Retinal status analysis method based on feature extraction and quantitative grading in OCT images.

PubMed

Fu, Dongmei; Tong, Hejun; Zheng, Shuang; Luo, Ling; Gao, Fulin; Minar, Jiri

2016-07-22

Optical coherence tomography (OCT) is widely used in ophthalmology for viewing the morphology of the retina, which is important for disease detection and assessing therapeutic effect. The diagnosis of retinal diseases is based primarily on the subjective analysis of OCT images by trained ophthalmologists. This paper describes an OCT images automatic analysis method for computer-aided disease diagnosis and it is a critical part of the eye fundus diagnosis. This study analyzed 300 OCT images acquired by Optovue Avanti RTVue XR (Optovue Corp., Fremont, CA). Firstly, the normal retinal reference model based on retinal boundaries was presented. Subsequently, two kinds of quantitative methods based on geometric features and morphological features were proposed. This paper put forward a retinal abnormal grading decision-making method which was used in actual analysis and evaluation of multiple OCT images. This paper showed detailed analysis process by four retinal OCT images with different abnormal degrees. The final grading results verified that the analysis method can distinguish abnormal severity and lesion regions. This paper presented the simulation of the 150 test images, where the results of analysis of retinal status showed that the sensitivity was 0.94 and specificity was 0.92.The proposed method can speed up diagnostic process and objectively evaluate the retinal status. This paper aims on studies of retinal status automatic analysis method based on feature extraction and quantitative grading in OCT images. The proposed method can obtain the parameters and the features that are associated with retinal morphology. Quantitative analysis and evaluation of these features are combined with reference model which can realize the target image abnormal judgment and provide a reference for disease diagnosis.

Comparison among Reconstruction Algorithms for Quantitative Analysis of 11C-Acetate Cardiac PET Imaging.

PubMed

Shi, Ximin; Li, Nan; Ding, Haiyan; Dang, Yonghong; Hu, Guilan; Liu, Shuai; Cui, Jie; Zhang, Yue; Li, Fang; Zhang, Hui; Huo, Li

2018-01-01

Kinetic modeling of dynamic 11 C-acetate PET imaging provides quantitative information for myocardium assessment. The quality and quantitation of PET images are known to be dependent on PET reconstruction methods. This study aims to investigate the impacts of reconstruction algorithms on the quantitative analysis of dynamic 11 C-acetate cardiac PET imaging. Suspected alcoholic cardiomyopathy patients ( N = 24) underwent 11 C-acetate dynamic PET imaging after low dose CT scan. PET images were reconstructed using four algorithms: filtered backprojection (FBP), ordered subsets expectation maximization (OSEM), OSEM with time-of-flight (TOF), and OSEM with both time-of-flight and point-spread-function (TPSF). Standardized uptake values (SUVs) at different time points were compared among images reconstructed using the four algorithms. Time-activity curves (TACs) in myocardium and blood pools of ventricles were generated from the dynamic image series. Kinetic parameters K 1 and k 2 were derived using a 1-tissue-compartment model for kinetic modeling of cardiac flow from 11 C-acetate PET images. Significant image quality improvement was found in the images reconstructed using iterative OSEM-type algorithms (OSME, TOF, and TPSF) compared with FBP. However, no statistical differences in SUVs were observed among the four reconstruction methods at the selected time points. Kinetic parameters K 1 and k 2 also exhibited no statistical difference among the four reconstruction algorithms in terms of mean value and standard deviation. However, for the correlation analysis, OSEM reconstruction presented relatively higher residual in correlation with FBP reconstruction compared with TOF and TPSF reconstruction, and TOF and TPSF reconstruction were highly correlated with each other. All the tested reconstruction algorithms performed similarly for quantitative analysis of 11 C-acetate cardiac PET imaging. TOF and TPSF yielded highly consistent kinetic parameter results with superior

Quantitative Medical Image Analysis for Clinical Development of Therapeutics

NASA Astrophysics Data System (ADS)

Analoui, Mostafa

There has been significant progress in development of therapeutics for prevention and management of several disease areas in recent years, leading to increased average life expectancy, as well as of quality of life, globally. However, due to complexity of addressing a number of medical needs and financial burden of development of new class of therapeutics, there is a need for better tools for decision making and validation of efficacy and safety of new compounds. Numerous biological markers (biomarkers) have been proposed either as adjunct to current clinical endpoints or as surrogates. Imaging biomarkers are among rapidly increasing biomarkers, being examined to expedite effective and rational drug development. Clinical imaging often involves a complex set of multi-modality data sets that require rapid and objective analysis, independent of reviewer's bias and training. In this chapter, an overview of imaging biomarkers for drug development is offered, along with challenges that necessitate quantitative and objective image analysis. Examples of automated and semi-automated analysis approaches are provided, along with technical review of such methods. These examples include the use of 3D MRI for osteoarthritis, ultrasound vascular imaging, and dynamic contrast enhanced MRI for oncology. Additionally, a brief overview of regulatory requirements is discussed. In conclusion, this chapter highlights key challenges and future directions in this area.

Quantitative image analysis for investigating cell-matrix interactions

NASA Astrophysics Data System (ADS)

Burkel, Brian; Notbohm, Jacob

2017-07-01

The extracellular matrix provides both chemical and physical cues that control cellular processes such as migration, division, differentiation, and cancer progression. Cells can mechanically alter the matrix by applying forces that result in matrix displacements, which in turn may localize to form dense bands along which cells may migrate. To quantify the displacements, we use confocal microscopy and fluorescent labeling to acquire high-contrast images of the fibrous material. Using a technique for quantitative image analysis called digital volume correlation, we then compute the matrix displacements. Our experimental technology offers a means to quantify matrix mechanics and cell-matrix interactions. We are now using these experimental tools to modulate mechanical properties of the matrix to study cell contraction and migration.

Visualisation and quantitative analysis of the rodent malaria liver stage by real time imaging.

PubMed

Ploemen, Ivo H J; Prudêncio, Miguel; Douradinha, Bruno G; Ramesar, Jai; Fonager, Jannik; van Gemert, Geert-Jan; Luty, Adrian J F; Hermsen, Cornelus C; Sauerwein, Robert W; Baptista, Fernanda G; Mota, Maria M; Waters, Andrew P; Que, Ivo; Lowik, Clemens W G M; Khan, Shahid M; Janse, Chris J; Franke-Fayard, Blandine M D

2009-11-18

The quantitative analysis of Plasmodium development in the liver in laboratory animals in cultured cells is hampered by low parasite infection rates and the complicated methods required to monitor intracellular development. As a consequence, this important phase of the parasite's life cycle has been poorly studied compared to blood stages, for example in screening anti-malarial drugs. Here we report the use of a transgenic P. berghei parasite, PbGFP-Luc(con), expressing the bioluminescent reporter protein luciferase to visualize and quantify parasite development in liver cells both in culture and in live mice using real-time luminescence imaging. The reporter-parasite based quantification in cultured hepatocytes by real-time imaging or using a microplate reader correlates very well with established quantitative RT-PCR methods. For the first time the liver stage of Plasmodium is visualized in whole bodies of live mice and we were able to discriminate as few as 1-5 infected hepatocytes per liver in mice using 2D-imaging and to identify individual infected hepatocytes by 3D-imaging. The analysis of liver infections by whole body imaging shows a good correlation with quantitative RT-PCR analysis of extracted livers. The luminescence-based analysis of the effects of various drugs on in vitro hepatocyte infection shows that this method can effectively be used for in vitro screening of compounds targeting Plasmodium liver stages. Furthermore, by analysing the effect of primaquine and tafenoquine in vivo we demonstrate the applicability of real time imaging to assess parasite drug sensitivity in the liver. The simplicity and speed of quantitative analysis of liver-stage development by real-time imaging compared to the PCR methodologies, as well as the possibility to analyse liver development in live mice without surgery, opens up new possibilities for research on Plasmodium liver infections and for validating the effect of drugs and vaccines on the liver stage of

Visualisation and Quantitative Analysis of the Rodent Malaria Liver Stage by Real Time Imaging

PubMed Central

Douradinha, Bruno G.; Ramesar, Jai; Fonager, Jannik; van Gemert, Geert-Jan; Luty, Adrian J. F.; Hermsen, Cornelus C.; Sauerwein, Robert W.; Baptista, Fernanda G.; Mota, Maria M.; Waters, Andrew P.; Que, Ivo; Lowik, Clemens W. G. M.; Khan, Shahid M.; Janse, Chris J.; Franke-Fayard, Blandine M. D.

2009-01-01

The quantitative analysis of Plasmodium development in the liver in laboratory animals in cultured cells is hampered by low parasite infection rates and the complicated methods required to monitor intracellular development. As a consequence, this important phase of the parasite's life cycle has been poorly studied compared to blood stages, for example in screening anti-malarial drugs. Here we report the use of a transgenic P. berghei parasite, PbGFP-Luccon, expressing the bioluminescent reporter protein luciferase to visualize and quantify parasite development in liver cells both in culture and in live mice using real-time luminescence imaging. The reporter-parasite based quantification in cultured hepatocytes by real-time imaging or using a microplate reader correlates very well with established quantitative RT-PCR methods. For the first time the liver stage of Plasmodium is visualized in whole bodies of live mice and we were able to discriminate as few as 1–5 infected hepatocytes per liver in mice using 2D-imaging and to identify individual infected hepatocytes by 3D-imaging. The analysis of liver infections by whole body imaging shows a good correlation with quantitative RT-PCR analysis of extracted livers. The luminescence-based analysis of the effects of various drugs on in vitro hepatocyte infection shows that this method can effectively be used for in vitro screening of compounds targeting Plasmodium liver stages. Furthermore, by analysing the effect of primaquine and tafenoquine in vivo we demonstrate the applicability of real time imaging to assess parasite drug sensitivity in the liver. The simplicity and speed of quantitative analysis of liver-stage development by real-time imaging compared to the PCR methodologies, as well as the possibility to analyse liver development in live mice without surgery, opens up new possibilities for research on Plasmodium liver infections and for validating the effect of drugs and vaccines on the liver stage of

Qualitative and quantitative interpretation of SEM image using digital image processing.

PubMed

Saladra, Dawid; Kopernik, Magdalena

2016-10-01

The aim of the this study is improvement of qualitative and quantitative analysis of scanning electron microscope micrographs by development of computer program, which enables automatic crack analysis of scanning electron microscopy (SEM) micrographs. Micromechanical tests of pneumatic ventricular assist devices result in a large number of micrographs. Therefore, the analysis must be automatic. Tests for athrombogenic titanium nitride/gold coatings deposited on polymeric substrates (Bionate II) are performed. These tests include microshear, microtension and fatigue analysis. Anisotropic surface defects observed in the SEM micrographs require support for qualitative and quantitative interpretation. Improvement of qualitative analysis of scanning electron microscope images was achieved by a set of computational tools that includes binarization, simplified expanding, expanding, simple image statistic thresholding, the filters Laplacian 1, and Laplacian 2, Otsu and reverse binarization. Several modifications of the known image processing techniques and combinations of the selected image processing techniques were applied. The introduced quantitative analysis of digital scanning electron microscope images enables computation of stereological parameters such as area, crack angle, crack length, and total crack length per unit area. This study also compares the functionality of the developed computer program of digital image processing with existing applications. The described pre- and postprocessing may be helpful in scanning electron microscopy and transmission electron microscopy surface investigations. © 2016 The Authors Journal of Microscopy © 2016 Royal Microscopical Society.

Quantitative analysis for peripheral vascularity assessment based on clinical photoacoustic and ultrasound images

NASA Astrophysics Data System (ADS)

Murakoshi, Dai; Hirota, Kazuhiro; Ishii, Hiroyasu; Hashimoto, Atsushi; Ebata, Tetsurou; Irisawa, Kaku; Wada, Takatsugu; Hayakawa, Toshiro; Itoh, Kenji; Ishihara, Miya

2018-02-01

Photoacoustic (PA) imaging technology is expected to be applied to clinical assessment for peripheral vascularity. We started a clinical evaluation with the prototype PA imaging system we recently developed. Prototype PA imaging system was composed with in-house Q-switched Alexandrite laser system which emits short-pulsed laser with 750 nm wavelength, handheld ultrasound transducer where illumination optics were integrated and signal processing for PA image reconstruction implemented in the clinical ultrasound (US) system. For the purpose of quantitative assessment of PA images, an image analyzing function has been developed and applied to clinical PA images. In this analyzing function, vascularity derived from PA signal intensity ranged for prescribed threshold was defined as a numerical index of vessel fulfillment and calculated for the prescribed region of interest (ROI). Skin surface was automatically detected by utilizing B-mode image acquired simultaneously with PA image. Skinsurface position is utilized to place the ROI objectively while avoiding unwanted signals such as artifacts which were imposed due to melanin pigment in the epidermal layer which absorbs laser emission and generates strong PA signals. Multiple images were available to support the scanned image set for 3D viewing. PA images for several fingers of patients with systemic sclerosis (SSc) were quantitatively assessed. Since the artifact region is trimmed off in PA images, the visibility of vessels with rather low PA signal intensity on the 3D projection image was enhanced and the reliability of the quantitative analysis was improved.

Quantitative Doppler Analysis Using Conventional Color Flow Imaging Acquisitions.

PubMed

Karabiyik, Yucel; Ekroll, Ingvild Kinn; Eik-Nes, Sturla H; Lovstakken, Lasse

2018-05-01

Interleaved acquisitions used in conventional triplex mode result in a tradeoff between the frame rate and the quality of velocity estimates. On the other hand, workflow becomes inefficient when the user has to switch between different modes, and measurement variability is increased. This paper investigates the use of power spectral Capon estimator in quantitative Doppler analysis using data acquired with conventional color flow imaging (CFI) schemes. To preserve the number of samples used for velocity estimation, only spatial averaging was utilized, and clutter rejection was performed after spectral estimation. The resulting velocity spectra were evaluated in terms of spectral width using a recently proposed spectral envelope estimator. The spectral envelopes were also used for Doppler index calculations using in vivo and string phantom acquisitions. In vivo results demonstrated that the Capon estimator can provide spectral estimates with sufficient quality for quantitative analysis using packet-based CFI acquisitions. The calculated Doppler indices were similar to the values calculated using spectrograms estimated on a commercial ultrasound scanner.

Use of local noise power spectrum and wavelet analysis in quantitative image quality assurance for EPIDs

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

Lee, Soyoung

Purpose: To investigate the use of local noise power spectrum (NPS) to characterize image noise and wavelet analysis to isolate defective pixels and inter-subpanel flat-fielding artifacts for quantitative quality assurance (QA) of electronic portal imaging devices (EPIDs). Methods: A total of 93 image sets including custom-made bar-pattern images and open exposure images were collected from four iViewGT a-Si EPID systems over three years. Global quantitative metrics such as modulation transform function (MTF), NPS, and detective quantum efficiency (DQE) were computed for each image set. Local NPS was also calculated for individual subpanels by sampling region of interests within each subpanelmore » of the EPID. The 1D NPS, obtained by radially averaging the 2D NPS, was fitted to a power-law function. The r-square value of the linear regression analysis was used as a singular metric to characterize the noise properties of individual subpanels of the EPID. The sensitivity of the local NPS was first compared with the global quantitative metrics using historical image sets. It was then compared with two commonly used commercial QA systems with images collected after applying two different EPID calibration methods (single-level gain and multilevel gain). To detect isolated defective pixels and inter-subpanel flat-fielding artifacts, Haar wavelet transform was applied on the images. Results: Global quantitative metrics including MTF, NPS, and DQE showed little change over the period of data collection. On the contrary, a strong correlation between the local NPS (r-square values) and the variation of the EPID noise condition was observed. The local NPS analysis indicated image quality improvement with the r-square values increased from 0.80 ± 0.03 (before calibration) to 0.85 ± 0.03 (after single-level gain calibration) and to 0.96 ± 0.03 (after multilevel gain calibration), while the commercial QA systems failed to distinguish the image quality improvement between

Quantitative histogram analysis of images

NASA Astrophysics Data System (ADS)

Holub, Oliver; Ferreira, Sérgio T.

2006-11-01

A routine for histogram analysis of images has been written in the object-oriented, graphical development environment LabVIEW. The program converts an RGB bitmap image into an intensity-linear greyscale image according to selectable conversion coefficients. This greyscale image is subsequently analysed by plots of the intensity histogram and probability distribution of brightness, and by calculation of various parameters, including average brightness, standard deviation, variance, minimal and maximal brightness, mode, skewness and kurtosis of the histogram and the median of the probability distribution. The program allows interactive selection of specific regions of interest (ROI) in the image and definition of lower and upper threshold levels (e.g., to permit the removal of a constant background signal). The results of the analysis of multiple images can be conveniently saved and exported for plotting in other programs, which allows fast analysis of relatively large sets of image data. The program file accompanies this manuscript together with a detailed description of two application examples: The analysis of fluorescence microscopy images, specifically of tau-immunofluorescence in primary cultures of rat cortical and hippocampal neurons, and the quantification of protein bands by Western-blot. The possibilities and limitations of this kind of analysis are discussed. Program summaryTitle of program: HAWGC Catalogue identifier: ADXG_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/ADXG_v1_0 Program obtainable from: CPC Program Library, Queen's University of Belfast, N. Ireland Computers: Mobile Intel Pentium III, AMD Duron Installations: No installation necessary—Executable file together with necessary files for LabVIEW Run-time engine Operating systems or monitors under which the program has been tested: WindowsME/2000/XP Programming language used: LabVIEW 7.0 Memory required to execute with typical data:˜16MB for starting and ˜160MB used for

Quantitative Myocardial Perfusion Imaging Versus Visual Analysis in Diagnosing Myocardial Ischemia: A CE-MARC Substudy.

PubMed

Biglands, John D; Ibraheem, Montasir; Magee, Derek R; Radjenovic, Aleksandra; Plein, Sven; Greenwood, John P

2018-05-01

This study sought to compare the diagnostic accuracy of visual and quantitative analyses of myocardial perfusion cardiovascular magnetic resonance against a reference standard of quantitative coronary angiography. Visual analysis of perfusion cardiovascular magnetic resonance studies for assessing myocardial perfusion has been shown to have high diagnostic accuracy for coronary artery disease. However, only a few small studies have assessed the diagnostic accuracy of quantitative myocardial perfusion. This retrospective study included 128 patients randomly selected from the CE-MARC (Clinical Evaluation of Magnetic Resonance Imaging in Coronary Heart Disease) study population such that the distribution of risk factors and disease status was proportionate to the full population. Visual analysis results of cardiovascular magnetic resonance perfusion images, by consensus of 2 expert readers, were taken from the original study reports. Quantitative myocardial blood flow estimates were obtained using Fermi-constrained deconvolution. The reference standard for myocardial ischemia was a quantitative coronary x-ray angiogram stenosis severity of ≥70% diameter in any coronary artery of >2 mm diameter, or ≥50% in the left main stem. Diagnostic performance was calculated using receiver-operating characteristic curve analysis. The area under the curve for visual analysis was 0.88 (95% confidence interval: 0.81 to 0.95) with a sensitivity of 81.0% (95% confidence interval: 69.1% to 92.8%) and specificity of 86.0% (95% confidence interval: 78.7% to 93.4%). For quantitative stress myocardial blood flow the area under the curve was 0.89 (95% confidence interval: 0.83 to 0.96) with a sensitivity of 87.5% (95% confidence interval: 77.3% to 97.7%) and specificity of 84.5% (95% confidence interval: 76.8% to 92.3%). There was no statistically significant difference between the diagnostic performance of quantitative and visual analyses (p = 0.72). Incorporating rest myocardial

Identification and quantitation of semi-crystalline microplastics using image analysis and differential scanning calorimetry.

PubMed

Rodríguez Chialanza, Mauricio; Sierra, Ignacio; Pérez Parada, Andrés; Fornaro, Laura

2018-06-01

There are several techniques used to analyze microplastics. These are often based on a combination of visual and spectroscopic techniques. Here we introduce an alternative workflow for identification and mass quantitation through a combination of optical microscopy with image analysis (IA) and differential scanning calorimetry (DSC). We studied four synthetic polymers with environmental concern: low and high density polyethylene (LDPE and HDPE, respectively), polypropylene (PP), and polyethylene terephthalate (PET). Selected experiments were conducted to investigate (i) particle characterization and counting procedures based on image analysis with open-source software, (ii) chemical identification of microplastics based on DSC signal processing, (iii) dependence of particle size on DSC signal, and (iv) quantitation of microplastics mass based on DSC signal. We describe the potential and limitations of these techniques to increase reliability for microplastic analysis. Particle size demonstrated to have particular incidence in the qualitative and quantitative performance of DSC signals. Both, identification (based on characteristic onset temperature) and mass quantitation (based on heat flow) showed to be affected by particle size. As a result, a proper sample treatment which includes sieving of suspended particles is particularly required for this analytical approach.

Quantitative Analysis of Rat Dorsal Root Ganglion Neurons Cultured on Microelectrode Arrays Based on Fluorescence Microscopy Image Processing.

PubMed

Mari, João Fernando; Saito, José Hiroki; Neves, Amanda Ferreira; Lotufo, Celina Monteiro da Cruz; Destro-Filho, João-Batista; Nicoletti, Maria do Carmo

2015-12-01

Microelectrode Arrays (MEA) are devices for long term electrophysiological recording of extracellular spontaneous or evocated activities on in vitro neuron culture. This work proposes and develops a framework for quantitative and morphological analysis of neuron cultures on MEAs, by processing their corresponding images, acquired by fluorescence microscopy. The neurons are segmented from the fluorescence channel images using a combination of segmentation by thresholding, watershed transform, and object classification. The positioning of microelectrodes is obtained from the transmitted light channel images using the circular Hough transform. The proposed method was applied to images of dissociated culture of rat dorsal root ganglion (DRG) neuronal cells. The morphological and topological quantitative analysis carried out produced information regarding the state of culture, such as population count, neuron-to-neuron and neuron-to-microelectrode distances, soma morphologies, neuron sizes, neuron and microelectrode spatial distributions. Most of the analysis of microscopy images taken from neuronal cultures on MEA only consider simple qualitative analysis. Also, the proposed framework aims to standardize the image processing and to compute quantitative useful measures for integrated image-signal studies and further computational simulations. As results show, the implemented microelectrode identification method is robust and so are the implemented neuron segmentation and classification one (with a correct segmentation rate up to 84%). The quantitative information retrieved by the method is highly relevant to assist the integrated signal-image study of recorded electrophysiological signals as well as the physical aspects of the neuron culture on MEA. Although the experiments deal with DRG cell images, cortical and hippocampal cell images could also be processed with small adjustments in the image processing parameter estimation.

AUTOMATED ANALYSIS OF QUANTITATIVE IMAGE DATA USING ISOMORPHIC FUNCTIONAL MIXED MODELS, WITH APPLICATION TO PROTEOMICS DATA.

PubMed

Morris, Jeffrey S; Baladandayuthapani, Veerabhadran; Herrick, Richard C; Sanna, Pietro; Gutstein, Howard

2011-01-01

Image data are increasingly encountered and are of growing importance in many areas of science. Much of these data are quantitative image data, which are characterized by intensities that represent some measurement of interest in the scanned images. The data typically consist of multiple images on the same domain and the goal of the research is to combine the quantitative information across images to make inference about populations or interventions. In this paper, we present a unified analysis framework for the analysis of quantitative image data using a Bayesian functional mixed model approach. This framework is flexible enough to handle complex, irregular images with many local features, and can model the simultaneous effects of multiple factors on the image intensities and account for the correlation between images induced by the design. We introduce a general isomorphic modeling approach to fitting the functional mixed model, of which the wavelet-based functional mixed model is one special case. With suitable modeling choices, this approach leads to efficient calculations and can result in flexible modeling and adaptive smoothing of the salient features in the data. The proposed method has the following advantages: it can be run automatically, it produces inferential plots indicating which regions of the image are associated with each factor, it simultaneously considers the practical and statistical significance of findings, and it controls the false discovery rate. Although the method we present is general and can be applied to quantitative image data from any application, in this paper we focus on image-based proteomic data. We apply our method to an animal study investigating the effects of opiate addiction on the brain proteome. Our image-based functional mixed model approach finds results that are missed with conventional spot-based analysis approaches. In particular, we find that the significant regions of the image identified by the proposed method

3D Slicer as an Image Computing Platform for the Quantitative Imaging Network

PubMed Central

Fedorov, Andriy; Beichel, Reinhard; Kalpathy-Cramer, Jayashree; Finet, Julien; Fillion-Robin, Jean-Christophe; Pujol, Sonia; Bauer, Christian; Jennings, Dominique; Fennessy, Fiona; Sonka, Milan; Buatti, John; Aylward, Stephen; Miller, James V.; Pieper, Steve; Kikinis, Ron

2012-01-01

Quantitative analysis has tremendous but mostly unrealized potential in healthcare to support objective and accurate interpretation of the clinical imaging. In 2008, the National Cancer Institute began building the Quantitative Imaging Network (QIN) initiative with the goal of advancing quantitative imaging in the context of personalized therapy and evaluation of treatment response. Computerized analysis is an important component contributing to reproducibility and efficiency of the quantitative imaging techniques. The success of quantitative imaging is contingent on robust analysis methods and software tools to bring these methods from bench to bedside. 3D Slicer is a free open source software application for medical image computing. As a clinical research tool, 3D Slicer is similar to a radiology workstation that supports versatile visualizations but also provides advanced functionality such as automated segmentation and registration for a variety of application domains. Unlike a typical radiology workstation, 3D Slicer is free and is not tied to specific hardware. As a programming platform, 3D Slicer facilitates translation and evaluation of the new quantitative methods by allowing the biomedical researcher to focus on the implementation of the algorithm, and providing abstractions for the common tasks of data communication, visualization and user interface development. Compared to other tools that provide aspects of this functionality, 3D Slicer is fully open source and can be readily extended and redistributed. In addition, 3D Slicer is designed to facilitate the development of new functionality in the form of 3D Slicer extensions. In this paper, we present an overview of 3D Slicer as a platform for prototyping, development and evaluation of image analysis tools for clinical research applications. To illustrate the utility of the platform in the scope of QIN, we discuss several use cases of 3D Slicer by the existing QIN teams, and we elaborate on the future

WE-G-207-05: Relationship Between CT Image Quality, Segmentation Performance, and Quantitative Image Feature Analysis

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

Lee, J; Nishikawa, R; Reiser, I

Purpose: Segmentation quality can affect quantitative image feature analysis. The objective of this study is to examine the relationship between computed tomography (CT) image quality, segmentation performance, and quantitative image feature analysis. Methods: A total of 90 pathology proven breast lesions in 87 dedicated breast CT images were considered. An iterative image reconstruction (IIR) algorithm was used to obtain CT images with different quality. With different combinations of 4 variables in the algorithm, this study obtained a total of 28 different qualities of CT images. Two imaging tasks/objectives were considered: 1) segmentation and 2) classification of the lesion as benignmore » or malignant. Twenty-three image features were extracted after segmentation using a semi-automated algorithm and 5 of them were selected via a feature selection technique. Logistic regression was trained and tested using leave-one-out-cross-validation and its area under the ROC curve (AUC) was recorded. The standard deviation of a homogeneous portion and the gradient of a parenchymal portion of an example breast were used as an estimate of image noise and sharpness. The DICE coefficient was computed using a radiologist’s drawing on the lesion. Mean DICE and AUC were used as performance metrics for each of the 28 reconstructions. The relationship between segmentation and classification performance under different reconstructions were compared. Distributions (median, 95% confidence interval) of DICE and AUC for each reconstruction were also compared. Results: Moderate correlation (Pearson’s rho = 0.43, p-value = 0.02) between DICE and AUC values was found. However, the variation between DICE and AUC values for each reconstruction increased as the image sharpness increased. There was a combination of IIR parameters that resulted in the best segmentation with the worst classification performance. Conclusion: There are certain images that yield better segmentation or

Quantitative Hyperspectral Reflectance Imaging

PubMed Central

Klein, Marvin E.; Aalderink, Bernard J.; Padoan, Roberto; de Bruin, Gerrit; Steemers, Ted A.G.

2008-01-01

Hyperspectral imaging is a non-destructive optical analysis technique that can for instance be used to obtain information from cultural heritage objects unavailable with conventional colour or multi-spectral photography. This technique can be used to distinguish and recognize materials, to enhance the visibility of faint or obscured features, to detect signs of degradation and study the effect of environmental conditions on the object. We describe the basic concept, working principles, construction and performance of a laboratory instrument specifically developed for the analysis of historical documents. The instrument measures calibrated spectral reflectance images at 70 wavelengths ranging from 365 to 1100 nm (near-ultraviolet, visible and near-infrared). By using a wavelength tunable narrow-bandwidth light-source, the light energy used to illuminate the measured object is minimal, so that any light-induced degradation can be excluded. Basic analysis of the hyperspectral data includes a qualitative comparison of the spectral images and the extraction of quantitative data such as mean spectral reflectance curves and statistical information from user-defined regions-of-interest. More sophisticated mathematical feature extraction and classification techniques can be used to map areas on the document, where different types of ink had been applied or where one ink shows various degrees of degradation. The developed quantitative hyperspectral imager is currently in use by the Nationaal Archief (National Archives of The Netherlands) to study degradation effects of artificial samples and original documents, exposed in their permanent exhibition area or stored in their deposit rooms. PMID:27873831

Quantitative imaging biomarker ontology (QIBO) for knowledge representation of biomedical imaging biomarkers.

PubMed

Buckler, Andrew J; Liu, Tiffany Ting; Savig, Erica; Suzek, Baris E; Ouellette, M; Danagoulian, J; Wernsing, G; Rubin, Daniel L; Paik, David

2013-08-01

A widening array of novel imaging biomarkers is being developed using ever more powerful clinical and preclinical imaging modalities. These biomarkers have demonstrated effectiveness in quantifying biological processes as they occur in vivo and in the early prediction of therapeutic outcomes. However, quantitative imaging biomarker data and knowledge are not standardized, representing a critical barrier to accumulating medical knowledge based on quantitative imaging data. We use an ontology to represent, integrate, and harmonize heterogeneous knowledge across the domain of imaging biomarkers. This advances the goal of developing applications to (1) improve precision and recall of storage and retrieval of quantitative imaging-related data using standardized terminology; (2) streamline the discovery and development of novel imaging biomarkers by normalizing knowledge across heterogeneous resources; (3) effectively annotate imaging experiments thus aiding comprehension, re-use, and reproducibility; and (4) provide validation frameworks through rigorous specification as a basis for testable hypotheses and compliance tests. We have developed the Quantitative Imaging Biomarker Ontology (QIBO), which currently consists of 488 terms spanning the following upper classes: experimental subject, biological intervention, imaging agent, imaging instrument, image post-processing algorithm, biological target, indicated biology, and biomarker application. We have demonstrated that QIBO can be used to annotate imaging experiments with standardized terms in the ontology and to generate hypotheses for novel imaging biomarker-disease associations. Our results established the utility of QIBO in enabling integrated analysis of quantitative imaging data.

Image velocimetry and spectral analysis enable quantitative characterization of larval zebrafish gut motility.

PubMed

Ganz, J; Baker, R P; Hamilton, M K; Melancon, E; Diba, P; Eisen, J S; Parthasarathy, R

2018-05-02

Normal gut function requires rhythmic and coordinated movements that are affected by developmental processes, physical and chemical stimuli, and many debilitating diseases. The imaging and characterization of gut motility, especially regarding periodic, propagative contractions driving material transport, are therefore critical goals. Previous image analysis approaches have successfully extracted properties related to the temporal frequency of motility modes, but robust measures of contraction magnitude, especially from in vivo image data, remain challenging to obtain. We developed a new image analysis method based on image velocimetry and spectral analysis that reveals temporal characteristics such as frequency and wave propagation speed, while also providing quantitative measures of the amplitude of gut motion. We validate this approach using several challenges to larval zebrafish, imaged with differential interference contrast microscopy. Both acetylcholine exposure and feeding increase frequency and amplitude of motility. Larvae lacking enteric nervous system gut innervation show the same average motility frequency, but reduced and less variable amplitude compared to wild types. Our image analysis approach enables insights into gut dynamics in a wide variety of developmental and physiological contexts and can also be extended to analyze other types of cell movements. © 2018 John Wiley & Sons Ltd.

Anniversary Paper: History and status of CAD and quantitative image analysis: The role of Medical Physics and AAPM

PubMed Central

Giger, Maryellen L.; Chan, Heang-Ping; Boone, John

2008-01-01

algorithms using appropriate cases to measure performance and robustness; conducting observer studies with which to evaluate radiologists in the diagnostic task without and with the use of the computer aid; and ultimately assessing performance with a clinical trial. Medical physicists also have an important role in quantitative imaging, by validating the quantitative integrity of scanners and developing imaging techniques, and image analysis tools that extract quantitative data in a more accurate and automated fashion. As imaging systems become more complex and the need for better quantitative information from images grows, the future includes the combined research efforts from physicists working in CAD with those working on quantitative imaging systems to readily yield information on morphology, function, molecular structure, and more—from animal imaging research to clinical patient care. A historical review of CAD and a discussion of challenges for the future are presented here, along with the extension to quantitative image analysis. PMID:19175137

Anniversary Paper: History and status of CAD and quantitative image analysis: The role of Medical Physics and AAPM

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

Giger, Maryellen L.; Chan, Heang-Ping; Boone, John

2008-12-15

algorithms using appropriate cases to measure performance and robustness; conducting observer studies with which to evaluate radiologists in the diagnostic task without and with the use of the computer aid; and ultimately assessing performance with a clinical trial. Medical physicists also have an important role in quantitative imaging, by validating the quantitative integrity of scanners and developing imaging techniques, and image analysis tools that extract quantitative data in a more accurate and automated fashion. As imaging systems become more complex and the need for better quantitative information from images grows, the future includes the combined research efforts from physicists working in CAD with those working on quantitative imaging systems to readily yield information on morphology, function, molecular structure, and more--from animal imaging research to clinical patient care. A historical review of CAD and a discussion of challenges for the future are presented here, along with the extension to quantitative image analysis.« less

Evaluation of coronary stenosis with the aid of quantitative image analysis in histological cross sections.

PubMed

Dulohery, Kate; Papavdi, Asteria; Michalodimitrakis, Manolis; Kranioti, Elena F

2012-11-01

Coronary artery atherosclerosis is a hugely prevalent condition in the Western World and is often encountered during autopsy. Atherosclerotic plaques can cause luminal stenosis: which, if over a significant level (75%), is said to contribute to cause of death. Estimation of stenosis can be macroscopically performed by the forensic pathologists at the time of autopsy or by microscopic examination. This study compares macroscopic estimation with quantitative microscopic image analysis with a particular focus on the assessment of significant stenosis (>75%). A total of 131 individuals were analysed. The sample consists of an atherosclerotic group (n=122) and a control group (n=9). The results of the two methods were significantly different from each other (p=0.001) and the macroscopic method gave a greater percentage stenosis by an average of 3.5%. Also, histological examination of coronary artery stenosis yielded a difference in significant stenosis in 11.5% of cases. The differences were attributed to either histological quantitative image analysis underestimation; gross examination overestimation; or, a combination of both. The underestimation may have come from tissue shrinkage during tissue processing for histological specimen. The overestimation from the macroscopic assessment can be attributed to the lumen shape, to the examiner observer error or to a possible bias to diagnose coronary disease when no other cause of death is apparent. The results indicate that the macroscopic estimation is open to more biases and that histological quantitative image analysis only gives a precise assessment of stenosis ex vivo. Once tissue shrinkage, if any, is accounted for then histological quantitative image analysis will yield a more accurate assessment of in vivo stenosis. It may then be considered a complementary tool for the examination of coronary stenosis. Copyright © 2012 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.

An Improved Method for Measuring Quantitative Resistance to the Wheat Pathogen Zymoseptoria tritici Using High-Throughput Automated Image Analysis.

PubMed

Stewart, Ethan L; Hagerty, Christina H; Mikaberidze, Alexey; Mundt, Christopher C; Zhong, Ziming; McDonald, Bruce A

2016-07-01

Zymoseptoria tritici causes Septoria tritici blotch (STB) on wheat. An improved method of quantifying STB symptoms was developed based on automated analysis of diseased leaf images made using a flatbed scanner. Naturally infected leaves (n = 949) sampled from fungicide-treated field plots comprising 39 wheat cultivars grown in Switzerland and 9 recombinant inbred lines (RIL) grown in Oregon were included in these analyses. Measures of quantitative resistance were percent leaf area covered by lesions, pycnidia size and gray value, and pycnidia density per leaf and lesion. These measures were obtained automatically with a batch-processing macro utilizing the image-processing software ImageJ. All phenotypes in both locations showed a continuous distribution, as expected for a quantitative trait. The trait distributions at both sites were largely overlapping even though the field and host environments were quite different. Cultivars and RILs could be assigned to two or more statistically different groups for each measured phenotype. Traditional visual assessments of field resistance were highly correlated with quantitative resistance measures based on image analysis for the Oregon RILs. These results show that automated image analysis provides a promising tool for assessing quantitative resistance to Z. tritici under field conditions.

Quantitative analysis of geomorphic processes using satellite image data at different scales

NASA Technical Reports Server (NTRS)

Williams, R. S., Jr.

1985-01-01

When aerial and satellite photographs and images are used in the quantitative analysis of geomorphic processes, either through direct observation of active processes or by analysis of landforms resulting from inferred active or dormant processes, a number of limitations in the use of such data must be considered. Active geomorphic processes work at different scales and rates. Therefore, the capability of imaging an active or dormant process depends primarily on the scale of the process and the spatial-resolution characteristic of the imaging system. Scale is an important factor in recording continuous and discontinuous active geomorphic processes, because what is not recorded will not be considered or even suspected in the analysis of orbital images. If the geomorphic process of landform change caused by the process is less than 200 m in x to y dimension, then it will not be recorded. Although the scale factor is critical, in the recording of discontinuous active geomorphic processes, the repeat interval of orbital-image acquisition of a planetary surface also is a consideration in order to capture a recurring short-lived geomorphic process or to record changes caused by either a continuous or a discontinuous geomorphic process.

Quantitative fluorescence microscopy and image deconvolution.

PubMed

Swedlow, Jason R

2013-01-01

Quantitative imaging and image deconvolution have become standard techniques for the modern cell biologist because they can form the basis of an increasing number of assays for molecular function in a cellular context. There are two major types of deconvolution approaches--deblurring and restoration algorithms. Deblurring algorithms remove blur but treat a series of optical sections as individual two-dimensional entities and therefore sometimes mishandle blurred light. Restoration algorithms determine an object that, when convolved with the point-spread function of the microscope, could produce the image data. The advantages and disadvantages of these methods are discussed in this chapter. Image deconvolution in fluorescence microscopy has usually been applied to high-resolution imaging to improve contrast and thus detect small, dim objects that might otherwise be obscured. Their proper use demands some consideration of the imaging hardware, the acquisition process, fundamental aspects of photon detection, and image processing. This can prove daunting for some cell biologists, but the power of these techniques has been proven many times in the works cited in the chapter and elsewhere. Their usage is now well defined, so they can be incorporated into the capabilities of most laboratories. A major application of fluorescence microscopy is the quantitative measurement of the localization, dynamics, and interactions of cellular factors. The introduction of green fluorescent protein and its spectral variants has led to a significant increase in the use of fluorescence microscopy as a quantitative assay system. For quantitative imaging assays, it is critical to consider the nature of the image-acquisition system and to validate its response to known standards. Any image-processing algorithms used before quantitative analysis should preserve the relative signal levels in different parts of the image. A very common image-processing algorithm, image deconvolution, is used

Quantitative diagnosis of bladder cancer by morphometric analysis of HE images

NASA Astrophysics Data System (ADS)

Wu, Binlin; Nebylitsa, Samantha V.; Mukherjee, Sushmita; Jain, Manu

2015-02-01

In clinical practice, histopathological analysis of biopsied tissue is the main method for bladder cancer diagnosis and prognosis. The diagnosis is performed by a pathologist based on the morphological features in the image of a hematoxylin and eosin (HE) stained tissue sample. This manuscript proposes algorithms to perform morphometric analysis on the HE images, quantify the features in the images, and discriminate bladder cancers with different grades, i.e. high grade and low grade. The nuclei are separated from the background and other types of cells such as red blood cells (RBCs) and immune cells using manual outlining, color deconvolution and image segmentation. A mask of nuclei is generated for each image for quantitative morphometric analysis. The features of the nuclei in the mask image including size, shape, orientation, and their spatial distributions are measured. To quantify local clustering and alignment of nuclei, we propose a 1-nearest-neighbor (1-NN) algorithm which measures nearest neighbor distance and nearest neighbor parallelism. The global distributions of the features are measured using statistics of the proposed parameters. A linear support vector machine (SVM) algorithm is used to classify the high grade and low grade bladder cancers. The results show using a particular group of nuclei such as large ones, and combining multiple parameters can achieve better discrimination. This study shows the proposed approach can potentially help expedite pathological diagnosis by triaging potentially suspicious biopsies.

Putting tools in the toolbox: Development of a free, open-source toolbox for quantitative image analysis of porous media.

NASA Astrophysics Data System (ADS)

Iltis, G.; Caswell, T. A.; Dill, E.; Wilkins, S.; Lee, W. K.

2014-12-01

X-ray tomographic imaging of porous media has proven to be a valuable tool for investigating and characterizing the physical structure and state of both natural and synthetic porous materials, including glass bead packs, ceramics, soil and rock. Given that most synchrotron facilities have user programs which grant academic researchers access to facilities and x-ray imaging equipment free of charge, a key limitation or hindrance for small research groups interested in conducting x-ray imaging experiments is the financial cost associated with post-experiment data analysis. While the cost of high performance computing hardware continues to decrease, expenses associated with licensing commercial software packages for quantitative image analysis continue to increase, with current prices being as high as $24,000 USD, for a single user license. As construction of the Nation's newest synchrotron accelerator nears completion, a significant effort is being made here at the National Synchrotron Light Source II (NSLS-II), Brookhaven National Laboratory (BNL), to provide an open-source, experiment-to-publication toolbox that reduces the financial and technical 'activation energy' required for performing sophisticated quantitative analysis of multidimensional porous media data sets, collected using cutting-edge x-ray imaging techniques. Implementation focuses on leveraging existing open-source projects and developing additional tools for quantitative analysis. We will present an overview of the software suite that is in development here at BNL including major design decisions, a demonstration of several test cases illustrating currently available quantitative tools for analysis and characterization of multidimensional porous media image data sets and plans for their future development.

Tissue microarrays and quantitative tissue-based image analysis as a tool for oncology biomarker and diagnostic development.

PubMed

Dolled-Filhart, Marisa P; Gustavson, Mark D

2012-11-01

Translational oncology has been improved by using tissue microarrays (TMAs), which facilitate biomarker analysis of large cohorts on a single slide. This has allowed for rapid analysis and validation of potential biomarkers for prognostic and predictive value, as well as for evaluation of biomarker prevalence. Coupled with quantitative analysis of immunohistochemical (IHC) staining, objective and standardized biomarker data from tumor samples can further advance companion diagnostic approaches for the identification of drug-responsive or resistant patient subpopulations. This review covers the advantages, disadvantages and applications of TMAs for biomarker research. Research literature and reviews of TMAs and quantitative image analysis methodology have been surveyed for this review (with an AQUA® analysis focus). Applications such as multi-marker diagnostic development and pathway-based biomarker subpopulation analyses are described. Tissue microarrays are a useful tool for biomarker analyses including prevalence surveys, disease progression assessment and addressing potential prognostic or predictive value. By combining quantitative image analysis with TMAs, analyses will be more objective and reproducible, allowing for more robust IHC-based diagnostic test development. Quantitative multi-biomarker IHC diagnostic tests that can predict drug response will allow for greater success of clinical trials for targeted therapies and provide more personalized clinical decision making.

Mammographic quantitative image analysis and biologic image composition for breast lesion characterization and classification

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

Drukker, Karen, E-mail: kdrukker@uchicago.edu; Giger, Maryellen L.; Li, Hui

2014-03-15

Purpose: To investigate whether biologic image composition of mammographic lesions can improve upon existing mammographic quantitative image analysis (QIA) in estimating the probability of malignancy. Methods: The study population consisted of 45 breast lesions imaged with dual-energy mammography prior to breast biopsy with final diagnosis resulting in 10 invasive ductal carcinomas, 5 ductal carcinomain situ, 11 fibroadenomas, and 19 other benign diagnoses. Analysis was threefold: (1) The raw low-energy mammographic images were analyzed with an established in-house QIA method, “QIA alone,” (2) the three-compartment breast (3CB) composition measure—derived from the dual-energy mammography—of water, lipid, and protein thickness were assessed, “3CBmore » alone”, and (3) information from QIA and 3CB was combined, “QIA + 3CB.” Analysis was initiated from radiologist-indicated lesion centers and was otherwise fully automated. Steps of the QIA and 3CB methods were lesion segmentation, characterization, and subsequent classification for malignancy in leave-one-case-out cross-validation. Performance assessment included box plots, Bland–Altman plots, and Receiver Operating Characteristic (ROC) analysis. Results: The area under the ROC curve (AUC) for distinguishing between benign and malignant lesions (invasive and DCIS) was 0.81 (standard error 0.07) for the “QIA alone” method, 0.72 (0.07) for “3CB alone” method, and 0.86 (0.04) for “QIA+3CB” combined. The difference in AUC was 0.043 between “QIA + 3CB” and “QIA alone” but failed to reach statistical significance (95% confidence interval [–0.17 to + 0.26]). Conclusions: In this pilot study analyzing the new 3CB imaging modality, knowledge of the composition of breast lesions and their periphery appeared additive in combination with existing mammographic QIA methods for the distinction between different benign and malignant lesion types.« less

Quantitative image analysis of cellular heterogeneity in breast tumors complements genomic profiling.

PubMed

Yuan, Yinyin; Failmezger, Henrik; Rueda, Oscar M; Ali, H Raza; Gräf, Stefan; Chin, Suet-Feung; Schwarz, Roland F; Curtis, Christina; Dunning, Mark J; Bardwell, Helen; Johnson, Nicola; Doyle, Sarah; Turashvili, Gulisa; Provenzano, Elena; Aparicio, Sam; Caldas, Carlos; Markowetz, Florian

2012-10-24

Solid tumors are heterogeneous tissues composed of a mixture of cancer and normal cells, which complicates the interpretation of their molecular profiles. Furthermore, tissue architecture is generally not reflected in molecular assays, rendering this rich information underused. To address these challenges, we developed a computational approach based on standard hematoxylin and eosin-stained tissue sections and demonstrated its power in a discovery and validation cohort of 323 and 241 breast tumors, respectively. To deconvolute cellular heterogeneity and detect subtle genomic aberrations, we introduced an algorithm based on tumor cellularity to increase the comparability of copy number profiles between samples. We next devised a predictor for survival in estrogen receptor-negative breast cancer that integrated both image-based and gene expression analyses and significantly outperformed classifiers that use single data types, such as microarray expression signatures. Image processing also allowed us to describe and validate an independent prognostic factor based on quantitative analysis of spatial patterns between stromal cells, which are not detectable by molecular assays. Our quantitative, image-based method could benefit any large-scale cancer study by refining and complementing molecular assays of tumor samples.

Mechanical Model Analysis for Quantitative Evaluation of Liver Fibrosis Based on Ultrasound Tissue Elasticity Imaging

NASA Astrophysics Data System (ADS)

Shiina, Tsuyoshi; Maki, Tomonori; Yamakawa, Makoto; Mitake, Tsuyoshi; Kudo, Masatoshi; Fujimoto, Kenji

2012-07-01

Precise evaluation of the stage of chronic hepatitis C with respect to fibrosis has become an important issue to prevent the occurrence of cirrhosis and to initiate appropriate therapeutic intervention such as viral eradication using interferon. Ultrasound tissue elasticity imaging, i.e., elastography can visualize tissue hardness/softness, and its clinical usefulness has been studied to detect and evaluate tumors. We have recently reported that the texture of elasticity image changes as fibrosis progresses. To evaluate fibrosis progression quantitatively on the basis of ultrasound tissue elasticity imaging, we introduced a mechanical model of fibrosis progression and simulated the process by which hepatic fibrosis affects elasticity images and compared the results with those clinical data analysis. As a result, it was confirmed that even in diffuse diseases like chronic hepatitis, the patterns of elasticity images are related to fibrous structural changes caused by hepatic disease and can be used to derive features for quantitative evaluation of fibrosis stage.

Development of CD3 cell quantitation algorithms for renal allograft biopsy rejection assessment utilizing open source image analysis software.

PubMed

Moon, Andres; Smith, Geoffrey H; Kong, Jun; Rogers, Thomas E; Ellis, Carla L; Farris, Alton B Brad

2018-02-01

Renal allograft rejection diagnosis depends on assessment of parameters such as interstitial inflammation; however, studies have shown interobserver variability regarding interstitial inflammation assessment. Since automated image analysis quantitation can be reproducible, we devised customized analysis methods for CD3+ T-cell staining density as a measure of rejection severity and compared them with established commercial methods along with visual assessment. Renal biopsy CD3 immunohistochemistry slides (n = 45), including renal allografts with various degrees of acute cellular rejection (ACR) were scanned for whole slide images (WSIs). Inflammation was quantitated in the WSIs using pathologist visual assessment, commercial algorithms (Aperio nuclear algorithm for CD3+ cells/mm 2 and Aperio positive pixel count algorithm), and customized open source algorithms developed in ImageJ with thresholding/positive pixel counting (custom CD3+%) and identification of pixels fulfilling "maxima" criteria for CD3 expression (custom CD3+ cells/mm 2 ). Based on visual inspections of "markup" images, CD3 quantitation algorithms produced adequate accuracy. Additionally, CD3 quantitation algorithms correlated between each other and also with visual assessment in a statistically significant manner (r = 0.44 to 0.94, p = 0.003 to < 0.0001). Methods for assessing inflammation suggested a progression through the tubulointerstitial ACR grades, with statistically different results in borderline versus other ACR types, in all but the custom methods. Assessment of CD3-stained slides using various open source image analysis algorithms presents salient correlations with established methods of CD3 quantitation. These analysis techniques are promising and highly customizable, providing a form of on-slide "flow cytometry" that can facilitate additional diagnostic accuracy in tissue-based assessments.

Quantitative image analysis of WE43-T6 cracking behavior

NASA Astrophysics Data System (ADS)

Ahmad, A.; Yahya, Z.

2013-06-01

Environment-assisted cracking of WE43 cast magnesium (4.2 wt.% Yt, 2.3 wt.% Nd, 0.7% Zr, 0.8% HRE) in the T6 peak-aged condition was induced in ambient air in notched specimens. The mechanism of fracture was studied using electron backscatter diffraction, serial sectioning and in situ observations of crack propagation. The intermetallic (rare earthed-enriched divorced intermetallic retained at grain boundaries and predominantly at triple points) material was found to play a significant role in initiating cracks which leads to failure of this material. Quantitative measurements were required for this project. The populations of the intermetallic and clusters of intermetallic particles were analyzed using image analysis of metallographic images. This is part of the work to generate a theoretical model of the effect of notch geometry on the static fatigue strength of this material.

Quantitative image processing in fluid mechanics

NASA Technical Reports Server (NTRS)

Hesselink, Lambertus; Helman, James; Ning, Paul

1992-01-01

The current status of digital image processing in fluid flow research is reviewed. In particular, attention is given to a comprehensive approach to the extraction of quantitative data from multivariate databases and examples of recent developments. The discussion covers numerical simulations and experiments, data processing, generation and dissemination of knowledge, traditional image processing, hybrid processing, fluid flow vector field topology, and isosurface analysis using Marching Cubes.

Quantitative analysis of brain magnetic resonance imaging for hepatic encephalopathy

NASA Astrophysics Data System (ADS)

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

1992-06-01

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

Hyperspectral Imaging and SPA-LDA Quantitative Analysis for Detection of Colon Cancer Tissue

NASA Astrophysics Data System (ADS)

Yuan, X.; Zhang, D.; Wang, Ch.; Dai, B.; Zhao, M.; Li, B.

2018-05-01

Hyperspectral imaging (HSI) has been demonstrated to provide a rapid, precise, and noninvasive method for cancer detection. However, because HSI contains many data, quantitative analysis is often necessary to distill information useful for distinguishing cancerous from normal tissue. To demonstrate that HSI with our proposed algorithm can make this distinction, we built a Vis-NIR HSI setup and made many spectral images of colon tissues, and then used a successive projection algorithm (SPA) to analyze the hyperspectral image data of the tissues. This was used to build an identification model based on linear discrimination analysis (LDA) using the relative reflectance values of the effective wavelengths. Other tissues were used as a prediction set to verify the reliability of the identification model. The results suggest that Vis-NIR hyperspectral images, together with the spectroscopic classification method, provide a new approach for reliable and safe diagnosis of colon cancer and could lead to advances in cancer diagnosis generally.

