; Polin, Lisa A; Shen, Yimin; Haacke, E Mark; Allen, Matthew J

2016-07-01

Eu(II) -based contrast agents offer physiologically relevant, metal-based redox sensing that is unachievable with Gd(III) -based contrast agents. To evaluate the in vivo contrast enhancement of Eu(II) as a function of injection type, we performed intravenous, intraperitoneal, and subcutaneous injections in mice. Our data reveal a correlation between reported oxygen content and expected rates of diffusion with the persistence of Eu(II) -based contrast enhancement. Biodistribution studies revealed europium clearance through the liver and kidneys for intravenous and intraperitoneal injections, but no contrast enhancement was observed in organs associated with clearance. These data represent a step toward understanding the behavior of Eu(II) -based complexes in vivo. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

A simple method for low-contrast detectability, image quality and dose optimisation with CT iterative reconstruction algorithms and model observers.

PubMed

Bellesi, Luca; Wyttenbach, Rolf; Gaudino, Diego; Colleoni, Paolo; Pupillo, Francesco; Carrara, Mauro; Braghetti, Antonio; Puligheddu, Carla; Presilla, Stefano

2017-01-01

The aim of this work was to evaluate detection of low-contrast objects and image quality in computed tomography (CT) phantom images acquired at different tube loadings (i.e. mAs) and reconstructed with different algorithms, in order to find appropriate settings to reduce the dose to the patient without any image detriment. Images of supraslice low-contrast objects of a CT phantom were acquired using different mAs values. Images were reconstructed using filtered back projection (FBP), hybrid and iterative model-based methods. Image quality parameters were evaluated in terms of modulation transfer function; noise, and uniformity using two software resources. For the definition of low-contrast detectability, studies based on both human (i.e. four-alternative forced-choice test) and model observers were performed across the various images. Compared to FBP, image quality parameters were improved by using iterative reconstruction (IR) algorithms. In particular, IR model-based methods provided a 60% noise reduction and a 70% dose reduction, preserving image quality and low-contrast detectability for human radiological evaluation. According to the model observer, the diameters of the minimum detectable detail were around 2 mm (up to 100 mAs). Below 100 mAs, the model observer was unable to provide a result. IR methods improve CT protocol quality, providing a potential dose reduction while maintaining a good image detectability. Model observer can in principle be useful to assist human performance in CT low-contrast detection tasks and in dose optimisation.

Bayesian Quantification of Contrast-Enhanced Ultrasound Images With Adaptive Inclusion of an Irreversible Component.

PubMed

Rizzo, Gaia; Tonietto, Matteo; Castellaro, Marco; Raffeiner, Bernd; Coran, Alessandro; Fiocco, Ugo; Stramare, Roberto; Grisan, Enrico

2017-04-01

Contrast Enhanced Ultrasound (CEUS) is a sensitive imaging technique to assess tissue vascularity and it can be particularly useful in early detection and grading of arthritis. In a recent study we have shown that a Gamma-variate can accurately quantify synovial perfusion and it is flexible enough to describe many heterogeneous patterns. However, in some cases the heterogeneity of the kinetics can be such that even the Gamma model does not properly describe the curve, with a high number of outliers. In this work we apply to CEUS data the single compartment recirculation model (SCR) which takes explicitly into account the trapping of the microbubbles contrast agent by adding to the single Gamma-variate model its integral. The SCR model, originally proposed for dynamic-susceptibility magnetic resonance imaging, is solved here at pixel level within a Bayesian framework using Variational Bayes (VB). We also include the automatic relevant determination (ARD) algorithm to automatically infer the model complexity (SCR vs. Gamma model) from the data. We demonstrate that the inclusion of trapping best describes the CEUS patterns in 50% of the pixels, with the other 50% best fitted by a single Gamma. Such results highlight the necessity of the use ARD, to automatically exclude the irreversible component where not supported by the data. VB with ARD returns precise estimates in the majority of the kinetics (88% of total percentage of pixels) in a limited computational time (on average, 3.6 min per subject). Moreover, the impact of the additional trapping component has been evaluated for the differentiation of rheumatoid and non-rheumatoid patients, by means of a support vector machine classifier with backward feature selection. The results show that the trapping parameter is always present in the selected feature set, and improves the classification.

