Bio-inspired approach to multistage image processing

NASA Astrophysics Data System (ADS)

Timchenko, Leonid I.; Pavlov, Sergii V.; Kokryatskaya, Natalia I.; Poplavska, Anna A.; Kobylyanska, Iryna M.; Burdenyuk, Iryna I.; Wójcik, Waldemar; Uvaysova, Svetlana; Orazbekov, Zhassulan; Kashaganova, Gulzhan

2017-08-01

Multistage integration of visual information in the brain allows people to respond quickly to most significant stimuli while preserving the ability to recognize small details in the image. Implementation of this principle in technical systems can lead to more efficient processing procedures. The multistage approach to image processing, described in this paper, comprises main types of cortical multistage convergence. One of these types occurs within each visual pathway and the other between the pathways. This approach maps input images into a flexible hierarchy which reflects the complexity of the image data. The procedures of temporal image decomposition and hierarchy formation are described in mathematical terms. The multistage system highlights spatial regularities, which are passed through a number of transformational levels to generate a coded representation of the image which encapsulates, in a computer manner, structure on different hierarchical levels in the image. At each processing stage a single output result is computed to allow a very quick response from the system. The result is represented as an activity pattern, which can be compared with previously computed patterns on the basis of the closest match.

Image processing and classification procedures for analysis of sub-decimeter imagery acquired with an unmanned aircraft over arid rangelands

USDA-ARS?s Scientific Manuscript database

Using five centimeter resolution images acquired with an unmanned aircraft system (UAS), we developed and evaluated an image processing workflow that included the integration of resolution-appropriate field sampling, feature selection, object-based image analysis, and processing approaches for UAS i...

Advanced Imaging Methods for Long-Baseline Optical Interferometry

NASA Astrophysics Data System (ADS)

Le Besnerais, G.; Lacour, S.; Mugnier, L. M.; Thiebaut, E.; Perrin, G.; Meimon, S.

2008-11-01

We address the data processing methods needed for imaging with a long baseline optical interferometer. We first describe parametric reconstruction approaches and adopt a general formulation of nonparametric image reconstruction as the solution of a constrained optimization problem. Within this framework, we present two recent reconstruction methods, Mira and Wisard, representative of the two generic approaches for dealing with the missing phase information. Mira is based on an implicit approach and a direct optimization of a Bayesian criterion while Wisard adopts a self-calibration approach and an alternate minimization scheme inspired from radio-astronomy. Both methods can handle various regularization criteria. We review commonly used regularization terms and introduce an original quadratic regularization called ldquosoft support constraintrdquo that favors the object compactness. It yields images of quality comparable to nonquadratic regularizations on the synthetic data we have processed. We then perform image reconstructions, both parametric and nonparametric, on astronomical data from the IOTA interferometer, and discuss the respective roles of parametric and nonparametric approaches for optical interferometric imaging.

Advanced Secure Optical Image Processing for Communications

NASA Astrophysics Data System (ADS)

Al Falou, Ayman

2018-04-01

New image processing tools and data-processing network systems have considerably increased the volume of transmitted information such as 2D and 3D images with high resolution. Thus, more complex networks and long processing times become necessary, and high image quality and transmission speeds are requested for an increasing number of applications. To satisfy these two requests, several either numerical or optical solutions were offered separately. This book explores both alternatives and describes research works that are converging towards optical/numerical hybrid solutions for high volume signal and image processing and transmission. Without being limited to hybrid approaches, the latter are particularly investigated in this book in the purpose of combining the advantages of both techniques. Additionally, pure numerical or optical solutions are also considered since they emphasize the advantages of one of the two approaches separately.

Realization of the FPGA-based reconfigurable computing environment by the example of morphological processing of a grayscale image

NASA Astrophysics Data System (ADS)

Shatravin, V.; Shashev, D. V.

2018-05-01

Currently, robots are increasingly being used in every industry. One of the most high-tech areas is creation of completely autonomous robotic devices including vehicles. The results of various global research prove the efficiency of vision systems in autonomous robotic devices. However, the use of these systems is limited because of the computational and energy resources available in the robot device. The paper describes the results of applying the original approach for image processing on reconfigurable computing environments by the example of morphological operations over grayscale images. This approach is prospective for realizing complex image processing algorithms and real-time image analysis in autonomous robotic devices.

Voyager image processing at the Image Processing Laboratory

NASA Astrophysics Data System (ADS)

Jepsen, P. L.; Mosher, J. A.; Yagi, G. M.; Avis, C. C.; Lorre, J. J.; Garneau, G. W.

1980-09-01

This paper discusses new digital processing techniques as applied to the Voyager Imaging Subsystem and devised to explore atmospheric dynamics, spectral variations, and the morphology of Jupiter, Saturn and their satellites. Radiometric and geometric decalibration processes, the modulation transfer function, and processes to determine and remove photometric properties of the atmosphere and surface of Jupiter and its satellites are examined. It is exhibited that selected images can be processed into 'approach at constant longitude' time lapse movies which are useful in observing atmospheric changes of Jupiter. Photographs are included to illustrate various image processing techniques.

Voyager image processing at the Image Processing Laboratory

NASA Technical Reports Server (NTRS)

Jepsen, P. L.; Mosher, J. A.; Yagi, G. M.; Avis, C. C.; Lorre, J. J.; Garneau, G. W.

1980-01-01

This paper discusses new digital processing techniques as applied to the Voyager Imaging Subsystem and devised to explore atmospheric dynamics, spectral variations, and the morphology of Jupiter, Saturn and their satellites. Radiometric and geometric decalibration processes, the modulation transfer function, and processes to determine and remove photometric properties of the atmosphere and surface of Jupiter and its satellites are examined. It is exhibited that selected images can be processed into 'approach at constant longitude' time lapse movies which are useful in observing atmospheric changes of Jupiter. Photographs are included to illustrate various image processing techniques.

A modified approach combining FNEA and watershed algorithms for segmenting remotely-sensed optical images

NASA Astrophysics Data System (ADS)

Liu, Likun

2018-01-01

In the field of remote sensing image processing, remote sensing image segmentation is a preliminary step for later analysis of remote sensing image processing and semi-auto human interpretation, fully-automatic machine recognition and learning. Since 2000, a technique of object-oriented remote sensing image processing method and its basic thought prevails. The core of the approach is Fractal Net Evolution Approach (FNEA) multi-scale segmentation algorithm. The paper is intent on the research and improvement of the algorithm, which analyzes present segmentation algorithms and selects optimum watershed algorithm as an initialization. Meanwhile, the algorithm is modified by modifying an area parameter, and then combining area parameter with a heterogeneous parameter further. After that, several experiments is carried on to prove the modified FNEA algorithm, compared with traditional pixel-based method (FCM algorithm based on neighborhood information) and combination of FNEA and watershed, has a better segmentation result.

Identifying regions of interest in medical images using self-organizing maps.

PubMed

Teng, Wei-Guang; Chang, Ping-Lin

2012-10-01

Advances in data acquisition, processing and visualization techniques have had a tremendous impact on medical imaging in recent years. However, the interpretation of medical images is still almost always performed by radiologists. Developments in artificial intelligence and image processing have shown the increasingly great potential of computer-aided diagnosis (CAD). Nevertheless, it has remained challenging to develop a general approach to process various commonly used types of medical images (e.g., X-ray, MRI, and ultrasound images). To facilitate diagnosis, we recommend the use of image segmentation to discover regions of interest (ROI) using self-organizing maps (SOM). We devise a two-stage SOM approach that can be used to precisely identify the dominant colors of a medical image and then segment it into several small regions. In addition, by appropriately conducting the recursive merging steps to merge smaller regions into larger ones, radiologists can usually identify one or more ROIs within a medical image.

Watch your step! A frustrated total internal reflection approach to forensic footwear imaging

NASA Astrophysics Data System (ADS)

Needham, J. A.; Sharp, J. S.

2016-02-01

Forensic image retrieval and processing are vital tools in the fight against crime e.g. during fingerprint capture. However, despite recent advances in machine vision technology and image processing techniques (and contrary to the claims of popular fiction) forensic image retrieval is still widely being performed using outdated practices involving inkpads and paper. Ongoing changes in government policy, increasing crime rates and the reduction of forensic service budgets increasingly require that evidence be gathered and processed more rapidly and efficiently. A consequence of this is that new, low-cost imaging technologies are required to simultaneously increase the quality and throughput of the processing of evidence. This is particularly true in the burgeoning field of forensic footwear analysis, where images of shoe prints are being used to link individuals to crime scenes. Here we describe one such approach based upon frustrated total internal reflection imaging that can be used to acquire images of regions where shoes contact rigid surfaces.

Watch your step! A frustrated total internal reflection approach to forensic footwear imaging.

PubMed

Needham, J A; Sharp, J S

2016-02-16

Forensic image retrieval and processing are vital tools in the fight against crime e.g. during fingerprint capture. However, despite recent advances in machine vision technology and image processing techniques (and contrary to the claims of popular fiction) forensic image retrieval is still widely being performed using outdated practices involving inkpads and paper. Ongoing changes in government policy, increasing crime rates and the reduction of forensic service budgets increasingly require that evidence be gathered and processed more rapidly and efficiently. A consequence of this is that new, low-cost imaging technologies are required to simultaneously increase the quality and throughput of the processing of evidence. This is particularly true in the burgeoning field of forensic footwear analysis, where images of shoe prints are being used to link individuals to crime scenes. Here we describe one such approach based upon frustrated total internal reflection imaging that can be used to acquire images of regions where shoes contact rigid surfaces.

Image segmentation by iterative parallel region growing with application to data compression and image analysis

NASA Technical Reports Server (NTRS)

Tilton, James C.

1988-01-01

Image segmentation can be a key step in data compression and image analysis. However, the segmentation results produced by most previous approaches to region growing are suspect because they depend on the order in which portions of the image are processed. An iterative parallel segmentation algorithm avoids this problem by performing globally best merges first. Such a segmentation approach, and two implementations of the approach on NASA's Massively Parallel Processor (MPP) are described. Application of the segmentation approach to data compression and image analysis is then described, and results of such application are given for a LANDSAT Thematic Mapper image.

Band co-registration modeling of LAPAN-A3/IPB multispectral imager based on satellite attitude

NASA Astrophysics Data System (ADS)

Hakim, P. R.; Syafrudin, A. H.; Utama, S.; Jayani, A. P. S.

2018-05-01

One of significant geometric distortion on images of LAPAN-A3/IPB multispectral imager is co-registration error between each color channel detector. Band co-registration distortion usually can be corrected by using several approaches, which are manual method, image matching algorithm, or sensor modeling and calibration approach. This paper develops another approach to minimize band co-registration distortion on LAPAN-A3/IPB multispectral image by using supervised modeling of image matching with respect to satellite attitude. Modeling results show that band co-registration error in across-track axis is strongly influenced by yaw angle, while error in along-track axis is fairly influenced by both pitch and roll angle. Accuracy of the models obtained is pretty good, which lies between 1-3 pixels error for each axis of each pair of band co-registration. This mean that the model can be used to correct the distorted images without the need of slower image matching algorithm, nor the laborious effort needed in manual approach and sensor calibration. Since the calculation can be executed in order of seconds, this approach can be used in real time quick-look image processing in ground station or even in satellite on-board image processing.

Video image processing

NASA Technical Reports Server (NTRS)

Murray, N. D.

1985-01-01

Current technology projections indicate a lack of availability of special purpose computing for Space Station applications. Potential functions for video image special purpose processing are being investigated, such as smoothing, enhancement, restoration and filtering, data compression, feature extraction, object detection and identification, pixel interpolation/extrapolation, spectral estimation and factorization, and vision synthesis. Also, architectural approaches are being identified and a conceptual design generated. Computationally simple algorithms will be research and their image/vision effectiveness determined. Suitable algorithms will be implimented into an overall architectural approach that will provide image/vision processing at video rates that are flexible, selectable, and programmable. Information is given in the form of charts, diagrams and outlines.

Stable image acquisition for mobile image processing applications

NASA Astrophysics Data System (ADS)

Henning, Kai-Fabian; Fritze, Alexander; Gillich, Eugen; Mönks, Uwe; Lohweg, Volker

2015-02-01

Today, mobile devices (smartphones, tablets, etc.) are widespread and of high importance for their users. Their performance as well as versatility increases over time. This leads to the opportunity to use such devices for more specific tasks like image processing in an industrial context. For the analysis of images requirements like image quality (blur, illumination, etc.) as well as a defined relative position of the object to be inspected are crucial. Since mobile devices are handheld and used in constantly changing environments the challenge is to fulfill these requirements. We present an approach to overcome the obstacles and stabilize the image capturing process such that image analysis becomes significantly improved on mobile devices. Therefore, image processing methods are combined with sensor fusion concepts. The approach consists of three main parts. First, pose estimation methods are used to guide a user moving the device to a defined position. Second, the sensors data and the pose information are combined for relative motion estimation. Finally, the image capturing process is automated. It is triggered depending on the alignment of the device and the object as well as the image quality that can be achieved under consideration of motion and environmental effects.

Video image processing to create a speed sensor

DOT National Transportation Integrated Search

1999-11-01

Image processing has been applied to traffic analysis in recent years, with different goals. In the report, a new approach is presented for extracting vehicular speed information, given a sequence of real-time traffic images. We extract moving edges ...

Electronic photography at NASA Langley Research Center

NASA Technical Reports Server (NTRS)

Holm, Jack M.

1994-01-01

The field of photography began a metamorphosis several years ago which promises to fundamentally change how images are captured, transmitted, and output. At this time the metamorphosis is still in the early stages, but already new processes, hardware, and software are allowing many individuals and organizations to explore the entry of imaging into the information revolution. Exploration at this time is prerequisite to leading expertise in the future, and a number of branches at LaRC have ventured into electronic and digital imaging. Their progress until recently has been limited by two factors: the lack of an integrated approach and the lack of an electronic photographic capability. The purpose of the research conducted was to address these two items. In some respects, the lack of electronic photographs has prevented application of an integrated imaging approach. Since everything could not be electronic, the tendency was to work with hard copy. Over the summer, the Photographics Section has set up an Electronic Photography Laboratory. This laboratory now has the capability to scan film images, process the images, and output the images in a variety of forms. Future plans also include electronic capture capability. The current forms of image processing available include sharpening, noise reduction, dust removal, tone correction, color balancing, image editing, cropping, electronic separations, and halftoning. Output choices include customer specified electronic file formats which can be output on magnetic or optical disks or over the network, 4400 line photographic quality prints and transparencies to 8.5 by 11 inches, and 8000 line film negatives and transparencies to 4 by 5 inches. The problem of integrated imaging involves a number of branches at LaRC including Visual Imaging, Research Printing and Publishing, Data Visualization and Animation, Advanced Computing, and various research groups. These units must work together to develop common approaches to image processing and archiving. The ultimate goal is to be able to search for images using an on-line database and image catalog. These images could then be retrieved over the network as needed, along with information on the acquisition and processing prior to storage. For this goal to be realized, a number of standard processing protocols must be developed to allow the classification of images into categories. Standard series of processing algorithms can then be applied to each category (although many of these may be adaptive between images). Since the archived image files would be standardized, it should also be possible to develop standard output processing protocols for a number of output devices. If LaRC continues the research effort begun this summer, it may be one of the first organizations to develop an integrated approach to imaging. As such, it could serve as a model for other organizations in government and the private sector.

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.

3D image restoration for confocal microscopy: toward a wavelet deconvolution for the study of complex biological structures

NASA Astrophysics Data System (ADS)

Boutet de Monvel, Jacques; Le Calvez, Sophie; Ulfendahl, Mats

2000-05-01

Image restoration algorithms provide efficient tools for recovering part of the information lost in the imaging process of a microscope. We describe recent progress in the application of deconvolution to confocal microscopy. The point spread function of a Biorad-MRC1024 confocal microscope was measured under various imaging conditions, and used to process 3D-confocal images acquired in an intact preparation of the inner ear developed at Karolinska Institutet. Using these experiments we investigate the application of denoising methods based on wavelet analysis as a natural regularization of the deconvolution process. Within the Bayesian approach to image restoration, we compare wavelet denoising with the use of a maximum entropy constraint as another natural regularization method. Numerical experiments performed with test images show a clear advantage of the wavelet denoising approach, allowing to `cool down' the image with respect to the signal, while suppressing much of the fine-scale artifacts appearing during deconvolution due to the presence of noise, incomplete knowledge of the point spread function, or undersampling problems. We further describe a natural development of this approach, which consists of performing the Bayesian inference directly in the wavelet domain.

Hybrid wavefront sensing and image correction algorithm for imaging through turbulent media

NASA Astrophysics Data System (ADS)

Wu, Chensheng; Robertson Rzasa, John; Ko, Jonathan; Davis, Christopher C.

2017-09-01

It is well known that passive image correction of turbulence distortions often involves using geometry-dependent deconvolution algorithms. On the other hand, active imaging techniques using adaptive optic correction should use the distorted wavefront information for guidance. Our work shows that a hybrid hardware-software approach is possible to obtain accurate and highly detailed images through turbulent media. The processing algorithm also takes much fewer iteration steps in comparison with conventional image processing algorithms. In our proposed approach, a plenoptic sensor is used as a wavefront sensor to guide post-stage image correction on a high-definition zoomable camera. Conversely, we show that given the ground truth of the highly detailed image and the plenoptic imaging result, we can generate an accurate prediction of the blurred image on a traditional zoomable camera. Similarly, the ground truth combined with the blurred image from the zoomable camera would provide the wavefront conditions. In application, our hybrid approach can be used as an effective way to conduct object recognition in a turbulent environment where the target has been significantly distorted or is even unrecognizable.

Developing image processing meta-algorithms with data mining of multiple metrics.

PubMed

Leung, Kelvin; Cunha, Alexandre; Toga, A W; Parker, D Stott

2014-01-01

People often use multiple metrics in image processing, but here we take a novel approach of mining the values of batteries of metrics on image processing results. We present a case for extending image processing methods to incorporate automated mining of multiple image metric values. Here by a metric we mean any image similarity or distance measure, and in this paper we consider intensity-based and statistical image measures and focus on registration as an image processing problem. We show how it is possible to develop meta-algorithms that evaluate different image processing results with a number of different metrics and mine the results in an automated fashion so as to select the best results. We show that the mining of multiple metrics offers a variety of potential benefits for many image processing problems, including improved robustness and validation.

Finite grade pheromone ant colony optimization for image segmentation

NASA Astrophysics Data System (ADS)

Yuanjing, F.; Li, Y.; Liangjun, K.

2008-06-01

By combining the decision process of ant colony optimization (ACO) with the multistage decision process of image segmentation based on active contour model (ACM), an algorithm called finite grade ACO (FACO) for image segmentation is proposed. This algorithm classifies pheromone into finite grades and updating of the pheromone is achieved by changing the grades and the updated quantity of pheromone is independent from the objective function. The algorithm that provides a new approach to obtain precise contour is proved to converge to the global optimal solutions linearly by means of finite Markov chains. The segmentation experiments with ultrasound heart image show the effectiveness of the algorithm. Comparing the results for segmentation of left ventricle images shows that the ACO for image segmentation is more effective than the GA approach and the new pheromone updating strategy appears good time performance in optimization process.

Superresolution with the focused plenoptic camera

NASA Astrophysics Data System (ADS)

Georgiev, Todor; Chunev, Georgi; Lumsdaine, Andrew

2011-03-01

Digital images from a CCD or CMOS sensor with a color filter array must undergo a demosaicing process to combine the separate color samples into a single color image. This interpolation process can interfere with the subsequent superresolution process. Plenoptic superresolution, which relies on precise sub-pixel sampling across captured microimages, is particularly sensitive to such resampling of the raw data. In this paper we present an approach for superresolving plenoptic images that takes place at the time of demosaicing the raw color image data. Our approach exploits the interleaving provided by typical color filter arrays (e.g., Bayer filter) to further refine plenoptic sub-pixel sampling. Our rendering algorithm treats the color channels in a plenoptic image separately, which improves final superresolution by a factor of two. With appropriate plenoptic capture we show the theoretical possibility for rendering final images at full sensor resolution.

Machine processing of remotely sensed data - quantifying global process: Models, sensor systems, and analytical methods; Proceedings of the Eleventh International Symposium, Purdue University, West Lafayette, IN, June 25-27, 1985

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

Mengel, S.K.; Morrison, D.B.

1985-01-01

Consideration is given to global biogeochemical issues, image processing, remote sensing of tropical environments, global processes, geology, landcover hydrology, and ecosystems modeling. Topics discussed include multisensor remote sensing strategies, geographic information systems, radars, and agricultural remote sensing. Papers are presented on fast feature extraction; a computational approach for adjusting TM imagery terrain distortions; the segmentation of a textured image by a maximum likelihood classifier; analysis of MSS Landsat data; sun angle and background effects on spectral response of simulated forest canopies; an integrated approach for vegetation/landcover mapping with digital Landsat images; geological and geomorphological studies using an image processing technique;more » and wavelength intensity indices in relation to tree conditions and leaf-nutrient content.« less

Parallel asynchronous systems and image processing algorithms

NASA Technical Reports Server (NTRS)

Coon, D. D.; Perera, A. G. U.

1989-01-01

A new hardware approach to implementation of image processing algorithms is described. The approach is based on silicon devices which would permit an independent analog processing channel to be dedicated to evey pixel. A laminar architecture consisting of a stack of planar arrays of the device would form a two-dimensional array processor with a 2-D array of inputs located directly behind a focal plane detector array. A 2-D image data stream would propagate in neuronlike asynchronous pulse coded form through the laminar processor. Such systems would integrate image acquisition and image processing. Acquisition and processing would be performed concurrently as in natural vision systems. The research is aimed at implementation of algorithms, such as the intensity dependent summation algorithm and pyramid processing structures, which are motivated by the operation of natural vision systems. Implementation of natural vision algorithms would benefit from the use of neuronlike information coding and the laminar, 2-D parallel, vision system type architecture. Besides providing a neural network framework for implementation of natural vision algorithms, a 2-D parallel approach could eliminate the serial bottleneck of conventional processing systems. Conversion to serial format would occur only after raw intensity data has been substantially processed. An interesting challenge arises from the fact that the mathematical formulation of natural vision algorithms does not specify the means of implementation, so that hardware implementation poses intriguing questions involving vision science.

Comparison of remote sensing image processing techniques to identify tornado damage areas from Landsat TM data

USGS Publications Warehouse

Myint, S.W.; Yuan, M.; Cerveny, R.S.; Giri, C.P.

2008-01-01

Remote sensing techniques have been shown effective for large-scale damage surveys after a hazardous event in both near real-time or post-event analyses. The paper aims to compare accuracy of common imaging processing techniques to detect tornado damage tracks from Landsat TM data. We employed the direct change detection approach using two sets of images acquired before and after the tornado event to produce a principal component composite images and a set of image difference bands. Techniques in the comparison include supervised classification, unsupervised classification, and objectoriented classification approach with a nearest neighbor classifier. Accuracy assessment is based on Kappa coefficient calculated from error matrices which cross tabulate correctly identified cells on the TM image and commission and omission errors in the result. Overall, the Object-oriented Approach exhibits the highest degree of accuracy in tornado damage detection. PCA and Image Differencing methods show comparable outcomes. While selected PCs can improve detection accuracy 5 to 10%, the Object-oriented Approach performs significantly better with 15-20% higher accuracy than the other two techniques. ?? 2008 by MDPI.

Comparison of Remote Sensing Image Processing Techniques to Identify Tornado Damage Areas from Landsat TM Data

PubMed Central

Myint, Soe W.; Yuan, May; Cerveny, Randall S.; Giri, Chandra P.

2008-01-01

Remote sensing techniques have been shown effective for large-scale damage surveys after a hazardous event in both near real-time or post-event analyses. The paper aims to compare accuracy of common imaging processing techniques to detect tornado damage tracks from Landsat TM data. We employed the direct change detection approach using two sets of images acquired before and after the tornado event to produce a principal component composite images and a set of image difference bands. Techniques in the comparison include supervised classification, unsupervised classification, and object-oriented classification approach with a nearest neighbor classifier. Accuracy assessment is based on Kappa coefficient calculated from error matrices which cross tabulate correctly identified cells on the TM image and commission and omission errors in the result. Overall, the Object-oriented Approach exhibits the highest degree of accuracy in tornado damage detection. PCA and Image Differencing methods show comparable outcomes. While selected PCs can improve detection accuracy 5 to 10%, the Object-oriented Approach performs significantly better with 15-20% higher accuracy than the other two techniques. PMID:27879757

Application of image processing to calculate the number of fish seeds using raspberry-pi

NASA Astrophysics Data System (ADS)

Rahmadiansah, A.; Kusumawardhani, A.; Duanto, F. N.; Qoonita, F.

2018-03-01

Many fish cultivator in Indonesia who suffered losses due to the sale and purchase of fish seeds did not match the agreed amount. The loss is due to the calculation of fish seed still using manual method. To overcome these problems, then in this study designed fish counting system automatically and real-time fish using the image processing based on Raspberry Pi. Used image processing because it can calculate moving objects and eliminate noise. Image processing method used to calculate moving object is virtual loop detector or virtual detector method and the approach used is “double difference image”. The “double difference” approach uses information from the previous frame and the next frame to estimate the shape and position of the object. Using these methods and approaches, the results obtained were quite good with an average error of 1.0% for 300 individuals in a test with a virtual detector width of 96 pixels and a slope of 1 degree test plane.

Digital Image Processing in Private Industry.

ERIC Educational Resources Information Center

Moore, Connie

1986-01-01

Examines various types of private industry optical disk installations in terms of business requirements for digital image systems in five areas: records management; transaction processing; engineering/manufacturing; information distribution; and office automation. Approaches for implementing image systems are addressed as well as key success…

Telerobotic Surgery: An Intelligent Systems Approach to Mitigate the Adverse Effects of Communication Delay. Chapter 4

NASA Technical Reports Server (NTRS)

Cardullo, Frank M.; Lewis, Harold W., III; Panfilov, Peter B.

2007-01-01

An extremely innovative approach has been presented, which is to have the surgeon operate through a simulator running in real-time enhanced with an intelligent controller component to enhance the safety and efficiency of a remotely conducted operation. The use of a simulator enables the surgeon to operate in a virtual environment free from the impediments of telecommunication delay. The simulator functions as a predictor and periodically the simulator state is corrected with truth data. Three major research areas must be explored in order to ensure achieving the objectives. They are: simulator as predictor, image processing, and intelligent control. Each is equally necessary for success of the project and each of these involves a significant intelligent component in it. These are diverse, interdisciplinary areas of investigation, thereby requiring a highly coordinated effort by all the members of our team, to ensure an integrated system. The following is a brief discussion of those areas. Simulator as a predictor: The delays encountered in remote robotic surgery will be greater than any encountered in human-machine systems analysis, with the possible exception of remote operations in space. Therefore, novel compensation techniques will be developed. Included will be the development of the real-time simulator, which is at the heart of our approach. The simulator will present real-time, stereoscopic images and artificial haptic stimuli to the surgeon. Image processing: Because of the delay and the possibility of insufficient bandwidth a high level of novel image processing is necessary. This image processing will include several innovative aspects, including image interpretation, video to graphical conversion, texture extraction, geometric processing, image compression and image generation at the surgeon station. Intelligent control: Since the approach we propose is in a sense predictor based, albeit a very sophisticated predictor, a controller, which not only optimizes end effector trajectory but also avoids error, is essential. We propose to investigate two different approaches to the controller design. One approach employs an optimal controller based on modern control theory; the other one involves soft computing techniques, i.e. fuzzy logic, neural networks, genetic algorithms and hybrids of these.

Initial Navigation Alignment of Optical Instruments on GOES-R

NASA Astrophysics Data System (ADS)

Isaacson, P.; DeLuccia, F.; Reth, A. D.; Igli, D. A.; Carter, D.

2016-12-01

The GOES-R satellite is the first in NOAA's next-generation series of geostationary weather satellites. In addition to a number of space weather sensors, it will carry two principal optical earth-observing instruments, the Advanced Baseline Imager (ABI) and the Geostationary Lightning Mapper (GLM). During launch, currently scheduled for November of 2016, the alignment of these optical instruments is anticipated to shift from that measured during pre-launch characterization. While both instruments have image navigation and registration (INR) processing algorithms to enable automated geolocation of the collected data, the launch-derived misalignment may be too large for these approaches to function without an initial adjustment to calibration parameters. The parameters that may require adjustment are for Line of Sight Motion Compensation (LMC), and the adjustments will be estimated on orbit during the post-launch test (PLT) phase. We have developed approaches to estimate the initial alignment errors for both ABI and GLM image products. Our approaches involve comparison of ABI and GLM images collected during PLT to a set of reference ("truth") images using custom image processing tools and other software (the INR Performance Assessment Tool Set, or "IPATS") being developed for other INR assessments of ABI and GLM data. IPATS is based on image correlation approaches to determine offsets between input and reference images, and these offsets are the fundamental input to our estimate of the initial alignment errors. Initial testing of our alignment algorithms on proxy datasets lends high confidence that their application will determine the initial alignment errors to within sufficient accuracy to enable the operational INR processing approaches to proceed in a nominal fashion. We will report on the algorithms, implementation approach, and status of these initial alignment tools being developed for the GOES-R ABI and GLM instruments.

Low-cost digital image processing at the University of Oklahoma

NASA Technical Reports Server (NTRS)

Harrington, J. A., Jr.

1981-01-01

Computer assisted instruction in remote sensing at the University of Oklahoma involves two separate approaches and is dependent upon initial preprocessing of a LANDSAT computer compatible tape using software developed for an IBM 370/158 computer. In-house generated preprocessing algorithms permits students or researchers to select a subset of a LANDSAT scene for subsequent analysis using either general purpose statistical packages or color graphic image processing software developed for Apple II microcomputers. Procedures for preprocessing the data and image analysis using either of the two approaches for low-cost LANDSAT data processing are described.

A synoptic description of coal basins via image processing

NASA Technical Reports Server (NTRS)

Farrell, K. W., Jr.; Wherry, D. B.

1978-01-01

An existing image processing system is adapted to describe the geologic attributes of a regional coal basin. This scheme handles a map as if it were a matrix, in contrast to more conventional approaches which represent map information in terms of linked polygons. The utility of the image processing approach is demonstrated by a multiattribute analysis of the Herrin No. 6 coal seam in Illinois. Findings include the location of a resource and estimation of tonnage corresponding to constraints on seam thickness, overburden, and Btu value, which are illustrative of the need for new mining technology.

Developing Image Processing Meta-Algorithms with Data Mining of Multiple Metrics

PubMed Central

Cunha, Alexandre; Toga, A. W.; Parker, D. Stott

2014-01-01

People often use multiple metrics in image processing, but here we take a novel approach of mining the values of batteries of metrics on image processing results. We present a case for extending image processing methods to incorporate automated mining of multiple image metric values. Here by a metric we mean any image similarity or distance measure, and in this paper we consider intensity-based and statistical image measures and focus on registration as an image processing problem. We show how it is possible to develop meta-algorithms that evaluate different image processing results with a number of different metrics and mine the results in an automated fashion so as to select the best results. We show that the mining of multiple metrics offers a variety of potential benefits for many image processing problems, including improved robustness and validation. PMID:24653748

MEMS-based system and image processing strategy for epiretinal prosthesis.

PubMed

Xia, Peng; Hu, Jie; Qi, Jin; Gu, Chaochen; Peng, Yinghong

2015-01-01

Retinal prostheses have the potential to restore some level of visual function to the patients suffering from retinal degeneration. In this paper, an epiretinal approach with active stimulation devices is presented. The MEMS-based processing system consists of an external micro-camera, an information processor, an implanted electrical stimulator and a microelectrode array. The image processing strategy combining image clustering and enhancement techniques was proposed and evaluated by psychophysical experiments. The results indicated that the image processing strategy improved the visual performance compared with direct merging pixels to low resolution. The image processing methods assist epiretinal prosthesis for vision restoration.

Thermal imaging as a biometrics approach to facial signature authentication.