Quantitative imaging methods in osteoporosis.

PubMed

Oei, Ling; Koromani, Fjorda; Rivadeneira, Fernando; Zillikens, M Carola; Oei, Edwin H G

2016-12-01

Osteoporosis is characterized by a decreased bone mass and quality resulting in an increased fracture risk. Quantitative imaging methods are critical in the diagnosis and follow-up of treatment effects in osteoporosis. Prior radiographic vertebral fractures and bone mineral density (BMD) as a quantitative parameter derived from dual-energy X-ray absorptiometry (DXA) are among the strongest known predictors of future osteoporotic fractures. Therefore, current clinical decision making relies heavily on accurate assessment of these imaging features. Further, novel quantitative techniques are being developed to appraise additional characteristics of osteoporosis including three-dimensional bone architecture with quantitative computed tomography (QCT). Dedicated high-resolution (HR) CT equipment is available to enhance image quality. At the other end of the spectrum, by utilizing post-processing techniques such as the trabecular bone score (TBS) information on three-dimensional architecture can be derived from DXA images. Further developments in magnetic resonance imaging (MRI) seem promising to not only capture bone micro-architecture but also characterize processes at the molecular level. This review provides an overview of various quantitative imaging techniques based on different radiological modalities utilized in clinical osteoporosis care and research.

Quantitative analysis of histopathological findings using image processing software.

PubMed

Horai, Yasushi; Kakimoto, Tetsuhiro; Takemoto, Kana; Tanaka, Masaharu

2017-10-01

In evaluating pathological changes in drug efficacy and toxicity studies, morphometric analysis can be quite robust. In this experiment, we examined whether morphometric changes of major pathological findings in various tissue specimens stained with hematoxylin and eosin could be recognized and quantified using image processing software. Using Tissue Studio, hypertrophy of hepatocytes and adrenocortical cells could be quantified based on the method of a previous report, but the regions of red pulp, white pulp, and marginal zones in the spleen could not be recognized when using one setting condition. Using Image-Pro Plus, lipid-derived vacuoles in the liver and mucin-derived vacuoles in the intestinal mucosa could be quantified using two criteria (area and/or roundness). Vacuoles derived from phospholipid could not be quantified when small lipid deposition coexisted in the liver and adrenal cortex. Mononuclear inflammatory cell infiltration in the liver could be quantified to some extent, except for specimens with many clustered infiltrating cells. Adipocyte size and the mean linear intercept could be quantified easily and efficiently using morphological processing and the macro tool equipped in Image-Pro Plus. These methodologies are expected to form a base system that can recognize morphometric features and analyze quantitatively pathological findings through the use of information technology.

Cancer imaging phenomics toolkit: quantitative imaging analytics for precision diagnostics and predictive modeling of clinical outcome.

PubMed

Davatzikos, Christos; Rathore, Saima; Bakas, Spyridon; Pati, Sarthak; Bergman, Mark; Kalarot, Ratheesh; Sridharan, Patmaa; Gastounioti, Aimilia; Jahani, Nariman; Cohen, Eric; Akbari, Hamed; Tunc, Birkan; Doshi, Jimit; Parker, Drew; Hsieh, Michael; Sotiras, Aristeidis; Li, Hongming; Ou, Yangming; Doot, Robert K; Bilello, Michel; Fan, Yong; Shinohara, Russell T; Yushkevich, Paul; Verma, Ragini; Kontos, Despina

2018-01-01

The growth of multiparametric imaging protocols has paved the way for quantitative imaging phenotypes that predict treatment response and clinical outcome, reflect underlying cancer molecular characteristics and spatiotemporal heterogeneity, and can guide personalized treatment planning. This growth has underlined the need for efficient quantitative analytics to derive high-dimensional imaging signatures of diagnostic and predictive value in this emerging era of integrated precision diagnostics. This paper presents cancer imaging phenomics toolkit (CaPTk), a new and dynamically growing software platform for analysis of radiographic images of cancer, currently focusing on brain, breast, and lung cancer. CaPTk leverages the value of quantitative imaging analytics along with machine learning to derive phenotypic imaging signatures, based on two-level functionality. First, image analysis algorithms are used to extract comprehensive panels of diverse and complementary features, such as multiparametric intensity histogram distributions, texture, shape, kinetics, connectomics, and spatial patterns. At the second level, these quantitative imaging signatures are fed into multivariate machine learning models to produce diagnostic, prognostic, and predictive biomarkers. Results from clinical studies in three areas are shown: (i) computational neuro-oncology of brain gliomas for precision diagnostics, prediction of outcome, and treatment planning; (ii) prediction of treatment response for breast and lung cancer, and (iii) risk assessment for breast cancer.

Simulation of realistic abnormal SPECT brain perfusion images: application in semi-quantitative analysis

NASA Astrophysics Data System (ADS)

Ward, T.; Fleming, J. S.; Hoffmann, S. M. A.; Kemp, P. M.

2005-11-01

Simulation is useful in the validation of functional image analysis methods, particularly when considering the number of analysis techniques currently available lacking thorough validation. Problems exist with current simulation methods due to long run times or unrealistic results making it problematic to generate complete datasets. A method is presented for simulating known abnormalities within normal brain SPECT images using a measured point spread function (PSF), and incorporating a stereotactic atlas of the brain for anatomical positioning. This allows for the simulation of realistic images through the use of prior information regarding disease progression. SPECT images of cerebral perfusion have been generated consisting of a control database and a group of simulated abnormal subjects that are to be used in a UK audit of analysis methods. The abnormality is defined in the stereotactic space, then transformed to the individual subject space, convolved with a measured PSF and removed from the normal subject image. The dataset was analysed using SPM99 (Wellcome Department of Imaging Neuroscience, University College, London) and the MarsBaR volume of interest (VOI) analysis toolbox. The results were evaluated by comparison with the known ground truth. The analysis showed improvement when using a smoothing kernel equal to system resolution over the slightly larger kernel used routinely. Significant correlation was found between effective volume of a simulated abnormality and the detected size using SPM99. Improvements in VOI analysis sensitivity were found when using the region median over the region mean. The method and dataset provide an efficient methodology for use in the comparison and cross validation of semi-quantitative analysis methods in brain SPECT, and allow the optimization of analysis parameters.

Three modality image registration of brain SPECT/CT and MR images for quantitative analysis of dopamine transporter imaging

NASA Astrophysics Data System (ADS)

Yamaguchi, Yuzuho; Takeda, Yuta; Hara, Takeshi; Zhou, Xiangrong; Matsusako, Masaki; Tanaka, Yuki; Hosoya, Kazuhiko; Nihei, Tsutomu; Katafuchi, Tetsuro; Fujita, Hiroshi

2016-03-01

Important features in Parkinson's disease (PD) are degenerations and losses of dopamine neurons in corpus striatum. 123I-FP-CIT can visualize activities of the dopamine neurons. The activity radio of background to corpus striatum is used for diagnosis of PD and Dementia with Lewy Bodies (DLB). The specific activity can be observed in the corpus striatum on SPECT images, but the location and the shape of the corpus striatum on SPECT images only are often lost because of the low uptake. In contrast, MR images can visualize the locations of the corpus striatum. The purpose of this study was to realize a quantitative image analysis for the SPECT images by using image registration technique with brain MR images that can determine the region of corpus striatum. In this study, the image fusion technique was used to fuse SPECT and MR images by intervening CT image taken by SPECT/CT. The mutual information (MI) for image registration between CT and MR images was used for the registration. Six SPECT/CT and four MR scans of phantom materials are taken by changing the direction. As the results of the image registrations, 16 of 24 combinations were registered within 1.3mm. By applying the approach to 32 clinical SPECT/CT and MR cases, all of the cases were registered within 0.86mm. In conclusions, our registration method has a potential in superimposing MR images on SPECT images.

Quantitative Imaging in Cancer Evolution and Ecology

PubMed Central

Grove, Olya; Gillies, Robert J.

2013-01-01

Cancer therapy, even when highly targeted, typically fails because of the remarkable capacity of malignant cells to evolve effective adaptations. These evolutionary dynamics are both a cause and a consequence of cancer system heterogeneity at many scales, ranging from genetic properties of individual cells to large-scale imaging features. Tumors of the same organ and cell type can have remarkably diverse appearances in different patients. Furthermore, even within a single tumor, marked variations in imaging features, such as necrosis or contrast enhancement, are common. Similar spatial variations recently have been reported in genetic profiles. Radiologic heterogeneity within tumors is usually governed by variations in blood flow, whereas genetic heterogeneity is typically ascribed to random mutations. However, evolution within tumors, as in all living systems, is subject to Darwinian principles; thus, it is governed by predictable and reproducible interactions between environmental selection forces and cell phenotype (not genotype). This link between regional variations in environmental properties and cellular adaptive strategies may permit clinical imaging to be used to assess and monitor intratumoral evolution in individual patients. This approach is enabled by new methods that extract, report, and analyze quantitative, reproducible, and mineable clinical imaging data. However, most current quantitative metrics lack spatialness, expressing quantitative radiologic features as a single value for a region of interest encompassing the whole tumor. In contrast, spatially explicit image analysis recognizes that tumors are heterogeneous but not well mixed and defines regionally distinct habitats, some of which appear to harbor tumor populations that are more aggressive and less treatable than others. By identifying regional variations in key environmental selection forces and evidence of cellular adaptation, clinical imaging can enable us to define intratumoral

[Scanning electron microscope observation and image quantitative analysis of Hippocampi].

PubMed

Zhang, Z; Pu, Z; Xu, L; Xu, G; Wang, Q; Xu, G; Wu, L; Chen, J

1998-12-01

The "scale-like projects" on the derma of 3 species of Hippocampi, H. kuda Bleerer, H. trimaculatus Leach and H. japonicus Kaup were observed by scanning electron microscope (SEM). Results showed that some characteristics such us size, shape and type of arrangement of the "scale-like projects" can be considered as the evidence for microanalysis. Image quantitative analysis of the "scale-like project" was carried out on 45 pieces of photograph using area, long diameter, short diameter and shape factor as parameters. No difference among the different parts of the same species was observed, but significant differences were found among the above 3 species.

Applying quantitative benefit-risk analysis to aid regulatory decision making in diagnostic imaging: methods, challenges, and opportunities.

PubMed

Agapova, Maria; Devine, Emily Beth; Bresnahan, Brian W; Higashi, Mitchell K; Garrison, Louis P

2014-09-01

Health agencies making regulatory marketing-authorization decisions use qualitative and quantitative approaches to assess expected benefits and expected risks associated with medical interventions. There is, however, no universal standard approach that regulatory agencies consistently use to conduct benefit-risk assessment (BRA) for pharmaceuticals or medical devices, including for imaging technologies. Economics, health services research, and health outcomes research use quantitative approaches to elicit preferences of stakeholders, identify priorities, and model health conditions and health intervention effects. Challenges to BRA in medical devices are outlined, highlighting additional barriers in radiology. Three quantitative methods--multi-criteria decision analysis, health outcomes modeling and stated-choice survey--are assessed using criteria that are important in balancing benefits and risks of medical devices and imaging technologies. To be useful in regulatory BRA, quantitative methods need to: aggregate multiple benefits and risks, incorporate qualitative considerations, account for uncertainty, and make clear whose preferences/priorities are being used. Each quantitative method performs differently across these criteria and little is known about how BRA estimates and conclusions vary by approach. While no specific quantitative method is likely to be the strongest in all of the important areas, quantitative methods may have a place in BRA of medical devices and radiology. Quantitative BRA approaches have been more widely applied in medicines, with fewer BRAs in devices. Despite substantial differences in characteristics of pharmaceuticals and devices, BRA methods may be as applicable to medical devices and imaging technologies as they are to pharmaceuticals. Further research to guide the development and selection of quantitative BRA methods for medical devices and imaging technologies is needed. Copyright © 2014 AUR. Published by Elsevier Inc. All rights

A Computer-Aided Analysis Method of SPECT Brain Images for Quantitative Treatment Monitoring: Performance Evaluations and Clinical Applications.

PubMed

Zheng, Xiujuan; Wei, Wentao; Huang, Qiu; Song, Shaoli; Wan, Jieqing; Huang, Gang

2017-01-01

The objective and quantitative analysis of longitudinal single photon emission computed tomography (SPECT) images are significant for the treatment monitoring of brain disorders. Therefore, a computer aided analysis (CAA) method is introduced to extract a change-rate map (CRM) as a parametric image for quantifying the changes of regional cerebral blood flow (rCBF) in longitudinal SPECT brain images. The performances of the CAA-CRM approach in treatment monitoring are evaluated by the computer simulations and clinical applications. The results of computer simulations show that the derived CRMs have high similarities with their ground truths when the lesion size is larger than system spatial resolution and the change rate is higher than 20%. In clinical applications, the CAA-CRM approach is used to assess the treatment of 50 patients with brain ischemia. The results demonstrate that CAA-CRM approach has a 93.4% accuracy of recovered region's localization. Moreover, the quantitative indexes of recovered regions derived from CRM are all significantly different among the groups and highly correlated with the experienced clinical diagnosis. In conclusion, the proposed CAA-CRM approach provides a convenient solution to generate a parametric image and derive the quantitative indexes from the longitudinal SPECT brain images for treatment monitoring.

Quantitative Image Restoration in Bright Field Optical Microscopy.

PubMed

Gutiérrez-Medina, Braulio; Sánchez Miranda, Manuel de Jesús

2017-11-07

Bright field (BF) optical microscopy is regarded as a poor method to observe unstained biological samples due to intrinsic low image contrast. We introduce quantitative image restoration in bright field (QRBF), a digital image processing method that restores out-of-focus BF images of unstained cells. Our procedure is based on deconvolution, using a point spread function modeled from theory. By comparing with reference images of bacteria observed in fluorescence, we show that QRBF faithfully recovers shape and enables quantify size of individual cells, even from a single input image. We applied QRBF in a high-throughput image cytometer to assess shape changes in Escherichia coli during hyperosmotic shock, finding size heterogeneity. We demonstrate that QRBF is also applicable to eukaryotic cells (yeast). Altogether, digital restoration emerges as a straightforward alternative to methods designed to generate contrast in BF imaging for quantitative analysis. Copyright © 2017 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Quantitative imaging biomarkers: a review of statistical methods for technical performance assessment.

PubMed

Raunig, David L; McShane, Lisa M; Pennello, Gene; Gatsonis, Constantine; Carson, Paul L; Voyvodic, James T; Wahl, Richard L; Kurland, Brenda F; Schwarz, Adam J; Gönen, Mithat; Zahlmann, Gudrun; Kondratovich, Marina V; O'Donnell, Kevin; Petrick, Nicholas; Cole, Patricia E; Garra, Brian; Sullivan, Daniel C

2015-02-01

Technological developments and greater rigor in the quantitative measurement of biological features in medical images have given rise to an increased interest in using quantitative imaging biomarkers to measure changes in these features. Critical to the performance of a quantitative imaging biomarker in preclinical or clinical settings are three primary metrology areas of interest: measurement linearity and bias, repeatability, and the ability to consistently reproduce equivalent results when conditions change, as would be expected in any clinical trial. Unfortunately, performance studies to date differ greatly in designs, analysis method, and metrics used to assess a quantitative imaging biomarker for clinical use. It is therefore difficult or not possible to integrate results from different studies or to use reported results to design studies. The Radiological Society of North America and the Quantitative Imaging Biomarker Alliance with technical, radiological, and statistical experts developed a set of technical performance analysis methods, metrics, and study designs that provide terminology, metrics, and methods consistent with widely accepted metrological standards. This document provides a consistent framework for the conduct and evaluation of quantitative imaging biomarker performance studies so that results from multiple studies can be compared, contrasted, or combined. © The Author(s) 2014 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

Fundamentals of quantitative dynamic contrast-enhanced MR imaging.

PubMed

Paldino, Michael J; Barboriak, Daniel P

2009-05-01

Quantitative analysis of dynamic contrast-enhanced MR imaging (DCE-MR imaging) has the power to provide information regarding physiologic characteristics of the microvasculature and is, therefore, of great potential value to the practice of oncology. In particular, these techniques could have a significant impact on the development of novel anticancer therapies as a promising biomarker of drug activity. Standardization of DCE-MR imaging acquisition and analysis to provide more reproducible measures of tumor vessel physiology is of crucial importance to realize this potential. The purpose of this article is to review the pathophysiologic basis and technical aspects of DCE-MR imaging techniques.

Analysis of vaginal microbicide film hydration kinetics by quantitative imaging refractometry.

PubMed

Rinehart, Matthew; Grab, Sheila; Rohan, Lisa; Katz, David; Wax, Adam

2014-01-01

We have developed a quantitative imaging refractometry technique, based on holographic phase microscopy, as a tool for investigating microscopic structural changes in water-soluble polymeric materials. Here we apply the approach to analyze the structural degradation of vaginal topical microbicide films due to water uptake. We implemented transmission imaging of 1-mm diameter film samples loaded into a flow chamber with a 1.5×2 mm field of view. After water was flooded into the chamber, interference images were captured and analyzed to obtain high resolution maps of the local refractive index and subsequently the volume fraction and mass density of film material at each spatial location. Here, we compare the hydration dynamics of a panel of films with varying thicknesses and polymer compositions, demonstrating that quantitative imaging refractometry can be an effective tool for evaluating and characterizing the performance of candidate microbicide film designs for anti-HIV drug delivery.

Differentiation of Glioblastoma from Brain Metastasis: Qualitative and Quantitative Analysis Using Arterial Spin Labeling MR Imaging.

PubMed

Sunwoo, Leonard; Yun, Tae Jin; You, Sung-Hye; Yoo, Roh-Eul; Kang, Koung Mi; Choi, Seung Hong; Kim, Ji-Hoon; Sohn, Chul-Ho; Park, Sun-Won; Jung, Cheolkyu; Park, Chul-Kee

2016-01-01

To evaluate the diagnostic performance of cerebral blood flow (CBF) by using arterial spin labeling (ASL) perfusion magnetic resonance (MR) imaging to differentiate glioblastoma (GBM) from brain metastasis. The institutional review board of our hospital approved this retrospective study. The study population consisted of 128 consecutive patients who underwent surgical resection and were diagnosed as either GBM (n = 89) or brain metastasis (n = 39). All participants underwent preoperative MR imaging including ASL. For qualitative analysis, the tumors were visually graded into five categories based on ASL-CBF maps by two blinded reviewers. For quantitative analysis, the reviewers drew regions of interest (ROIs) on ASL-CBF maps upon the most hyperperfused portion within the tumor and upon peritumoral T2 hyperintensity area. Signal intensities of intratumoral and peritumoral ROIs for each subject were normalized by dividing the values by those of contralateral normal gray matter (nCBFintratumoral and nCBFperitumoral, respectively). Visual grading scales and quantitative parameters between GBM and brain metastasis were compared. In addition, the area under the receiver-operating characteristic curve was used to evaluate the diagnostic performance of ASL-driven CBF to differentiate GBM from brain metastasis. For qualitative analysis, GBM group showed significantly higher grade compared to metastasis group (p = 0.001). For quantitative analysis, both nCBFintratumoral and nCBFperitumoral in GBM were significantly higher than those in metastasis (both p < 0.001). The areas under the curve were 0.677, 0.714, and 0.835 for visual grading, nCBFintratumoral, and nCBFperitumoral, respectively (all p < 0.001). ASL perfusion MR imaging can aid in the differentiation of GBM from brain metastasis.

Analysis of Vaginal Microbicide Film Hydration Kinetics by Quantitative Imaging Refractometry

PubMed Central

Rinehart, Matthew; Grab, Sheila; Rohan, Lisa; Katz, David; Wax, Adam

2014-01-01

We have developed a quantitative imaging refractometry technique, based on holographic phase microscopy, as a tool for investigating microscopic structural changes in water-soluble polymeric materials. Here we apply the approach to analyze the structural degradation of vaginal topical microbicide films due to water uptake. We implemented transmission imaging of 1-mm diameter film samples loaded into a flow chamber with a 1.5×2 mm field of view. After water was flooded into the chamber, interference images were captured and analyzed to obtain high resolution maps of the local refractive index and subsequently the volume fraction and mass density of film material at each spatial location. Here, we compare the hydration dynamics of a panel of films with varying thicknesses and polymer compositions, demonstrating that quantitative imaging refractometry can be an effective tool for evaluating and characterizing the performance of candidate microbicide film designs for anti-HIV drug delivery. PMID:24736376

Dependence of quantitative accuracy of CT perfusion imaging on system parameters

NASA Astrophysics Data System (ADS)

Li, Ke; Chen, Guang-Hong

2017-03-01

Deconvolution is a popular method to calculate parametric perfusion parameters from four dimensional CT perfusion (CTP) source images. During the deconvolution process, the four dimensional space is squeezed into three-dimensional space by removing the temporal dimension, and a prior knowledge is often used to suppress noise associated with the process. These additional complexities confound the understanding about deconvolution-based CTP imaging system and how its quantitative accuracy depends on parameters and sub-operations involved in the image formation process. Meanwhile, there has been a strong clinical need in answering this question, as physicians often rely heavily on the quantitative values of perfusion parameters to make diagnostic decisions, particularly during an emergent clinical situation (e.g. diagnosis of acute ischemic stroke). The purpose of this work was to develop a theoretical framework that quantitatively relates the quantification accuracy of parametric perfusion parameters with CTP acquisition and post-processing parameters. This goal was achieved with the help of a cascaded systems analysis for deconvolution-based CTP imaging systems. Based on the cascaded systems analysis, the quantitative relationship between regularization strength, source image noise, arterial input function, and the quantification accuracy of perfusion parameters was established. The theory could potentially be used to guide developments of CTP imaging technology for better quantification accuracy and lower radiation dose.

Quantitative imaging of protein targets in the human brain with PET

NASA Astrophysics Data System (ADS)

Gunn, Roger N.; Slifstein, Mark; Searle, Graham E.; Price, Julie C.

2015-11-01

PET imaging of proteins in the human brain with high affinity radiolabelled molecules has a history stretching back over 30 years. During this period the portfolio of protein targets that can be imaged has increased significantly through successes in radioligand discovery and development. This portfolio now spans six major categories of proteins; G-protein coupled receptors, membrane transporters, ligand gated ion channels, enzymes, misfolded proteins and tryptophan-rich sensory proteins. In parallel to these achievements in radiochemical sciences there have also been significant advances in the quantitative analysis and interpretation of the imaging data including the development of methods for image registration, image segmentation, tracer compartmental modeling, reference tissue kinetic analysis and partial volume correction. In this review, we analyze the activity of the field around each of the protein targets in order to give a perspective on the historical focus and the possible future trajectory of the field. The important neurobiology and pharmacology is introduced for each of the six protein classes and we present established radioligands for each that have successfully transitioned to quantitative imaging in humans. We present a standard quantitative analysis workflow for these radioligands which takes the dynamic PET data, associated blood and anatomical MRI data as the inputs to a series of image processing and bio-mathematical modeling steps before outputting the outcome measure of interest on either a regional or parametric image basis. The quantitative outcome measures are then used in a range of different imaging studies including tracer discovery and development studies, cross sectional studies, classification studies, intervention studies and longitudinal studies. Finally we consider some of the confounds, challenges and subtleties that arise in practice when trying to quantify and interpret PET neuroimaging data including motion artifacts

Image registration and analysis for quantitative myocardial perfusion: application to dynamic circular cardiac CT.

PubMed

Isola, A A; Schmitt, H; van Stevendaal, U; Begemann, P G; Coulon, P; Boussel, L; Grass, M

2011-09-21

Large area detector computed tomography systems with fast rotating gantries enable volumetric dynamic cardiac perfusion studies. Prospectively, ECG-triggered acquisitions limit the data acquisition to a predefined cardiac phase and thereby reduce x-ray dose and limit motion artefacts. Even in the case of highly accurate prospective triggering and stable heart rate, spatial misalignment of the cardiac volumes acquired and reconstructed per cardiac cycle may occur due to small motion pattern variations from cycle to cycle. These misalignments reduce the accuracy of the quantitative analysis of myocardial perfusion parameters on a per voxel basis. An image-based solution to this problem is elastic 3D image registration of dynamic volume sequences with variable contrast, as it is introduced in this contribution. After circular cone-beam CT reconstruction of cardiac volumes covering large areas of the myocardial tissue, the complete series is aligned with respect to a chosen reference volume. The results of the registration process and the perfusion analysis with and without registration are evaluated quantitatively in this paper. The spatial alignment leads to improved quantification of myocardial perfusion for three different pig data sets.

Methods in quantitative image analysis.

PubMed

Oberholzer, M; Ostreicher, M; Christen, H; Brühlmann, M

1996-05-01

The main steps of image analysis are image capturing, image storage (compression), correcting imaging defects (e.g. non-uniform illumination, electronic-noise, glare effect), image enhancement, segmentation of objects in the image and image measurements. Digitisation is made by a camera. The most modern types include a frame-grabber, converting the analog-to-digital signal into digital (numerical) information. The numerical information consists of the grey values describing the brightness of every point within the image, named a pixel. The information is stored in bits. Eight bits are summarised in one byte. Therefore, grey values can have a value between 0 and 256 (2(8)). The human eye seems to be quite content with a display of 5-bit images (corresponding to 64 different grey values). In a digitised image, the pixel grey values can vary within regions that are uniform in the original scene: the image is noisy. The noise is mainly manifested in the background of the image. For an optimal discrimination between different objects or features in an image, uniformity of illumination in the whole image is required. These defects can be minimised by shading correction [subtraction of a background (white) image from the original image, pixel per pixel, or division of the original image by the background image]. The brightness of an image represented by its grey values can be analysed for every single pixel or for a group of pixels. The most frequently used pixel-based image descriptors are optical density, integrated optical density, the histogram of the grey values, mean grey value and entropy. The distribution of the grey values existing within an image is one of the most important characteristics of the image. However, the histogram gives no information about the texture of the image. The simplest way to improve the contrast of an image is to expand the brightness scale by spreading the histogram out to the full available range. Rules for transforming the grey value

A method for rapid quantitative assessment of biofilms with biomolecular staining and image analysis

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

Larimer, Curtis J.; Winder, Eric M.; Jeters, Robert T.

Here, the accumulation of bacteria in surface attached biofilms, or biofouling, can be detrimental to human health, dental hygiene, and many industrial processes. A critical need in identifying and preventing the deleterious effects of biofilms is the ability to observe and quantify their development. Analytical methods capable of assessing early stage fouling are cumbersome or lab-confined, subjective, and qualitative. Herein, a novel photographic method is described that uses biomolecular staining and image analysis to enhance contrast of early stage biofouling. A robust algorithm was developed to objectively and quantitatively measure surface accumulation of Pseudomonas putida from photographs and results weremore » compared to independent measurements of cell density. Results from image analysis quantified biofilm growth intensity accurately and with approximately the same precision of the more laborious cell counting method. This simple method for early stage biofilm detection enables quantifiable measurement of surface fouling and is flexible enough to be applied from the laboratory to the field. Broad spectrum staining highlights fouling biomass, photography quickly captures a large area of interest, and image analysis rapidly quantifies fouling in the image.« less

A method for rapid quantitative assessment of biofilms with biomolecular staining and image analysis

DOE PAGES

Larimer, Curtis J.; Winder, Eric M.; Jeters, Robert T.; ...

2015-12-07

Here, the accumulation of bacteria in surface attached biofilms, or biofouling, can be detrimental to human health, dental hygiene, and many industrial processes. A critical need in identifying and preventing the deleterious effects of biofilms is the ability to observe and quantify their development. Analytical methods capable of assessing early stage fouling are cumbersome or lab-confined, subjective, and qualitative. Herein, a novel photographic method is described that uses biomolecular staining and image analysis to enhance contrast of early stage biofouling. A robust algorithm was developed to objectively and quantitatively measure surface accumulation of Pseudomonas putida from photographs and results weremore » compared to independent measurements of cell density. Results from image analysis quantified biofilm growth intensity accurately and with approximately the same precision of the more laborious cell counting method. This simple method for early stage biofilm detection enables quantifiable measurement of surface fouling and is flexible enough to be applied from the laboratory to the field. Broad spectrum staining highlights fouling biomass, photography quickly captures a large area of interest, and image analysis rapidly quantifies fouling in the image.« less

Automated image analysis for quantitative fluorescence in situ hybridization with environmental samples.

PubMed

Zhou, Zhi; Pons, Marie Noëlle; Raskin, Lutgarde; Zilles, Julie L

2007-05-01

When fluorescence in situ hybridization (FISH) analyses are performed with complex environmental samples, difficulties related to the presence of microbial cell aggregates and nonuniform background fluorescence are often encountered. The objective of this study was to develop a robust and automated quantitative FISH method for complex environmental samples, such as manure and soil. The method and duration of sample dispersion were optimized to reduce the interference of cell aggregates. An automated image analysis program that detects cells from 4',6'-diamidino-2-phenylindole (DAPI) micrographs and extracts the maximum and mean fluorescence intensities for each cell from corresponding FISH images was developed with the software Visilog. Intensity thresholds were not consistent even for duplicate analyses, so alternative ways of classifying signals were investigated. In the resulting method, the intensity data were divided into clusters using fuzzy c-means clustering, and the resulting clusters were classified as target (positive) or nontarget (negative). A manual quality control confirmed this classification. With this method, 50.4, 72.1, and 64.9% of the cells in two swine manure samples and one soil sample, respectively, were positive as determined with a 16S rRNA-targeted bacterial probe (S-D-Bact-0338-a-A-18). Manual counting resulted in corresponding values of 52.3, 70.6, and 61.5%, respectively. In two swine manure samples and one soil sample 21.6, 12.3, and 2.5% of the cells were positive with an archaeal probe (S-D-Arch-0915-a-A-20), respectively. Manual counting resulted in corresponding values of 22.4, 14.0, and 2.9%, respectively. This automated method should facilitate quantitative analysis of FISH images for a variety of complex environmental samples.

Segmentation of fluorescence microscopy images for quantitative analysis of cell nuclear architecture.

PubMed

Russell, Richard A; Adams, Niall M; Stephens, David A; Batty, Elizabeth; Jensen, Kirsten; Freemont, Paul S

2009-04-22

Considerable advances in microscopy, biophysics, and cell biology have provided a wealth of imaging data describing the functional organization of the cell nucleus. Until recently, cell nuclear architecture has largely been assessed by subjective visual inspection of fluorescently labeled components imaged by the optical microscope. This approach is inadequate to fully quantify spatial associations, especially when the patterns are indistinct, irregular, or highly punctate. Accurate image processing techniques as well as statistical and computational tools are thus necessary to interpret this data if meaningful spatial-function relationships are to be established. Here, we have developed a thresholding algorithm, stable count thresholding (SCT), to segment nuclear compartments in confocal laser scanning microscopy image stacks to facilitate objective and quantitative analysis of the three-dimensional organization of these objects using formal statistical methods. We validate the efficacy and performance of the SCT algorithm using real images of immunofluorescently stained nuclear compartments and fluorescent beads as well as simulated images. In all three cases, the SCT algorithm delivers a segmentation that is far better than standard thresholding methods, and more importantly, is comparable to manual thresholding results. By applying the SCT algorithm and statistical analysis, we quantify the spatial configuration of promyelocytic leukemia nuclear bodies with respect to irregular-shaped SC35 domains. We show that the compartments are closer than expected under a null model for their spatial point distribution, and furthermore that their spatial association varies according to cell state. The methods reported are general and can readily be applied to quantify the spatial interactions of other nuclear compartments.

Segmentation of Fluorescence Microscopy Images for Quantitative Analysis of Cell Nuclear Architecture

PubMed Central

Russell, Richard A.; Adams, Niall M.; Stephens, David A.; Batty, Elizabeth; Jensen, Kirsten; Freemont, Paul S.

2009-01-01

Abstract Considerable advances in microscopy, biophysics, and cell biology have provided a wealth of imaging data describing the functional organization of the cell nucleus. Until recently, cell nuclear architecture has largely been assessed by subjective visual inspection of fluorescently labeled components imaged by the optical microscope. This approach is inadequate to fully quantify spatial associations, especially when the patterns are indistinct, irregular, or highly punctate. Accurate image processing techniques as well as statistical and computational tools are thus necessary to interpret this data if meaningful spatial-function relationships are to be established. Here, we have developed a thresholding algorithm, stable count thresholding (SCT), to segment nuclear compartments in confocal laser scanning microscopy image stacks to facilitate objective and quantitative analysis of the three-dimensional organization of these objects using formal statistical methods. We validate the efficacy and performance of the SCT algorithm using real images of immunofluorescently stained nuclear compartments and fluorescent beads as well as simulated images. In all three cases, the SCT algorithm delivers a segmentation that is far better than standard thresholding methods, and more importantly, is comparable to manual thresholding results. By applying the SCT algorithm and statistical analysis, we quantify the spatial configuration of promyelocytic leukemia nuclear bodies with respect to irregular-shaped SC35 domains. We show that the compartments are closer than expected under a null model for their spatial point distribution, and furthermore that their spatial association varies according to cell state. The methods reported are general and can readily be applied to quantify the spatial interactions of other nuclear compartments. PMID:19383481

Label-free cell-cycle analysis by high-throughput quantitative phase time-stretch imaging flow cytometry

NASA Astrophysics Data System (ADS)

Mok, Aaron T. Y.; Lee, Kelvin C. M.; Wong, Kenneth K. Y.; Tsia, Kevin K.

2018-02-01

Biophysical properties of cells could complement and correlate biochemical markers to characterize a multitude of cellular states. Changes in cell size, dry mass and subcellular morphology, for instance, are relevant to cell-cycle progression which is prevalently evaluated by DNA-targeted fluorescence measurements. Quantitative-phase microscopy (QPM) is among the effective biophysical phenotyping tools that can quantify cell sizes and sub-cellular dry mass density distribution of single cells at high spatial resolution. However, limited camera frame rate and thus imaging throughput makes QPM incompatible with high-throughput flow cytometry - a gold standard in multiparametric cell-based assay. Here we present a high-throughput approach for label-free analysis of cell cycle based on quantitative-phase time-stretch imaging flow cytometry at a throughput of > 10,000 cells/s. Our time-stretch QPM system enables sub-cellular resolution even at high speed, allowing us to extract a multitude (at least 24) of single-cell biophysical phenotypes (from both amplitude and phase images). Those phenotypes can be combined to track cell-cycle progression based on a t-distributed stochastic neighbor embedding (t-SNE) algorithm. Using multivariate analysis of variance (MANOVA) discriminant analysis, cell-cycle phases can also be predicted label-free with high accuracy at >90% in G1 and G2 phase, and >80% in S phase. We anticipate that high throughput label-free cell cycle characterization could open new approaches for large-scale single-cell analysis, bringing new mechanistic insights into complex biological processes including diseases pathogenesis.

Infrared thermography quantitative image processing

NASA Astrophysics Data System (ADS)

Skouroliakou, A.; Kalatzis, I.; Kalyvas, N.; Grivas, TB

2017-11-01

Infrared thermography is an imaging technique that has the ability to provide a map of temperature distribution of an object’s surface. It is considered for a wide range of applications in medicine as well as in non-destructive testing procedures. One of its promising medical applications is in orthopaedics and diseases of the musculoskeletal system where temperature distribution of the body’s surface can contribute to the diagnosis and follow up of certain disorders. Although the thermographic image can give a fairly good visual estimation of distribution homogeneity and temperature pattern differences between two symmetric body parts, it is important to extract a quantitative measurement characterising temperature. Certain approaches use temperature of enantiomorphic anatomical points, or parameters extracted from a Region of Interest (ROI). A number of indices have been developed by researchers to that end. In this study a quantitative approach in thermographic image processing is attempted based on extracting different indices for symmetric ROIs on thermograms of the lower back area of scoliotic patients. The indices are based on first order statistical parameters describing temperature distribution. Analysis and comparison of these indices result in evaluating the temperature distribution pattern of the back trunk expected in healthy, regarding spinal problems, subjects.

Automatic quantitative analysis of in-stent restenosis using FD-OCT in vivo intra-arterial imaging.

PubMed

Mandelias, Kostas; Tsantis, Stavros; Spiliopoulos, Stavros; Katsakiori, Paraskevi F; Karnabatidis, Dimitris; Nikiforidis, George C; Kagadis, George C

2013-06-01

A new segmentation technique is implemented for automatic lumen area extraction and stent strut detection in intravascular optical coherence tomography (OCT) images for the purpose of quantitative analysis of in-stent restenosis (ISR). In addition, a user-friendly graphical user interface (GUI) is developed based on the employed algorithm toward clinical use. Four clinical datasets of frequency-domain OCT scans of the human femoral artery were analyzed. First, a segmentation method based on fuzzy C means (FCM) clustering and wavelet transform (WT) was applied toward inner luminal contour extraction. Subsequently, stent strut positions were detected by utilizing metrics derived from the local maxima of the wavelet transform into the FCM membership function. The inner lumen contour and the position of stent strut were extracted with high precision. Compared to manual segmentation by an expert physician, the automatic lumen contour delineation had an average overlap value of 0.917 ± 0.065 for all OCT images included in the study. The strut detection procedure achieved an overall accuracy of 93.80% and successfully identified 9.57 ± 0.5 struts for every OCT image. Processing time was confined to approximately 2.5 s per OCT frame. A new fast and robust automatic segmentation technique combining FCM and WT for lumen border extraction and strut detection in intravascular OCT images was designed and implemented. The proposed algorithm integrated in a GUI represents a step forward toward the employment of automated quantitative analysis of ISR in clinical practice.

Quantitative Chemical Imaging and Unsupervised Analysis Using Hyperspectral Coherent Anti-Stokes Raman Scattering Microscopy

PubMed Central

2013-01-01

In this work, we report a method to acquire and analyze hyperspectral coherent anti-Stokes Raman scattering (CARS) microscopy images of organic materials and biological samples resulting in an unbiased quantitative chemical analysis. The method employs singular value decomposition on the square root of the CARS intensity, providing an automatic determination of the components above noise, which are retained. Complex CARS susceptibility spectra, which are linear in the chemical composition, are retrieved from the CARS intensity spectra using the causality of the susceptibility by two methods, and their performance is evaluated by comparison with Raman spectra. We use non-negative matrix factorization applied to the imaginary part and the nonresonant real part of the susceptibility with an additional concentration constraint to obtain absolute susceptibility spectra of independently varying chemical components and their absolute concentration. We demonstrate the ability of the method to provide quantitative chemical analysis on known lipid mixtures. We then show the relevance of the method by imaging lipid-rich stem-cell-derived mouse adipocytes as well as differentiated embryonic stem cells with a low density of lipids. We retrieve and visualize the most significant chemical components with spectra given by water, lipid, and proteins segmenting the image into the cell surrounding, lipid droplets, cytosol, and the nucleus, and we reveal the chemical structure of the cells, with details visualized by the projection of the chemical contrast into a few relevant channels. PMID:24099603

Informatics methods to enable sharing of quantitative imaging research data.

PubMed

Levy, Mia A; Freymann, John B; Kirby, Justin S; Fedorov, Andriy; Fennessy, Fiona M; Eschrich, Steven A; Berglund, Anders E; Fenstermacher, David A; Tan, Yongqiang; Guo, Xiaotao; Casavant, Thomas L; Brown, Bartley J; Braun, Terry A; Dekker, Andre; Roelofs, Erik; Mountz, James M; Boada, Fernando; Laymon, Charles; Oborski, Matt; Rubin, Daniel L

2012-11-01

The National Cancer Institute Quantitative Research Network (QIN) is a collaborative research network whose goal is to share data, algorithms and research tools to accelerate quantitative imaging research. A challenge is the variability in tools and analysis platforms used in quantitative imaging. Our goal was to understand the extent of this variation and to develop an approach to enable sharing data and to promote reuse of quantitative imaging data in the community. We performed a survey of the current tools in use by the QIN member sites for representation and storage of their QIN research data including images, image meta-data and clinical data. We identified existing systems and standards for data sharing and their gaps for the QIN use case. We then proposed a system architecture to enable data sharing and collaborative experimentation within the QIN. There are a variety of tools currently used by each QIN institution. We developed a general information system architecture to support the QIN goals. We also describe the remaining architecture gaps we are developing to enable members to share research images and image meta-data across the network. As a research network, the QIN will stimulate quantitative imaging research by pooling data, algorithms and research tools. However, there are gaps in current functional requirements that will need to be met by future informatics development. Special attention must be given to the technical requirements needed to translate these methods into the clinical research workflow to enable validation and qualification of these novel imaging biomarkers. Copyright © 2012 Elsevier Inc. All rights reserved.

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

Quantitative imaging of aggregated emulsions.

PubMed

Penfold, Robert; Watson, Andrew D; Mackie, Alan R; Hibberd, David J

2006-02-28

Noise reduction, restoration, and segmentation methods are developed for the quantitative structural analysis in three dimensions of aggregated oil-in-water emulsion systems imaged by fluorescence confocal laser scanning microscopy. Mindful of typical industrial formulations, the methods are demonstrated for concentrated (30% volume fraction) and polydisperse emulsions. Following a regularized deconvolution step using an analytic optical transfer function and appropriate binary thresholding, novel application of the Euclidean distance map provides effective discrimination of closely clustered emulsion droplets with size variation over at least 1 order of magnitude. The a priori assumption of spherical nonintersecting objects provides crucial information to combat the ill-posed inverse problem presented by locating individual particles. Position coordinates and size estimates are recovered with sufficient precision to permit quantitative study of static geometrical features. In particular, aggregate morphology is characterized by a novel void distribution measure based on the generalized Apollonius problem. This is also compared with conventional Voronoi/Delauney analysis.

Principles of Quantitative MR Imaging with Illustrated Review of Applicable Modular Pulse Diagrams.

PubMed

Mills, Andrew F; Sakai, Osamu; Anderson, Stephan W; Jara, Hernan

2017-01-01

Continued improvements in diagnostic accuracy using magnetic resonance (MR) imaging will require development of methods for tissue analysis that complement traditional qualitative MR imaging studies. Quantitative MR imaging is based on measurement and interpretation of tissue-specific parameters independent of experimental design, compared with qualitative MR imaging, which relies on interpretation of tissue contrast that results from experimental pulse sequence parameters. Quantitative MR imaging represents a natural next step in the evolution of MR imaging practice, since quantitative MR imaging data can be acquired using currently available qualitative imaging pulse sequences without modifications to imaging equipment. The article presents a review of the basic physical concepts used in MR imaging and how quantitative MR imaging is distinct from qualitative MR imaging. Subsequently, the article reviews the hierarchical organization of major applicable pulse sequences used in this article, with the sequences organized into conventional, hybrid, and multispectral sequences capable of calculating the main tissue parameters of T1, T2, and proton density. While this new concept offers the potential for improved diagnostic accuracy and workflow, awareness of this extension to qualitative imaging is generally low. This article reviews the basic physical concepts in MR imaging, describes commonly measured tissue parameters in quantitative MR imaging, and presents the major available pulse sequences used for quantitative MR imaging, with a focus on the hierarchical organization of these sequences. © RSNA, 2017.

Toward Quantitative Small Animal Pinhole SPECT: Assessment of Quantitation Accuracy Prior to Image Compensations

PubMed Central

Chen, Chia-Lin; Wang, Yuchuan; Lee, Jason J. S.; Tsui, Benjamin M. W.

2011-01-01

Purpose We assessed the quantitation accuracy of small animal pinhole single photon emission computed tomography (SPECT) under the current preclinical settings, where image compensations are not routinely applied. Procedures The effects of several common image-degrading factors and imaging parameters on quantitation accuracy were evaluated using Monte-Carlo simulation methods. Typical preclinical imaging configurations were modeled, and quantitative analyses were performed based on image reconstructions without compensating for attenuation, scatter, and limited system resolution. Results Using mouse-sized phantom studies as examples, attenuation effects alone degraded quantitation accuracy by up to −18% (Tc-99m or In-111) or −41% (I-125). The inclusion of scatter effects changed the above numbers to −12% (Tc-99m or In-111) and −21% (I-125), respectively, indicating the significance of scatter in quantitative I-125 imaging. Region-of-interest (ROI) definitions have greater impacts on regional quantitation accuracy for small sphere sources as compared to attenuation and scatter effects. For the same ROI, SPECT acquisitions using pinhole apertures of different sizes could significantly affect the outcome, whereas the use of different radii-of-rotation yielded negligible differences in quantitation accuracy for the imaging configurations simulated. Conclusions We have systematically quantified the influence of several factors affecting the quantitation accuracy of small animal pinhole SPECT. In order to consistently achieve accurate quantitation within 5% of the truth, comprehensive image compensation methods are needed. PMID:19048346

Quantitative fractography by digital image processing: NIH Image macro tools for stereo pair analysis and 3-D reconstruction.

PubMed

Hein, L R

2001-10-01

A set of NIH Image macro programs was developed to make qualitative and quantitative analyses from digital stereo pictures produced by scanning electron microscopes. These tools were designed for image alignment, anaglyph representation, animation, reconstruction of true elevation surfaces, reconstruction of elevation profiles, true-scale elevation mapping and, for the quantitative approach, surface area and roughness calculations. Limitations on time processing, scanning techniques and programming concepts are also discussed.

Comparison of longitudinal excursion of a nerve-phantom model using quantitative ultrasound imaging and motion analysis system methods: A convergent validity study.

PubMed

Paquette, Philippe; El Khamlichi, Youssef; Lamontagne, Martin; Higgins, Johanne; Gagnon, Dany H

2017-08-01

Quantitative ultrasound imaging is gaining popularity in research and clinical settings to measure the neuromechanical properties of the peripheral nerves such as their capability to glide in response to body segment movement. Increasing evidence suggests that impaired median nerve longitudinal excursion is associated with carpal tunnel syndrome. To date, psychometric properties of longitudinal nerve excursion measurements using quantitative ultrasound imaging have not been extensively investigated. This study investigates the convergent validity of the longitudinal nerve excursion by comparing measures obtained using quantitative ultrasound imaging with those determined with a motion analysis system. A 38-cm long rigid nerve-phantom model was used to assess the longitudinal excursion in a laboratory environment. The nerve-phantom model, immersed in a 20-cm deep container filled with a gelatin-based solution, was moved 20 times using a linear forward and backward motion. Three light-emitting diodes were used to record nerve-phantom excursion with a motion analysis system, while a 5-cm linear transducer allowed simultaneous recording via ultrasound imaging. Both measurement techniques yielded excellent association ( r  = 0.99) and agreement (mean absolute difference between methods = 0.85 mm; mean relative difference between methods = 7.48 %). Small discrepancies were largely found when larger excursions (i.e. > 10 mm) were performed, revealing slight underestimation of the excursion by the ultrasound imaging analysis software. Quantitative ultrasound imaging is an accurate method to assess the longitudinal excursion of an in vitro nerve-phantom model and appears relevant for future research protocols investigating the neuromechanical properties of the peripheral nerves.

Quantitative Analysis of Subcellular Distribution of the SUMO Conjugation System by Confocal Microscopy Imaging.

PubMed

Mas, Abraham; Amenós, Montse; Lois, L Maria

2016-01-01

Different studies point to an enrichment in SUMO conjugation in the cell nucleus, although non-nuclear SUMO targets also exist. In general, the study of subcellular localization of proteins is essential for understanding their function within a cell. Fluorescence microscopy is a powerful tool for studying subcellular protein partitioning in living cells, since fluorescent proteins can be fused to proteins of interest to determine their localization. Subcellular distribution of proteins can be influenced by binding to other biomolecules and by posttranslational modifications. Sometimes these changes affect only a portion of the protein pool or have a partial effect, and a quantitative evaluation of fluorescence images is required to identify protein redistribution among subcellular compartments. In order to obtain accurate data about the relative subcellular distribution of SUMO conjugation machinery members, and to identify the molecular determinants involved in their localization, we have applied quantitative confocal microscopy imaging. In this chapter, we will describe the fluorescent protein fusions used in these experiments, and how to measure, evaluate, and compare average fluorescence intensities in cellular compartments by image-based analysis. We show the distribution of some components of the Arabidopsis SUMOylation machinery in epidermal onion cells and how they change their distribution in the presence of interacting partners or even when its activity is affected.

Quantitative Imaging Biomarkers of NAFLD

PubMed Central

Kinner, Sonja; Reeder, Scott B.

2016-01-01

Conventional imaging modalities, including ultrasonography (US), computed tomography (CT), and magnetic resonance (MR), play an important role in the diagnosis and management of patients with nonalcoholic fatty liver disease (NAFLD) by allowing noninvasive diagnosis of hepatic steatosis. However, conventional imaging modalities are limited as biomarkers of NAFLD for various reasons. Multi-parametric quantitative MRI techniques overcome many of the shortcomings of conventional imaging and allow comprehensive and objective evaluation of NAFLD. MRI can provide unconfounded biomarkers of hepatic fat, iron, and fibrosis in a single examination—a virtual biopsy has become a clinical reality. In this article, we will review the utility and limitation of conventional US, CT, and MR imaging for the diagnosis NAFLD. Recent advances in imaging biomarkers of NAFLD are also discussed with an emphasis in multi-parametric quantitative MRI. PMID:26848588

Quantitative phase imaging of arthropods

PubMed Central

Sridharan, Shamira; Katz, Aron; Soto-Adames, Felipe; Popescu, Gabriel

2015-01-01

Abstract. Classification of arthropods is performed by characterization of fine features such as setae and cuticles. An unstained whole arthropod specimen mounted on a slide can be preserved for many decades, but is difficult to study since current methods require sample manipulation or tedious image processing. Spatial light interference microscopy (SLIM) is a quantitative phase imaging (QPI) technique that is an add-on module to a commercial phase contrast microscope. We use SLIM to image a whole organism springtail Ceratophysella denticulata mounted on a slide. This is the first time, to our knowledge, that an entire organism has been imaged using QPI. We also demonstrate the ability of SLIM to image fine structures in addition to providing quantitative data that cannot be obtained by traditional bright field microscopy. PMID:26334858

Analysis of airborne MAIS imaging spectrometric data for mineral exploration

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

Wang Jinnian; Zheng Lanfen; Tong Qingxi

1996-11-01

The high spectral resolution imaging spectrometric system made quantitative analysis and mapping of surface composition possible. The key issue will be the quantitative approach for analysis of surface parameters for imaging spectrometer data. This paper describes the methods and the stages of quantitative analysis. (1) Extracting surface reflectance from imaging spectrometer image. Lab. and inflight field measurements are conducted for calibration of imaging spectrometer data, and the atmospheric correction has also been used to obtain ground reflectance by using empirical line method and radiation transfer modeling. (2) Determining quantitative relationship between absorption band parameters from the imaging spectrometer data andmore » chemical composition of minerals. (3) Spectral comparison between the spectra of spectral library and the spectra derived from the imagery. The wavelet analysis-based spectrum-matching techniques for quantitative analysis of imaging spectrometer data has beer, developed. Airborne MAIS imaging spectrometer data were used for analysis and the analysis results have been applied to the mineral and petroleum exploration in Tarim Basin area china. 8 refs., 8 figs.« less

Qualitative and quantitative mass spectrometry imaging of drugs and metabolites

PubMed Central

Lietz, Christopher B.; Gemperline, Erin; Li, Lingjun

2013-01-01

Mass spectrometric imaging (MSI) has rapidly increased its presence in the pharmaceutical sciences. While quantitative whole-body autoradiography and microautoradiography are the traditional techniques for molecular imaging of drug delivery and metabolism, MSI provides advantageous specificity that can distinguish the parent drug from metabolites and modified endogenous molecules. This review begins with the fundamentals of MSI sample preparation/ionization, and then moves on to both qualitative and quantitative applications with special emphasis on drug discovery and delivery. Cutting-edge investigations on sub-cellular imaging and endogenous signaling peptides are also highlighted, followed by perspectives on emerging technology and the path for MSI to become a routine analysis technique. PMID:23603211

Performing Quantitative Imaging Acquisition, Analysis and Visualization Using the Best of Open Source and Commercial Software Solutions.

PubMed

Shenoy, Shailesh M

2016-07-01

A challenge in any imaging laboratory, especially one that uses modern techniques, is to achieve a sustainable and productive balance between using open source and commercial software to perform quantitative image acquisition, analysis and visualization. In addition to considering the expense of software licensing, one must consider factors such as the quality and usefulness of the software's support, training and documentation. Also, one must consider the reproducibility with which multiple people generate results using the same software to perform the same analysis, how one may distribute their methods to the community using the software and the potential for achieving automation to improve productivity.

Characterization of breast lesion using T1-perfusion magnetic resonance imaging: Qualitative vs. quantitative analysis.