Contrast-enhanced CT- and MRI-based perfusion assessment for pulmonary diseases: basics and clinical applications

PubMed Central

Ohno, Yoshiharu; Koyama, Hisanobu; Lee, Ho Yun; Miura, Sachiko; Yoshikawa, Takeshi; Sugimura, Kazuro

2016-01-01

Assessment of regional pulmonary perfusion as well as nodule and tumor perfusions in various pulmonary diseases are currently performed by means of nuclear medicine studies requiring radioactive macroaggregates, dual-energy computed tomography (CT), and dynamic first-pass contrast-enhanced perfusion CT techniques and unenhanced and dynamic first-pass contrast enhanced perfusion magnetic resonance imaging (MRI), as well as time-resolved three-dimensional or four-dimensional contrast-enhanced magnetic resonance angiography (MRA). Perfusion scintigraphy, single-photon emission tomography (SPECT) and SPECT fused with CT have been established as clinically available scintigraphic methods; however, they are limited by perfusion information with poor spatial resolution and other shortcomings. Although positron emission tomography with 15O water can measure absolute pulmonary perfusion, it requires a cyclotron for generation of a tracer with an extremely short half-life (2 min), and can only be performed for academic purposes. Therefore, clinicians are concentrating their efforts on the application of CT-based and MRI-based quantitative and qualitative perfusion assessment to various pulmonary diseases. This review article covers 1) the basics of dual-energy CT and dynamic first-pass contrast-enhanced perfusion CT techniques, 2) the basics of time-resolved contrast-enhanced MRA and dynamic first-pass contrast-enhanced perfusion MRI, and 3) clinical applications of contrast-enhanced CT- and MRI-based perfusion assessment for patients with pulmonary nodule, lung cancer, and pulmonary vascular diseases. We believe that these new techniques can be useful in routine clinical practice for not only thoracic oncology patients, but also patients with different pulmonary vascular diseases. PMID:27523813

Investigating the enhanced Best Performance Algorithm for Annual Crop Planning problem based on economic factors.

PubMed

Adewumi, Aderemi Oluyinka; Chetty, Sivashan

2017-01-01

The Annual Crop Planning (ACP) problem was a recently introduced problem in the literature. This study further expounds on this problem by presenting a new mathematical formulation, which is based on market economic factors. To determine solutions, a new local search metaheuristic algorithm is investigated which is called the enhanced Best Performance Algorithm (eBPA). eBPA's results are compared against two well-known local search metaheuristic algorithms; these include Tabu Search and Simulated Annealing. The results show the potential of the eBPA for continuous optimization problems.

Dual-energy contrast-enhanced spectral mammography (CESM).

PubMed

Daniaux, Martin; De Zordo, Tobias; Santner, Wolfram; Amort, Birgit; Koppelstätter, Florian; Jaschke, Werner; Dromain, Clarisse; Oberaigner, Willi; Hubalek, Michael; Marth, Christian

2015-10-01

Dual-energy contrast-enhanced mammography is one of the latest developments in breast care. Imaging with contrast agents in breast cancer was already known from previous magnetic resonance imaging and computed tomography studies. However, high costs, limited availability-or high radiation dose-led to the development of contrast-enhanced spectral mammography (CESM). We reviewed the current literature, present our experience, discuss the advantages and drawbacks of CESM and look at the future of this innovative technique.

SU-F-J-71: Improving CT Quality for Radiation Therapy Planning and Delivery Guidance Using a Non-Linear Contrast Enhancement Technique

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

Noid, G; Tai, A; Li, X

2016-06-15

Purpose: Advanced image post-processing techniques which enhance soft-tissue contrast in CT have not been widely employed for RT planning or delivery guidance. The purpose of this work is to assess the soft-tissue contrast enhancement from non-linear contrast enhancing filters and its impact in RT. The contrast enhancement reduces patient alignment uncertainties. Methods: Non-linear contrast enhancing methods, such as Best Contrast (Siemens), amplify small differences in X-ray attenuation between two adjacent structure without significantly increasing noise. Best Contrast (BC) separates a CT into two frequency bands. The low frequency band is modified by a non-linear scaling function before recombination with themore » high frequency band. CT data collected using a CT-on-rails (Definition AS Open, Siemens) during daily CT-guided RT for 6 prostate cancer patients and an image quality phantom (The Phantom Laboratory) were analyzed. Images acquired with a standard protocol (120 kVp, 0.6 pitch, 18 mGy CTDIvol) were processed before comparison to the unaltered images. Contrast and noise were measured in the the phantom. Inter-observer variation was assessed by placing prostate contours on the 12 CT study sets, 6 enhanced and 6 unaltered, in a blinded study involving 8 observers. Results: The phantom data demonstrate that BC increased the contrast between the 1.0% supra-slice element and the background substrate by 46.5 HU while noise increased by only 2.3 HU. Thus the contrast to noise ratio increased from 1.28 to 6.71. Furthermore, the variation in centroid position of the prostate contours was decreased from 1.3±0.4 mm to 0.8±0.3 mm. Thus the CTV-to-PTV margin was reduced by 1.1 mm. The uncertainty in delineation of the prostate/rectum edge decreased by 0.5 mm. Conclusion: As demonstrated in phantom and patient scans the BC filter accentuates soft-tissue contrast. This enhancement leads to reduced inter-observer variation, which should improve RT planning and delivery. Supported by Siemens.« less