PubMed

Guzman, A M; Goryawala, M; Wang, Jin; Barreto, A; Andrian, J; Rishe, N; Adjouadi, M

2013-01-01

A new thermal imaging framework with unique feature extraction and similarity measurements for face recognition is presented. The research premise is to design specialized algorithms that would extract vasculature information, create a thermal facial signature and identify the individual. The proposed algorithm is fully integrated and consolidates the critical steps of feature extraction through the use of morphological operators, registration using the Linear Image Registration Tool and matching through unique similarity measures designed for this task. The novel approach at developing a thermal signature template using four images taken at various instants of time ensured that unforeseen changes in the vasculature over time did not affect the biometric matching process as the authentication process relied only on consistent thermal features. Thirteen subjects were used for testing the developed technique on an in-house thermal imaging system. The matching using the similarity measures showed an average accuracy of 88.46% for skeletonized signatures and 90.39% for anisotropically diffused signatures. The highly accurate results obtained in the matching process clearly demonstrate the ability of the thermal infrared system to extend in application to other thermal imaging based systems. Empirical results applying this approach to an existing database of thermal images proves this assertion.

Reduced exposure using asymmetric cone beam processing for wide area detector cardiac CT

PubMed Central

Bedayat, Arash; Kumamaru, Kanako; Powers, Sara L.; Signorelli, Jason; Steigner, Michael L.; Steveson, Chloe; Soga, Shigeyoshi; Adams, Kimberly; Mitsouras, Dimitrios; Clouse, Melvin; Mather, Richard T.

2011-01-01

The purpose of this study was to estimate dose reduction after implementation of asymmetrical cone beam processing using exposure differences measured in a water phantom and a small cohort of clinical coronary CTA patients. Two separate 320 × 0.5 mm detector row scans of a water phantom used identical cardiac acquisition parameters before and after software modifications from symmetric to asymmetric cone beam acquisition and processing. Exposure was measured at the phantom surface with Optically Stimulated Luminescence (OSL) dosimeters at 12 equally spaced angular locations. Mean HU and standard deviation (SD) for both approaches were compared using ROI measurements obtained at the center plus four peripheral locations in the water phantom. To assess image quality, mean HU and standard deviation (SD) for both approaches were compared using ROI measurements obtained at five points within the water phantom. Retrospective evaluation of 64 patients (37 symmetric; 27 asymmetric acquisition) included clinical data, scanning parameters, quantitative plus qualitative image assessment, and estimated radiation dose. In the water phantom, the asymmetric cone beam processing reduces exposure by approximately 20% with no change in image quality. The clinical coronary CTA patient groups had comparable demographics. The estimated dose reduction after implementation of the asymmetric approach was roughly 24% with no significant difference between the symmetric and asymmetric approach with respect to objective measures of image quality or subjective assessment using a four point scale. When compared to a symmetric approach, the decreased exposure, subsequent lower patient radiation dose, and similar image quality from asymmetric cone beam processing supports its routine clinical use. PMID:21336552

Reduced exposure using asymmetric cone beam processing for wide area detector cardiac CT.

PubMed

Bedayat, Arash; Rybicki, Frank J; Kumamaru, Kanako; Powers, Sara L; Signorelli, Jason; Steigner, Michael L; Steveson, Chloe; Soga, Shigeyoshi; Adams, Kimberly; Mitsouras, Dimitrios; Clouse, Melvin; Mather, Richard T

2012-02-01

The purpose of this study was to estimate dose reduction after implementation of asymmetrical cone beam processing using exposure differences measured in a water phantom and a small cohort of clinical coronary CTA patients. Two separate 320 × 0.5 mm detector row scans of a water phantom used identical cardiac acquisition parameters before and after software modifications from symmetric to asymmetric cone beam acquisition and processing. Exposure was measured at the phantom surface with Optically Stimulated Luminescence (OSL) dosimeters at 12 equally spaced angular locations. Mean HU and standard deviation (SD) for both approaches were compared using ROI measurements obtained at the center plus four peripheral locations in the water phantom. To assess image quality, mean HU and standard deviation (SD) for both approaches were compared using ROI measurements obtained at five points within the water phantom. Retrospective evaluation of 64 patients (37 symmetric; 27 asymmetric acquisition) included clinical data, scanning parameters, quantitative plus qualitative image assessment, and estimated radiation dose. In the water phantom, the asymmetric cone beam processing reduces exposure by approximately 20% with no change in image quality. The clinical coronary CTA patient groups had comparable demographics. The estimated dose reduction after implementation of the asymmetric approach was roughly 24% with no significant difference between the symmetric and asymmetric approach with respect to objective measures of image quality or subjective assessment using a four point scale. When compared to a symmetric approach, the decreased exposure, subsequent lower patient radiation dose, and similar image quality from asymmetric cone beam processing supports its routine clinical use.

Photogrammetric Processing of Planetary Linear Pushbroom Images Based on Approximate Orthophotos

NASA Astrophysics Data System (ADS)

Geng, X.; Xu, Q.; Xing, S.; Hou, Y. F.; Lan, C. Z.; Zhang, J. J.

2018-04-01

It is still a great challenging task to efficiently produce planetary mapping products from orbital remote sensing images. There are many disadvantages in photogrammetric processing of planetary stereo images, such as lacking ground control information and informative features. Among which, image matching is the most difficult job in planetary photogrammetry. This paper designs a photogrammetric processing framework for planetary remote sensing images based on approximate orthophotos. Both tie points extraction for bundle adjustment and dense image matching for generating digital terrain model (DTM) are performed on approximate orthophotos. Since most of planetary remote sensing images are acquired by linear scanner cameras, we mainly deal with linear pushbroom images. In order to improve the computational efficiency of orthophotos generation and coordinates transformation, a fast back-projection algorithm of linear pushbroom images is introduced. Moreover, an iteratively refined DTM and orthophotos scheme was adopted in the DTM generation process, which is helpful to reduce search space of image matching and improve matching accuracy of conjugate points. With the advantages of approximate orthophotos, the matching results of planetary remote sensing images can be greatly improved. We tested the proposed approach with Mars Express (MEX) High Resolution Stereo Camera (HRSC) and Lunar Reconnaissance Orbiter (LRO) Narrow Angle Camera (NAC) images. The preliminary experimental results demonstrate the feasibility of the proposed approach.

The correlation study of parallel feature extractor and noise reduction approaches

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

Dewi, Deshinta Arrova; Sundararajan, Elankovan; Prabuwono, Anton Satria

2015-05-15

This paper presents literature reviews that show variety of techniques to develop parallel feature extractor and finding its correlation with noise reduction approaches for low light intensity images. Low light intensity images are normally displayed as darker images and low contrast. Without proper handling techniques, those images regularly become evidences of misperception of objects and textures, the incapability to section them. The visual illusions regularly clues to disorientation, user fatigue, poor detection and classification performance of humans and computer algorithms. Noise reduction approaches (NR) therefore is an essential step for other image processing steps such as edge detection, image segmentation,more » image compression, etc. Parallel Feature Extractor (PFE) meant to capture visual contents of images involves partitioning images into segments, detecting image overlaps if any, and controlling distributed and redistributed segments to extract the features. Working on low light intensity images make the PFE face challenges and closely depend on the quality of its pre-processing steps. Some papers have suggested many well established NR as well as PFE strategies however only few resources have suggested or mentioned the correlation between them. This paper reviews best approaches of the NR and the PFE with detailed explanation on the suggested correlation. This finding may suggest relevant strategies of the PFE development. With the help of knowledge based reasoning, computational approaches and algorithms, we present the correlation study between the NR and the PFE that can be useful for the development and enhancement of other existing PFE.« less

On techniques for angle compensation in nonideal iris recognition.

PubMed

Schuckers, Stephanie A C; Schmid, Natalia A; Abhyankar, Aditya; Dorairaj, Vivekanand; Boyce, Christopher K; Hornak, Lawrence A

2007-10-01

The popularity of the iris biometric has grown considerably over the past two to three years. Most research has been focused on the development of new iris processing and recognition algorithms for frontal view iris images. However, a few challenging directions in iris research have been identified, including processing of a nonideal iris and iris at a distance. In this paper, we describe two nonideal iris recognition systems and analyze their performance. The word "nonideal" is used in the sense of compensating for off-angle occluded iris images. The system is designed to process nonideal iris images in two steps: 1) compensation for off-angle gaze direction and 2) processing and encoding of the rotated iris image. Two approaches are presented to account for angular variations in the iris images. In the first approach, we use Daugman's integrodifferential operator as an objective function to estimate the gaze direction. After the angle is estimated, the off-angle iris image undergoes geometric transformations involving the estimated angle and is further processed as if it were a frontal view image. The encoding technique developed for a frontal image is based on the application of the global independent component analysis. The second approach uses an angular deformation calibration model. The angular deformations are modeled, and calibration parameters are calculated. The proposed method consists of a closed-form solution, followed by an iterative optimization procedure. The images are projected on the plane closest to the base calibrated plane. Biorthogonal wavelets are used for encoding to perform iris recognition. We use a special dataset of the off-angle iris images to quantify the performance of the designed systems. A series of receiver operating characteristics demonstrate various effects on the performance of the nonideal-iris-based recognition system.

Adaptive multifocus image fusion using block compressed sensing with smoothed projected Landweber integration in the wavelet domain.

PubMed

V S, Unni; Mishra, Deepak; Subrahmanyam, G R K S

2016-12-01

The need for image fusion in current image processing systems is increasing mainly due to the increased number and variety of image acquisition techniques. Image fusion is the process of combining substantial information from several sensors using mathematical techniques in order to create a single composite image that will be more comprehensive and thus more useful for a human operator or other computer vision tasks. This paper presents a new approach to multifocus image fusion based on sparse signal representation. Block-based compressive sensing integrated with a projection-driven compressive sensing (CS) recovery that encourages sparsity in the wavelet domain is used as a method to get the focused image from a set of out-of-focus images. Compression is achieved during the image acquisition process using a block compressive sensing method. An adaptive thresholding technique within the smoothed projected Landweber recovery process reconstructs high-resolution focused images from low-dimensional CS measurements of out-of-focus images. Discrete wavelet transform and dual-tree complex wavelet transform are used as the sparsifying basis for the proposed fusion. The main finding lies in the fact that sparsification enables a better selection of the fusion coefficients and hence better fusion. A Laplacian mixture model fit is done in the wavelet domain and estimation of the probability density function (pdf) parameters by expectation maximization leads us to the proper selection of the coefficients of the fused image. Using the proposed method compared with the fusion scheme without employing the projected Landweber (PL) scheme and the other existing CS-based fusion approaches, it is observed that with fewer samples itself, the proposed method outperforms other approaches.

Image enhancement and color constancy for a vehicle-mounted change detection system

NASA Astrophysics Data System (ADS)

Tektonidis, Marco; Monnin, David

2016-10-01

Vehicle-mounted change detection systems allow to improve situational awareness on outdoor itineraries of inter- est. Since the visibility of acquired images is often affected by illumination effects (e.g., shadows) it is important to enhance local contrast. For the analysis and comparison of color images depicting the same scene at different time points it is required to compensate color and lightness inconsistencies caused by the different illumination conditions. We have developed an approach for image enhancement and color constancy based on the center/surround Retinex model and the Gray World hypothesis. The combination of the two methods using a color processing function improves color rendition, compared to both methods. The use of stacked integral images (SII) allows to efficiently perform local image processing. Our combined Retinex/Gray World approach has been successfully applied to image sequences acquired on outdoor itineraries at different time points and a comparison with previous Retinex-based approaches has been carried out.

Development of a fusion approach selection tool

NASA Astrophysics Data System (ADS)

Pohl, C.; Zeng, Y.

2015-06-01

During the last decades number and quality of available remote sensing satellite sensors for Earth observation has grown significantly. The amount of available multi-sensor images along with their increased spatial and spectral resolution provides new challenges to Earth scientists. With a Fusion Approach Selection Tool (FAST) the remote sensing community would obtain access to an optimized and improved image processing technology. Remote sensing image fusion is a mean to produce images containing information that is not inherent in the single image alone. In the meantime the user has access to sophisticated commercialized image fusion techniques plus the option to tune the parameters of each individual technique to match the anticipated application. This leaves the operator with an uncountable number of options to combine remote sensing images, not talking about the selection of the appropriate images, resolution and bands. Image fusion can be a machine and time-consuming endeavour. In addition it requires knowledge about remote sensing, image fusion, digital image processing and the application. FAST shall provide the user with a quick overview of processing flows to choose from to reach the target. FAST will ask for available images, application parameters and desired information to process this input to come out with a workflow to quickly obtain the best results. It will optimize data and image fusion techniques. It provides an overview on the possible results from which the user can choose the best. FAST will enable even inexperienced users to use advanced processing methods to maximize the benefit of multi-sensor image exploitation.

Mapping spatial patterns with morphological image processing

Treesearch

Peter Vogt; Kurt H. Riitters; Christine Estreguil; Jacek Kozak; Timothy G. Wade; James D. Wickham

2006-01-01

We use morphological image processing for classifying spatial patterns at the pixel level on binary land-cover maps. Land-cover pattern is classified as 'perforated,' 'edge,' 'patch,' and 'core' with higher spatial precision and thematic accuracy compared to a previous approach based on image convolution, while retaining the...

Pre-processing, registration and selection of adaptive optics corrected retinal images.

PubMed

Ramaswamy, Gomathy; Devaney, Nicholas

2013-07-01

In this paper, the aim is to demonstrate enhanced processing of sequences of fundus images obtained using a commercial AO flood illumination system. The purpose of the work is to (1) correct for uneven illumination at the retina (2) automatically select the best quality images and (3) precisely register the best images. Adaptive optics corrected retinal images are pre-processed to correct uneven illumination using different methods; subtracting or dividing by the average filtered image, homomorphic filtering and a wavelet based approach. These images are evaluated to measure the image quality using various parameters, including sharpness, variance, power spectrum kurtosis and contrast. We have carried out the registration in two stages; a coarse stage using cross-correlation followed by fine registration using two approaches; parabolic interpolation on the peak of the cross-correlation and maximum-likelihood estimation. The angle of rotation of the images is measured using a combination of peak tracking and Procrustes transformation. We have found that a wavelet approach (Daubechies 4 wavelet at 6th level decomposition) provides good illumination correction with clear improvement in image sharpness and contrast. The assessment of image quality using a 'Designer metric' works well when compared to visual evaluation, although it is highly correlated with other metrics. In image registration, sub-pixel translation measured using parabolic interpolation on the peak of the cross-correlation function and maximum-likelihood estimation are found to give very similar results (RMS difference 0.047 pixels). We have confirmed that correcting rotation of the images provides a significant improvement, especially at the edges of the image. We observed that selecting the better quality frames (e.g. best 75% images) for image registration gives improved resolution, at the expense of poorer signal-to-noise. The sharpness map of the registered and de-rotated images shows increased sharpness over most of the field of view. Adaptive optics assisted images of the cone photoreceptors can be better pre-processed using a wavelet approach. These images can be assessed for image quality using a 'Designer Metric'. Two-stage image registration including correcting for rotation significantly improves the final image contrast and sharpness. © 2013 The Authors Ophthalmic & Physiological Optics © 2013 The College of Optometrists.

Population-based imaging biobanks as source of big data.

PubMed

Gatidis, Sergios; Heber, Sophia D; Storz, Corinna; Bamberg, Fabian

2017-06-01

Advances of computational sciences over the last decades have enabled the introduction of novel methodological approaches in biomedical research. Acquiring extensive and comprehensive data about a research subject and subsequently extracting significant information has opened new possibilities in gaining insight into biological and medical processes. This so-called big data approach has recently found entrance into medical imaging and numerous epidemiological studies have been implementing advanced imaging to identify imaging biomarkers that provide information about physiological processes, including normal development and aging but also on the development of pathological disease states. The purpose of this article is to present existing epidemiological imaging studies and to discuss opportunities, methodological and organizational aspects, and challenges that population imaging poses to the field of big data research.

Oil Spill Detection in Terma-Side-Looking Airborne Radar Images Using Image Features and Region Segmentation

PubMed Central

Alacid, Beatriz

2018-01-01

This work presents a method for oil-spill detection on Spanish coasts using aerial Side-Looking Airborne Radar (SLAR) images, which are captured using a Terma sensor. The proposed method uses grayscale image processing techniques to identify the dark spots that represent oil slicks on the sea. The approach is based on two steps. First, the noise regions caused by aircraft movements are detected and labeled in order to avoid the detection of false-positives. Second, a segmentation process guided by a map saliency technique is used to detect image regions that represent oil slicks. The results show that the proposed method is an improvement on the previous approaches for this task when employing SLAR images. PMID:29316716

Fixed-Cell Imaging of Schizosaccharomyces pombe.

PubMed

Hagan, Iain M; Bagley, Steven

2016-07-01

The acknowledged genetic malleability of fission yeast has been matched by impressive cytology to drive major advances in our understanding of basic molecular cell biological processes. In many of the more recent studies, traditional approaches of fixation followed by processing to accommodate classical staining procedures have been superseded by live-cell imaging approaches that monitor the distribution of fusion proteins between a molecule of interest and a fluorescent protein. Although such live-cell imaging is uniquely informative for many questions, fixed-cell imaging remains the better option for others and is an important-sometimes critical-complement to the analysis of fluorescent fusion proteins by live-cell imaging. Here, we discuss the merits of fixed- and live-cell imaging as well as specific issues for fluorescence microscopy imaging of fission yeast. © 2016 Cold Spring Harbor Laboratory Press.

Analysis of the Growth Process of Neural Cells in Culture Environment Using Image Processing Techniques

NASA Astrophysics Data System (ADS)

Mirsafianf, Atefeh S.; Isfahani, Shirin N.; Kasaei, Shohreh; Mobasheri, Hamid

Here we present an approach for processing neural cells images to analyze their growth process in culture environment. We have applied several image processing techniques for: 1- Environmental noise reduction, 2- Neural cells segmentation, 3- Neural cells classification based on their dendrites' growth conditions, and 4- neurons' features Extraction and measurement (e.g., like cell body area, number of dendrites, axon's length, and so on). Due to the large amount of noise in the images, we have used feed forward artificial neural networks to detect edges more precisely.

Analysis of contour images using optics of spiral beams

NASA Astrophysics Data System (ADS)

Volostnikov, V. G.; Kishkin, S. A.; Kotova, S. P.

2018-03-01

An approach is outlined to the recognition of contour images using computer technology based on coherent optics principles. A mathematical description of the recognition process algorithm and the results of numerical modelling are presented. The developed approach to the recognition of contour images using optics of spiral beams is described and justified.

Models of formation and some algorithms of hyperspectral image processing

NASA Astrophysics Data System (ADS)

Achmetov, R. N.; Stratilatov, N. R.; Yudakov, A. A.; Vezenov, V. I.; Eremeev, V. V.

2014-12-01

Algorithms and information technologies for processing Earth hyperspectral imagery are presented. Several new approaches are discussed. Peculiar properties of processing the hyperspectral imagery, such as multifold signal-to-noise reduction, atmospheric distortions, access to spectral characteristics of every image point, and high dimensionality of data, were studied. Different measures of similarity between individual hyperspectral image points and the effect of additive uncorrelated noise on these measures were analyzed. It was shown that these measures are substantially affected by noise, and a new measure free of this disadvantage was proposed. The problem of detecting the observed scene object boundaries, based on comparing the spectral characteristics of image points, is considered. It was shown that contours are processed much better when spectral characteristics are used instead of energy brightness. A statistical approach to the correction of atmospheric distortions, which makes it possible to solve the stated problem based on analysis of a distorted image in contrast to analytical multiparametric models, was proposed. Several algorithms used to integrate spectral zonal images with data from other survey systems, which make it possible to image observed scene objects with a higher quality, are considered. Quality characteristics of hyperspectral data processing were proposed and studied.

"Minding the gap": imagination, creativity and human cognition.

PubMed

Pelaprat, Etienne; Cole, Michael

2011-12-01

Inquiry into the nature of mental images is a major topic in psychology where research is focused on the psychological faculties of imagination and creativity. In this paper, we draw on the work of L.S. Vygotsky to develop a cultural-historical approach to the study of imagination as central to human cognitive processes. We characterize imagination as a process of image making that resolves "gaps" arising from biological and cultural-historical constraints, and that enables ongoing time-space coordination necessary for thought and action. After presenting some basic theoretical considerations, we offer a series of examples to illustrate for the reader the diversity of processes of imagination as image making. Applying our arguments to contemporary digital media, we argue that a cultural-historical approach to image formation is important for understanding how imagination and creativity are distinct, yet inter-penetrating processes.

Quantitative evaluation of phase processing approaches in susceptibility weighted imaging

NASA Astrophysics Data System (ADS)

Li, Ningzhi; Wang, Wen-Tung; Sati, Pascal; Pham, Dzung L.; Butman, John A.

2012-03-01

Susceptibility weighted imaging (SWI) takes advantage of the local variation in susceptibility between different tissues to enable highly detailed visualization of the cerebral venous system and sensitive detection of intracranial hemorrhages. Thus, it has been increasingly used in magnetic resonance imaging studies of traumatic brain injury as well as other intracranial pathologies. In SWI, magnitude information is combined with phase information to enhance the susceptibility induced image contrast. Because of global susceptibility variations across the image, the rate of phase accumulation varies widely across the image resulting in phase wrapping artifacts that interfere with the local assessment of phase variation. Homodyne filtering is a common approach to eliminate this global phase variation. However, filter size requires careful selection in order to preserve image contrast and avoid errors resulting from residual phase wraps. An alternative approach is to apply phase unwrapping prior to high pass filtering. A suitable phase unwrapping algorithm guarantees no residual phase wraps but additional computational steps are required. In this work, we quantitatively evaluate these two phase processing approaches on both simulated and real data using different filters and cutoff frequencies. Our analysis leads to an improved understanding of the relationship between phase wraps, susceptibility effects, and acquisition parameters. Although homodyne filtering approaches are faster and more straightforward, phase unwrapping approaches perform more accurately in a wider variety of acquisition scenarios.

New approach to gallbladder ultrasonic images analysis and lesions recognition.

PubMed

Bodzioch, Sławomir; Ogiela, Marek R

2009-03-01

This paper presents a new approach to gallbladder ultrasonic image processing and analysis towards detection of disease symptoms on processed images. First, in this paper, there is presented a new method of filtering gallbladder contours from USG images. A major stage in this filtration is to segment and section off areas occupied by the said organ. In most cases this procedure is based on filtration that plays a key role in the process of diagnosing pathological changes. Unfortunately ultrasound images present among the most troublesome methods of analysis owing to the echogenic inconsistency of structures under observation. This paper provides for an inventive algorithm for the holistic extraction of gallbladder image contours. The algorithm is based on rank filtration, as well as on the analysis of histogram sections on tested organs. The second part concerns detecting lesion symptoms of the gallbladder. Automating a process of diagnosis always comes down to developing algorithms used to analyze the object of such diagnosis and verify the occurrence of symptoms related to given affection. Usually the final stage is to make a diagnosis based on the detected symptoms. This last stage can be carried out through either dedicated expert systems or more classic pattern analysis approach like using rules to determine illness basing on detected symptoms. This paper discusses the pattern analysis algorithms for gallbladder image interpretation towards classification of the most frequent illness symptoms of this organ.

High Throughput Multispectral Image Processing with Applications in Food Science.

PubMed

Tsakanikas, Panagiotis; Pavlidis, Dimitris; Nychas, George-John

2015-01-01

Recently, machine vision is gaining attention in food science as well as in food industry concerning food quality assessment and monitoring. Into the framework of implementation of Process Analytical Technology (PAT) in the food industry, image processing can be used not only in estimation and even prediction of food quality but also in detection of adulteration. Towards these applications on food science, we present here a novel methodology for automated image analysis of several kinds of food products e.g. meat, vanilla crème and table olives, so as to increase objectivity, data reproducibility, low cost information extraction and faster quality assessment, without human intervention. Image processing's outcome will be propagated to the downstream analysis. The developed multispectral image processing method is based on unsupervised machine learning approach (Gaussian Mixture Models) and a novel unsupervised scheme of spectral band selection for segmentation process optimization. Through the evaluation we prove its efficiency and robustness against the currently available semi-manual software, showing that the developed method is a high throughput approach appropriate for massive data extraction from food samples.

Advanced biologically plausible algorithms for low-level image processing

NASA Astrophysics Data System (ADS)

Gusakova, Valentina I.; Podladchikova, Lubov N.; Shaposhnikov, Dmitry G.; Markin, Sergey N.; Golovan, Alexander V.; Lee, Seong-Whan

1999-08-01

At present, in computer vision, the approach based on modeling the biological vision mechanisms is extensively developed. However, up to now, real world image processing has no effective solution in frameworks of both biologically inspired and conventional approaches. Evidently, new algorithms and system architectures based on advanced biological motivation should be developed for solution of computational problems related to this visual task. Basic problems that should be solved for creation of effective artificial visual system to process real world imags are a search for new algorithms of low-level image processing that, in a great extent, determine system performance. In the present paper, the result of psychophysical experiments and several advanced biologically motivated algorithms for low-level processing are presented. These algorithms are based on local space-variant filter, context encoding visual information presented in the center of input window, and automatic detection of perceptually important image fragments. The core of latter algorithm are using local feature conjunctions such as noncolinear oriented segment and composite feature map formation. Developed algorithms were integrated into foveal active vision model, the MARR. It is supposed that proposed algorithms may significantly improve model performance while real world image processing during memorizing, search, and recognition.

A digital ISO expansion technique for digital cameras

NASA Astrophysics Data System (ADS)

Yoo, Youngjin; Lee, Kangeui; Choe, Wonhee; Park, SungChan; Lee, Seong-Deok; Kim, Chang-Yong

2010-01-01

Market's demands of digital cameras for higher sensitivity capability under low-light conditions are remarkably increasing nowadays. The digital camera market is now a tough race for providing higher ISO capability. In this paper, we explore an approach for increasing maximum ISO capability of digital cameras without changing any structure of an image sensor or CFA. Our method is directly applied to the raw Bayer pattern CFA image to avoid non-linearity characteristics and noise amplification which are usually deteriorated after ISP (Image Signal Processor) of digital cameras. The proposed method fuses multiple short exposed images which are noisy, but less blurred. Our approach is designed to avoid the ghost artifact caused by hand-shaking and object motion. In order to achieve a desired ISO image quality, both low frequency chromatic noise and fine-grain noise that usually appear in high ISO images are removed and then we modify the different layers which are created by a two-scale non-linear decomposition of an image. Once our approach is performed on an input Bayer pattern CFA image, the resultant Bayer image is further processed by ISP to obtain a fully processed RGB image. The performance of our proposed approach is evaluated by comparing SNR (Signal to Noise Ratio), MTF50 (Modulation Transfer Function), color error ~E*ab and visual quality with reference images whose exposure times are properly extended into a variety of target sensitivity.

Acousto-optic time- and space-integrating spotlight-mode SAR processor

NASA Astrophysics Data System (ADS)

Haney, Michael W.; Levy, James J.; Michael, Robert R., Jr.

1993-09-01

The technical approach and recent experimental results for the acousto-optic time- and space- integrating real-time SAR image formation processor program are reported. The concept overcomes the size and power consumption limitations of electronic approaches by using compact, rugged, and low-power analog optical signal processing techniques for the most computationally taxing portions of the SAR imaging problem. Flexibility and performance are maintained by the use of digital electronics for the critical low-complexity filter generation and output image processing functions. The results include a demonstration of the processor's ability to perform high-resolution spotlight-mode SAR imaging by simultaneously compensating for range migration and range/azimuth coupling in the analog optical domain, thereby avoiding a highly power-consuming digital interpolation or reformatting operation usually required in all-electronic approaches.

A fourth order PDE based fuzzy c- means approach for segmentation of microscopic biopsy images in presence of Poisson noise for cancer detection.

PubMed

Kumar, Rajesh; Srivastava, Subodh; Srivastava, Rajeev

2017-07-01

For cancer detection from microscopic biopsy images, image segmentation step used for segmentation of cells and nuclei play an important role. Accuracy of segmentation approach dominate the final results. Also the microscopic biopsy images have intrinsic Poisson noise and if it is present in the image the segmentation results may not be accurate. The objective is to propose an efficient fuzzy c-means based segmentation approach which can also handle the noise present in the image during the segmentation process itself i.e. noise removal and segmentation is combined in one step. To address the above issues, in this paper a fourth order partial differential equation (FPDE) based nonlinear filter adapted to Poisson noise with fuzzy c-means segmentation method is proposed. This approach is capable of effectively handling the segmentation problem of blocky artifacts while achieving good tradeoff between Poisson noise removals and edge preservation of the microscopic biopsy images during segmentation process for cancer detection from cells. The proposed approach is tested on breast cancer microscopic biopsy data set with region of interest (ROI) segmented ground truth images. The microscopic biopsy data set contains 31 benign and 27 malignant images of size 896 × 768. The region of interest selected ground truth of all 58 images are also available for this data set. Finally, the result obtained from proposed approach is compared with the results of popular segmentation algorithms; fuzzy c-means, color k-means, texture based segmentation, and total variation fuzzy c-means approaches. The experimental results shows that proposed approach is providing better results in terms of various performance measures such as Jaccard coefficient, dice index, Tanimoto coefficient, area under curve, accuracy, true positive rate, true negative rate, false positive rate, false negative rate, random index, global consistency error, and variance of information as compared to other segmentation approaches used for cancer detection. Copyright © 2017 Elsevier B.V. All rights reserved.

a Fast Approach for Stitching of Aerial Images

NASA Astrophysics Data System (ADS)

Moussa, A.; El-Sheimy, N.

2016-06-01

The last few years have witnessed an increasing volume of aerial image data because of the extensive improvements of the Unmanned Aerial Vehicles (UAVs). These newly developed UAVs have led to a wide variety of applications. A fast assessment of the achieved coverage and overlap of the acquired images of a UAV flight mission is of great help to save the time and cost of the further steps. A fast automatic stitching of the acquired images can help to visually assess the achieved coverage and overlap during the flight mission. This paper proposes an automatic image stitching approach that creates a single overview stitched image using the acquired images during a UAV flight mission along with a coverage image that represents the count of overlaps between the acquired images. The main challenge of such task is the huge number of images that are typically involved in such scenarios. A short flight mission with image acquisition frequency of one second can capture hundreds to thousands of images. The main focus of the proposed approach is to reduce the processing time of the image stitching procedure by exploiting the initial knowledge about the images positions provided by the navigation sensors. The proposed approach also avoids solving for all the transformation parameters of all the photos together to save the expected long computation time if all the parameters were considered simultaneously. After extracting the points of interest of all the involved images using Scale-Invariant Feature Transform (SIFT) algorithm, the proposed approach uses the initial image's coordinates to build an incremental constrained Delaunay triangulation that represents the neighborhood of each image. This triangulation helps to match only the neighbor images and therefore reduces the time-consuming features matching step. The estimated relative orientation between the matched images is used to find a candidate seed image for the stitching process. The pre-estimated transformation parameters of the images are employed successively in a growing fashion to create the stitched image and the coverage image. The proposed approach is implemented and tested using the images acquired through a UAV flight mission and the achieved results are presented and discussed.

A New Approach to Create Image Control Networks in ISIS

NASA Astrophysics Data System (ADS)

Becker, K. J.; Berry, K. L.; Mapel, J. A.; Walldren, J. C.

2017-06-01

A new approach was used to create a feature-based control point network that required the development of new tools in the Integrated Software for Imagers and Spectrometers (ISIS3) system to process very large datasets.

A Unified Mathematical Approach to Image Analysis.

DTIC Science & Technology

1987-08-31

describes four instances of the paradigm in detail. Directions for ongoing and future research are also indicated. Keywords: Image processing; Algorithms; Segmentation; Boundary detection; tomography; Global image analysis .

Single-Scale Fusion: An Effective Approach to Merging Images.

PubMed

Ancuti, Codruta O; Ancuti, Cosmin; De Vleeschouwer, Christophe; Bovik, Alan C

2017-01-01

Due to its robustness and effectiveness, multi-scale fusion (MSF) based on the Laplacian pyramid decomposition has emerged as a popular technique that has shown utility in many applications. Guided by several intuitive measures (weight maps) the MSF process is versatile and straightforward to be implemented. However, the number of pyramid levels increases with the image size, which implies sophisticated data management and memory accesses, as well as additional computations. Here, we introduce a simplified formulation that reduces MSF to only a single level process. Starting from the MSF decomposition, we explain both mathematically and intuitively (visually) a way to simplify the classical MSF approach with minimal loss of information. The resulting single-scale fusion (SSF) solution is a close approximation of the MSF process that eliminates important redundant computations. It also provides insights regarding why MSF is so effective. While our simplified expression is derived in the context of high dynamic range imaging, we show its generality on several well-known fusion-based applications, such as image compositing, extended depth of field, medical imaging, and blending thermal (infrared) images with visible light. Besides visual validation, quantitative evaluations demonstrate that our SSF strategy is able to yield results that are highly competitive with traditional MSF approaches.