PubMed

Thakran, S; Gupta, P K; Kabra, V; Saha, I; Jain, P; Gupta, R K; Singh, A

2018-06-14

The objective of this study was to quantify the hemodynamic parameters using first pass analysis of T 1 -perfusion magnetic resonance imaging (MRI) data of human breast and to compare these parameters with the existing tracer kinetic parameters, semi-quantitative and qualitative T 1 -perfusion analysis in terms of lesion characterization. MRI of the breast was performed in 50 women (mean age, 44±11 [SD] years; range: 26-75) years with a total of 15 benign and 35 malignant breast lesions. After pre-processing, T 1 -perfusion MRI data was analyzed using qualitative approach by two radiologists (visual inspection of the kinetic curve into types I, II or III), semi-quantitative (characterization of kinetic curve types using empirical parameters), generalized-tracer-kinetic-model (tracer kinetic parameters) and first pass analysis (hemodynamic-parameters). Chi-squared test, t-test, one-way analysis-of-variance (ANOVA) using Bonferroni post-hoc test and receiver-operating-characteristic (ROC) curve were used for statistical analysis. All quantitative parameters except leakage volume (Ve), qualitative (type-I and III) and semi-quantitative curves (type-I and III) provided significant differences (P<0.05) between benign and malignant lesions. Kinetic parameters, particularly volume transfer coefficient (K trans ) provided a significant difference (P<0.05) between all grades except grade-II vs III. The hemodynamic parameter (relative-leakage-corrected-breast-blood-volume [rBBVcorr) provided a statistically significant difference (P<0.05) between all grades. It also provided highest sensitivity and specificity among all parameters in differentiation between different grades of malignant breast lesions. Quantitative parameters, particularly rBBVcorr and K trans provided similar sensitivity and specificity in differentiating benign from malignant breast lesions for this cohort. Moreover, rBBVcorr provided better differentiation between different grades of malignant breast

A Quantitative Three-Dimensional Image Analysis Tool for Maximal Acquisition of Spatial Heterogeneity Data.

PubMed

Allenby, Mark C; Misener, Ruth; Panoskaltsis, Nicki; Mantalaris, Athanasios

2017-02-01

Three-dimensional (3D) imaging techniques provide spatial insight into environmental and cellular interactions and are implemented in various fields, including tissue engineering, but have been restricted by limited quantification tools that misrepresent or underutilize the cellular phenomena captured. This study develops image postprocessing algorithms pairing complex Euclidean metrics with Monte Carlo simulations to quantitatively assess cell and microenvironment spatial distributions while utilizing, for the first time, the entire 3D image captured. Although current methods only analyze a central fraction of presented confocal microscopy images, the proposed algorithms can utilize 210% more cells to calculate 3D spatial distributions that can span a 23-fold longer distance. These algorithms seek to leverage the high sample cost of 3D tissue imaging techniques by extracting maximal quantitative data throughout the captured image.

Quantitative Multispectral Analysis Of Discrete Subcellular Particles By Digital Imaging Fluorescence Microscopy (DIFM)

NASA Astrophysics Data System (ADS)

Dorey, C. K.; Ebenstein, David B.

1988-10-01

Subcellular localization of multiple biochemical markers is readily achieved through their characteristic autofluorescence or through use of appropriately labelled antibodies. Recent development of specific probes has permitted elegant studies in calcium and pH in living cells. However, each of these methods measured fluorescence at one wavelength; precise quantitation of multiple fluorophores at individual sites within a cell has not been possible. Using DIFM, we have achieved spectral analysis of discrete subcellular particles 1-2 gm in diameter. The fluorescence emission is broken into narrow bands by an interference monochromator and visualized through the combined use of a silicon intensified target (SIT) camera, a microcomputer based framegrabber with 8 bit resolution, and a color video monitor. Image acquisition, processing, analysis and display are under software control. The digitized image can be corrected for the spectral distortions induced by the wavelength dependent sensitivity of the camera, and the displayed image can be enhanced or presented in pseudocolor to facilitate discrimination of variation in pixel intensity of individual particles. For rapid comparison of the fluorophore composition of granules, a ratio image is produced by dividing the image captured at one wavelength by that captured at another. In the resultant ratio image, a granule which has a fluorophore composition different from the majority is selectively colored. This powerful system has been utilized to obtain spectra of endogenous autofluorescent compounds in discrete cellular organelles of human retinal pigment epithelium, and to measure immunohistochemically labelled components of the extracellular matrix associated with the human optic nerve.

Digital pathology and image analysis for robust high-throughput quantitative assessment of Alzheimer disease neuropathologic changes.

PubMed

Neltner, Janna Hackett; Abner, Erin Lynn; Schmitt, Frederick A; Denison, Stephanie Kay; Anderson, Sonya; Patel, Ela; Nelson, Peter T

2012-12-01

Quantitative neuropathologic methods provide information that is important for both research and clinical applications. The technologic advancement of digital pathology and image analysis offers new solutions to enable valid quantification of pathologic severity that is reproducible between raters regardless of experience. Using an Aperio ScanScope XT and its accompanying image analysis software, we designed algorithms for quantitation of amyloid and tau pathologies on 65 β-amyloid (6F/3D antibody) and 48 phospho-tau (PHF-1)-immunostained sections of human temporal neocortex. Quantitative digital pathologic data were compared with manual pathology counts. There were excellent correlations between manually counted and digitally analyzed neuropathologic parameters (R² = 0.56-0.72). Data were highly reproducible among 3 participants with varying degrees of expertise in neuropathology (intraclass correlation coefficient values, >0.910). Digital quantification also provided additional parameters, including average plaque area, which shows statistically significant differences when samples are stratified according to apolipoprotein E allele status (average plaque area, 380.9 μm² in apolipoprotein E [Latin Small Letter Open E]4 carriers vs 274.4 μm² for noncarriers; p < 0.001). Thus, digital pathology offers a rigorous and reproducible method for quantifying Alzheimer disease neuropathologic changes and may provide additional insights into morphologic characteristics that were previously more challenging to assess because of technical limitations.

Radiologic-Pathologic Analysis of Contrast-enhanced and Diffusion-weighted MR Imaging in Patients with HCC after TACE: Diagnostic Accuracy of 3D Quantitative Image Analysis

PubMed Central

Chapiro, Julius; Wood, Laura D.; Lin, MingDe; Duran, Rafael; Cornish, Toby; Lesage, David; Charu, Vivek; Schernthaner, Rüdiger; Wang, Zhijun; Tacher, Vania; Savic, Lynn Jeanette; Kamel, Ihab R.

2014-01-01

Purpose To evaluate the diagnostic performance of three-dimensional (3Dthree-dimensional) quantitative enhancement-based and diffusion-weighted volumetric magnetic resonance (MR) imaging assessment of hepatocellular carcinoma (HCChepatocellular carcinoma) lesions in determining the extent of pathologic tumor necrosis after transarterial chemoembolization (TACEtransarterial chemoembolization). Materials and Methods This institutional review board–approved retrospective study included 17 patients with HCChepatocellular carcinoma who underwent TACEtransarterial chemoembolization before surgery. Semiautomatic 3Dthree-dimensional volumetric segmentation of target lesions was performed at the last MR examination before orthotopic liver transplantation or surgical resection. The amount of necrotic tumor tissue on contrast material–enhanced arterial phase MR images and the amount of diffusion-restricted tumor tissue on apparent diffusion coefficient (ADCapparent diffusion coefficient) maps were expressed as a percentage of the total tumor volume. Visual assessment of the extent of tumor necrosis and tumor response according to European Association for the Study of the Liver (EASLEuropean Association for the Study of the Liver) criteria was performed. Pathologic tumor necrosis was quantified by using slide-by-slide segmentation. Correlation analysis was performed to evaluate the predictive values of the radiologic techniques. Results At histopathologic examination, the mean percentage of tumor necrosis was 70% (range, 10%–100%). Both 3Dthree-dimensional quantitative techniques demonstrated a strong correlation with tumor necrosis at pathologic examination (R2 = 0.9657 and R2 = 0.9662 for quantitative EASLEuropean Association for the Study of the Liver and quantitative ADCapparent diffusion coefficient, respectively) and a strong intermethod agreement (R2 = 0.9585). Both methods showed a significantly lower discrepancy with pathologically measured necrosis (residual

Retinal imaging and image analysis.

PubMed

Abràmoff, Michael D; Garvin, Mona K; Sonka, Milan

2010-01-01

Many important eye diseases as well as systemic diseases manifest themselves in the retina. While a number of other anatomical structures contribute to the process of vision, this review focuses on retinal imaging and image analysis. Following a brief overview of the most prevalent causes of blindness in the industrialized world that includes age-related macular degeneration, diabetic retinopathy, and glaucoma, the review is devoted to retinal imaging and image analysis methods and their clinical implications. Methods for 2-D fundus imaging and techniques for 3-D optical coherence tomography (OCT) imaging are reviewed. Special attention is given to quantitative techniques for analysis of fundus photographs with a focus on clinically relevant assessment of retinal vasculature, identification of retinal lesions, assessment of optic nerve head (ONH) shape, building retinal atlases, and to automated methods for population screening for retinal diseases. A separate section is devoted to 3-D analysis of OCT images, describing methods for segmentation and analysis of retinal layers, retinal vasculature, and 2-D/3-D detection of symptomatic exudate-associated derangements, as well as to OCT-based analysis of ONH morphology and shape. Throughout the paper, aspects of image acquisition, image analysis, and clinical relevance are treated together considering their mutually interlinked relationships.

Retinal Imaging and Image Analysis

PubMed Central

Abràmoff, Michael D.; Garvin, Mona K.; Sonka, Milan

2011-01-01

Many important eye diseases as well as systemic diseases manifest themselves in the retina. While a number of other anatomical structures contribute to the process of vision, this review focuses on retinal imaging and image analysis. Following a brief overview of the most prevalent causes of blindness in the industrialized world that includes age-related macular degeneration, diabetic retinopathy, and glaucoma, the review is devoted to retinal imaging and image analysis methods and their clinical implications. Methods for 2-D fundus imaging and techniques for 3-D optical coherence tomography (OCT) imaging are reviewed. Special attention is given to quantitative techniques for analysis of fundus photographs with a focus on clinically relevant assessment of retinal vasculature, identification of retinal lesions, assessment of optic nerve head (ONH) shape, building retinal atlases, and to automated methods for population screening for retinal diseases. A separate section is devoted to 3-D analysis of OCT images, describing methods for segmentation and analysis of retinal layers, retinal vasculature, and 2-D/3-D detection of symptomatic exudate-associated derangements, as well as to OCT-based analysis of ONH morphology and shape. Throughout the paper, aspects of image acquisition, image analysis, and clinical relevance are treated together considering their mutually interlinked relationships. PMID:22275207

Quantitative, spectrally-resolved intraoperative fluorescence imaging

PubMed Central

Valdés, Pablo A.; Leblond, Frederic; Jacobs, Valerie L.; Wilson, Brian C.; Paulsen, Keith D.; Roberts, David W.

2012-01-01

Intraoperative visual fluorescence imaging (vFI) has emerged as a promising aid to surgical guidance, but does not fully exploit the potential of the fluorescent agents that are currently available. Here, we introduce a quantitative fluorescence imaging (qFI) approach that converts spectrally-resolved data into images of absolute fluorophore concentration pixel-by-pixel across the surgical field of view (FOV). The resulting estimates are linear, accurate, and precise relative to true values, and spectral decomposition of multiple fluorophores is also achieved. Experiments with protoporphyrin IX in a glioma rodent model demonstrate in vivo quantitative and spectrally-resolved fluorescence imaging of infiltrating tumor margins for the first time. Moreover, we present images from human surgery which detect residual tumor not evident with state-of-the-art vFI. The wide-field qFI technique has broad implications for intraoperative surgical guidance because it provides near real-time quantitative assessment of multiple fluorescent biomarkers across the operative field. PMID:23152935

Quantitative TLC-Image Analysis of Urinary Creatinine Using Iodine Staining and RGB Values.

PubMed

Kerr, Emily; West, Caroline; Kradtap Hartwell, Supaporn

2016-04-01

Digital image analysis of the separation results of colorless analytes on thin-layer chromatography (TLC) plates usually involves using specially tailored software to analyze the images generated from either a UV scanner or UV lamp station with a digital camera or a densitometer. Here, a low-cost alternative setup for quantitative TLC-digital image analysis is demonstrated using a universal staining reagent (iodine vapor), an office scanner and a commonly available software (Microsoft Paint) for analysis of red, green and blue colors (RGB values). Urinary creatinine is used as a model analyte to represent a sample in complicated biological matrices. Separation was carried out on a silica gel plate using a butanol-NH4OH-H2O (40 : 10 : 50, v/v) mobile phase with a 6-cm solvent front. It is important that the TLC plate be stained evenly and with sufficient staining time. Staining the TLC plate in a 23.4 × 18.8 × 6.8 cm chamber containing about 70 g iodine crystals yielded comparable results for the staining times of 30-60 min. The Green value offered the best results in the linear working range (0.0810-0.9260 mg/mL) and precision (2.03% RSD, n = 10). The detection limit was found to be 0.24 µg per 3 µL spot. Urinary creatinine concentrations determined by TLC-digital image analysis using the green value calibration graph agree well with results obtained from high-pressure liquid chromatography (HPLC). © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Chromatic Image Analysis For Quantitative Thermal Mapping

NASA Technical Reports Server (NTRS)

Buck, Gregory M.

1995-01-01

Chromatic image analysis system (CIAS) developed for use in noncontact measurements of temperatures on aerothermodynamic models in hypersonic wind tunnels. Based on concept of temperature coupled to shift in color spectrum for optical measurement. Video camera images fluorescence emitted by phosphor-coated model at two wavelengths. Temperature map of model then computed from relative brightnesses in video images of model at those wavelengths. Eliminates need for intrusive, time-consuming, contact temperature measurements by gauges, making it possible to map temperatures on complex surfaces in timely manner and at reduced cost.

Quantitative Imaging in Cancer Clinical Trials

PubMed Central

Yankeelov, Thomas E.; Mankoff, David A.; Schwartz, Lawrence H.; Lieberman, Frank S.; Buatti, John M.; Mountz, James M.; Erickson, Bradley J.; Fennessy, Fiona M.M.; Huang, Wei; Kalpathy-Cramer, Jayashree; Wahl, Richard L.; Linden, Hannah M.; Kinahan, Paul; Zhao, Binsheng; Hylton, Nola M.; Gillies, Robert J.; Clarke, Laurence; Nordstrom, Robert; Rubin, Daniel L.

2015-01-01

As anti-cancer therapies designed to target specific molecular pathways have been developed, it has become critical to develop methods to assess the response induced by such agents. While traditional, anatomic CT and MRI exams are useful in many settings, there is increasing evidence that these methods cannot answer the fundamental biological and physiological questions essential for assessment and, eventually, prediction of treatment response in the clinical trial setting, especially in the critical period soon after treatment is initiated. To optimally apply advances in quantitative imaging methods to trials of targeted cancer therapy, new infrastructure improvements are needed that incorporate these emerging techniques into the settings where they are most likely to have impact. In this review, we first elucidate the needs for therapeutic response assessment in the era of molecularly targeted therapy and describe how quantitative imaging can most effectively provide scientifically and clinically relevant data. We then describe the tools and methods required to apply quantitative imaging and provide concrete examples of work making these advances practically available for routine application in clinical trials. We conclude by proposing strategies to surmount barriers to wider incorporation of these quantitative imaging methods into clinical trials and, eventually, clinical practice. Our goal is to encourage and guide the oncology community to deploy standardized quantitative imaging techniques in clinical trials to further personalize care for cancer patients, and to provide a more efficient path for the development of improved targeted therapies. PMID:26773162

Quantitative assessment of dynamic PET imaging data in cancer imaging.

PubMed

Muzi, Mark; O'Sullivan, Finbarr; Mankoff, David A; Doot, Robert K; Pierce, Larry A; Kurland, Brenda F; Linden, Hannah M; Kinahan, Paul E

2012-11-01

Clinical imaging in positron emission tomography (PET) is often performed using single-time-point estimates of tracer uptake or static imaging that provides a spatial map of regional tracer concentration. However, dynamic tracer imaging can provide considerably more information about in vivo biology by delineating both the temporal and spatial pattern of tracer uptake. In addition, several potential sources of error that occur in static imaging can be mitigated. This review focuses on the application of dynamic PET imaging to measuring regional cancer biologic features and especially in using dynamic PET imaging for quantitative therapeutic response monitoring for cancer clinical trials. Dynamic PET imaging output parameters, particularly transport (flow) and overall metabolic rate, have provided imaging end points for clinical trials at single-center institutions for years. However, dynamic imaging poses many challenges for multicenter clinical trial implementations from cross-center calibration to the inadequacy of a common informatics infrastructure. Underlying principles and methodology of PET dynamic imaging are first reviewed, followed by an examination of current approaches to dynamic PET image analysis with a specific case example of dynamic fluorothymidine imaging to illustrate the approach. Copyright © 2012 Elsevier Inc. All rights reserved.

Segmentation and Quantitative Analysis of Apoptosis of Chinese Hamster Ovary Cells from Fluorescence Microscopy Images.

PubMed

Du, Yuncheng; Budman, Hector M; Duever, Thomas A

2017-06-01

Accurate and fast quantitative analysis of living cells from fluorescence microscopy images is useful for evaluating experimental outcomes and cell culture protocols. An algorithm is developed in this work to automatically segment and distinguish apoptotic cells from normal cells. The algorithm involves three steps consisting of two segmentation steps and a classification step. The segmentation steps are: (i) a coarse segmentation, combining a range filter with a marching square method, is used as a prefiltering step to provide the approximate positions of cells within a two-dimensional matrix used to store cells' images and the count of the number of cells for a given image; and (ii) a fine segmentation step using the Active Contours Without Edges method is applied to the boundaries of cells identified in the coarse segmentation step. Although this basic two-step approach provides accurate edges when the cells in a given image are sparsely distributed, the occurrence of clusters of cells in high cell density samples requires further processing. Hence, a novel algorithm for clusters is developed to identify the edges of cells within clusters and to approximate their morphological features. Based on the segmentation results, a support vector machine classifier that uses three morphological features: the mean value of pixel intensities in the cellular regions, the variance of pixel intensities in the vicinity of cell boundaries, and the lengths of the boundaries, is developed for distinguishing apoptotic cells from normal cells. The algorithm is shown to be efficient in terms of computational time, quantitative analysis, and differentiation accuracy, as compared with the use of the active contours method without the proposed preliminary coarse segmentation step.

High-resolution dynamic imaging and quantitative analysis of lung cancer xenografts in nude mice using clinical PET/CT

PubMed Central

Wang, Ying Yi; Wang, Kai; Xu, Zuo Yu; Song, Yan; Wang, Chu Nan; Zhang, Chong Qing; Sun, Xi Lin; Shen, Bao Zhong

2017-01-01

Considering the general application of dedicated small-animal positron emission tomography/computed tomography is limited, an acceptable alternative in many situations might be clinical PET/CT. To estimate the feasibility of using clinical PET/CT with [F-18]-fluoro-2-deoxy-D-glucose for high-resolution dynamic imaging and quantitative analysis of cancer xenografts in nude mice. Dynamic clinical PET/CT scans were performed on xenografts for 60 min after injection with [F-18]-fluoro-2-deoxy-D-glucose. Scans were reconstructed with or without SharpIR method in two phases. And mice were sacrificed to extracting major organs and tumors, using ex vivo γ-counting as a reference. Strikingly, we observed that the image quality and the correlation between the all quantitive data from clinical PET/CT and the ex vivo counting was better with the SharpIR reconstructions than without. Our data demonstrate that clinical PET/CT scanner with SharpIR reconstruction is a valuable tool for imaging small animals in preclinical cancer research, offering dynamic imaging parameters, good image quality and accurate data quatification. PMID:28881772

High-resolution dynamic imaging and quantitative analysis of lung cancer xenografts in nude mice using clinical PET/CT.

PubMed

Wang, Ying Yi; Wang, Kai; Xu, Zuo Yu; Song, Yan; Wang, Chu Nan; Zhang, Chong Qing; Sun, Xi Lin; Shen, Bao Zhong

2017-08-08

Considering the general application of dedicated small-animal positron emission tomography/computed tomography is limited, an acceptable alternative in many situations might be clinical PET/CT. To estimate the feasibility of using clinical PET/CT with [F-18]-fluoro-2-deoxy-D-glucose for high-resolution dynamic imaging and quantitative analysis of cancer xenografts in nude mice. Dynamic clinical PET/CT scans were performed on xenografts for 60 min after injection with [F-18]-fluoro-2-deoxy-D-glucose. Scans were reconstructed with or without SharpIR method in two phases. And mice were sacrificed to extracting major organs and tumors, using ex vivo γ-counting as a reference. Strikingly, we observed that the image quality and the correlation between the all quantitive data from clinical PET/CT and the ex vivo counting was better with the SharpIR reconstructions than without. Our data demonstrate that clinical PET/CT scanner with SharpIR reconstruction is a valuable tool for imaging small animals in preclinical cancer research, offering dynamic imaging parameters, good image quality and accurate data quatification.

Qualitative and quantitative mass spectrometry imaging of drugs and metabolites.

PubMed

Lietz, Christopher B; Gemperline, Erin; Li, Lingjun

2013-07-01

Mass spectrometric imaging (MSI) has rapidly increased its presence in the pharmaceutical sciences. While quantitative whole-body autoradiography and microautoradiography are the traditional techniques for molecular imaging of drug delivery and metabolism, MSI provides advantageous specificity that can distinguish the parent drug from metabolites and modified endogenous molecules. This review begins with the fundamentals of MSI sample preparation/ionization, and then moves on to both qualitative and quantitative applications with special emphasis on drug discovery and delivery. Cutting-edge investigations on sub-cellular imaging and endogenous signaling peptides are also highlighted, followed by perspectives on emerging technology and the path for MSI to become a routine analysis technique. Copyright © 2013 Elsevier B.V. All rights reserved.

Quantitative imaging assay for NF-κB nuclear translocation in primary human macrophages

PubMed Central

Noursadeghi, Mahdad; Tsang, Jhen; Haustein, Thomas; Miller, Robert F.; Chain, Benjamin M.; Katz, David R.

2008-01-01

Quantitative measurement of NF-κB nuclear translocation is an important research tool in cellular immunology. Established methodologies have a number of limitations, such as poor sensitivity, high cost or dependence on cell lines. Novel imaging methods to measure nuclear translocation of transcriptionally active components of NF-κB are being used but are also partly limited by the need for specialist imaging equipment or image analysis software. Herein we present a method for quantitative detection of NF-κB rel A nuclear translocation, using immunofluorescence microscopy and the public domain image analysis software ImageJ that can be easily adopted for cellular immunology research without the need for specialist image analysis expertise and at low cost. The method presented here is validated by demonstrating the time course and dose response of NF-κB nuclear translocation in primary human macrophages stimulated with LPS, and by comparison with a commercial NF-κB activation reporter cell line. PMID:18036607

Subsurface imaging and cell refractometry using quantitative phase/ shear-force feedback microscopy

NASA Astrophysics Data System (ADS)

Edward, Kert; Farahi, Faramarz

2009-10-01

Over the last few years, several novel quantitative phase imaging techniques have been developed for the study of biological cells. However, many of these techniques are encumbered by inherent limitations including 2π phase ambiguities and diffraction limited spatial resolution. In addition, subsurface information in the phase data is not exploited. We hereby present a novel quantitative phase imaging system without 2 π ambiguities, which also allows for subsurface imaging and cell refractometry studies. This is accomplished by utilizing simultaneously obtained shear-force topography information. We will demonstrate how the quantitative phase and topography data can be used for subsurface and cell refractometry analysis and will present results for a fabricated structure and a malaria infected red blood cell.

Quantitative assessment of image motion blur in diffraction images of moving biological cells

NASA Astrophysics Data System (ADS)

Wang, He; Jin, Changrong; Feng, Yuanming; Qi, Dandan; Sa, Yu; Hu, Xin-Hua

2016-02-01

Motion blur (MB) presents a significant challenge for obtaining high-contrast image data from biological cells with a polarization diffraction imaging flow cytometry (p-DIFC) method. A new p-DIFC experimental system has been developed to evaluate the MB and its effect on image analysis using a time-delay-integration (TDI) CCD camera. Diffraction images of MCF-7 and K562 cells have been acquired with different speed-mismatch ratios and compared to characterize MB quantitatively. Frequency analysis of the diffraction images shows that the degree of MB can be quantified by bandwidth variations of the diffraction images along the motion direction. The analytical results were confirmed by the p-DIFC image data acquired at different speed-mismatch ratios and used to validate a method of numerical simulation of MB on blur-free diffraction images, which provides a useful tool to examine the blurring effect on diffraction images acquired from the same cell. These results provide insights on the dependence of diffraction image on MB and allow significant improvement on rapid biological cell assay with the p-DIFC method.

Automated classification and quantitative analysis of arterial and venous vessels in fundus images

NASA Astrophysics Data System (ADS)

Alam, Minhaj; Son, Taeyoon; Toslak, Devrim; Lim, Jennifer I.; Yao, Xincheng

2018-02-01

It is known that retinopathies may affect arteries and veins differently. Therefore, reliable differentiation of arteries and veins is essential for computer-aided analysis of fundus images. The purpose of this study is to validate one automated method for robust classification of arteries and veins (A-V) in digital fundus images. We combine optical density ratio (ODR) analysis and blood vessel tracking algorithm to classify arteries and veins. A matched filtering method is used to enhance retinal blood vessels. Bottom hat filtering and global thresholding are used to segment the vessel and skeleton individual blood vessels. The vessel tracking algorithm is used to locate the optic disk and to identify source nodes of blood vessels in optic disk area. Each node can be identified as vein or artery using ODR information. Using the source nodes as starting point, the whole vessel trace is then tracked and classified as vein or artery using vessel curvature and angle information. 50 color fundus images from diabetic retinopathy patients were used to test the algorithm. Sensitivity, specificity, and accuracy metrics were measured to assess the validity of the proposed classification method compared to ground truths created by two independent observers. The algorithm demonstrated 97.52% accuracy in identifying blood vessels as vein or artery. A quantitative analysis upon A-V classification showed that average A-V ratio of width for NPDR subjects with hypertension decreased significantly (43.13%).

High PRF ultrafast sliding compound doppler imaging: fully qualitative and quantitative analysis of blood flow

NASA Astrophysics Data System (ADS)

Kang, Jinbum; Jang, Won Seuk; Yoo, Yangmo

2018-02-01

Ultrafast compound Doppler imaging based on plane-wave excitation (UCDI) can be used to evaluate cardiovascular diseases using high frame rates. In particular, it provides a fully quantifiable flow analysis over a large region of interest with high spatio-temporal resolution. However, the pulse-repetition frequency (PRF) in the UCDI method is limited for high-velocity flow imaging since it has a tradeoff between the number of plane-wave angles (N) and acquisition time. In this paper, we present high PRF ultrafast sliding compound Doppler imaging method (HUSDI) to improve quantitative flow analysis. With the HUSDI method, full scanline images (i.e. each tilted plane wave data) in a Doppler frame buffer are consecutively summed using a sliding window to create high-quality ensemble data so that there is no reduction in frame rate and flow sensitivity. In addition, by updating a new compounding set with a certain time difference (i.e. sliding window step size or L), the HUSDI method allows various Doppler PRFs with the same acquisition data to enable a fully qualitative, retrospective flow assessment. To evaluate the performance of the proposed HUSDI method, simulation, in vitro and in vivo studies were conducted under diverse flow circumstances. In the simulation and in vitro studies, the HUSDI method showed improved hemodynamic representations without reducing either temporal resolution or sensitivity compared to the UCDI method. For the quantitative analysis, the root mean squared velocity error (RMSVE) was measured using 9 angles (-12° to 12°) with L of 1-9, and the results were found to be comparable to those of the UCDI method (L  =  N  =  9), i.e.  ⩽0.24 cm s-1, for all L values. For the in vivo study, the flow data acquired from a full cardiac cycle of the femoral vessels of a healthy volunteer were analyzed using a PW spectrogram, and arterial and venous flows were successfully assessed with high Doppler PRF (e.g. 5 kHz at L�

High PRF ultrafast sliding compound doppler imaging: fully qualitative and quantitative analysis of blood flow.

PubMed

Kang, Jinbum; Jang, Won Seuk; Yoo, Yangmo

2018-02-09

Ultrafast compound Doppler imaging based on plane-wave excitation (UCDI) can be used to evaluate cardiovascular diseases using high frame rates. In particular, it provides a fully quantifiable flow analysis over a large region of interest with high spatio-temporal resolution. However, the pulse-repetition frequency (PRF) in the UCDI method is limited for high-velocity flow imaging since it has a tradeoff between the number of plane-wave angles (N) and acquisition time. In this paper, we present high PRF ultrafast sliding compound Doppler imaging method (HUSDI) to improve quantitative flow analysis. With the HUSDI method, full scanline images (i.e. each tilted plane wave data) in a Doppler frame buffer are consecutively summed using a sliding window to create high-quality ensemble data so that there is no reduction in frame rate and flow sensitivity. In addition, by updating a new compounding set with a certain time difference (i.e. sliding window step size or L), the HUSDI method allows various Doppler PRFs with the same acquisition data to enable a fully qualitative, retrospective flow assessment. To evaluate the performance of the proposed HUSDI method, simulation, in vitro and in vivo studies were conducted under diverse flow circumstances. In the simulation and in vitro studies, the HUSDI method showed improved hemodynamic representations without reducing either temporal resolution or sensitivity compared to the UCDI method. For the quantitative analysis, the root mean squared velocity error (RMSVE) was measured using 9 angles (-12° to 12°) with L of 1-9, and the results were found to be comparable to those of the UCDI method (L  =  N  =  9), i.e.  ⩽0.24 cm s -1 , for all L values. For the in vivo study, the flow data acquired from a full cardiac cycle of the femoral vessels of a healthy volunteer were analyzed using a PW spectrogram, and arterial and venous flows were successfully assessed with high Doppler PRF (e.g. 5 kHz at L�

Biomarkers and Surrogate Endpoints in Uveitis: The Impact of Quantitative Imaging.

PubMed

Denniston, Alastair K; Keane, Pearse A; Srivastava, Sunil K

2017-05-01

Uveitis is a major cause of sight loss across the world. The reliable assessment of intraocular inflammation in uveitis ('disease activity') is essential in order to score disease severity and response to treatment. In this review, we describe how 'quantitative imaging', the approach of using automated analysis and measurement algorithms across both standard and emerging imaging modalities, can develop objective instrument-based measures of disease activity. This is a narrative review based on searches of the current world literature using terms related to quantitative imaging techniques in uveitis, supplemented by clinical trial registry data, and expert knowledge of surrogate endpoints and outcome measures in ophthalmology. Current measures of disease activity are largely based on subjective clinical estimation, and are relatively insensitive, with poor discrimination and reliability. The development of quantitative imaging in uveitis is most established in the use of optical coherence tomographic (OCT) measurement of central macular thickness (CMT) to measure severity of macular edema (ME). The transformative effect of CMT in clinical assessment of patients with ME provides a paradigm for the development and impact of other forms of quantitative imaging. Quantitative imaging approaches are now being developed and validated for other key inflammatory parameters such as anterior chamber cells, vitreous haze, retinovascular leakage, and chorioretinal infiltrates. As new forms of quantitative imaging in uveitis are proposed, the uveitis community will need to evaluate these tools against the current subjective clinical estimates and reach a new consensus for how disease activity in uveitis should be measured. The development, validation, and adoption of sensitive and discriminatory measures of disease activity is an unmet need that has the potential to transform both drug development and routine clinical care for the patient with uveitis.

Differentiating invasive and pre-invasive lung cancer by quantitative analysis of histopathologic images

NASA Astrophysics Data System (ADS)

Zhou, Chuan; Sun, Hongliu; Chan, Heang-Ping; Chughtai, Aamer; Wei, Jun; Hadjiiski, Lubomir; Kazerooni, Ella

2018-02-01

We are developing automated radiopathomics method for diagnosis of lung nodule subtypes. In this study, we investigated the feasibility of using quantitative methods to analyze the tumor nuclei and cytoplasm in pathologic wholeslide images for the classification of pathologic subtypes of invasive nodules and pre-invasive nodules. We developed a multiscale blob detection method with watershed transform (MBD-WT) to segment the tumor cells. Pathomic features were extracted to characterize the size, morphology, sharpness, and gray level variation in each segmented nucleus and the heterogeneity patterns of tumor nuclei and cytoplasm. With permission of the National Lung Screening Trial (NLST) project, a data set containing 90 digital haematoxylin and eosin (HE) whole-slide images from 48 cases was used in this study. The 48 cases contain 77 regions of invasive subtypes and 43 regions of pre-invasive subtypes outlined by a pathologist on the HE images using the pathological tumor region description provided by NLST as reference. A logistic regression model (LRM) was built using leave-one-case-out resampling and receiver operating characteristic (ROC) analysis for classification of invasive and pre-invasive subtypes. With 11 selected features, the LRM achieved a test area under the ROC curve (AUC) value of 0.91+/-0.03. The results demonstrated that the pathologic invasiveness of lung adenocarcinomas could be categorized with high accuracy using pathomics analysis.

Spotsizer: High-throughput quantitative analysis of microbial growth.

PubMed

Bischof, Leanne; Převorovský, Martin; Rallis, Charalampos; Jeffares, Daniel C; Arzhaeva, Yulia; Bähler, Jürg

2016-10-01

Microbial colony growth can serve as a useful readout in assays for studying complex genetic interactions or the effects of chemical compounds. Although computational tools for acquiring quantitative measurements of microbial colonies have been developed, their utility can be compromised by inflexible input image requirements, non-trivial installation procedures, or complicated operation. Here, we present the Spotsizer software tool for automated colony size measurements in images of robotically arrayed microbial colonies. Spotsizer features a convenient graphical user interface (GUI), has both single-image and batch-processing capabilities, and works with multiple input image formats and different colony grid types. We demonstrate how Spotsizer can be used for high-throughput quantitative analysis of fission yeast growth. The user-friendly Spotsizer tool provides rapid, accurate, and robust quantitative analyses of microbial growth in a high-throughput format. Spotsizer is freely available at https://data.csiro.au/dap/landingpage?pid=csiro:15330 under a proprietary CSIRO license.

Quantitative analysis of diffusion tensor imaging (DTI) using statistical parametric mapping (SPM) for brain disorders

NASA Astrophysics Data System (ADS)

Lee, Jae-Seung; Im, In-Chul; Kang, Su-Man; Goo, Eun-Hoe; Kwak, Byung-Joon

2013-07-01

This study aimed to quantitatively analyze data from diffusion tensor imaging (DTI) using statistical parametric mapping (SPM) in patients with brain disorders and to assess its potential utility for analyzing brain function. DTI was obtained by performing 3.0-T magnetic resonance imaging for patients with Alzheimer's disease (AD) and vascular dementia (VD), and the data were analyzed using Matlab-based SPM software. The two-sample t-test was used for error analysis of the location of the activated pixels. We compared regions of white matter where the fractional anisotropy (FA) values were low and the apparent diffusion coefficients (ADCs) were increased. In the AD group, the FA values were low in the right superior temporal gyrus, right inferior temporal gyrus, right sub-lobar insula, and right occipital lingual gyrus whereas the ADCs were significantly increased in the right inferior frontal gyrus and right middle frontal gyrus. In the VD group, the FA values were low in the right superior temporal gyrus, right inferior temporal gyrus, right limbic cingulate gyrus, and right sub-lobar caudate tail whereas the ADCs were significantly increased in the left lateral globus pallidus and left medial globus pallidus. In conclusion by using DTI and SPM analysis, we were able to not only determine the structural state of the regions affected by brain disorders but also quantitatively analyze and assess brain function.

Diffraction enhance x-ray imaging for quantitative phase contrast studies

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

Agrawal, A. K.; Singh, B., E-mail: balwants@rrcat.gov.in; Kashyap, Y. S.

2016-05-23

Conventional X-ray imaging based on absorption contrast permits limited visibility of feature having small density and thickness variations. For imaging of weakly absorbing material or materials possessing similar densities, a novel phase contrast imaging techniques called diffraction enhanced imaging has been designed and developed at imaging beamline Indus-2 RRCAT Indore. The technique provides improved visibility of the interfaces and show high contrast in the image forsmall density or thickness gradients in the bulk. This paper presents basic principle, instrumentation and analysis methods for this technique. Initial results of quantitative phase retrieval carried out on various samples have also been presented.

[Study of Cervical Exfoliated Cell's DNA Quantitative Analysis Based on Multi-Spectral Imaging Technology].

PubMed

Wu, Zheng; Zeng, Li-bo; Wu, Qiong-shui

2016-02-01

The conventional cervical cancer screening methods mainly include TBS (the bethesda system) classification method and cellular DNA quantitative analysis, however, by using multiple staining method in one cell slide, which is staining the cytoplasm with Papanicolaou reagent and the nucleus with Feulgen reagent, the study of achieving both two methods in the cervical cancer screening at the same time is still blank. Because the difficulty of this multiple staining method is that the absorbance of the non-DNA material may interfere with the absorbance of DNA, so that this paper has set up a multi-spectral imaging system, and established an absorbance unmixing model by using multiple linear regression method based on absorbance's linear superposition character, and successfully stripped out the absorbance of DNA to run the DNA quantitative analysis, and achieved the perfect combination of those two kinds of conventional screening method. Through a series of experiment we have proved that between the absorbance of DNA which is calculated by the absorbance unmixxing model and the absorbance of DNA which is measured there is no significant difference in statistics when the test level is 1%, also the result of actual application has shown that there is no intersection between the confidence interval of the DNA index of the tetraploid cells which are screened by using this paper's analysis method when the confidence level is 99% and the DNA index's judging interval of cancer cells, so that the accuracy and feasibility of the quantitative DNA analysis with multiple staining method expounded by this paper have been verified, therefore this analytical method has a broad application prospect and considerable market potential in early diagnosis of cervical cancer and other cancers.

Multi-observation PET image analysis for patient follow-up quantitation and therapy assessment

NASA Astrophysics Data System (ADS)

David, S.; Visvikis, D.; Roux, C.; Hatt, M.

2011-09-01

In positron emission tomography (PET) imaging, an early therapeutic response is usually characterized by variations of semi-quantitative parameters restricted to maximum SUV measured in PET scans during the treatment. Such measurements do not reflect overall tumor volume and radiotracer uptake variations. The proposed approach is based on multi-observation image analysis for merging several PET acquisitions to assess tumor metabolic volume and uptake variations. The fusion algorithm is based on iterative estimation using a stochastic expectation maximization (SEM) algorithm. The proposed method was applied to simulated and clinical follow-up PET images. We compared the multi-observation fusion performance to threshold-based methods, proposed for the assessment of the therapeutic response based on functional volumes. On simulated datasets the adaptive threshold applied independently on both images led to higher errors than the ASEM fusion and on clinical datasets it failed to provide coherent measurements for four patients out of seven due to aberrant delineations. The ASEM method demonstrated improved and more robust estimation of the evaluation leading to more pertinent measurements. Future work will consist in extending the methodology and applying it to clinical multi-tracer datasets in order to evaluate its potential impact on the biological tumor volume definition for radiotherapy applications.

Segmentation-based retrospective shading correction in fluorescence microscopy E. coli images for quantitative analysis

NASA Astrophysics Data System (ADS)

Mai, Fei; Chang, Chunqi; Liu, Wenqing; Xu, Weichao; Hung, Yeung S.

2009-10-01

Due to the inherent imperfections in the imaging process, fluorescence microscopy images often suffer from spurious intensity variations, which is usually referred to as intensity inhomogeneity, intensity non uniformity, shading or bias field. In this paper, a retrospective shading correction method for fluorescence microscopy Escherichia coli (E. Coli) images is proposed based on segmentation result. Segmentation and shading correction are coupled together, so we iteratively correct the shading effects based on segmentation result and refine the segmentation by segmenting the image after shading correction. A fluorescence microscopy E. Coli image can be segmented (based on its intensity value) into two classes: the background and the cells, where the intensity variation within each class is close to zero if there is no shading. Therefore, we make use of this characteristics to correct the shading in each iteration. Shading is mathematically modeled as a multiplicative component and an additive noise component. The additive component is removed by a denoising process, and the multiplicative component is estimated using a fast algorithm to minimize the intra-class intensity variation. We tested our method on synthetic images and real fluorescence E.coli images. It works well not only for visual inspection, but also for numerical evaluation. Our proposed method should be useful for further quantitative analysis especially for protein expression value comparison.

[Evaluation of dental plaque by quantitative digital image analysis system].

PubMed

Huang, Z; Luan, Q X

2016-04-18

To analyze the plaque staining image by using image analysis software, to verify the maneuverability, practicability and repeatability of this technique, and to evaluate the influence of different plaque stains. In the study, 30 volunteers were enrolled from the new dental students of Peking University Health Science Center in accordance with the inclusion criteria. The digital images of the anterior teeth were acquired after plaque stained according to filming standardization.The image analysis was performed using Image Pro Plus 7.0, and the Quigley-Hein plaque indexes of the anterior teeth were evaluated. The plaque stain area percentage and the corresponding dental plaque index were highly correlated,and the Spearman correlation coefficient was 0.776 (P<0.01). Intraclass correlation coefficients of the tooth area and plaque area which two researchers used the software to calculate were 0.956 and 0.930 (P<0.01).The Bland-Altman analysis chart showed only a few spots outside the 95% consistency boundaries. The different plaque stains image analysis results showed that the difference of the tooth area measurements was not significant, while the difference of the plaque area measurements significant (P<0.01). This method is easy in operation and control,highly related to the calculated percentage of plaque area and traditional plaque index, and has good reproducibility.The different plaque staining method has little effect on image segmentation results.The sensitive plaque stain for image analysis is suggested.

Quantitative Analysis of the Cervical Texture by Ultrasound and Correlation with Gestational Age.

PubMed

Baños, Núria; Perez-Moreno, Alvaro; Migliorelli, Federico; Triginer, Laura; Cobo, Teresa; Bonet-Carne, Elisenda; Gratacos, Eduard; Palacio, Montse

2017-01-01

Quantitative texture analysis has been proposed to extract robust features from the ultrasound image to detect subtle changes in the textures of the images. The aim of this study was to evaluate the feasibility of quantitative cervical texture analysis to assess cervical tissue changes throughout pregnancy. This was a cross-sectional study including singleton pregnancies between 20.0 and 41.6 weeks of gestation from women who delivered at term. Cervical length was measured, and a selected region of interest in the cervix was delineated. A model to predict gestational age based on features extracted from cervical images was developed following three steps: data splitting, feature transformation, and regression model computation. Seven hundred images, 30 per gestational week, were included for analysis. There was a strong correlation between the gestational age at which the images were obtained and the estimated gestational age by quantitative analysis of the cervical texture (R = 0.88). This study provides evidence that quantitative analysis of cervical texture can extract features from cervical ultrasound images which correlate with gestational age. Further research is needed to evaluate its applicability as a biomarker of the risk of spontaneous preterm birth, as well as its role in cervical assessment in other clinical situations in which cervical evaluation might be relevant. © 2016 S. Karger AG, Basel.

Assessing treatment response in triple-negative breast cancer from quantitative image analysis in perfusion magnetic resonance imaging.

PubMed

Banerjee, Imon; Malladi, Sadhika; Lee, Daniela; Depeursinge, Adrien; Telli, Melinda; Lipson, Jafi; Golden, Daniel; Rubin, Daniel L

2018-01-01

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is sensitive but not specific to determining treatment response in early stage triple-negative breast cancer (TNBC) patients. We propose an efficient computerized technique for assessing treatment response, specifically the residual tumor (RT) status and pathological complete response (pCR), in response to neoadjuvant chemotherapy. The proposed approach is based on Riesz wavelet analysis of pharmacokinetic maps derived from noninvasive DCE-MRI scans, obtained before and after treatment. We compared the performance of Riesz features with the traditional gray level co-occurrence matrices and a comprehensive characterization of the lesion that includes a wide range of quantitative features (e.g., shape and boundary). We investigated a set of predictive models ([Formula: see text]) incorporating distinct combinations of quantitative characterizations and statistical models at different time points of the treatment and some area under the receiver operating characteristic curve (AUC) values we reported are above 0.8. The most efficient models are based on first-order statistics and Riesz wavelets, which predicted RT with an AUC value of 0.85 and pCR with an AUC value of 0.83, improving results reported in a previous study by [Formula: see text]. Our findings suggest that Riesz texture analysis of TNBC lesions can be considered a potential framework for optimizing TNBC patient care.

A quantitative reconstruction software suite for SPECT imaging

NASA Astrophysics Data System (ADS)

Namías, Mauro; Jeraj, Robert

2017-11-01

Quantitative Single Photon Emission Tomography (SPECT) imaging allows for measurement of activity concentrations of a given radiotracer in vivo. Although SPECT has usually been perceived as non-quantitative by the medical community, the introduction of accurate CT based attenuation correction and scatter correction from hybrid SPECT/CT scanners has enabled SPECT systems to be as quantitative as Positron Emission Tomography (PET) systems. We implemented a software suite to reconstruct quantitative SPECT images from hybrid or dedicated SPECT systems with a separate CT scanner. Attenuation, scatter and collimator response corrections were included in an Ordered Subset Expectation Maximization (OSEM) algorithm. A novel scatter fraction estimation technique was introduced. The SPECT/CT system was calibrated with a cylindrical phantom and quantitative accuracy was assessed with an anthropomorphic phantom and a NEMA/IEC image quality phantom. Accurate activity measurements were achieved at an organ level. This software suite helps increasing quantitative accuracy of SPECT scanners.

Multimodal quantitative phase and fluorescence imaging of cell apoptosis

NASA Astrophysics Data System (ADS)

Fu, Xinye; Zuo, Chao; Yan, Hao

2017-06-01

Fluorescence microscopy, utilizing fluorescence labeling, has the capability to observe intercellular changes which transmitted and reflected light microscopy techniques cannot resolve. However, the parts without fluorescence labeling are not imaged. Hence, the processes simultaneously happen in these parts cannot be revealed. Meanwhile, fluorescence imaging is 2D imaging where information in the depth is missing. Therefore the information in labeling parts is also not complete. On the other hand, quantitative phase imaging is capable to image cells in 3D in real time through phase calculation. However, its resolution is limited by the optical diffraction and cannot observe intercellular changes below 200 nanometers. In this work, fluorescence imaging and quantitative phase imaging are combined to build a multimodal imaging system. Such system has the capability to simultaneously observe the detailed intercellular phenomenon and 3D cell morphology. In this study the proposed multimodal imaging system is used to observe the cell behavior in the cell apoptosis. The aim is to highlight the limitations of fluorescence microscopy and to point out the advantages of multimodal quantitative phase and fluorescence imaging. The proposed multimodal quantitative phase imaging could be further applied in cell related biomedical research, such as tumor.

Groping for quantitative digital 3-D image analysis: an approach to quantitative fluorescence in situ hybridization in thick tissue sections of prostate carcinoma.

PubMed

Rodenacker, K; Aubele, M; Hutzler, P; Adiga, P S

1997-01-01

In molecular pathology numerical chromosome aberrations have been found to be decisive for the prognosis of malignancy in tumours. The existence of such aberrations can be detected by interphase fluorescence in situ hybridization (FISH). The gain or loss of certain base sequences in the desoxyribonucleic acid (DNA) can be estimated by counting the number of FISH signals per cell nucleus. The quantitative evaluation of such events is a necessary condition for a prospective use in diagnostic pathology. To avoid occlusions of signals, the cell nucleus has to be analyzed in three dimensions. Confocal laser scanning microscopy is the means to obtain series of optical thin sections from fluorescence stained or marked material to fulfill the conditions mentioned above. A graphical user interface (GUI) to a software package for display, inspection, count and (semi-)automatic analysis of 3-D images for pathologists is outlined including the underlying methods of 3-D image interaction and segmentation developed. The preparative methods are briefly described. Main emphasis is given to the methodical questions of computer-aided analysis of large 3-D image data sets for pathologists. Several automated analysis steps can be performed for segmentation and succeeding quantification. However tumour material is in contrast to isolated or cultured cells even for visual inspection, a difficult material. For the present a fully automated digital image analysis of 3-D data is not in sight. A semi-automatic segmentation method is thus presented here.

Quantitative SIMS Imaging of Agar-Based Microbial Communities.

PubMed

Dunham, Sage J B; Ellis, Joseph F; Baig, Nameera F; Morales-Soto, Nydia; Cao, Tianyuan; Shrout, Joshua D; Bohn, Paul W; Sweedler, Jonathan V

2018-05-01

After several decades of widespread use for mapping elemental ions and small molecular fragments in surface science, secondary ion mass spectrometry (SIMS) has emerged as a powerful analytical tool for molecular imaging in biology. Biomolecular SIMS imaging has primarily been used as a qualitative technique; although the distribution of a single analyte can be accurately determined, it is difficult to map the absolute quantity of a compound or even to compare the relative abundance of one molecular species to that of another. We describe a method for quantitative SIMS imaging of small molecules in agar-based microbial communities. The microbes are cultivated on a thin film of agar, dried under nitrogen, and imaged directly with SIMS. By use of optical microscopy, we show that the area of the agar is reduced by 26 ± 2% (standard deviation) during dehydration, but the overall biofilm morphology and analyte distribution are largely retained. We detail a quantitative imaging methodology, in which the ion intensity of each analyte is (1) normalized to an external quadratic regression curve, (2) corrected for isomeric interference, and (3) filtered for sample-specific noise and lower and upper limits of quantitation. The end result is a two-dimensional surface density image for each analyte. The sample preparation and quantitation methods are validated by quantitatively imaging four alkyl-quinolone and alkyl-quinoline N-oxide signaling molecules (including Pseudomonas quinolone signal) in Pseudomonas aeruginosa colony biofilms. We show that the relative surface densities of the target biomolecules are substantially different from values inferred through direct intensity comparison and that the developed methodologies can be used to quantitatively compare as many ions as there are available standards.

Quantitative and qualitative measure of intralaboratory two-dimensional protein gel reproducibility and the effects of sample preparation, sample load, and image analysis.

PubMed

Choe, Leila H; Lee, Kelvin H

2003-10-01

We investigate one approach to assess the quantitative variability in two-dimensional gel electrophoresis (2-DE) separations based on gel-to-gel variability, sample preparation variability, sample load differences, and the effect of automation on image analysis. We observe that 95% of spots present in three out of four replicate gels exhibit less than a 0.52 coefficient of variation (CV) in fluorescent stain intensity (% volume) for a single sample run on multiple gels. When four parallel sample preparations are performed, this value increases to 0.57. We do not observe any significant change in quantitative value for an increase or decrease in sample load of 30% when using appropriate image analysis variables. Increasing use of automation, while necessary in modern 2-DE experiments, does change the observed level of quantitative and qualitative variability among replicate gels. The number of spots that change qualitatively for a single sample run in parallel varies from a CV = 0.03 for fully manual analysis to CV = 0.20 for a fully automated analysis. We present a systematic method by which a single laboratory can measure gel-to-gel variability using only three gel runs.

New Tools for Comparing Microscopy Images: Quantitative Analysis of Cell Types in Bacillus subtilis

PubMed Central

van Gestel, Jordi; Vlamakis, Hera

2014-01-01

Fluorescence microscopy is a method commonly used to examine individual differences between bacterial cells, yet many studies still lack a quantitative analysis of fluorescence microscopy data. Here we introduce some simple tools that microbiologists can use to analyze and compare their microscopy images. We show how image data can be converted to distribution data. These data can be subjected to a cluster analysis that makes it possible to objectively compare microscopy images. The distribution data can further be analyzed using distribution fitting. We illustrate our methods by scrutinizing two independently acquired data sets, each containing microscopy images of a doubly labeled Bacillus subtilis strain. For the first data set, we examined the expression of srfA and tapA, two genes which are expressed in surfactin-producing and matrix-producing cells, respectively. For the second data set, we examined the expression of eps and tapA; these genes are expressed in matrix-producing cells. We show that srfA is expressed by all cells in the population, a finding which contrasts with a previously reported bimodal distribution of srfA expression. In addition, we show that eps and tapA do not always have the same expression profiles, despite being expressed in the same cell type: both operons are expressed in cell chains, while single cells mainly express eps. These findings exemplify that the quantification and comparison of microscopy data can yield insights that otherwise would go unnoticed. PMID:25448819

Agreement between clinical estimation and a new quantitative analysis by Photoshop software in fundus and angiographic image variables.