Iplt--image processing library and toolkit for the electron microscopy community.

PubMed

Philippsen, Ansgar; Schenk, Andreas D; Stahlberg, Henning; Engel, Andreas

2003-01-01

We present the foundation for establishing a modular, collaborative, integrated, open-source architecture for image processing of electron microscopy images, named iplt. It is designed around object oriented paradigms and implemented using the programming languages C++ and Python. In many aspects it deviates from classical image processing approaches. This paper intends to motivate developers within the community to participate in this on-going project. The iplt homepage can be found at http://www.iplt.org.

Approach motivation and cognitive resources combine to influence memory for positive emotional stimuli.

PubMed

Crowell, Adrienne; Schmeichel, Brandon J

2016-01-01

Inspired by the elaborated intrusion theory of desire, the current research tested the hypothesis that persons higher in trait approach motivation process positive stimuli deeply, which enhances memory for them. Ninety-four undergraduates completed a measure of trait approach motivation, viewed positive or negative image slideshows in the presence or absence of a cognitive load, and one week later completed an image memory test. Higher trait approach motivation predicted better memory for the positive slideshow, but this memory boost disappeared under cognitive load. Approach motivation did not influence memory for the negative slideshow. The current findings support the idea that individuals higher in approach motivation spontaneously devote limited resources to processing positive stimuli.

Aberration-free superresolution imaging via binary speckle pattern encoding and processing

NASA Astrophysics Data System (ADS)

Ben-Eliezer, Eyal; Marom, Emanuel

2007-04-01

We present an approach that provides superresolution beyond the classical limit as well as image restoration in the presence of aberrations; in particular, the ability to obtain superresolution while extending the depth of field (DOF) simultaneously is tested experimentally. It is based on an approach, recently proposed, shown to increase the resolution significantly for in-focus images by speckle encoding and decoding. In our approach, an object multiplied by a fine binary speckle pattern may be located anywhere along an extended DOF region. Since the exact magnification is not known in the presence of defocus aberration, the acquired low-resolution image is electronically processed via a parallel-branch decoding scheme, where in each branch the image is multiplied by the same high-resolution synchronized time-varying binary speckle but with different magnification. Finally, a hard-decision algorithm chooses the branch that provides the highest-resolution output image, thus achieving insensitivity to aberrations as well as DOF variations. Simulation as well as experimental results are presented, exhibiting significant resolution improvement factors.

The infection algorithm: an artificial epidemic approach for dense stereo correspondence.

PubMed

Olague, Gustavo; Fernández, Francisco; Pérez, Cynthia B; Lutton, Evelyne

2006-01-01

We present a new bio-inspired approach applied to a problem of stereo image matching. This approach is based on an artificial epidemic process, which we call the infection algorithm. The problem at hand is a basic one in computer vision for 3D scene reconstruction. It has many complex aspects and is known as an extremely difficult one. The aim is to match the contents of two images in order to obtain 3D information that allows the generation of simulated projections from a viewpoint that is different from the ones of the initial photographs. This process is known as view synthesis. The algorithm we propose exploits the image contents in order to produce only the necessary 3D depth information, while saving computational time. It is based on a set of distributed rules, which propagate like an artificial epidemic over the images. Experiments on a pair of real images are presented, and realistic reprojected images have been generated.

Complex Event Processing for Content-Based Text, Image, and Video Retrieval

DTIC Science & Technology

2016-06-01

NY): Wiley- Interscience; 2000. Feldman R, Sanger J. The text mining handbook: advanced approaches in analyzing unstructured data. New York (NY...ARL-TR-7705 ● JUNE 2016 US Army Research Laboratory Complex Event Processing for Content-Based Text , Image, and Video Retrieval...ARL-TR-7705 ● JUNE 2016 US Army Research Laboratory Complex Event Processing for Content-Based Text , Image, and Video Retrieval

Point Cloud Generation from Aerial Image Data Acquired by a Quadrocopter Type Micro Unmanned Aerial Vehicle and a Digital Still Camera

PubMed Central

Rosnell, Tomi; Honkavaara, Eija

2012-01-01

The objective of this investigation was to develop and investigate methods for point cloud generation by image matching using aerial image data collected by quadrocopter type micro unmanned aerial vehicle (UAV) imaging systems. Automatic generation of high-quality, dense point clouds from digital images by image matching is a recent, cutting-edge step forward in digital photogrammetric technology. The major components of the system for point cloud generation are a UAV imaging system, an image data collection process using high image overlaps, and post-processing with image orientation and point cloud generation. Two post-processing approaches were developed: one of the methods is based on Bae Systems’ SOCET SET classical commercial photogrammetric software and another is built using Microsoft®’s Photosynth™ service available in the Internet. Empirical testing was carried out in two test areas. Photosynth processing showed that it is possible to orient the images and generate point clouds fully automatically without any a priori orientation information or interactive work. The photogrammetric processing line provided dense and accurate point clouds that followed the theoretical principles of photogrammetry, but also some artifacts were detected. The point clouds from the Photosynth processing were sparser and noisier, which is to a large extent due to the fact that the method is not optimized for dense point cloud generation. Careful photogrammetric processing with self-calibration is required to achieve the highest accuracy. Our results demonstrate the high performance potential of the approach and that with rigorous processing it is possible to reach results that are consistent with theory. We also point out several further research topics. Based on theoretical and empirical results, we give recommendations for properties of imaging sensor, data collection and processing of UAV image data to ensure accurate point cloud generation. PMID:22368479

Point cloud generation from aerial image data acquired by a quadrocopter type micro unmanned aerial vehicle and a digital still camera.

PubMed

Rosnell, Tomi; Honkavaara, Eija

2012-01-01

The objective of this investigation was to develop and investigate methods for point cloud generation by image matching using aerial image data collected by quadrocopter type micro unmanned aerial vehicle (UAV) imaging systems. Automatic generation of high-quality, dense point clouds from digital images by image matching is a recent, cutting-edge step forward in digital photogrammetric technology. The major components of the system for point cloud generation are a UAV imaging system, an image data collection process using high image overlaps, and post-processing with image orientation and point cloud generation. Two post-processing approaches were developed: one of the methods is based on Bae Systems' SOCET SET classical commercial photogrammetric software and another is built using Microsoft(®)'s Photosynth™ service available in the Internet. Empirical testing was carried out in two test areas. Photosynth processing showed that it is possible to orient the images and generate point clouds fully automatically without any a priori orientation information or interactive work. The photogrammetric processing line provided dense and accurate point clouds that followed the theoretical principles of photogrammetry, but also some artifacts were detected. The point clouds from the Photosynth processing were sparser and noisier, which is to a large extent due to the fact that the method is not optimized for dense point cloud generation. Careful photogrammetric processing with self-calibration is required to achieve the highest accuracy. Our results demonstrate the high performance potential of the approach and that with rigorous processing it is possible to reach results that are consistent with theory. We also point out several further research topics. Based on theoretical and empirical results, we give recommendations for properties of imaging sensor, data collection and processing of UAV image data to ensure accurate point cloud generation.

Effect of wavefront aberrations on a focused plenoptic imaging system: a wave optics simulation approach

NASA Astrophysics Data System (ADS)

Turola, Massimo; Meah, Chris J.; Marshall, Richard J.; Styles, Iain B.; Gruppetta, Stephen

2015-06-01

A plenoptic imaging system records simultaneously the intensity and the direction of the rays of light. This additional information allows many post processing features such as 3D imaging, synthetic refocusing and potentially evaluation of wavefront aberrations. In this paper the effects of low order aberrations on a simple plenoptic imaging system have been investigated using a wave optics simulations approach.

Optronic System Imaging Simulator (OSIS): imager simulation tool of the ECOMOS project

NASA Astrophysics Data System (ADS)

Wegner, D.; Repasi, E.

2018-04-01

ECOMOS is a multinational effort within the framework of an EDA Project Arrangement. Its aim is to provide a generally accepted and harmonized European computer model for computing nominal Target Acquisition (TA) ranges of optronic imagers operating in the Visible or thermal Infrared (IR). The project involves close co-operation of defense and security industry and public research institutes from France, Germany, Italy, The Netherlands and Sweden. ECOMOS uses two approaches to calculate Target Acquisition (TA) ranges, the analytical TRM4 model and the image-based Triangle Orientation Discrimination model (TOD). In this paper the IR imager simulation tool, Optronic System Imaging Simulator (OSIS), is presented. It produces virtual camera imagery required by the TOD approach. Pristine imagery is degraded by various effects caused by atmospheric attenuation, optics, detector footprint, sampling, fixed pattern noise, temporal noise and digital signal processing. Resulting images might be presented to observers or could be further processed for automatic image quality calculations. For convenience OSIS incorporates camera descriptions and intermediate results provided by TRM4. For input OSIS uses pristine imagery tied with meta information about scene content, its physical dimensions, and gray level interpretation. These images represent planar targets placed at specified distances to the imager. Furthermore, OSIS is extended by a plugin functionality that enables integration of advanced digital signal processing techniques in ECOMOS such as compression, local contrast enhancement, digital turbulence mitiga- tion, to name but a few. By means of this image-based approach image degradations and image enhancements can be investigated, which goes beyond the scope of the analytical TRM4 model.

Systems Biology-Driven Hypotheses Tested In Vivo: The Need to Advancing Molecular Imaging Tools.

PubMed

Verma, Garima; Palombo, Alessandro; Grigioni, Mauro; La Monaca, Morena; D'Avenio, Giuseppe

2018-01-01

Processing and interpretation of biological images may provide invaluable insights on complex, living systems because images capture the overall dynamics as a "whole." Therefore, "extraction" of key, quantitative morphological parameters could be, at least in principle, helpful in building a reliable systems biology approach in understanding living objects. Molecular imaging tools for system biology models have attained widespread usage in modern experimental laboratories. Here, we provide an overview on advances in the computational technology and different instrumentations focused on molecular image processing and analysis. Quantitative data analysis through various open source software and algorithmic protocols will provide a novel approach for modeling the experimental research program. Besides this, we also highlight the predictable future trends regarding methods for automatically analyzing biological data. Such tools will be very useful to understand the detailed biological and mathematical expressions under in-silico system biology processes with modeling properties.

A New Parallel Approach for Accelerating the GPU-Based Execution of Edge Detection Algorithms

PubMed Central

Emrani, Zahra; Bateni, Soroosh; Rabbani, Hossein

2017-01-01

Real-time image processing is used in a wide variety of applications like those in medical care and industrial processes. This technique in medical care has the ability to display important patient information graphi graphically, which can supplement and help the treatment process. Medical decisions made based on real-time images are more accurate and reliable. According to the recent researches, graphic processing unit (GPU) programming is a useful method for improving the speed and quality of medical image processing and is one of the ways of real-time image processing. Edge detection is an early stage in most of the image processing methods for the extraction of features and object segments from a raw image. The Canny method, Sobel and Prewitt filters, and the Roberts’ Cross technique are some examples of edge detection algorithms that are widely used in image processing and machine vision. In this work, these algorithms are implemented using the Compute Unified Device Architecture (CUDA), Open Source Computer Vision (OpenCV), and Matrix Laboratory (MATLAB) platforms. An existing parallel method for Canny approach has been modified further to run in a fully parallel manner. This has been achieved by replacing the breadth- first search procedure with a parallel method. These algorithms have been compared by testing them on a database of optical coherence tomography images. The comparison of results shows that the proposed implementation of the Canny method on GPU using the CUDA platform improves the speed of execution by 2–100× compared to the central processing unit-based implementation using the OpenCV and MATLAB platforms. PMID:28487831

A New Parallel Approach for Accelerating the GPU-Based Execution of Edge Detection Algorithms.

PubMed

Emrani, Zahra; Bateni, Soroosh; Rabbani, Hossein

2017-01-01

Real-time image processing is used in a wide variety of applications like those in medical care and industrial processes. This technique in medical care has the ability to display important patient information graphi graphically, which can supplement and help the treatment process. Medical decisions made based on real-time images are more accurate and reliable. According to the recent researches, graphic processing unit (GPU) programming is a useful method for improving the speed and quality of medical image processing and is one of the ways of real-time image processing. Edge detection is an early stage in most of the image processing methods for the extraction of features and object segments from a raw image. The Canny method, Sobel and Prewitt filters, and the Roberts' Cross technique are some examples of edge detection algorithms that are widely used in image processing and machine vision. In this work, these algorithms are implemented using the Compute Unified Device Architecture (CUDA), Open Source Computer Vision (OpenCV), and Matrix Laboratory (MATLAB) platforms. An existing parallel method for Canny approach has been modified further to run in a fully parallel manner. This has been achieved by replacing the breadth- first search procedure with a parallel method. These algorithms have been compared by testing them on a database of optical coherence tomography images. The comparison of results shows that the proposed implementation of the Canny method on GPU using the CUDA platform improves the speed of execution by 2-100× compared to the central processing unit-based implementation using the OpenCV and MATLAB platforms.

A Quality Sorting of Fruit Using a New Automatic Image Processing Method

NASA Astrophysics Data System (ADS)

Amenomori, Michihiro; Yokomizu, Nobuyuki

This paper presents an innovative approach for quality sorting of objects such as apples sorting in an agricultural factory, using an image processing algorithm. The objective of our approach are; firstly to sort the objects by their colors precisely; secondly to detect any irregularity of the colors surrounding the apples efficiently. An experiment has been conducted and the results have been obtained and compared with that has been preformed by human sorting process and by color sensor sorting devices. The results demonstrate that our approach is capable to sort the objects rapidly and the percentage of classification valid rate was 100 %.

Image fusion via nonlocal sparse K-SVD dictionary learning.

PubMed

Li, Ying; Li, Fangyi; Bai, Bendu; Shen, Qiang

2016-03-01

Image fusion aims to merge two or more images captured via various sensors of the same scene to construct a more informative image by integrating their details. Generally, such integration is achieved through the manipulation of the representations of the images concerned. Sparse representation plays an important role in the effective description of images, offering a great potential in a variety of image processing tasks, including image fusion. Supported by sparse representation, in this paper, an approach for image fusion by the use of a novel dictionary learning scheme is proposed. The nonlocal self-similarity property of the images is exploited, not only at the stage of learning the underlying description dictionary but during the process of image fusion. In particular, the property of nonlocal self-similarity is combined with the traditional sparse dictionary. This results in an improved learned dictionary, hereafter referred to as the nonlocal sparse K-SVD dictionary (where K-SVD stands for the K times singular value decomposition that is commonly used in the literature), and abbreviated to NL_SK_SVD. The performance of the NL_SK_SVD dictionary is applied for image fusion using simultaneous orthogonal matching pursuit. The proposed approach is evaluated with different types of images, and compared with a number of alternative image fusion techniques. The resultant superior fused images using the present approach demonstrates the efficacy of the NL_SK_SVD dictionary in sparse image representation.

Dual-modality brain PET-CT image segmentation based on adaptive use of functional and anatomical information.

PubMed

Xia, Yong; Eberl, Stefan; Wen, Lingfeng; Fulham, Michael; Feng, David Dagan

2012-01-01

Dual medical imaging modalities, such as PET-CT, are now a routine component of clinical practice. Medical image segmentation methods, however, have generally only been applied to single modality images. In this paper, we propose the dual-modality image segmentation model to segment brain PET-CT images into gray matter, white matter and cerebrospinal fluid. This model converts PET-CT image segmentation into an optimization process controlled simultaneously by PET and CT voxel values and spatial constraints. It is innovative in the creation and application of the modality discriminatory power (MDP) coefficient as a weighting scheme to adaptively combine the functional (PET) and anatomical (CT) information on a voxel-by-voxel basis. Our approach relies upon allowing the modality with higher discriminatory power to play a more important role in the segmentation process. We compared the proposed approach to three other image segmentation strategies, including PET-only based segmentation, combination of the results of independent PET image segmentation and CT image segmentation, and simultaneous segmentation of joint PET and CT images without an adaptive weighting scheme. Our results in 21 clinical studies showed that our approach provides the most accurate and reliable segmentation for brain PET-CT images. Copyright © 2011 Elsevier Ltd. All rights reserved.

Imaging model for the scintillator and its application to digital radiography image enhancement.

PubMed

Wang, Qian; Zhu, Yining; Li, Hongwei

2015-12-28

Digital Radiography (DR) images obtained by OCD-based (optical coupling detector) Micro-CT system usually suffer from low contrast. In this paper, a mathematical model is proposed to describe the image formation process in scintillator. By solving the correlative inverse problem, the quality of DR images is improved, i.e. higher contrast and spatial resolution. By analyzing the radiative transfer process of visible light in scintillator, scattering is recognized as the main factor leading to low contrast. Moreover, involved blurring effect is also concerned and described as point spread function (PSF). Based on these physical processes, the scintillator imaging model is then established. When solving the inverse problem, pre-correction to the intensity of x-rays, dark channel prior based haze removing technique, and an effective blind deblurring approach are employed. Experiments on a variety of DR images show that the proposed approach could improve the contrast of DR images dramatically as well as eliminate the blurring vision effectively. Compared with traditional contrast enhancement methods, such as CLAHE, our method could preserve the relative absorption values well.

Theoretical and Empirical Comparison of Big Data Image Processing with Apache Hadoop and Sun Grid Engine.

PubMed

Bao, Shunxing; Weitendorf, Frederick D; Plassard, Andrew J; Huo, Yuankai; Gokhale, Aniruddha; Landman, Bennett A

2017-02-11

The field of big data is generally concerned with the scale of processing at which traditional computational paradigms break down. In medical imaging, traditional large scale processing uses a cluster computer that combines a group of workstation nodes into a functional unit that is controlled by a job scheduler. Typically, a shared-storage network file system (NFS) is used to host imaging data. However, data transfer from storage to processing nodes can saturate network bandwidth when data is frequently uploaded/retrieved from the NFS, e.g., "short" processing times and/or "large" datasets. Recently, an alternative approach using Hadoop and HBase was presented for medical imaging to enable co-location of data storage and computation while minimizing data transfer. The benefits of using such a framework must be formally evaluated against a traditional approach to characterize the point at which simply "large scale" processing transitions into "big data" and necessitates alternative computational frameworks. The proposed Hadoop system was implemented on a production lab-cluster alongside a standard Sun Grid Engine (SGE). Theoretical models for wall-clock time and resource time for both approaches are introduced and validated. To provide real example data, three T1 image archives were retrieved from a university secure, shared web database and used to empirically assess computational performance under three configurations of cluster hardware (using 72, 109, or 209 CPU cores) with differing job lengths. Empirical results match the theoretical models. Based on these data, a comparative analysis is presented for when the Hadoop framework will be relevant and non-relevant for medical imaging.

Theoretical and empirical comparison of big data image processing with Apache Hadoop and Sun Grid Engine

NASA Astrophysics Data System (ADS)

Bao, Shunxing; Weitendorf, Frederick D.; Plassard, Andrew J.; Huo, Yuankai; Gokhale, Aniruddha; Landman, Bennett A.

2017-03-01

The field of big data is generally concerned with the scale of processing at which traditional computational paradigms break down. In medical imaging, traditional large scale processing uses a cluster computer that combines a group of workstation nodes into a functional unit that is controlled by a job scheduler. Typically, a shared-storage network file system (NFS) is used to host imaging data. However, data transfer from storage to processing nodes can saturate network bandwidth when data is frequently uploaded/retrieved from the NFS, e.g., "short" processing times and/or "large" datasets. Recently, an alternative approach using Hadoop and HBase was presented for medical imaging to enable co-location of data storage and computation while minimizing data transfer. The benefits of using such a framework must be formally evaluated against a traditional approach to characterize the point at which simply "large scale" processing transitions into "big data" and necessitates alternative computational frameworks. The proposed Hadoop system was implemented on a production lab-cluster alongside a standard Sun Grid Engine (SGE). Theoretical models for wall-clock time and resource time for both approaches are introduced and validated. To provide real example data, three T1 image archives were retrieved from a university secure, shared web database and used to empirically assess computational performance under three configurations of cluster hardware (using 72, 109, or 209 CPU cores) with differing job lengths. Empirical results match the theoretical models. Based on these data, a comparative analysis is presented for when the Hadoop framework will be relevant and nonrelevant for medical imaging.

Cloud Engineering Principles and Technology Enablers for Medical Image Processing-as-a-Service.

PubMed

Bao, Shunxing; Plassard, Andrew J; Landman, Bennett A; Gokhale, Aniruddha

2017-04-01

Traditional in-house, laboratory-based medical imaging studies use hierarchical data structures (e.g., NFS file stores) or databases (e.g., COINS, XNAT) for storage and retrieval. The resulting performance from these approaches is, however, impeded by standard network switches since they can saturate network bandwidth during transfer from storage to processing nodes for even moderate-sized studies. To that end, a cloud-based "medical image processing-as-a-service" offers promise in utilizing the ecosystem of Apache Hadoop, which is a flexible framework providing distributed, scalable, fault tolerant storage and parallel computational modules, and HBase, which is a NoSQL database built atop Hadoop's distributed file system. Despite this promise, HBase's load distribution strategy of region split and merge is detrimental to the hierarchical organization of imaging data (e.g., project, subject, session, scan, slice). This paper makes two contributions to address these concerns by describing key cloud engineering principles and technology enhancements we made to the Apache Hadoop ecosystem for medical imaging applications. First, we propose a row-key design for HBase, which is a necessary step that is driven by the hierarchical organization of imaging data. Second, we propose a novel data allocation policy within HBase to strongly enforce collocation of hierarchically related imaging data. The proposed enhancements accelerate data processing by minimizing network usage and localizing processing to machines where the data already exist. Moreover, our approach is amenable to the traditional scan, subject, and project-level analysis procedures, and is compatible with standard command line/scriptable image processing software. Experimental results for an illustrative sample of imaging data reveals that our new HBase policy results in a three-fold time improvement in conversion of classic DICOM to NiFTI file formats when compared with the default HBase region split policy, and nearly a six-fold improvement over a commonly available network file system (NFS) approach even for relatively small file sets. Moreover, file access latency is lower than network attached storage.

A new approach towards image based virtual 3D city modeling by using close range photogrammetry

NASA Astrophysics Data System (ADS)

Singh, S. P.; Jain, K.; Mandla, V. R.

2014-05-01

3D city model is a digital representation of the Earth's surface and it's related objects such as building, tree, vegetation, and some manmade feature belonging to urban area. The demand of 3D city modeling is increasing day to day for various engineering and non-engineering applications. Generally three main image based approaches are using for virtual 3D city models generation. In first approach, researchers used Sketch based modeling, second method is Procedural grammar based modeling and third approach is Close range photogrammetry based modeling. Literature study shows that till date, there is no complete solution available to create complete 3D city model by using images. These image based methods also have limitations This paper gives a new approach towards image based virtual 3D city modeling by using close range photogrammetry. This approach is divided into three sections. First, data acquisition process, second is 3D data processing, and third is data combination process. In data acquisition process, a multi-camera setup developed and used for video recording of an area. Image frames created from video data. Minimum required and suitable video image frame selected for 3D processing. In second section, based on close range photogrammetric principles and computer vision techniques, 3D model of area created. In third section, this 3D model exported to adding and merging of other pieces of large area. Scaling and alignment of 3D model was done. After applying the texturing and rendering on this model, a final photo-realistic textured 3D model created. This 3D model transferred into walk-through model or in movie form. Most of the processing steps are automatic. So this method is cost effective and less laborious. Accuracy of this model is good. For this research work, study area is the campus of department of civil engineering, Indian Institute of Technology, Roorkee. This campus acts as a prototype for city. Aerial photography is restricted in many country and high resolution satellite images are costly. In this study, proposed method is based on only simple video recording of area. Thus this proposed method is suitable for 3D city modeling. Photo-realistic, scalable, geo-referenced virtual 3D city model is useful for various kinds of applications such as for planning in navigation, tourism, disasters management, transportations, municipality, urban and environmental managements, real-estate industry. Thus this study will provide a good roadmap for geomatics community to create photo-realistic virtual 3D city model by using close range photogrammetry.

Wood industrial application for quality control using image processing

NASA Astrophysics Data System (ADS)

Ferreira, M. J. O.; Neves, J. A. C.

1994-11-01

This paper describes an application of image processing for the furniture industry. It uses an input data, images acquired directly from wood planks where defects were previously marked by an operator. A set of image processing algorithms separates and codes each defect and detects a polygonal approach of the line representing them. For such a purpose we developed a pattern classification algorithm and a new technique of segmenting defects by carving the convex hull of the binary shape representing each isolated defect.

A novel data processing technique for image reconstruction of penumbral imaging

NASA Astrophysics Data System (ADS)

Xie, Hongwei; Li, Hongyun; Xu, Zeping; Song, Guzhou; Zhang, Faqiang; Zhou, Lin

2011-06-01

CT image reconstruction technique was applied to the data processing of the penumbral imaging. Compared with other traditional processing techniques for penumbral coded pinhole image such as Wiener, Lucy-Richardson and blind technique, this approach is brand new. In this method, the coded aperture processing method was used for the first time independent to the point spread function of the image diagnostic system. In this way, the technical obstacles was overcome in the traditional coded pinhole image processing caused by the uncertainty of point spread function of the image diagnostic system. Then based on the theoretical study, the simulation of penumbral imaging and image reconstruction was carried out to provide fairly good results. While in the visible light experiment, the point source of light was used to irradiate a 5mm×5mm object after diffuse scattering and volume scattering. The penumbral imaging was made with aperture size of ~20mm. Finally, the CT image reconstruction technique was used for image reconstruction to provide a fairly good reconstruction result.

Evaluation of security algorithms used for security processing on DICOM images

NASA Astrophysics Data System (ADS)

Chen, Xiaomeng; Shuai, Jie; Zhang, Jianguo; Huang, H. K.

2005-04-01

In this paper, we developed security approach to provide security measures and features in PACS image acquisition and Tele-radiology image transmission. The security processing on medical images was based on public key infrastructure (PKI) and including digital signature and data encryption to achieve the security features of confidentiality, privacy, authenticity, integrity, and non-repudiation. There are many algorithms which can be used in PKI for data encryption and digital signature. In this research, we select several algorithms to perform security processing on different DICOM images in PACS environment, evaluate the security processing performance of these algorithms, and find the relationship between performance with image types, sizes and the implementation methods.

Gaussian Process Interpolation for Uncertainty Estimation in Image Registration

PubMed Central

Wachinger, Christian; Golland, Polina; Reuter, Martin; Wells, William

2014-01-01

Intensity-based image registration requires resampling images on a common grid to evaluate the similarity function. The uncertainty of interpolation varies across the image, depending on the location of resampled points relative to the base grid. We propose to perform Bayesian inference with Gaussian processes, where the covariance matrix of the Gaussian process posterior distribution estimates the uncertainty in interpolation. The Gaussian process replaces a single image with a distribution over images that we integrate into a generative model for registration. Marginalization over resampled images leads to a new similarity measure that includes the uncertainty of the interpolation. We demonstrate that our approach increases the registration accuracy and propose an efficient approximation scheme that enables seamless integration with existing registration methods. PMID:25333127

EVALUATION OF REGISTRATION, COMPRESSION AND CLASSIFICATION ALGORITHMS

NASA Technical Reports Server (NTRS)

Jayroe, R. R.

1994-01-01

Several types of algorithms are generally used to process digital imagery such as Landsat data. The most commonly used algorithms perform the task of registration, compression, and classification. Because there are different techniques available for performing registration, compression, and classification, imagery data users need a rationale for selecting a particular approach to meet their particular needs. This collection of registration, compression, and classification algorithms was developed so that different approaches could be evaluated and the best approach for a particular application determined. Routines are included for six registration algorithms, six compression algorithms, and two classification algorithms. The package also includes routines for evaluating the effects of processing on the image data. This collection of routines should be useful to anyone using or developing image processing software. Registration of image data involves the geometrical alteration of the imagery. Registration routines available in the evaluation package include image magnification, mapping functions, partitioning, map overlay, and data interpolation. The compression of image data involves reducing the volume of data needed for a given image. Compression routines available in the package include adaptive differential pulse code modulation, two-dimensional transforms, clustering, vector reduction, and picture segmentation. Classification of image data involves analyzing the uncompressed or compressed image data to produce inventories and maps of areas of similar spectral properties within a scene. The classification routines available include a sequential linear technique and a maximum likelihood technique. The choice of the appropriate evaluation criteria is quite important in evaluating the image processing functions. The user is therefore given a choice of evaluation criteria with which to investigate the available image processing functions. All of the available evaluation criteria basically compare the observed results with the expected results. For the image reconstruction processes of registration and compression, the expected results are usually the original data or some selected characteristics of the original data. For classification processes the expected result is the ground truth of the scene. Thus, the comparison process consists of determining what changes occur in processing, where the changes occur, how much change occurs, and the amplitude of the change. The package includes evaluation routines for performing such comparisons as average uncertainty, average information transfer, chi-square statistics, multidimensional histograms, and computation of contingency matrices. This collection of routines is written in FORTRAN IV for batch execution and has been implemented on an IBM 360 computer with a central memory requirement of approximately 662K of 8 bit bytes. This collection of image processing and evaluation routines was developed in 1979.

Real time mitigation of atmospheric turbulence in long distance imaging using the lucky region fusion algorithm with FPGA and GPU hardware acceleration

NASA Astrophysics Data System (ADS)

Jackson, Christopher Robert

"Lucky-region" fusion (LRF) is a synthetic imaging technique that has proven successful in enhancing the quality of images distorted by atmospheric turbulence. The LRF algorithm selects sharp regions of an image obtained from a series of short exposure frames, and fuses the sharp regions into a final, improved image. In previous research, the LRF algorithm had been implemented on a PC using the C programming language. However, the PC did not have sufficient sequential processing power to handle real-time extraction, processing and reduction required when the LRF algorithm was applied to real-time video from fast, high-resolution image sensors. This thesis describes two hardware implementations of the LRF algorithm to achieve real-time image processing. The first was created with a VIRTEX-7 field programmable gate array (FPGA). The other developed using the graphics processing unit (GPU) of a NVIDIA GeForce GTX 690 video card. The novelty in the FPGA approach is the creation of a "black box" LRF video processing system with a general camera link input, a user controller interface, and a camera link video output. We also describe a custom hardware simulation environment we have built to test the FPGA LRF implementation. The advantage of the GPU approach is significantly improved development time, integration of image stabilization into the system, and comparable atmospheric turbulence mitigation.

Comparing multiple turbulence restoration algorithms performance on noisy anisoplanatic imagery

NASA Astrophysics Data System (ADS)

Rucci, Michael A.; Hardie, Russell C.; Dapore, Alexander J.

2017-05-01

In this paper, we compare the performance of multiple turbulence mitigation algorithms to restore imagery degraded by atmospheric turbulence and camera noise. In order to quantify and compare algorithm performance, imaging scenes were simulated by applying noise and varying levels of turbulence. For the simulation, a Monte-Carlo wave optics approach is used to simulate the spatially and temporally varying turbulence in an image sequence. A Poisson-Gaussian noise mixture model is then used to add noise to the observed turbulence image set. These degraded image sets are processed with three separate restoration algorithms: Lucky Look imaging, bispectral speckle imaging, and a block matching method with restoration filter. These algorithms were chosen because they incorporate different approaches and processing techniques. The results quantitatively show how well the algorithms are able to restore the simulated degraded imagery.

Novel wavelength diversity technique for high-speed atmospheric turbulence compensation

NASA Astrophysics Data System (ADS)

Arrasmith, William W.; Sullivan, Sean F.