PubMed

Ramezani, Alireza; Ahmadieh, Hamid; Azarmina, Mohsen; Soheilian, Masoud; Dehghan, Mohammad H; Mohebbi, Mohammad R

2009-12-01

To evaluate the validity of a new method for the quantitative analysis of fundus or angiographic images using Photoshop 7.0 (Adobe, USA) software by comparing with clinical evaluation. Four hundred and eighteen fundus and angiographic images of diabetic patients were evaluated by three retina specialists and then by computing using Photoshop 7.0 software. Four variables were selected for comparison: amount of hard exudates (HE) on color pictures, amount of HE on red-free pictures, severity of leakage, and the size of the foveal avascular zone (FAZ). The coefficient of agreement (Kappa) between the two methods in the amount of HE on color and red-free photographs were 85% (0.69) and 79% (0.59), respectively. The agreement for severity of leakage was 72% (0.46). In the two methods for the evaluation of the FAZ size using the magic and lasso software tools, the agreement was 54% (0.09) and 89% (0.77), respectively. Agreement in the estimation of the FAZ size by the lasso magnetic tool was excellent and was almost as good in the quantification of HE on color and on red-free images. Considering the agreement of this new technique for the measurement of variables in fundus images using Photoshop software with the clinical evaluation, this method seems to have sufficient validity to be used for the quantitative analysis of HE, leakage, and FAZ size on the angiograms of diabetic patients.

Quantitative Image Analysis Techniques with High-Speed Schlieren Photography

NASA Technical Reports Server (NTRS)

Pollard, Victoria J.; Herron, Andrew J.

2017-01-01

Optical flow visualization techniques such as schlieren and shadowgraph photography are essential to understanding fluid flow when interpreting acquired wind tunnel test data. Output of the standard implementations of these visualization techniques in test facilities are often limited only to qualitative interpretation of the resulting images. Although various quantitative optical techniques have been developed, these techniques often require special equipment or are focused on obtaining very precise and accurate data about the visualized flow. These systems are not practical in small, production wind tunnel test facilities. However, high-speed photography capability has become a common upgrade to many test facilities in order to better capture images of unsteady flow phenomena such as oscillating shocks and flow separation. This paper describes novel techniques utilized by the authors to analyze captured high-speed schlieren and shadowgraph imagery from wind tunnel testing for quantification of observed unsteady flow frequency content. Such techniques have applications in parametric geometry studies and in small facilities where more specialized equipment may not be available.

Multiplexed immunohistochemistry, imaging, and quantitation: a review, with an assessment of Tyramide signal amplification, multispectral imaging and multiplex analysis.

PubMed

Stack, Edward C; Wang, Chichung; Roman, Kristin A; Hoyt, Clifford C

2014-11-01

Tissue sections offer the opportunity to understand a patient's condition, to make better prognostic evaluations and to select optimum treatments, as evidenced by the place pathology holds today in clinical practice. Yet, there is a wealth of information locked up in a tissue section that is only partially accessed, due mainly to the limitations of tools and methods. Often tissues are assessed primarily based on visual analysis of one or two proteins, or 2-3 DNA or RNA molecules. Even while analysis is still based on visual perception, image analysis is starting to address the variability of human perception. This is in contrast to measuring characteristics that are substantially out of reach of human perception, such as parameters revealed through co-expression, spatial relationships, heterogeneity, and low abundance molecules. What is not routinely accessed is the information revealed through simultaneous detection of multiple markers, the spatial relationships among cells and tissue in disease, and the heterogeneity now understood to be critical to developing effective therapeutic strategies. Our purpose here is to review and assess methods for multiplexed, quantitative, image analysis based approaches, using new multicolor immunohistochemistry methods, automated multispectral slide imaging, and advanced trainable pattern recognition software. A key aspect of our approach is presenting imagery in a workflow that engages the pathologist to utilize the strengths of human perception and judgment, while significantly expanding the range of metrics collectable from tissue sections and also provide a level of consistency and precision needed to support the complexities of personalized medicine. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

Computer-aided analysis with Image J for quantitatively assessing psoriatic lesion area.

PubMed

Sun, Z; Wang, Y; Ji, S; Wang, K; Zhao, Y

2015-11-01

Body surface area is important in determining the severity of psoriasis. However, objective, reliable, and practical method is still in need for this purpose. We performed a computer image analysis (CIA) of psoriatic area using the image J freeware to determine whether this method could be used for objective evaluation of psoriatic area. Fifteen psoriasis patients were randomized to be treated with adalimumab or placebo in a clinical trial. At each visit, the psoriasis area of each body site was estimated by two physicians (E-method), and standard photographs were taken. The psoriasis area in the pictures was assessed with CIA using semi-automatic threshold selection (T-method), or manual selection (M-method, gold standard). The results assessed by the three methods were analyzed with reliability and affecting factors evaluated. Both T- and E-method correlated strongly with M-method, and T-method had a slightly stronger correlation with M-method. Both T- and E-methods had a good consistency between the evaluators. All the three methods were able to detect the change in the psoriatic area after treatment, while the E-method tends to overestimate. The CIA with image J freeware is reliable and practicable in quantitatively assessing the lesional of psoriasis area. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Ranking Quantitative Resistance to Septoria tritici Blotch in Elite Wheat Cultivars Using Automated Image Analysis.

PubMed

Karisto, Petteri; Hund, Andreas; Yu, Kang; Anderegg, Jonas; Walter, Achim; Mascher, Fabio; McDonald, Bruce A; Mikaberidze, Alexey

2018-05-01

Quantitative resistance is likely to be more durable than major gene resistance for controlling Septoria tritici blotch (STB) on wheat. Earlier studies hypothesized that resistance affecting the degree of host damage, as measured by the percentage of leaf area covered by STB lesions, is distinct from resistance that affects pathogen reproduction, as measured by the density of pycnidia produced within lesions. We tested this hypothesis using a collection of 335 elite European winter wheat cultivars that was naturally infected by a diverse population of Zymoseptoria tritici in a replicated field experiment. We used automated image analysis of 21,420 scanned wheat leaves to obtain quantitative measures of conditional STB intensity that were precise, objective, and reproducible. These measures allowed us to explicitly separate resistance affecting host damage from resistance affecting pathogen reproduction, enabling us to confirm that these resistance traits are largely independent. The cultivar rankings based on host damage were different from the rankings based on pathogen reproduction, indicating that the two forms of resistance should be considered separately in breeding programs aiming to increase STB resistance. We hypothesize that these different forms of resistance are under separate genetic control, enabling them to be recombined to form new cultivars that are highly resistant to STB. We found a significant correlation between rankings based on automated image analysis and rankings based on traditional visual scoring, suggesting that image analysis can complement conventional measurements of STB resistance, based largely on host damage, while enabling a much more precise measure of pathogen reproduction. We showed that measures of pathogen reproduction early in the growing season were the best predictors of host damage late in the growing season, illustrating the importance of breeding for resistance that reduces pathogen reproduction in order to minimize

A novel spectral imaging system for quantitative analysis of hypertrophic scar

NASA Astrophysics Data System (ADS)

Ghassemi, Pejhman; Shupp, Jeffrey W.; Moffatt, Lauren T.; Ramella-Roman, Jessica C.

2013-03-01

Scarring can lead to significant cosmetic, psychosocial, and functional consequences in patients with hypertrophic scars from burn and trauma injuries. Therefore, quantitative assessment of scar is needed in clinical diagnosis and treatment. The Vancouver Scar Scale (VSS), the accepted clinical scar assessment tool, was introduced in the nineties and relies only on the physician subjective evaluation of skin pliability, height, vascularity, and pigmentation. To date, no entirely objective method has been available for scar assessment. So, there is a continued need for better techniques to monitor patients with scars. We introduce a new spectral imaging system combining out-of-plane Stokes polarimetry, Spatial Frequency Domain Imaging (SFDI), and three-dimensional (3D) reconstruction. The main idea behind this system is to estimate hemoglobin and melanin contents of scar using SFDI technique, roughness and directional anisotropy features with Stokes polarimetry, and height and general shape with 3D reconstruction. Our proposed tool has several advantages compared to current methodologies. First and foremost, it is non-contact and non-invasive and thus can be used at any stage in wound healing without causing harm to the patient. Secondarily, the height, pigmentation, and hemoglobin assessments are co-registered and are based on imaging and not point measurement, allowing for more meaningful interpretation of the data. Finally, the algorithms used in the data analysis are physics based which will be very beneficial in the standardization of the technique. A swine model has also been developed for hypertrophic scarring and an ongoing pre-clinical evaluation of the technique is being conducted.

FFDM image quality assessment using computerized image texture analysis

NASA Astrophysics Data System (ADS)

Berger, Rachelle; Carton, Ann-Katherine; Maidment, Andrew D. A.; Kontos, Despina

2010-04-01

Quantitative measures of image quality (IQ) are routinely obtained during the evaluation of imaging systems. These measures, however, do not necessarily correlate with the IQ of the actual clinical images, which can also be affected by factors such as patient positioning. No quantitative method currently exists to evaluate clinical IQ. Therefore, we investigated the potential of using computerized image texture analysis to quantitatively assess IQ. Our hypothesis is that image texture features can be used to assess IQ as a measure of the image signal-to-noise ratio (SNR). To test feasibility, the "Rachel" anthropomorphic breast phantom (Model 169, Gammex RMI) was imaged with a Senographe 2000D FFDM system (GE Healthcare) using 220 unique exposure settings (target/filter, kVs, and mAs combinations). The mAs were varied from 10%-300% of that required for an average glandular dose (AGD) of 1.8 mGy. A 2.5cm2 retroareolar region of interest (ROI) was segmented from each image. The SNR was computed from the ROIs segmented from images linear with dose (i.e., raw images) after flat-field and off-set correction. Image texture features of skewness, coarseness, contrast, energy, homogeneity, and fractal dimension were computed from the Premium ViewTM postprocessed image ROIs. Multiple linear regression demonstrated a strong association between the computed image texture features and SNR (R2=0.92, p<=0.001). When including kV, target and filter as additional predictor variables, a stronger association with SNR was observed (R2=0.95, p<=0.001). The strong associations indicate that computerized image texture analysis can be used to measure image SNR and potentially aid in automating IQ assessment as a component of the clinical workflow. Further work is underway to validate our findings in larger clinical datasets.

Confidence estimation for quantitative photoacoustic imaging

NASA Astrophysics Data System (ADS)

Gröhl, Janek; Kirchner, Thomas; Maier-Hein, Lena

2018-02-01

Quantification of photoacoustic (PA) images is one of the major challenges currently being addressed in PA research. Tissue properties can be quantified by correcting the recorded PA signal with an estimation of the corresponding fluence. Fluence estimation itself, however, is an ill-posed inverse problem which usually needs simplifying assumptions to be solved with state-of-the-art methods. These simplifications, as well as noise and artifacts in PA images reduce the accuracy of quantitative PA imaging (PAI). This reduction in accuracy is often localized to image regions where the assumptions do not hold true. This impedes the reconstruction of functional parameters when averaging over entire regions of interest (ROI). Averaging over a subset of voxels with a high accuracy would lead to an improved estimation of such parameters. To achieve this, we propose a novel approach to the local estimation of confidence in quantitative reconstructions of PA images. It makes use of conditional probability densities to estimate confidence intervals alongside the actual quantification. It encapsulates an estimation of the errors introduced by fluence estimation as well as signal noise. We validate the approach using Monte Carlo generated data in combination with a recently introduced machine learning-based approach to quantitative PAI. Our experiments show at least a two-fold improvement in quantification accuracy when evaluating on voxels with high confidence instead of thresholding signal intensity.

Advanced forensic validation for human spermatozoa identification using SPERM HY-LITER™ Express with quantitative image analysis.

PubMed

Takamura, Ayari; Watanabe, Ken; Akutsu, Tomoko

2017-07-01

Identification of human semen is indispensable for the investigation of sexual assaults. Fluorescence staining methods using commercial kits, such as the series of SPERM HY-LITER™ kits, have been useful to detect human sperm via strong fluorescence. These kits have been examined from various forensic aspects. However, because of a lack of evaluation methods, these studies did not provide objective, or quantitative, descriptions of the results nor clear criteria for the decisions reached. In addition, the variety of validations was considerably limited. In this study, we conducted more advanced validations of SPERM HY-LITER™ Express using our established image analysis method. Use of this method enabled objective and specific identification of fluorescent sperm's spots and quantitative comparisons of the sperm detection performance under complex experimental conditions. For body fluid mixtures, we examined interference with the fluorescence staining from other body fluid components. Effects of sample decomposition were simulated in high humidity and high temperature conditions. Semen with quite low sperm concentrations, such as azoospermia and oligospermia samples, represented the most challenging cases in application of the kit. Finally, the tolerance of the kit against various acidic and basic environments was analyzed. The validations herein provide useful information for the practical applications of the SPERM HY-LITER™ Express kit, which were previously unobtainable. Moreover, the versatility of our image analysis method toward various complex cases was demonstrated.

Standardisation of DNA quantitation by image analysis: quality control of instrumentation.

PubMed

Puech, M; Giroud, F

1999-05-01

DNA image analysis is frequently performed in clinical practice as a prognostic tool and to improve diagnosis. The precision of prognosis and diagnosis depends on the accuracy of analysis and particularly on the quality of image analysis systems. It has been reported that image analysis systems used for DNA quantification differ widely in their characteristics (Thunissen et al.: Cytometry 27: 21-25, 1997). This induces inter-laboratory variations when the same sample is analysed in different laboratories. In microscopic image analysis, the principal instrumentation errors arise from the optical and electronic parts of systems. They bring about problems of instability, non-linearity, and shading and glare phenomena. The aim of this study is to establish tools and standardised quality control procedures for microscopic image analysis systems. Specific reference standard slides have been developed to control instability, non-linearity, shading and glare phenomena and segmentation efficiency. Some systems have been controlled with these tools and these quality control procedures. Interpretation criteria and accuracy limits of these quality control procedures are proposed according to the conclusions of a European project called PRESS project (Prototype Reference Standard Slide). Beyond these limits, tested image analysis systems are not qualified to realise precise DNA analysis. The different procedures presented in this work determine if an image analysis system is qualified to deliver sufficiently precise DNA measurements for cancer case analysis. If the controlled systems are beyond the defined limits, some recommendations are given to find a solution to the problem.

Improving image segmentation performance and quantitative analysis via a computer-aided grading methodology for optical coherence tomography retinal image analysis.

PubMed

Debuc, Delia Cabrera; Salinas, Harry M; Ranganathan, Sudarshan; Tátrai, Erika; Gao, Wei; Shen, Meixiao; Wang, Jianhua; Somfai, Gábor M; Puliafito, Carmen A

2010-01-01

We demonstrate quantitative analysis and error correction of optical coherence tomography (OCT) retinal images by using a custom-built, computer-aided grading methodology. A total of 60 Stratus OCT (Carl Zeiss Meditec, Dublin, California) B-scans collected from ten normal healthy eyes are analyzed by two independent graders. The average retinal thickness per macular region is compared with the automated Stratus OCT results. Intergrader and intragrader reproducibility is calculated by Bland-Altman plots of the mean difference between both gradings and by Pearson correlation coefficients. In addition, the correlation between Stratus OCT and our methodology-derived thickness is also presented. The mean thickness difference between Stratus OCT and our methodology is 6.53 microm and 26.71 microm when using the inner segment/outer segment (IS/OS) junction and outer segment/retinal pigment epithelium (OS/RPE) junction as the outer retinal border, respectively. Overall, the median of the thickness differences as a percentage of the mean thickness is less than 1% and 2% for the intragrader and intergrader reproducibility test, respectively. The measurement accuracy range of the OCT retinal image analysis (OCTRIMA) algorithm is between 0.27 and 1.47 microm and 0.6 and 1.76 microm for the intragrader and intergrader reproducibility tests, respectively. Pearson correlation coefficients demonstrate R(2)>0.98 for all Early Treatment Diabetic Retinopathy Study (ETDRS) regions. Our methodology facilitates a more robust and localized quantification of the retinal structure in normal healthy controls and patients with clinically significant intraretinal features.

Analytical robustness of quantitative NIR chemical imaging for Islamic paper characterization

NASA Astrophysics Data System (ADS)

Mahgoub, Hend; Gilchrist, John R.; Fearn, Thomas; Strlič, Matija

2017-07-01

Recently, spectral imaging techniques such as Multispectral (MSI) and Hyperspectral Imaging (HSI) have gained importance in the field of heritage conservation. This paper explores the analytical robustness of quantitative chemical imaging for Islamic paper characterization by focusing on the effect of different measurement and processing parameters, i.e. acquisition conditions and calibration on the accuracy of the collected spectral data. This will provide a better understanding of the technique that can provide a measure of change in collections through imaging. For the quantitative model, special calibration target was devised using 105 samples from a well-characterized reference Islamic paper collection. Two material properties were of interest: starch sizing and cellulose degree of polymerization (DP). Multivariate data analysis methods were used to develop discrimination and regression models which were used as an evaluation methodology for the metrology of quantitative NIR chemical imaging. Spectral data were collected using a pushbroom HSI scanner (Gilden Photonics Ltd) in the 1000-2500 nm range with a spectral resolution of 6.3 nm using a mirror scanning setup and halogen illumination. Data were acquired at different measurement conditions and acquisition parameters. Preliminary results showed the potential of the evaluation methodology to show that measurement parameters such as the use of different lenses and different scanning backgrounds may not have a great influence on the quantitative results. Moreover, the evaluation methodology allowed for the selection of the best pre-treatment method to be applied to the data.

Quantitative analysis of image quality for acceptance and commissioning of an MRI simulator with a semiautomatic method.

PubMed

Chen, Xinyuan; Dai, Jianrong

2018-05-01

Magnetic Resonance Imaging (MRI) simulation differs from diagnostic MRI in purpose, technical requirements, and implementation. We propose a semiautomatic method for image acceptance and commissioning for the scanner, the radiofrequency (RF) coils, and pulse sequences for an MRI simulator. The ACR MRI accreditation large phantom was used for image quality analysis with seven parameters. Standard ACR sequences with a split head coil were adopted to examine the scanner's basic performance. The performance of simulation RF coils were measured and compared using the standard sequence with different clinical diagnostic coils. We used simulation sequences with simulation coils to test the quality of image and advanced performance of the scanner. Codes and procedures were developed for semiautomatic image quality analysis. When using standard ACR sequences with a split head coil, image quality passed all ACR recommended criteria. The image intensity uniformity with a simulation RF coil decreased about 34% compared with the eight-channel diagnostic head coil, while the other six image quality parameters were acceptable. Those two image quality parameters could be improved to more than 85% by built-in intensity calibration methods. In the simulation sequences test, the contrast resolution was sensitive to the FOV and matrix settings. The geometric distortion of simulation sequences such as T1-weighted and T2-weighted images was well-controlled in the isocenter and 10 cm off-center within a range of ±1% (2 mm). We developed a semiautomatic image quality analysis method for quantitative evaluation of images and commissioning of an MRI simulator. The baseline performances of simulation RF coils and pulse sequences have been established for routine QA. © 2018 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.

Quantitative 3D breast magnetic resonance imaging fibroglandular tissue analysis and correlation with qualitative assessments: a feasibility study.

PubMed

Ha, Richard; Mema, Eralda; Guo, Xiaotao; Mango, Victoria; Desperito, Elise; Ha, Jason; Wynn, Ralph; Zhao, Binsheng

2016-04-01

The amount of fibroglandular tissue (FGT) has been linked to breast cancer risk based on mammographic density studies. Currently, the qualitative assessment of FGT on mammogram (MG) and magnetic resonance imaging (MRI) is prone to intra and inter-observer variability. The purpose of this study is to develop an objective quantitative FGT measurement tool for breast MRI that could provide significant clinical value. An IRB approved study was performed. Sixty breast MRI cases with qualitative assessment of mammographic breast density and MRI FGT were randomly selected for quantitative analysis from routine breast MRIs performed at our institution from 1/2013 to 12/2014. Blinded to the qualitative data, whole breast and FGT contours were delineated on T1-weighted pre contrast sagittal images using an in-house, proprietary segmentation algorithm which combines the region-based active contours and a level set approach. FGT (%) was calculated by: [segmented volume of FGT (mm(3))/(segmented volume of whole breast (mm(3))] ×100. Statistical correlation analysis was performed between quantified FGT (%) on MRI and qualitative assessments of mammographic breast density and MRI FGT. There was a significant positive correlation between quantitative MRI FGT assessment and qualitative MRI FGT (r=0.809, n=60, P<0.001) and mammographic density assessment (r=0.805, n=60, P<0.001). There was a significant correlation between qualitative MRI FGT assessment and mammographic density assessment (r=0.725, n=60, P<0.001). The four qualitative assessment categories of FGT correlated with the calculated mean quantitative FGT (%) of 4.61% (95% CI, 0-12.3%), 8.74% (7.3-10.2%), 18.1% (15.1-21.1%), 37.4% (29.5-45.3%). Quantitative measures of FGT (%) were computed with data derived from breast MRI and correlated significantly with conventional qualitative assessments. This quantitative technique may prove to be a valuable tool in clinical use by providing computer generated standardized

Diagnostic performance of semi-quantitative and quantitative stress CMR perfusion analysis: a meta-analysis.

PubMed

van Dijk, R; van Assen, M; Vliegenthart, R; de Bock, G H; van der Harst, P; Oudkerk, M

2017-11-27

Stress cardiovascular magnetic resonance (CMR) perfusion imaging is a promising modality for the evaluation of coronary artery disease (CAD) due to high spatial resolution and absence of radiation. Semi-quantitative and quantitative analysis of CMR perfusion are based on signal-intensity curves produced during the first-pass of gadolinium contrast. Multiple semi-quantitative and quantitative parameters have been introduced. Diagnostic performance of these parameters varies extensively among studies and standardized protocols are lacking. This study aims to determine the diagnostic accuracy of semi- quantitative and quantitative CMR perfusion parameters, compared to multiple reference standards. Pubmed, WebOfScience, and Embase were systematically searched using predefined criteria (3272 articles). A check for duplicates was performed (1967 articles). Eligibility and relevance of the articles was determined by two reviewers using pre-defined criteria. The primary data extraction was performed independently by two researchers with the use of a predefined template. Differences in extracted data were resolved by discussion between the two researchers. The quality of the included studies was assessed using the 'Quality Assessment of Diagnostic Accuracy Studies Tool' (QUADAS-2). True positives, false positives, true negatives, and false negatives were subtracted/calculated from the articles. The principal summary measures used to assess diagnostic accuracy were sensitivity, specificity, andarea under the receiver operating curve (AUC). Data was pooled according to analysis territory, reference standard and perfusion parameter. Twenty-two articles were eligible based on the predefined study eligibility criteria. The pooled diagnostic accuracy for segment-, territory- and patient-based analyses showed good diagnostic performance with sensitivity of 0.88, 0.82, and 0.83, specificity of 0.72, 0.83, and 0.76 and AUC of 0.90, 0.84, and 0.87, respectively. In per territory

Quantitative Imaging Biomarkers: A Review of Statistical Methods for Technical Performance Assessment

PubMed Central

2017-01-01

Technological developments and greater rigor in the quantitative measurement of biological features in medical images have given rise to an increased interest in using quantitative imaging biomarkers (QIBs) to measure changes in these features. Critical to the performance of a QIB in preclinical or clinical settings are three primary metrology areas of interest: measurement linearity and bias, repeatability, and the ability to consistently reproduce equivalent results when conditions change, as would be expected in any clinical trial. Unfortunately, performance studies to date differ greatly in designs, analysis method and metrics used to assess a QIB for clinical use. It is therefore, difficult or not possible to integrate results from different studies or to use reported results to design studies. The Radiological Society of North America (RSNA) and the Quantitative Imaging Biomarker Alliance (QIBA) with technical, radiological and statistical experts developed a set of technical performance analysis methods, metrics and study designs that provide terminology, metrics and methods consistent with widely accepted metrological standards. This document provides a consistent framework for the conduct and evaluation of QIB performance studies so that results from multiple studies can be compared, contrasted or combined. PMID:24919831

Quantitative analysis of drug distribution by ambient mass spectrometry imaging method with signal extinction normalization strategy and inkjet-printing technology.

PubMed

Luo, Zhigang; He, Jingjing; He, Jiuming; Huang, Lan; Song, Xiaowei; Li, Xin; Abliz, Zeper

2018-03-01

Quantitative mass spectrometry imaging (MSI) is a robust approach that provides both quantitative and spatial information for drug candidates' research. However, because of complicated signal suppression and interference, acquiring accurate quantitative information from MSI data remains a challenge, especially for whole-body tissue sample. Ambient MSI techniques using spray-based ionization appear to be ideal for pharmaceutical quantitative MSI analysis. However, it is more challenging, as it involves almost no sample preparation and is more susceptible to ion suppression/enhancement. Herein, based on our developed air flow-assisted desorption electrospray ionization (AFADESI)-MSI technology, an ambient quantitative MSI method was introduced by integrating inkjet-printing technology with normalization of the signal extinction coefficient (SEC) using the target compound itself. The method utilized a single calibration curve to quantify multiple tissue types. Basic blue 7 and an antitumor drug candidate (S-(+)-deoxytylophorinidine, CAT) were chosen to initially validate the feasibility and reliability of the quantitative MSI method. Rat tissue sections (heart, kidney, and brain) administered with CAT was then analyzed. The quantitative MSI analysis results were cross-validated by LC-MS/MS analysis data of the same tissues. The consistency suggests that the approach is able to fast obtain the quantitative MSI data without introducing interference into the in-situ environment of the tissue sample, and is potential to provide a high-throughput, economical and reliable approach for drug discovery and development. Copyright © 2017 Elsevier B.V. All rights reserved.

A method for rapid quantitative assessment of biofilms with biomolecular staining and image analysis.

PubMed

Larimer, Curtis; Winder, Eric; Jeters, Robert; Prowant, Matthew; Nettleship, Ian; Addleman, Raymond Shane; Bonheyo, George T

2016-01-01

The accumulation of bacteria in surface-attached biofilms can be detrimental to human health, dental hygiene, and many industrial processes. Natural biofilms are soft and often transparent, and they have heterogeneous biological composition and structure over micro- and macroscales. As a result, it is challenging to quantify the spatial distribution and overall intensity of biofilms. In this work, a new method was developed to enhance the visibility and quantification of bacterial biofilms. First, broad-spectrum biomolecular staining was used to enhance the visibility of the cells, nucleic acids, and proteins that make up biofilms. Then, an image analysis algorithm was developed to objectively and quantitatively measure biofilm accumulation from digital photographs and results were compared to independent measurements of cell density. This new method was used to quantify the growth intensity of Pseudomonas putida biofilms as they grew over time. This method is simple and fast, and can quantify biofilm growth over a large area with approximately the same precision as the more laborious cell counting method. Stained and processed images facilitate assessment of spatial heterogeneity of a biofilm across a surface. This new approach to biofilm analysis could be applied in studies of natural, industrial, and environmental biofilms.

Movement Correction Method for Human Brain PET Images: Application to Quantitative Analysis of Dynamic [18F]-FDDNP Scans

PubMed Central

Wardak, Mirwais; Wong, Koon-Pong; Shao, Weber; Dahlbom, Magnus; Kepe, Vladimir; Satyamurthy, Nagichettiar; Small, Gary W.; Barrio, Jorge R.; Huang, Sung-Cheng

2010-01-01

Head movement during a PET scan (especially, dynamic scan) can affect both the qualitative and quantitative aspects of an image, making it difficult to accurately interpret the results. The primary objective of this study was to develop a retrospective image-based movement correction (MC) method and evaluate its implementation on dynamic [18F]-FDDNP PET images of cognitively intact controls and patients with Alzheimer’s disease (AD). Methods Dynamic [18F]-FDDNP PET images, used for in vivo imaging of beta-amyloid plaques and neurofibrillary tangles, were obtained from 12 AD and 9 age-matched controls. For each study, a transmission scan was first acquired for attenuation correction. An accurate retrospective MC method that corrected for transmission-emission misalignment as well as emission-emission misalignment was applied to all studies. No restriction was assumed for zero movement between the transmission scan and first emission scan. Logan analysis with cerebellum as the reference region was used to estimate various regional distribution volume ratio (DVR) values in the brain before and after MC. Discriminant analysis was used to build a predictive model for group membership, using data with and without MC. Results MC improved the image quality and quantitative values in [18F]-FDDNP PET images. In this subject population, medial temporal (MTL) did not show a significant difference between controls and AD before MC. However, after MC, significant differences in DVR values were seen in frontal, parietal, posterior cingulate (PCG), MTL, lateral temporal (LTL), and global between the two groups (P < 0.05). In controls and AD, the variability of regional DVR values (as measured by the coefficient of variation) decreased on average by >18% after MC. Mean DVR separation between controls and ADs was higher in frontal, MTL, LTL and global after MC. Group classification by discriminant analysis based on [18F]-FDDNP DVR values was markedly improved after MC. Conclusion

Quantitative image fusion in infrared radiometry

NASA Astrophysics Data System (ADS)

Romm, Iliya; Cukurel, Beni

2018-05-01

Towards high-accuracy infrared radiance estimates, measurement practices and processing techniques aimed to achieve quantitative image fusion using a set of multi-exposure images of a static scene are reviewed. The conventional non-uniformity correction technique is extended, as the original is incompatible with quantitative fusion. Recognizing the inherent limitations of even the extended non-uniformity correction, an alternative measurement methodology, which relies on estimates of the detector bias using self-calibration, is developed. Combining data from multi-exposure images, two novel image fusion techniques that ultimately provide high tonal fidelity of a photoquantity are considered: ‘subtract-then-fuse’, which conducts image subtraction in the camera output domain and partially negates the bias frame contribution common to both the dark and scene frames; and ‘fuse-then-subtract’, which reconstructs the bias frame explicitly and conducts image fusion independently for the dark and the scene frames, followed by subtraction in the photoquantity domain. The performances of the different techniques are evaluated for various synthetic and experimental data, identifying the factors contributing to potential degradation of the image quality. The findings reflect the superiority of the ‘fuse-then-subtract’ approach, conducting image fusion via per-pixel nonlinear weighted least squares optimization.

New tools for comparing microscopy images: quantitative analysis of cell types in Bacillus subtilis.

PubMed

van Gestel, Jordi; Vlamakis, Hera; Kolter, Roberto

2015-02-15

Fluorescence microscopy is a method commonly used to examine individual differences between bacterial cells, yet many studies still lack a quantitative analysis of fluorescence microscopy data. Here we introduce some simple tools that microbiologists can use to analyze and compare their microscopy images. We show how image data can be converted to distribution data. These data can be subjected to a cluster analysis that makes it possible to objectively compare microscopy images. The distribution data can further be analyzed using distribution fitting. We illustrate our methods by scrutinizing two independently acquired data sets, each containing microscopy images of a doubly labeled Bacillus subtilis strain. For the first data set, we examined the expression of srfA and tapA, two genes which are expressed in surfactin-producing and matrix-producing cells, respectively. For the second data set, we examined the expression of eps and tapA; these genes are expressed in matrix-producing cells. We show that srfA is expressed by all cells in the population, a finding which contrasts with a previously reported bimodal distribution of srfA expression. In addition, we show that eps and tapA do not always have the same expression profiles, despite being expressed in the same cell type: both operons are expressed in cell chains, while single cells mainly express eps. These findings exemplify that the quantification and comparison of microscopy data can yield insights that otherwise would go unnoticed. Copyright © 2015, American Society for Microbiology. All Rights Reserved.

Two-dimensional auto-correlation analysis and Fourier-transform analysis of second-harmonic-generation image for quantitative analysis of collagen fiber in human facial skin

NASA Astrophysics Data System (ADS)

Ogura, Yuki; Tanaka, Yuji; Hase, Eiji; Yamashita, Toyonobu; Yasui, Takeshi

2018-02-01

We compare two-dimensional auto-correlation (2D-AC) analysis and two-dimensional Fourier transform (2D-FT) for evaluation of age-dependent structural change of facial dermal collagen fibers caused by intrinsic aging and extrinsic photo-aging. The age-dependent structural change of collagen fibers for female subjects' cheek skin in their 20s, 40s, and 60s were more noticeably reflected in 2D-AC analysis than in 2D-FT analysis. Furthermore, 2D-AC analysis indicated significantly higher correlation with the skin elasticity measured by Cutometer® than 2D-AC analysis. 2D-AC analysis of SHG image has a high potential for quantitative evaluation of not only age-dependent structural change of collagen fibers but also skin elasticity.

Quantitative method to assess caries via fluorescence imaging from the perspective of autofluorescence spectral analysis

NASA Astrophysics Data System (ADS)

Chen, Q. G.; Zhu, H. H.; Xu, Y.; Lin, B.; Chen, H.

2015-08-01

A quantitative method to discriminate caries lesions for a fluorescence imaging system is proposed in this paper. The autofluorescence spectral investigation of 39 teeth samples classified by the International Caries Detection and Assessment System levels was performed at 405 nm excitation. The major differences in the different caries lesions focused on the relative spectral intensity range of 565-750 nm. The spectral parameter, defined as the ratio of wavebands at 565-750 nm to the whole spectral range, was calculated. The image component ratio R/(G + B) of color components was statistically computed by considering the spectral parameters (e.g. autofluorescence, optical filter, and spectral sensitivity) in our fluorescence color imaging system. Results showed that the spectral parameter and image component ratio presented a linear relation. Therefore, the image component ratio was graded as <0.66, 0.66-1.06, 1.06-1.62, and >1.62 to quantitatively classify sound, early decay, established decay, and severe decay tissues, respectively. Finally, the fluorescence images of caries were experimentally obtained, and the corresponding image component ratio distribution was compared with the classification result. A method to determine the numerical grades of caries using a fluorescence imaging system was proposed. This method can be applied to similar imaging systems.

Inverse transport problems in quantitative PAT for molecular imaging

NASA Astrophysics Data System (ADS)

Ren, Kui; Zhang, Rongting; Zhong, Yimin

2015-12-01

Fluorescence photoacoustic tomography (fPAT) is a molecular imaging modality that combines photoacoustic tomography with fluorescence imaging to obtain high-resolution imaging of fluorescence distributions inside heterogeneous media. The objective of this work is to study inverse problems in the quantitative step of fPAT where we intend to reconstruct physical coefficients in a coupled system of radiative transport equations using internal data recovered from ultrasound measurements. We derive uniqueness and stability results on the inverse problems and develop some efficient algorithms for image reconstructions. Numerical simulations based on synthetic data are presented to validate the theoretical analysis. The results we present here complement these in Ren K and Zhao H (2013 SIAM J. Imaging Sci. 6 2024-49) on the same problem but in the diffusive regime.

Quantitative analysis of ultrasonic images of fibrotic liver using co-occurrence matrix based on multi-Rayleigh model

NASA Astrophysics Data System (ADS)

Isono, Hiroshi; Hirata, Shinnosuke; Hachiya, Hiroyuki

2015-07-01

In medical ultrasonic images of liver disease, a texture with a speckle pattern indicates a microscopic structure such as nodules surrounded by fibrous tissues in hepatitis or cirrhosis. We have been applying texture analysis based on a co-occurrence matrix to ultrasonic images of fibrotic liver for quantitative tissue characterization. A co-occurrence matrix consists of the probability distribution of brightness of pixel pairs specified with spatial parameters and gives new information on liver disease. Ultrasonic images of different types of fibrotic liver were simulated and the texture-feature contrast was calculated to quantify the co-occurrence matrices generated from the images. The results show that the contrast converges with a value that can be theoretically estimated using a multi-Rayleigh model of echo signal amplitude distribution. We also found that the contrast value increases as liver fibrosis progresses and fluctuates depending on the size of fibrotic structure.

A novel image-based quantitative method for the characterization of NETosis

PubMed Central

Zhao, Wenpu; Fogg, Darin K.; Kaplan, Mariana J.

2015-01-01

NETosis is a newly recognized mechanism of programmed neutrophil death. It is characterized by a stepwise progression of chromatin decondensation, membrane rupture, and release of bactericidal DNA-based structures called neutrophil extracellular traps (NETs). Conventional ‘suicidal’ NETosis has been described in pathogenic models of systemic autoimmune disorders. Recent in vivo studies suggest that a process of ‘vital’ NETosis also exists, in which chromatin is condensed and membrane integrity is preserved. Techniques to assess ‘suicidal’ or ‘vital’ NET formation in a specific, quantitative, rapid and semiautomated way have been lacking, hindering the characterization of this process. Here we have developed a new method to simultaneously assess both ‘suicidal’ and ‘vital’ NETosis, using high-speed multi-spectral imaging coupled to morphometric image analysis, to quantify spontaneous NET formation observed ex-vivo or stimulus-induced NET formation triggered in vitro. Use of imaging flow cytometry allows automated, quantitative and rapid analysis of subcellular morphology and texture, and introduces the potential for further investigation using NETosis as a biomarker in pre-clinical and clinical studies. PMID:26003624

UK audit of quantitative thyroid uptake imaging.

PubMed

Taylor, Jonathan C; Murray, Anthony W; Hall, David O; Barnfield, Mark C; O'Shaugnessy, Emma R; Carson, Kathryn J; Cullis, James; Towey, David J; Kenny, Bob

2017-07-01

A national audit of quantitative thyroid uptake imaging was conducted by the Nuclear Medicine Software Quality Group of the Institute of Physics and Engineering in Medicine in 2014/2015. The aims of the audit were to measure and assess the variability in thyroid uptake results across the UK and to compare local protocols with British Nuclear Medicine Society (BNMS) guidelines. Participants were invited through a combination of emails on a public mailbase and targeted invitations from regional co-ordinators. All participants were given a set of images from which to calculate quantitative measures and a spreadsheet for capturing results. The image data consisted of two sets of 10 anterior thyroid images, half of which were acquired after administration of Tc-pertechnetate and the other half after administration of I-iodide. Images of the administration syringes or thyroid phantoms were also included. In total, 54 participants responded to the audit. The median number of scans conducted per year was 50. A majority of centres had at least one noncompliance in comparison with BNMS guidelines. Of most concern was the widespread lack of injection-site imaging. Quantitative results showed that both intersite and intrasite variability were low for the Tc dataset. The coefficient of quartile deviation was between 0.03 and 0.13 for measurements of overall percentage uptake. Although the number of returns for the I dataset was smaller, the level of variability between participants was greater (the coefficient of quartile deviation was between 0.17 and 0.25). A UK-wide audit showed that thyroid uptake imaging is still a common test in the UK. It was found that most centres do not adhere to all aspects of the BNMS practice guidelines but that quantitative results are reasonably consistent for Tc-based scans.

Alzheimer disease: Quantitative analysis of I-123-iodoamphetamine SPECT brain imaging

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

Hellman, R.S.; Tikofsky, R.S.; Collier, B.D.

1989-07-01

To enable a more quantitative diagnosis of senile dementia of the Alzheimer type (SDAT), the authors developed and tested a semiautomated method to define regions of interest (ROIs) to be used in quantitating results from single photon emission computed tomography (SPECT) of regional cerebral blood flow performed with N-isopropyl iodine-123-iodoamphetamine. SPECT/IMP imaging was performed in ten patients with probable SDAT and seven healthy subjects. Multiple ROIs were manually and semiautomatically generated, and uptake was quantitated for each ROI. Mean cortical activity was estimated as the average of the mean activity in 24 semiautomatically generated ROIs; mean cerebellar activity was determinedmore » from the mean activity in separate ROIs. A ratio of parietal to cerebellar activity less than 0.60 and a ratio of parietal to mean cortical activity less than 0.90 allowed correct categorization of nine of ten and eight of ten patients, respectively, with SDAT and all control subjects. The degree of diminished mental status observed in patients with SDAT correlated with both global and regional changes in IMP uptake.« less

I Vivo Quantitative Ultrasound Imaging and Scatter Assessments.

NASA Astrophysics Data System (ADS)

Lu, Zheng Feng

^{-1} in controls compared with 74times 10^{-4}cm^{-1}sr^ {-1} (at 6 MHz) in treated animals. A simplified quantitative approach using video image signals was developed. Results derived both from the r.f. signal analysis and from the video signal analysis are sensitive to the changes in the liver in this animal model.

Quantitative imaging features: extension of the oncology medical image database

NASA Astrophysics Data System (ADS)

Patel, M. N.; Looney, P. T.; Young, K. C.; Halling-Brown, M. D.

2015-03-01

Radiological imaging is fundamental within the healthcare industry and has become routinely adopted for diagnosis, disease monitoring and treatment planning. With the advent of digital imaging modalities and the rapid growth in both diagnostic and therapeutic imaging, the ability to be able to harness this large influx of data is of paramount importance. The Oncology Medical Image Database (OMI-DB) was created to provide a centralized, fully annotated dataset for research. The database contains both processed and unprocessed images, associated data, and annotations and where applicable expert determined ground truths describing features of interest. Medical imaging provides the ability to detect and localize many changes that are important to determine whether a disease is present or a therapy is effective by depicting alterations in anatomic, physiologic, biochemical or molecular processes. Quantitative imaging features are sensitive, specific, accurate and reproducible imaging measures of these changes. Here, we describe an extension to the OMI-DB whereby a range of imaging features and descriptors are pre-calculated using a high throughput approach. The ability to calculate multiple imaging features and data from the acquired images would be valuable and facilitate further research applications investigating detection, prognosis, and classification. The resultant data store contains more than 10 million quantitative features as well as features derived from CAD predictions. Theses data can be used to build predictive models to aid image classification, treatment response assessment as well as to identify prognostic imaging biomarkers.

On-site semi-quantitative analysis for ammonium nitrate detection using digital image colourimetry.

PubMed

Choodum, Aree; Boonsamran, Pichapat; NicDaeid, Niamh; Wongniramaikul, Worawit

2015-12-01

Digital image colourimetry was successfully applied in the semi-quantitative analysis of ammonium nitrate using Griess's test with zinc reduction. A custom-built detection box was developed to enable reproducible lighting of samples, and was used with the built-in webcams of a netbook and an ultrabook for on-site detection. The webcams were used for colour imaging of chemical reaction products in the samples, while the netbook was used for on-site colour analysis. The analytical performance was compared to a commercial external webcam and a digital single-lens reflex (DSLR) camera. The relationship between Red-Green-Blue intensities and ammonium nitrate concentration was investigated. The green channel intensity (IG) was the most sensitive for the pink-violet products from ammonium nitrate that revealed a spectrometric absorption peak at 546 nm. A wide linear range (5 to 250 mgL⁻¹) with a high sensitivity was obtained with the built-in webcam of the ultrabook. A considerably lower detection limit (1.34 ± 0.05mgL⁻¹) was also obtained using the ultrabook, in comparison with the netbook (2.6 ± 0.2 mgL⁻¹), the external web cam (3.4 ± 0.1 mgL⁻¹) and the DSLR (8.0 ± 0.5 mgL⁻¹). The best inter-day precision (over 3 days) was obtained with the external webcam (0.40 to 1.34%RSD), while the netbook and the ultrabook had 0.52 to 3.62% and 1.25 to 4.99% RSDs, respectively. The relative errors were +3.6, +5.6 and -7.1%, on analysing standard ammonium nitrate solutions of known concentration using IG, for the ultrabook, the external webcam, and the netbook, respectively, while the DSLR gave -4.4% relative error. However, the IG of the pink-violet reaction product suffers from interference by soil, so that blank subtraction (|IG-IGblank| or |AG-AGblank|) is recommended for soil sample analysis. This method also gave very good accuracies of -0.11 to -5.61% for spiked soil samples and the results presented for five seized samples showed good correlations between

Improving image segmentation performance and quantitative analysis via a computer-aided grading methodology for optical coherence tomography retinal image analysis

NASA Astrophysics Data System (ADS)

Cabrera Debuc, Delia; Salinas, Harry M.; Ranganathan, Sudarshan; Tátrai, Erika; Gao, Wei; Shen, Meixiao; Wang, Jianhua; Somfai, Gábor M.; Puliafito, Carmen A.

2010-07-01

We demonstrate quantitative analysis and error correction of optical coherence tomography (OCT) retinal images by using a custom-built, computer-aided grading methodology. A total of 60 Stratus OCT (Carl Zeiss Meditec, Dublin, California) B-scans collected from ten normal healthy eyes are analyzed by two independent graders. The average retinal thickness per macular region is compared with the automated Stratus OCT results. Intergrader and intragrader reproducibility is calculated by Bland-Altman plots of the mean difference between both gradings and by Pearson correlation coefficients. In addition, the correlation between Stratus OCT and our methodology-derived thickness is also presented. The mean thickness difference between Stratus OCT and our methodology is 6.53 μm and 26.71 μm when using the inner segment/outer segment (IS/OS) junction and outer segment/retinal pigment epithelium (OS/RPE) junction as the outer retinal border, respectively. Overall, the median of the thickness differences as a percentage of the mean thickness is less than 1% and 2% for the intragrader and intergrader reproducibility test, respectively. The measurement accuracy range of the OCT retinal image analysis (OCTRIMA) algorithm is between 0.27 and 1.47 μm and 0.6 and 1.76 μm for the intragrader and intergrader reproducibility tests, respectively. Pearson correlation coefficients demonstrate R2>0.98 for all Early Treatment Diabetic Retinopathy Study (ETDRS) regions. Our methodology facilitates a more robust and localized quantification of the retinal structure in normal healthy controls and patients with clinically significant intraretinal features.

Hyperspectral and differential CARS microscopy for quantitative chemical imaging in human adipocytes

PubMed Central

Di Napoli, Claudia; Pope, Iestyn; Masia, Francesco; Watson, Peter; Langbein, Wolfgang; Borri, Paola

2014-01-01

In this work, we demonstrate the applicability of coherent anti-Stokes Raman scattering (CARS) micro-spectroscopy for quantitative chemical imaging of saturated and unsaturated lipids in human stem-cell derived adipocytes. We compare dual-frequency/differential CARS (D-CARS), which enables rapid imaging and simple data analysis, with broadband hyperspectral CARS microscopy analyzed using an unsupervised phase-retrieval and factorization method recently developed by us for quantitative chemical image analysis. Measurements were taken in the vibrational fingerprint region (1200–2000/cm) and in the CH stretch region (2600–3300/cm) using a home-built CARS set-up which enables hyperspectral imaging with 10/cm resolution via spectral focussing from a single broadband 5 fs Ti:Sa laser source. Through a ratiometric analysis, both D-CARS and phase-retrieved hyperspectral CARS determine the concentration of unsaturated lipids with comparable accuracy in the fingerprint region, while in the CH stretch region D-CARS provides only a qualitative contrast owing to its non-linear behavior. When analyzing hyperspectral CARS images using the blind factorization into susceptibilities and concentrations of chemical components recently demonstrated by us, we are able to determine vol:vol concentrations of different lipid components and spatially resolve inhomogeneities in lipid composition with superior accuracy compared to state-of-the art ratiometric methods. PMID:24877002

Isolating specific cell and tissue compartments from 3D images for quantitative regional distribution analysis using novel computer algorithms.

PubMed

Fenrich, Keith K; Zhao, Ethan Y; Wei, Yuan; Garg, Anirudh; Rose, P Ken

2014-04-15

Isolating specific cellular and tissue compartments from 3D image stacks for quantitative distribution analysis is crucial for understanding cellular and tissue physiology under normal and pathological conditions. Current approaches are limited because they are designed to map the distributions of synapses onto the dendrites of stained neurons and/or require specific proprietary software packages for their implementation. To overcome these obstacles, we developed algorithms to Grow and Shrink Volumes of Interest (GSVI) to isolate specific cellular and tissue compartments from 3D image stacks for quantitative analysis and incorporated these algorithms into a user-friendly computer program that is open source and downloadable at no cost. The GSVI algorithm was used to isolate perivascular regions in the cortex of live animals and cell membrane regions of stained spinal motoneurons in histological sections. We tracked the real-time, intravital biodistribution of injected fluorophores with sub-cellular resolution from the vascular lumen to the perivascular and parenchymal space following a vascular microlesion, and mapped the precise distributions of membrane-associated KCC2 and gephyrin immunolabeling in dendritic and somatic regions of spinal motoneurons. Compared to existing approaches, the GSVI approach is specifically designed for isolating perivascular regions and membrane-associated regions for quantitative analysis, is user-friendly, and free. The GSVI algorithm is useful to quantify regional differences of stained biomarkers (e.g., cell membrane-associated channels) in relation to cell functions, and the effects of therapeutic strategies on the redistributions of biomolecules, drugs, and cells in diseased or injured tissues. Copyright © 2014 Elsevier B.V. All rights reserved.

Quantitative Imaging with a Mobile Phone Microscope

PubMed Central

Skandarajah, Arunan; Reber, Clay D.; Switz, Neil A.; Fletcher, Daniel A.

2014-01-01

Use of optical imaging for medical and scientific applications requires accurate quantification of features such as object size, color, and brightness. High pixel density cameras available on modern mobile phones have made photography simple and convenient for consumer applications; however, the camera hardware and software that enables this simplicity can present a barrier to accurate quantification of image data. This issue is exacerbated by automated settings, proprietary image processing algorithms, rapid phone evolution, and the diversity of manufacturers. If mobile phone cameras are to live up to their potential to increase access to healthcare in low-resource settings, limitations of mobile phone–based imaging must be fully understood and addressed with procedures that minimize their effects on image quantification. Here we focus on microscopic optical imaging using a custom mobile phone microscope that is compatible with phones from multiple manufacturers. We demonstrate that quantitative microscopy with micron-scale spatial resolution can be carried out with multiple phones and that image linearity, distortion, and color can be corrected as needed. Using all versions of the iPhone and a selection of Android phones released between 2007 and 2012, we show that phones with greater than 5 MP are capable of nearly diffraction-limited resolution over a broad range of magnifications, including those relevant for single cell imaging. We find that automatic focus, exposure, and color gain standard on mobile phones can degrade image resolution and reduce accuracy of color capture if uncorrected, and we devise procedures to avoid these barriers to quantitative imaging. By accommodating the differences between mobile phone cameras and the scientific cameras, mobile phone microscopes can be reliably used to increase access to quantitative imaging for a variety of medical and scientific applications. PMID:24824072

Developing the Quantitative Histopathology Image Ontology (QHIO): A case study using the hot spot detection problem.

PubMed

Gurcan, Metin N; Tomaszewski, John; Overton, James A; Doyle, Scott; Ruttenberg, Alan; Smith, Barry

2017-02-01

Interoperability across data sets is a key challenge for quantitative histopathological imaging. There is a need for an ontology that can support effective merging of pathological image data with associated clinical and demographic data. To foster organized, cross-disciplinary, information-driven collaborations in the pathological imaging field, we propose to develop an ontology to represent imaging data and methods used in pathological imaging and analysis, and call it Quantitative Histopathological Imaging Ontology - QHIO. We apply QHIO to breast cancer hot-spot detection with the goal of enhancing reliability of detection by promoting the sharing of data between image analysts. Copyright © 2016 Elsevier Inc. All rights reserved.

Meeting Report: Tissue-based Image Analysis.

PubMed

Saravanan, Chandra; Schumacher, Vanessa; Brown, Danielle; Dunstan, Robert; Galarneau, Jean-Rene; Odin, Marielle; Mishra, Sasmita

2017-10-01

Quantitative image analysis (IA) is a rapidly evolving area of digital pathology. Although not a new concept, the quantification of histological features on photomicrographs used to be cumbersome, resource-intensive, and limited to specialists and specialized laboratories. Recent technological advances like highly efficient automated whole slide digitizer (scanner) systems, innovative IA platforms, and the emergence of pathologist-friendly image annotation and analysis systems mean that quantification of features on histological digital images will become increasingly prominent in pathologists' daily professional lives. The added value of quantitative IA in pathology includes confirmation of equivocal findings noted by a pathologist, increasing the sensitivity of feature detection, quantification of signal intensity, and improving efficiency. There is no denying that quantitative IA is part of the future of pathology; however, there are also several potential pitfalls when trying to estimate volumetric features from limited 2-dimensional sections. This continuing education session on quantitative IA offered a broad overview of the field; a hands-on toxicologic pathologist experience with IA principles, tools, and workflows; a discussion on how to apply basic stereology principles in order to minimize bias in IA; and finally, a reflection on the future of IA in the toxicologic pathology field.

Brain Injury Lesion Imaging Using Preconditioned Quantitative Susceptibility Mapping without Skull Stripping.