2010-04-01

The defense, intelligence, and homeland security communities are driving a need for software dominant, real-time or near-real time atmospheric turbulence compensated imagery. The development of parallel processing capabilities are finding application in diverse areas including image processing, target tracking, pattern recognition, and image fusion to name a few. A novel approach to the computationally intensive case of software dominant optical and near infrared imaging through atmospheric turbulence is addressed in this paper. Previously, the somewhat conventional wavelength diversity method has been used to compensate for atmospheric turbulence with great success. We apply a new correlation based approach to the wavelength diversity methodology using a parallel processing architecture enabling high speed atmospheric turbulence compensation. Methods for optical imaging through distributed turbulence are discussed, simulation results are presented, and computational and performance assessments are provided.

Application of machine learning methods for traffic signs recognition

NASA Astrophysics Data System (ADS)

Filatov, D. V.; Ignatev, K. V.; Deviatkin, A. V.; Serykh, E. V.

2018-02-01

This paper focuses on solving a relevant and pressing safety issue on intercity roads. Two approaches were considered for solving the problem of traffic signs recognition; the approaches involved neural networks to analyze images obtained from a camera in the real-time mode. The first approach is based on a sequential image processing. At the initial stage, with the help of color filters and morphological operations (dilatation and erosion), the area containing the traffic sign is located on the image, then the selected and scaled fragment of the image is analyzed using a feedforward neural network to determine the meaning of the found traffic sign. Learning of the neural network in this approach is carried out using a backpropagation method. The second approach involves convolution neural networks at both stages, i.e. when searching and selecting the area of the image containing the traffic sign, and when determining its meaning. Learning of the neural network in the second approach is carried out using the intersection over union function and a loss function. For neural networks to learn and the proposed algorithms to be tested, a series of videos from a dash cam were used that were shot under various weather and illumination conditions. As a result, the proposed approaches for traffic signs recognition were analyzed and compared by key indicators such as recognition rate percentage and the complexity of neural networks’ learning process.

Near-infrared spectroscopic tissue imaging for medical applications

DOEpatents

Demos,; Stavros, Staggs [Livermore, CA; Michael, C [Tracy, CA

2006-03-21

Near infrared imaging using elastic light scattering and tissue autofluorescence are explored for medical applications. The approach involves imaging using cross-polarized elastic light scattering and tissue autofluorescence in the Near Infra-Red (NIR) coupled with image processing and inter-image operations to differentiate human tissue components.

Near-infrared spectroscopic tissue imaging for medical applications

DOEpatents

Demos, Stavros [Livermore, CA; Staggs, Michael C [Tracy, CA

2006-12-12

Near infrared imaging using elastic light scattering and tissue autofluorescence are explored for medical applications. The approach involves imaging using cross-polarized elastic light scattering and tissue autofluorescence in the Near Infra-Red (NIR) coupled with image processing and inter-image operations to differentiate human tissue components.

Discriminative feature representation: an effective postprocessing solution to low dose CT imaging

NASA Astrophysics Data System (ADS)

Chen, Yang; Liu, Jin; Hu, Yining; Yang, Jian; Shi, Luyao; Shu, Huazhong; Gui, Zhiguo; Coatrieux, Gouenou; Luo, Limin

2017-03-01

This paper proposes a concise and effective approach termed discriminative feature representation (DFR) for low dose computerized tomography (LDCT) image processing, which is currently a challenging problem in medical imaging field. This DFR method assumes LDCT images as the superposition of desirable high dose CT (HDCT) 3D features and undesirable noise-artifact 3D features (the combined term of noise and artifact features induced by low dose scan protocols), and the decomposed HDCT features are used to provide the processed LDCT images with higher quality. The target HDCT features are solved via the DFR algorithm using a featured dictionary composed by atoms representing HDCT features and noise-artifact features. In this study, the featured dictionary is efficiently built using physical phantom images collected from the same CT scanner as the target clinical LDCT images to process. The proposed DFR method also has good robustness in parameter setting for different CT scanner types. This DFR method can be directly applied to process DICOM formatted LDCT images, and has good applicability to current CT systems. Comparative experiments with abdomen LDCT data validate the good performance of the proposed approach. This research was supported by National Natural Science Foundation under grants (81370040, 81530060), the Fundamental Research Funds for the Central Universities, and the Qing Lan Project in Jiangsu Province.

Non-rigid multi-frame registration of cell nuclei in live cell fluorescence microscopy image data.

PubMed

Tektonidis, Marco; Kim, Il-Han; Chen, Yi-Chun M; Eils, Roland; Spector, David L; Rohr, Karl

2015-01-01

The analysis of the motion of subcellular particles in live cell microscopy images is essential for understanding biological processes within cells. For accurate quantification of the particle motion, compensation of the motion and deformation of the cell nucleus is required. We introduce a non-rigid multi-frame registration approach for live cell fluorescence microscopy image data. Compared to existing approaches using pairwise registration, our approach exploits information from multiple consecutive images simultaneously to improve the registration accuracy. We present three intensity-based variants of the multi-frame registration approach and we investigate two different temporal weighting schemes. The approach has been successfully applied to synthetic and live cell microscopy image sequences, and an experimental comparison with non-rigid pairwise registration has been carried out. Copyright © 2014 Elsevier B.V. All rights reserved.

Analysis of x-ray tomography data of an extruded low density styrenic foam: an image analysis study

NASA Astrophysics Data System (ADS)

Lin, Jui-Ching; Heeschen, William

2016-10-01

Extruded styrenic foams are low density foams that are widely used for thermal insulation. It is difficult to precisely characterize the structure of the cells in low density foams by traditional cross-section viewing due to the frailty of the walls of the cells. X-ray computed tomography (CT) is a non-destructive, three dimensional structure characterization technique that has great potential for structure characterization of styrenic foams. Unfortunately the intrinsic artifacts of the data and the artifacts generated during image reconstruction are often comparable in size and shape to the thin walls of the foam, making robust and reliable analysis of cell sizes challenging. We explored three different image processing methods to clean up artifacts in the reconstructed images, thus allowing quantitative three dimensional determination of cell size in a low density styrenic foam. Three image processing approaches - an intensity based approach, an intensity variance based approach, and a machine learning based approach - are explored in this study, and the machine learning image feature classification method was shown to be the best. Individual cells are segmented within the images after the images were cleaned up using the three different methods and the cell sizes are measured and compared in the study. Although the collected data with the image analysis methods together did not yield enough measurements for a good statistic of the measurement of cell sizes, the problem can be resolved by measuring multiple samples or increasing imaging field of view.

Functional Imaging Biomarkers: Potential to Guide an Individualised Approach to Radiotherapy.

PubMed

Prestwich, R J D; Vaidyanathan, S; Scarsbrook, A F

2015-10-01

The identification of robust prognostic and predictive biomarkers would transform the ability to implement an individualised approach to radiotherapy. In this regard, there has been a surge of interest in the use of functional imaging to assess key underlying biological processes within tumours and their response to therapy. Importantly, functional imaging biomarkers hold the potential to evaluate tumour heterogeneity/biology both spatially and temporally. An ever-increasing range of functional imaging techniques is now available primarily involving positron emission tomography and magnetic resonance imaging. Small-scale studies across multiple tumour types have consistently been able to correlate changes in functional imaging parameters during radiotherapy with disease outcomes. Considerable challenges remain before the implementation of functional imaging biomarkers into routine clinical practice, including the inherent temporal variability of biological processes within tumours, reproducibility of imaging, determination of optimal imaging technique/combinations, timing during treatment and design of appropriate validation studies. Copyright © 2015 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

Restoration of color in a remote sensing image and its quality evaluation

NASA Astrophysics Data System (ADS)

Zhang, Zuxun; Li, Zhijiang; Zhang, Jianqing; Wang, Zhihe

2003-09-01

This paper is focused on the restoration of color remote sensing (including airborne photo). A complete approach is recommended. It propose that two main aspects should be concerned in restoring a remote sensing image, that are restoration of space information, restoration of photometric information. In this proposal, the restoration of space information can be performed by making the modulation transfer function (MTF) as degradation function, in which the MTF is obtained by measuring the edge curve of origin image. The restoration of photometric information can be performed by improved local maximum entropy algorithm. What's more, a valid approach in processing color remote sensing image is recommended. That is splits the color remote sensing image into three monochromatic images which corresponding three visible light bands and synthesizes the three images after being processed separately with psychological color vision restriction. Finally, three novel evaluation variables are obtained based on image restoration to evaluate the image restoration quality in space restoration quality and photometric restoration quality. An evaluation is provided at last.

Three-dimensional image contrast using biospeckle

NASA Astrophysics Data System (ADS)

Godinho, Robson Pierangeli; Braga, Roberto A., Jr.

2010-09-01

The biospeckle laser (BSL) has been applied in many areas of knowledge and a variety of approaches has been presented to address the best results in biological and non-biological samples, in fast or slow activities, or else in defined flow of materials or in random activities. The methodologies accounted in the literature consider the apparatus used in the image assembling and the way the collected data is processed. The image processing steps presents in turn a variety of procedures with first or second order statistics analysis, and as well with different sizes of data collected. One way to access the biospeckle in defined flow, such as in capillary blood flow in alive animals, was the adoption of the image contrast technique which uses only one image from the illuminated sample. That approach presents some problems related to the resolution of the image, which is reduced during the image contrast processing. In order to help the visualization of the low resolution image formed by the contrast technique, this work presents the three-dimensional procedure as a reliable alternative to enhance the final image. The work based on a parallel processing, with the generation of a virtual map of amplitudes, and maintaining the quasi-online characteristic of the contrast technique. Therefore, it was possible to generate in the same display the observed material, the image contrast result and in addiction the three-dimensional image with adjustable options of rotation. The platform also offers to the user the possibility to access the 3D image offline.

Dual Systems Competence [Image Omitted] Procedural Processing: A Relational Developmental Systems Approach to Reasoning

ERIC Educational Resources Information Center

Ricco, Robert B.; Overton, Willis F.

2011-01-01

Many current psychological models of reasoning minimize the role of deductive processes in human thought. In the present paper, we argue that deduction is an important part of ordinary cognition and we propose that a dual systems Competence [image omitted] Procedural processing model conceptualized within relational developmental systems theory…

A smart technique for attendance system to recognize faces through parallelism

NASA Astrophysics Data System (ADS)

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

2017-11-01

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

Predictive images of postoperative levator resection outcome using image processing software.

PubMed

Mawatari, Yuki; Fukushima, Mikiko

2016-01-01

This study aims to evaluate the efficacy of processed images to predict postoperative appearance following levator resection. Analysis involved 109 eyes from 65 patients with blepharoptosis who underwent advancement of levator aponeurosis and Müller's muscle complex (levator resection). Predictive images were prepared from preoperative photographs using the image processing software (Adobe Photoshop ® ). Images of selected eyes were digitally enlarged in an appropriate manner and shown to patients prior to surgery. Approximately 1 month postoperatively, we surveyed our patients using questionnaires. Fifty-six patients (89.2%) were satisfied with their postoperative appearances, and 55 patients (84.8%) positively responded to the usefulness of processed images to predict postoperative appearance. Showing processed images that predict postoperative appearance to patients prior to blepharoptosis surgery can be useful for those patients concerned with their postoperative appearance. This approach may serve as a useful tool to simulate blepharoptosis surgery.

Predictive images of postoperative levator resection outcome using image processing software

PubMed Central

Mawatari, Yuki; Fukushima, Mikiko

2016-01-01

Purpose This study aims to evaluate the efficacy of processed images to predict postoperative appearance following levator resection. Methods Analysis involved 109 eyes from 65 patients with blepharoptosis who underwent advancement of levator aponeurosis and Müller’s muscle complex (levator resection). Predictive images were prepared from preoperative photographs using the image processing software (Adobe Photoshop®). Images of selected eyes were digitally enlarged in an appropriate manner and shown to patients prior to surgery. Results Approximately 1 month postoperatively, we surveyed our patients using questionnaires. Fifty-six patients (89.2%) were satisfied with their postoperative appearances, and 55 patients (84.8%) positively responded to the usefulness of processed images to predict postoperative appearance. Conclusion Showing processed images that predict postoperative appearance to patients prior to blepharoptosis surgery can be useful for those patients concerned with their postoperative appearance. This approach may serve as a useful tool to simulate blepharoptosis surgery. PMID:27757008

Real-time interactive virtual tour on the World Wide Web (WWW)

NASA Astrophysics Data System (ADS)

Yoon, Sanghyuk; Chen, Hai-jung; Hsu, Tom; Yoon, Ilmi

2003-12-01

Web-based Virtual Tour has become a desirable and demanded application, yet challenging due to the nature of web application's running environment such as limited bandwidth and no guarantee of high computation power on the client side. Image-based rendering approach has attractive advantages over traditional 3D rendering approach in such Web Applications. Traditional approach, such as VRML, requires labor-intensive 3D modeling process, high bandwidth and computation power especially for photo-realistic virtual scenes. QuickTime VR and IPIX as examples of image-based approach, use panoramic photos and the virtual scenes that can be generated from photos directly skipping the modeling process. But, these image-based approaches may require special cameras or effort to take panoramic views and provide only one fixed-point look-around and zooming in-out rather than 'walk around', that is a very important feature to provide immersive experience to virtual tourists. The Web-based Virtual Tour using Tour into the Picture employs pseudo 3D geometry with image-based rendering approach to provide viewers with immersive experience of walking around the virtual space with several snap shots of conventional photos.

An incompressible fluid flow model with mutual information for MR image registration

NASA Astrophysics Data System (ADS)

Tsai, Leo; Chang, Herng-Hua

2013-03-01

Image registration is one of the fundamental and essential tasks within image processing. It is a process of determining the correspondence between structures in two images, which are called the template image and the reference image, respectively. The challenge of registration is to find an optimal geometric transformation between corresponding image data. This paper develops a new MR image registration algorithm that uses a closed incompressible viscous fluid model associated with mutual information. In our approach, we treat the image pixels as the fluid elements of a viscous fluid flow governed by the nonlinear Navier-Stokes partial differential equation (PDE). We replace the pressure term with the body force mainly used to guide the transformation with a weighting coefficient, which is expressed by the mutual information between the template and reference images. To solve this modified Navier-Stokes PDE, we adopted the fast numerical techniques proposed by Seibold1. The registration process of updating the body force, the velocity and deformation fields is repeated until the mutual information weight reaches a prescribed threshold. We applied our approach to the BrainWeb and real MR images. As consistent with the theory of the proposed fluid model, we found that our method accurately transformed the template images into the reference images based on the intensity flow. Experimental results indicate that our method is of potential in a wide variety of medical image registration applications.

Current Trends in Image Assessment. Working Paper Series.

ERIC Educational Resources Information Center

Fellers, John

Image assessment in higher education and procedures for conducting image assessments are discussed. Image assessment is the process of finding out what others think about an organization. It is proposed that when image assessments are approached objectively, the results can help determine constituent needs, anticipate vocational trends, survey…

Automatic image enhancement based on multi-scale image decomposition

NASA Astrophysics Data System (ADS)

Feng, Lu; Wu, Zhuangzhi; Pei, Luo; Long, Xiong

2014-01-01

In image processing and computational photography, automatic image enhancement is one of the long-range objectives. Recently the automatic image enhancement methods not only take account of the globe semantics, like correct color hue and brightness imbalances, but also the local content of the image, such as human face and sky of landscape. In this paper we describe a new scheme for automatic image enhancement that considers both global semantics and local content of image. Our automatic image enhancement method employs the multi-scale edge-aware image decomposition approach to detect the underexposure regions and enhance the detail of the salient content. The experiment results demonstrate the effectiveness of our approach compared to existing automatic enhancement methods.

A concept-based interactive biomedical image retrieval approach using visualness and spatial information

NASA Astrophysics Data System (ADS)

Rahman, Md M.; Antani, Sameer K.; Demner-Fushman, Dina; Thoma, George R.

2015-03-01

This paper presents a novel approach to biomedical image retrieval by mapping image regions to local concepts and represent images in a weighted entropy-based concept feature space. The term concept refers to perceptually distinguishable visual patches that are identified locally in image regions and can be mapped to a glossary of imaging terms. Further, the visual significance (e.g., visualness) of concepts is measured as Shannon entropy of pixel values in image patches and is used to refine the feature vector. Moreover, the system can assist user in interactively select a Region-Of-Interest (ROI) and search for similar image ROIs. Further, a spatial verification step is used as a post-processing step to improve retrieval results based on location information. The hypothesis that such approaches would improve biomedical image retrieval, is validated through experiments on a data set of 450 lung CT images extracted from journal articles from four different collections.

New Martian satellite search

NASA Technical Reports Server (NTRS)

1983-01-01

The approach pictures taken by the Viking 1 and Viking 2 spacecrafts two days before their Mars orbital insertion maneuvers were analyzed in order to search for new satellites within the orbit of Phobos. To accomplish this task, search procedure and analysis strategy were formulated, developed and executed using the substantial image processing capabilities of the Image Processing Laboratory at the Jet Propulsion Laboratory. The development of these new search capabilities should prove to be valuable to NASA in processing of image data obtained from other spacecraft missions. The result of applying the search procedures to the Viking approach pictures was as follows: no new satellites of comparable size (approx. 20 km) and brightness to Phobos or Demios were detected within the orbit of Phobos.

Parallel exploitation of a spatial-spectral classification approach for hyperspectral images on RVC-CAL

NASA Astrophysics Data System (ADS)

Lazcano, R.; Madroñal, D.; Fabelo, H.; Ortega, S.; Salvador, R.; Callicó, G. M.; Juárez, E.; Sanz, C.

2017-10-01

Hyperspectral Imaging (HI) assembles high resolution spectral information from hundreds of narrow bands across the electromagnetic spectrum, thus generating 3D data cubes in which each pixel gathers the spectral information of the reflectance of every spatial pixel. As a result, each image is composed of large volumes of data, which turns its processing into a challenge, as performance requirements have been continuously tightened. For instance, new HI applications demand real-time responses. Hence, parallel processing becomes a necessity to achieve this requirement, so the intrinsic parallelism of the algorithms must be exploited. In this paper, a spatial-spectral classification approach has been implemented using a dataflow language known as RVCCAL. This language represents a system as a set of functional units, and its main advantage is that it simplifies the parallelization process by mapping the different blocks over different processing units. The spatial-spectral classification approach aims at refining the classification results previously obtained by using a K-Nearest Neighbors (KNN) filtering process, in which both the pixel spectral value and the spatial coordinates are considered. To do so, KNN needs two inputs: a one-band representation of the hyperspectral image and the classification results provided by a pixel-wise classifier. Thus, spatial-spectral classification algorithm is divided into three different stages: a Principal Component Analysis (PCA) algorithm for computing the one-band representation of the image, a Support Vector Machine (SVM) classifier, and the KNN-based filtering algorithm. The parallelization of these algorithms shows promising results in terms of computational time, as the mapping of them over different cores presents a speedup of 2.69x when using 3 cores. Consequently, experimental results demonstrate that real-time processing of hyperspectral images is achievable.

Theoretical and Empirical Comparison of Big Data Image Processing with Apache Hadoop and Sun Grid Engine

PubMed Central

Bao, Shunxing; Weitendorf, Frederick D.; Plassard, Andrew J.; Huo, Yuankai; Gokhale, Aniruddha; Landman, Bennett A.

2016-01-01

The field of big data is generally concerned with the scale of processing at which traditional computational paradigms break down. In medical imaging, traditional large scale processing uses a cluster computer that combines a group of workstation nodes into a functional unit that is controlled by a job scheduler. Typically, a shared-storage network file system (NFS) is used to host imaging data. However, data transfer from storage to processing nodes can saturate network bandwidth when data is frequently uploaded/retrieved from the NFS, e.g., “short” processing times and/or “large” datasets. Recently, an alternative approach using Hadoop and HBase was presented for medical imaging to enable co-location of data storage and computation while minimizing data transfer. The benefits of using such a framework must be formally evaluated against a traditional approach to characterize the point at which simply “large scale” processing transitions into “big data” and necessitates alternative computational frameworks. The proposed Hadoop system was implemented on a production lab-cluster alongside a standard Sun Grid Engine (SGE). Theoretical models for wall-clock time and resource time for both approaches are introduced and validated. To provide real example data, three T1 image archives were retrieved from a university secure, shared web database and used to empirically assess computational performance under three configurations of cluster hardware (using 72, 109, or 209 CPU cores) with differing job lengths. Empirical results match the theoretical models. Based on these data, a comparative analysis is presented for when the Hadoop framework will be relevant and non-relevant for medical imaging. PMID:28736473

Novel image processing approach to detect malaria

NASA Astrophysics Data System (ADS)

Mas, David; Ferrer, Belen; Cojoc, Dan; Finaurini, Sara; Mico, Vicente; Garcia, Javier; Zalevsky, Zeev

2015-09-01

In this paper we present a novel image processing algorithm providing good preliminary capabilities for in vitro detection of malaria. The proposed concept is based upon analysis of the temporal variation of each pixel. Changes in dark pixels mean that inter cellular activity happened, indicating the presence of the malaria parasite inside the cell. Preliminary experimental results involving analysis of red blood cells being either healthy or infected with malaria parasites, validated the potential benefit of the proposed numerical approach.

a Metadata Based Approach for Analyzing Uav Datasets for Photogrammetric Applications

NASA Astrophysics Data System (ADS)

Dhanda, A.; Remondino, F.; Santana Quintero, M.

2018-05-01

This paper proposes a methodology for pre-processing and analysing Unmanned Aerial Vehicle (UAV) datasets before photogrammetric processing. In cases where images are gathered without a detailed flight plan and at regular acquisition intervals the datasets can be quite large and be time consuming to process. This paper proposes a method to calculate the image overlap and filter out images to reduce large block sizes and speed up photogrammetric processing. The python-based algorithm that implements this methodology leverages the metadata in each image to determine the end and side overlap of grid-based UAV flights. Utilizing user input, the algorithm filters out images that are unneeded for photogrammetric processing. The result is an algorithm that can speed up photogrammetric processing and provide valuable information to the user about the flight path.

A Bayesian model for highly accelerated phase-contrast MRI.

PubMed

Rich, Adam; Potter, Lee C; Jin, Ning; Ash, Joshua; Simonetti, Orlando P; Ahmad, Rizwan

2016-08-01

Phase-contrast magnetic resonance imaging is a noninvasive tool to assess cardiovascular disease by quantifying blood flow; however, low data acquisition efficiency limits the spatial and temporal resolutions, real-time application, and extensions to four-dimensional flow imaging in clinical settings. We propose a new data processing approach called Reconstructing Velocity Encoded MRI with Approximate message passing aLgorithms (ReVEAL) that accelerates the acquisition by exploiting data structure unique to phase-contrast magnetic resonance imaging. The proposed approach models physical correlations across space, time, and velocity encodings. The proposed Bayesian approach exploits the relationships in both magnitude and phase among velocity encodings. A fast iterative recovery algorithm is introduced based on message passing. For validation, prospectively undersampled data are processed from a pulsatile flow phantom and five healthy volunteers. The proposed approach is in good agreement, quantified by peak velocity and stroke volume (SV), with reference data for acceleration rates R≤10. For SV, Pearson r≥0.99 for phantom imaging (n = 24) and r≥0.96 for prospectively accelerated in vivo imaging (n = 10) for R≤10. The proposed approach enables accurate quantification of blood flow from highly undersampled data. The technique is extensible to four-dimensional flow imaging, where higher acceleration may be possible due to additional redundancy. Magn Reson Med 76:689-701, 2016. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

Neural network and its application to CT imaging

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

Nikravesh, M.; Kovscek, A.R.; Patzek, T.W.

We present an integrated approach to imaging the progress of air displacement by spontaneous imbibition of oil into sandstone. We combine Computerized Tomography (CT) scanning and neural network image processing. The main aspects of our approach are (I) visualization of the distribution of oil and air saturation by CT, (II) interpretation of CT scans using neural networks, and (III) reconstruction of 3-D images of oil saturation from the CT scans with a neural network model. Excellent agreement between the actual images and the neural network predictions is found.

The cognitive structural approach for image restoration

NASA Astrophysics Data System (ADS)

Mardare, Igor; Perju, Veacheslav; Casasent, David

2008-03-01

It is analyzed the important and actual problem of the defective images of scenes restoration. The proposed approach provides restoration of scenes by a system on the basis of human intelligence phenomena reproduction used for restoration-recognition of images. The cognitive models of the restoration process are elaborated. The models are realized by the intellectual processors constructed on the base of neural networks and associative memory using neural network simulator NNToolbox from MATLAB 7.0. The models provides restoration and semantic designing of images of scenes under defective images of the separate objects.

Research on pre-processing of QR Code

NASA Astrophysics Data System (ADS)

Sun, Haixing; Xia, Haojie; Dong, Ning

2013-10-01

QR code encodes many kinds of information because of its advantages: large storage capacity, high reliability, full arrange of utter-high-speed reading, small printing size and high-efficient representation of Chinese characters, etc. In order to obtain the clearer binarization image from complex background, and improve the recognition rate of QR code, this paper researches on pre-processing methods of QR code (Quick Response Code), and shows algorithms and results of image pre-processing for QR code recognition. Improve the conventional method by changing the Souvola's adaptive text recognition method. Additionally, introduce the QR code Extraction which adapts to different image size, flexible image correction approach, and improve the efficiency and accuracy of QR code image processing.

Fast segmentation and high-quality three-dimensional volume mesh creation from medical images for diffuse optical tomography

NASA Astrophysics Data System (ADS)

Jermyn, Michael; Ghadyani, Hamid; Mastanduno, Michael A.; Turner, Wes; Davis, Scott C.; Dehghani, Hamid; Pogue, Brian W.

2013-08-01

Multimodal approaches that combine near-infrared (NIR) and conventional imaging modalities have been shown to improve optical parameter estimation dramatically and thus represent a prevailing trend in NIR imaging. These approaches typically involve applying anatomical templates from magnetic resonance imaging/computed tomography/ultrasound images to guide the recovery of optical parameters. However, merging these data sets using current technology requires multiple software packages, substantial expertise, significant time-commitment, and often results in unacceptably poor mesh quality for optical image reconstruction, a reality that represents a significant roadblock for translational research of multimodal NIR imaging. This work addresses these challenges directly by introducing automated digital imaging and communications in medicine image stack segmentation and a new one-click three-dimensional mesh generator optimized for multimodal NIR imaging, and combining these capabilities into a single software package (available for free download) with a streamlined workflow. Image processing time and mesh quality benchmarks were examined for four common multimodal NIR use-cases (breast, brain, pancreas, and small animal) and were compared to a commercial image processing package. Applying these tools resulted in a fivefold decrease in image processing time and 62% improvement in minimum mesh quality, in the absence of extra mesh postprocessing. These capabilities represent a significant step toward enabling translational multimodal NIR research for both expert and nonexpert users in an open-source platform.

Volumetric segmentation of range images for printed circuit board inspection

NASA Astrophysics Data System (ADS)

Van Dop, Erik R.; Regtien, Paul P. L.

1996-10-01

Conventional computer vision approaches towards object recognition and pose estimation employ 2D grey-value or color imaging. As a consequence these images contain information about projections of a 3D scene only. The subsequent image processing will then be difficult, because the object coordinates are represented with just image coordinates. Only complicated low-level vision modules like depth from stereo or depth from shading can recover some of the surface geometry of the scene. Recent advances in fast range imaging have however paved the way towards 3D computer vision, since range data of the scene can now be obtained with sufficient accuracy and speed for object recognition and pose estimation purposes. This article proposes the coded-light range-imaging method together with superquadric segmentation to approach this task. Superquadric segments are volumetric primitives that describe global object properties with 5 parameters, which provide the main features for object recognition. Besides, the principle axes of a superquadric segment determine the phase of an object in the scene. The volumetric segmentation of a range image can be used to detect missing, false or badly placed components on assembled printed circuit boards. Furthermore, this approach will be useful to recognize and extract valuable or toxic electronic components on printed circuit boards scrap that currently burden the environment during electronic waste processing. Results on synthetic range images with errors constructed according to a verified noise model illustrate the capabilities of this approach.

Mountain building processes in the Central Andes

NASA Technical Reports Server (NTRS)

Bloom, A. L.; Isacks, B. L.

1986-01-01

False color composite images of the Thematic Mapper (TM) bands 5, 4, and 2 were examined to make visual interpretations of geological features. The use of the roam mode of image display with the International Imaging Systems (IIS) System 600 image processing package running on the IIS Model 75 was very useful. Several areas in which good comparisons with ground data existed, were examined in detail. Parallel to the visual approach, image processing methods are being developed which allow the complete use of the seven TM bands. The data was organized into easily accessible files and a visual cataloging of the quads (quarter TM scenes) with preliminary registration with the best available charts for the region. The catalog has proved to be a valuable tool for the rapid scanning of quads for a specific investigation. Integration of the data into a complete approach to the problems of uplift, deformation, and magnetism in relation to the Nazca-South American plate interaction is at an initial stage.

Mountain building processes in the Central Andes

NASA Astrophysics Data System (ADS)

Bloom, A. L.; Isacks, B. L.

False color composite images of the Thematic Mapper (TM) bands 5, 4, and 2 were examined to make visual interpretations of geological features. The use of the roam mode of image display with the International Imaging Systems (IIS) System 600 image processing package running on the IIS Model 75 was very useful. Several areas in which good comparisons with ground data existed, were examined in detail. Parallel to the visual approach, image processing methods are being developed which allow the complete use of the seven TM bands. The data was organized into easily accessible files and a visual cataloging of the quads (quarter TM scenes) with preliminary registration with the best available charts for the region. The catalog has proved to be a valuable tool for the rapid scanning of quads for a specific investigation. Integration of the data into a complete approach to the problems of uplift, deformation, and magnetism in relation to the Nazca-South American plate interaction is at an initial stage.

Real-time image dehazing using local adaptive neighborhoods and dark-channel-prior

NASA Astrophysics Data System (ADS)

Valderrama, Jesus A.; Díaz-Ramírez, Víctor H.; Kober, Vitaly; Hernandez, Enrique

2015-09-01

A real-time algorithm for single image dehazing is presented. The algorithm is based on calculation of local neighborhoods of a hazed image inside a moving window. The local neighborhoods are constructed by computing rank-order statistics. Next the dark-channel-prior approach is applied to the local neighborhoods to estimate the transmission function of the scene. By using the suggested approach there is no need for applying a refining algorithm to the estimated transmission such as the soft matting algorithm. To achieve high-rate signal processing the proposed algorithm is implemented exploiting massive parallelism on a graphics processing unit (GPU). Computer simulation results are carried out to test the performance of the proposed algorithm in terms of dehazing efficiency and speed of processing. These tests are performed using several synthetic and real images. The obtained results are analyzed and compared with those obtained with existing dehazing algorithms.

Towards the Automatic Detection of Pre-Existing Termite Mounds through UAS and Hyperspectral Imagery.

PubMed

Sandino, Juan; Wooler, Adam; Gonzalez, Felipe

2017-09-24

The increased technological developments in Unmanned Aerial Vehicles (UAVs) combined with artificial intelligence and Machine Learning (ML) approaches have opened the possibility of remote sensing of extensive areas of arid lands. In this paper, a novel approach towards the detection of termite mounds with the use of a UAV, hyperspectral imagery, ML and digital image processing is intended. A new pipeline process is proposed to detect termite mounds automatically and to reduce, consequently, detection times. For the classification stage, several ML classification algorithms' outcomes were studied, selecting support vector machines as the best approach for their role in image classification of pre-existing termite mounds. Various test conditions were applied to the proposed algorithm, obtaining an overall accuracy of 68%. Images with satisfactory mound detection proved that the method is "resolution-dependent". These mounds were detected regardless of their rotation and position in the aerial image. However, image distortion reduced the number of detected mounds due to the inclusion of a shape analysis method in the object detection phase, and image resolution is still determinant to obtain accurate results. Hyperspectral imagery demonstrated better capabilities to classify a huge set of materials than implementing traditional segmentation methods on RGB images only.

A soft kinetic data structure for lesion border detection.

PubMed

Kockara, Sinan; Mete, Mutlu; Yip, Vincent; Lee, Brendan; Aydin, Kemal

2010-06-15

The medical imaging and image processing techniques, ranging from microscopic to macroscopic, has become one of the main components of diagnostic procedures to assist dermatologists in their medical decision-making processes. Computer-aided segmentation and border detection on dermoscopic images is one of the core components of diagnostic procedures and therapeutic interventions for skin cancer. Automated assessment tools for dermoscopic images have become an important research field mainly because of inter- and intra-observer variations in human interpretations. In this study, a novel approach-graph spanner-for automatic border detection in dermoscopic images is proposed. In this approach, a proximity graph representation of dermoscopic images in order to detect regions and borders in skin lesion is presented. Graph spanner approach is examined on a set of 100 dermoscopic images whose manually drawn borders by a dermatologist are used as the ground truth. Error rates, false positives and false negatives along with true positives and true negatives are quantified by digitally comparing results with manually determined borders from a dermatologist. The results show that the highest precision and recall rates obtained to determine lesion boundaries are 100%. However, accuracy of assessment averages out at 97.72% and borders errors' mean is 2.28% for whole dataset.

Calibrated Multi-Temporal Edge Images for City Infrastructure Growth Assessment and Prediction

NASA Astrophysics Data System (ADS)

Al-Ruzouq, R.; Shanableh, A.; Boharoon, Z.; Khalil, M.

2018-03-01

Urban Growth or urbanization can be defined as the gradual process of city's population growth and infrastructure development. It is typically demonstrated by the expansion of a city's infrastructure, mainly development of its roads and buildings. Uncontrolled urban Growth in cities has been responsible for several problems that include living environment, drinking water, noise and air pollution, waste management, traffic congestion and hydraulic processes. Accurate identification of urban growth is of great importance for urban planning and water/land management. Recent advances in satellite imagery, in terms of improved spatial and temporal resolutions, allows for efficient identification of change patterns and the prediction of built-up areas. In this study, two approaches were adapted to quantify and assess the pattern of urbanization, in Ajman City at UAE, during the last three decades. The first approach relies on image processing techniques and multi-temporal Landsat satellite images with ground resolution varying between 15 to 60 meters. In this approach, the derived edge images (roads and buildings) were used as the basis of change detection. The second approach relies on digitizing features from high-resolution images captured at different years. The latest approach was adopted, as a reference and ground truth, to calibrate extracted edges from Landsat images. It has been found that urbanized area almost increased by 12 folds during the period 1975-2015 where the growth of buildings and roads were almost parallel until 2005 when the roads spatial expansion witnessed a steep increase due to the vertical expansion of the City. Extracted Edges features, were successfully used for change detection and quantification in term of buildings and roads.

Silhouette-based approach of 3D image reconstruction for automated image acquisition using robotic arm

NASA Astrophysics Data System (ADS)

Azhar, N.; Saad, W. H. M.; Manap, N. A.; Saad, N. M.; Syafeeza, A. R.

2017-06-01

This study presents the approach of 3D image reconstruction using an autonomous robotic arm for the image acquisition process. A low cost of the automated imaging platform is created using a pair of G15 servo motor connected in series to an Arduino UNO as a main microcontroller. Two sets of sequential images were obtained using different projection angle of the camera. The silhouette-based approach is used in this study for 3D reconstruction from the sequential images captured from several different angles of the object. Other than that, an analysis based on the effect of different number of sequential images on the accuracy of 3D model reconstruction was also carried out with a fixed projection angle of the camera. The effecting elements in the 3D reconstruction are discussed and the overall result of the analysis is concluded according to the prototype of imaging platform.

Information Acquisition, Analysis and Integration

DTIC Science & Technology

2016-08-03

of sensing and processing, theory, applications, signal processing, image and video processing, machine learning , technology transfer. 16. SECURITY... learning . 5. Solved elegantly old problems like image and video debluring, intro- ducing new revolutionary approaches. 1 DISTRIBUTION A: Distribution...Polatkan, G. Sapiro, D. Blei, D. B. Dunson, and L. Carin, “ Deep learning with hierarchical convolution factor analysis,” IEEE 6 DISTRIBUTION A

Image gathering, coding, and processing: End-to-end optimization for efficient and robust acquisition of visual information

NASA Technical Reports Server (NTRS)

Huck, Friedrich O.; Fales, Carl L.

1990-01-01

Researchers are concerned with the end-to-end performance of image gathering, coding, and processing. The applications range from high-resolution television to vision-based robotics, wherever the resolution, efficiency and robustness of visual information acquisition and processing are critical. For the presentation at this workshop, it is convenient to divide research activities into the following two overlapping areas: The first is the development of focal-plane processing techniques and technology to effectively combine image gathering with coding, with an emphasis on low-level vision processing akin to the retinal processing in human vision. The approach includes the familiar Laplacian pyramid, the new intensity-dependent spatial summation, and parallel sensing/processing networks. Three-dimensional image gathering is attained by combining laser ranging with sensor-array imaging. The second is the rigorous extension of information theory and optimal filtering to visual information acquisition and processing. The goal is to provide a comprehensive methodology for quantitatively assessing the end-to-end performance of image gathering, coding, and processing.

Investigations of image fusion

NASA Astrophysics Data System (ADS)

Zhang, Zhong

1999-12-01

The objective of image fusion is to combine information from multiple images of the same scene. The result of image fusion is a single image which is more suitable for the purpose of human visual perception or further image processing tasks. In this thesis, a region-based fusion algorithm using the wavelet transform is proposed. The identification of important features in each image, such as edges and regions of interest, are used to guide the fusion process. The idea of multiscale grouping is also introduced and a generic image fusion framework based on multiscale decomposition is studied. The framework includes all of the existing multiscale-decomposition- based fusion approaches we found in the literature which did not assume a statistical model for the source images. Comparisons indicate that our framework includes some new approaches which outperform the existing approaches for the cases we consider. Registration must precede our fusion algorithms. So we proposed a hybrid scheme which uses both feature-based and intensity-based methods. The idea of robust estimation of optical flow from time- varying images is employed with a coarse-to-fine multi- resolution approach and feature-based registration to overcome some of the limitations of the intensity-based schemes. Experiments show that this approach is robust and efficient. Assessing image fusion performance in a real application is a complicated issue. In this dissertation, a mixture probability density function model is used in conjunction with the Expectation- Maximization algorithm to model histograms of edge intensity. Some new techniques are proposed for estimating the quality of a noisy image of a natural scene. Such quality measures can be used to guide the fusion. Finally, we study fusion of images obtained from several copies of a new type of camera developed for video surveillance. Our techniques increase the capability and reliability of the surveillance system and provide an easy way to obtain 3-D information of objects in the space monitored by the system.

Partial differential equation-based approach for empirical mode decomposition: application on image analysis.

PubMed

Niang, Oumar; Thioune, Abdoulaye; El Gueirea, Mouhamed Cheikh; Deléchelle, Eric; Lemoine, Jacques

2012-09-01

The major problem with the empirical mode decomposition (EMD) algorithm is its lack of a theoretical framework. So, it is difficult to characterize and evaluate this approach. In this paper, we propose, in the 2-D case, the use of an alternative implementation to the algorithmic definition of the so-called "sifting process" used in the original Huang's EMD method. This approach, especially based on partial differential equations (PDEs), was presented by Niang in previous works, in 2005 and 2007, and relies on a nonlinear diffusion-based filtering process to solve the mean envelope estimation problem. In the 1-D case, the efficiency of the PDE-based method, compared to the original EMD algorithmic version, was also illustrated in a recent paper. Recently, several 2-D extensions of the EMD method have been proposed. Despite some effort, 2-D versions for EMD appear poorly performing and are very time consuming. So in this paper, an extension to the 2-D space of the PDE-based approach is extensively described. This approach has been applied in cases of both signal and image decomposition. The obtained results confirm the usefulness of the new PDE-based sifting process for the decomposition of various kinds of data. Some results have been provided in the case of image decomposition. The effectiveness of the approach encourages its use in a number of signal and image applications such as denoising, detrending, or texture analysis.

A sparse representation-based approach for copy-move image forgery detection in smooth regions

NASA Astrophysics Data System (ADS)

Abdessamad, Jalila; ElAdel, Asma; Zaied, Mourad

2017-03-01

Copy-move image forgery is the act of cloning a restricted region in the image and pasting it once or multiple times within that same image. This procedure intends to cover a certain feature, probably a person or an object, in the processed image or emphasize it through duplication. Consequences of this malicious operation can be unexpectedly harmful. Hence, the present paper proposes a new approach that automatically detects Copy-move Forgery (CMF). In particular, this work broaches a widely common open issue in CMF research literature that is detecting CMF within smooth areas. Indeed, the proposed approach represents the image blocks as a sparse linear combination of pre-learned bases (a mixture of texture and color-wise small patches) which allows a robust description of smooth patches. The reported experimental results demonstrate the effectiveness of the proposed approach in identifying the forged regions in CM attacks.

Spatial vision processes: From the optical image to the symbolic structures of contour information

NASA Technical Reports Server (NTRS)

Jobson, Daniel J.

1988-01-01

The significance of machine and natural vision is discussed together with the need for a general approach to image acquisition and processing aimed at recognition. An exploratory scheme is proposed which encompasses the definition of spatial primitives, intrinsic image properties and sampling, 2-D edge detection at the smallest scale, the construction of spatial primitives from edges, and the isolation of contour information from textural information. Concepts drawn from or suggested by natural vision at both perceptual and physiological levels are relied upon heavily to guide the development of the overall scheme. The scheme is intended to provide a larger context in which to place the emerging technology of detector array focal-plane processors. The approach differs from many recent efforts in edge detection and image coding by emphasizing smallest scale edge detection as a foundation for multi-scale symbolic processing while diminishing somewhat the importance of image convolutions with multi-scale edge operators. Cursory treatments of information theory illustrate that the direct application of this theory to structural information in images could not be realized.

Efficiency analysis for 3D filtering of multichannel images

NASA Astrophysics Data System (ADS)

Kozhemiakin, Ruslan A.; Rubel, Oleksii; Abramov, Sergey K.; Lukin, Vladimir V.; Vozel, Benoit; Chehdi, Kacem

2016-10-01

Modern remote sensing systems basically acquire images that are multichannel (dual- or multi-polarization, multi- and hyperspectral) where noise, usually with different characteristics, is present in all components. If noise is intensive, it is desirable to remove (suppress) it before applying methods of image classification, interpreting, and information extraction. This can be done using one of two approaches - by component-wise or by vectorial (3D) filtering. The second approach has shown itself to have higher efficiency if there is essential correlation between multichannel image components as this often happens for multichannel remote sensing data of different origin. Within the class of 3D filtering techniques, there are many possibilities and variations. In this paper, we consider filtering based on discrete cosine transform (DCT) and pay attention to two aspects of processing. First, we study in detail what changes in DCT coefficient statistics take place for 3D denoising compared to component-wise processing. Second, we analyze how selection of component images united into 3D data array influences efficiency of filtering and can the observed tendencies be exploited in processing of images with rather large number of channels.

Training system for digital mammographic diagnoses of breast cancer

NASA Astrophysics Data System (ADS)

Thomaz, R. L.; Nirschl Crozara, M. G.; Patrocinio, A. C.

2013-03-01

As the technology evolves, the analog mammography systems are being replaced by digital systems. The digital system uses video monitors as the display of mammographic images instead of the previously used screen-film and negatoscope for analog images. The change in the way of visualizing mammographic images may require a different approach for training the health care professionals in diagnosing the breast cancer with digital mammography. Thus, this paper presents a computational approach to train the health care professionals providing a smooth transition between analog and digital technology also training to use the advantages of digital image processing tools to diagnose the breast cancer. This computational approach consists of a software where is possible to open, process and diagnose a full mammogram case from a database, which has the digital images of each of the mammographic views. The software communicates with a gold standard digital mammogram cases database. This database contains the digital images in Tagged Image File Format (TIFF) and the respective diagnoses according to BI-RADSTM, these files are read by software and shown to the user as needed. There are also some digital image processing tools that can be used to provide better visualization of each single image. The software was built based on a minimalist and a user-friendly interface concept that might help in the smooth transition. It also has an interface for inputting diagnoses from the professional being trained, providing a result feedback. This system has been already completed, but hasn't been applied to any professional training yet.

Intellicount: High-Throughput Quantification of Fluorescent Synaptic Protein Puncta by Machine Learning

PubMed Central

Fantuzzo, J. A.; Mirabella, V. R.; Zahn, J. D.

2017-01-01

Abstract Synapse formation analyses can be performed by imaging and quantifying fluorescent signals of synaptic markers. Traditionally, these analyses are done using simple or multiple thresholding and segmentation approaches or by labor-intensive manual analysis by a human observer. Here, we describe Intellicount, a high-throughput, fully-automated synapse quantification program which applies a novel machine learning (ML)-based image processing algorithm to systematically improve region of interest (ROI) identification over simple thresholding techniques. Through processing large datasets from both human and mouse neurons, we demonstrate that this approach allows image processing to proceed independently of carefully set thresholds, thus reducing the need for human intervention. As a result, this method can efficiently and accurately process large image datasets with minimal interaction by the experimenter, making it less prone to bias and less liable to human error. Furthermore, Intellicount is integrated into an intuitive graphical user interface (GUI) that provides a set of valuable features, including automated and multifunctional figure generation, routine statistical analyses, and the ability to run full datasets through nested folders, greatly expediting the data analysis process. PMID:29218324

Automatic bladder segmentation from CT images using deep CNN and 3D fully connected CRF-RNN.

PubMed

Xu, Xuanang; Zhou, Fugen; Liu, Bo

2018-03-19

Automatic approach for bladder segmentation from computed tomography (CT) images is highly desirable in clinical practice. It is a challenging task since the bladder usually suffers large variations of appearance and low soft-tissue contrast in CT images. In this study, we present a deep learning-based approach which involves a convolutional neural network (CNN) and a 3D fully connected conditional random fields recurrent neural network (CRF-RNN) to perform accurate bladder segmentation. We also propose a novel preprocessing method, called dual-channel preprocessing, to further advance the segmentation performance of our approach. The presented approach works as following: first, we apply our proposed preprocessing method on the input CT image and obtain a dual-channel image which consists of the CT image and an enhanced bladder density map. Second, we exploit a CNN to predict a coarse voxel-wise bladder score map on this dual-channel image. Finally, a 3D fully connected CRF-RNN refines the coarse bladder score map and produce final fine-localized segmentation result. We compare our approach to the state-of-the-art V-net on a clinical dataset. Results show that our approach achieves superior segmentation accuracy, outperforming the V-net by a significant margin. The Dice Similarity Coefficient of our approach (92.24%) is 8.12% higher than that of the V-net. Moreover, the bladder probability maps performed by our approach present sharper boundaries and more accurate localizations compared with that of the V-net. Our approach achieves higher segmentation accuracy than the state-of-the-art method on clinical data. Both the dual-channel processing and the 3D fully connected CRF-RNN contribute to this improvement. The united deep network composed of the CNN and 3D CRF-RNN also outperforms a system where the CRF model acts as a post-processing method disconnected from the CNN.

Innovative visualization and segmentation approaches for telemedicine

NASA Astrophysics Data System (ADS)

Nguyen, D.; Roehrig, Hans; Borders, Marisa H.; Fitzpatrick, Kimberly A.; Roveda, Janet

2014-09-01

In health care applications, we obtain, manage, store and communicate using high quality, large volume of image data through integrated devices. In this paper we propose several promising methods that can assist physicians in image data process and communication. We design a new semi-automated segmentation approach for radiological images, such as CT and MRI to clearly identify the areas of interest. This approach combines the advantages from both the region-based method and boundary-based methods. It has three key steps compose: coarse segmentation by using fuzzy affinity and homogeneity operator, image division and reclassification using the Voronoi Diagram, and refining boundary lines using the level set model.

Examining the Role of the Human Hippocampus in Approach-Avoidance Decision Making Using a Novel Conflict Paradigm and Multivariate Functional Magnetic Resonance Imaging.

PubMed

O'Neil, Edward B; Newsome, Rachel N; Li, Iris H N; Thavabalasingam, Sathesan; Ito, Rutsuko; Lee, Andy C H

2015-11-11

Rodent models of anxiety have implicated the ventral hippocampus in approach-avoidance conflict processing. Few studies have, however, examined whether the human hippocampus plays a similar role. We developed a novel decision-making paradigm to examine neural activity when participants made approach/avoidance decisions under conditions of high or absent approach-avoidance conflict. Critically, our task required participants to learn the associated reward/punishment values of previously neutral stimuli and controlled for mnemonic and spatial processing demands, both important issues given approach-avoidance behavior in humans is less tied to predation and foraging compared to rodents. Participants played a points-based game where they first attempted to maximize their score by determining which of a series of previously neutral image pairs should be approached or avoided. During functional magnetic resonance imaging, participants were then presented with novel pairings of these images. These pairings consisted of images of congruent or opposing learned valences, the latter creating conditions of high approach-avoidance conflict. A data-driven partial least squares multivariate analysis revealed two reliable patterns of activity, each revealing differential activity in the anterior hippocampus, the homolog of the rodent ventral hippocampus. The first was associated with greater hippocampal involvement during trials with high as opposed to no approach-avoidance conflict, regardless of approach or avoidance behavior. The second pattern encompassed greater hippocampal activity in a more anterior aspect during approach compared to avoid responses, for conflict and no-conflict conditions. Multivoxel pattern classification analyses yielded converging findings, underlining a role of the anterior hippocampus in approach-avoidance conflict decision making. Approach-avoidance conflict has been linked to anxiety and occurs when a stimulus or situation is associated with reward and punishment. Although rodent work has implicated the hippocampus in approach-avoidance conflict processing, there is limited data on whether this role applies to learned, as opposed to innate, incentive values, and whether the human hippocampus plays a similar role. Using functional neuroimaging with a novel decision-making task that controlled for perceptual and mnemonic processing, we found that the human hippocampus was significantly active when approach-avoidance conflict was present for stimuli with learned incentive values. These findings demonstrate a role for the human hippocampus in approach-avoidance decision making that cannot be explained easily by hippocampal-dependent long-term memory or spatial cognition. Copyright © 2015 the authors 0270-6474/15/3515040-11$15.00/0.

Clinical and mathematical introduction to computer processing of scintigraphic images

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

Goris, M.L.; Briandet, P.A.

The authors state in their preface:''...we believe that there is no book yet available in which computing in nuclear medicine has been approached in a reasonable manner. This book is our attempt to correct the situation.'' The book is divided into four sections: (1) Clinical Applications of Quantitative Scintigraphic Analysis; (2) Mathematical Derivations; (3) Processing Methods of Scintigraphic Images; and (4) The (Computer) System. Section 1 has chapters on quantitative approaches to congenital and acquired heart diseases, nephrology and urology, and pulmonary medicine.

Separation of irradiance and reflectance from observed color images by logarithmical nonlinear diffusion process

NASA Astrophysics Data System (ADS)

Saito, Takahiro; Takahashi, Hiromi; Komatsu, Takashi

2006-02-01

The Retinex theory was first proposed by Land, and deals with separation of irradiance from reflectance in an observed image. The separation problem is an ill-posed problem. Land and others proposed various Retinex separation algorithms. Recently, Kimmel and others proposed a variational framework that unifies the previous Retinex algorithms such as the Poisson-equation-type Retinex algorithms developed by Horn and others, and presented a Retinex separation algorithm with the time-evolution of a linear diffusion process. However, the Kimmel's separation algorithm cannot achieve physically rational separation, if true irradiance varies among color channels. To cope with this problem, we introduce a nonlinear diffusion process into the time-evolution. Moreover, as to its extension to color images, we present two approaches to treat color channels: the independent approach to treat each color channel separately and the collective approach to treat all color channels collectively. The latter approach outperforms the former. Furthermore, we apply our separation algorithm to a high quality chroma key in which before combining a foreground frame and a background frame into an output image a color of each pixel in the foreground frame are spatially adaptively corrected through transformation of the separated irradiance. Experiments demonstrate superiority of our separation algorithm over the Kimmel's separation algorithm.

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.

Cloud Engineering Principles and Technology Enablers for Medical Image Processing-as-a-Service

PubMed Central

Bao, Shunxing; Plassard, Andrew J.; Landman, Bennett A.; Gokhale, Aniruddha

2017-01-01

Traditional in-house, laboratory-based medical imaging studies use hierarchical data structures (e.g., NFS file stores) or databases (e.g., COINS, XNAT) for storage and retrieval. The resulting performance from these approaches is, however, impeded by standard network switches since they can saturate network bandwidth during transfer from storage to processing nodes for even moderate-sized studies. To that end, a cloud-based “medical image processing-as-a-service” offers promise in utilizing the ecosystem of Apache Hadoop, which is a flexible framework providing distributed, scalable, fault tolerant storage and parallel computational modules, and HBase, which is a NoSQL database built atop Hadoop’s distributed file system. Despite this promise, HBase’s load distribution strategy of region split and merge is detrimental to the hierarchical organization of imaging data (e.g., project, subject, session, scan, slice). This paper makes two contributions to address these concerns by describing key cloud engineering principles and technology enhancements we made to the Apache Hadoop ecosystem for medical imaging applications. First, we propose a row-key design for HBase, which is a necessary step that is driven by the hierarchical organization of imaging data. Second, we propose a novel data allocation policy within HBase to strongly enforce collocation of hierarchically related imaging data. The proposed enhancements accelerate data processing by minimizing network usage and localizing processing to machines where the data already exist. Moreover, our approach is amenable to the traditional scan, subject, and project-level analysis procedures, and is compatible with standard command line/scriptable image processing software. Experimental results for an illustrative sample of imaging data reveals that our new HBase policy results in a three-fold time improvement in conversion of classic DICOM to NiFTI file formats when compared with the default HBase region split policy, and nearly a six-fold improvement over a commonly available network file system (NFS) approach even for relatively small file sets. Moreover, file access latency is lower than network attached storage. PMID:28884169

Robust algebraic image enhancement for intelligent control systems

NASA Technical Reports Server (NTRS)

Lerner, Bao-Ting; Morrelli, Michael

1993-01-01

Robust vision capability for intelligent control systems has been an elusive goal in image processing. The computationally intensive techniques a necessary for conventional image processing make real-time applications, such as object tracking and collision avoidance difficult. In order to endow an intelligent control system with the needed vision robustness, an adequate image enhancement subsystem capable of compensating for the wide variety of real-world degradations, must exist between the image capturing and the object recognition subsystems. This enhancement stage must be adaptive and must operate with consistency in the presence of both statistical and shape-based noise. To deal with this problem, we have developed an innovative algebraic approach which provides a sound mathematical framework for image representation and manipulation. Our image model provides a natural platform from which to pursue dynamic scene analysis, and its incorporation into a vision system would serve as the front-end to an intelligent control system. We have developed a unique polynomial representation of gray level imagery and applied this representation to develop polynomial operators on complex gray level scenes. This approach is highly advantageous since polynomials can be manipulated very easily, and are readily understood, thus providing a very convenient environment for image processing. Our model presents a highly structured and compact algebraic representation of grey-level images which can be viewed as fuzzy sets.

Automated detection of changes in sequential color ocular fundus images

NASA Astrophysics Data System (ADS)

Sakuma, Satoshi; Nakanishi, Tadashi; Takahashi, Yasuko; Fujino, Yuichi; Tsubouchi, Tetsuro; Nakanishi, Norimasa

1998-06-01

A recent trend is the automatic screening of color ocular fundus images. The examination of such images is used in the early detection of several adult diseases such as hypertension and diabetes. Since this type of examination is easier than CT, costs less, and has no harmful side effects, it will become a routine medical examination. Normal ocular fundus images are found in more than 90% of all people. To deal with the increasing number of such images, this paper proposes a new approach to process them automatically and accurately. Our approach, based on individual comparison, identifies changes in sequential images: a previously diagnosed normal reference image is compared to a non- diagnosed image.

Dendritic tree extraction from noisy maximum intensity projection images in C. elegans.

PubMed

Greenblum, Ayala; Sznitman, Raphael; Fua, Pascal; Arratia, Paulo E; Oren, Meital; Podbilewicz, Benjamin; Sznitman, Josué

2014-06-12

Maximum Intensity Projections (MIP) of neuronal dendritic trees obtained from confocal microscopy are frequently used to study the relationship between tree morphology and mechanosensory function in the model organism C. elegans. Extracting dendritic trees from noisy images remains however a strenuous process that has traditionally relied on manual approaches. Here, we focus on automated and reliable 2D segmentations of dendritic trees following a statistical learning framework. Our dendritic tree extraction (DTE) method uses small amounts of labelled training data on MIPs to learn noise models of texture-based features from the responses of tree structures and image background. Our strategy lies in evaluating statistical models of noise that account for both the variability generated from the imaging process and from the aggregation of information in the MIP images. These noisy models are then used within a probabilistic, or Bayesian framework to provide a coarse 2D dendritic tree segmentation. Finally, some post-processing is applied to refine the segmentations and provide skeletonized trees using a morphological thinning process. Following a Leave-One-Out Cross Validation (LOOCV) method for an MIP databse with available "ground truth" images, we demonstrate that our approach provides significant improvements in tree-structure segmentations over traditional intensity-based methods. Improvements for MIPs under various imaging conditions are both qualitative and quantitative, as measured from Receiver Operator Characteristic (ROC) curves and the yield and error rates in the final segmentations. In a final step, we demonstrate our DTE approach on previously unseen MIP samples including the extraction of skeletonized structures, and compare our method to a state-of-the art dendritic tree tracing software. Overall, our DTE method allows for robust dendritic tree segmentations in noisy MIPs, outperforming traditional intensity-based methods. Such approach provides a useable segmentation framework, ultimately delivering a speed-up for dendritic tree identification on the user end and a reliable first step towards further morphological characterizations of tree arborization.

Unified modeling language and design of a case-based retrieval system in medical imaging.

PubMed Central

LeBozec, C.; Jaulent, M. C.; Zapletal, E.; Degoulet, P.

1998-01-01

One goal of artificial intelligence research into case-based reasoning (CBR) systems is to develop approaches for designing useful and practical interactive case-based environments. Explaining each step of the design of the case-base and of the retrieval process is critical for the application of case-based systems to the real world. We describe herein our approach to the design of IDEM--Images and Diagnosis from Examples in Medicine--a medical image case-based retrieval system for pathologists. Our approach is based on the expressiveness of an object-oriented modeling language standard: the Unified Modeling Language (UML). We created a set of diagrams in UML notation illustrating the steps of the CBR methodology we used. The key aspect of this approach was selecting the relevant objects of the system according to user requirements and making visualization of cases and of the components of the case retrieval process. Further evaluation of the expressiveness of the design document is required but UML seems to be a promising formalism, improving the communication between the developers and users. Images Figure 6 Figure 7 PMID:9929346

Automatic Detection of Optic Disc in Retinal Image by Using Keypoint Detection, Texture Analysis, and Visual Dictionary Techniques

PubMed Central

Bayır, Şafak

2016-01-01

With the advances in the computer field, methods and techniques in automatic image processing and analysis provide the opportunity to detect automatically the change and degeneration in retinal images. Localization of the optic disc is extremely important for determining the hard exudate lesions or neovascularization, which is the later phase of diabetic retinopathy, in computer aided eye disease diagnosis systems. Whereas optic disc detection is fairly an easy process in normal retinal images, detecting this region in the retinal image which is diabetic retinopathy disease may be difficult. Sometimes information related to optic disc and hard exudate information may be the same in terms of machine learning. We presented a novel approach for efficient and accurate localization of optic disc in retinal images having noise and other lesions. This approach is comprised of five main steps which are image processing, keypoint extraction, texture analysis, visual dictionary, and classifier techniques. We tested our proposed technique on 3 public datasets and obtained quantitative results. Experimental results show that an average optic disc detection accuracy of 94.38%, 95.00%, and 90.00% is achieved, respectively, on the following public datasets: DIARETDB1, DRIVE, and ROC. PMID:27110272

A global "imaging'' view on systems approaches in immunology.

PubMed

Ludewig, Burkhard; Stein, Jens V; Sharpe, James; Cervantes-Barragan, Luisa; Thiel, Volker; Bocharov, Gennady

2012-12-01

The immune system exhibits an enormous complexity. High throughput methods such as the "-omic'' technologies generate vast amounts of data that facilitate dissection of immunological processes at ever finer resolution. Using high-resolution data-driven systems analysis, causal relationships between complex molecular processes and particular immunological phenotypes can be constructed. However, processes in tissues, organs, and the organism itself (so-called higher level processes) also control and regulate the molecular (lower level) processes. Reverse systems engineering approaches, which focus on the examination of the structure, dynamics and control of the immune system, can help to understand the construction principles of the immune system. Such integrative mechanistic models can properly describe, explain, and predict the behavior of the immune system in health and disease by combining both higher and lower level processes. Moving from molecular and cellular levels to a multiscale systems understanding requires the development of methodologies that integrate data from different biological levels into multiscale mechanistic models. In particular, 3D imaging techniques and 4D modeling of the spatiotemporal dynamics of immune processes within lymphoid tissues are central for such integrative approaches. Both dynamic and global organ imaging technologies will be instrumental in facilitating comprehensive multiscale systems immunology analyses as discussed in this review. © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

3D-resolved fluorescence and phosphorescence lifetime imaging using temporal focusing wide-field two-photon excitation

PubMed Central

Choi, Heejin; Tzeranis, Dimitrios S.; Cha, Jae Won; Clémenceau, Philippe; de Jong, Sander J. G.; van Geest, Lambertus K.; Moon, Joong Ho; Yannas, Ioannis V.; So, Peter T. C.

2012-01-01

Fluorescence and phosphorescence lifetime imaging are powerful techniques for studying intracellular protein interactions and for diagnosing tissue pathophysiology. While lifetime-resolved microscopy has long been in the repertoire of the biophotonics community, current implementations fall short in terms of simultaneously providing 3D resolution, high throughput, and good tissue penetration. This report describes a new highly efficient lifetime-resolved imaging method that combines temporal focusing wide-field multiphoton excitation and simultaneous acquisition of lifetime information in frequency domain using a nanosecond gated imager from a 3D-resolved plane. This approach is scalable allowing fast volumetric imaging limited only by the available laser peak power. The accuracy and performance of the proposed method is demonstrated in several imaging studies important for understanding peripheral nerve regeneration processes. Most importantly, the parallelism of this approach may enhance the imaging speed of long lifetime processes such as phosphorescence by several orders of magnitude. PMID:23187477

Information theoretic analysis of linear shift-invariant edge-detection operators

NASA Astrophysics Data System (ADS)

Jiang, Bo; Rahman, Zia-ur

2012-06-01

Generally, the designs of digital image processing algorithms and image gathering devices remain separate. Consequently, the performance of digital image processing algorithms is evaluated without taking into account the influences by the image gathering process. However, experiments show that the image gathering process has a profound impact on the performance of digital image processing and the quality of the resulting images. Huck et al. proposed one definitive theoretic analysis of visual communication channels, where the different parts, such as image gathering, processing, and display, are assessed in an integrated manner using Shannon's information theory. We perform an end-to-end information theory based system analysis to assess linear shift-invariant edge-detection algorithms. We evaluate the performance of the different algorithms as a function of the characteristics of the scene and the parameters, such as sampling, additive noise etc., that define the image gathering system. The edge-detection algorithm is regarded as having high performance only if the information rate from the scene to the edge image approaches its maximum possible. This goal can be achieved only by jointly optimizing all processes. Our information-theoretic assessment provides a new tool that allows us to compare different linear shift-invariant edge detectors in a common environment.

Parametric boundary reconstruction algorithm for industrial CT metrology application.

PubMed

Yin, Zhye; Khare, Kedar; De Man, Bruno

2009-01-01

High-energy X-ray computed tomography (CT) systems have been recently used to produce high-resolution images in various nondestructive testing and evaluation (NDT/NDE) applications. The accuracy of the dimensional information extracted from CT images is rapidly approaching the accuracy achieved with a coordinate measuring machine (CMM), the conventional approach to acquire the metrology information directly. On the other hand, CT systems generate the sinogram which is transformed mathematically to the pixel-based images. The dimensional information of the scanned object is extracted later by performing edge detection on reconstructed CT images. The dimensional accuracy of this approach is limited by the grid size of the pixel-based representation of CT images since the edge detection is performed on the pixel grid. Moreover, reconstructed CT images usually display various artifacts due to the underlying physical process and resulting object boundaries from the edge detection fail to represent the true boundaries of the scanned object. In this paper, a novel algorithm to reconstruct the boundaries of an object with uniform material composition and uniform density is presented. There are three major benefits in the proposed approach. First, since the boundary parameters are reconstructed instead of image pixels, the complexity of the reconstruction algorithm is significantly reduced. The iterative approach, which can be computationally intensive, will be practical with the parametric boundary reconstruction. Second, the object of interest in metrology can be represented more directly and accurately by the boundary parameters instead of the image pixels. By eliminating the extra edge detection step, the overall dimensional accuracy and process time can be improved. Third, since the parametric reconstruction approach shares the boundary representation with other conventional metrology modalities such as CMM, boundary information from other modalities can be directly incorporated as prior knowledge to improve the convergence of an iterative approach. In this paper, the feasibility of parametric boundary reconstruction algorithm is demonstrated with both simple and complex simulated objects. Finally, the proposed algorithm is applied to the experimental industrial CT system data.