PubMed

Soman, S; Liu, Z; Kim, G; Nemec, U; Holdsworth, S J; Main, K; Lee, B; Kolakowsky-Hayner, S; Selim, M; Furst, A J; Massaband, P; Yesavage, J; Adamson, M M; Spincemallie, P; Moseley, M; Wang, Y

2018-04-01

Identifying cerebral microhemorrhage burden can aid in the diagnosis and management of traumatic brain injury, stroke, hypertension, and cerebral amyloid angiopathy. MR imaging susceptibility-based methods are more sensitive than CT for detecting cerebral microhemorrhage, but methods other than quantitative susceptibility mapping provide results that vary with field strength and TE, require additional phase maps to distinguish blood from calcification, and depict cerebral microhemorrhages as bloom artifacts. Quantitative susceptibility mapping provides universal quantification of tissue magnetic property without these constraints but traditionally requires a mask generated by skull-stripping, which can pose challenges at tissue interphases. We evaluated the preconditioned quantitative susceptibility mapping MR imaging method, which does not require skull-stripping, for improved depiction of brain parenchyma and pathology. Fifty-six subjects underwent brain MR imaging with a 3D multiecho gradient recalled echo acquisition. Mask-based quantitative susceptibility mapping images were created using a commonly used mask-based quantitative susceptibility mapping method, and preconditioned quantitative susceptibility images were made using precondition-based total field inversion. All images were reviewed by a neuroradiologist and a radiology resident. Ten subjects (18%), all with traumatic brain injury, demonstrated blood products on 3D gradient recalled echo imaging. All lesions were visible on preconditioned quantitative susceptibility mapping, while 6 were not visible on mask-based quantitative susceptibility mapping. Thirty-one subjects (55%) demonstrated brain parenchyma and/or lesions that were visible on preconditioned quantitative susceptibility mapping but not on mask-based quantitative susceptibility mapping. Six subjects (11%) demonstrated pons artifacts on preconditioned quantitative susceptibility mapping and mask-based quantitative susceptibility mapping

Image analysis and modeling in medical image computing. Recent developments and advances.

PubMed

Handels, H; Deserno, T M; Meinzer, H-P; Tolxdorff, T

2012-01-01

Medical image computing is of growing importance in medical diagnostics and image-guided therapy. Nowadays, image analysis systems integrating advanced image computing methods are used in practice e.g. to extract quantitative image parameters or to support the surgeon during a navigated intervention. However, the grade of automation, accuracy, reproducibility and robustness of medical image computing methods has to be increased to meet the requirements in clinical routine. In the focus theme, recent developments and advances in the field of modeling and model-based image analysis are described. The introduction of models in the image analysis process enables improvements of image analysis algorithms in terms of automation, accuracy, reproducibility and robustness. Furthermore, model-based image computing techniques open up new perspectives for prediction of organ changes and risk analysis of patients. Selected contributions are assembled to present latest advances in the field. The authors were invited to present their recent work and results based on their outstanding contributions to the Conference on Medical Image Computing BVM 2011 held at the University of Lübeck, Germany. All manuscripts had to pass a comprehensive peer review. Modeling approaches and model-based image analysis methods showing new trends and perspectives in model-based medical image computing are described. Complex models are used in different medical applications and medical images like radiographic images, dual-energy CT images, MR images, diffusion tensor images as well as microscopic images are analyzed. The applications emphasize the high potential and the wide application range of these methods. The use of model-based image analysis methods can improve segmentation quality as well as the accuracy and reproducibility of quantitative image analysis. Furthermore, image-based models enable new insights and can lead to a deeper understanding of complex dynamic mechanisms in the human body

Mild hypothermia for treatment of diffuse axonal injury: a quantitative analysis of diffusion tensor imaging

PubMed Central

Jing, Guojie; Yao, Xiaoteng; Li, Yiyi; Xie, Yituan; Li, Wang#x2019;an; Liu, Kejun; Jing, Yingchao; Li, Baisheng; Lv, Yifan; Ma, Baoxin

2014-01-01

Fractional anisotropy values in diffusion tensor imaging can quantitatively reflect the consistency of nerve fibers after brain damage, where higher values generally indicate less damage to nerve fibers. Therefore, we hypothesized that diffusion tensor imaging could be used to evaluate the effect of mild hypothermia on diffuse axonal injury. A total of 102 patients with diffuse axonal injury were randomly divided into two groups: normothermic and mild hypothermic treatment groups. Patient's modified Rankin scale scores 2 months after mild hypothermia were significantly lower than those for the normothermia group. The difference in average fractional anisotropy value for each region of interest before and after mild hypothermia was 1.32-1.36 times higher than the value in the normothermia group. Quantitative assessment of diffusion tensor imaging indicates that mild hypothermia therapy may be beneficial for patients with diffuse axonal injury. PMID:25206800

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

Live-cell confocal microscopy and quantitative 4D image analysis of anchor cell invasion through the basement membrane in C. elegans

PubMed Central

Kelley, Laura C.; Wang, Zheng; Hagedorn, Elliott J.; Wang, Lin; Shen, Wanqing; Lei, Shijun; Johnson, Sam A.; Sherwood, David R.

2018-01-01

Cell invasion through basement membrane (BM) barriers is crucial during development, leukocyte trafficking, and for the spread of cancer. Despite its importance in normal and diseased states, the mechanisms that direct invasion are poorly understood, in large part because of the inability to visualize dynamic cell-basement membrane interactions in vivo. This protocol describes multi-channel time-lapse confocal imaging of anchor cell invasion in live C. elegans. Methods presented include outline slide preparation and worm growth synchronization (15 min), mounting (20 min), image acquisition (20-180 min), image processing (20 min), and quantitative analysis (variable timing). Images acquired enable direct measurement of invasive dynamics including invadopodia formation, cell membrane protrusions, and BM removal. This protocol can be combined with genetic analysis, molecular activity probes, and optogenetic approaches to uncover molecular mechanisms underlying cell invasion. These methods can also be readily adapted for real-time analysis of cell migration, basement membrane turnover, and cell membrane dynamics by any worm laboratory. PMID:28880279

Multiparametric Quantitative Ultrasound Imaging in Assessment of Chronic Kidney Disease.

PubMed

Gao, Jing; Perlman, Alan; Kalache, Safa; Berman, Nathaniel; Seshan, Surya; Salvatore, Steven; Smith, Lindsey; Wehrli, Natasha; Waldron, Levi; Kodali, Hanish; Chevalier, James

2017-11-01

To evaluate the value of multiparametric quantitative ultrasound imaging in assessing chronic kidney disease (CKD) using kidney biopsy pathologic findings as reference standards. We prospectively measured multiparametric quantitative ultrasound markers with grayscale, spectral Doppler, and acoustic radiation force impulse imaging in 25 patients with CKD before kidney biopsy and 10 healthy volunteers. Based on all pathologic (glomerulosclerosis, interstitial fibrosis/tubular atrophy, arteriosclerosis, and edema) scores, the patients with CKD were classified into mild (no grade 3 and <2 of grade 2) and moderate to severe (at least 2 of grade 2 or 1 of grade 3) CKD groups. Multiparametric quantitative ultrasound parameters included kidney length, cortical thickness, pixel intensity, parenchymal shear wave velocity, intrarenal artery peak systolic velocity (PSV), end-diastolic velocity (EDV), and resistive index. We tested the difference in quantitative ultrasound parameters among mild CKD, moderate to severe CKD, and healthy controls using analysis of variance, analyzed correlations of quantitative ultrasound parameters with pathologic scores and the estimated glomerular filtration rate (GFR) using Pearson correlation coefficients, and examined the diagnostic performance of quantitative ultrasound parameters in determining moderate CKD and an estimated GFR of less than 60 mL/min/1.73 m 2 using receiver operating characteristic curve analysis. There were significant differences in cortical thickness, pixel intensity, PSV, and EDV among the 3 groups (all P < .01). Among quantitative ultrasound parameters, the top areas under the receiver operating characteristic curves for PSV and EDV were 0.88 and 0.97, respectively, for determining pathologic moderate to severe CKD, and 0.76 and 0.86 for estimated GFR of less than 60 mL/min/1.73 m 2 . Moderate to good correlations were found for PSV, EDV, and pixel intensity with pathologic scores and estimated GFR. The

Multispectral Imaging Broadens Cellular Analysis

NASA Technical Reports Server (NTRS)

2007-01-01

Amnis Corporation, a Seattle-based biotechnology company, developed ImageStream to produce sensitive fluorescence images of cells in flow. The company responded to an SBIR solicitation from Ames Research Center, and proposed to evaluate several methods of extending the depth of field for its ImageStream system and implement the best as an upgrade to its commercial products. This would allow users to view whole cells at the same time, rather than just one section of each cell. Through Phase I and II SBIR contracts, Ames provided Amnis the funding the company needed to develop this extended functionality. For NASA, the resulting high-speed image flow cytometry process made its way into Medusa, a life-detection instrument built to collect, store, and analyze sample organisms from erupting hydrothermal vents, and has the potential to benefit space flight health monitoring. On the commercial end, Amnis has implemented the process in ImageStream, combining high-resolution microscopy and flow cytometry in a single instrument, giving researchers the power to conduct quantitative analyses of individual cells and cell populations at the same time, in the same experiment. ImageStream is also built for many other applications, including cell signaling and pathway analysis; classification and characterization of peripheral blood mononuclear cell populations; quantitative morphology; apoptosis (cell death) assays; gene expression analysis; analysis of cell conjugates; molecular distribution; and receptor mapping and distribution.

Application of automatic image analysis in wood science

Treesearch

Charles W. McMillin

1982-01-01

In this paper I describe an image analysis system and illustrate with examples the application of automatic quantitative measurement to wood science. Automatic image analysis, a powerful and relatively new technology, uses optical, video, electronic, and computer components to rapidly derive information from images with minimal operator interaction. Such instruments...

A quantitative method to measure biofilm removal efficiency from complex biomaterial surfaces using SEM and image analysis

NASA Astrophysics Data System (ADS)

Vyas, N.; Sammons, R. L.; Addison, O.; Dehghani, H.; Walmsley, A. D.

2016-09-01

Biofilm accumulation on biomaterial surfaces is a major health concern and significant research efforts are directed towards producing biofilm resistant surfaces and developing biofilm removal techniques. To accurately evaluate biofilm growth and disruption on surfaces, accurate methods which give quantitative information on biofilm area are needed, as current methods are indirect and inaccurate. We demonstrate the use of machine learning algorithms to segment biofilm from scanning electron microscopy images. A case study showing disruption of biofilm from rough dental implant surfaces using cavitation bubbles from an ultrasonic scaler is used to validate the imaging and analysis protocol developed. Streptococcus mutans biofilm was disrupted from sandblasted, acid etched (SLA) Ti discs and polished Ti discs. Significant biofilm removal occurred due to cavitation from ultrasonic scaling (p < 0.001). The mean sensitivity and specificity values for segmentation of the SLA surface images were 0.80 ± 0.18 and 0.62 ± 0.20 respectively and 0.74 ± 0.13 and 0.86 ± 0.09 respectively for polished surfaces. Cavitation has potential to be used as a novel way to clean dental implants. This imaging and analysis method will be of value to other researchers and manufacturers wishing to study biofilm growth and removal.

A computational image analysis glossary for biologists.

PubMed

Roeder, Adrienne H K; Cunha, Alexandre; Burl, Michael C; Meyerowitz, Elliot M

2012-09-01

Recent advances in biological imaging have resulted in an explosion in the quality and quantity of images obtained in a digital format. Developmental biologists are increasingly acquiring beautiful and complex images, thus creating vast image datasets. In the past, patterns in image data have been detected by the human eye. Larger datasets, however, necessitate high-throughput objective analysis tools to computationally extract quantitative information from the images. These tools have been developed in collaborations between biologists, computer scientists, mathematicians and physicists. In this Primer we present a glossary of image analysis terms to aid biologists and briefly discuss the importance of robust image analysis in developmental studies.

Phase calibration target for quantitative phase imaging with ptychography.

PubMed

Godden, T M; Muñiz-Piniella, A; Claverley, J D; Yacoot, A; Humphry, M J

2016-04-04

Quantitative phase imaging (QPI) utilizes refractive index and thickness variations that lead to optical phase shifts. This gives contrast to images of transparent objects. In quantitative biology, phase images are used to accurately segment cells and calculate properties such as dry mass, volume and proliferation rate. The fidelity of the measured phase shifts is of critical importance in this field. However to date, there has been no standardized method for characterizing the performance of phase imaging systems. Consequently, there is an increasing need for protocols to test the performance of phase imaging systems using well-defined phase calibration and resolution targets. In this work, we present a candidate for a standardized phase resolution target, and measurement protocol for the determination of the transfer of spatial frequencies, and sensitivity of a phase imaging system. The target has been carefully designed to contain well-defined depth variations over a broadband range of spatial frequencies. In order to demonstrate the utility of the target, we measure quantitative phase images on a ptychographic microscope, and compare the measured optical phase shifts with Atomic Force Microscopy (AFM) topography maps and surface profile measurements from coherence scanning interferometry. The results show that ptychography has fully quantitative nanometer sensitivity in optical path differences over a broadband range of spatial frequencies for feature sizes ranging from micrometers to hundreds of micrometers.

Detection of Prostate Cancer: Quantitative Multiparametric MR Imaging Models Developed Using Registered Correlative Histopathology.

PubMed

Metzger, Gregory J; Kalavagunta, Chaitanya; Spilseth, Benjamin; Bolan, Patrick J; Li, Xiufeng; Hutter, Diane; Nam, Jung W; Johnson, Andrew D; Henriksen, Jonathan C; Moench, Laura; Konety, Badrinath; Warlick, Christopher A; Schmechel, Stephen C; Koopmeiners, Joseph S

2016-06-01

Purpose To develop multiparametric magnetic resonance (MR) imaging models to generate a quantitative, user-independent, voxel-wise composite biomarker score (CBS) for detection of prostate cancer by using coregistered correlative histopathologic results, and to compare performance of CBS-based detection with that of single quantitative MR imaging parameters. Materials and Methods Institutional review board approval and informed consent were obtained. Patients with a diagnosis of prostate cancer underwent multiparametric MR imaging before surgery for treatment. All MR imaging voxels in the prostate were classified as cancer or noncancer on the basis of coregistered histopathologic data. Predictive models were developed by using more than one quantitative MR imaging parameter to generate CBS maps. Model development and evaluation of quantitative MR imaging parameters and CBS were performed separately for the peripheral zone and the whole gland. Model accuracy was evaluated by using the area under the receiver operating characteristic curve (AUC), and confidence intervals were calculated with the bootstrap procedure. The improvement in classification accuracy was evaluated by comparing the AUC for the multiparametric model and the single best-performing quantitative MR imaging parameter at the individual level and in aggregate. Results Quantitative T2, apparent diffusion coefficient (ADC), volume transfer constant (K(trans)), reflux rate constant (kep), and area under the gadolinium concentration curve at 90 seconds (AUGC90) were significantly different between cancer and noncancer voxels (P < .001), with ADC showing the best accuracy (peripheral zone AUC, 0.82; whole gland AUC, 0.74). Four-parameter models demonstrated the best performance in both the peripheral zone (AUC, 0.85; P = .010 vs ADC alone) and whole gland (AUC, 0.77; P = .043 vs ADC alone). Individual-level analysis showed statistically significant improvement in AUC in 82% (23 of 28) and 71% (24 of 34

Quantitative imaging test approval and biomarker qualification: interrelated but distinct activities.

PubMed

Buckler, Andrew J; Bresolin, Linda; Dunnick, N Reed; Sullivan, Daniel C; Aerts, Hugo J W L; Bendriem, Bernard; Bendtsen, Claus; Boellaard, Ronald; Boone, John M; Cole, Patricia E; Conklin, James J; Dorfman, Gary S; Douglas, Pamela S; Eidsaunet, Willy; Elsinger, Cathy; Frank, Richard A; Gatsonis, Constantine; Giger, Maryellen L; Gupta, Sandeep N; Gustafson, David; Hoekstra, Otto S; Jackson, Edward F; Karam, Lisa; Kelloff, Gary J; Kinahan, Paul E; McLennan, Geoffrey; Miller, Colin G; Mozley, P David; Muller, Keith E; Patt, Rick; Raunig, David; Rosen, Mark; Rupani, Haren; Schwartz, Lawrence H; Siegel, Barry A; Sorensen, A Gregory; Wahl, Richard L; Waterton, John C; Wolf, Walter; Zahlmann, Gudrun; Zimmerman, Brian

2011-06-01

Quantitative imaging biomarkers could speed the development of new treatments for unmet medical needs and improve routine clinical care. However, it is not clear how the various regulatory and nonregulatory (eg, reimbursement) processes (often referred to as pathways) relate, nor is it clear which data need to be collected to support these different pathways most efficiently, given the time- and cost-intensive nature of doing so. The purpose of this article is to describe current thinking regarding these pathways emerging from diverse stakeholders interested and active in the definition, validation, and qualification of quantitative imaging biomarkers and to propose processes to facilitate the development and use of quantitative imaging biomarkers. A flexible framework is described that may be adapted for each imaging application, providing mechanisms that can be used to develop, assess, and evaluate relevant biomarkers. From this framework, processes can be mapped that would be applicable to both imaging product development and to quantitative imaging biomarker development aimed at increasing the effectiveness and availability of quantitative imaging. http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.10100800/-/DC1. RSNA, 2011

Non-interferometric quantitative phase imaging of yeast cells

NASA Astrophysics Data System (ADS)

Poola, Praveen K.; Pandiyan, Vimal Prabhu; John, Renu

2015-12-01

Real-time imaging of live cells is quite difficult without the addition of external contrast agents. Various methods for quantitative phase imaging of living cells have been proposed like digital holographic microscopy and diffraction phase microscopy. In this paper, we report theoretical and experimental results of quantitative phase imaging of live yeast cells with nanometric precision using transport of intensity equations (TIE). We demonstrate nanometric depth sensitivity in imaging live yeast cells using this technique. This technique being noninterferometric, does not need any coherent light sources and images can be captured through a regular bright-field microscope. This real-time imaging technique would deliver the depth or 3-D volume information of cells and is highly promising in real-time digital pathology applications, screening of pathogens and staging of diseases like malaria as it does not need any preprocessing of samples.

Dual-isotope Cryo-imaging Quantitative Autoradiography (CIQA): Anvestigating Antibody-Drug Conjugate Distribution And Payload Delivery Through Imaging.

PubMed

Ilovich, Ohad; Qutaish, Mohammed; Hesterman, Jacob; Orcutt, Kelly; Hoppin, Jack; Polyak, Ildiko; Seaman, Marc; Abu-Yousif, Adnan; Cvet, Donna; Bradley, Daniel

2018-05-04

In vitro properties of antibody drug conjugates (ADCs) such as binding, internalization, and cytotoxicity are often well characterized prior to in vivo studies. Interpretation of in vivo studies could significantly be enhanced by molecular imaging tools. We present here a dual-isotope cryo-imaging quantitative autoradiography (CIQA) methodology combined with advanced 3D imaging and analysis allowing for the simultaneous study of both antibody and payload distribution in tissues of interest. in a pre-clinical setting. Methods: TAK-264, an investigational anti-guanylyl cyclase C (GCC) targeting ADC was synthesized utilizing tritiated Monomethyl auristatin E (MMAE). The tritiated ADC was then conjugated to DTPA, labeled with indium-111 and evaluated in vivo in GCC-positive and GCC-negative tumor-bearing animals. Results: Cryo-imaging Quantitative Autoradiography (CIQA) reveals the time course of drug release from ADC and its distribution into various tumor regions seemingly impenetrablethat are less accessible to the antibody. For GCC-positive tumors, a representative section obtained 96 hours post tracer injection showed only 0.8% of the voxels have co-localized signal versus over 15% of the voxels for a GCC-negative tumor section., suggesting successful and specific cleaving of the toxin in the antigen positive lesions. Conclusion: The combination of a veteran established autoradiography technology with advanced image analysis methodologies affords an experimental tool that can support detailed characterization of ADC tumor penetration and pharmacokinetics. Copyright © 2018 by the Society of Nuclear Medicine and Molecular Imaging, Inc.

Quantitative imaging as cancer biomarker

NASA Astrophysics Data System (ADS)

Mankoff, David A.

2015-03-01

The ability to assay tumor biologic features and the impact of drugs on tumor biology is fundamental to drug development. Advances in our ability to measure genomics, gene expression, protein expression, and cellular biology have led to a host of new targets for anticancer drug therapy. In translating new drugs into clinical trials and clinical practice, these same assays serve to identify patients most likely to benefit from specific anticancer treatments. As cancer therapy becomes more individualized and targeted, there is an increasing need to characterize tumors and identify therapeutic targets to select therapy most likely to be successful in treating the individual patient's cancer. Thus far assays to identify cancer therapeutic targets or anticancer drug pharmacodynamics have been based upon in vitro assay of tissue or blood samples. Advances in molecular imaging, particularly PET, have led to the ability to perform quantitative non-invasive molecular assays. Imaging has traditionally relied on structural and anatomic features to detect cancer and determine its extent. More recently, imaging has expanded to include the ability to image regional biochemistry and molecular biology, often termed molecular imaging. Molecular imaging can be considered an in vivo assay technique, capable of measuring regional tumor biology without perturbing it. This makes molecular imaging a unique tool for cancer drug development, complementary to traditional assay methods, and a potentially powerful method for guiding targeted therapy in clinical trials and clinical practice. The ability to quantify, in absolute measures, regional in vivo biologic parameters strongly supports the use of molecular imaging as a tool to guide therapy. This review summarizes current and future applications of quantitative molecular imaging as a biomarker for cancer therapy, including the use of imaging to (1) identify patients whose tumors express a specific therapeutic target; (2) determine

Holographic Interferometry and Image Analysis for Aerodynamic Testing

DTIC Science & Technology

1980-09-01

tunnels, (2) development of automated image analysis techniques for reducing quantitative flow-field data from holographic interferograms, and (3...investigation and development of software for the application of digital image analysis to other photographic techniques used in wind tunnel testing.

CALIPSO: an interactive image analysis software package for desktop PACS workstations

NASA Astrophysics Data System (ADS)

Ratib, Osman M.; Huang, H. K.

1990-07-01

The purpose of this project is to develop a low cost workstation for quantitative analysis of multimodality images using a Macintosh II personal computer. In the current configuration the Macintosh operates as a stand alone workstation where images are imported either from a central PACS server through a standard Ethernet network or recorded through video digitizer board. The CALIPSO software developed contains a large variety ofbasic image display and manipulation tools. We focused our effort however on the design and implementation ofquantitative analysis methods that can be applied to images from different imaging modalities. Analysis modules currently implemented include geometric and densitometric volumes and ejection fraction calculation from radionuclide and cine-angiograms Fourier analysis ofcardiac wall motion vascular stenosis measurement color coded parametric display of regional flow distribution from dynamic coronary angiograms automatic analysis ofmyocardial distribution ofradiolabelled tracers from tomoscintigraphic images. Several of these analysis tools were selected because they use similar color coded andparametric display methods to communicate quantitative data extracted from the images. 1. Rationale and objectives of the project Developments of Picture Archiving and Communication Systems (PACS) in clinical environment allow physicians and radiologists to assess radiographic images directly through imaging workstations (''). This convenient access to the images is often limited by the number of workstations available due in part to their high cost. There is also an increasing need for quantitative analysis ofthe images. During thepast decade

Precision of quantitative computed tomography texture analysis using image filtering: A phantom study for scanner variability.

PubMed

Yasaka, Koichiro; Akai, Hiroyuki; Mackin, Dennis; Court, Laurence; Moros, Eduardo; Ohtomo, Kuni; Kiryu, Shigeru

2017-05-01

Quantitative computed tomography (CT) texture analyses for images with and without filtration are gaining attention to capture the heterogeneity of tumors. The aim of this study was to investigate how quantitative texture parameters using image filtering vary among different computed tomography (CT) scanners using a phantom developed for radiomics studies.A phantom, consisting of 10 different cartridges with various textures, was scanned under 6 different scanning protocols using four CT scanners from four different vendors. CT texture analyses were performed for both unfiltered images and filtered images (using a Laplacian of Gaussian spatial band-pass filter) featuring fine, medium, and coarse textures. Forty-five regions of interest were placed for each cartridge (x) in a specific scan image set (y), and the average of the texture values (T(x,y)) was calculated. The interquartile range (IQR) of T(x,y) among the 6 scans was calculated for a specific cartridge (IQR(x)), while the IQR of T(x,y) among the 10 cartridges was calculated for a specific scan (IQR(y)), and the median IQR(y) was then calculated for the 6 scans (as the control IQR, IQRc). The median of their quotient (IQR(x)/IQRc) among the 10 cartridges was defined as the variability index (VI).The VI was relatively small for the mean in unfiltered images (0.011) and for standard deviation (0.020-0.044) and entropy (0.040-0.044) in filtered images. Skewness and kurtosis in filtered images featuring medium and coarse textures were relatively variable across different CT scanners, with VIs of 0.638-0.692 and 0.430-0.437, respectively.Various quantitative CT texture parameters are robust and variable among different scanners, and the behavior of these parameters should be taken into consideration.

The Spectral Image Processing System (SIPS) - Interactive visualization and analysis of imaging spectrometer data

NASA Technical Reports Server (NTRS)

Kruse, F. A.; Lefkoff, A. B.; Boardman, J. W.; Heidebrecht, K. B.; Shapiro, A. T.; Barloon, P. J.; Goetz, A. F. H.

1993-01-01

The Center for the Study of Earth from Space (CSES) at the University of Colorado, Boulder, has developed a prototype interactive software system called the Spectral Image Processing System (SIPS) using IDL (the Interactive Data Language) on UNIX-based workstations. SIPS is designed to take advantage of the combination of high spectral resolution and spatial data presentation unique to imaging spectrometers. It streamlines analysis of these data by allowing scientists to rapidly interact with entire datasets. SIPS provides visualization tools for rapid exploratory analysis and numerical tools for quantitative modeling. The user interface is X-Windows-based, user friendly, and provides 'point and click' operation. SIPS is being used for multidisciplinary research concentrating on use of physically based analysis methods to enhance scientific results from imaging spectrometer data. The objective of this continuing effort is to develop operational techniques for quantitative analysis of imaging spectrometer data and to make them available to the scientific community prior to the launch of imaging spectrometer satellite systems such as the Earth Observing System (EOS) High Resolution Imaging Spectrometer (HIRIS).

Prospects and challenges of quantitative phase imaging in tumor cell biology

NASA Astrophysics Data System (ADS)

Kemper, Björn; Götte, Martin; Greve, Burkhard; Ketelhut, Steffi

2016-03-01

Quantitative phase imaging (QPI) techniques provide high resolution label-free quantitative live cell imaging. Here, prospects and challenges of QPI in tumor cell biology are presented, using the example of digital holographic microscopy (DHM). It is shown that the evaluation of quantitative DHM phase images allows the retrieval of different parameter sets for quantification of cellular motion changes in migration and motility assays that are caused by genetic modifications. Furthermore, we demonstrate simultaneously label-free imaging of cell growth and morphology properties.

Quantitative Analysis of {sup 18}F-Fluorodeoxyglucose Positron Emission Tomography Identifies Novel Prognostic Imaging Biomarkers in Locally Advanced Pancreatic Cancer Patients Treated With Stereotactic Body Radiation Therapy

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

Cui, Yi; Global Institution for Collaborative Research and Education, Hokkaido University, Sapporo; Song, Jie

Purpose: To identify prognostic biomarkers in pancreatic cancer using high-throughput quantitative image analysis. Methods and Materials: In this institutional review board–approved study, we retrospectively analyzed images and outcomes for 139 locally advanced pancreatic cancer patients treated with stereotactic body radiation therapy (SBRT). The overall population was split into a training cohort (n=90) and a validation cohort (n=49) according to the time of treatment. We extracted quantitative imaging characteristics from pre-SBRT {sup 18}F-fluorodeoxyglucose positron emission tomography, including statistical, morphologic, and texture features. A Cox proportional hazard regression model was built to predict overall survival (OS) in the training cohort using 162more » robust image features. To avoid over-fitting, we applied the elastic net to obtain a sparse set of image features, whose linear combination constitutes a prognostic imaging signature. Univariate and multivariate Cox regression analyses were used to evaluate the association with OS, and concordance index (CI) was used to evaluate the survival prediction accuracy. Results: The prognostic imaging signature included 7 features characterizing different tumor phenotypes, including shape, intensity, and texture. On the validation cohort, univariate analysis showed that this prognostic signature was significantly associated with OS (P=.002, hazard ratio 2.74), which improved upon conventional imaging predictors including tumor volume, maximum standardized uptake value, and total legion glycolysis (P=.018-.028, hazard ratio 1.51-1.57). On multivariate analysis, the proposed signature was the only significant prognostic index (P=.037, hazard ratio 3.72) when adjusted for conventional imaging and clinical factors (P=.123-.870, hazard ratio 0.53-1.30). In terms of CI, the proposed signature scored 0.66 and was significantly better than competing prognostic indices (CI 0.48-0.64, Wilcoxon rank sum test P<1e

Quantitative x-ray phase-contrast imaging using a single grating of comparable pitch to sample feature size.

PubMed

Morgan, Kaye S; Paganin, David M; Siu, Karen K W

2011-01-01

The ability to quantitatively retrieve transverse phase maps during imaging by using coherent x rays often requires a precise grating or analyzer-crystal-based setup. Imaging of live animals presents further challenges when these methods require multiple exposures for image reconstruction. We present a simple method of single-exposure, single-grating quantitative phase contrast for a regime in which the grating period is much greater than the effective pixel size. A grating is used to create a high-visibility reference pattern incident on the sample, which is distorted according to the complex refractive index and thickness of the sample. The resolution, along a line parallel to the grating, is not restricted by the grating spacing, and the detector resolution becomes the primary determinant of the spatial resolution. We present a method of analysis that maps the displacement of interrogation windows in order to retrieve a quantitative phase map. Application of this analysis to the imaging of known phantoms shows excellent correspondence.

Toward standardized quantitative image quality (IQ) assessment in computed tomography (CT): A comprehensive framework for automated and comparative IQ analysis based on ICRU Report 87.

PubMed

Pahn, Gregor; Skornitzke, Stephan; Schlemmer, Hans-Peter; Kauczor, Hans-Ulrich; Stiller, Wolfram

2016-01-01

Based on the guidelines from "Report 87: Radiation Dose and Image-quality Assessment in Computed Tomography" of the International Commission on Radiation Units and Measurements (ICRU), a software framework for automated quantitative image quality analysis was developed and its usability for a variety of scientific questions demonstrated. The extendable framework currently implements the calculation of the recommended Fourier image quality (IQ) metrics modulation transfer function (MTF) and noise-power spectrum (NPS), and additional IQ quantities such as noise magnitude, CT number accuracy, uniformity across the field-of-view, contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR) of simulated lesions for a commercially available cone-beam phantom. Sample image data were acquired with different scan and reconstruction settings on CT systems from different manufacturers. Spatial resolution is analyzed in terms of edge-spread function, line-spread-function, and MTF. 3D NPS is calculated according to ICRU Report 87, and condensed to 2D and radially averaged 1D representations. Noise magnitude, CT numbers, and uniformity of these quantities are assessed on large samples of ROIs. Low-contrast resolution (CNR, SNR) is quantitatively evaluated as a function of lesion contrast and diameter. Simultaneous automated processing of several image datasets allows for straightforward comparative assessment. The presented framework enables systematic, reproducible, automated and time-efficient quantitative IQ analysis. Consistent application of the ICRU guidelines facilitates standardization of quantitative assessment not only for routine quality assurance, but for a number of research questions, e.g. the comparison of different scanner models or acquisition protocols, and the evaluation of new technology or reconstruction methods. Copyright © 2015 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

Quantitative Image Informatics for Cancer Research (QIICR) | Informatics Technology for Cancer Research (ITCR)

Cancer.gov

Imaging has enormous untapped potential to improve cancer research through software to extract and process morphometric and functional biomarkers. In the era of non-cytotoxic treatment agents, multi- modality image-guided ablative therapies and rapidly evolving computational resources, quantitative imaging software can be transformative in enabling minimally invasive, objective and reproducible evaluation of cancer treatment response. Post-processing algorithms are integral to high-throughput analysis and fine- grained differentiation of multiple molecular targets.

PCA-based groupwise image registration for quantitative MRI.

PubMed

Huizinga, W; Poot, D H J; Guyader, J-M; Klaassen, R; Coolen, B F; van Kranenburg, M; van Geuns, R J M; Uitterdijk, A; Polfliet, M; Vandemeulebroucke, J; Leemans, A; Niessen, W J; Klein, S

2016-04-01

Quantitative magnetic resonance imaging (qMRI) is a technique for estimating quantitative tissue properties, such as the T1 and T2 relaxation times, apparent diffusion coefficient (ADC), and various perfusion measures. This estimation is achieved by acquiring multiple images with different acquisition parameters (or at multiple time points after injection of a contrast agent) and by fitting a qMRI signal model to the image intensities. Image registration is often necessary to compensate for misalignments due to subject motion and/or geometric distortions caused by the acquisition. However, large differences in image appearance make accurate image registration challenging. In this work, we propose a groupwise image registration method for compensating misalignment in qMRI. The groupwise formulation of the method eliminates the requirement of choosing a reference image, thus avoiding a registration bias. The method minimizes a cost function that is based on principal component analysis (PCA), exploiting the fact that intensity changes in qMRI can be described by a low-dimensional signal model, but not requiring knowledge on the specific acquisition model. The method was evaluated on 4D CT data of the lungs, and both real and synthetic images of five different qMRI applications: T1 mapping in a porcine heart, combined T1 and T2 mapping in carotid arteries, ADC mapping in the abdomen, diffusion tensor mapping in the brain, and dynamic contrast-enhanced mapping in the abdomen. Each application is based on a different acquisition model. The method is compared to a mutual information-based pairwise registration method and four other state-of-the-art groupwise registration methods. Registration accuracy is evaluated in terms of the precision of the estimated qMRI parameters, overlap of segmented structures, distance between corresponding landmarks, and smoothness of the deformation. In all qMRI applications the proposed method performed better than or equally well as

Quantitative multimodality imaging in cancer research and therapy.

PubMed

Yankeelov, Thomas E; Abramson, Richard G; Quarles, C Chad

2014-11-01

Advances in hardware and software have enabled the realization of clinically feasible, quantitative multimodality imaging of tissue pathophysiology. Earlier efforts relating to multimodality imaging of cancer have focused on the integration of anatomical and functional characteristics, such as PET-CT and single-photon emission CT (SPECT-CT), whereas more-recent advances and applications have involved the integration of multiple quantitative, functional measurements (for example, multiple PET tracers, varied MRI contrast mechanisms, and PET-MRI), thereby providing a more-comprehensive characterization of the tumour phenotype. The enormous amount of complementary quantitative data generated by such studies is beginning to offer unique insights into opportunities to optimize care for individual patients. Although important technical optimization and improved biological interpretation of multimodality imaging findings are needed, this approach can already be applied informatively in clinical trials of cancer therapeutics using existing tools. These concepts are discussed herein.

An Image Analysis Method for the Precise Selection and Quantitation of Fluorescently Labeled Cellular Constituents

PubMed Central

Agley, Chibeza C.; Velloso, Cristiana P.; Lazarus, Norman R.

2012-01-01

The accurate measurement of the morphological characteristics of cells with nonuniform conformations presents difficulties. We report here a straightforward method using immunofluorescent staining and the commercially available imaging program Adobe Photoshop, which allows objective and precise information to be gathered on irregularly shaped cells. We have applied this measurement technique to the analysis of human muscle cells and their immunologically marked intracellular constituents, as these cells are prone to adopting a highly branched phenotype in culture. Use of this method can be used to overcome many of the long-standing limitations of conventional approaches for quantifying muscle cell size in vitro. In addition, wider applications of Photoshop as a quantitative and semiquantitative tool in immunocytochemistry are explored. PMID:22511600

Cardiac imaging: working towards fully-automated machine analysis & interpretation.

PubMed

Slomka, Piotr J; Dey, Damini; Sitek, Arkadiusz; Motwani, Manish; Berman, Daniel S; Germano, Guido

2017-03-01

Non-invasive imaging plays a critical role in managing patients with cardiovascular disease. Although subjective visual interpretation remains the clinical mainstay, quantitative analysis facilitates objective, evidence-based management, and advances in clinical research. This has driven developments in computing and software tools aimed at achieving fully automated image processing and quantitative analysis. In parallel, machine learning techniques have been used to rapidly integrate large amounts of clinical and quantitative imaging data to provide highly personalized individual patient-based conclusions. Areas covered: This review summarizes recent advances in automated quantitative imaging in cardiology and describes the latest techniques which incorporate machine learning principles. The review focuses on the cardiac imaging techniques which are in wide clinical use. It also discusses key issues and obstacles for these tools to become utilized in mainstream clinical practice. Expert commentary: Fully-automated processing and high-level computer interpretation of cardiac imaging are becoming a reality. Application of machine learning to the vast amounts of quantitative data generated per scan and integration with clinical data also facilitates a move to more patient-specific interpretation. These developments are unlikely to replace interpreting physicians but will provide them with highly accurate tools to detect disease, risk-stratify, and optimize patient-specific treatment. However, with each technological advance, we move further from human dependence and closer to fully-automated machine interpretation.

Cardiac imaging: working towards fully-automated machine analysis & interpretation

PubMed Central

Slomka, Piotr J; Dey, Damini; Sitek, Arkadiusz; Motwani, Manish; Berman, Daniel S; Germano, Guido

2017-01-01

Introduction Non-invasive imaging plays a critical role in managing patients with cardiovascular disease. Although subjective visual interpretation remains the clinical mainstay, quantitative analysis facilitates objective, evidence-based management, and advances in clinical research. This has driven developments in computing and software tools aimed at achieving fully automated image processing and quantitative analysis. In parallel, machine learning techniques have been used to rapidly integrate large amounts of clinical and quantitative imaging data to provide highly personalized individual patient-based conclusions. Areas covered This review summarizes recent advances in automated quantitative imaging in cardiology and describes the latest techniques which incorporate machine learning principles. The review focuses on the cardiac imaging techniques which are in wide clinical use. It also discusses key issues and obstacles for these tools to become utilized in mainstream clinical practice. Expert commentary Fully-automated processing and high-level computer interpretation of cardiac imaging are becoming a reality. Application of machine learning to the vast amounts of quantitative data generated per scan and integration with clinical data also facilitates a move to more patient-specific interpretation. These developments are unlikely to replace interpreting physicians but will provide them with highly accurate tools to detect disease, risk-stratify, and optimize patient-specific treatment. However, with each technological advance, we move further from human dependence and closer to fully-automated machine interpretation. PMID:28277804

NiftyPET: a High-throughput Software Platform for High Quantitative Accuracy and Precision PET Imaging and Analysis.

PubMed

Markiewicz, Pawel J; Ehrhardt, Matthias J; Erlandsson, Kjell; Noonan, Philip J; Barnes, Anna; Schott, Jonathan M; Atkinson, David; Arridge, Simon R; Hutton, Brian F; Ourselin, Sebastien

2018-01-01

We present a standalone, scalable and high-throughput software platform for PET image reconstruction and analysis. We focus on high fidelity modelling of the acquisition processes to provide high accuracy and precision quantitative imaging, especially for large axial field of view scanners. All the core routines are implemented using parallel computing available from within the Python package NiftyPET, enabling easy access, manipulation and visualisation of data at any processing stage. The pipeline of the platform starts from MR and raw PET input data and is divided into the following processing stages: (1) list-mode data processing; (2) accurate attenuation coefficient map generation; (3) detector normalisation; (4) exact forward and back projection between sinogram and image space; (5) estimation of reduced-variance random events; (6) high accuracy fully 3D estimation of scatter events; (7) voxel-based partial volume correction; (8) region- and voxel-level image analysis. We demonstrate the advantages of this platform using an amyloid brain scan where all the processing is executed from a single and uniform computational environment in Python. The high accuracy acquisition modelling is achieved through span-1 (no axial compression) ray tracing for true, random and scatter events. Furthermore, the platform offers uncertainty estimation of any image derived statistic to facilitate robust tracking of subtle physiological changes in longitudinal studies. The platform also supports the development of new reconstruction and analysis algorithms through restricting the axial field of view to any set of rings covering a region of interest and thus performing fully 3D reconstruction and corrections using real data significantly faster. All the software is available as open source with the accompanying wiki-page and test data.

Application of image analysis in studies of quantitative disease resistance, exemplified using common bacterial blight-common bean pathosystem.

PubMed

Xie, Weilong; Yu, Kangfu; Pauls, K Peter; Navabi, Alireza

2012-04-01

The effectiveness of image analysis (IA) compared with an ordinal visual scale, for quantitative measurement of disease severity, its application in quantitative genetic studies, and its effect on the estimates of genetic parameters were investigated. Studies were performed using eight backcross-derived families of common bean (Phaseolus vulgaris) (n = 172) segregating for the molecular marker SU91, known to be associated with a quantitative trait locus (QTL) for resistance to common bacterial blight (CBB), caused by Xanthomonas campestris pv. phaseoli and X. fuscans subsp. fuscans. Even though both IA and visual assessments were highly repeatable, IA was more sensitive in detecting quantitative differences between bean genotypes. The CBB phenotypic difference between the two SU91 genotypic groups was consistently more than fivefold for IA assessments but generally only two- to threefold for visual assessments. Results suggest that the visual assessment results in overestimation of the effect of QTL in genetic studies. This may have been caused by lack of additivity and uneven intervals of the visual scale. Although visual assessment of disease severity is a useful tool for general selection in breeding programs, assessments using IA may be more suitable for phenotypic evaluations in quantitative genetic studies involving CBB resistance as well as other foliar diseases.

Quantitative Phase Imaging in a Volume Holographic Microscope

NASA Astrophysics Data System (ADS)

Waller, Laura; Luo, Yuan; Barbastathis, George

2010-04-01

We demonstrate a method for quantitative phase imaging in a Volume Holographic Microscope (VHM) from a single exposure, describe the properties of the system and show experimental results. The VHM system uses a multiplexed volume hologram (VH) to laterally separate images from different focal planes. This 3D intensity information is then used to solve the transport of intensity (TIE) equation and recover phase quantitatively. We discuss the modifications to the technique that were made in order to give accurate results.

Versatile quantitative phase imaging system applied to high-speed, low noise and multimodal imaging (Conference Presentation)

NASA Astrophysics Data System (ADS)

Federici, Antoine; Aknoun, Sherazade; Savatier, Julien; Wattellier, Benoit F.

2017-02-01

Quadriwave lateral shearing interferometry (QWLSI) is a well-established quantitative phase imaging (QPI) technique based on the analysis of interference patterns of four diffraction orders by an optical grating set in front of an array detector [1]. As a QPI modality, this is a non-invasive imaging technique which allow to measure the optical path difference (OPD) of semi-transparent samples. We present a system enabling QWLSI with high-performance sCMOS cameras [2] and apply it to perform high-speed imaging, low noise as well as multimodal imaging. This modified QWLSI system contains a versatile optomechanical device which images the optical grating near the detector plane. Such a device is coupled with any kind of camera by varying its magnification. In this paper, we study the use of a sCMOS Zyla5.5 camera from Andor along with our modified QWLSI system. We will present high-speed live cell imaging, up to 200Hz frame rate, in order to follow intracellular fast motions while measuring the quantitative phase information. The structural and density information extracted from the OPD signal is complementary to the specific and localized fluorescence signal [2]. In addition, QPI detects cells even when the fluorophore is not expressed. This is very useful to follow a protein expression with time. The 10 µm spatial pixel resolution of our modified QWLSI associated to the high sensitivity of the Zyla5.5 enabling to perform high quality fluorescence imaging, we have carried out multimodal imaging revealing fine structures cells, like actin filaments, merged with the morphological information of the phase. References [1]. P. Bon, G. Maucort, B. Wattellier, and S. Monneret, "Quadriwave lateral shearing interferometry for quantitative phase microscopy of living cells," Opt. Express, vol. 17, pp. 13080-13094, 2009. [2] P. Bon, S. Lécart, E. Fort and S. Lévêque-Fort, "Fast label-free cytoskeletal network imaging in living mammalian cells," Biophysical journal, 106

Quantitatively in Situ Imaging Silver Nanowire Hollowing Kinetics

DOE PAGES

Yu, Le; Yan, Zhongying; Cai, Zhonghou; ...

2016-09-28

We report the in-situ investigation of the morphological evolution of silver nanowires to hollow silver oxide nanotubes using transmission x-ray microscopy (TXM). Complex silver diffusion kinetics and hollowing process via the Kirkendall effect have been captured in real time. Further quantitative x-ray absorption analysis reveals the difference between the longitudinal and radial diffusions. In conclusion, the diffusion coefficient of silver in its oxide nanoshell is, for the first time, calculated to be 1.2 × 10 -13 cm 2/s from the geometrical parameters extracted from the TXM images.

The Use of Mouse Models of Breast Cancer and Quantitative Image Analysis to Evaluate Hormone Receptor Antigenicity after Microwave-assisted Formalin Fixation

PubMed Central

Engelberg, Jesse A.; Giberson, Richard T.; Young, Lawrence J.T.; Hubbard, Neil E.

2014-01-01

Microwave methods of fixation can dramatically shorten fixation times while preserving tissue structure; however, it remains unclear if adequate tissue antigenicity is preserved. To assess and validate antigenicity, robust quantitative methods and animal disease models are needed. We used two mouse mammary models of human breast cancer to evaluate microwave-assisted and standard 24-hr formalin fixation. The mouse models expressed four antigens prognostic for breast cancer outcome: estrogen receptor, progesterone receptor, Ki67, and human epidermal growth factor receptor 2. Using pathologist evaluation and novel methods of quantitative image analysis, we measured and compared the quality of antigen preservation, percentage of positive cells, and line plots of cell intensity. Visual evaluations by pathologists established that the amounts and patterns of staining were similar in tissues fixed by the different methods. The results of the quantitative image analysis provided a fine-grained evaluation, demonstrating that tissue antigenicity is preserved in tissues fixed using microwave methods. Evaluation of the results demonstrated that a 1-hr, 150-W fixation is better than a 45-min, 150-W fixation followed by a 15-min, 650-W fixation. The results demonstrated that microwave-assisted formalin fixation can standardize fixation times to 1 hr and produce immunohistochemistry that is in every way commensurate with longer conventional fixation methods. PMID:24682322

A Workstation for Interactive Display and Quantitative Analysis of 3-D and 4-D Biomedical Images

PubMed Central

Robb, R.A.; Heffeman, P.B.; Camp, J.J.; Hanson, D.P.

1986-01-01

The capability to extract objective and quantitatively accurate information from 3-D radiographic biomedical images has not kept pace with the capabilities to produce the images themselves. This is rather an ironic paradox, since on the one hand the new 3-D and 4-D imaging capabilities promise significant potential for providing greater specificity and sensitivity (i.e., precise objective discrimination and accurate quantitative measurement of body tissue characteristics and function) in clinical diagnostic and basic investigative imaging procedures than ever possible before, but on the other hand, the momentous advances in computer and associated electronic imaging technology which have made these 3-D imaging capabilities possible have not been concomitantly developed for full exploitation of these capabilities. Therefore, we have developed a powerful new microcomputer-based system which permits detailed investigations and evaluation of 3-D and 4-D (dynamic 3-D) biomedical images. The system comprises a special workstation to which all the information in a large 3-D image data base is accessible for rapid display, manipulation, and measurement. The system provides important capabilities for simultaneously representing and analyzing both structural and functional data and their relationships in various organs of the body. This paper provides a detailed description of this system, as well as some of the rationale, background, theoretical concepts, and practical considerations related to system implementation. ImagesFigure 5Figure 7Figure 8Figure 9Figure 10Figure 11Figure 12Figure 13Figure 14Figure 15Figure 16

Quantitative photoacoustic elasticity and viscosity imaging for cirrhosis detection

NASA Astrophysics Data System (ADS)

Wang, Qian; Shi, Yujiao; Yang, Fen; Yang, Sihua

2018-05-01

Elasticity and viscosity assessments are essential for understanding and characterizing the physiological and pathological states of tissue. In this work, by establishing a photoacoustic (PA) shear wave model, an approach for quantitative PA elasticity imaging based on measurement of the rise time of the thermoelastic displacement was developed. Thus, using an existing PA viscoelasticity imaging method that features a phase delay measurement, quantitative PA elasticity imaging and viscosity imaging can be obtained in a simultaneous manner. The method was tested and validated by imaging viscoelastic agar phantoms prepared at different agar concentrations, and the imaging data were in good agreement with rheometry results. Ex vivo experiments on liver pathological models demonstrated the capability for cirrhosis detection, and the results were consistent with the corresponding histological results. This method expands the scope of conventional PA imaging and has potential to become an important alternative imaging modality.

A simple approach to quantitative analysis using three-dimensional spectra based on selected Zernike moments.

PubMed

Zhai, Hong Lin; Zhai, Yue Yuan; Li, Pei Zhen; Tian, Yue Li

2013-01-21

A very simple approach to quantitative analysis is proposed based on the technology of digital image processing using three-dimensional (3D) spectra obtained by high-performance liquid chromatography coupled with a diode array detector (HPLC-DAD). As the region-based shape features of a grayscale image, Zernike moments with inherently invariance property were employed to establish the linear quantitative models. This approach was applied to the quantitative analysis of three compounds in mixed samples using 3D HPLC-DAD spectra, and three linear models were obtained, respectively. The correlation coefficients (R(2)) for training and test sets were more than 0.999, and the statistical parameters and strict validation supported the reliability of established models. The analytical results suggest that the Zernike moment selected by stepwise regression can be used in the quantitative analysis of target compounds. Our study provides a new idea for quantitative analysis using 3D spectra, which can be extended to the analysis of other 3D spectra obtained by different methods or instruments.

Clinical Utility of Quantitative Imaging

PubMed Central

Rosenkrantz, Andrew B; Mendiratta-Lala, Mishal; Bartholmai, Brian J.; Ganeshan, Dhakshinamoorthy; Abramson, Richard G.; Burton, Kirsteen R.; Yu, John-Paul J.; Scalzetti, Ernest M.; Yankeelov, Thomas E.; Subramaniam, Rathan M.; Lenchik, Leon

2014-01-01

Quantitative imaging (QI) is increasingly applied in modern radiology practice, assisting in the clinical assessment of many patients and providing a source of biomarkers for a spectrum of diseases. QI is commonly used to inform patient diagnosis or prognosis, determine the choice of therapy, or monitor therapy response. Because most radiologists will likely implement some QI tools to meet the patient care needs of their referring clinicians, it is important for all radiologists to become familiar with the strengths and limitations of QI. The Association of University Radiologists Radiology Research Alliance Quantitative Imaging Task Force has explored the clinical application of QI and summarizes its work in this review. We provide an overview of the clinical use of QI by discussing QI tools that are currently employed in clinical practice, clinical applications of these tools, approaches to reporting of QI, and challenges to implementing QI. It is hoped that these insights will help radiologists recognize the tangible benefits of QI to their patients, their referring clinicians, and their own radiology practice. PMID:25442800

Quantitative comparison of 3D third harmonic generation and fluorescence microscopy images.

PubMed

Zhang, Zhiqing; Kuzmin, Nikolay V; Groot, Marie Louise; de Munck, Jan C

2018-01-01

Third harmonic generation (THG) microscopy is a label-free imaging technique that shows great potential for rapid pathology of brain tissue during brain tumor surgery. However, the interpretation of THG brain images should be quantitatively linked to images of more standard imaging techniques, which so far has been done qualitatively only. We establish here such a quantitative link between THG images of mouse brain tissue and all-nuclei-highlighted fluorescence images, acquired simultaneously from the same tissue area. For quantitative comparison of a substantial pair of images, we present here a segmentation workflow that is applicable for both THG and fluorescence images, with a precision of 91.3 % and 95.8 % achieved respectively. We find that the correspondence between the main features of the two imaging modalities amounts to 88.9 %, providing quantitative evidence of the interpretation of dark holes as brain cells. Moreover, 80 % bright objects in THG images overlap with nuclei highlighted in the fluorescence images, and they are 2 times smaller than the dark holes, showing that cells of different morphologies can be recognized in THG images. We expect that the described quantitative comparison is applicable to other types of brain tissue and with more specific staining experiments for cell type identification. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Photoacoustic image reconstruction: a quantitative analysis

NASA Astrophysics Data System (ADS)

Sperl, Jonathan I.; Zell, Karin; Menzenbach, Peter; Haisch, Christoph; Ketzer, Stephan; Marquart, Markus; Koenig, Hartmut; Vogel, Mika W.