Real-Time Nonlinear Optical Information Processing.

DTIC Science & Technology

1979-06-01

operations aree presented. One approach realizes the halftone method of nonlinear optical processing in real time by replacing the conventional...photographic recording medium with a real-time image transducer. In the second approach halftoning is eliminated and the real-time device is used directly

Unified Digital Image Display And Processing System

NASA Astrophysics Data System (ADS)

Horii, Steven C.; Maguire, Gerald Q.; Noz, Marilyn E.; Schimpf, James H.

1981-11-01

Our institution like many others, is faced with a proliferation of medical imaging techniques. Many of these methods give rise to digital images (e.g. digital radiography, computerized tomography (CT) , nuclear medicine and ultrasound). We feel that a unified, digital system approach to image management (storage, transmission and retrieval), image processing and image display will help in integrating these new modalities into the present diagnostic radiology operations. Future techniques are likely to employ digital images, so such a system could readily be expanded to include other image sources. We presently have the core of such a system. We can both view and process digital nuclear medicine (conventional gamma camera) images, positron emission tomography (PET) and CT images on a single system. Images from our recently installed digital radiographic unit can be added. Our paper describes our present system, explains the rationale for its configuration, and describes the directions in which it will expand.

Heterogeneous Optimization Framework: Reproducible Preprocessing of Multi-Spectral Clinical MRI for Neuro-Oncology Imaging Research.

PubMed

Milchenko, Mikhail; Snyder, Abraham Z; LaMontagne, Pamela; Shimony, Joshua S; Benzinger, Tammie L; Fouke, Sarah Jost; Marcus, Daniel S

2016-07-01

Neuroimaging research often relies on clinically acquired magnetic resonance imaging (MRI) datasets that can originate from multiple institutions. Such datasets are characterized by high heterogeneity of modalities and variability of sequence parameters. This heterogeneity complicates the automation of image processing tasks such as spatial co-registration and physiological or functional image analysis. Given this heterogeneity, conventional processing workflows developed for research purposes are not optimal for clinical data. In this work, we describe an approach called Heterogeneous Optimization Framework (HOF) for developing image analysis pipelines that can handle the high degree of clinical data non-uniformity. HOF provides a set of guidelines for configuration, algorithm development, deployment, interpretation of results and quality control for such pipelines. At each step, we illustrate the HOF approach using the implementation of an automated pipeline for Multimodal Glioma Analysis (MGA) as an example. The MGA pipeline computes tissue diffusion characteristics of diffusion tensor imaging (DTI) acquisitions, hemodynamic characteristics using a perfusion model of susceptibility contrast (DSC) MRI, and spatial cross-modal co-registration of available anatomical, physiological and derived patient images. Developing MGA within HOF enabled the processing of neuro-oncology MR imaging studies to be fully automated. MGA has been successfully used to analyze over 160 clinical tumor studies to date within several research projects. Introduction of the MGA pipeline improved image processing throughput and, most importantly, effectively produced co-registered datasets that were suitable for advanced analysis despite high heterogeneity in acquisition protocols.

Dynamic deformation image de-blurring and image processing for digital imaging correlation measurement

NASA Astrophysics Data System (ADS)

Guo, X.; Li, Y.; Suo, T.; Liu, H.; Zhang, C.

2017-11-01

This paper proposes a method for de-blurring of images captured in the dynamic deformation of materials. De-blurring is achieved based on the dynamic-based approach, which is used to estimate the Point Spread Function (PSF) during the camera exposure window. The deconvolution process involving iterative matrix calculations of pixels, is then performed on the GPU to decrease the time cost. Compared to the Gauss method and the Lucy-Richardson method, it has the best result of the image restoration. The proposed method has been evaluated by using the Hopkinson bar loading system. In comparison to the blurry image, the proposed method has successfully restored the image. It is also demonstrated from image processing applications that the de-blurring method can improve the accuracy and the stability of the digital imaging correlation measurement.

Information granules in image histogram analysis.

PubMed

Wieclawek, Wojciech

2018-04-01

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

Microtomography imaging of an isolated plant fiber: a digital holographic approach.

PubMed

Malek, Mokrane; Khelfa, Haithem; Picart, Pascal; Mounier, Denis; Poilâne, Christophe

2016-01-20

This paper describes a method for optical projection tomography for the 3D in situ characterization of micrometric plant fibers. The proposed approach is based on digital holographic microscopy, the holographic capability being convenient to compensate for the runout of the fiber during rotations. The setup requires a telecentric alignment to prevent from the changes in the optical magnification, and calibration results show the very good experimental adjustment. Amplitude images are obtained from the set of recorded and digitally processed holograms. Refocusing of blurred images and correction of both runout and jitter are carried out to get appropriate amplitude images. The 3D data related to the plant fiber are computed from the set of images using a dedicated numerical processing. Experimental results exhibit the internal and external shapes of the plant fiber. These experimental results constitute the first attempt to obtain 3D data of flax fiber, about 12  μm×17  μm in apparent diameter, with a full-field optical tomography approach using light in the visible range.

Segmentation of Brain Lesions in MRI and CT Scan Images: A Hybrid Approach Using k-Means Clustering and Image Morphology

NASA Astrophysics Data System (ADS)

Agrawal, Ritu; Sharma, Manisha; Singh, Bikesh Kumar

2018-04-01

Manual segmentation and analysis of lesions in medical images is time consuming and subjected to human errors. Automated segmentation has thus gained significant attention in recent years. This article presents a hybrid approach for brain lesion segmentation in different imaging modalities by combining median filter, k means clustering, Sobel edge detection and morphological operations. Median filter is an essential pre-processing step and is used to remove impulsive noise from the acquired brain images followed by k-means segmentation, Sobel edge detection and morphological processing. The performance of proposed automated system is tested on standard datasets using performance measures such as segmentation accuracy and execution time. The proposed method achieves a high accuracy of 94% when compared with manual delineation performed by an expert radiologist. Furthermore, the statistical significance test between lesion segmented using automated approach and that by expert delineation using ANOVA and correlation coefficient achieved high significance values of 0.986 and 1 respectively. The experimental results obtained are discussed in lieu of some recently reported studies.

Autofluorescence detection and imaging of bladder cancer realized through a cystoscope

DOEpatents

Demos, Stavros G [Livermore, CA; deVere White, Ralph W [Sacramento, CA

2007-08-14

Near infrared imaging using elastic light scattering and tissue autofluorescence and utilizing interior examination techniques and equipment are explored for medical applications. The approach involves imaging using cross-polarized elastic light scattering and/or tissue autofluorescence in the Near Infra-Red (NIR) coupled with image processing and inter-image operations to differentiate human tissue components.

Identification of suitable fundus images using automated quality assessment methods.

PubMed

Şevik, Uğur; Köse, Cemal; Berber, Tolga; Erdöl, Hidayet

2014-04-01

Retinal image quality assessment (IQA) is a crucial process for automated retinal image analysis systems to obtain an accurate and successful diagnosis of retinal diseases. Consequently, the first step in a good retinal image analysis system is measuring the quality of the input image. We present an approach for finding medically suitable retinal images for retinal diagnosis. We used a three-class grading system that consists of good, bad, and outlier classes. We created a retinal image quality dataset with a total of 216 consecutive images called the Diabetic Retinopathy Image Database. We identified the suitable images within the good images for automatic retinal image analysis systems using a novel method. Subsequently, we evaluated our retinal image suitability approach using the Digital Retinal Images for Vessel Extraction and Standard Diabetic Retinopathy Database Calibration level 1 public datasets. The results were measured through the F1 metric, which is a harmonic mean of precision and recall metrics. The highest F1 scores of the IQA tests were 99.60%, 96.50%, and 85.00% for good, bad, and outlier classes, respectively. Additionally, the accuracy of our suitable image detection approach was 98.08%. Our approach can be integrated into any automatic retinal analysis system with sufficient performance scores.

Wavelet-space correlation imaging for high-speed MRI without motion monitoring or data segmentation.

PubMed

Li, Yu; Wang, Hui; Tkach, Jean; Roach, David; Woods, Jason; Dumoulin, Charles

2015-12-01

This study aims to (i) develop a new high-speed MRI approach by implementing correlation imaging in wavelet-space, and (ii) demonstrate the ability of wavelet-space correlation imaging to image human anatomy with involuntary or physiological motion. Correlation imaging is a high-speed MRI framework in which image reconstruction relies on quantification of data correlation. The presented work integrates correlation imaging with a wavelet transform technique developed originally in the field of signal and image processing. This provides a new high-speed MRI approach to motion-free data collection without motion monitoring or data segmentation. The new approach, called "wavelet-space correlation imaging", is investigated in brain imaging with involuntary motion and chest imaging with free-breathing. Wavelet-space correlation imaging can exceed the speed limit of conventional parallel imaging methods. Using this approach with high acceleration factors (6 for brain MRI, 16 for cardiac MRI, and 8 for lung MRI), motion-free images can be generated in static brain MRI with involuntary motion and nonsegmented dynamic cardiac/lung MRI with free-breathing. Wavelet-space correlation imaging enables high-speed MRI in the presence of involuntary motion or physiological dynamics without motion monitoring or data segmentation. © 2014 Wiley Periodicals, Inc.

Automatic Assessment and Reduction of Noise using Edge Pattern Analysis in Non-Linear Image Enhancement

NASA Technical Reports Server (NTRS)

Jobson, Daniel J.; Rahman, Zia-Ur; Woodell, Glenn A.; Hines, Glenn D.

2004-01-01

Noise is the primary visibility limit in the process of non-linear image enhancement, and is no longer a statistically stable additive noise in the post-enhancement image. Therefore novel approaches are needed to both assess and reduce spatially variable noise at this stage in overall image processing. Here we will examine the use of edge pattern analysis both for automatic assessment of spatially variable noise and as a foundation for new noise reduction methods.

Effectiveness of image features and similarity measures in cluster-based approaches for content-based image retrieval

NASA Astrophysics Data System (ADS)

Du, Hongbo; Al-Jubouri, Hanan; Sellahewa, Harin

2014-05-01

Content-based image retrieval is an automatic process of retrieving images according to image visual contents instead of textual annotations. It has many areas of application from automatic image annotation and archive, image classification and categorization to homeland security and law enforcement. The key issues affecting the performance of such retrieval systems include sensible image features that can effectively capture the right amount of visual contents and suitable similarity measures to find similar and relevant images ranked in a meaningful order. Many different approaches, methods and techniques have been developed as a result of very intensive research in the past two decades. Among many existing approaches, is a cluster-based approach where clustering methods are used to group local feature descriptors into homogeneous regions, and search is conducted by comparing the regions of the query image against those of the stored images. This paper serves as a review of works in this area. The paper will first summarize the existing work reported in the literature and then present the authors' own investigations in this field. The paper intends to highlight not only achievements made by recent research but also challenges and difficulties still remaining in this area.

Adaptive windowing in contrast-enhanced intravascular ultrasound imaging

PubMed Central

Lindsey, Brooks D.; Martin, K. Heath; Jiang, Xiaoning; Dayton, Paul A.

2016-01-01

Intravascular ultrasound (IVUS) is one of the most commonly-used interventional imaging techniques and has seen recent innovations which attempt to characterize the risk posed by atherosclerotic plaques. One such development is the use of microbubble contrast agents to image vasa vasorum, fine vessels which supply oxygen and nutrients to the walls of coronary arteries and typically have diameters less than 200 µm. The degree of vasa vasorum neovascularization within plaques is positively correlated with plaque vulnerability. Having recently presented a prototype dual-frequency transducer for contrast agent-specific intravascular imaging, here we describe signal processing approaches based on minimum variance (MV) beamforming and the phase coherence factor (PCF) for improving the spatial resolution and contrast-to-tissue ratio (CTR) in IVUS imaging. These approaches are examined through simulations, phantom studies, ex vivo studies in porcine arteries, and in vivo studies in chicken embryos. In phantom studies, PCF processing improved CTR by a mean of 4.2 dB, while combined MV and PCF processing improved spatial resolution by 41.7%. Improvements of 2.2 dB in CTR and 37.2% in resolution were observed in vivo. Applying these processing strategies can enhance image quality in conventional B-mode IVUS or in contrast-enhanced IVUS, where signal-to-noise ratio is relatively low and resolution is at a premium. PMID:27161022

A De-Identification Pipeline for Ultrasound Medical Images in DICOM Format.

PubMed

Monteiro, Eriksson; Costa, Carlos; Oliveira, José Luís

2017-05-01

Clinical data sharing between healthcare institutions, and between practitioners is often hindered by privacy protection requirements. This problem is critical in collaborative scenarios where data sharing is fundamental for establishing a workflow among parties. The anonymization of patient information burned in DICOM images requires elaborate processes somewhat more complex than simple de-identification of textual information. Usually, before sharing, there is a need for manual removal of specific areas containing sensitive information in the images. In this paper, we present a pipeline for ultrasound medical image de-identification, provided as a free anonymization REST service for medical image applications, and a Software-as-a-Service to streamline automatic de-identification of medical images, which is freely available for end-users. The proposed approach applies image processing functions and machine-learning models to bring about an automatic system to anonymize medical images. To perform character recognition, we evaluated several machine-learning models, being Convolutional Neural Networks (CNN) selected as the best approach. For accessing the system quality, 500 processed images were manually inspected showing an anonymization rate of 89.2%. The tool can be accessed at https://bioinformatics.ua.pt/dicom/anonymizer and it is available with the most recent version of Google Chrome, Mozilla Firefox and Safari. A Docker image containing the proposed service is also publicly available for the community.

Gradient-based multiresolution image fusion.

PubMed

Petrović, Valdimir S; Xydeas, Costas S

2004-02-01

A novel approach to multiresolution signal-level image fusion is presented for accurately transferring visual information from any number of input image signals, into a single fused image without loss of information or the introduction of distortion. The proposed system uses a "fuse-then-decompose" technique realized through a novel, fusion/decomposition system architecture. In particular, information fusion is performed on a multiresolution gradient map representation domain of image signal information. At each resolution, input images are represented as gradient maps and combined to produce new, fused gradient maps. Fused gradient map signals are processed, using gradient filters derived from high-pass quadrature mirror filters to yield a fused multiresolution pyramid representation. The fused output image is obtained by applying, on the fused pyramid, a reconstruction process that is analogous to that of conventional discrete wavelet transform. This new gradient fusion significantly reduces the amount of distortion artefacts and the loss of contrast information usually observed in fused images obtained from conventional multiresolution fusion schemes. This is because fusion in the gradient map domain significantly improves the reliability of the feature selection and information fusion processes. Fusion performance is evaluated through informal visual inspection and subjective psychometric preference tests, as well as objective fusion performance measurements. Results clearly demonstrate the superiority of this new approach when compared to conventional fusion systems.

A fast image registration approach of neural activities in light-sheet fluorescence microscopy images

NASA Astrophysics Data System (ADS)

Meng, Hui; Hui, Hui; Hu, Chaoen; Yang, Xin; Tian, Jie

2017-03-01

The ability of fast and single-neuron resolution imaging of neural activities enables light-sheet fluorescence microscopy (LSFM) as a powerful imaging technique in functional neural connection applications. The state-of-art LSFM imaging system can record the neuronal activities of entire brain for small animal, such as zebrafish or C. elegans at single-neuron resolution. However, the stimulated and spontaneous movements in animal brain result in inconsistent neuron positions during recording process. It is time consuming to register the acquired large-scale images with conventional method. In this work, we address the problem of fast registration of neural positions in stacks of LSFM images. This is necessary to register brain structures and activities. To achieve fast registration of neural activities, we present a rigid registration architecture by implementation of Graphics Processing Unit (GPU). In this approach, the image stacks were preprocessed on GPU by mean stretching to reduce the computation effort. The present image was registered to the previous image stack that considered as reference. A fast Fourier transform (FFT) algorithm was used for calculating the shift of the image stack. The calculations for image registration were performed in different threads while the preparation functionality was refactored and called only once by the master thread. We implemented our registration algorithm on NVIDIA Quadro K4200 GPU under Compute Unified Device Architecture (CUDA) programming environment. The experimental results showed that the registration computation can speed-up to 550ms for a full high-resolution brain image. Our approach also has potential to be used for other dynamic image registrations in biomedical applications.

Image-Based Airborne Sensors: A Combined Approach for Spectral Signatures Classification through Deterministic Simulated Annealing

PubMed Central

Guijarro, María; Pajares, Gonzalo; Herrera, P. Javier

2009-01-01

The increasing technology of high-resolution image airborne sensors, including those on board Unmanned Aerial Vehicles, demands automatic solutions for processing, either on-line or off-line, the huge amountds of image data sensed during the flights. The classification of natural spectral signatures in images is one potential application. The actual tendency in classification is oriented towards the combination of simple classifiers. In this paper we propose a combined strategy based on the Deterministic Simulated Annealing (DSA) framework. The simple classifiers used are the well tested supervised parametric Bayesian estimator and the Fuzzy Clustering. The DSA is an optimization approach, which minimizes an energy function. The main contribution of DSA is its ability to avoid local minima during the optimization process thanks to the annealing scheme. It outperforms simple classifiers used for the combination and some combined strategies, including a scheme based on the fuzzy cognitive maps and an optimization approach based on the Hopfield neural network paradigm. PMID:22399989

Directional ratio based on parabolic molecules and its application to the analysis of tubular structures

NASA Astrophysics Data System (ADS)

Labate, Demetrio; Negi, Pooran; Ozcan, Burcin; Papadakis, Manos

2015-09-01

As advances in imaging technologies make more and more data available for biomedical applications, there is an increasing need to develop efficient quantitative algorithms for the analysis and processing of imaging data. In this paper, we introduce an innovative multiscale approach called Directional Ratio which is especially effective to distingush isotropic from anisotropic structures. This task is especially useful in the analysis of images of neurons, the main units of the nervous systems which consist of a main cell body called the soma and many elongated processes called neurites. We analyze the theoretical properties of our method on idealized models of neurons and develop a numerical implementation of this approach for analysis of fluorescent images of cultured neurons. We show that this algorithm is very effective for the detection of somas and the extraction of neurites in images of small circuits of neurons.

SAR processing using SHARC signal processing systems

NASA Astrophysics Data System (ADS)

Huxtable, Barton D.; Jackson, Christopher R.; Skaron, Steve A.

1998-09-01

Synthetic aperture radar (SAR) is uniquely suited to help solve the Search and Rescue problem since it can be utilized either day or night and through both dense fog or thick cloud cover. Other papers in this session, and in this session in 1997, describe the various SAR image processing algorithms that are being developed and evaluated within the Search and Rescue Program. All of these approaches to using SAR data require substantial amounts of digital signal processing: for the SAR image formation, and possibly for the subsequent image processing. In recognition of the demanding processing that will be required for an operational Search and Rescue Data Processing System (SARDPS), NASA/Goddard Space Flight Center and NASA/Stennis Space Center are conducting a technology demonstration utilizing SHARC multi-chip modules from Boeing to perform SAR image formation processing.

Experimental results in autonomous landing approaches by dynamic machine vision

NASA Astrophysics Data System (ADS)

Dickmanns, Ernst D.; Werner, Stefan; Kraus, S.; Schell, R.

1994-07-01

The 4-D approach to dynamic machine vision, exploiting full spatio-temporal models of the process to be controlled, has been applied to on board autonomous landing approaches of aircraft. Aside from image sequence processing, for which it was developed initially, it is also used for data fusion from a range of sensors. By prediction error feedback an internal representation of the aircraft state relative to the runway in 3-D space and time is servo- maintained in the interpretation process, from which the control applications required are being derived. The validity and efficiency of the approach have been proven both in hardware- in-the-loop simulations and in flight experiments with a twin turboprop aircraft Do128 under perturbations from cross winds and wind gusts. The software package has been ported to `C' and onto a new transputer image processing platform; the system has been expanded for bifocal vision with two cameras of different focal length mounted fixed relative to each other on a two-axes platform for viewing direction control.

Wavelet-space Correlation Imaging for High-speed MRI without Motion Monitoring or Data Segmentation

PubMed Central

Li, Yu; Wang, Hui; Tkach, Jean; Roach, David; Woods, Jason; Dumoulin, Charles

2014-01-01

Purpose This study aims to 1) develop a new high-speed MRI approach by implementing correlation imaging in wavelet-space, and 2) demonstrate the ability of wavelet-space correlation imaging to image human anatomy with involuntary or physiological motion. Methods Correlation imaging is a high-speed MRI framework in which image reconstruction relies on quantification of data correlation. The presented work integrates correlation imaging with a wavelet transform technique developed originally in the field of signal and image processing. This provides a new high-speed MRI approach to motion-free data collection without motion monitoring or data segmentation. The new approach, called “wavelet-space correlation imaging”, is investigated in brain imaging with involuntary motion and chest imaging with free-breathing. Results Wavelet-space correlation imaging can exceed the speed limit of conventional parallel imaging methods. Using this approach with high acceleration factors (6 for brain MRI, 16 for cardiac MRI and 8 for lung MRI), motion-free images can be generated in static brain MRI with involuntary motion and nonsegmented dynamic cardiac/lung MRI with free-breathing. Conclusion Wavelet-space correlation imaging enables high-speed MRI in the presence of involuntary motion or physiological dynamics without motion monitoring or data segmentation. PMID:25470230

Managing complex processing of medical image sequences by program supervision techniques

NASA Astrophysics Data System (ADS)

Crubezy, Monica; Aubry, Florent; Moisan, Sabine; Chameroy, Virginie; Thonnat, Monique; Di Paola, Robert

1997-05-01

Our objective is to offer clinicians wider access to evolving medical image processing (MIP) techniques, crucial to improve assessment and quantification of physiological processes, but difficult to handle for non-specialists in MIP. Based on artificial intelligence techniques, our approach consists in the development of a knowledge-based program supervision system, automating the management of MIP libraries. It comprises a library of programs, a knowledge base capturing the expertise about programs and data and a supervision engine. It selects, organizes and executes the appropriate MIP programs given a goal to achieve and a data set, with dynamic feedback based on the results obtained. It also advises users in the development of new procedures chaining MIP programs.. We have experimented the approach for an application of factor analysis of medical image sequences as a means of predicting the response of osteosarcoma to chemotherapy, with both MRI and NM dynamic image sequences. As a result our program supervision system frees clinical end-users from performing tasks outside their competence, permitting them to concentrate on clinical issues. Therefore our approach enables a better exploitation of possibilities offered by MIP and higher quality results, both in terms of robustness and reliability.

An approach for traffic prohibition sign detection

NASA Astrophysics Data System (ADS)

Li, Qingquan; Xu, Dihong; Li, Bijun; Zeng, Zhe

2006-10-01

This paper presents an off-line traffic prohibition sign detection approach, whose core is based on combination with the color feature of traffic prohibition signs, shape feature and degree of circularity. Matlab-Image-processing toolbox is used for this purpose. In order to reduce the computational cost, a pre-processing of the image is applied before the core. Then, we employ the obvious redness attribute of prohibition signs to coarsely eliminate the non-redness image in the input data. Again, a edge-detection operator, Canny edge detector, is applied to extract the potential edge. Finally, Degree of circularity is used to verdict the traffic prohibition sign. Experimental results show that our systems offer satisfactory performance.

A novel double patterning approach for 30nm dense holes

NASA Astrophysics Data System (ADS)

Hsu, Dennis Shu-Hao; Wang, Walter; Hsieh, Wei-Hsien; Huang, Chun-Yen; Wu, Wen-Bin; Shih, Chiang-Lin; Shih, Steven

2011-04-01

Double Patterning Technology (DPT) was commonly accepted as the major workhorse beyond water immersion lithography for sub-38nm half-pitch line patterning before the EUV production. For dense hole patterning, classical DPT employs self-aligned spacer deposition and uses the intersection of horizontal and vertical lines to define the desired hole patterns. However, the increase in manufacturing cost and process complexity is tremendous. Several innovative approaches have been proposed and experimented to address the manufacturing and technical challenges. A novel process of double patterned pillars combined image reverse will be proposed for the realization of low cost dense holes in 30nm node DRAM. The nature of pillar formation lithography provides much better optical contrast compared to the counterpart hole patterning with similar CD requirements. By the utilization of a reliable freezing process, double patterned pillars can be readily implemented. A novel image reverse process at the last stage defines the hole patterns with high fidelity. In this paper, several freezing processes for the construction of the double patterned pillars were tested and compared, and 30nm double patterning pillars were demonstrated successfully. A variety of different image reverse processes will be investigated and discussed for their pros and cons. An economic approach with the optimized lithography performance will be proposed for the application of 30nm DRAM node.

Beam-hardening correction by a surface fitting and phase classification by a least square support vector machine approach for tomography images of geological samples

NASA Astrophysics Data System (ADS)

Khan, F.; Enzmann, F.; Kersten, M.

2015-12-01

In X-ray computed microtomography (μXCT) image processing is the most important operation prior to image analysis. Such processing mainly involves artefact reduction and image segmentation. We propose a new two-stage post-reconstruction procedure of an image of a geological rock core obtained by polychromatic cone-beam μXCT technology. In the first stage, the beam-hardening (BH) is removed applying a best-fit quadratic surface algorithm to a given image data set (reconstructed slice), which minimizes the BH offsets of the attenuation data points from that surface. The final BH-corrected image is extracted from the residual data, or the difference between the surface elevation values and the original grey-scale values. For the second stage, we propose using a least square support vector machine (a non-linear classifier algorithm) to segment the BH-corrected data as a pixel-based multi-classification task. A combination of the two approaches was used to classify a complex multi-mineral rock sample. The Matlab code for this approach is provided in the Appendix. A minor drawback is that the proposed segmentation algorithm may become computationally demanding in the case of a high dimensional training data set.

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

Smith, Donald F.; Schulz, Carl; Konijnenburg, Marco

High-resolution Fourier transform ion cyclotron resonance (FT-ICR) mass spectrometry imaging enables the spatial mapping and identification of biomolecules from complex surfaces. The need for long time-domain transients, and thus large raw file sizes, results in a large amount of raw data (“big data”) that must be processed efficiently and rapidly. This can be compounded by largearea imaging and/or high spatial resolution imaging. For FT-ICR, data processing and data reduction must not compromise the high mass resolution afforded by the mass spectrometer. The continuous mode “Mosaic Datacube” approach allows high mass resolution visualization (0.001 Da) of mass spectrometry imaging data, butmore » requires additional processing as compared to featurebased processing. We describe the use of distributed computing for processing of FT-ICR MS imaging datasets with generation of continuous mode Mosaic Datacubes for high mass resolution visualization. An eight-fold improvement in processing time is demonstrated using a Dutch nationally available cloud service.« less

Photoacoustic imaging of lymphatic pumping

NASA Astrophysics Data System (ADS)

Forbrich, Alex; Heinmiller, Andrew; Zemp, Roger J.

2017-10-01

The lymphatic system is responsible for fluid homeostasis and immune cell trafficking and has been implicated in several diseases, including obesity, diabetes, and cancer metastasis. Despite its importance, the lack of suitable in vivo imaging techniques has hampered our understanding of the lymphatic system. This is, in part, due to the limited contrast of lymphatic fluids and structures. Photoacoustic imaging, in combination with optically absorbing dyes or nanoparticles, has great potential for noninvasively visualizing the lymphatic vessels deep in tissues. Multispectral photoacoustic imaging is capable of separating the components; however, the slow wavelength switching speed of most laser systems is inadequate for imaging lymphatic pumping without motion artifacts being introduced into the processed images. We investigate two approaches for visualizing lymphatic processes in vivo. First, single-wavelength differential photoacoustic imaging is used to visualize lymphatic pumping in the hindlimb of a mouse in real time. Second, a fast-switching multiwavelength photoacoustic imaging system was used to assess the propulsion profile of dyes through the lymphatics in real time. These approaches may have profound impacts in noninvasively characterizing and investigating the lymphatic system.

Automatic detection of the macula in retinal fundus images using seeded mode tracking approach.

PubMed

Wong, Damon W K; Liu, Jiang; Tan, Ngan-Meng; Yin, Fengshou; Cheng, Xiangang; Cheng, Ching-Yu; Cheung, Gemmy C M; Wong, Tien Yin

2012-01-01

The macula is the part of the eye responsible for central high acuity vision. Detection of the macula is an important task in retinal image processing as a landmark for subsequent disease assessment, such as for age-related macula degeneration. In this paper, we have presented an approach to automatically determine the macula centre in retinal fundus images. First contextual information on the image is combined with a statistical model to obtain an approximate macula region of interest localization. Subsequently, we propose the use of a seeded mode tracking technique to locate the macula centre. The proposed approach is tested on a large dataset composed of 482 normal images and 162 glaucoma images from the ORIGA database and an additional 96 AMD images. The results show a ROI detection of 97.5%, and 90.5% correct detection of the macula within 1/3DD from a manual reference, which outperforms other current methods. The results are promising for the use of the proposed approach to locate the macula for the detection of macula diseases from retinal images.

Astigmatism compensation in digital holographic microscopy using complex-amplitude correlation

NASA Astrophysics Data System (ADS)

Tamrin, Khairul Fikri; Rahmatullah, Bahbibi; Samuri, Suzani Mohamad

2015-07-01

Digital holographic microscopy (DHM) is a promising tool for a three-dimensional imaging of microscopic particles. It offers the possibility of wavefront processing by manipulating amplitude and phase of the recorded digital holograms. With a view to compensate for aberration in the reconstructed particle images, this paper discusses a new approach of aberration compensation based on complex amplitude correlation and the use of a priori information. The approach is applied to holograms of microscopic particles flowing inside a cylindrical micro-channel recorded using an off-axis digital holographic microscope. The approach results in improvements in the image and signal qualities.

Short Project-Based Learning with MATLAB Applications to Support the Learning of Video-Image Processing

ERIC Educational Resources Information Center

Gil, Pablo

2017-01-01

University courses concerning Computer Vision and Image Processing are generally taught using a traditional methodology that is focused on the teacher rather than on the students. This approach is consequently not effective when teachers seek to attain cognitive objectives involving their students' critical thinking. This manuscript covers the…

Linear Algebra and Image Processing

ERIC Educational Resources Information Center

Allali, Mohamed

2010-01-01

We use the computing technology digital image processing (DIP) to enhance the teaching of linear algebra so as to make the course more visual and interesting. Certainly, this visual approach by using technology to link linear algebra to DIP is interesting and unexpected to both students as well as many faculty. (Contains 2 tables and 11 figures.)

Image processing and reconstruction

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

Chartrand, Rick

2012-06-15

This talk will examine some mathematical methods for image processing and the solution of underdetermined, linear inverse problems. The talk will have a tutorial flavor, mostly accessible to undergraduates, while still presenting research results. The primary approach is the use of optimization problems. We will find that relaxing the usual assumption of convexity will give us much better results.

Breakup phenomena of a coaxial jet in the non-dilute region using real-time X-ray radiography

NASA Astrophysics Data System (ADS)

Cheung, F. B.; Kuo, K. K.; Woodward, R. D.; Garner, K. N.

1990-07-01

An innovative approach to the investigation of liquid jet breakup processes in the near-injector region has been developed to overcome the experimental difficulties associated with optically opaque, dense sprays. Real-time X-ray radiography (RTR) has been employed to observe the inner structure and breakup phenomena of coaxial jets. In the atomizing regime, droplets much smaller than the exit diameter are formed beginning essentially at the injector exit. Through the use of RTR, the instantaneous contour of the liquid core was visualized. Experimental results consist of controlled-exposure digital video images of the liquid jet breakup process. Time-averaged video images have also been recorded for comparison. A digital image processing system is used to analyze the recorded images by creating radiance level distributions of the jet. A rudimentary method for deducing intact-liquid-core length has been suggested. The technique of real-time X-ray radiography has been shown to be a viable approach to the study of the breakup processes of high-speed liquid jets.