2007-07-01

Photoacoustic imaging is a promising new way to generate unprecedented contrast in ultrasound diagnostic imaging. It differs from other medical imaging approaches, in that it provides spatially resolved information about optical absorption of targeted tissue structures. Because the data acquisition process deviates from standard clinical ultrasound, choice of the proper image reconstruction method is crucial for successful application of the technique. In the literature, multiple approaches have been advocated, and the purpose of this paper is to compare four reconstruction techniques. Thereby, we focused on resolution limits, stability, reconstruction speed, and SNR. We generated experimental and simulated data and reconstructed images of the pressure distribution using four different methods: delay-and-sum (DnS), circular backprojection (CBP), generalized 2D Hough transform (HTA), and Fourier transform (FTA). All methods were able to depict the point sources properly. DnS and CBP produce blurred images containing typical superposition artifacts. The HTA provides excellent SNR and allows a good point source separation. The FTA is the fastest and shows the best FWHM. In our study, we found the FTA to show the best overall performance. It allows a very fast and theoretically exact reconstruction. Only a hardware-implemented DnS might be faster and enable real-time imaging. A commercial system may also perform several methods to fully utilize the new contrast mechanism and guarantee optimal resolution and fidelity.

Calibration of HST wide field camera for quantitative analysis of faint galaxy images

NASA Technical Reports Server (NTRS)

Ratnatunga, Kavan U.; Griffiths, Richard E.; Casertano, Stefano; Neuschaefer, Lyman W.; Wyckoff, Eric W.

1994-01-01

We present the methods adopted to optimize the calibration of images obtained with the Hubble Space Telescope (HST) Wide Field Camera (WFC) (1991-1993). Our main goal is to improve quantitative measurement of faint images, with special emphasis on the faint (I approximately 20-24 mag) stars and galaxies observed as a part of the Medium-Deep Survey. Several modifications to the standard calibration procedures have been introduced, including improved bias and dark images, and a new supersky flatfield obtained by combining a large number of relatively object-free Medium-Deep Survey exposures of random fields. The supersky flat has a pixel-to-pixel rms error of about 2.0% in F555W and of 2.4% in F785LP; large-scale variations are smaller than 1% rms. Overall, our modifications improve the quality of faint images with respect to the standard calibration by about a factor of five in photometric accuracy and about 0.3 mag in sensitivity, corresponding to about a factor of two in observing time. The relevant calibration images have been made available to the scientific community.

Quantitative analysis of autophagic flux by confocal pH-imaging of autophagic intermediates

PubMed Central

Maulucci, Giuseppe; Chiarpotto, Michela; Papi, Massimiliano; Samengo, Daniela; Pani, Giovambattista; De Spirito, Marco

2015-01-01

Although numerous techniques have been developed to monitor autophagy and to probe its cellular functions, these methods cannot evaluate in sufficient detail the autophagy process, and suffer limitations from complex experimental setups and/or systematic errors. Here we developed a method to image, contextually, the number and pH of autophagic intermediates by using the probe mRFP-GFP-LC3B as a ratiometric pH sensor. This information is expressed functionally by AIPD, the pH distribution of the number of autophagic intermediates per cell. AIPD analysis reveals how intermediates are characterized by a continuous pH distribution, in the range 4.5–6.5, and therefore can be described by a more complex set of states rather than the usual biphasic one (autophagosomes and autolysosomes). AIPD shape and amplitude are sensitive to alterations in the autophagy pathway induced by drugs or environmental states, and allow a quantitative estimation of autophagic flux by retrieving the concentrations of autophagic intermediates. PMID:26506895

Oufti: An integrated software package for high-accuracy, high-throughput quantitative microscopy analysis

PubMed Central

Paintdakhi, Ahmad; Parry, Bradley; Campos, Manuel; Irnov, Irnov; Elf, Johan; Surovtsev, Ivan; Jacobs-Wagner, Christine

2016-01-01

Summary With the realization that bacteria display phenotypic variability among cells and exhibit complex subcellular organization critical for cellular function and behavior, microscopy has re-emerged as a primary tool in bacterial research during the last decade. However, the bottleneck in today’s single-cell studies is quantitative image analysis of cells and fluorescent signals. Here, we address current limitations through the development of Oufti, a stand-alone, open-source software package for automated measurements of microbial cells and fluorescence signals from microscopy images. Oufti provides computational solutions for tracking touching cells in confluent samples, handles various cell morphologies, offers algorithms for quantitative analysis of both diffraction and non-diffraction-limited fluorescence signals, and is scalable for high-throughput analysis of massive datasets, all with subpixel precision. All functionalities are integrated in a single package. The graphical user interface, which includes interactive modules for segmentation, image analysis, and post-processing analysis, makes the software broadly accessible to users irrespective of their computational skills. PMID:26538279

Cardiovascular and pulmonary dynamics by quantitative imaging

NASA Technical Reports Server (NTRS)

Wood, E. H.

1976-01-01

The accuracy and range of studies on cardiovascular and pulmonary functions can be greatly facilitated if the motions of the underlying organ systems throughout individual cycles can be directly visualized and readily measured with minimum or preferably no effect on these motions. Achievement of this objective requires development of techniques for quantitative noninvasive or minimally invasive dynamic and stop-action imaging of the organ systems. A review of advances in dynamic quantitative imaging of moving organs reveals that the revolutionary value of cross-sectional and three-dimensional images produced by various types of radiant energy such as X-rays and gamma rays, positrons, electrons, protons, light, and ultrasound for clinical diagnostic and biomedical research applications is just beginning to be realized. The fabrication of a clinically useful cross-section reconstruction device with sensing capabilities for both anatomical structural composition and chemical composition may be possible and awaits future development.

Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach

PubMed Central

Aerts, Hugo J. W. L.; Velazquez, Emmanuel Rios; Leijenaar, Ralph T. H.; Parmar, Chintan; Grossmann, Patrick; Cavalho, Sara; Bussink, Johan; Monshouwer, René; Haibe-Kains, Benjamin; Rietveld, Derek; Hoebers, Frank; Rietbergen, Michelle M.; Leemans, C. René; Dekker, Andre; Quackenbush, John; Gillies, Robert J.; Lambin, Philippe

2014-01-01

Human cancers exhibit strong phenotypic differences that can be visualized noninvasively by medical imaging. Radiomics refers to the comprehensive quantification of tumour phenotypes by applying a large number of quantitative image features. Here we present a radiomic analysis of 440 features quantifying tumour image intensity, shape and texture, which are extracted from computed tomography data of 1,019 patients with lung or head-and-neck cancer. We find that a large number of radiomic features have prognostic power in independent data sets of lung and head-and-neck cancer patients, many of which were not identified as significant before. Radiogenomics analysis reveals that a prognostic radiomic signature, capturing intratumour heterogeneity, is associated with underlying gene-expression patterns. These data suggest that radiomics identifies a general prognostic phenotype existing in both lung and head-and-neck cancer. This may have a clinical impact as imaging is routinely used in clinical practice, providing an unprecedented opportunity to improve decision-support in cancer treatment at low cost. PMID:24892406

Quantitative magnetic resonance imaging in traumatic brain injury.

PubMed

Bigler, E D

2001-04-01

Quantitative neuroimaging has now become a well-established method for analyzing magnetic resonance imaging in traumatic brain injury (TBI). A general review of studies that have examined quantitative changes following TBI is presented. The consensus of quantitative neuroimaging studies is that most brain structures demonstrate changes in volume or surface area after injury. The patterns of atrophy are consistent with the generalized nature of brain injury and diffuse axonal injury. Various clinical caveats are provided including how quantitative neuroimaging findings can be used clinically and in predicting rehabilitation outcome. The future of quantitative neuroimaging also is discussed.

Diagnosis of breast cancer biopsies using quantitative phase imaging

NASA Astrophysics Data System (ADS)

Majeed, Hassaan; Kandel, Mikhail E.; Han, Kevin; Luo, Zelun; Macias, Virgilia; Tangella, Krishnarao; Balla, Andre; Popescu, Gabriel

2015-03-01

The standard practice in the histopathology of breast cancers is to examine a hematoxylin and eosin (H&E) stained tissue biopsy under a microscope. The pathologist looks at certain morphological features, visible under the stain, to diagnose whether a tumor is benign or malignant. This determination is made based on qualitative inspection making it subject to investigator bias. Furthermore, since this method requires a microscopic examination by the pathologist it suffers from low throughput. A quantitative, label-free and high throughput method for detection of these morphological features from images of tissue biopsies is, hence, highly desirable as it would assist the pathologist in making a quicker and more accurate diagnosis of cancers. We present here preliminary results showing the potential of using quantitative phase imaging for breast cancer screening and help with differential diagnosis. We generated optical path length maps of unstained breast tissue biopsies using Spatial Light Interference Microscopy (SLIM). As a first step towards diagnosis based on quantitative phase imaging, we carried out a qualitative evaluation of the imaging resolution and contrast of our label-free phase images. These images were shown to two pathologists who marked the tumors present in tissue as either benign or malignant. This diagnosis was then compared against the diagnosis of the two pathologists on H&E stained tissue images and the number of agreements were counted. In our experiment, the agreement between SLIM and H&E based diagnosis was measured to be 88%. Our preliminary results demonstrate the potential and promise of SLIM for a push in the future towards quantitative, label-free and high throughput diagnosis.

Quantitative analysis of ex vivo colorectal epithelium using an automated feature extraction algorithm for microendoscopy image data

PubMed Central

Prieto, Sandra P.; Lai, Keith K.; Laryea, Jonathan A.; Mizell, Jason S.; Muldoon, Timothy J.

2016-01-01

Abstract. Qualitative screening for colorectal polyps via fiber bundle microendoscopy imaging has shown promising results, with studies reporting high rates of sensitivity and specificity, as well as low interobserver variability with trained clinicians. A quantitative image quality control and image feature extraction algorithm (QFEA) was designed to lessen the burden of training and provide objective data for improved clinical efficacy of this method. After a quantitative image quality control step, QFEA extracts field-of-view area, crypt area, crypt circularity, and crypt number per image. To develop and validate this QFEA, a training set of microendoscopy images was collected from freshly resected porcine colon epithelium. The algorithm was then further validated on ex vivo image data collected from eight human subjects, selected from clinically normal appearing regions distant from grossly visible tumor in surgically resected colorectal tissue. QFEA has proven flexible in application to both mosaics and individual images, and its automated crypt detection sensitivity ranges from 71 to 94% despite intensity and contrast variation within the field of view. It also demonstrates the ability to detect and quantify differences in grossly normal regions among different subjects, suggesting the potential efficacy of this approach in detecting occult regions of dysplasia. PMID:27335893

Rapid analysis and exploration of fluorescence microscopy images.

PubMed

Pavie, Benjamin; Rajaram, Satwik; Ouyang, Austin; Altschuler, Jason M; Steininger, Robert J; Wu, Lani F; Altschuler, Steven J

2014-03-19

Despite rapid advances in high-throughput microscopy, quantitative image-based assays still pose significant challenges. While a variety of specialized image analysis tools are available, most traditional image-analysis-based workflows have steep learning curves (for fine tuning of analysis parameters) and result in long turnaround times between imaging and analysis. In particular, cell segmentation, the process of identifying individual cells in an image, is a major bottleneck in this regard. Here we present an alternate, cell-segmentation-free workflow based on PhenoRipper, an open-source software platform designed for the rapid analysis and exploration of microscopy images. The pipeline presented here is optimized for immunofluorescence microscopy images of cell cultures and requires minimal user intervention. Within half an hour, PhenoRipper can analyze data from a typical 96-well experiment and generate image profiles. Users can then visually explore their data, perform quality control on their experiment, ensure response to perturbations and check reproducibility of replicates. This facilitates a rapid feedback cycle between analysis and experiment, which is crucial during assay optimization. This protocol is useful not just as a first pass analysis for quality control, but also may be used as an end-to-end solution, especially for screening. The workflow described here scales to large data sets such as those generated by high-throughput screens, and has been shown to group experimental conditions by phenotype accurately over a wide range of biological systems. The PhenoBrowser interface provides an intuitive framework to explore the phenotypic space and relate image properties to biological annotations. Taken together, the protocol described here will lower the barriers to adopting quantitative analysis of image based screens.

Microbial-based evaluation of foaming events in full-scale wastewater treatment plants by microscopy survey and quantitative image analysis.

PubMed

Leal, Cristiano; Amaral, António Luís; Costa, Maria de Lourdes

2016-08-01

Activated sludge systems are prone to be affected by foaming occurrences causing the sludge to rise in the reactor and affecting the wastewater treatment plant (WWTP) performance. Nonetheless, there is currently a knowledge gap hindering the development of foaming events prediction tools that may be fulfilled by the quantitative monitoring of AS systems biota and sludge characteristics. As such, the present study focuses on the assessment of foaming events in full-scale WWTPs, by quantitative protozoa, metazoa, filamentous bacteria, and sludge characteristics analysis, further used to enlighten the inner relationships between these parameters. In the current study, a conventional activated sludge system (CAS) and an oxidation ditch (OD) were surveyed throughout a period of 2 and 3 months, respectively, regarding their biota and sludge characteristics. The biota community was monitored by microscopic observation, and a new filamentous bacteria index was developed to quantify their occurrence. Sludge characteristics (aggregated and filamentous biomass contents and aggregate size) were determined by quantitative image analysis (QIA). The obtained data was then processed by principal components analysis (PCA), cross-correlation analysis, and decision trees to assess the foaming occurrences, and enlighten the inner relationships. It was found that such events were best assessed by the combined use of the relative abundance of testate amoeba and nocardioform filamentous index, presenting a 92.9 % success rate for overall foaming events, and 87.5 and 100 %, respectively, for persistent and mild events.

Large-scale quantitative analysis of painting arts.

PubMed

Kim, Daniel; Son, Seung-Woo; Jeong, Hawoong

2014-12-11

Scientists have made efforts to understand the beauty of painting art in their own languages. As digital image acquisition of painting arts has made rapid progress, researchers have come to a point where it is possible to perform statistical analysis of a large-scale database of artistic paints to make a bridge between art and science. Using digital image processing techniques, we investigate three quantitative measures of images - the usage of individual colors, the variety of colors, and the roughness of the brightness. We found a difference in color usage between classical paintings and photographs, and a significantly low color variety of the medieval period. Interestingly, moreover, the increment of roughness exponent as painting techniques such as chiaroscuro and sfumato have advanced is consistent with historical circumstances.

Comparative study between quantitative digital image analysis and fluorescence in situ hybridization of breast cancer equivocal human epidermal growth factor receptors 2 score 2(+) cases.

PubMed

Ayad, Essam; Mansy, Mina; Elwi, Dalal; Salem, Mostafa; Salama, Mohamed; Kayser, Klaus

2015-01-01

Optimization of workflow for breast cancer samples with equivocal human epidermal growth factor receptors 2 (HER2)/neu score 2(+) results in routine practice, remains to be a central focus of the on-going efforts to assess HER2 status. According to the College of American Pathologists/American Society of Clinical Oncology guidelines equivocal HER2/neu score 2(+) cases are subject for further testing, usually by fluorescence in situ hybridization (FISH) investigations. It still remains on open question, whether quantitative digital image analysis of HER2 immunohistochemistry (IHC) stained slides can assist in further refining the HER2 score 2(+). To assess utility of quantitative digital analysis of IHC stained slides and compare its performance to FISH in cases of breast cancer with equivocal HER2 score 2(+). Fifteen specimens (previously diagnosed as breast cancer and was evaluated as HER 2(-) score 2(+)) represented the study population. Contemporary new cuts were prepared for re-evaluation of HER2 immunohistochemical studies and FISH examination. All the cases were digitally scanned by iScan (Produced by BioImagene [Now Roche-Ventana]). The IHC signals of HER2 were measured using an automated image analyzing system (MECES, www.Diagnomx.eu/meces). Finally, a comparative study was done between the results of the FISH and the quantitative analysis of the virtual slides. Three out of the 15 cases with equivocal HER2 score 2(+), turned out to be positive (3(+)) by quantitative digital analysis, and 12 were found to be negative in FISH too. Two of these three positive cases proved to be positive with FISH, and only one was negative. Quantitative digital analysis is highly sensitive and relatively specific when compared to FISH in detecting HER2/neu overexpression. Therefore, it represents a potential reliable substitute for FISH in breast cancer cases, which desire further refinement of equivocal IHC results.

Pilot study of quantitative analysis of background enhancement on breast MR images: association with menstrual cycle and mammographic breast density.

PubMed

Scaranelo, Anabel M; Carrillo, Maria Claudia; Fleming, Rachel; Jacks, Lindsay M; Kulkarni, Supriya R; Crystal, Pavel

2013-06-01

To perform semiautomated quantitative analysis of the background enhancement (BE) in a cohort of patients with newly diagnosed breast cancer and to correlate it with mammographic breast density and menstrual cycle. Informed consent was waived after the research ethics board approved this study. Results of 177 consecutive preoperative breast magnetic resonance (MR) examinations performed from February to December 2009 were reviewed; 147 female patients (median age, 48 years; range, 26-86 years) were included. Ordinal values of BE and breast density were described by two independent readers by using the Breast Imaging Reporting and Data System lexicon. The BE coefficient (BEC) was calculated thus: (SI2 · 100/SI1) - 100, where SI is signal intensity, SI2 is the SI enhancement measured in the largest anteroposterior dimension in the axial plane 1 minute after the contrast agent injection, and SI1is the SI before contrast agent injection. BEC was used for the quantitative analysis of BE. Menstrual cycle status was based on the last menstrual period. The Wilcoxon rank-sum or Kruskal-Wallis test was used to compare quantitative assessment groups. Cohen weighted κ was used to evaluate agreement. Of 147 patients, 68 (46%) were premenopausal and 79 (54%) were postmenopausal. The quantitative BEC was associated with the menstrual status (BEC in premenopausal women, 31.48 ± 20.68 [standard deviation]; BEC in postmenopausal women, 25.65 ± 16.74; P = .02). The percentage of overall BE was higher when the MR imaging was performed in women in the inadequate phase of the cycle (<35 days, not 7-14 days; mean BEC, 35.7) compared with women in the postmenopausal group (P = .001). Premenopausal women had significantly higher BEC when compared with postmenopausal women (P = .03). There was no significant difference in the percentage of BE between breast density groups. Premenopausal women with breast cancer, and specifically women in the inadequate phase of the cycle, presented with

Toward uniform implementation of parametric map Digital Imaging and Communication in Medicine standard in multisite quantitative diffusion imaging studies.

PubMed

Malyarenko, Dariya; Fedorov, Andriy; Bell, Laura; Prah, Melissa; Hectors, Stefanie; Arlinghaus, Lori; Muzi, Mark; Solaiyappan, Meiyappan; Jacobs, Michael; Fung, Maggie; Shukla-Dave, Amita; McManus, Kevin; Boss, Michael; Taouli, Bachir; Yankeelov, Thomas E; Quarles, Christopher Chad; Schmainda, Kathleen; Chenevert, Thomas L; Newitt, David C

2018-01-01

This paper reports on results of a multisite collaborative project launched by the MRI subgroup of Quantitative Imaging Network to assess current capability and provide future guidelines for generating a standard parametric diffusion map Digital Imaging and Communication in Medicine (DICOM) in clinical trials that utilize quantitative diffusion-weighted imaging (DWI). Participating sites used a multivendor DWI DICOM dataset of a single phantom to generate parametric maps (PMs) of the apparent diffusion coefficient (ADC) based on two models. The results were evaluated for numerical consistency among models and true phantom ADC values, as well as for consistency of metadata with attributes required by the DICOM standards. This analysis identified missing metadata descriptive of the sources for detected numerical discrepancies among ADC models. Instead of the DICOM PM object, all sites stored ADC maps as DICOM MR objects, generally lacking designated attributes and coded terms for quantitative DWI modeling. Source-image reference, model parameters, ADC units and scale, deemed important for numerical consistency, were either missing or stored using nonstandard conventions. Guided by the identified limitations, the DICOM PM standard has been amended to include coded terms for the relevant diffusion models. Open-source software has been developed to support conversion of site-specific formats into the standard representation.

Structural Image Analysis of the Brain in Neuropsychology Using Magnetic Resonance Imaging (MRI) Techniques.

PubMed

Bigler, Erin D

2015-09-01

Magnetic resonance imaging (MRI) of the brain provides exceptional image quality for visualization and neuroanatomical classification of brain structure. A variety of image analysis techniques provide both qualitative as well as quantitative methods to relate brain structure with neuropsychological outcome and are reviewed herein. Of particular importance are more automated methods that permit analysis of a broad spectrum of anatomical measures including volume, thickness and shape. The challenge for neuropsychology is which metric to use, for which disorder and the timing of when image analysis methods are applied to assess brain structure and pathology. A basic overview is provided as to the anatomical and pathoanatomical relations of different MRI sequences in assessing normal and abnormal findings. Some interpretive guidelines are offered including factors related to similarity and symmetry of typical brain development along with size-normalcy features of brain anatomy related to function. The review concludes with a detailed example of various quantitative techniques applied to analyzing brain structure for neuropsychological outcome studies in traumatic brain injury.

ImmunoRatio: a publicly available web application for quantitative image analysis of estrogen receptor (ER), progesterone receptor (PR), and Ki-67

PubMed Central

2010-01-01

Introduction Accurate assessment of estrogen receptor (ER), progesterone receptor (PR), and Ki-67 is essential in the histopathologic diagnostics of breast cancer. Commercially available image analysis systems are usually bundled with dedicated analysis hardware and, to our knowledge, no easily installable, free software for immunostained slide scoring has been described. In this study, we describe a free, Internet-based web application for quantitative image analysis of ER, PR, and Ki-67 immunohistochemistry in breast cancer tissue sections. Methods The application, named ImmunoRatio, calculates the percentage of positively stained nuclear area (labeling index) by using a color deconvolution algorithm for separating the staining components (diaminobenzidine and hematoxylin) and adaptive thresholding for nuclear area segmentation. ImmunoRatio was calibrated using cell counts defined visually as the gold standard (training set, n = 50). Validation was done using a separate set of 50 ER, PR, and Ki-67 stained slides (test set, n = 50). In addition, Ki-67 labeling indexes determined by ImmunoRatio were studied for their prognostic value in a retrospective cohort of 123 breast cancer patients. Results The labeling indexes by calibrated ImmunoRatio analyses correlated well with those defined visually in the test set (correlation coefficient r = 0.98). Using the median Ki-67 labeling index (20%) as a cutoff, a hazard ratio of 2.2 was obtained in the survival analysis (n = 123, P = 0.01). ImmunoRatio was shown to adapt to various staining protocols, microscope setups, digital camera models, and image acquisition settings. The application can be used directly with web browsers running on modern operating systems (e.g., Microsoft Windows, Linux distributions, and Mac OS). No software downloads or installations are required. ImmunoRatio is open source software, and the web application is publicly accessible on our website. Conclusions We anticipate that free web applications

ImmunoRatio: a publicly available web application for quantitative image analysis of estrogen receptor (ER), progesterone receptor (PR), and Ki-67.

PubMed

Tuominen, Vilppu J; Ruotoistenmäki, Sanna; Viitanen, Arttu; Jumppanen, Mervi; Isola, Jorma

2010-01-01

Accurate assessment of estrogen receptor (ER), progesterone receptor (PR), and Ki-67 is essential in the histopathologic diagnostics of breast cancer. Commercially available image analysis systems are usually bundled with dedicated analysis hardware and, to our knowledge, no easily installable, free software for immunostained slide scoring has been described. In this study, we describe a free, Internet-based web application for quantitative image analysis of ER, PR, and Ki-67 immunohistochemistry in breast cancer tissue sections. The application, named ImmunoRatio, calculates the percentage of positively stained nuclear area (labeling index) by using a color deconvolution algorithm for separating the staining components (diaminobenzidine and hematoxylin) and adaptive thresholding for nuclear area segmentation. ImmunoRatio was calibrated using cell counts defined visually as the gold standard (training set, n = 50). Validation was done using a separate set of 50 ER, PR, and Ki-67 stained slides (test set, n = 50). In addition, Ki-67 labeling indexes determined by ImmunoRatio were studied for their prognostic value in a retrospective cohort of 123 breast cancer patients. The labeling indexes by calibrated ImmunoRatio analyses correlated well with those defined visually in the test set (correlation coefficient r = 0.98). Using the median Ki-67 labeling index (20%) as a cutoff, a hazard ratio of 2.2 was obtained in the survival analysis (n = 123, P = 0.01). ImmunoRatio was shown to adapt to various staining protocols, microscope setups, digital camera models, and image acquisition settings. The application can be used directly with web browsers running on modern operating systems (e.g., Microsoft Windows, Linux distributions, and Mac OS). No software downloads or installations are required. ImmunoRatio is open source software, and the web application is publicly accessible on our website. We anticipate that free web applications, such as ImmunoRatio, will make the

Single and two-shot quantitative phase imaging using Hilbert-Huang Transform based fringe pattern analysis

NASA Astrophysics Data System (ADS)

Trusiak, Maciej; Micó, Vicente; Patorski, Krzysztof; García-Monreal, Javier; Sluzewski, Lukasz; Ferreira, Carlos

2016-08-01

In this contribution we propose two Hilbert-Huang Transform based algorithms for fast and accurate single-shot and two-shot quantitative phase imaging applicable in both on-axis and off-axis configurations. In the first scheme a single fringe pattern containing information about biological phase-sample under study is adaptively pre-filtered using empirical mode decomposition based approach. Further it is phase demodulated by the Hilbert Spiral Transform aided by the Principal Component Analysis for the local fringe orientation estimation. Orientation calculation enables closed fringes efficient analysis and can be avoided using arbitrary phase-shifted two-shot Gram-Schmidt Orthonormalization scheme aided by Hilbert-Huang Transform pre-filtering. This two-shot approach is a trade-off between single-frame and temporal phase shifting demodulation. Robustness of the proposed techniques is corroborated using experimental digital holographic microscopy studies of polystyrene micro-beads and red blood cells. Both algorithms compare favorably with the temporal phase shifting scheme which is used as a reference method.

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

PubMed

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

2011-06-01

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

A quantitative image cytometry technique for time series or population analyses of signaling networks.

PubMed

Ozaki, Yu-ichi; Uda, Shinsuke; Saito, Takeshi H; Chung, Jaehoon; Kubota, Hiroyuki; Kuroda, Shinya

2010-04-01

Modeling of cellular functions on the basis of experimental observation is increasingly common in the field of cellular signaling. However, such modeling requires a large amount of quantitative data of signaling events with high spatio-temporal resolution. A novel technique which allows us to obtain such data is needed for systems biology of cellular signaling. We developed a fully automatable assay technique, termed quantitative image cytometry (QIC), which integrates a quantitative immunostaining technique and a high precision image-processing algorithm for cell identification. With the aid of an automated sample preparation system, this device can quantify protein expression, phosphorylation and localization with subcellular resolution at one-minute intervals. The signaling activities quantified by the assay system showed good correlation with, as well as comparable reproducibility to, western blot analysis. Taking advantage of the high spatio-temporal resolution, we investigated the signaling dynamics of the ERK pathway in PC12 cells. The QIC technique appears as a highly quantitative and versatile technique, which can be a convenient replacement for the most conventional techniques including western blot, flow cytometry and live cell imaging. Thus, the QIC technique can be a powerful tool for investigating the systems biology of cellular signaling.

Quantitative magnetic resonance micro-imaging methods for pharmaceutical research.

PubMed

Mantle, M D

2011-09-30

The use of magnetic resonance imaging (MRI) as a tool in pharmaceutical research is now well established and the current literature covers a multitude of different pharmaceutically relevant research areas. This review focuses on the use of quantitative magnetic resonance micro-imaging techniques and how they have been exploited to extract information that is of direct relevance to the pharmaceutical industry. The article is divided into two main areas. The first half outlines the theoretical aspects of magnetic resonance and deals with basic magnetic resonance theory, the effects of nuclear spin-lattice (T(1)), spin-spin (T(2)) relaxation and molecular diffusion upon image quantitation, and discusses the applications of rapid magnetic resonance imaging techniques. In addition to the theory, the review aims to provide some practical guidelines for the pharmaceutical researcher with an interest in MRI as to which MRI pulse sequences/protocols should be used and when. The second half of the article reviews the recent advances and developments that have appeared in the literature concerning the use of quantitative micro-imaging methods to pharmaceutically relevant research. Copyright © 2010 Elsevier B.V. All rights reserved.

Qualitative and quantitative effects of harmonic echocardiographic imaging on endocardial edge definition and side-lobe artifacts

NASA Technical Reports Server (NTRS)

Rubin, D. N.; Yazbek, N.; Garcia, M. J.; Stewart, W. J.; Thomas, J. D.

2000-01-01

Harmonic imaging is a new ultrasonographic technique that is designed to improve image quality by exploiting the spontaneous generation of higher frequencies as ultrasound propagates through tissue. We studied 51 difficult-to-image patients with blinded side-by-side cineloop evaluation of endocardial border definition by harmonic versus fundamental imaging. In addition, quantitative intensities from cavity versus wall were compared for harmonic versus fundamental imaging. Harmonic imaging improved left ventricular endocardial border delineation over fundamental imaging (superior: harmonic = 71.1%, fundamental = 18.7%; similar: 10.2%; P <.001). Quantitative analysis of 100 wall/cavity combinations demonstrated brighter wall segments and more strikingly darker cavities during harmonic imaging (cavity intensity on a 0 to 255 scale: fundamental = 15.6 +/- 8.6; harmonic = 6.0 +/- 5.3; P <.0001), which led to enhanced contrast between the wall and cavity (1.89 versus 1.19, P <.0001). Harmonic imaging reduces side-lobe artifacts, resulting in a darker cavity and brighter walls, thereby improving image contrast and endocardial delineation.

Cerebral Metabolic Rate of Oxygen (CMRO2 ) Mapping by Combining Quantitative Susceptibility Mapping (QSM) and Quantitative Blood Oxygenation Level-Dependent Imaging (qBOLD).

PubMed

Cho, Junghun; Kee, Youngwook; Spincemaille, Pascal; Nguyen, Thanh D; Zhang, Jingwei; Gupta, Ajay; Zhang, Shun; Wang, Yi

2018-03-07

To map the cerebral metabolic rate of oxygen (CMRO 2 ) by estimating the oxygen extraction fraction (OEF) from gradient echo imaging (GRE) using phase and magnitude of the GRE data. 3D multi-echo gradient echo imaging and perfusion imaging with arterial spin labeling were performed in 11 healthy subjects. CMRO 2 and OEF maps were reconstructed by joint quantitative susceptibility mapping (QSM) to process GRE phases and quantitative blood oxygen level-dependent (qBOLD) modeling to process GRE magnitudes. Comparisons with QSM and qBOLD alone were performed using ROI analysis, paired t-tests, and Bland-Altman plot. The average CMRO 2 value in cortical gray matter across subjects were 140.4 ± 14.9, 134.1 ± 12.5, and 184.6 ± 17.9 μmol/100 g/min, with corresponding OEFs of 30.9 ± 3.4%, 30.0 ± 1.8%, and 40.9 ± 2.4% for methods based on QSM, qBOLD, and QSM+qBOLD, respectively. QSM+qBOLD provided the highest CMRO 2 contrast between gray and white matter, more uniform OEF than QSM, and less noisy OEF than qBOLD. Quantitative CMRO 2 mapping that fits the entire complex GRE data is feasible by combining QSM analysis of phase and qBOLD analysis of magnitude. © 2018 International Society for Magnetic Resonance in Medicine.

An image analysis system for near-infrared (NIR) fluorescence lymph imaging

NASA Astrophysics Data System (ADS)

Zhang, Jingdan; Zhou, Shaohua Kevin; Xiang, Xiaoyan; Rasmussen, John C.; Sevick-Muraca, Eva M.

2011-03-01

Quantitative analysis of lymphatic function is crucial for understanding the lymphatic system and diagnosing the associated diseases. Recently, a near-infrared (NIR) fluorescence imaging system is developed for real-time imaging lymphatic propulsion by intradermal injection of microdose of a NIR fluorophore distal to the lymphatics of interest. However, the previous analysis software3, 4 is underdeveloped, requiring extensive time and effort to analyze a NIR image sequence. In this paper, we develop a number of image processing techniques to automate the data analysis workflow, including an object tracking algorithm to stabilize the subject and remove the motion artifacts, an image representation named flow map to characterize lymphatic flow more reliably, and an automatic algorithm to compute lymph velocity and frequency of propulsion. By integrating all these techniques to a system, the analysis workflow significantly reduces the amount of required user interaction and improves the reliability of the measurement.

Segmentation and learning in the quantitative analysis of microscopy images

NASA Astrophysics Data System (ADS)

Ruggiero, Christy; Ross, Amy; Porter, Reid

2015-02-01

In material science and bio-medical domains the quantity and quality of microscopy images is rapidly increasing and there is a great need to automatically detect, delineate and quantify particles, grains, cells, neurons and other functional "objects" within these images. These are challenging problems for image processing because of the variability in object appearance that inevitably arises in real world image acquisition and analysis. One of the most promising (and practical) ways to address these challenges is interactive image segmentation. These algorithms are designed to incorporate input from a human operator to tailor the segmentation method to the image at hand. Interactive image segmentation is now a key tool in a wide range of applications in microscopy and elsewhere. Historically, interactive image segmentation algorithms have tailored segmentation on an image-by-image basis, and information derived from operator input is not transferred between images. But recently there has been increasing interest to use machine learning in segmentation to provide interactive tools that accumulate and learn from the operator input over longer periods of time. These new learning algorithms reduce the need for operator input over time, and can potentially provide a more dynamic balance between customization and automation for different applications. This paper reviews the state of the art in this area, provides a unified view of these algorithms, and compares the segmentation performance of various design choices.

Molecular spectral imaging system for quantitative immunohistochemical analysis of early diabetic retinopathy.

PubMed

Li, Qingli; Zhang, Jingfa; Wang, Yiting; Xu, Guoteng

2009-12-01

A molecular spectral imaging system has been developed based on microscopy and spectral imaging technology. The system is capable of acquiring molecular spectral images from 400 nm to 800 nm with 2 nm wavelength increments. The basic principles, instrumental systems, and system calibration method as well as its applications for the calculation of the stain-uptake by tissues are introduced. As a case study, the system is used for determining the pathogenesis of diabetic retinopathy and evaluating the therapeutic effects of erythropoietin. Some molecular spectral images of retinal sections of normal, diabetic, and treated rats were collected and analyzed. The typical transmittance curves of positive spots stained for albumin and advanced glycation end products are retrieved from molecular spectral data with the spectral response calibration algorithm. To explore and evaluate the protective effect of erythropoietin (EPO) on retinal albumin leakage of streptozotocin-induced diabetic rats, an algorithm based on Beer-Lambert's law is presented. The algorithm can assess the uptake by histologic retinal sections of stains used in quantitative pathology to label albumin leakage and advanced glycation end products formation. Experimental results show that the system is helpful for the ophthalmologist to reveal the pathogenesis of diabetic retinopathy and explore the protective effect of erythropoietin on retinal cells of diabetic rats. It also highlights the potential of molecular spectral imaging technology to provide more effective and reliable diagnostic criteria in pathology.

Real time quantitative imaging for semiconductor crystal growth, control and characterization

NASA Technical Reports Server (NTRS)

Wargo, Michael J.

1991-01-01

A quantitative real time image processing system has been developed which can be software-reconfigured for semiconductor processing and characterization tasks. In thermal imager mode, 2D temperature distributions of semiconductor melt surfaces (900-1600 C) can be obtained with temperature and spatial resolutions better than 0.5 C and 0.5 mm, respectively, as demonstrated by analysis of melt surface thermal distributions. Temporal and spatial image processing techniques and multitasking computational capabilities convert such thermal imaging into a multimode sensor for crystal growth control. A second configuration of the image processing engine in conjunction with bright and dark field transmission optics is used to nonintrusively determine the microdistribution of free charge carriers and submicron sized crystalline defects in semiconductors. The IR absorption characteristics of wafers are determined with 10-micron spatial resolution and, after calibration, are converted into charge carrier density.

Quantitative single-molecule imaging by confocal laser scanning microscopy.

PubMed

Vukojevic, Vladana; Heidkamp, Marcus; Ming, Yu; Johansson, Björn; Terenius, Lars; Rigler, Rudolf

2008-11-25

A new approach to quantitative single-molecule imaging by confocal laser scanning microscopy (CLSM) is presented. It relies on fluorescence intensity distribution to analyze the molecular occurrence statistics captured by digital imaging and enables direct determination of the number of fluorescent molecules and their diffusion rates without resorting to temporal or spatial autocorrelation analyses. Digital images of fluorescent molecules were recorded by using fast scanning and avalanche photodiode detectors. In this way the signal-to-background ratio was significantly improved, enabling direct quantitative imaging by CLSM. The potential of the proposed approach is demonstrated by using standard solutions of fluorescent dyes, fluorescently labeled DNA molecules, quantum dots, and the Enhanced Green Fluorescent Protein in solution and in live cells. The method was verified by using fluorescence correlation spectroscopy. The relevance for biological applications, in particular, for live cell imaging, is discussed.

Patient-specific coronary blood supply territories for quantitative perfusion analysis

PubMed Central

Zakkaroff, Constantine; Biglands, John D.; Greenwood, John P.; Plein, Sven; Boyle, Roger D.; Radjenovic, Aleksandra; Magee, Derek R.

2018-01-01

Abstract Myocardial perfusion imaging, coupled with quantitative perfusion analysis, provides an important diagnostic tool for the identification of ischaemic heart disease caused by coronary stenoses. The accurate mapping between coronary anatomy and under-perfused areas of the myocardium is important for diagnosis and treatment. However, in the absence of the actual coronary anatomy during the reporting of perfusion images, areas of ischaemia are allocated to a coronary territory based on a population-derived 17-segment (American Heart Association) AHA model of coronary blood supply. This work presents a solution for the fusion of 2D Magnetic Resonance (MR) myocardial perfusion images and 3D MR angiography data with the aim to improve the detection of ischaemic heart disease. The key contribution of this work is a novel method for the mediated spatiotemporal registration of perfusion and angiography data and a novel method for the calculation of patient-specific coronary supply territories. The registration method uses 4D cardiac MR cine series spanning the complete cardiac cycle in order to overcome the under-constrained nature of non-rigid slice-to-volume perfusion-to-angiography registration. This is achieved by separating out the deformable registration problem and solving it through phase-to-phase registration of the cine series. The use of patient-specific blood supply territories in quantitative perfusion analysis (instead of the population-based model of coronary blood supply) has the potential of increasing the accuracy of perfusion analysis. Quantitative perfusion analysis diagnostic accuracy evaluation with patient-specific territories against the AHA model demonstrates the value of the mediated spatiotemporal registration in the context of ischaemic heart disease diagnosis. PMID:29392098

Magnetic Resonance-based Motion Correction for Quantitative PET in Simultaneous PET-MR Imaging.

PubMed

Rakvongthai, Yothin; El Fakhri, Georges

2017-07-01

Motion degrades image quality and quantitation of PET images, and is an obstacle to quantitative PET imaging. Simultaneous PET-MR offers a tool that can be used for correcting the motion in PET images by using anatomic information from MR imaging acquired concurrently. Motion correction can be performed by transforming a set of reconstructed PET images into the same frame or by incorporating the transformation into the system model and reconstructing the motion-corrected image. Several phantom and patient studies have validated that MR-based motion correction strategies have great promise for quantitative PET imaging in simultaneous PET-MR. Copyright © 2017 Elsevier Inc. All rights reserved.

A new method to evaluate image quality of CBCT images quantitatively without observers

PubMed Central

Shimizu, Mayumi; Okamura, Kazutoshi; Yoshida, Shoko; Weerawanich, Warangkana; Tokumori, Kenji; Jasa, Gainer R; Yoshiura, Kazunori

2017-01-01

Objectives: To develop an observer-free method for quantitatively evaluating the image quality of CBCT images by applying just-noticeable difference (JND). Methods: We used two test objects: (1) a Teflon (polytetrafluoroethylene) plate phantom attached to a dry human mandible; and (2) a block phantom consisting of a Teflon step phantom and an aluminium step phantom. These phantoms had holes with different depths. They were immersed in water and scanned with a CB MercuRay (Hitachi Medical Corporation, Tokyo, Japan) at tube voltages of 120 kV, 100 kV, 80 kV and 60 kV. Superimposed images of the phantoms with holes were used for evaluation. The number of detectable holes was used as an index of image quality. In detecting holes quantitatively, the threshold grey value (ΔG), which differentiated holes from the background, was calculated using a specific threshold (the JND), and we extracted the holes with grey values above ΔG. The indices obtained by this quantitative method (the extracted hole values) were compared with the observer evaluations (the observed hole values). In addition, the contrast-to-noise ratio (CNR) of the shallowest detectable holes and the deepest undetectable holes were measured to evaluate the contribution of CNR to detectability. Results: The results of this evaluation method corresponded almost exactly with the evaluations made by observers. The extracted hole values reflected the influence of different tube voltages. All extracted holes had an area with a CNR of ≥1.5. Conclusions: This quantitative method of evaluating CBCT image quality may be more useful and less time-consuming than evaluation by observation. PMID:28045343

Quantitative Ultrasound Imaging Using Acoustic Backscatter Coefficients.

NASA Astrophysics Data System (ADS)

Boote, Evan Jeffery

Current clinical ultrasound scanners render images which have brightness levels related to the degree of backscattered energy from the tissue being imaged. These images offer the interpreter a qualitative impression of the scattering characteristics of the tissue being examined, but due to the complex factors which affect the amplitude and character of the echoed acoustic energy, it is difficult to make quantitative assessments of scattering nature of the tissue, and thus, difficult to make precise diagnosis when subtle disease effects are present. In this dissertation, a method of data reduction for determining acoustic backscatter coefficients is adapted for use in forming quantitative ultrasound images of this parameter. In these images, the brightness level of an individual pixel corresponds to the backscatter coefficient determined for the spatial position represented by that pixel. The data reduction method utilized rigorously accounts for extraneous factors which affect the scattered echo waveform and has been demonstrated to accurately determine backscatter coefficients under a wide range of conditions. The algorithms and procedures used to form backscatter coefficient images are described. These were tested using tissue-mimicking phantoms which have regions of varying scattering levels. Another phantom has a fat-mimicking layer for testing these techniques under more clinically relevant conditions. Backscatter coefficient images were also formed of in vitro human liver tissue. A clinical ultrasound scanner has been adapted for use as a backscatter coefficient imaging platform. The digital interface between the scanner and the computer used for data reduction are described. Initial tests, using phantoms are presented. A study of backscatter coefficient imaging of in vivo liver was performed using several normal, healthy human subjects.

Optical Ptychographic Microscope for Quantitative Bio-Mechanical Imaging

NASA Astrophysics Data System (ADS)

Anthony, Nicholas; Cadenazzi, Guido; Nugent, Keith; Abbey, Brian

The role that mechanical forces play in biological processes such as cell movement and death is becoming of significant interest to further develop our understanding of the inner workings of cells. The most common method used to obtain stress information is photoelasticity which maps a samples birefringence, or its direction dependent refractive indices, using polarized light. However this method only provides qualitative data and for stress information to be useful quantitative data is required. Ptychography is a method for quantitatively determining the phase of a samples complex transmission function. The technique relies upon the collection of multiple overlapping coherent diffraction patterns from laterally displaced points on the sample. The overlap of measurement points provides complementary information that significantly aids in the reconstruction of the complex wavefield exiting the sample and allows for quantitative imaging of weakly interacting specimens. Here we describe recent advances at La Trobe University Melbourne on achieving quantitative birefringence mapping using polarized light ptychography with applications in cell mechanics. Australian Synchrotron, ARC Centre of Excellence for Advanced Molecular Imaging.

Large-Scale Quantitative Analysis of Painting Arts

PubMed Central

Kim, Daniel; Son, Seung-Woo; Jeong, Hawoong

2014-01-01

Scientists have made efforts to understand the beauty of painting art in their own languages. As digital image acquisition of painting arts has made rapid progress, researchers have come to a point where it is possible to perform statistical analysis of a large-scale database of artistic paints to make a bridge between art and science. Using digital image processing techniques, we investigate three quantitative measures of images – the usage of individual colors, the variety of colors, and the roughness of the brightness. We found a difference in color usage between classical paintings and photographs, and a significantly low color variety of the medieval period. Interestingly, moreover, the increment of roughness exponent as painting techniques such as chiaroscuro and sfumato have advanced is consistent with historical circumstances. PMID:25501877

Dual function microscope for quantitative DIC and birefringence imaging

NASA Astrophysics Data System (ADS)

Li, Chengshuai; Zhu, Yizheng

2016-03-01

A spectral multiplexing interferometry (SXI) method is presented for integrated birefringence and phase gradient measurement on label-free biological specimens. With SXI, the retardation and orientation of sample birefringence are simultaneously encoded onto two separate spectral carrier waves, generated by a crystal retarder oriented at a specific angle. Thus sufficient information for birefringence determination can be obtained from a single interference spectrum, eliminating the need for multiple acquisitions with mechanical rotation or electrical modulation. In addition, with the insertion of a Nomarski prism, the setup can then acquire quantitative differential interference contrast images. Red blood cells infected by malaria parasites are imaged for birefringence retardation as well as phase gradient. The results demonstrate that the SXI approach can achieve both quantitative phase imaging and birefringence imaging with a single, high-sensitivity system.

Metrology Standards for Quantitative Imaging Biomarkers

PubMed Central

Obuchowski, Nancy A.; Kessler, Larry G.; Raunig, David L.; Gatsonis, Constantine; Huang, Erich P.; Kondratovich, Marina; McShane, Lisa M.; Reeves, Anthony P.; Barboriak, Daniel P.; Guimaraes, Alexander R.; Wahl, Richard L.

2015-01-01

Although investigators in the imaging community have been active in developing and evaluating quantitative imaging biomarkers (QIBs), the development and implementation of QIBs have been hampered by the inconsistent or incorrect use of terminology or methods for technical performance and statistical concepts. Technical performance is an assessment of how a test performs in reference objects or subjects under controlled conditions. In this article, some of the relevant statistical concepts are reviewed, methods that can be used for evaluating and comparing QIBs are described, and some of the technical performance issues related to imaging biomarkers are discussed. More consistent and correct use of terminology and study design principles will improve clinical research, advance regulatory science, and foster better care for patients who undergo imaging studies. © RSNA, 2015 PMID:26267831

Simple and fast spectral domain algorithm for quantitative phase imaging of living cells with digital holographic microscopy

NASA Astrophysics Data System (ADS)

Min, Junwei; Yao, Baoli; Ketelhut, Steffi; Kemper, Björn

2017-02-01

The modular combination of optical microscopes with digital holographic microscopy (DHM) has been proven to be a powerful tool for quantitative live cell imaging. The introduction of condenser and different microscope objectives (MO) simplifies the usage of the technique and makes it easier to measure different kinds of specimens with different magnifications. However, the high flexibility of illumination and imaging also causes variable phase aberrations that need to be eliminated for high resolution quantitative phase imaging. The existent phase aberrations compensation methods either require add additional elements into the reference arm or need specimen free reference areas or separate reference holograms to build up suitable digital phase masks. These inherent requirements make them unpractical for usage with highly variable illumination and imaging systems and prevent on-line monitoring of living cells. In this paper, we present a simple numerical method for phase aberration compensation based on the analysis of holograms in spatial frequency domain with capabilities for on-line quantitative phase imaging. From a single shot off-axis hologram, the whole phase aberration can be eliminated automatically without numerical fitting or pre-knowledge of the setup. The capabilities and robustness for quantitative phase imaging of living cancer cells are demonstrated.

UK quantitative WB-DWI technical workgroup: consensus meeting recommendations on optimisation, quality control, processing and analysis of quantitative whole-body diffusion-weighted imaging for cancer.

PubMed

Barnes, Anna; Alonzi, Roberto; Blackledge, Matthew; Charles-Edwards, Geoff; Collins, David J; Cook, Gary; Coutts, Glynn; Goh, Vicky; Graves, Martin; Kelly, Charles; Koh, Dow-Mu; McCallum, Hazel; Miquel, Marc E; O'Connor, James; Padhani, Anwar; Pearson, Rachel; Priest, Andrew; Rockall, Andrea; Stirling, James; Taylor, Stuart; Tunariu, Nina; van der Meulen, Jan; Walls, Darren; Winfield, Jessica; Punwani, Shonit

2018-01-01

Application of whole body diffusion-weighted MRI (WB-DWI) for oncology are rapidly increasing within both research and routine clinical domains. However, WB-DWI as a quantitative imaging biomarker (QIB) has significantly slower adoption. To date, challenges relating to accuracy and reproducibility, essential criteria for a good QIB, have limited widespread clinical translation. In recognition, a UK workgroup was established in 2016 to provide technical consensus guidelines (to maximise accuracy and reproducibility of WB-MRI QIBs) and accelerate the clinical translation of quantitative WB-DWI applications for oncology. A panel of experts convened from cancer centres around the UK with subspecialty expertise in quantitative imaging and/or the use of WB-MRI with DWI. A formal consensus method was used to obtain consensus agreement regarding best practice. Questions were asked about the appropriateness or otherwise on scanner hardware and software, sequence optimisation, acquisition protocols, reporting, and ongoing quality control programs to monitor precision and accuracy and agreement on quality control. The consensus panel was able to reach consensus on 73% (255/351) items and based on consensus areas made recommendations to maximise accuracy and reproducibly of quantitative WB-DWI studies performed at 1.5T. The panel were unable to reach consensus on the majority of items related to quantitative WB-DWI performed at 3T. This UK Quantitative WB-DWI Technical Workgroup consensus provides guidance on maximising accuracy and reproducibly of quantitative WB-DWI for oncology. The consensus guidance can be used by researchers and clinicians to harmonise WB-DWI protocols which will accelerate clinical translation of WB-DWI-derived QIBs.

Quantitative phase imaging and complex field reconstruction by pupil modulation differential phase contrast

PubMed Central

Lu, Hangwen; Chung, Jaebum; Ou, Xiaoze; Yang, Changhuei

2016-01-01

Differential phase contrast (DPC) is a non-interferometric quantitative phase imaging method achieved by using an asymmetric imaging procedure. We report a pupil modulation differential phase contrast (PMDPC) imaging method by filtering a sample’s Fourier domain with half-circle pupils. A phase gradient image is captured with each half-circle pupil, and a quantitative high resolution phase image is obtained after a deconvolution process with a minimum of two phase gradient images. Here, we introduce PMDPC quantitative phase image reconstruction algorithm and realize it experimentally in a 4f system with an SLM placed at the pupil plane. In our current experimental setup with the numerical aperture of 0.36, we obtain a quantitative phase image with a resolution of 1.73μm after computationally removing system aberrations and refocusing. We also extend the depth of field digitally by 20 times to ±50μm with a resolution of 1.76μm. PMID:27828473

Development of quantitative analysis method for stereotactic brain image: assessment of reduced accumulation in extent and severity using anatomical segmentation.

PubMed

Mizumura, Sunao; Kumita, Shin-ichiro; Cho, Keiichi; Ishihara, Makiko; Nakajo, Hidenobu; Toba, Masahiro; Kumazaki, Tatsuo

2003-06-01

Through visual assessment by three-dimensional (3D) brain image analysis methods using stereotactic brain coordinates system, such as three-dimensional stereotactic surface projections and statistical parametric mapping, it is difficult to quantitatively assess anatomical information and the range of extent of an abnormal region. In this study, we devised a method to quantitatively assess local abnormal findings by segmenting a brain map according to anatomical structure. Through quantitative local abnormality assessment using this method, we studied the characteristics of distribution of reduced blood flow in cases with dementia of the Alzheimer type (DAT). Using twenty-five cases with DAT (mean age, 68.9 years old), all of whom were diagnosed as probable Alzheimer's disease based on NINCDS-ADRDA, we collected I-123 iodoamphetamine SPECT data. A 3D brain map using the 3D-SSP program was compared with the data of 20 cases in the control group, who age-matched the subject cases. To study local abnormalities on the 3D images, we divided the whole brain into 24 segments based on anatomical classification. We assessed the extent of an abnormal region in each segment (rate of the coordinates with a Z-value that exceeds the threshold value, in all coordinates within a segment), and severity (average Z-value of the coordinates with a Z-value that exceeds the threshold value). This method clarified orientation and expansion of reduced accumulation, through classifying stereotactic brain coordinates according to the anatomical structure. This method was considered useful for quantitatively grasping distribution abnormalities in the brain and changes in abnormality distribution.

Analysis of objects in binary images. M.S. Thesis - Old Dominion Univ.

NASA Technical Reports Server (NTRS)

Leonard, Desiree M.

1991-01-01

Digital image processing techniques are typically used to produce improved digital images through the application of successive enhancement techniques to a given image or to generate quantitative data about the objects within that image. In support of and to assist researchers in a wide range of disciplines, e.g., interferometry, heavy rain effects on aerodynamics, and structure recognition research, it is often desirable to count objects in an image and compute their geometric properties. Therefore, an image analysis application package, focusing on a subset of image analysis techniques used for object recognition in binary images, was developed. This report describes the techniques and algorithms utilized in three main phases of the application and are categorized as: image segmentation, object recognition, and quantitative analysis. Appendices provide supplemental formulas for the algorithms employed as well as examples and results from the various image segmentation techniques and the object recognition algorithm implemented.

Kinetic Analysis of Amylase Using Quantitative Benedict's and Iodine Starch Reagents

ERIC Educational Resources Information Center

Cochran, Beverly; Lunday, Deborah; Miskevich, Frank

2008-01-01

Quantitative analysis of carbohydrates is a fundamental analytical tool used in many aspects of biology and chemistry. We have adapted a technique developed by Mathews et al. using an inexpensive scanner and open-source image analysis software to quantify amylase activity using both the breakdown of starch and the appearance of glucose. Breakdown…

Quantitative imaging of bilirubin by photoacoustic microscopy

NASA Astrophysics Data System (ADS)

Zhou, Yong; Zhang, Chi; Yao, Da-Kang; Wang, Lihong V.

2013-03-01

Noninvasive detection of both bilirubin concentration and its distribution is important for disease diagnosis. Here we implemented photoacoustic microscopy (PAM) to detect bilirubin distribution. We first demonstrate that our PAM system can measure the absorption spectra of bilirubin and blood. We also image bilirubin distributions in tissuemimicking samples, both without and with blood mixed. Our results show that PAM has the potential to quantitatively image bilirubin in vivo for clinical applications.

Characterization of a neutron sensitive MCP/Timepix detector for quantitative image analysis at a pulsed neutron source

NASA Astrophysics Data System (ADS)

Watanabe, Kenichi; Minniti, Triestino; Kockelmann, Winfried; Dalgliesh, Robert; Burca, Genoveva; Tremsin, Anton S.

2017-07-01

The uncertainties and the stability of a neutron sensitive MCP/Timepix detector when operating in the event timing mode for quantitative image analysis at a pulsed neutron source were investigated. The dominant component to the uncertainty arises from the counting statistics. The contribution of the overlap correction to the uncertainty was concluded to be negligible from considerations based on the error propagation even if a pixel occupation probability is more than 50%. We, additionally, have taken into account the multiple counting effect in consideration of the counting statistics. Furthermore, the detection efficiency of this detector system changes under relatively high neutron fluxes due to the ageing effects of current Microchannel Plates. Since this efficiency change is position-dependent, it induces a memory image. The memory effect can be significantly reduced with correction procedures using the rate equations describing the permanent gain degradation and the scrubbing effect on the inner surfaces of the MCP pores.

Quantitative image analysis of immunohistochemical stains using a CMYK color model

PubMed Central

Pham, Nhu-An; Morrison, Andrew; Schwock, Joerg; Aviel-Ronen, Sarit; Iakovlev, Vladimir; Tsao, Ming-Sound; Ho, James; Hedley, David W

2007-01-01

Background Computer image analysis techniques have decreased effects of observer biases, and increased the sensitivity and the throughput of immunohistochemistry (IHC) as a tissue-based procedure for the evaluation of diseases. Methods We adapted a Cyan/Magenta/Yellow/Key (CMYK) model for automated computer image analysis to quantify IHC stains in hematoxylin counterstained histological sections. Results The spectral characteristics of the chromogens AEC, DAB and NovaRed as well as the counterstain hematoxylin were first determined using CMYK, Red/Green/Blue (RGB), normalized RGB and Hue/Saturation/Lightness (HSL) color models. The contrast of chromogen intensities on a 0–255 scale (24-bit image file) as well as compared to the hematoxylin counterstain was greatest using the Yellow channel of a CMYK color model, suggesting an improved sensitivity for IHC evaluation compared to other color models. An increase in activated STAT3 levels due to growth factor stimulation, quantified using the Yellow channel image analysis was associated with an increase detected by Western blotting. Two clinical image data sets were used to compare the Yellow channel automated method with observer-dependent methods. First, a quantification of DAB-labeled carbonic anhydrase IX hypoxia marker in 414 sections obtained from 138 biopsies of cervical carcinoma showed strong association between Yellow channel and positive color selection results. Second, a linear relationship was also demonstrated between Yellow intensity and visual scoring for NovaRed-labeled epidermal growth factor receptor in 256 non-small cell lung cancer biopsies. Conclusion The Yellow channel image analysis method based on a CMYK color model is independent of observer biases for threshold and positive color selection, applicable to different chromogens, tolerant of hematoxylin, sensitive to small changes in IHC intensity and is applicable to simple automation procedures. These characteristics are advantageous for both

Application of multispectral imaging in quantitative immunohistochemistry study of breast cancer: a comparative study.

PubMed

Liu, Wen-Lou; Wang, Lin-Wei; Chen, Jia-Mei; Yuan, Jing-Ping; Xiang, Qing-Ming; Yang, Gui-Fang; Qu, Ai-Ping; Liu, Juan; Li, Yan

2016-04-01

Multispectral imaging (MSI) based on imaging and spectroscopy, as relatively novel to the field of histopathology, has been used in biomedical multidisciplinary researches. We analyzed and compared the utility of multispectral (MS) versus conventional red-green-blue (RGB) images for immunohistochemistry (IHC) staining to explore the advantages of MSI in clinical-pathological diagnosis. The MS images acquired of IHC-stained membranous marker human epidermal growth factor receptor 2 (HER2), cytoplasmic marker cytokeratin5/6 (CK5/6), and nuclear marker estrogen receptor (ER) have higher resolution, stronger contrast, and more accurate segmentation than the RGB images. The total signal optical density (OD) values for each biomarker were higher in MS images than in RGB images (all P < 0.05). Moreover, receiver operator characteristic (ROC) analysis revealed that a greater area under the curve (AUC), higher sensitivity, and specificity in evaluation of HER2 gene were achieved by MS images (AUC = 0.91, 89.1 %, 83.2 %) than RGB images (AUC = 0.87, 84.5, and 81.8 %). There was no significant difference between quantitative results of RGB images and clinico-pathological characteristics (P > 0.05). However, by quantifying MS images, the total signal OD values of HER2 positive expression were correlated with lymph node status and histological grades (P = 0.02 and 0.04). Additionally, the consistency test results indicated the inter-observer agreement was more robust in MS images for HER2 (inter-class correlation coefficient (ICC) = 0.95, r s = 0.94), CK5/6 (ICC = 0.90, r s = 0.88), and ER (ICC = 0.94, r s = 0.94) (all P < 0.001) than that in RGB images for HER2 (ICC = 0.91, r s = 0.89), CK5/6 (ICC = 0.85, r s = 0.84), and ER (ICC = 0.90, r s = 0.89) (all P < 0.001). Our results suggest that the application of MS images in quantitative IHC analysis could obtain higher accuracy, reliability, and more

DICOM for quantitative imaging biomarker development: a standards based approach to sharing clinical data and structured PET/CT analysis results in head and neck cancer research.

PubMed

Fedorov, Andriy; Clunie, David; Ulrich, Ethan; Bauer, Christian; Wahle, Andreas; Brown, Bartley; Onken, Michael; Riesmeier, Jörg; Pieper, Steve; Kikinis, Ron; Buatti, John; Beichel, Reinhard R

2016-01-01

Background. Imaging biomarkers hold tremendous promise for precision medicine clinical applications. Development of such biomarkers relies heavily on image post-processing tools for automated image quantitation. Their deployment in the context of clinical research necessitates interoperability with the clinical systems. Comparison with the established outcomes and evaluation tasks motivate integration of the clinical and imaging data, and the use of standardized approaches to support annotation and sharing of the analysis results and semantics. We developed the methodology and tools to support these tasks in Positron Emission Tomography and Computed Tomography (PET/CT) quantitative imaging (QI) biomarker development applied to head and neck cancer (HNC) treatment response assessment, using the Digital Imaging and Communications in Medicine (DICOM(®)) international standard and free open-source software. Methods. Quantitative analysis of PET/CT imaging data collected on patients undergoing treatment for HNC was conducted. Processing steps included Standardized Uptake Value (SUV) normalization of the images, segmentation of the tumor using manual and semi-automatic approaches, automatic segmentation of the reference regions, and extraction of the volumetric segmentation-based measurements. Suitable components of the DICOM standard were identified to model the various types of data produced by the analysis. A developer toolkit of conversion routines and an Application Programming Interface (API) were contributed and applied to create a standards-based representation of the data. Results. DICOM Real World Value Mapping, Segmentation and Structured Reporting objects were utilized for standards-compliant representation of the PET/CT QI analysis results and relevant clinical data. A number of correction proposals to the standard were developed. The open-source DICOM toolkit (DCMTK) was improved to simplify the task of DICOM encoding by introducing new API abstractions

DICOM for quantitative imaging biomarker development: a standards based approach to sharing clinical data and structured PET/CT analysis results in head and neck cancer research

PubMed Central

Clunie, David; Ulrich, Ethan; Bauer, Christian; Wahle, Andreas; Brown, Bartley; Onken, Michael; Riesmeier, Jörg; Pieper, Steve; Kikinis, Ron; Buatti, John; Beichel, Reinhard R.

2016-01-01

Background. Imaging biomarkers hold tremendous promise for precision medicine clinical applications. Development of such biomarkers relies heavily on image post-processing tools for automated image quantitation. Their deployment in the context of clinical research necessitates interoperability with the clinical systems. Comparison with the established outcomes and evaluation tasks motivate integration of the clinical and imaging data, and the use of standardized approaches to support annotation and sharing of the analysis results and semantics. We developed the methodology and tools to support these tasks in Positron Emission Tomography and Computed Tomography (PET/CT) quantitative imaging (QI) biomarker development applied to head and neck cancer (HNC) treatment response assessment, using the Digital Imaging and Communications in Medicine (DICOM®) international standard and free open-source software. Methods. Quantitative analysis of PET/CT imaging data collected on patients undergoing treatment for HNC was conducted. Processing steps included Standardized Uptake Value (SUV) normalization of the images, segmentation of the tumor using manual and semi-automatic approaches, automatic segmentation of the reference regions, and extraction of the volumetric segmentation-based measurements. Suitable components of the DICOM standard were identified to model the various types of data produced by the analysis. A developer toolkit of conversion routines and an Application Programming Interface (API) were contributed and applied to create a standards-based representation of the data. Results. DICOM Real World Value Mapping, Segmentation and Structured Reporting objects were utilized for standards-compliant representation of the PET/CT QI analysis results and relevant clinical data. A number of correction proposals to the standard were developed. The open-source DICOM toolkit (DCMTK) was improved to simplify the task of DICOM encoding by introducing new API abstractions

Quantitative assessment of the impact of biomedical image acquisition on the results obtained from image analysis and processing.

PubMed

Koprowski, Robert

2014-07-04

Dedicated, automatic algorithms for image analysis and processing are becoming more and more common in medical diagnosis. When creating dedicated algorithms, many factors must be taken into consideration. They are associated with selecting the appropriate algorithm parameters and taking into account the impact of data acquisition on the results obtained. An important feature of algorithms is the possibility of their use in other medical units by other operators. This problem, namely operator's (acquisition) impact on the results obtained from image analysis and processing, has been shown on a few examples. The analysed images were obtained from a variety of medical devices such as thermal imaging, tomography devices and those working in visible light. The objects of imaging were cellular elements, the anterior segment and fundus of the eye, postural defects and others. In total, almost 200'000 images coming from 8 different medical units were analysed. All image analysis algorithms were implemented in C and Matlab. For various algorithms and methods of medical imaging, the impact of image acquisition on the results obtained is different. There are different levels of algorithm sensitivity to changes in the parameters, for example: (1) for microscope settings and the brightness assessment of cellular elements there is a difference of 8%; (2) for the thyroid ultrasound images there is a difference in marking the thyroid lobe area which results in a brightness assessment difference of 2%. The method of image acquisition in image analysis and processing also affects: (3) the accuracy of determining the temperature in the characteristic areas on the patient's back for the thermal method - error of 31%; (4) the accuracy of finding characteristic points in photogrammetric images when evaluating postural defects - error of 11%; (5) the accuracy of performing ablative and non-ablative treatments in cosmetology - error of 18% for the nose, 10% for the cheeks, and 7% for the

A specialized plug-in software module for computer-aided quantitative measurement of medical images.

PubMed

Wang, Q; Zeng, Y J; Huo, P; Hu, J L; Zhang, J H

2003-12-01

This paper presents a specialized system for quantitative measurement of medical images. Using Visual C++, we developed a computer-aided software based on Image-Pro Plus (IPP), a software development platform. When transferred to the hard disk of a computer by an MVPCI-V3A frame grabber, medical images can be automatically processed by our own IPP plug-in for immunohistochemical analysis, cytomorphological measurement and blood vessel segmentation. In 34 clinical studies, the system has shown its high stability, reliability and ease of utility.

Automated thermal mapping techniques using chromatic image analysis

NASA Technical Reports Server (NTRS)

Buck, Gregory M.

1989-01-01

Thermal imaging techniques are introduced using a chromatic image analysis system and temperature sensitive coatings. These techniques are used for thermal mapping and surface heat transfer measurements on aerothermodynamic test models in hypersonic wind tunnels. Measurements are made on complex vehicle configurations in a timely manner and at minimal expense. The image analysis system uses separate wavelength filtered images to analyze surface spectral intensity data. The system was initially developed for quantitative surface temperature mapping using two-color thermographic phosphors but was found useful in interpreting phase change paint and liquid crystal data as well.

Widefield quantitative multiplex surface enhanced Raman scattering imaging in vivo

NASA Astrophysics Data System (ADS)

McVeigh, Patrick Z.; Mallia, Rupananda J.; Veilleux, Israel; Wilson, Brian C.

2013-04-01

In recent years numerous studies have shown the potential advantages of molecular imaging in vitro and in vivo using contrast agents based on surface enhanced Raman scattering (SERS), however the low throughput of traditional point-scanned imaging methodologies have limited their use in biological imaging. In this work we demonstrate that direct widefield Raman imaging based on a tunable filter is capable of quantitative multiplex SERS imaging in vivo, and that this imaging is possible with acquisition times which are orders of magnitude lower than achievable with comparable point-scanned methodologies. The system, designed for small animal imaging, has a linear response from (0.01 to 100 pM), acquires typical in vivo images in <10 s, and with suitable SERS reporter molecules is capable of multiplex imaging without compensation for spectral overlap. To demonstrate the utility of widefield Raman imaging in biological applications, we show quantitative imaging of four simultaneous SERS reporter molecules in vivo with resulting probe quantification that is in excellent agreement with known quantities (R2>0.98).

Quantitative refractive index distribution of single cell by combining phase-shifting interferometry and AFM imaging.

PubMed

Zhang, Qinnan; Zhong, Liyun; Tang, Ping; Yuan, Yingjie; Liu, Shengde; Tian, Jindong; Lu, Xiaoxu

2017-05-31

Cell refractive index, an intrinsic optical parameter, is closely correlated with the intracellular mass and concentration. By combining optical phase-shifting interferometry (PSI) and atomic force microscope (AFM) imaging, we constructed a label free, non-invasive and quantitative refractive index of single cell measurement system, in which the accurate phase map of single cell was retrieved with PSI technique and the cell morphology with nanoscale resolution was achieved with AFM imaging. Based on the proposed AFM/PSI system, we achieved quantitative refractive index distributions of single red blood cell and Jurkat cell, respectively. Further, the quantitative change of refractive index distribution during Daunorubicin (DNR)-induced Jurkat cell apoptosis was presented, and then the content changes of intracellular biochemical components were achieved. Importantly, these results were consistent with Raman spectral analysis, indicating that the proposed PSI/AFM based refractive index system is likely to become a useful tool for intracellular biochemical components analysis measurement, and this will facilitate its application for revealing cell structure and pathological state from a new perspective.

FLIM-FRET image analysis of tryptophan in prostate cancer cells

NASA Astrophysics Data System (ADS)

Periasamy, Ammasi; Alam, Shagufta R.; Svindrych, Zdenek; Wallrabe, Horst

2017-07-01

A region of interest (ROI) based quantitative FLIM-FRET image analysis is developed to quantitate the autofluorescence signals of the essential amino acid tryptophan as a biomarker to investigate the metabolism in prostate cancer cells.

Use of MRI in Differentiation of Papillary Renal Cell Carcinoma Subtypes: Qualitative and Quantitative Analysis.

PubMed

Doshi, Ankur M; Ream, Justin M; Kierans, Andrea S; Bilbily, Matthew; Rusinek, Henry; Huang, William C; Chandarana, Hersh

2016-03-01

The purpose of this study was to determine whether qualitative and quantitative MRI feature analysis is useful for differentiating type 1 from type 2 papillary renal cell carcinoma (PRCC). This retrospective study included 21 type 1 and 17 type 2 PRCCs evaluated with preoperative MRI. Two radiologists independently evaluated various qualitative features, including signal intensity, heterogeneity, and margin. For the quantitative analysis, a radiology fellow and a medical student independently drew 3D volumes of interest over the entire tumor on T2-weighted HASTE images, apparent diffusion coefficient parametric maps, and nephrographic phase contrast-enhanced MR images to derive first-order texture metrics. Qualitative and quantitative features were compared between the groups. For both readers, qualitative features with greater frequency in type 2 PRCC included heterogeneous enhancement, indistinct margin, and T2 heterogeneity (all, p < 0.035). Indistinct margins and heterogeneous enhancement were independent predictors (AUC, 0.822). Quantitative analysis revealed that apparent diffusion coefficient, HASTE, and contrast-enhanced entropy were greater in type 2 PRCC (p < 0.05; AUC, 0.682-0.716). A combined quantitative and qualitative model had an AUC of 0.859. Qualitative features within the model had interreader concordance of 84-95%, and the quantitative data had intraclass coefficients of 0.873-0.961. Qualitative and quantitative features can help discriminate between type 1 and type 2 PRCC. Quantitative analysis may capture useful information that complements the qualitative appearance while benefiting from high interobserver agreement.

General Staining and Segmentation Procedures for High Content Imaging and Analysis.

PubMed

Chambers, Kevin M; Mandavilli, Bhaskar S; Dolman, Nick J; Janes, Michael S

2018-01-01

Automated quantitative fluorescence microscopy, also known as high content imaging (HCI), is a rapidly growing analytical approach in cell biology. Because automated image analysis relies heavily on robust demarcation of cells and subcellular regions, reliable methods for labeling cells is a critical component of the HCI workflow. Labeling of cells for image segmentation is typically performed with fluorescent probes that bind DNA for nuclear-based cell demarcation or with those which react with proteins for image analysis based on whole cell staining. These reagents, along with instrument and software settings, play an important role in the successful segmentation of cells in a population for automated and quantitative image analysis. In this chapter, we describe standard procedures for labeling and image segmentation in both live and fixed cell samples. The chapter will also provide troubleshooting guidelines for some of the common problems associated with these aspects of HCI.

New approaches for the analysis of confluent cell layers with quantitative phase digital holographic microscopy

NASA Astrophysics Data System (ADS)

Pohl, L.; Kaiser, M.; Ketelhut, S.; Pereira, S.; Goycoolea, F.; Kemper, Björn

2016-03-01

Digital holographic microscopy (DHM) enables high resolution non-destructive inspection of technical surfaces and minimally-invasive label-free live cell imaging. However, the analysis of confluent cell layers represents a challenge as quantitative DHM phase images in this case do not provide sufficient information for image segmentation, determination of the cellular dry mass or calculation of the cell thickness. We present novel strategies for the analysis of confluent cell layers with quantitative DHM phase contrast utilizing a histogram based-evaluation procedure. The applicability of our approach is illustrated by quantification of drug induced cell morphology changes and it is shown that the method is capable to quantify reliable global morphology changes of confluent cell layers.

Biostatistical analysis of quantitative immunofluorescence microscopy images.

PubMed

Giles, C; Albrecht, M A; Lam, V; Takechi, R; Mamo, J C

2016-12-01

Semiquantitative immunofluorescence microscopy has become a key methodology in biomedical research. Typical statistical workflows are considered in the context of avoiding pseudo-replication and marginalising experimental error. However, immunofluorescence microscopy naturally generates hierarchically structured data that can be leveraged to improve statistical power and enrich biological interpretation. Herein, we describe a robust distribution fitting procedure and compare several statistical tests, outlining their potential advantages/disadvantages in the context of biological interpretation. Further, we describe tractable procedures for power analysis that incorporates the underlying distribution, sample size and number of images captured per sample. The procedures outlined have significant potential for increasing understanding of biological processes and decreasing both ethical and financial burden through experimental optimization. © 2016 The Authors Journal of Microscopy © 2016 Royal Microscopical Society.

Diagnostic efficacy of contrast-enhanced sonography by combined qualitative and quantitative analysis in breast lesions: a comparative study with magnetic resonance imaging.

PubMed

Wang, Lin; Du, Jing; Li, Feng-Hua; Fang, Hua; Hua, Jia; Wan, Cai-Feng

2013-10-01

The purpose of this study was to evaluate the diagnostic efficacy of contrast-enhanced sonography for differentiation of breast lesions by combined qualitative and quantitative analyses in comparison to magnetic resonance imaging (MRI). Fifty-six patients with American College of Radiology Breast Imaging Reporting and Data System category 3 to 5 breast lesions on conventional sonography were evaluated by contrast-enhanced sonography and MRI. A comparative analysis of diagnostic results between contrast-enhanced sonography and MRI was conducted in light of the pathologic findings. Pathologic analysis showed 26 benign and 30 malignant lesions. The predominant enhancement patterns of the benign lesions on contrast-enhanced sonography were homogeneous, centrifugal, and isoenhancement or hypoenhancement, whereas the patterns of the malignant lesions were mainly heterogeneous, centripetal, and hyperenhancement. The detection rates for perfusion defects and peripheral radial vessels in the malignant group were much higher than those in the benign group (P < .05). As to quantitative analysis, statistically significant differences were found in peak and time-to-peak values between the groups (P < .05). With pathologic findings as the reference standard, the sensitivity, specificity, and accuracy of contrast-enhanced sonography and MRI were 90.0%, 92.3%, 91.1% and 96.7%, 88.5%, and 92.9%, respectively. The two methods had a concordant rate of 87.5% (49 of 56), and the concordance test gave a value of κ = 0.75, indicating that there was high concordance in breast lesion assessment between the two diagnostic modalities. Contrast-enhanced sonography provided typical enhancement patterns and valuable quantitative parameters, which showed good agreement with MRI in diagnostic efficacy and may potentially improve characterization of breast lesions.

Image-derived arterial input function for quantitative fluorescence imaging of receptor-drug binding in vivo

PubMed Central

Elliott, Jonathan T.; Samkoe, Kimberley S.; Davis, Scott C.; Gunn, Jason R.; Paulsen, Keith D.; Roberts, David W.; Pogue, Brian W.

2017-01-01

Receptor concentration imaging (RCI) with targeted-untargeted optical dye pairs has enabled in vivo immunohistochemistry analysis in preclinical subcutaneous tumors. Successful application of RCI to fluorescence guided resection (FGR), so that quantitative molecular imaging of tumor-specific receptors could be performed in situ, would have a high impact. However, assumptions of pharmacokinetics, permeability and retention, as well as the lack of a suitable reference region limit the potential for RCI in human neurosurgery. In this study, an arterial input graphic analysis (AIGA) method is presented which is enabled by independent component analysis (ICA). The percent difference in arterial concentration between the image-derived arterial input function (AIFICA) and that obtained by an invasive method (ICACAR) was 2.0 ± 2.7% during the first hour of circulation of a targeted-untargeted dye pair in mice. Estimates of distribution volume and receptor concentration in tumor bearing mice (n = 5) recovered using the AIGA technique did not differ significantly from values obtained using invasive AIF measurements (p=0.12). The AIGA method, enabled by the subject-specific AIFICA, was also applied in a rat orthotopic model of U-251 glioblastoma to obtain the first reported receptor concentration and distribution volume maps during open craniotomy. PMID:26349671

Visualization and Quantitative Analysis of Crack-Tip Plastic Zone in Pure Nickel

NASA Astrophysics Data System (ADS)

Kelton, Randall; Sola, Jalal Fathi; Meletis, Efstathios I.; Huang, Haiying

2018-05-01

Changes in surface morphology have long been thought to be associated with crack propagation in metallic materials. We have studied areal surface texture changes around crack tips in an attempt to understand the correlations between surface texture changes and crack growth behavior. Detailed profiling of the fatigue sample surface was carried out at short fatigue intervals. An image processing algorithm was developed to calculate the surface texture changes. Quantitative analysis of the crack-tip plastic zone, crack-arrested sites near triple points, and large surface texture changes associated with crack release from arrested locations was carried out. The results indicate that surface texture imaging enables visualization of the development of plastic deformation around a crack tip. Quantitative analysis of the surface texture changes reveals the effects of local microstructures on the crack growth behavior.

Quantitative Analysis of Human Cancer Cell Extravasation Using Intravital Imaging.

PubMed

Willetts, Lian; Bond, David; Stoletov, Konstantin; Lewis, John D

2016-01-01

Metastasis, or the spread of cancer cells from a primary tumor to distant sites, is the leading cause of cancer-associated death. Metastasis is a complex multi-step process comprised of invasion, intravasation, survival in circulation, extravasation, and formation of metastatic colonies. Currently, in vitro assays are limited in their ability to investigate these intricate processes and do not faithfully reflect metastasis as it occurs in vivo. Traditional in vivo models of metastasis are limited by their ability to visualize the seemingly sporadic behavior of where and when cancer cells spread (Reymond et al., Nat Rev Cancer 13:858-870, 2013). The avian embryo model of metastasis is a powerful platform to study many of the critical steps in the metastatic cascade including the migration, extravasation, and invasion of human cancer cells in vivo (Sung et al., Nat Commun 6:7164, 2015; Leong et al., Cell Rep 8, 1558-1570, 2014; Kain et al., Dev Dyn 243:216-28, 2014; Leong et al., Nat Protoc 5:1406-17, 2010; Zijlstra et al., Cancer Cell 13:221-234, 2008; Palmer et al., J Vis Exp 51:2815, 2011). The chicken chorioallantoic membrane (CAM) is a readily accessible and well-vascularized tissue that surrounds the developing embryo. When the chicken embryo is grown in a shell-less, ex ovo environment, the nearly transparent CAM provides an ideal environment for high-resolution fluorescent microcopy approaches. In this model, the embryonic chicken vasculature and labeled cancer cells can be visualized simultaneously to investigate specific steps in the metastatic cascade including extravasation. When combined with the proper image analysis tools, the ex ovo chicken embryo model offers a cost-effective and high-throughput platform for the quantitative analysis of tumor cell metastasis in a physiologically relevant in vivo setting. Here we discuss detailed procedures to quantify cancer cell extravasation in the shell-less chicken embryo model with advanced fluorescence

Image Harvest: an open-source platform for high-throughput plant image processing and analysis

PubMed Central

Knecht, Avi C.; Campbell, Malachy T.; Caprez, Adam; Swanson, David R.; Walia, Harkamal

2016-01-01

High-throughput plant phenotyping is an effective approach to bridge the genotype-to-phenotype gap in crops. Phenomics experiments typically result in large-scale image datasets, which are not amenable for processing on desktop computers, thus creating a bottleneck in the image-analysis pipeline. Here, we present an open-source, flexible image-analysis framework, called Image Harvest (IH), for processing images originating from high-throughput plant phenotyping platforms. Image Harvest is developed to perform parallel processing on computing grids and provides an integrated feature for metadata extraction from large-scale file organization. Moreover, the integration of IH with the Open Science Grid provides academic researchers with the computational resources required for processing large image datasets at no cost. Image Harvest also offers functionalities to extract digital traits from images to interpret plant architecture-related characteristics. To demonstrate the applications of these digital traits, a rice (Oryza sativa) diversity panel was phenotyped and genome-wide association mapping was performed using digital traits that are used to describe different plant ideotypes. Three major quantitative trait loci were identified on rice chromosomes 4 and 6, which co-localize with quantitative trait loci known to regulate agronomically important traits in rice. Image Harvest is an open-source software for high-throughput image processing that requires a minimal learning curve for plant biologists to analyzephenomics datasets. PMID:27141917

Remote sensing image denoising application by generalized morphological component analysis

NASA Astrophysics Data System (ADS)

Yu, Chong; Chen, Xiong

2014-12-01

In this paper, we introduced a remote sensing image denoising method based on generalized morphological component analysis (GMCA). This novel algorithm is the further extension of morphological component analysis (MCA) algorithm to the blind source separation framework. The iterative thresholding strategy adopted by GMCA algorithm firstly works on the most significant features in the image, and then progressively incorporates smaller features to finely tune the parameters of whole model. Mathematical analysis of the computational complexity of GMCA algorithm is provided. Several comparison experiments with state-of-the-art denoising algorithms are reported. In order to make quantitative assessment of algorithms in experiments, Peak Signal to Noise Ratio (PSNR) index and Structural Similarity (SSIM) index are calculated to assess the denoising effect from the gray-level fidelity aspect and the structure-level fidelity aspect, respectively. Quantitative analysis on experiment results, which is consistent with the visual effect illustrated by denoised images, has proven that the introduced GMCA algorithm possesses a marvelous remote sensing image denoising effectiveness and ability. It is even hard to distinguish the original noiseless image from the recovered image by adopting GMCA algorithm through visual effect.

Quantitative imaging features of pretreatment CT predict volumetric response to chemotherapy in patients with colorectal liver metastases.

PubMed

Creasy, John M; Midya, Abhishek; Chakraborty, Jayasree; Adams, Lauryn B; Gomes, Camilla; Gonen, Mithat; Seastedt, Kenneth P; Sutton, Elizabeth J; Cercek, Andrea; Kemeny, Nancy E; Shia, Jinru; Balachandran, Vinod P; Kingham, T Peter; Allen, Peter J; DeMatteo, Ronald P; Jarnagin, William R; D'Angelica, Michael I; Do, Richard K G; Simpson, Amber L

2018-06-19

This study investigates whether quantitative image analysis of pretreatment CT scans can predict volumetric response to chemotherapy for patients with colorectal liver metastases (CRLM). Patients treated with chemotherapy for CRLM (hepatic artery infusion (HAI) combined with systemic or systemic alone) were included in the study. Patients were imaged at baseline and approximately 8 weeks after treatment. Response was measured as the percentage change in tumour volume from baseline. Quantitative imaging features were derived from the index hepatic tumour on pretreatment CT, and features statistically significant on univariate analysis were included in a linear regression model to predict volumetric response. The regression model was constructed from 70% of data, while 30% were reserved for testing. Test data were input into the trained model. Model performance was evaluated with mean absolute prediction error (MAPE) and R 2 . Clinicopatholologic factors were assessed for correlation with response. 157 patients were included, split into training (n = 110) and validation (n = 47) sets. MAPE from the multivariate linear regression model was 16.5% (R 2 = 0.774) and 21.5% in the training and validation sets, respectively. Stratified by HAI utilisation, MAPE in the validation set was 19.6% for HAI and 25.1% for systemic chemotherapy alone. Clinical factors associated with differences in median tumour response were treatment strategy, systemic chemotherapy regimen, age and KRAS mutation status (p < 0.05). Quantitative imaging features extracted from pretreatment CT are promising predictors of volumetric response to chemotherapy in patients with CRLM. Pretreatment predictors of response have the potential to better select patients for specific therapies. • Colorectal liver metastases (CRLM) are downsized with chemotherapy but predicting the patients that will respond to chemotherapy is currently not possible. • Heterogeneity and enhancement patterns of CRLM can be

Hyperplasia of type 2 pneumocytes following 0. 34 ppm nitrogen dioxide exposure: quantitation by image analysis. [Mice

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

Sherwin, R.P.; Richters, V.

1982-09-01

Swiss Webster male mice were exposed to intermittent 0.34 ppm nitrogen dioxide for 6 wk. Quantitative image analysis showed increased Type 2 cell numbers in each of the three lobes measured, with and without adjustment to alveolar wall measurements for lung volume normalization (e.g., P < .037 for Type 2 cell number adjusted to alveolar wall perimeters, combined lobe analysis of variance). The exposed animals dominated the upper quartile ranking of the cell number/alveolar area ratio computations (P < .025), which implied the presence of an especially susceptible subpopulation of animals. The Type 2 cell increase is believed to resultmore » from damage and loss of Type 1 cells, the reversibility and progression of which are presently unknown. The data also suggest an increased size of the Type 2 cell, and possibly slight atelectasis and/or edema of the alveolar walls.« less

Automatic registration of ICG images using mutual information and perfusion analysis

NASA Astrophysics Data System (ADS)

Kim, Namkug; Seo, Jong-Mo; Lee, June-goo; Kim, Jong Hyo; Park, Kwangsuk; Yu, Hyeong-Gon; Yu, Young Suk; Chung, Hum

2005-04-01

Introduction: Indocyanin green fundus angiographic images (ICGA) of the eyes is useful method in detecting and characterizing the choroidal neovascularization (CNV), which is the major cause of the blindness over 65 years of age. To investigate the quantitative analysis of the blood flow on ICGA, systematic approach for automatic registration of using mutual information and a quantitative analysis was developed. Methods: Intermittent sequential images of indocyanin green angiography were acquired by Heidelberg retinal angiography that uses the laser scanning system for the image acquisition. Misalignment of the each image generated by the minute eye movement of the patients was corrected by the mutual information method because the distribution of the contrast media on image is changing throughout the time sequences. Several region of interest (ROI) were selected by a physician and the intensities of the selected region were plotted according to the time sequences. Results: The registration of ICGA time sequential images is required not only translate transform but also rotational transform. Signal intensities showed variation based on gamma-variate function depending on ROIs and capillary vessels show more variance of signal intensity than major vessels. CNV showed intermediate variance of signal intensity and prolonged transit time. Conclusion: The resulting registered images can be used not only for quantitative analysis, but also for perfusion analysis. Various investigative approached on CNV using this method will be helpful in the characterization of the lesion and follow-up.

The study on the parallel processing based time series correlation analysis of RBC membrane flickering in quantitative phase imaging

NASA Astrophysics Data System (ADS)

Lee, Minsuk; Won, Youngjae; Park, Byungjun; Lee, Seungrag

2017-02-01

Not only static characteristics but also dynamic characteristics of the red blood cell (RBC) contains useful information for the blood diagnosis. Quantitative phase imaging (QPI) can capture sample images with subnanometer scale depth resolution and millisecond scale temporal resolution. Various researches have been used QPI for the RBC diagnosis, and recently many researches has been developed to decrease the process time of RBC information extraction using QPI by the parallel computing algorithm, however previous studies are interested in the static parameters such as morphology of the cells or simple dynamic parameters such as root mean square (RMS) of the membrane fluctuations. Previously, we presented a practical blood test method using the time series correlation analysis of RBC membrane flickering with QPI. However, this method has shown that there is a limit to the clinical application because of the long computation time. In this study, we present an accelerated time series correlation analysis of RBC membrane flickering using the parallel computing algorithm. This method showed consistent fractal scaling exponent results of the surrounding medium and the normal RBC with our previous research.

Quantitative phase-digital holographic microscopy: a new imaging modality to identify original cellular biomarkers of diseases

NASA Astrophysics Data System (ADS)

Marquet, P.; Rothenfusser, K.; Rappaz, B.; Depeursinge, C.; Jourdain, P.; Magistretti, P. J.

2016-03-01

Quantitative phase microscopy (QPM) has recently emerged as a powerful label-free technique in the field of living cell imaging allowing to non-invasively measure with a nanometric axial sensitivity cell structure and dynamics. Since the phase retardation of a light wave when transmitted through the observed cells, namely the quantitative phase signal (QPS), is sensitive to both cellular thickness and intracellular refractive index related to the cellular content, its accurate analysis allows to derive various cell parameters and monitor specific cell processes, which are very likely to identify new cell biomarkers. Specifically, quantitative phase-digital holographic microscopy (QP-DHM), thanks to its numerical flexibility facilitating parallelization and automation processes, represents an appealing imaging modality to both identify original cellular biomarkers of diseases as well to explore the underlying pathophysiological processes.

Assessing agreement between preclinical magnetic resonance imaging and histology: An evaluation of their image qualities and quantitative results

PubMed Central

Elschner, Cindy; Korn, Paula; Hauptstock, Maria; Schulz, Matthias C.; Range, Ursula; Jünger, Diana; Scheler, Ulrich

2017-01-01

One consequence of demographic change is the increasing demand for biocompatible materials for use in implants and prostheses. This is accompanied by a growing number of experimental animals because the interactions between new biomaterials and its host tissue have to be investigated. To evaluate novel materials and engineered tissues the use of non-destructive imaging modalities have been identified as a strategic priority. This provides the opportunity for studying interactions repeatedly with individual animals, along with the advantages of reduced biological variability and decreased number of laboratory animals. However, histological techniques are still the golden standard in preclinical biomaterial research. The present article demonstrates a detailed method comparison between histology and magnetic resonance imaging. This includes the presentation of their image qualities as well as the detailed statistical analysis for assessing agreement between quantitative measures. Exemplarily, the bony ingrowth of tissue engineered bone substitutes for treatment of a cleft-like maxillary bone defect has been evaluated. By using a graphical concordance analysis the mean difference between MRI results and histomorphometrical measures has been examined. The analysis revealed a slightly but significant bias in the case of the bone volume (biasHisto−MRI:Bone volume=2.40 %, p<0.005) and a clearly significant deviation for the remaining defect width (biasHisto−MRI:Defect width=−6.73 %, p≪0.005). But the study although showed a considerable effect of the analyzed section position to the quantitative result. It could be proven, that the bias of the data sets was less originated due to the imaging modalities, but mainly on the evaluation of different slice positions. The article demonstrated that method comparisons not always need the use of an independent animal study, additionally. PMID:28666026

Sequential processing of quantitative phase images for the study of cell behaviour in real-time digital holographic microscopy.

PubMed

Zikmund, T; Kvasnica, L; Týč, M; Křížová, A; Colláková, J; Chmelík, R

2014-11-01

Transmitted light holographic microscopy is particularly used for quantitative phase imaging of transparent microscopic objects such as living cells. The study of the cell is based on extraction of the dynamic data on cell behaviour from the time-lapse sequence of the phase images. However, the phase images are affected by the phase aberrations that make the analysis particularly difficult. This is because the phase deformation is prone to change during long-term experiments. Here, we present a novel algorithm for sequential processing of living cells phase images in a time-lapse sequence. The algorithm compensates for the deformation of a phase image using weighted least-squares surface fitting. Moreover, it identifies and segments the individual cells in the phase image. All these procedures are performed automatically and applied immediately after obtaining every single phase image. This property of the algorithm is important for real-time cell quantitative phase imaging and instantaneous control of the course of the experiment by playback of the recorded sequence up to actual time. Such operator's intervention is a forerunner of process automation derived from image analysis. The efficiency of the propounded algorithm is demonstrated on images of rat fibrosarcoma cells using an off-axis holographic microscope. © 2014 The Authors Journal of Microscopy © 2014 Royal Microscopical Society.

Practical considerations of image analysis and quantification of signal transduction IHC staining.

PubMed

Grunkin, Michael; Raundahl, Jakob; Foged, Niels T

2011-01-01

The dramatic increase in computer processing power in combination with the availability of high-quality digital cameras during the last 10 years has fertilized the grounds for quantitative microscopy based on digital image analysis. With the present introduction of robust scanners for whole slide imaging in both research and routine, the benefits of automation and objectivity in the analysis of tissue sections will be even more obvious. For in situ studies of signal transduction, the combination of tissue microarrays, immunohistochemistry, digital imaging, and quantitative image analysis will be central operations. However, immunohistochemistry is a multistep procedure including a lot of technical pitfalls leading to intra- and interlaboratory variability of its outcome. The resulting variations in staining intensity and disruption of original morphology are an extra challenge for the image analysis software, which therefore preferably should be dedicated to the detection and quantification of histomorphometrical end points.

Quantitative Imaging in Laboratory: Fast Kinetics and Fluorescence Quenching

ERIC Educational Resources Information Center

Cumberbatch, Tanya; Hanley, Quentin S.

2007-01-01

The process of quantitative imaging, which is very commonly used in laboratory, is shown to be very useful for studying the fast kinetics and fluorescence quenching of many experiments. The imaging technique is extremely cheap and hence can be used in many absorption and luminescence experiments.

Characterization of human oral tissues based on quantitative analysis of optical coherence tomography images

NASA Astrophysics Data System (ADS)

Salehi, Hassan S.; Kosa, Ali; Mahdian, Mina; Moslehpour, Saeid; Alnajjar, Hisham; Tadinada, Aditya

2017-02-01

In this paper, five types of tissues, human enamel, human cortical bone, human trabecular bone, muscular tissue, and fatty tissue were imaged ex vivo using optical coherence tomography (OCT). The specimens were prepared in blocks of 5 x 5 x 3 mm (width x length x height). The OCT imaging system was a swept source OCT system operating at wavelengths ranging between 1250 nm and 1360 nm with an average power of 18 mW and a scan rate of 50 to 100 kHz. The imaging probe was placed on top of a 2 x 2 cm stabilizing device to maintain a standard distance from the samples. Ten image samples from each type of tissue were obtained. To acquire images with minimum inhomogeneity, imaging was performed multiple times at different points. Based on the observed texture differences between OCT images of soft and hard tissues, spatial and spectral features were quantitatively extracted from the OCT images. The Radon transform from angles of 0 deg to 90 deg was computed, averaged over all the angles, normalized to peak at unity, and then fitted with Gaussian function. The mean absolute values of the spatial frequency components of the OCT image were considered as a feature, where 2-D fast Fourier transform (FFT) was done to OCT images. These OCT features can reliably differentiate between a range of hard and soft tissues, and could be extremely valuable in assisting dentists for in vivo evaluation of oral tissues and early detection of pathologic changes in tissues.

Quantitative Infrared Image Analysis Of Simultaneous Upstream and Downstream Microgravity Flame Spread over Thermally-Thin Cellulose in Low Speed Forced Flow

NASA Technical Reports Server (NTRS)

Olson, S. L.; Lee, J. R.; Fujita, O.; Kikuchi, M.; Kashiwagi, T.

2013-01-01

The effect of low velocity forced flow on microgravity flame spread is examined using quantitative analysis of infrared video imaging. The objective of the quantitative analysis is to provide insight into the mechanisms of flame spread in microgravity where the flame is able to spread from a central location on the fuel surface, rather than from an edge. Surface view calibrated infrared images of ignition and flame spread over a thin cellulose fuel were obtained along with a color video of the surface view and color images of the edge view using 35 mm color film at 2 Hz. The cellulose fuel samples were mounted in the center of a 12 cm wide by 16 cm tall flow duct and were ignited in microgravity using a straight hot wire across the center of the 7.5 cm wide by 14 cm long samples. Four cases, at 1 atm. 35%O2 in N2, at forced flows from 2 cm/s to 20 cm/s are presented here. This flow range captures flame spread from strictly upstream spread at low flows, to predominantly downstream spread at high flow. Surface temperature profiles are evaluated as a function of time, and temperature gradients for upstream and downstream flame spread are measured. Flame spread rates from IR image data are compared to visible image spread rate data. IR blackbody temperatures are compared to surface thermocouple readings to evaluate the effective emissivity of the pyrolyzing surface. Preheat lengths and pyrolysis lengths are evaluated both upstream and downstream of the central ignition point. A surface energy balance estimates the net heat flux from the flame to the fuel surface along the length of the fuel. Surface radiative loss and gas-phase radiation from soot are measured relative to the net heat feedback from the flame. At high surface heat loss relative to heat feedback, the downstream flame spread does not occur.

Quantitative background parenchymal uptake on molecular breast imaging and breast cancer risk: a case-control study.

PubMed

Hruska, Carrie B; Geske, Jennifer R; Swanson, Tiffinee N; Mammel, Alyssa N; Lake, David S; Manduca, Armando; Conners, Amy Lynn; Whaley, Dana H; Scott, Christopher G; Carter, Rickey E; Rhodes, Deborah J; O'Connor, Michael K; Vachon, Celine M

2018-06-05

Background parenchymal uptake (BPU), which refers to the level of Tc-99m sestamibi uptake within normal fibroglandular tissue on molecular breast imaging (MBI), has been identified as a breast cancer risk factor, independent of mammographic density. Prior analyses have used subjective categories to describe BPU. We evaluate a new quantitative method for assessing BPU by testing its reproducibility, comparing quantitative results with previously established subjective BPU categories, and determining the association of quantitative BPU with breast cancer risk. Two nonradiologist operators independently performed region-of-interest analysis on MBI images viewed in conjunction with corresponding digital mammograms. Quantitative BPU was defined as a unitless ratio of the average pixel intensity (counts/pixel) within the fibroglandular tissue versus the average pixel intensity in fat. Operator agreement and the correlation of quantitative BPU measures with subjective BPU categories assessed by expert radiologists were determined. Percent density on mammograms was estimated using Cumulus. The association of quantitative BPU with breast cancer (per one unit BPU) was examined within an established case-control study of 62 incident breast cancer cases and 177 matched controls. Quantitative BPU ranged from 0.4 to 3.2 across all subjects and was on average higher in cases compared to controls (1.4 versus 1.2, p < 0.007 for both operators). Quantitative BPU was strongly correlated with subjective BPU categories (Spearman's r = 0.59 to 0.69, p < 0.0001, for each paired combination of two operators and two radiologists). Interoperator and intraoperator agreement in the quantitative BPU measure, assessed by intraclass correlation, was 0.92 and 0.98, respectively. Quantitative BPU measures showed either no correlation or weak negative correlation with mammographic percent density. In a model adjusted for body mass index and percent density, higher quantitative BPU was

Quantitative nanoscopy: Tackling sampling limitations in (S)TEM imaging of polymers and composites.

PubMed

Gnanasekaran, Karthikeyan; Snel, Roderick; de With, Gijsbertus; Friedrich, Heiner

2016-01-01

Sampling limitations in electron microscopy questions whether the analysis of a bulk material is representative, especially while analyzing hierarchical morphologies that extend over multiple length scales. We tackled this problem by automatically acquiring a large series of partially overlapping (S)TEM images with sufficient resolution, subsequently stitched together to generate a large-area map using an in-house developed acquisition toolbox (TU/e Acquisition ToolBox) and stitching module (TU/e Stitcher). In addition, we show that quantitative image analysis of the large scale maps provides representative information that can be related to the synthesis and process conditions of hierarchical materials, which moves electron microscopy analysis towards becoming a bulk characterization tool. We demonstrate the power of such an analysis by examining two different multi-phase materials that are structured over multiple length scales. Copyright © 2015 Elsevier B.V. All rights reserved.

A quantitative comparison of moldic and vuggy porosity structure in karst aquifers using image and geospatial analysis

NASA Astrophysics Data System (ADS)

Culpepper, A. R.; Manda, A. K.

2011-12-01

Limestone aquifers are vital sources of groundwater for domestic and industrial use throughout the world. To sustain rising population throughout the southeastern United States, aquifers are increasingly exploited to provide the populace clean and reliable water resources. The moldic Castle Hayne and the vuggy Biscayne aquifer systems are two highly productive aquifers that provide critical water resources to millions of citizens in eastern North Carolina and southeastern Florida, respectively. In order to better understand karst aquifers and evaluate the potential for contaminant transport, detailed investigation of 2D porosity and pore geometry using image and geospatial analysis were undertaken. The objective of this study is to compare and contrast the porosity structure of moldic and vuggy karst aquifers by quantifying 2D porosity and pore geometry from images of slabbed core samples and optical televiewer images. Televiewer images and images of painted core samples from the Spring Garden Member of the Castle Hayne aquifer and Miami Limestone Formation of the Biscayne aquifer were acquired for analysis of porosity structure. The procedure for converting images of slabbed core and televiewer images to a GIS useable format consisted of rectification, calibration, image enhancement, classification, recoding and filtering. In GIS, raster or vector formats were used to assess pore attributes (e.g., area and perimeter) and structure. Preliminary results show that both pore area and perimeter for the Spring Garden Member of the Castle Hayne and Miami Limestone Formation of the Biscayne aquifers can be described by exponential distributions. In both sets of slabbed core images the relatively small pores have the highest occurrence, whereas larger pores occur less frequently. However, the moldic Spring Garden Member of the Castle Hayne aquifer has larger pore sizes derived from cores images than the vuggy Miami Limestone Formation of Biscayne aquifer. Total porosity

Multiscale Analysis of Solar Image Data

NASA Astrophysics Data System (ADS)

Young, C. A.; Myers, D. C.

2001-12-01

It is often said that the blessing and curse of solar physics is that there is too much data. Solar missions such as Yohkoh, SOHO and TRACE have shown us the Sun with amazing clarity but have also cursed us with an increased amount of higher complexity data than previous missions. We have improved our view of the Sun yet we have not improved our analysis techniques. The standard techniques used for analysis of solar images generally consist of observing the evolution of features in a sequence of byte scaled images or a sequence of byte scaled difference images. The determination of features and structures in the images are done qualitatively by the observer. There is little quantitative and objective analysis done with these images. Many advances in image processing techniques have occured in the past decade. Many of these methods are possibly suited for solar image analysis. Multiscale/Multiresolution methods are perhaps the most promising. These methods have been used to formulate the human ability to view and comprehend phenomena on different scales. So these techniques could be used to quantitify the imaging processing done by the observers eyes and brains. In this work we present a preliminary analysis of multiscale techniques applied to solar image data. Specifically, we explore the use of the 2-d wavelet transform and related transforms with EIT, LASCO and TRACE images. This work was supported by NASA contract NAS5-00220.

SU-E-J-260: Quantitative Image Feature Analysis of Multiphase Liver CT for Hepatocellular Carcinoma (HCC) in Radiation Therapy

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

Choi, W; Wang, J; Lu, W

Purpose: To identify the effective quantitative image features (radiomics features) for prediction of response, survival, recurrence and metastasis of hepatocellular carcinoma (HCC) in radiotherapy. Methods: Multiphase contrast enhanced liver CT images were acquired in 16 patients with HCC on pre and post radiation therapy (RT). In this study, arterial phase CT images were selected to analyze the effectiveness of image features for the prediction of treatment outcome of HCC to RT. Response evaluated by RECIST criteria, survival, local recurrence (LR), distant metastasis (DM) and liver metastasis (LM) were examined. A radiation oncologist manually delineated the tumor and normal liver onmore » pre and post CT scans, respectively. Quantitative image features were extracted to characterize the intensity distribution (n=8), spatial patterns (texture, n=36), and shape (n=16) of the tumor and liver, respectively. Moreover, differences between pre and post image features were calculated (n=120). A total of 360 features were extracted and then analyzed by unpaired student’s t-test to rank the effectiveness of features for the prediction of response. Results: The five most effective features were selected for prediction of each outcome. Significant predictors for tumor response and survival are changes in tumor shape (Second Major Axes Length, p= 0.002; Eccentricity, p=0.0002), for LR, liver texture (Standard Deviation (SD) of High Grey Level Run Emphasis and SD of Entropy, both p=0.005) on pre and post CT images, for DM, tumor texture (SD of Entropy, p=0.01) on pre CT image and for LM, liver (Mean of Cluster Shade, p=0.004) and tumor texture (SD of Entropy, p=0.006) on pre CT image. Intensity distribution features were not significant (p>0.09). Conclusion: Quantitative CT image features were found to be potential predictors of the five endpoints of HCC in RT. This work was supported in part by the National Cancer Institute Grant R01CA172638.« less

Petrographic characterization of lunar soils: Application of x ray digital-imaging to quantitative and automated analysis

NASA Technical Reports Server (NTRS)

Higgins, Stefan J.; Patchen, Allan; Chambers, John G.; Taylor, Lawrence A.; Mckay, David S.

1994-01-01

The rocks and soils of the moon will be the raw materials for various engineering needs at a lunar base, such as sources of hydrogen, oxygen, metals, etc. The material of choice for most of the bulk needs is the regolith and its less than 1 cm fraction, the soil. For specific mineral resources it may be necessary to concentrate minerals from either rocks or soils. Therefore, quantitative characterizations of these rocks and soils are necessary in order to better define their mineral resource potential. However, using standard point-counting microscopic procedures, it is difficult to quantitatively determine mineral abundances and virtually impossible to obtain data on mineral distributions within grains. As a start to fulfilling these needs, Taylor et al. and Chambers et al. have developed a procedure for characterization of crushed lunar rocks using x ray digital imaging. The development of a similar digital imaging procedure for lunar soils as obtained from a spectrometer is described.