Framework for hyperspectral image processing and quantification for cancer detection during animal tumor surgery.

PubMed

Lu, Guolan; Wang, Dongsheng; Qin, Xulei; Halig, Luma; Muller, Susan; Zhang, Hongzheng; Chen, Amy; Pogue, Brian W; Chen, Zhuo Georgia; Fei, Baowei

2015-01-01

Hyperspectral imaging (HSI) is an imaging modality that holds strong potential for rapid cancer detection during image-guided surgery. But the data from HSI often needs to be processed appropriately in order to extract the maximum useful information that differentiates cancer from normal tissue. We proposed a framework for hyperspectral image processing and quantification, which includes a set of steps including image preprocessing, glare removal, feature extraction, and ultimately image classification. The framework has been tested on images from mice with head and neck cancer, using spectra from 450- to 900-nm wavelength. The image analysis computed Fourier coefficients, normalized reflectance, mean, and spectral derivatives for improved accuracy. The experimental results demonstrated the feasibility of the hyperspectral image processing and quantification framework for cancer detection during animal tumor surgery, in a challenging setting where sensitivity can be low due to a modest number of features present, but potential for fast image classification can be high. This HSI approach may have potential application in tumor margin assessment during image-guided surgery, where speed of assessment may be the dominant factor.

Framework for hyperspectral image processing and quantification for cancer detection during animal tumor surgery

NASA Astrophysics Data System (ADS)

Lu, Guolan; Wang, Dongsheng; Qin, Xulei; Halig, Luma; Muller, Susan; Zhang, Hongzheng; Chen, Amy; Pogue, Brian W.; Chen, Zhuo Georgia; Fei, Baowei

2015-12-01

Hyperspectral imaging (HSI) is an imaging modality that holds strong potential for rapid cancer detection during image-guided surgery. But the data from HSI often needs to be processed appropriately in order to extract the maximum useful information that differentiates cancer from normal tissue. We proposed a framework for hyperspectral image processing and quantification, which includes a set of steps including image preprocessing, glare removal, feature extraction, and ultimately image classification. The framework has been tested on images from mice with head and neck cancer, using spectra from 450- to 900-nm wavelength. The image analysis computed Fourier coefficients, normalized reflectance, mean, and spectral derivatives for improved accuracy. The experimental results demonstrated the feasibility of the hyperspectral image processing and quantification framework for cancer detection during animal tumor surgery, in a challenging setting where sensitivity can be low due to a modest number of features present, but potential for fast image classification can be high. This HSI approach may have potential application in tumor margin assessment during image-guided surgery, where speed of assessment may be the dominant factor.

AstroImageJ: Image Processing and Photometric Extraction for Ultra-precise Astronomical Light Curves

NASA Astrophysics Data System (ADS)

Collins, Karen A.; Kielkopf, John F.; Stassun, Keivan G.; Hessman, Frederic V.

2017-02-01

ImageJ is a graphical user interface (GUI) driven, public domain, Java-based, software package for general image processing traditionally used mainly in life sciences fields. The image processing capabilities of ImageJ are useful and extendable to other scientific fields. Here we present AstroImageJ (AIJ), which provides an astronomy specific image display environment and tools for astronomy specific image calibration and data reduction. Although AIJ maintains the general purpose image processing capabilities of ImageJ, AIJ is streamlined for time-series differential photometry, light curve detrending and fitting, and light curve plotting, especially for applications requiring ultra-precise light curves (e.g., exoplanet transits). AIJ reads and writes standard Flexible Image Transport System (FITS) files, as well as other common image formats, provides FITS header viewing and editing, and is World Coordinate System aware, including an automated interface to the astrometry.net web portal for plate solving images. AIJ provides research grade image calibration and analysis tools with a GUI driven approach, and easily installed cross-platform compatibility. It enables new users, even at the level of undergraduate student, high school student, or amateur astronomer, to quickly start processing, modeling, and plotting astronomical image data with one tightly integrated software package.

Data Processing of LAPAN-A3 Thermal Imager

NASA Astrophysics Data System (ADS)

Hartono, R.; Hakim, P. R.; Syafrudin, AH

2018-04-01

As an experimental microsatellite, LAPAN-A3/IPB satellite has an experimental thermal imager, which is called as micro-bolometer, to observe earth surface temperature for horizon observation. The imager data is transmitted from satellite to ground station by S-band video analog signal transmission, and then processed by ground station to become sequence of 8-bit enhanced and contrasted images. Data processing of LAPAN-A3/IPB thermal imager is more difficult than visual digital camera, especially for mosaic and classification purpose. This research aims to describe simple mosaic and classification process of LAPAN-A3/IPB thermal imager based on several videos data produced by the imager. The results show that stitching using Adobe Photoshop produces excellent result but can only process small area, while manual approach using ImageJ software can produce a good result but need a lot of works and time consuming. The mosaic process using image cross-correlation by Matlab offers alternative solution, which can process significantly bigger area in significantly shorter time processing. However, the quality produced is not as good as mosaic images of the other two methods. The simple classifying process that has been done shows that the thermal image can classify three distinct objects, i.e.: clouds, sea, and land surface. However, the algorithm fail to classify any other object which might be caused by distortions in the images. All of these results can be used as reference for development of thermal imager in LAPAN-A4 satellite.

SHORT COMMUNICATION: An image processing approach to calibration of hydrometers

NASA Astrophysics Data System (ADS)

Lorefice, S.; Malengo, A.

2004-06-01

The usual method adopted for multipoint calibration of glass hydrometers is based on the measurement of the buoyancy by hydrostatic weighing when the hydrometer is plunged in a reference liquid up to the scale mark to be calibrated. An image processing approach is proposed by the authors to align the relevant scale mark with the reference liquid surface level. The method uses image analysis with a data processing technique and takes into account the perspective error. For this purpose a CCD camera with a pixel matrix of 604H × 576V and a lens of 16 mm focal length were used. High accuracy in the hydrometer reading was obtained as the resulting reading uncertainty was lower than 0.02 mm, about a fifth of the usual figure with the visual reading made by an operator.

Color image analysis of contaminants and bacteria transport in porous media

NASA Astrophysics Data System (ADS)

Rashidi, Mehdi; Dehmeshki, Jamshid; Daemi, Mohammad F.; Cole, Larry; Dickenson, Eric

1997-10-01

Transport of contaminants and bacteria in aqueous heterogeneous saturated porous systems have been studied experimentally using a novel fluorescent microscopic imaging technique. The approach involves color visualization and quantification of bacterium and contaminant distributions within a transparent porous column. By introducing stained bacteria and an organic dye as a contaminant into the column and illuminating the porous regions with a planar sheet of laser beam, contaminant and bacterial transport processes through the porous medium can be observed and measured microscopically. A computer controlled color CCD camera is used to record the fluorescent images as a function of time. These images are recorded by a frame accurate high resolution VCR and are then analyzed using a color image analysis code written in our laboratories. The color images are digitized this way and simultaneous concentration and velocity distributions of both contaminant and bacterium are evaluated as a function of time and pore characteristics. The approach provides a unique dynamic probe to observe these transport processes microscopically. These results are extremely valuable in in-situ bioremediation problems since microscopic particle-contaminant- bacterium interactions are the key to understanding and optimization of these processes.

Singular value decomposition for photon-processing nuclear imaging systems and applications for reconstruction and computing null functions.

PubMed

Jha, Abhinav K; Barrett, Harrison H; Frey, Eric C; Clarkson, Eric; Caucci, Luca; Kupinski, Matthew A

2015-09-21

Recent advances in technology are enabling a new class of nuclear imaging systems consisting of detectors that use real-time maximum-likelihood (ML) methods to estimate the interaction position, deposited energy, and other attributes of each photon-interaction event and store these attributes in a list format. This class of systems, which we refer to as photon-processing (PP) nuclear imaging systems, can be described by a fundamentally different mathematical imaging operator that allows processing of the continuous-valued photon attributes on a per-photon basis. Unlike conventional photon-counting (PC) systems that bin the data into images, PP systems do not have any binning-related information loss. Mathematically, while PC systems have an infinite-dimensional null space due to dimensionality considerations, PP systems do not necessarily suffer from this issue. Therefore, PP systems have the potential to provide improved performance in comparison to PC systems. To study these advantages, we propose a framework to perform the singular-value decomposition (SVD) of the PP imaging operator. We use this framework to perform the SVD of operators that describe a general two-dimensional (2D) planar linear shift-invariant (LSIV) PP system and a hypothetical continuously rotating 2D single-photon emission computed tomography (SPECT) PP system. We then discuss two applications of the SVD framework. The first application is to decompose the object being imaged by the PP imaging system into measurement and null components. We compare these components to the measurement and null components obtained with PC systems. In the process, we also present a procedure to compute the null functions for a PC system. The second application is designing analytical reconstruction algorithms for PP systems. The proposed analytical approach exploits the fact that PP systems acquire data in a continuous domain to estimate a continuous object function. The approach is parallelizable and implemented for graphics processing units (GPUs). Further, this approach leverages another important advantage of PP systems, namely the possibility to perform photon-by-photon real-time reconstruction. We demonstrate the application of the approach to perform reconstruction in a simulated 2D SPECT system. The results help to validate and demonstrate the utility of the proposed method and show that PP systems can help overcome the aliasing artifacts that are otherwise intrinsically present in PC systems.

Singular value decomposition for photon-processing nuclear imaging systems and applications for reconstruction and computing null functions

NASA Astrophysics Data System (ADS)

Jha, Abhinav K.; Barrett, Harrison H.; Frey, Eric C.; Clarkson, Eric; Caucci, Luca; Kupinski, Matthew A.

2015-09-01

Recent advances in technology are enabling a new class of nuclear imaging systems consisting of detectors that use real-time maximum-likelihood (ML) methods to estimate the interaction position, deposited energy, and other attributes of each photon-interaction event and store these attributes in a list format. This class of systems, which we refer to as photon-processing (PP) nuclear imaging systems, can be described by a fundamentally different mathematical imaging operator that allows processing of the continuous-valued photon attributes on a per-photon basis. Unlike conventional photon-counting (PC) systems that bin the data into images, PP systems do not have any binning-related information loss. Mathematically, while PC systems have an infinite-dimensional null space due to dimensionality considerations, PP systems do not necessarily suffer from this issue. Therefore, PP systems have the potential to provide improved performance in comparison to PC systems. To study these advantages, we propose a framework to perform the singular-value decomposition (SVD) of the PP imaging operator. We use this framework to perform the SVD of operators that describe a general two-dimensional (2D) planar linear shift-invariant (LSIV) PP system and a hypothetical continuously rotating 2D single-photon emission computed tomography (SPECT) PP system. We then discuss two applications of the SVD framework. The first application is to decompose the object being imaged by the PP imaging system into measurement and null components. We compare these components to the measurement and null components obtained with PC systems. In the process, we also present a procedure to compute the null functions for a PC system. The second application is designing analytical reconstruction algorithms for PP systems. The proposed analytical approach exploits the fact that PP systems acquire data in a continuous domain to estimate a continuous object function. The approach is parallelizable and implemented for graphics processing units (GPUs). Further, this approach leverages another important advantage of PP systems, namely the possibility to perform photon-by-photon real-time reconstruction. We demonstrate the application of the approach to perform reconstruction in a simulated 2D SPECT system. The results help to validate and demonstrate the utility of the proposed method and show that PP systems can help overcome the aliasing artifacts that are otherwise intrinsically present in PC systems.

Focus measure method based on the modulus of the gradient of the color planes for digital microscopy

NASA Astrophysics Data System (ADS)

Hurtado-Pérez, Román; Toxqui-Quitl, Carina; Padilla-Vivanco, Alfonso; Aguilar-Valdez, J. Félix; Ortega-Mendoza, Gabriel

2018-02-01

The modulus of the gradient of the color planes (MGC) is implemented to transform multichannel information to a grayscale image. This digital technique is used in two applications: (a) focus measurements during autofocusing (AF) process and (b) extending the depth of field (EDoF) by means of multifocus image fusion. In the first case, the MGC procedure is based on an edge detection technique and is implemented in over 15 focus metrics that are typically handled in digital microscopy. The MGC approach is tested on color images of histological sections for the selection of in-focus images. An appealing attribute of all the AF metrics working in the MGC space is their monotonic behavior even up to a magnification of 100×. An advantage of the MGC method is its computational simplicity and inherent parallelism. In the second application, a multifocus image fusion algorithm based on the MGC approach has been implemented on graphics processing units (GPUs). The resulting fused images are evaluated using a nonreference image quality metric. The proposed fusion method reveals a high-quality image independently of faulty illumination during the image acquisition. Finally, the three-dimensional visualization of the in-focus image is shown.

Motion-Blurred Particle Image Restoration for On-Line Wear Monitoring

PubMed Central

Peng, Yeping; Wu, Tonghai; Wang, Shuo; Kwok, Ngaiming; Peng, Zhongxiao

2015-01-01

On-line images of wear debris contain important information for real-time condition monitoring, and a dynamic imaging technique can eliminate particle overlaps commonly found in static images, for instance, acquired using ferrography. However, dynamic wear debris images captured in a running machine are unavoidably blurred because the particles in lubricant are in motion. Hence, it is difficult to acquire reliable images of wear debris with an adequate resolution for particle feature extraction. In order to obtain sharp wear particle images, an image processing approach is proposed. Blurred particles were firstly separated from the static background by utilizing a background subtraction method. Second, the point spread function was estimated using power cepstrum to determine the blur direction and length. Then, the Wiener filter algorithm was adopted to perform image restoration to improve the image quality. Finally, experiments were conducted with a large number of dynamic particle images to validate the effectiveness of the proposed method and the performance of the approach was also evaluated. This study provides a new practical approach to acquire clear images for on-line wear monitoring. PMID:25856328

Image detection and compression for memory efficient system analysis

NASA Astrophysics Data System (ADS)

Bayraktar, Mustafa

2015-02-01

The advances in digital signal processing have been progressing towards efficient use of memory and processing. Both of these factors can be utilized efficiently by using feasible techniques of image storage by computing the minimum information of image which will enhance computation in later processes. Scale Invariant Feature Transform (SIFT) can be utilized to estimate and retrieve of an image. In computer vision, SIFT can be implemented to recognize the image by comparing its key features from SIFT saved key point descriptors. The main advantage of SIFT is that it doesn't only remove the redundant information from an image but also reduces the key points by matching their orientation and adding them together in different windows of image [1]. Another key property of this approach is that it works on highly contrasted images more efficiently because it`s design is based on collecting key points from the contrast shades of image.

Single image interpolation via adaptive nonlocal sparsity-based modeling.

PubMed

Romano, Yaniv; Protter, Matan; Elad, Michael

2014-07-01

Single image interpolation is a central and extensively studied problem in image processing. A common approach toward the treatment of this problem in recent years is to divide the given image into overlapping patches and process each of them based on a model for natural image patches. Adaptive sparse representation modeling is one such promising image prior, which has been shown to be powerful in filling-in missing pixels in an image. Another force that such algorithms may use is the self-similarity that exists within natural images. Processing groups of related patches together exploits their correspondence, leading often times to improved results. In this paper, we propose a novel image interpolation method, which combines these two forces-nonlocal self-similarities and sparse representation modeling. The proposed method is contrasted with competitive and related algorithms, and demonstrated to achieve state-of-the-art results.

A Versatile Image Processor For Digital Diagnostic Imaging And Its Application In Computed Radiography

NASA Astrophysics Data System (ADS)

Blume, H.; Alexandru, R.; Applegate, R.; Giordano, T.; Kamiya, K.; Kresina, R.

1986-06-01

In a digital diagnostic imaging department, the majority of operations for handling and processing of images can be grouped into a small set of basic operations, such as image data buffering and storage, image processing and analysis, image display, image data transmission and image data compression. These operations occur in almost all nodes of the diagnostic imaging communications network of the department. An image processor architecture was developed in which each of these functions has been mapped into hardware and software modules. The modular approach has advantages in terms of economics, service, expandability and upgradeability. The architectural design is based on the principles of hierarchical functionality, distributed and parallel processing and aims at real time response. Parallel processing and real time response is facilitated in part by a dual bus system: a VME control bus and a high speed image data bus, consisting of 8 independent parallel 16-bit busses, capable of handling combined up to 144 MBytes/sec. The presented image processor is versatile enough to meet the video rate processing needs of digital subtraction angiography, the large pixel matrix processing requirements of static projection radiography, or the broad range of manipulation and display needs of a multi-modality diagnostic work station. Several hardware modules are described in detail. For illustrating the capabilities of the image processor, processed 2000 x 2000 pixel computed radiographs are shown and estimated computation times for executing the processing opera-tions are presented.

Solfatara volcano subsurface imaging: two different approaches to process and interpret multi-variate data sets

NASA Astrophysics Data System (ADS)

Bernardinetti, Stefano; Bruno, Pier Paolo; Lavoué, François; Gresse, Marceau; Vandemeulebrouck, Jean; Revil, André

2017-04-01

The need to reduce model uncertainty and produce a more reliable geophysical imaging and interpretations is nowadays a fundamental task required to geophysics techniques applied in complex environments such as Solfatara Volcano. The use of independent geophysical methods allows to obtain many information on the subsurface due to the different sensitivities of the data towards parameters such as compressional and shearing wave velocities, bulk electrical conductivity, or density. The joint processing of these multiple physical properties can lead to a very detailed characterization of the subsurface and therefore enhance our imaging and our interpretation. In this work, we develop two different processing approaches based on reflection seismology and seismic P-wave tomography on one hand, and electrical data acquired over the same line, on the other hand. From these data, we obtain an image-guided electrical resistivity tomography and a post processing integration of tomographic results. The image-guided electrical resistivity tomography is obtained by regularizing the inversion of the electrical data with structural constraints extracted from a migrated seismic section using image processing tools. This approach enables to focus the reconstruction of electrical resistivity anomalies along the features visible in the seismic section, and acts as a guide for interpretation in terms of subsurface structures and processes. To integrate co-registrated P-wave velocity and electrical resistivity values, we apply a data mining tool, the k-means algorithm, to individuate relationships between the two set of variables. This algorithm permits to individuate different clusters with the objective to minimize the sum of squared Euclidean distances within each cluster and maximize it between clusters for the multivariate data set. We obtain a partitioning of the multivariate data set in a finite number of well-correlated clusters, representative of the optimum clustering of our geophysical variables (P-wave velocities and electrical resistivities). The result is an integrated tomography that shows a finite number of homogeneous geophysical facies, and therefore permits to highlight the main geological features of the subsurface.

Single image super-resolution via an iterative reproducing kernel Hilbert space method.

PubMed

Deng, Liang-Jian; Guo, Weihong; Huang, Ting-Zhu

2016-11-01

Image super-resolution, a process to enhance image resolution, has important applications in satellite imaging, high definition television, medical imaging, etc. Many existing approaches use multiple low-resolution images to recover one high-resolution image. In this paper, we present an iterative scheme to solve single image super-resolution problems. It recovers a high quality high-resolution image from solely one low-resolution image without using a training data set. We solve the problem from image intensity function estimation perspective and assume the image contains smooth and edge components. We model the smooth components of an image using a thin-plate reproducing kernel Hilbert space (RKHS) and the edges using approximated Heaviside functions. The proposed method is applied to image patches, aiming to reduce computation and storage. Visual and quantitative comparisons with some competitive approaches show the effectiveness of the proposed method.

A comparison of basic deinterlacing approaches for a computer assisted diagnosis approach of videoscope images

NASA Astrophysics Data System (ADS)

Kage, Andreas; Canto, Marcia; Gorospe, Emmanuel; Almario, Antonio; Münzenmayer, Christian

2010-03-01

In the near future, Computer Assisted Diagnosis (CAD) which is well known in the area of mammography might be used to support clinical experts in the diagnosis of images derived from imaging modalities such as endoscopy. In the recent past, a few first approaches for computer assisted endoscopy have been presented already. These systems use a video signal as an input that is provided by the endoscopes video processor. Despite the advent of high-definition systems most standard endoscopy systems today still provide only analog video signals. These signals consist of interlaced images that can not be used in a CAD approach without deinterlacing. Of course, there are many different deinterlacing approaches known today. But most of them are specializations of some basic approaches. In this paper we present four basic deinterlacing approaches. We have used a database of non-interlaced images which have been degraded by artificial interlacing and afterwards processed by these approaches. The database contains regions of interest (ROI) of clinical relevance for the diagnosis of abnormalities in the esophagus. We compared the classification rates on these ROIs on the original images and after the deinterlacing. The results show that the deinterlacing has an impact on the classification rates. The Bobbing approach and the Motion Compensation approach achieved the best classification results in most cases.

Image processing for cryogenic transmission electron microscopy of symmetry-mismatched complexes.

PubMed

Huiskonen, Juha T

2018-02-08

Cryogenic transmission electron microscopy (cryo-TEM) is a high-resolution biological imaging method, whereby biological samples, such as purified proteins, macromolecular complexes, viral particles, organelles and cells, are embedded in vitreous ice preserving their native structures. Due to sensitivity of biological materials to the electron beam of the microscope, only relatively low electron doses can be applied during imaging. As a result, the signal arising from the structure of interest is overpowered by noise in the images. To increase the signal-to-noise ratio, different image processing-based strategies that aim at coherent averaging of signal have been devised. In such strategies, images are generally assumed to arise from multiple identical copies of the structure. Prior to averaging, the images must be grouped according to the view of the structure they represent and images representing the same view must be simultaneously aligned relatively to each other. For computational reconstruction of the three-dimensional structure, images must contain different views of the original structure. Structures with multiple symmetry-related substructures are advantageous in averaging approaches because each image provides multiple views of the substructures. However, the symmetry assumption may be valid for only parts of the structure, leading to incoherent averaging of the other parts. Several image processing approaches have been adapted to tackle symmetry-mismatched substructures with increasing success. Such structures are ubiquitous in nature and further computational method development is needed to understanding their biological functions. ©2018 The Author(s).

High-performance image processing on the desktop

NASA Astrophysics Data System (ADS)

Jordan, Stephen D.

1996-04-01

The suitability of computers to the task of medical image visualization for the purposes of primary diagnosis and treatment planning depends on three factors: speed, image quality, and price. To be widely accepted the technology must increase the efficiency of the diagnostic and planning processes. This requires processing and displaying medical images of various modalities in real-time, with accuracy and clarity, on an affordable system. Our approach to meeting this challenge began with market research to understand customer image processing needs. These needs were translated into system-level requirements, which in turn were used to determine which image processing functions should be implemented in hardware. The result is a computer architecture for 2D image processing that is both high-speed and cost-effective. The architectural solution is based on the high-performance PA-RISC workstation with an HCRX graphics accelerator. The image processing enhancements are incorporated into the image visualization accelerator (IVX) which attaches to the HCRX graphics subsystem. The IVX includes a custom VLSI chip which has a programmable convolver, a window/level mapper, and an interpolator supporting nearest-neighbor, bi-linear, and bi-cubic modes. This combination of features can be used to enable simultaneous convolution, pan, zoom, rotate, and window/level control into 1 k by 1 k by 16-bit medical images at 40 frames/second.

An integral design strategy combining optical system and image processing to obtain high resolution images

NASA Astrophysics Data System (ADS)

Wang, Jiaoyang; Wang, Lin; Yang, Ying; Gong, Rui; Shao, Xiaopeng; Liang, Chao; Xu, Jun

2016-05-01

In this paper, an integral design that combines optical system with image processing is introduced to obtain high resolution images, and the performance is evaluated and demonstrated. Traditional imaging methods often separate the two technical procedures of optical system design and imaging processing, resulting in the failures in efficient cooperation between the optical and digital elements. Therefore, an innovative approach is presented to combine the merit function during optical design together with the constraint conditions of image processing algorithms. Specifically, an optical imaging system with low resolution is designed to collect the image signals which are indispensable for imaging processing, while the ultimate goal is to obtain high resolution images from the final system. In order to optimize the global performance, the optimization function of ZEMAX software is utilized and the number of optimization cycles is controlled. Then Wiener filter algorithm is adopted to process the image simulation and mean squared error (MSE) is taken as evaluation criterion. The results show that, although the optical figures of merit for the optical imaging systems is not the best, it can provide image signals that are more suitable for image processing. In conclusion. The integral design of optical system and image processing can search out the overall optimal solution which is missed by the traditional design methods. Especially, when designing some complex optical system, this integral design strategy has obvious advantages to simplify structure and reduce cost, as well as to gain high resolution images simultaneously, which has a promising perspective of industrial application.

A Software Platform for Post-Processing Waveform-Based NDE

NASA Technical Reports Server (NTRS)

Roth, Donald J.; Martin, Richard E.; Seebo, Jeff P.; Trinh, Long B.; Walker, James L.; Winfree, William P.

2007-01-01

Ultrasonic, microwave, and terahertz nondestructive evaluation imaging systems generally require the acquisition of waveforms at each scan point to form an image. For such systems, signal and image processing methods are commonly needed to extract information from the waves and improve resolution of, and highlight, defects in the image. Since some similarity exists for all waveform-based NDE methods, it would seem a common software platform containing multiple signal and image processing techniques to process the waveforms and images makes sense where multiple techniques, scientists, engineers, and organizations are involved. This presentation describes NASA Glenn Research Center's approach in developing a common software platform for processing waveform-based NDE signals and images. This platform is currently in use at NASA Glenn and at Lockheed Martin Michoud Assembly Facility for processing of pulsed terahertz and ultrasonic data. Highlights of the software operation will be given. A case study will be shown for use with terahertz data. The authors also request scientists and engineers who are interested in sharing customized signal and image processing algorithms to contribute to this effort by letting the authors code up and include these algorithms in future releases.

Automatic brain MR image denoising based on texture feature-based artificial neural networks.

PubMed

Chang, Yu-Ning; Chang, Herng-Hua

2015-01-01

Noise is one of the main sources of quality deterioration not only for visual inspection but also in computerized processing in brain magnetic resonance (MR) image analysis such as tissue classification, segmentation and registration. Accordingly, noise removal in brain MR images is important for a wide variety of subsequent processing applications. However, most existing denoising algorithms require laborious tuning of parameters that are often sensitive to specific image features and textures. Automation of these parameters through artificial intelligence techniques will be highly beneficial. In the present study, an artificial neural network associated with image texture feature analysis is proposed to establish a predictable parameter model and automate the denoising procedure. In the proposed approach, a total of 83 image attributes were extracted based on four categories: 1) Basic image statistics. 2) Gray-level co-occurrence matrix (GLCM). 3) Gray-level run-length matrix (GLRLM) and 4) Tamura texture features. To obtain the ranking of discrimination in these texture features, a paired-samples t-test was applied to each individual image feature computed in every image. Subsequently, the sequential forward selection (SFS) method was used to select the best texture features according to the ranking of discrimination. The selected optimal features were further incorporated into a back propagation neural network to establish a predictable parameter model. A wide variety of MR images with various scenarios were adopted to evaluate the performance of the proposed framework. Experimental results indicated that this new automation system accurately predicted the bilateral filtering parameters and effectively removed the noise in a number of MR images. Comparing to the manually tuned filtering process, our approach not only produced better denoised results but also saved significant processing time.

Combined endeavor of Neutrosophic Set and Chan-Vese model to extract accurate liver image from CT scan.

PubMed

Siri, Sangeeta K; Latte, Mrityunjaya V

2017-11-01

Many different diseases can occur in the liver, including infections such as hepatitis, cirrhosis, cancer and over effect of medication or toxins. The foremost stage for computer-aided diagnosis of liver is the identification of liver region. Liver segmentation algorithms extract liver image from scan images which helps in virtual surgery simulation, speedup the diagnosis, accurate investigation and surgery planning. The existing liver segmentation algorithms try to extort exact liver image from abdominal Computed Tomography (CT) scan images. It is an open problem because of ambiguous boundaries, large variation in intensity distribution, variability of liver geometry from patient to patient and presence of noise. A novel approach is proposed to meet challenges in extracting the exact liver image from abdominal CT scan images. The proposed approach consists of three phases: (1) Pre-processing (2) CT scan image transformation to Neutrosophic Set (NS) and (3) Post-processing. In pre-processing, the noise is removed by median filter. The "new structure" is designed to transform a CT scan image into neutrosophic domain which is expressed using three membership subset: True subset (T), False subset (F) and Indeterminacy subset (I). This transform approximately extracts the liver image structure. In post processing phase, morphological operation is performed on indeterminacy subset (I) and apply Chan-Vese (C-V) model with detection of initial contour within liver without user intervention. This resulted in liver boundary identification with high accuracy. Experiments show that, the proposed method is effective, robust and comparable with existing algorithm for liver segmentation of CT scan images. Copyright © 2017 Elsevier B.V. All rights reserved.

Supervised restoration of degraded medical images using multiple-point geostatistics.

PubMed

Pham, Tuan D

2012-06-01

Reducing noise in medical images has been an important issue of research and development for medical diagnosis, patient treatment, and validation of biomedical hypotheses. Noise inherently exists in medical and biological images due to the acquisition and transmission in any imaging devices. Being different from image enhancement, the purpose of image restoration is the process of removing noise from a degraded image in order to recover as much as possible its original version. This paper presents a statistically supervised approach for medical image restoration using the concept of multiple-point geostatistics. Experimental results have shown the effectiveness of the proposed technique which has potential as a new methodology for medical and biological image processing. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

Fabrication of monodisperse magnetic nanoparticles released in solution using a block copolymer template

NASA Astrophysics Data System (ADS)

Morcrette, Mélissa; Ortiz, Guillermo; Tallegas, Salomé; Joisten, Hélène; Tiron, Raluca; Baron, Thierry; Hou, Yanxia; Lequien, Stéphane; Bsiesy, Ahmad; Dieny, Bernard

2017-07-01

This paper describes a fabrication process of monodisperse magnetic nanoparticles released in solution, based on combined ‘top-down’ and ‘bottom-up’ approaches. The process involves the use of a self-assembled PS-PMMA block copolymer formed on a sacrificial layer. Such an approach was so far mostly explored for the preparation of patterned magnetic media for ultrahigh density magnetic storage. It is here extended to the preparation of released monodisperse nanoparticles for biomedical applications. A special sacrificial layer had to be developed compatible with the copolymer self-organization. The resulting nanoparticles exhibit very narrow size dispersion (≈7%) and can be good candidates as contrast agents for medical imaging i.e. magnetic resonance imaging or magnetic particle imaging. The approach provides a great freedom in the choice of the particles shapes and compositions. In particular, they can be made of biocompatible magnetic material.

An Approach for Stitching Satellite Images in a Bigdata Mapreduce Framework

NASA Astrophysics Data System (ADS)

Sarı, H.; Eken, S.; Sayar, A.

2017-11-01

In this study we present a two-step map/reduce framework to stitch satellite mosaic images. The proposed system enable recognition and extraction of objects whose parts falling in separate satellite mosaic images. However this is a time and resource consuming process. The major aim of the study is improving the performance of the image stitching processes by utilizing big data framework. To realize this, we first convert the images into bitmaps (first mapper) and then String formats in the forms of 255s and 0s (second mapper), and finally, find the best possible matching position of the images by a reduce function.

Robust biological parametric mapping: an improved technique for multimodal brain image analysis

NASA Astrophysics Data System (ADS)

Yang, Xue; Beason-Held, Lori; Resnick, Susan M.; Landman, Bennett A.

2011-03-01

Mapping the quantitative relationship between structure and function in the human brain is an important and challenging problem. Numerous volumetric, surface, region of interest and voxelwise image processing techniques have been developed to statistically assess potential correlations between imaging and non-imaging metrics. Recently, biological parametric mapping has extended the widely popular statistical parametric approach to enable application of the general linear model to multiple image modalities (both for regressors and regressands) along with scalar valued observations. This approach offers great promise for direct, voxelwise assessment of structural and functional relationships with multiple imaging modalities. However, as presented, the biological parametric mapping approach is not robust to outliers and may lead to invalid inferences (e.g., artifactual low p-values) due to slight mis-registration or variation in anatomy between subjects. To enable widespread application of this approach, we introduce robust regression and robust inference in the neuroimaging context of application of the general linear model. Through simulation and empirical studies, we demonstrate that our robust approach reduces sensitivity to outliers without substantial degradation in power. The robust approach and associated software package provides a reliable way to quantitatively assess voxelwise correlations between structural and functional neuroimaging modalities.