Techniques in helical scanning, dynamic imaging and image segmentation for improved quantitative analysis with X-ray micro-CT

NASA Astrophysics Data System (ADS)

Sheppard, Adrian; Latham, Shane; Middleton, Jill; Kingston, Andrew; Myers, Glenn; Varslot, Trond; Fogden, Andrew; Sawkins, Tim; Cruikshank, Ron; Saadatfar, Mohammad; Francois, Nicolas; Arns, Christoph; Senden, Tim

2014-04-01

This paper reports on recent advances at the micro-computed tomography facility at the Australian National University. Since 2000 this facility has been a significant centre for developments in imaging hardware and associated software for image reconstruction, image analysis and image-based modelling. In 2010 a new instrument was constructed that utilises theoretically-exact image reconstruction based on helical scanning trajectories, allowing higher cone angles and thus better utilisation of the available X-ray flux. We discuss the technical hurdles that needed to be overcome to allow imaging with cone angles in excess of 60°. We also present dynamic tomography algorithms that enable the changes between one moment and the next to be reconstructed from a sparse set of projections, allowing higher speed imaging of time-varying samples. Researchers at the facility have also created a sizeable distributed-memory image analysis toolkit with capabilities ranging from tomographic image reconstruction to 3D shape characterisation. We show results from image registration and present some of the new imaging and experimental techniques that it enables. Finally, we discuss the crucial question of image segmentation and evaluate some recently proposed techniques for automated segmentation.

Carr-Purcell-Meiboom-Gill imaging of prostate cancer: quantitative T2 values for cancer discrimination.

PubMed

Roebuck, Joseph R; Haker, Steven J; Mitsouras, Dimitris; Rybicki, Frank J; Tempany, Clare M; Mulkern, Robert V

2009-05-01

Quantitative, apparent T(2) values of suspected prostate cancer and healthy peripheral zone tissue in men with prostate cancer were measured using a Carr-Purcell-Meiboom-Gill (CPMG) imaging sequence in order to assess the cancer discrimination potential of tissue T(2) values. The CPMG imaging sequence was used to image the prostates of 18 men with biopsy-proven prostate cancer. Whole gland coverage with nominal voxel volumes of 0.54 x 1.1 x 4 mm(3) was obtained in 10.7 min, resulting in data sets suitable for generating high-quality images with variable T(2)-weighting and for evaluating quantitative T(2) values on a pixel-by-pixel basis. Region-of-interest analysis of suspected healthy peripheral zone tissue and suspected cancer, identified on the basis of both T(1)- and T(2)-weighted signal intensities and available histopathology reports, yielded significantly (P<.0001) longer apparent T(2) values in suspected healthy tissue (193+/-49 ms) vs. suspected cancer (100+/-26 ms), suggesting potential utility of this method as a tissue specific discrimination index for prostate cancer. We conclude that CPMG imaging of the prostate can be performed in reasonable scan times and can provide advantages over T(2)-weighted fast spin echo (FSE) imaging alone, including quantitative T(2) values for cancer discrimination as well as proton density maps without the point spread function degradation associated with short effective echo time FSE sequences.

Quantitative imaging of heterogeneous dynamics in drying and aging paints

PubMed Central

van der Kooij, Hanne M.; Fokkink, Remco; van der Gucht, Jasper; Sprakel, Joris

2016-01-01

Drying and aging paint dispersions display a wealth of complex phenomena that make their study fascinating yet challenging. To meet the growing demand for sustainable, high-quality paints, it is essential to unravel the microscopic mechanisms underlying these phenomena. Visualising the governing dynamics is, however, intrinsically difficult because the dynamics are typically heterogeneous and span a wide range of time scales. Moreover, the high turbidity of paints precludes conventional imaging techniques from reaching deep inside the paint. To address these challenges, we apply a scattering technique, Laser Speckle Imaging, as a versatile and quantitative tool to elucidate the internal dynamics, with microscopic resolution and spanning seven decades of time. We present a toolbox of data analysis and image processing methods that allows a tailored investigation of virtually any turbid dispersion, regardless of the geometry and substrate. Using these tools we watch a variety of paints dry and age with unprecedented detail. PMID:27682840

Thermal image analysis using the serpentine method

NASA Astrophysics Data System (ADS)

Koprowski, Robert; Wilczyński, Sławomir

2018-03-01

Thermal imaging is an increasingly widespread alternative to other imaging methods. As a supplementary method in diagnostics, it can be used both statically and with dynamic temperature changes. The paper proposes a new image analysis method that allows for the acquisition of new diagnostic information as well as object segmentation. The proposed serpentine analysis uses known and new methods of image analysis and processing proposed by the authors. Affine transformations of an image and subsequent Fourier analysis provide a new diagnostic quality. The method is fully repeatable and automatic and independent of inter-individual variability in patients. The segmentation results are by 10% better than those obtained from the watershed method and the hybrid segmentation method based on the Canny detector. The first and second harmonics of serpentine analysis enable to determine the type of temperature changes in the region of interest (gradient, number of heat sources etc.). The presented serpentine method provides new quantitative information on thermal imaging and more. Since it allows for image segmentation and designation of contact points of two and more heat sources (local minimum), it can be used to support medical diagnostics in many areas of medicine.

Ultrasound introscopic image quantitative characteristics for medical diagnosis

NASA Astrophysics Data System (ADS)

Novoselets, Mikhail K.; Sarkisov, Sergey S.; Gridko, Alexander N.; Tcheban, Anatoliy K.

1993-09-01

The results on computer aided extraction of quantitative characteristics (QC) of ultrasound introscopic images for medical diagnosis are presented. Thyroid gland (TG) images of Chernobil Accident sufferers are considered. It is shown that TG diseases can be associated with some values of selected QCs of random echo distribution in the image. The possibility of these QCs usage for TG diseases recognition in accordance with calculated values is analyzed. The role of speckle noise elimination in the solution of the problem on TG diagnosis is considered too.

T1, diffusion tensor, and quantitative magnetization transfer imaging of the hippocampus in an Alzheimer's disease mouse model.

PubMed

Whittaker, Heather T; Zhu, Shenghua; Di Curzio, Domenico L; Buist, Richard; Li, Xin-Min; Noy, Suzanna; Wiseman, Frances K; Thiessen, Jonathan D; Martin, Melanie

2018-07-01

Alzheimer's disease (AD) pathology causes microstructural changes in the brain. These changes, if quantified with magnetic resonance imaging (MRI), could be studied for use as an early biomarker for AD. The aim of our study was to determine if T 1 relaxation, diffusion tensor imaging (DTI), and quantitative magnetization transfer imaging (qMTI) metrics could reveal changes within the hippocampus and surrounding white matter structures in ex vivo transgenic mouse brains overexpressing human amyloid precursor protein with the Swedish mutation. Delineation of hippocampal cell layers using DTI color maps allows more detailed analysis of T 1 -weighted imaging, DTI, and qMTI metrics, compared with segmentation of gross anatomy based on relaxation images, and with analysis of DTI or qMTI metrics alone. These alterations are observed in the absence of robust intracellular Aβ accumulation or plaque deposition as revealed by histology. This work demonstrates that multiparametric quantitative MRI methods are useful for characterizing changes within the hippocampal substructures and surrounding white matter tracts of mouse models of AD. Copyright © 2018. Published by Elsevier Inc.

MATtrack: A MATLAB-Based Quantitative Image Analysis Platform for Investigating Real-Time Photo-Converted Fluorescent Signals in Live Cells

PubMed Central

Courtney, Jane; Woods, Elena; Scholz, Dimitri; Hall, William W.; Gautier, Virginie W.

2015-01-01

We introduce here MATtrack, an open source MATLAB-based computational platform developed to process multi-Tiff files produced by a photo-conversion time lapse protocol for live cell fluorescent microscopy. MATtrack automatically performs a series of steps required for image processing, including extraction and import of numerical values from Multi-Tiff files, red/green image classification using gating parameters, noise filtering, background extraction, contrast stretching and temporal smoothing. MATtrack also integrates a series of algorithms for quantitative image analysis enabling the construction of mean and standard deviation images, clustering and classification of subcellular regions and injection point approximation. In addition, MATtrack features a simple user interface, which enables monitoring of Fluorescent Signal Intensity in multiple Regions of Interest, over time. The latter encapsulates a region growing method to automatically delineate the contours of Regions of Interest selected by the user, and performs background and regional Average Fluorescence Tracking, and automatic plotting. Finally, MATtrack computes convenient visualization and exploration tools including a migration map, which provides an overview of the protein intracellular trajectories and accumulation areas. In conclusion, MATtrack is an open source MATLAB-based software package tailored to facilitate the analysis and visualization of large data files derived from real-time live cell fluorescent microscopy using photoconvertible proteins. It is flexible, user friendly, compatible with Windows, Mac, and Linux, and a wide range of data acquisition software. MATtrack is freely available for download at eleceng.dit.ie/courtney/MATtrack.zip. PMID:26485569

MATtrack: A MATLAB-Based Quantitative Image Analysis Platform for Investigating Real-Time Photo-Converted Fluorescent Signals in Live Cells.

PubMed

Courtney, Jane; Woods, Elena; Scholz, Dimitri; Hall, William W; Gautier, Virginie W

2015-01-01

We introduce here MATtrack, an open source MATLAB-based computational platform developed to process multi-Tiff files produced by a photo-conversion time lapse protocol for live cell fluorescent microscopy. MATtrack automatically performs a series of steps required for image processing, including extraction and import of numerical values from Multi-Tiff files, red/green image classification using gating parameters, noise filtering, background extraction, contrast stretching and temporal smoothing. MATtrack also integrates a series of algorithms for quantitative image analysis enabling the construction of mean and standard deviation images, clustering and classification of subcellular regions and injection point approximation. In addition, MATtrack features a simple user interface, which enables monitoring of Fluorescent Signal Intensity in multiple Regions of Interest, over time. The latter encapsulates a region growing method to automatically delineate the contours of Regions of Interest selected by the user, and performs background and regional Average Fluorescence Tracking, and automatic plotting. Finally, MATtrack computes convenient visualization and exploration tools including a migration map, which provides an overview of the protein intracellular trajectories and accumulation areas. In conclusion, MATtrack is an open source MATLAB-based software package tailored to facilitate the analysis and visualization of large data files derived from real-time live cell fluorescent microscopy using photoconvertible proteins. It is flexible, user friendly, compatible with Windows, Mac, and Linux, and a wide range of data acquisition software. MATtrack is freely available for download at eleceng.dit.ie/courtney/MATtrack.zip.

Advances in Surface Plasmon Resonance Imaging allowing for quantitative measurement of laterally heterogeneous samples

NASA Astrophysics Data System (ADS)

Raegen, Adam; Reiter, Kyle; Clarke, Anthony; Lipkowski, Jacek; Dutcher, John

2012-02-01

The Surface Plasmon Resonance (SPR) phenomenon is routinely exploited to qualitatively probe changes to materials on metallic surfaces for use in probes and sensors. Unfortunately, extracting truly quantitative information is usually limited to a select few cases -- uniform absorption/desorption of small biomolecules and films, in which a continuous ``slab'' model is a good approximation. We present advancements in the SPR technique that expand the number of cases for which the technique can provide meaningful results. Use of a custom, angle-scanning SPR imaging system, together with a refined data analysis method, allow for quantitative kinetic measurements of laterally heterogeneous systems. The degradation of cellulose microfibrils and bundles of microfibrils due to the action of cellulolytic enzymes will be presented as an excellent example of the capabilities of the SPR imaging system.

Passive thermal infrared hyperspectral imaging for quantitative imaging of shale gas leaks

NASA Astrophysics Data System (ADS)

Gagnon, Marc-André; Tremblay, Pierre; Savary, Simon; Farley, Vincent; Guyot, Éric; Lagueux, Philippe; Morton, Vince; Giroux, Jean; Chamberland, Martin

2017-10-01

There are many types of natural gas fields including shale formations that are common especially in the St-Lawrence Valley (Canada). Since methane (CH4), the major component of shale gas, is odorless, colorless and highly flammable, in addition to being a greenhouse gas, methane emanations and/or leaks are important to consider for both safety and environmental reasons. Telops recently launched on the market the Hyper-Cam Methane, a field-deployable thermal infrared hyperspectral camera specially tuned for detecting methane infrared spectral features under ambient conditions and over large distances. In order to illustrate the benefits of this novel research instrument for natural gas imaging, the instrument was brought on a site where shale gas leaks unexpectedly happened during a geological survey near the Enfant-Jesus hospital in Quebec City, Canada, during December 2014. Quantitative methane imaging was carried out based on methane's unique infrared spectral signature. Optical flow analysis was also carried out on the data to estimate the methane mass flow rate. The results show how this novel technique could be used for advanced research on shale gases.

Quantitative high-speed laryngoscopic analysis of vocal fold vibration in fatigued voice of young karaoke singers.

PubMed

Yiu, Edwin M-L; Wang, Gaowu; Lo, Andy C Y; Chan, Karen M-K; Ma, Estella P-M; Kong, Jiangping; Barrett, Elizabeth Ann

2013-11-01

The present study aimed to determine whether there were physiological differences in the vocal fold vibration between nonfatigued and fatigued voices using high-speed laryngoscopic imaging and quantitative analysis. Twenty participants aged from 18 to 23 years (mean, 21.2 years; standard deviation, 1.3 years) with normal voice were recruited to participate in an extended singing task. Vocal fatigue was induced using a singing task. High-speed laryngoscopic image recordings of /i/ phonation were taken before and after the singing task. The laryngoscopic images were semiautomatically analyzed with the quantitative high-speed video processing program to extract indices related to the anteroposterior dimension (length), transverse dimension (width), and the speed of opening and closing. Significant reduction in the glottal length-to-width ratio index was found after vocal fatigue. Physiologically, this indicated either a significantly shorter (anteroposteriorly) or a wider (transversely) glottis after vocal fatigue. The high-speed imaging technique using quantitative analysis has the potential for early identification of vocally fatigued voice. Copyright © 2013 The Voice Foundation. All rights reserved.

Evaluation of a web based informatics system with data mining tools for predicting outcomes with quantitative imaging features in stroke rehabilitation clinical trials

NASA Astrophysics Data System (ADS)

Wang, Ximing; Kim, Bokkyu; Park, Ji Hoon; Wang, Erik; Forsyth, Sydney; Lim, Cody; Ravi, Ragini; Karibyan, Sarkis; Sanchez, Alexander; Liu, Brent

2017-03-01

Quantitative imaging biomarkers are used widely in clinical trials for tracking and evaluation of medical interventions. Previously, we have presented a web based informatics system utilizing quantitative imaging features for predicting outcomes in stroke rehabilitation clinical trials. The system integrates imaging features extraction tools and a web-based statistical analysis tool. The tools include a generalized linear mixed model(GLMM) that can investigate potential significance and correlation based on features extracted from clinical data and quantitative biomarkers. The imaging features extraction tools allow the user to collect imaging features and the GLMM module allows the user to select clinical data and imaging features such as stroke lesion characteristics from the database as regressors and regressands. This paper discusses the application scenario and evaluation results of the system in a stroke rehabilitation clinical trial. The system was utilized to manage clinical data and extract imaging biomarkers including stroke lesion volume, location and ventricle/brain ratio. The GLMM module was validated and the efficiency of data analysis was also evaluated.

The Use of Quantitative SPECT/CT Imaging to Assess Residual Limb Health

DTIC Science & Technology

2016-10-01

AWARD NUMBER: W81XWH-15-1-0669 TITLE: The Use of Quantitative SPECT/CT Imaging to Assess Residual Limb Health PRINCIPAL INVESTIGATOR...3. DATES COVERED 30 Sep 2015 - 29 Sep 2016 4. TITLE AND SUBTITLE The Use of Quantitative SPECT/CT Imaging to Assess Residual Limb Health 5a...amputation and subsequently evaluate the utility of non-invasive imaging for evaluating the impact of next-generation socket technologies on the health of

Quantitative analysis of microtubule orientation in interdigitated leaf pavement cells

PubMed Central

Akita, Kae; Higaki, Takumi; Kutsuna, Natsumaro; Hasezawa, Seiichiro

2015-01-01

Leaf pavement cells are shaped like a jigsaw puzzle in most dicotyledon species. Molecular genetic studies have identified several genes required for pavement cells morphogenesis and proposed that microtubules play crucial roles in the interdigitation of pavement cells. In this study, we performed quantitative analysis of cortical microtubule orientation in leaf pavement cells in Arabidopsis thaliana. We captured confocal images of cortical microtubules in cotyledon leaf epidermis expressing GFP-tubulinβ and quantitatively evaluated the microtubule orientations relative to the pavement cell growth axis using original image processing techniques. Our results showed that microtubules kept parallel orientations to the growth axis during pavement cell growth. In addition, we showed that immersion treatment of seed cotyledons in solutions containing tubulin polymerization and depolymerization inhibitors decreased pavement cell complexity. Treatment with oryzalin and colchicine inhibited the symmetric division of guard mother cells. PMID:26039484

Quantitative analysis of microtubule orientation in interdigitated leaf pavement cells.

PubMed

Akita, Kae; Higaki, Takumi; Kutsuna, Natsumaro; Hasezawa, Seiichiro

2015-01-01

Leaf pavement cells are shaped like a jigsaw puzzle in most dicotyledon species. Molecular genetic studies have identified several genes required for pavement cells morphogenesis and proposed that microtubules play crucial roles in the interdigitation of pavement cells. In this study, we performed quantitative analysis of cortical microtubule orientation in leaf pavement cells in Arabidopsis thaliana. We captured confocal images of cortical microtubules in cotyledon leaf epidermis expressing GFP-tubulinβ and quantitatively evaluated the microtubule orientations relative to the pavement cell growth axis using original image processing techniques. Our results showed that microtubules kept parallel orientations to the growth axis during pavement cell growth. In addition, we showed that immersion treatment of seed cotyledons in solutions containing tubulin polymerization and depolymerization inhibitors decreased pavement cell complexity. Treatment with oryzalin and colchicine inhibited the symmetric division of guard mother cells.

Image Harvest: an open-source platform for high-throughput plant image processing and analysis.

PubMed

Knecht, Avi C; Campbell, Malachy T; Caprez, Adam; Swanson, David R; Walia, Harkamal

2016-05-01

High-throughput plant phenotyping is an effective approach to bridge the genotype-to-phenotype gap in crops. Phenomics experiments typically result in large-scale image datasets, which are not amenable for processing on desktop computers, thus creating a bottleneck in the image-analysis pipeline. Here, we present an open-source, flexible image-analysis framework, called Image Harvest (IH), for processing images originating from high-throughput plant phenotyping platforms. Image Harvest is developed to perform parallel processing on computing grids and provides an integrated feature for metadata extraction from large-scale file organization. Moreover, the integration of IH with the Open Science Grid provides academic researchers with the computational resources required for processing large image datasets at no cost. Image Harvest also offers functionalities to extract digital traits from images to interpret plant architecture-related characteristics. To demonstrate the applications of these digital traits, a rice (Oryza sativa) diversity panel was phenotyped and genome-wide association mapping was performed using digital traits that are used to describe different plant ideotypes. Three major quantitative trait loci were identified on rice chromosomes 4 and 6, which co-localize with quantitative trait loci known to regulate agronomically important traits in rice. Image Harvest is an open-source software for high-throughput image processing that requires a minimal learning curve for plant biologists to analyzephenomics datasets. © The Author 2016. Published by Oxford University Press on behalf of the Society for Experimental Biology.

Quantitative surface topography assessment of directly compressed and roller compacted tablet cores using photometric stereo image analysis.

PubMed

Allesø, Morten; Holm, Per; Carstensen, Jens Michael; Holm, René

2016-05-25

Surface topography, in the context of surface smoothness/roughness, was investigated by the use of an image analysis technique, MultiRay™, related to photometric stereo, on different tablet batches manufactured either by direct compression or roller compaction. In the present study, oblique illumination of the tablet (darkfield) was considered and the area of cracks and pores in the surface was used as a measure of tablet surface topography; the higher a value, the rougher the surface. The investigations demonstrated a high precision of the proposed technique, which was able to rapidly (within milliseconds) and quantitatively measure the obtained surface topography of the produced tablets. Compaction history, in the form of applied roll force and tablet punch pressure, was also reflected in the measured smoothness of the tablet surfaces. Generally it was found that a higher degree of plastic deformation of the microcrystalline cellulose resulted in a smoother tablet surface. This altogether demonstrated that the technique provides the pharmaceutical developer with a reliable, quantitative response parameter for visual appearance of solid dosage forms, which may be used for process and ultimately product optimization. Copyright © 2015 Elsevier B.V. All rights reserved.

Characterization of Cerebral White Matter Properties Using Quantitative Magnetic Resonance Imaging Stains

PubMed Central

Hurley, Samuel A.; Samsonov, Alexey A.; Adluru, Nagesh; Hosseinbor, Ameer Pasha; Mossahebi, Pouria; Tromp, Do P.M.; Zakszewski, Elizabeth; Field, Aaron S.

2011-01-01

Abstract The image contrast in magnetic resonance imaging (MRI) is highly sensitive to several mechanisms that are modulated by the properties of the tissue environment. The degree and type of contrast weighting may be viewed as image filters that accentuate specific tissue properties. Maps of quantitative measures of these mechanisms, akin to microstructural/environmental-specific tissue stains, may be generated to characterize the MRI and physiological properties of biological tissues. In this article, three quantitative MRI (qMRI) methods for characterizing white matter (WM) microstructural properties are reviewed. All of these measures measure complementary aspects of how water interacts with the tissue environment. Diffusion MRI, including diffusion tensor imaging, characterizes the diffusion of water in the tissues and is sensitive to the microstructural density, spacing, and orientational organization of tissue membranes, including myelin. Magnetization transfer imaging characterizes the amount and degree of magnetization exchange between free water and macromolecules like proteins found in the myelin bilayers. Relaxometry measures the MRI relaxation constants T1 and T2, which in WM have a component associated with the water trapped in the myelin bilayers. The conduction of signals between distant brain regions occurs primarily through myelinated WM tracts; thus, these methods are potential indicators of pathology and structural connectivity in the brain. This article provides an overview of the qMRI stain mechanisms, acquisition and analysis strategies, and applications for these qMRI stains. PMID:22432902

Applications of pathology-assisted image analysis of immunohistochemistry-based biomarkers in oncology.

PubMed

Shinde, V; Burke, K E; Chakravarty, A; Fleming, M; McDonald, A A; Berger, A; Ecsedy, J; Blakemore, S J; Tirrell, S M; Bowman, D

2014-01-01

Immunohistochemistry-based biomarkers are commonly used to understand target inhibition in key cancer pathways in preclinical models and clinical studies. Automated slide-scanning and advanced high-throughput image analysis software technologies have evolved into a routine methodology for quantitative analysis of immunohistochemistry-based biomarkers. Alongside the traditional pathology H-score based on physical slides, the pathology world is welcoming digital pathology and advanced quantitative image analysis, which have enabled tissue- and cellular-level analysis. An automated workflow was implemented that includes automated staining, slide-scanning, and image analysis methodologies to explore biomarkers involved in 2 cancer targets: Aurora A and NEDD8-activating enzyme (NAE). The 2 workflows highlight the evolution of our immunohistochemistry laboratory and the different needs and requirements of each biological assay. Skin biopsies obtained from MLN8237 (Aurora A inhibitor) phase 1 clinical trials were evaluated for mitotic and apoptotic index, while mitotic index and defects in chromosome alignment and spindles were assessed in tumor biopsies to demonstrate Aurora A inhibition. Additionally, in both preclinical xenograft models and an acute myeloid leukemia phase 1 trial of the NAE inhibitor MLN4924, development of a novel image algorithm enabled measurement of downstream pathway modulation upon NAE inhibition. In the highlighted studies, developing a biomarker strategy based on automated image analysis solutions enabled project teams to confirm target and pathway inhibition and understand downstream outcomes of target inhibition with increased throughput and quantitative accuracy. These case studies demonstrate a strategy that combines a pathologist's expertise with automated image analysis to support oncology drug discovery and development programs.

Quantitative energy-filtered TEM imaging of interfaces

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

Bentley, J.; Kenik, E.A.; Siangchaew, K.

Quantitative elemental mapping by inner shell core-loss energy-filtered transmission electron microscopy (TEM) with a Gatan Imaging Filter (GIF) interfaced to a Philips CM30 TEM operated with a LaB{sub 6} filament at 300 kV has been applied to interfaces in a range of materials. In sensitized type 304L stainless steel aged 15 h at 600{degrees}C, grain-boundary Cr depletion occurs between Cr-rich intergranular M{sub 23}C{sub 6} particles. Images of net Cr L{sub 23} intensity show segregation profiles that agree quantitatively with focused-probe spectrum-line measurements recorded with a Gatan PEELS on a Philips EM400T/FEG (0.8 nA in 2-nm-diam probe) of the same regions.more » Rare-earth oxide additives that are used for the liquid-phase sintering of Si{sub 3}N{sub 4} generate second phases of complex composition at grain boundaries and edges. These grain boundary phases often control corrosion, crack growth and creep damage behavior. High resolution imaging has been widely and with focused probes can be compromised by beam damage, but elemental mapping by EFTEM appears not to cause appreciable beam damage.« less

Quantitative image quality evaluation of MR images using perceptual difference models

PubMed Central

Miao, Jun; Huo, Donglai; Wilson, David L.

2008-01-01

The authors are using a perceptual difference model (Case-PDM) to quantitatively evaluate image quality of the thousands of test images which can be created when optimizing fast magnetic resonance (MR) imaging strategies and reconstruction techniques. In this validation study, they compared human evaluation of MR images from multiple organs and from multiple image reconstruction algorithms to Case-PDM and similar models. The authors found that Case-PDM compared very favorably to human observers in double-stimulus continuous-quality scale and functional measurement theory studies over a large range of image quality. The Case-PDM threshold for nonperceptible differences in a 2-alternative forced choice study varied with the type of image under study, but was ≈1.1 for diffuse image effects, providing a rule of thumb. Ordering the image quality evaluation models, we found in overall Case-PDM ≈ IDM (Sarnoff Corporation) ≈ SSIM [Wang et al. IEEE Trans. Image Process. 13, 600–612 (2004)] > mean squared error ≈ NR [Wang et al. (2004) (unpublished)] > DCTune (NASA) > IQM (MITRE Corporation). The authors conclude that Case-PDM is very useful in MR image evaluation but that one should probably restrict studies to similar images and similar processing, normally not a limitation in image reconstruction studies. PMID:18649487

Towards real-time quantitative optical imaging for surgery

NASA Astrophysics Data System (ADS)

Gioux, Sylvain

2017-07-01

There is a pressing clinical need to provide image guidance during surgery. Currently, assessment of tissue that needs to be resected or avoided is performed subjectively leading to a large number of failures, patient morbidity and increased healthcare cost. Because near-infrared (NIR) optical imaging is safe, does not require contact, and can provide relatively deep information (several mm), it offers unparalleled capabilities for providing image guidance during surgery. In this work, we introduce a novel concept that enables the quantitative imaging of endogenous molecular information over large fields-of-view. Because this concept can be implemented in real-time, it is amenable to provide video-rate endogenous information during surgery.

Quantitative label-free sperm imaging by means of transport of intensity

NASA Astrophysics Data System (ADS)

Poola, Praveen Kumar; Pandiyan, Vimal Prabhu; Jayaraman, Varshini; John, Renu

2016-03-01

Most living cells are optically transparent which makes it difficult to visualize them under bright field microscopy. Use of contrast agents or markers and staining procedures are often followed to observe these cells. However, most of these staining agents are toxic and not applicable for live cell imaging. In the last decade, quantitative phase imaging has become an indispensable tool for morphological characterization of the phase objects without any markers. In this paper, we report noninterferometric quantitative phase imaging of live sperm cells by solving transport of intensity equations with recorded intensity measurements along optical axis on a commercial bright field microscope.

Iterative optimization method for design of quantitative magnetization transfer imaging experiments.

PubMed

Levesque, Ives R; Sled, John G; Pike, G Bruce

2011-09-01

Quantitative magnetization transfer imaging (QMTI) using spoiled gradient echo sequences with pulsed off-resonance saturation can be a time-consuming technique. A method is presented for selection of an optimum experimental design for quantitative magnetization transfer imaging based on the iterative reduction of a discrete sampling of the Z-spectrum. The applicability of the technique is demonstrated for human brain white matter imaging at 1.5 T and 3 T, and optimal designs are produced to target specific model parameters. The optimal number of measurements and the signal-to-noise ratio required for stable parameter estimation are also investigated. In vivo imaging results demonstrate that this optimal design approach substantially improves parameter map quality. The iterative method presented here provides an advantage over free form optimal design methods, in that pragmatic design constraints are readily incorporated. In particular, the presented method avoids clustering and repeated measures in the final experimental design, an attractive feature for the purpose of magnetization transfer model validation. The iterative optimal design technique is general and can be applied to any method of quantitative magnetization transfer imaging. Copyright © 2011 Wiley-Liss, Inc.

Real-time quantitative fluorescence imaging using a single snapshot optical properties technique for neurosurgical guidance

NASA Astrophysics Data System (ADS)

Valdes, Pablo A.; Angelo, Joseph; Gioux, Sylvain

2015-03-01

Fluorescence imaging has shown promise as an adjunct to improve the extent of resection in neurosurgery and oncologic surgery. Nevertheless, current fluorescence imaging techniques do not account for the heterogeneous attenuation effects of tissue optical properties. In this work, we present a novel imaging system that performs real time quantitative fluorescence imaging using Single Snapshot Optical Properties (SSOP) imaging. We developed the technique and performed initial phantom studies to validate the quantitative capabilities of the system for intraoperative feasibility. Overall, this work introduces a novel real-time quantitative fluorescence imaging method capable of being used intraoperatively for neurosurgical guidance.

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

PubMed

Tam, Allison; Barker, Jocelyn; Rubin, Daniel

2016-01-01

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

The cutting edge - Micro-CT for quantitative toolmark analysis of sharp force trauma to bone.

PubMed

Norman, D G; Watson, D G; Burnett, B; Fenne, P M; Williams, M A

2018-02-01

Toolmark analysis involves examining marks created on an object to identify the likely tool responsible for creating those marks (e.g., a knife). Although a potentially powerful forensic tool, knife mark analysis is still in its infancy and the validation of imaging techniques as well as quantitative approaches is ongoing. This study builds on previous work by simulating real-world stabbings experimentally and statistically exploring quantitative toolmark properties, such as cut mark angle captured by micro-CT imaging, to predict the knife responsible. In Experiment 1 a mechanical stab rig and two knives were used to create 14 knife cut marks on dry pig ribs. The toolmarks were laser and micro-CT scanned to allow for quantitative measurements of numerous toolmark properties. The findings from Experiment 1 demonstrated that both knives produced statistically different cut mark widths, wall angle and shapes. Experiment 2 examined knife marks created on fleshed pig torsos with conditions designed to better simulate real-world stabbings. Eight knives were used to generate 64 incision cut marks that were also micro-CT scanned. Statistical exploration of these cut marks suggested that knife type, serrated or plain, can be predicted from cut mark width and wall angle. Preliminary results suggest that knives type can be predicted from cut mark width, and that knife edge thickness correlates with cut mark width. An additional 16 cut marks walls were imaged for striation marks using scanning electron microscopy with results suggesting that this approach might not be useful for knife mark analysis. Results also indicated that observer judgements of cut mark shape were more consistent when rated from micro-CT images than light microscopy images. The potential to combine micro-CT data, medical grade CT data and photographs to develop highly realistic virtual models for visualisation and 3D printing is also demonstrated. This is the first study to statistically explore simulated

Quantitative phase imaging of human red blood cells using phase-shifting white light interference microscopy with colour fringe analysis

NASA Astrophysics Data System (ADS)

Singh Mehta, Dalip; Srivastava, Vishal

2012-11-01

We report quantitative phase imaging of human red blood cells (RBCs) using phase-shifting interference microscopy. Five phase-shifted white light interferograms are recorded using colour charge coupled device camera. White light interferograms were decomposed into red, green, and blue colour components. The phase-shifted interferograms of each colour were then processed by phase-shifting analysis and phase maps for red, green, and blue colours were reconstructed. Wavelength dependent refractive index profiles of RBCs were computed from the single set of white light interferogram. The present technique has great potential for non-invasive determination of refractive index variation and morphological features of cells and tissues.

Quantitative Analysis of Venus Radar Backscatter Data in ArcGIS

NASA Technical Reports Server (NTRS)

Long, S. M.; Grosfils, E. B.

2005-01-01

Ongoing mapping of the Ganiki Planitia (V14) quadrangle of Venus and definition of material units has involved an integrated but qualitative analysis of Magellan radar backscatter images and topography using standard geomorphological mapping techniques. However, such analyses do not take full advantage of the quantitative information contained within the images. Analysis of the backscatter coefficient allows a much more rigorous statistical comparison between mapped units, permitting first order selfsimilarity tests of geographically separated materials assigned identical geomorphological labels. Such analyses cannot be performed directly on pixel (DN) values from Magellan backscatter images, because the pixels are scaled to the Muhleman law for radar echoes on Venus and are not corrected for latitudinal variations in incidence angle. Therefore, DN values must be converted based on pixel latitude back to their backscatter coefficient values before accurate statistical analysis can occur. Here we present a method for performing the conversions and analysis of Magellan backscatter data using commonly available ArcGIS software and illustrate the advantages of the process for geological mapping.

A strategy to apply quantitative epistasis analysis on developmental traits.

PubMed

Labocha, Marta K; Yuan, Wang; Aleman-Meza, Boanerges; Zhong, Weiwei

2017-05-15

Genetic interactions are keys to understand complex traits and evolution. Epistasis analysis is an effective method to map genetic interactions. Large-scale quantitative epistasis analysis has been well established for single cells. However, there is a substantial lack of such studies in multicellular organisms and their complex phenotypes such as development. Here we present a method to extend quantitative epistasis analysis to developmental traits. In the nematode Caenorhabditis elegans, we applied RNA interference on mutants to inactivate two genes, used an imaging system to quantitatively measure phenotypes, and developed a set of statistical methods to extract genetic interactions from phenotypic measurement. Using two different C. elegans developmental phenotypes, body length and sex ratio, as examples, we showed that this method could accommodate various metazoan phenotypes with performances comparable to those methods in single cell growth studies. Comparing with qualitative observations, this method of quantitative epistasis enabled detection of new interactions involving subtle phenotypes. For example, several sex-ratio genes were found to interact with brc-1 and brd-1, the orthologs of the human breast cancer genes BRCA1 and BARD1, respectively. We confirmed the brc-1 interactions with the following genes in DNA damage response: C34F6.1, him-3 (ortholog of HORMAD1, HORMAD2), sdc-1, and set-2 (ortholog of SETD1A, SETD1B, KMT2C, KMT2D), validating the effectiveness of our method in detecting genetic interactions. We developed a reliable, high-throughput method for quantitative epistasis analysis of developmental phenotypes.

Quantitative oxygen concentration imaging in toluene atmospheres using Dual Imaging with Modeling Evaluation

NASA Astrophysics Data System (ADS)

Ehn, Andreas; Jonsson, Malin; Johansson, Olof; Aldén, Marcus; Bood, Joakim

2013-01-01

Fluorescence lifetimes of toluene as a function of oxygen concentration in toluene/nitrogen/oxygen mixtures have been measured at room temperature using picosecond-laser excitation of the S1-S0 transition at 266 nm. The data satisfy the Stern-Volmer relation with high accuracy, providing an updated value of the Stern-Volmer slope. A newly developed fluorescence lifetime imaging scheme, called Dual Imaging with Modeling Evaluation (DIME), is evaluated and successfully demonstrated for quantitative oxygen concentration imaging in toluene-seeded O2/N2 gas mixtures.

Quantitative oxygen concentration imaging in toluene atmospheres using Dual Imaging with Modeling Evaluation

NASA Astrophysics Data System (ADS)

Ehn, Andreas; Jonsson, Malin; Johansson, Olof; Aldén, Marcus; Bood, Joakim

2012-12-01

Fluorescence lifetimes of toluene as a function of oxygen concentration in toluene/nitrogen/oxygen mixtures have been measured at room temperature using picosecond-laser excitation of the S1-S0 transition at 266 nm. The data satisfy the Stern-Volmer relation with high accuracy, providing an updated value of the Stern-Volmer slope. A newly developed fluorescence lifetime imaging scheme, called Dual Imaging with Modeling Evaluation (DIME), is evaluated and successfully demonstrated for quantitative oxygen concentration imaging in toluene-seeded O2/N2 gas mixtures.

Quantitative analysis of skeletal muscle mass in patients with rheumatic diseases under glucocorticoid therapy--comparison among bioelectrical impedance analysis, computed tomography, and magnetic resonance imaging.

PubMed

Hosono, Osamu; Yoshikawa, Noritada; Shimizu, Noriaki; Kiryu, Shigeru; Uehara, Masaaki; Kobayashi, Hiroshi; Matsumiya, Ryo; Kuribara, Akiko; Maruyama, Takako; Tanaka, Hirotoshi

2015-03-01

To determine the availability of bioelectrical impedance analysis (BIA), computed tomography (CT), and magnetic resonance imaging (MRI) for measurement of skeletal muscle mass in patients with rheumatic diseases and quantitatively assess skeletal muscle loss after glucocorticoid (GC) treatment. The data from 22 patients with rheumatic diseases were retrospectively obtained. The muscle mass of body segments was measured with a BIA device in terms of skeletal muscle mass index (SMI). Cross-sectional area (CSA) was obtained from CT and MRI scans at the mid-thigh level using the image analysis program. We further assessed the data of three different measurements before and after GC treatment in 7 patients with rheumatic diseases. SMI of whole body was significantly correlated with estimated muscle volume and mid-thigh muscle CSA with CT and MRI (p < 0.01). Significant correlations between SMI and mid-thigh muscle CSA of each leg were also found (p < 0.01). All the three measurements were negatively correlated with GC dosage (p < 0.01). Significant decline in mid-thigh muscle CSA with CT and MRI was found after GC treatment in 7 patients (p < 0.02). Those patients showed significant decline in SMI of whole body after GC treatment, but not in SMI of each leg. On the other hand, significant correlations between mid-thigh muscle CSA with CT and MRI were found before and after GC treatment (p < 0.01). GC-related skeletal muscle loss could be quantitatively assessed with BIA, CT, or MRI in patients with rheumatic diseases, and CT and MRI appeared to be more accurate than BIA.

Effects of 99mTc-TRODAT-1 drug template on image quantitative analysis

PubMed Central

Yang, Bang-Hung; Chou, Yuan-Hwa; Wang, Shyh-Jen; Chen, Jyh-Cheng

2018-01-01

99mTc-TRODAT-1 is a type of drug that can bind to dopamine transporters in living organisms and is often used in SPCT imaging for observation of changes in the activity uptake of dopamine in the striatum. Therefore, it is currently widely used in studies on clinical diagnosis of Parkinson’s disease (PD) and movement-related disorders. In conventional 99mTc-TRODAT-1 SPECT image evaluation, visual inspection or manual selection of ROI for semiquantitative analysis is mainly used to observe and evaluate the degree of striatal defects. However, these methods are dependent on the subjective opinions of observers, which lead to human errors, have shortcomings such as long duration, increased effort, and have low reproducibility. To solve this problem, this study aimed to establish an automatic semiquantitative analytical method for 99mTc-TRODAT-1. This method combines three drug templates (one built-in SPECT template in SPM software and two self-generated MRI-based and HMPAO-based TRODAT-1 templates) for the semiquantitative analysis of the striatal phantom and clinical images. At the same time, the results of automatic analysis of the three templates were compared with results from a conventional manual analysis for examining the feasibility of automatic analysis and the effects of drug templates on automatic semiquantitative analysis results. After comparison, it was found that the MRI-based TRODAT-1 template generated from MRI images is the most suitable template for 99mTc-TRODAT-1 automatic semiquantitative analysis. PMID:29543874

Imaging biomarkers in multiple Sclerosis: From image analysis to population imaging.

PubMed

Barillot, Christian; Edan, Gilles; Commowick, Olivier

2016-10-01

The production of imaging data in medicine increases more rapidly than the capacity of computing models to extract information from it. The grand challenges of better understanding the brain, offering better care for neurological disorders, and stimulating new drug design will not be achieved without significant advances in computational neuroscience. The road to success is to develop a new, generic, computational methodology and to confront and validate this methodology on relevant diseases with adapted computational infrastructures. This new concept sustains the need to build new research paradigms to better understand the natural history of the pathology at the early phase; to better aggregate data that will provide the most complete representation of the pathology in order to better correlate imaging with other relevant features such as clinical, biological or genetic data. In this context, one of the major challenges of neuroimaging in clinical neurosciences is to detect quantitative signs of pathological evolution as early as possible to prevent disease progression, evaluate therapeutic protocols or even better understand and model the natural history of a given neurological pathology. Many diseases encompass brain alterations often not visible on conventional MRI sequences, especially in normal appearing brain tissues (NABT). MRI has often a low specificity for differentiating between possible pathological changes which could help in discriminating between the different pathological stages or grades. The objective of medical image analysis procedures is to define new quantitative neuroimaging biomarkers to track the evolution of the pathology at different levels. This paper illustrates this issue in one acute neuro-inflammatory pathology: Multiple Sclerosis (MS). It exhibits the current medical image analysis approaches and explains how this field of research will evolve in the next decade to integrate larger scale of information at the temporal, cellular

A programmable light engine for quantitative single molecule TIRF and HILO imaging.

PubMed

van 't Hoff, Marcel; de Sars, Vincent; Oheim, Martin

2008-10-27

We report on a simple yet powerful implementation of objective-type total internal reflection fluorescence (TIRF) and highly inclined and laminated optical sheet (HILO, a type of dark-field) illumination. Instead of focusing the illuminating laser beam to a single spot close to the edge of the microscope objective, we are scanning during the acquisition of a fluorescence image the focused spot in a circular orbit, thereby illuminating the sample from various directions. We measure parameters relevant for quantitative image analysis during fluorescence image acquisition by capturing an image of the excitation light distribution in an equivalent objective backfocal plane (BFP). Operating at scan rates above 1 MHz, our programmable light engine allows directional averaging by circular spinning the spot even for sub-millisecond exposure times. We show that restoring the symmetry of TIRF/HILO illumination reduces scattering and produces an evenly lit field-of-view that affords on-line analysis of evanescnt-field excited fluorescence without pre-processing. Utilizing crossed acousto-optical deflectors, our device generates arbitrary intensity profiles in BFP, permitting variable-angle, multi-color illumination, or objective lenses to be rapidly exchanged.

Quantitative evaluation of in vivo vital-dye fluorescence endoscopic imaging for the detection of Barrett's-associated neoplasia.

PubMed

Thekkek, Nadhi; Lee, Michelle H; Polydorides, Alexandros D; Rosen, Daniel G; Anandasabapathy, Sharmila; Richards-Kortum, Rebecca

2015-05-01

Current imaging tools are associated with inconsistent sensitivity and specificity for detection of Barrett's-associated neoplasia. Optical imaging has shown promise in improving the classification of neoplasia in vivo. The goal of this pilot study was to evaluate whether in vivo vital dye fluorescence imaging (VFI) has the potential to improve the accuracy of early-detection of Barrett's-associated neoplasia. In vivo endoscopic VFI images were collected from 65 sites in 14 patients with confirmed Barrett's esophagus (BE), dysplasia, oresophageal adenocarcinoma using a modular video endoscope and a high-resolution microendoscope(HRME). Qualitative image features were compared to histology; VFI and HRME images show changes in glandular structure associated with neoplastic progression. Quantitative image features in VFI images were identified for objective image classification of metaplasia and neoplasia, and a diagnostic algorithm was developed using leave-one-out cross validation. Three image features extracted from VFI images were used to classify tissue as neoplastic or not with a sensitivity of 87.8% and a specificity of 77.6% (AUC = 0.878). A multimodal approach incorporating VFI and HRME imaging can delineate epithelial changes present in Barrett's-associated neoplasia. Quantitative analysis of VFI images may provide a means for objective interpretation of BE during surveillance.

The evolution of medical imaging from qualitative to quantitative: opportunities, challenges, and approaches (Conference Presentation)

NASA Astrophysics Data System (ADS)

Jackson, Edward F.

2016-04-01

Over the past decade, there has been an increasing focus on quantitative imaging biomarkers (QIBs), which are defined as "objectively measured characteristics derived from in vivo images as indicators of normal biological processes, pathogenic processes, or response to a therapeutic intervention"1. To evolve qualitative imaging assessments to the use of QIBs requires the development and standardization of data acquisition, data analysis, and data display techniques, as well as appropriate reporting structures. As such, successful implementation of QIB applications relies heavily on expertise from the fields of medical physics, radiology, statistics, and informatics as well as collaboration from vendors of imaging acquisition, analysis, and reporting systems. When successfully implemented, QIBs will provide image-derived metrics with known bias and variance that can be validated with anatomically and physiologically relevant measures, including treatment response (and the heterogeneity of that response) and outcome. Such non-invasive quantitative measures can then be used effectively in clinical and translational research and will contribute significantly to the goals of precision medicine. This presentation will focus on 1) outlining the opportunities for QIB applications, with examples to demonstrate applications in both research and patient care, 2) discussing key challenges in the implementation of QIB applications, and 3) providing overviews of efforts to address such challenges from federal, scientific, and professional organizations, including, but not limited to, the RSNA, NCI, FDA, and NIST. 1Sullivan, Obuchowski, Kessler, et al. Radiology, epub August 2015.

Quantitative characterization of color Doppler images: reproducibility, accuracy, and limitations.

PubMed

Delorme, S; Weisser, G; Zuna, I; Fein, M; Lorenz, A; van Kaick, G

1995-01-01

A computer-based quantitative analysis for color Doppler images of complex vascular formations is presented. The red-green-blue-signal from an Acuson XP10 is frame-grabbed and digitized. By matching each image pixel with the color bar, color pixels are identified and assigned to the corresponding flow velocity (color value). Data analysis consists of delineation of a region of interest and calculation of the relative number of color pixels in this region (color pixel density) as well as the mean color value. The mean color value was compared to flow velocities in a flow phantom. The thyroid and carotid artery in a volunteer were repeatedly examined by a single examiner to assess intra-observer variability. The thyroids in five healthy controls were examined by three experienced physicians to assess the extent of inter-observer variability and observer bias. The correlation between the mean color value and flow velocity ranged from 0.94 to 0.96 for a range of velocities determined by pulse repetition frequency. The average deviation of the mean color value from the flow velocity was 22% to 41%, depending on the selected pulse repetition frequency (range of deviations, -46% to +66%). Flow velocity was underestimated with inadequately low pulse repetition frequency, or inadequately high reject threshold. An overestimation occurred with inadequately high pulse repetition frequency. The highest intra-observer variability was 22% (relative standard deviation) for the color pixel density, and 9.1% for the mean color value. The inter-observer variation was approximately 30% for the color pixel density, and 20% for the mean color value. In conclusion, computer assisted image analysis permits an objective description of color Doppler images. However, the user must be aware that image acquisition under in vivo conditions as well as physical and instrumental factors may considerably influence the results.

Quantitative imaging biomarkers: Effect of sample size and bias on confidence interval coverage.

PubMed

Obuchowski, Nancy A; Bullen, Jennifer

2017-01-01

Introduction Quantitative imaging biomarkers (QIBs) are being increasingly used in medical practice and clinical trials. An essential first step in the adoption of a quantitative imaging biomarker is the characterization of its technical performance, i.e. precision and bias, through one or more performance studies. Then, given the technical performance, a confidence interval for a new patient's true biomarker value can be constructed. Estimating bias and precision can be problematic because rarely are both estimated in the same study, precision studies are usually quite small, and bias cannot be measured when there is no reference standard. Methods A Monte Carlo simulation study was conducted to assess factors affecting nominal coverage of confidence intervals for a new patient's quantitative imaging biomarker measurement and for change in the quantitative imaging biomarker over time. Factors considered include sample size for estimating bias and precision, effect of fixed and non-proportional bias, clustered data, and absence of a reference standard. Results Technical performance studies of a quantitative imaging biomarker should include at least 35 test-retest subjects to estimate precision and 65 cases to estimate bias. Confidence intervals for a new patient's quantitative imaging biomarker measurement constructed under the no-bias assumption provide nominal coverage as long as the fixed bias is <12%. For confidence intervals of the true change over time, linearity must hold and the slope of the regression of the measurements vs. true values should be between 0.95 and 1.05. The regression slope can be assessed adequately as long as fixed multiples of the measurand can be generated. Even small non-proportional bias greatly reduces confidence interval coverage. Multiple lesions in the same subject can be treated as independent when estimating precision. Conclusion Technical performance studies of quantitative imaging biomarkers require moderate sample sizes in

Deep Learning Automates the Quantitative Analysis of Individual Cells in Live-Cell Imaging Experiments.

PubMed

Van Valen, David A; Kudo, Takamasa; Lane, Keara M; Macklin, Derek N; Quach, Nicolas T; DeFelice, Mialy M; Maayan, Inbal; Tanouchi, Yu; Ashley, Euan A; Covert, Markus W

2016-11-01

Live-cell imaging has opened an exciting window into the role cellular heterogeneity plays in dynamic, living systems. A major critical challenge for this class of experiments is the problem of image segmentation, or determining which parts of a microscope image correspond to which individual cells. Current approaches require many hours of manual curation and depend on approaches that are difficult to share between labs. They are also unable to robustly segment the cytoplasms of mammalian cells. Here, we show that deep convolutional neural networks, a supervised machine learning method, can solve this challenge for multiple cell types across the domains of life. We demonstrate that this approach can robustly segment fluorescent images of cell nuclei as well as phase images of the cytoplasms of individual bacterial and mammalian cells from phase contrast images without the need for a fluorescent cytoplasmic marker. These networks also enable the simultaneous segmentation and identification of different mammalian cell types grown in co-culture. A quantitative comparison with prior methods demonstrates that convolutional neural networks have improved accuracy and lead to a significant reduction in curation time. We relay our experience in designing and optimizing deep convolutional neural networks for this task and outline several design rules that we found led to robust performance. We conclude that deep convolutional neural networks are an accurate method that require less curation time, are generalizable to a multiplicity of cell types, from bacteria to mammalian cells, and expand live-cell imaging capabilities to include multi-cell type systems.

Deep Learning Automates the Quantitative Analysis of Individual Cells in Live-Cell Imaging Experiments

DOE PAGES

Van Valen, David A.; Kudo, Takamasa; Lane, Keara M.; ...

2016-11-04

Live-cell imaging has opened an exciting window into the role cellular heterogeneity plays in dynamic, living systems. A major critical challenge for this class of experiments is the problem of image segmentation, or determining which parts of a microscope image correspond to which individual cells. Current approaches require many hours of manual curation and depend on approaches that are difficult to share between labs. They are also unable to robustly segment the cytoplasms of mammalian cells. Here, we show that deep convolutional neural networks, a supervised machine learning method, can solve this challenge for multiple cell types across the domainsmore » of life. We demonstrate that this approach can robustly segment fluorescent images of cell nuclei as well as phase images of the cytoplasms of individual bacterial and mammalian cells from phase contrast images without the need for a fluorescent cytoplasmic marker. These networks also enable the simultaneous segmentation and identification of different mammalian cell types grown in co-culture. A quantitative comparison with prior methods demonstrates that convolutional neural networks have improved accuracy and lead to a significant reduction in curation time. We relay our experience in designing and optimizing deep convolutional neural networks for this task and outline several design rules that we found led to robust performance. We conclude that deep convolutional neural networks are an accurate method that require less curation time, are generalizable to a multiplicity of cell types, from bacteria to mammalian cells, and expand live-cell imaging capabilities to include multi-cell type systems.« less

Deep Learning Automates the Quantitative Analysis of Individual Cells in Live-Cell Imaging Experiments

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

Van Valen, David A.; Kudo, Takamasa; Lane, Keara M.

Live-cell imaging has opened an exciting window into the role cellular heterogeneity plays in dynamic, living systems. A major critical challenge for this class of experiments is the problem of image segmentation, or determining which parts of a microscope image correspond to which individual cells. Current approaches require many hours of manual curation and depend on approaches that are difficult to share between labs. They are also unable to robustly segment the cytoplasms of mammalian cells. Here, we show that deep convolutional neural networks, a supervised machine learning method, can solve this challenge for multiple cell types across the domainsmore » of life. We demonstrate that this approach can robustly segment fluorescent images of cell nuclei as well as phase images of the cytoplasms of individual bacterial and mammalian cells from phase contrast images without the need for a fluorescent cytoplasmic marker. These networks also enable the simultaneous segmentation and identification of different mammalian cell types grown in co-culture. A quantitative comparison with prior methods demonstrates that convolutional neural networks have improved accuracy and lead to a significant reduction in curation time. We relay our experience in designing and optimizing deep convolutional neural networks for this task and outline several design rules that we found led to robust performance. We conclude that deep convolutional neural networks are an accurate method that require less curation time, are generalizable to a multiplicity of cell types, from bacteria to mammalian cells, and expand live-cell imaging capabilities to include multi-cell type systems.« less