A computational approach to real-time image processing for serial time-encoded amplified microscopy

NASA Astrophysics Data System (ADS)

Oikawa, Minoru; Hiyama, Daisuke; Hirayama, Ryuji; Hasegawa, Satoki; Endo, Yutaka; Sugie, Takahisa; Tsumura, Norimichi; Kuroshima, Mai; Maki, Masanori; Okada, Genki; Lei, Cheng; Ozeki, Yasuyuki; Goda, Keisuke; Shimobaba, Tomoyoshi

2016-03-01

High-speed imaging is an indispensable technique, particularly for identifying or analyzing fast-moving objects. The serial time-encoded amplified microscopy (STEAM) technique was proposed to enable us to capture images with a frame rate 1,000 times faster than using conventional methods such as CCD (charge-coupled device) cameras. The application of this high-speed STEAM imaging technique to a real-time system, such as flow cytometry for a cell-sorting system, requires successively processing a large number of captured images with high throughput in real time. We are now developing a high-speed flow cytometer system including a STEAM camera. In this paper, we describe our approach to processing these large amounts of image data in real time. We use an analog-to-digital converter that has up to 7.0G samples/s and 8-bit resolution for capturing the output voltage signal that involves grayscale images from the STEAM camera. Therefore the direct data output from the STEAM camera generates 7.0G byte/s continuously. We provided a field-programmable gate array (FPGA) device as a digital signal pre-processor for image reconstruction and finding objects in a microfluidic channel with high data rates in real time. We also utilized graphics processing unit (GPU) devices for accelerating the calculation speed of identification of the reconstructed images. We built our prototype system, which including a STEAM camera, a FPGA device and a GPU device, and evaluated its performance in real-time identification of small particles (beads), as virtual biological cells, owing through a microfluidic channel.

Visual communication - Information and fidelity. [of images

NASA Technical Reports Server (NTRS)

Huck, Freidrich O.; Fales, Carl L.; Alter-Gartenberg, Rachel; Rahman, Zia-Ur; Reichenbach, Stephen E.

1993-01-01

This assessment of visual communication deals with image gathering, coding, and restoration as a whole rather than as separate and independent tasks. The approach focuses on two mathematical criteria, information and fidelity, and on their relationships to the entropy of the encoded data and to the visual quality of the restored image. Past applications of these criteria to the assessment of image coding and restoration have been limited to the link that connects the output of the image-gathering device to the input of the image-display device. By contrast, the approach presented in this paper explicitly includes the critical limiting factors that constrain image gathering and display. This extension leads to an end-to-end assessment theory of visual communication that combines optical design with digital processing.

Validation of Western North America Models based on finite-frequency and ray theory imaging methods

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

Larmat, Carene; Maceira, Monica; Porritt, Robert W.

2015-02-02

We validate seismic models developed for western North America with a focus on effect of imaging methods on data fit. We use the DNA09 models for which our collaborators provide models built with both the body-­wave FF approach and the RT approach, when the data selection, processing and reference models are the same.

Is the linear modeling technique good enough for optimal form design? A comparison of quantitative analysis models.

PubMed

Lin, Yang-Cheng; Yeh, Chung-Hsing; Wang, Chen-Cheng; Wei, Chun-Chun

2012-01-01

How to design highly reputable and hot-selling products is an essential issue in product design. Whether consumers choose a product depends largely on their perception of the product image. A consumer-oriented design approach presented in this paper helps product designers incorporate consumers' perceptions of product forms in the design process. The consumer-oriented design approach uses quantification theory type I, grey prediction (the linear modeling technique), and neural networks (the nonlinear modeling technique) to determine the optimal form combination of product design for matching a given product image. An experimental study based on the concept of Kansei Engineering is conducted to collect numerical data for examining the relationship between consumers' perception of product image and product form elements of personal digital assistants (PDAs). The result of performance comparison shows that the QTTI model is good enough to help product designers determine the optimal form combination of product design. Although the PDA form design is used as a case study, the approach is applicable to other consumer products with various design elements and product images. The approach provides an effective mechanism for facilitating the consumer-oriented product design process.

Is the Linear Modeling Technique Good Enough for Optimal Form Design? A Comparison of Quantitative Analysis Models

PubMed Central

Lin, Yang-Cheng; Yeh, Chung-Hsing; Wang, Chen-Cheng; Wei, Chun-Chun

2012-01-01

How to design highly reputable and hot-selling products is an essential issue in product design. Whether consumers choose a product depends largely on their perception of the product image. A consumer-oriented design approach presented in this paper helps product designers incorporate consumers' perceptions of product forms in the design process. The consumer-oriented design approach uses quantification theory type I, grey prediction (the linear modeling technique), and neural networks (the nonlinear modeling technique) to determine the optimal form combination of product design for matching a given product image. An experimental study based on the concept of Kansei Engineering is conducted to collect numerical data for examining the relationship between consumers' perception of product image and product form elements of personal digital assistants (PDAs). The result of performance comparison shows that the QTTI model is good enough to help product designers determine the optimal form combination of product design. Although the PDA form design is used as a case study, the approach is applicable to other consumer products with various design elements and product images. The approach provides an effective mechanism for facilitating the consumer-oriented product design process. PMID:23258961

Algorithms for Image Analysis and Combination of Pattern Classifiers with Application to Medical Diagnosis

NASA Astrophysics Data System (ADS)

Georgiou, Harris

2009-10-01

Medical Informatics and the application of modern signal processing in the assistance of the diagnostic process in medical imaging is one of the more recent and active research areas today. This thesis addresses a variety of issues related to the general problem of medical image analysis, specifically in mammography, and presents a series of algorithms and design approaches for all the intermediate levels of a modern system for computer-aided diagnosis (CAD). The diagnostic problem is analyzed with a systematic approach, first defining the imaging characteristics and features that are relevant to probable pathology in mammo-grams. Next, these features are quantified and fused into new, integrated radio-logical systems that exhibit embedded digital signal processing, in order to improve the final result and minimize the radiological dose for the patient. In a higher level, special algorithms are designed for detecting and encoding these clinically interest-ing imaging features, in order to be used as input to advanced pattern classifiers and machine learning models. Finally, these approaches are extended in multi-classifier models under the scope of Game Theory and optimum collective deci-sion, in order to produce efficient solutions for combining classifiers with minimum computational costs for advanced diagnostic systems. The material covered in this thesis is related to a total of 18 published papers, 6 in scientific journals and 12 in international conferences.

Classification of follicular lymphoma images: a holistic approach with symbol-based machine learning methods.

PubMed

Zorman, Milan; Sánchez de la Rosa, José Luis; Dinevski, Dejan

2011-12-01

It is not very often to see a symbol-based machine learning approach to be used for the purpose of image classification and recognition. In this paper we will present such an approach, which we first used on the follicular lymphoma images. Lymphoma is a broad term encompassing a variety of cancers of the lymphatic system. Lymphoma is differentiated by the type of cell that multiplies and how the cancer presents itself. It is very important to get an exact diagnosis regarding lymphoma and to determine the treatments that will be most effective for the patient's condition. Our work was focused on the identification of lymphomas by finding follicles in microscopy images provided by the Laboratory of Pathology in the University Hospital of Tenerife, Spain. We divided our work in two stages: in the first stage we did image pre-processing and feature extraction, and in the second stage we used different symbolic machine learning approaches for pixel classification. Symbolic machine learning approaches are often neglected when looking for image analysis tools. They are not only known for a very appropriate knowledge representation, but also claimed to lack computational power. The results we got are very promising and show that symbolic approaches can be successful in image analysis applications.

An improved K-means clustering algorithm in agricultural image segmentation

NASA Astrophysics Data System (ADS)

Cheng, Huifeng; Peng, Hui; Liu, Shanmei

Image segmentation is the first important step to image analysis and image processing. In this paper, according to color crops image characteristics, we firstly transform the color space of image from RGB to HIS, and then select proper initial clustering center and cluster number in application of mean-variance approach and rough set theory followed by clustering calculation in such a way as to automatically segment color component rapidly and extract target objects from background accurately, which provides a reliable basis for identification, analysis, follow-up calculation and process of crops images. Experimental results demonstrate that improved k-means clustering algorithm is able to reduce the computation amounts and enhance precision and accuracy of clustering.

Transform- and multi-domain deep learning for single-frame rapid autofocusing in whole slide imaging.

PubMed

Jiang, Shaowei; Liao, Jun; Bian, Zichao; Guo, Kaikai; Zhang, Yongbing; Zheng, Guoan

2018-04-01

A whole slide imaging (WSI) system has recently been approved for primary diagnostic use in the US. The image quality and system throughput of WSI is largely determined by the autofocusing process. Traditional approaches acquire multiple images along the optical axis and maximize a figure of merit for autofocusing. Here we explore the use of deep convolution neural networks (CNNs) to predict the focal position of the acquired image without axial scanning. We investigate the autofocusing performance with three illumination settings: incoherent Kohler illumination, partially coherent illumination with two plane waves, and one-plane-wave illumination. We acquire ~130,000 images with different defocus distances as the training data set. Different defocus distances lead to different spatial features of the captured images. However, solely relying on the spatial information leads to a relatively bad performance of the autofocusing process. It is better to extract defocus features from transform domains of the acquired image. For incoherent illumination, the Fourier cutoff frequency is directly related to the defocus distance. Similarly, autocorrelation peaks are directly related to the defocus distance for two-plane-wave illumination. In our implementation, we use the spatial image, the Fourier spectrum, the autocorrelation of the spatial image, and combinations thereof as the inputs for the CNNs. We show that the information from the transform domains can improve the performance and robustness of the autofocusing process. The resulting focusing error is ~0.5 µm, which is within the 0.8-µm depth-of-field range. The reported approach requires little hardware modification for conventional WSI systems and the images can be captured on the fly without focus map surveying. It may find applications in WSI and time-lapse microscopy. The transform- and multi-domain approaches may also provide new insights for developing microscopy-related deep-learning networks. We have made our training and testing data set (~12 GB) open-source for the broad research community.

Segmentation of anatomical structures of the heart based on echocardiography

NASA Astrophysics Data System (ADS)

Danilov, V. V.; Skirnevskiy, I. P.; Gerget, O. M.

2017-01-01

Nowadays, many practical applications in the field of medical image processing require valid and reliable segmentation of images in the capacity of input data. Some of the commonly used imaging techniques are ultrasound, CT, and MRI. However, the main difference between the other medical imaging equipment and EchoCG is that it is safer, low cost, non-invasive and non-traumatic. Three-dimensional EchoCG is a non-invasive imaging modality that is complementary and supplementary to two-dimensional imaging and can be used to examine the cardiovascular function and anatomy in different medical settings. The challenging problems, presented by EchoCG image processing, such as speckle phenomena, noise, temporary non-stationarity of processes, unsharp boundaries, attenuation, etc. forced us to consider and compare existing methods and then to develop an innovative approach that can tackle the problems connected with clinical applications. Actual studies are related to the analysis and development of a cardiac parameters automatic detection system by EchoCG that will provide new data on the dynamics of changes in cardiac parameters and improve the accuracy and reliability of the diagnosis. Research study in image segmentation has highlighted the capabilities of image-based methods for medical applications. The focus of the research is both theoretical and practical aspects of the application of the methods. Some of the segmentation approaches can be interesting for the imaging and medical community. Performance evaluation is carried out by comparing the borders, obtained from the considered methods to those manually prescribed by a medical specialist. Promising results demonstrate the possibilities and the limitations of each technique for image segmentation problems. The developed approach allows: to eliminate errors in calculating the geometric parameters of the heart; perform the necessary conditions, such as speed, accuracy, reliability; build a master model that will be an indispensable assistant for operations on a beating heart.

Region-based multifocus image fusion for the precise acquisition of Pap smear images.

PubMed

Tello-Mijares, Santiago; Bescós, Jesús

2018-05-01

A multifocus image fusion method to obtain a single focused image from a sequence of microscopic high-magnification Papanicolau source (Pap smear) images is presented. These images, captured each in a different position of the microscope lens, frequently show partially focused cells or parts of cells, which makes them unpractical for the direct application of image analysis techniques. The proposed method obtains a focused image with a high preservation of original pixels information while achieving a negligible visibility of the fusion artifacts. The method starts by identifying the best-focused image of the sequence; then, it performs a mean-shift segmentation over this image; the focus level of the segmented regions is evaluated in all the images of the sequence, and best-focused regions are merged in a single combined image; finally, this image is processed with an adaptive artifact removal process. The combination of a region-oriented approach, instead of block-based approaches, and a minimum modification of the value of focused pixels in the original images achieve a highly contrasted image with no visible artifacts, which makes this method especially convenient for the medical imaging domain. The proposed method is compared with several state-of-the-art alternatives over a representative dataset. The experimental results show that our proposal obtains the best and more stable quality indicators. (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).

Image Re-Ranking Based on Topic Diversity.

PubMed

Qian, Xueming; Lu, Dan; Wang, Yaxiong; Zhu, Li; Tang, Yuan Yan; Wang, Meng

2017-08-01

Social media sharing Websites allow users to annotate images with free tags, which significantly contribute to the development of the web image retrieval. Tag-based image search is an important method to find images shared by users in social networks. However, how to make the top ranked result relevant and with diversity is challenging. In this paper, we propose a topic diverse ranking approach for tag-based image retrieval with the consideration of promoting the topic coverage performance. First, we construct a tag graph based on the similarity between each tag. Then, the community detection method is conducted to mine the topic community of each tag. After that, inter-community and intra-community ranking are introduced to obtain the final retrieved results. In the inter-community ranking process, an adaptive random walk model is employed to rank the community based on the multi-information of each topic community. Besides, we build an inverted index structure for images to accelerate the searching process. Experimental results on Flickr data set and NUS-Wide data sets show the effectiveness of the proposed approach.

MR-guided prostate interventions.

PubMed

Tempany, Clare; Straus, Sarah; Hata, Nobuhiko; Haker, Steven

2008-02-01

In this article the current issues of diagnosis and detection of prostate cancer are reviewed. The limitations for current techniques are highlighted and some possible solutions with MR imaging and MR-guided biopsy approaches are reviewed. There are several different biopsy approaches under investigation. These include transperineal open magnet approaches to closed-bore 1.5T transrectal biopsies. The imaging, image processing, and tracking methods are also discussed. In the arena of therapy, MR guidance has been used in conjunction with radiation methods, either brachytherapy or external delivery. The principles of the radiation treatment, the toxicities, and use of images are outlined. The future role of imaging and image-guided interventions lie with providing a noninvasive surrogate for cancer surveillance or monitoring treatment response. The shift to minimally invasive focal therapies has already begun and will be very exciting when MR-guided focused ultrasound surgery reaches its full potential. (Copyright) 2008 Wiley-Liss, Inc.

MR-Guided Prostate Interventions

PubMed Central

Tempany, Clare; Straus, Sarah; Hata, Nobuhiko; Haker, Steven

2009-01-01

In this article the current issues of diagnosis and detection of prostate cancer are reviewed. The limitations for current techniques are highlighted and some possible solutions with MR imaging and MR-guided biopsy approaches are reviewed. There are several different biopsy approaches under investigation. These include transperineal open magnet approaches to closed-bore 1.5T transrectal biopsies. The imaging, image processing, and tracking methods are also discussed. In the arena of therapy, MR guidance has been used in conjunction with radiation methods, either brachytherapy or external delivery. The principles of the radiation treatment, the toxicities, and use of images are outlined. The future role of imaging and image-guided interventions lie with providing a noninvasive surrogate for cancer surveillance or monitoring treatment response. The shift to minimally invasive focal therapies has already begun and will be very exciting when MR-guided focused ultrasound surgery reaches its full potential. PMID:18219689

Understanding Appearance-Enhancing Drug Use in Sport Using an Enactive Approach to Body Image

PubMed Central

Hauw, Denis; Bilard, Jean

2017-01-01

From an enactive approach to human activity, we suggest that the use of appearance-enhancing drugs is better explained by the sense-making related to body image rather than the cognitive evaluation of social norms about appearance and consequent psychopathology-oriented approach. After reviewing the main psychological disorders thought to link body image issues to the use of appearance-enhancing substances, we sketch a flexible, dynamic and embedded account of body image defined as the individual’s propensity to act and experience in specific situations. We show how this enacted body image is a complex process of sense-making that people engage in when they are trying to adapt to specific situations. These adaptations of the enacted body image require effort, perseverance and time, and therefore any substance that accelerates this process appears to be an easy and attractive solution. In this enactive account of body image, we underline that the link between the enacted body image and substance use is also anchored in the history of the body’s previous interactions with the world. This emerges during periods of upheaval and hardship, especially in a context where athletes experience weak participatory sense-making in a sport community. We conclude by suggesting prevention and intervention designs that would promote a safe instrumental use of the body in sports and psychological helping procedures for athletes experiencing difficulties with substances use and body image. PMID:29238320

Enhancing facial features by using clear facial features

NASA Astrophysics Data System (ADS)

Rofoo, Fanar Fareed Hanna

2017-09-01

The similarity of features between individuals of same ethnicity motivated the idea of this project. The idea of this project is to extract features of clear facial image and impose them on blurred facial image of same ethnic origin as an approach to enhance a blurred facial image. A database of clear images containing 30 individuals equally divided to five different ethnicities which were Arab, African, Chines, European and Indian. Software was built to perform pre-processing on images in order to align the features of clear and blurred images. And the idea was to extract features of clear facial image or template built from clear facial images using wavelet transformation to impose them on blurred image by using reverse wavelet. The results of this approach did not come well as all the features did not align together as in most cases the eyes were aligned but the nose or mouth were not aligned. Then we decided in the next approach to deal with features separately but in the result in some cases a blocky effect was present on features due to not having close matching features. In general the available small database did not help to achieve the goal results, because of the number of available individuals. The color information and features similarity could be more investigated to achieve better results by having larger database as well as improving the process of enhancement by the availability of closer matches in each ethnicity.

Color preservation for tone reproduction and image enhancement

NASA Astrophysics Data System (ADS)

Hsin, Chengho; Lee, Zong Wei; Lee, Zheng Zhan; Shin, Shaw-Jyh

2014-01-01

Applications based on luminance processing often face the problem of recovering the original chrominance in the output color image. A common approach to reconstruct a color image from the luminance output is by preserving the original hue and saturation. However, this approach often produces a highly colorful image which is undesirable. We develop a color preservation method that not only retains the ratios of the input tri-chromatic values but also adjusts the output chroma in an appropriate way. Linearizing the output luminance is the key idea to realize this method. In addition, a lightness difference metric together with a colorfulness difference metric are proposed to evaluate the performance of the color preservation methods. It shows that the proposed method performs consistently better than the existing approaches.

On-Ground Processing of Yaogan-24 Remote Sensing Satellite Attitude Data and Verification Using Geometric Field Calibration

PubMed Central

Wang, Mi; Fan, Chengcheng; Yang, Bo; Jin, Shuying; Pan, Jun

2016-01-01

Satellite attitude accuracy is an important factor affecting the geometric processing accuracy of high-resolution optical satellite imagery. To address the problem whereby the accuracy of the Yaogan-24 remote sensing satellite’s on-board attitude data processing is not high enough and thus cannot meet its image geometry processing requirements, we developed an approach involving on-ground attitude data processing and digital orthophoto (DOM) and the digital elevation model (DEM) verification of a geometric calibration field. The approach focuses on three modules: on-ground processing based on bidirectional filter, overall weighted smoothing and fitting, and evaluation in the geometric calibration field. Our experimental results demonstrate that the proposed on-ground processing method is both robust and feasible, which ensures the reliability of the observation data quality, convergence and stability of the parameter estimation model. In addition, both the Euler angle and quaternion could be used to build a mathematical fitting model, while the orthogonal polynomial fitting model is more suitable for modeling the attitude parameter. Furthermore, compared to the image geometric processing results based on on-board attitude data, the image uncontrolled and relative geometric positioning result accuracy can be increased by about 50%. PMID:27483287

A Multi-Functional Imaging Approach to High-Content Protein Interaction Screening

PubMed Central

Matthews, Daniel R.; Fruhwirth, Gilbert O.; Weitsman, Gregory; Carlin, Leo M.; Ofo, Enyinnaya; Keppler, Melanie; Barber, Paul R.; Tullis, Iain D. C.; Vojnovic, Borivoj; Ng, Tony; Ameer-Beg, Simon M.

2012-01-01

Functional imaging can provide a level of quantification that is not possible in what might be termed traditional high-content screening. This is due to the fact that the current state-of-the-art high-content screening systems take the approach of scaling-up single cell assays, and are therefore based on essentially pictorial measures as assay indicators. Such phenotypic analyses have become extremely sophisticated, advancing screening enormously, but this approach can still be somewhat subjective. We describe the development, and validation, of a prototype high-content screening platform that combines steady-state fluorescence anisotropy imaging with fluorescence lifetime imaging (FLIM). This functional approach allows objective, quantitative screening of small molecule libraries in protein-protein interaction assays. We discuss the development of the instrumentation, the process by which information on fluorescence resonance energy transfer (FRET) can be extracted from wide-field, acceptor fluorescence anisotropy imaging and cross-checking of this modality using lifetime imaging by time-correlated single-photon counting. Imaging of cells expressing protein constructs where eGFP and mRFP1 are linked with amino-acid chains of various lengths (7, 19 and 32 amino acids) shows the two methodologies to be highly correlated. We validate our approach using a small-scale inhibitor screen of a Cdc42 FRET biosensor probe expressed in epidermoid cancer cells (A431) in a 96 microwell-plate format. We also show that acceptor fluorescence anisotropy can be used to measure variations in hetero-FRET in protein-protein interactions. We demonstrate this using a screen of inhibitors of internalization of the transmembrane receptor, CXCR4. These assays enable us to demonstrate all the capabilities of the instrument, image processing and analytical techniques that have been developed. Direct correlation between acceptor anisotropy and donor FLIM is observed for FRET assays, providing an opportunity to rapidly screen proteins, interacting on the nano-meter scale, using wide-field imaging. PMID:22506000

SU-E-J-108: Solving the Chinese Postman Problem for Effective Contour Deformation

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

Yang, J; Zhang, L; Balter, P

2015-06-15

Purpose: To develop a practical approach for accurate contour deformation when deformable image registration (DIR) is used for atlas-based segmentation or contour propagation in image-guided radiotherapy. Methods: A contour deformation approach was developed on the basis of 3D mesh operations. The 2D contours represented by a series of points in each slice were first converted to a 3D triangular mesh, which was deformed by the deformation vectors resulting from DIR. A set of parallel 2D planes then cut through the deformed 3D mesh, generating unordered points and line segments, which should be reorganized into a set of 2D contour points.more » It was realized that the reorganization problem was equivalent to solving the Chinese Postman Problem (CPP) by traversing a graph built from the unordered points with the least cost. Alternatively, deformation could be applied to a binary mask converted from the original contours. The deformed binary mask was then converted back into contours at the CT slice locations. We performed a qualitative comparison to validate the mesh-based approach against the image-based approach. Results: The DIR could considerably change the 3D mesh, making complicated 2D contour representations after deformation. CPP was able to effectively reorganize the points in 2D planes no matter how complicated the 2D contours were. The mesh-based approach did not require a post-processing of the contour, thus accurately showing the actual deformation in DIR. The mesh-based approach could keep some fine details and resulted in smoother contours than the image-based approach did, especially for the lung structure. Image-based approach appeared to over-process contours and suffered from image resolution limits. The mesh-based approach was integrated into in-house DIR software for use in routine clinic and research. Conclusion: We developed a practical approach for accurate contour deformation. The efficiency of this approach was demonstrated in both clinic and research applications. This work was partially supported by Cancer Prevention & Research Institute of Texas (CPRIT) RP110562.« less

A semi-automated image analysis procedure for in situ plankton imaging systems.

PubMed

Bi, Hongsheng; Guo, Zhenhua; Benfield, Mark C; Fan, Chunlei; Ford, Michael; Shahrestani, Suzan; Sieracki, Jeffery M

2015-01-01

Plankton imaging systems are capable of providing fine-scale observations that enhance our understanding of key physical and biological processes. However, processing the large volumes of data collected by imaging systems remains a major obstacle for their employment, and existing approaches are designed either for images acquired under laboratory controlled conditions or within clear waters. In the present study, we developed a semi-automated approach to analyze plankton taxa from images acquired by the ZOOplankton VISualization (ZOOVIS) system within turbid estuarine waters, in Chesapeake Bay. When compared to images under laboratory controlled conditions or clear waters, images from highly turbid waters are often of relatively low quality and more variable, due to the large amount of objects and nonlinear illumination within each image. We first customized a segmentation procedure to locate objects within each image and extracted them for classification. A maximally stable extremal regions algorithm was applied to segment large gelatinous zooplankton and an adaptive threshold approach was developed to segment small organisms, such as copepods. Unlike the existing approaches for images acquired from laboratory, controlled conditions or clear waters, the target objects are often the majority class, and the classification can be treated as a multi-class classification problem. We customized a two-level hierarchical classification procedure using support vector machines to classify the target objects (< 5%), and remove the non-target objects (> 95%). First, histograms of oriented gradients feature descriptors were constructed for the segmented objects. In the first step all non-target and target objects were classified into different groups: arrow-like, copepod-like, and gelatinous zooplankton. Each object was passed to a group-specific classifier to remove most non-target objects. After the object was classified, an expert or non-expert then manually removed the non-target objects that could not be removed by the procedure. The procedure was tested on 89,419 images collected in Chesapeake Bay, and results were consistent with visual counts with >80% accuracy for all three groups.

A Semi-Automated Image Analysis Procedure for In Situ Plankton Imaging Systems

PubMed Central

Bi, Hongsheng; Guo, Zhenhua; Benfield, Mark C.; Fan, Chunlei; Ford, Michael; Shahrestani, Suzan; Sieracki, Jeffery M.

2015-01-01

Plankton imaging systems are capable of providing fine-scale observations that enhance our understanding of key physical and biological processes. However, processing the large volumes of data collected by imaging systems remains a major obstacle for their employment, and existing approaches are designed either for images acquired under laboratory controlled conditions or within clear waters. In the present study, we developed a semi-automated approach to analyze plankton taxa from images acquired by the ZOOplankton VISualization (ZOOVIS) system within turbid estuarine waters, in Chesapeake Bay. When compared to images under laboratory controlled conditions or clear waters, images from highly turbid waters are often of relatively low quality and more variable, due to the large amount of objects and nonlinear illumination within each image. We first customized a segmentation procedure to locate objects within each image and extracted them for classification. A maximally stable extremal regions algorithm was applied to segment large gelatinous zooplankton and an adaptive threshold approach was developed to segment small organisms, such as copepods. Unlike the existing approaches for images acquired from laboratory, controlled conditions or clear waters, the target objects are often the majority class, and the classification can be treated as a multi-class classification problem. We customized a two-level hierarchical classification procedure using support vector machines to classify the target objects (< 5%), and remove the non-target objects (> 95%). First, histograms of oriented gradients feature descriptors were constructed for the segmented objects. In the first step all non-target and target objects were classified into different groups: arrow-like, copepod-like, and gelatinous zooplankton. Each object was passed to a group-specific classifier to remove most non-target objects. After the object was classified, an expert or non-expert then manually removed the non-target objects that could not be removed by the procedure. The procedure was tested on 89,419 images collected in Chesapeake Bay, and results were consistent with visual counts with >80% accuracy for all three groups. PMID:26010260

Segmentation Approach Towards Phase-Contrast Microscopic Images of Activated Sludge to Monitor the Wastewater Treatment.

PubMed

Khan, Muhammad Burhan; Nisar, Humaira; Ng, Choon Aun; Yeap, Kim Ho; Lai, Koon Chun

2017-12-01

Image processing and analysis is an effective tool for monitoring and fault diagnosis of activated sludge (AS) wastewater treatment plants. The AS image comprise of flocs (microbial aggregates) and filamentous bacteria. In this paper, nine different approaches are proposed for image segmentation of phase-contrast microscopic (PCM) images of AS samples. The proposed strategies are assessed for their effectiveness from the perspective of microscopic artifacts associated with PCM. The first approach uses an algorithm that is based on the idea that different color space representation of images other than red-green-blue may have better contrast. The second uses an edge detection approach. The third strategy, employs a clustering algorithm for the segmentation and the fourth applies local adaptive thresholding. The fifth technique is based on texture-based segmentation and the sixth uses watershed algorithm. The seventh adopts a split-and-merge approach. The eighth employs Kittler's thresholding. Finally, the ninth uses a top-hat and bottom-hat filtering-based technique. The approaches are assessed, and analyzed critically with reference to the artifacts of PCM. Gold approximations of ground truth images are prepared to assess the segmentations. Overall, the edge detection-based approach exhibits the best results in terms of accuracy, and the texture-based algorithm in terms of false negative ratio. The respective scenarios are explained for suitability of edge detection and texture-based algorithms.

Visualization and recommendation of large image collections toward effective sensemaking

NASA Astrophysics Data System (ADS)

Gu, Yi; Wang, Chaoli; Nemiroff, Robert; Kao, David; Parra, Denis

2016-03-01

In our daily lives, images are among the most commonly found data which we need to handle. We present iGraph, a graph-based approach for visual analytics of large image collections and their associated text information. Given such a collection, we compute the similarity between images, the distance between texts, and the connection between image and text to construct iGraph, a compound graph representation which encodes the underlying relationships among these images and texts. To enable effective visual navigation and comprehension of iGraph with tens of thousands of nodes and hundreds of millions of edges, we present a progressive solution that offers collection overview, node comparison, and visual recommendation. Our solution not only allows users to explore the entire collection with representative images and keywords but also supports detailed comparison for understanding and intuitive guidance for navigation. The visual exploration of iGraph is further enhanced with the implementation of bubble sets to highlight group memberships of nodes, suggestion of abnormal keywords or time periods based on text outlier detection, and comparison of four different recommendation solutions. For performance speedup, multiple graphics processing units and central processing units are utilized for processing and visualization in parallel. We experiment with two image collections and leverage a cluster driving a display wall of nearly 50 million pixels. We show the effectiveness of our approach by demonstrating experimental results and conducting a user study.

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

Real-time implementations of image segmentation algorithms on shared memory multicore architecture: a survey (Conference Presentation)

NASA Astrophysics Data System (ADS)

Akil, Mohamed

2017-05-01

The real-time processing is getting more and more important in many image processing applications. Image segmentation is one of the most fundamental tasks image analysis. As a consequence, many different approaches for image segmentation have been proposed. The watershed transform is a well-known image segmentation tool. The watershed transform is a very data intensive task. To achieve acceleration and obtain real-time processing of watershed algorithms, parallel architectures and programming models for multicore computing have been developed. This paper focuses on the survey of the approaches for parallel implementation of sequential watershed algorithms on multicore general purpose CPUs: homogeneous multicore processor with shared memory. To achieve an efficient parallel implementation, it's necessary to explore different strategies (parallelization/distribution/distributed scheduling) combined with different acceleration and optimization techniques to enhance parallelism. In this paper, we give a comparison of various parallelization of sequential watershed algorithms on shared memory multicore architecture. We analyze the performance measurements of each parallel implementation and the impact of the different sources of overhead on the performance of the parallel implementations. In this comparison study, we also discuss the advantages and disadvantages of the parallel programming models. Thus, we compare the OpenMP (an application programming interface for multi-Processing) with Ptheads (POSIX Threads) to illustrate the impact of each parallel programming model on the performance of the parallel implementations.

Adaptive windowing in contrast-enhanced intravascular ultrasound imaging.

PubMed

Lindsey, Brooks D; Martin, K Heath; Jiang, Xiaoning; Dayton, Paul A

2016-08-01

Intravascular ultrasound (IVUS) is one of the most commonly-used interventional imaging techniques and has seen recent innovations which attempt to characterize the risk posed by atherosclerotic plaques. One such development is the use of microbubble contrast agents to image vasa vasorum, fine vessels which supply oxygen and nutrients to the walls of coronary arteries and typically have diameters less than 200μm. The degree of vasa vasorum neovascularization within plaques is positively correlated with plaque vulnerability. Having recently presented a prototype dual-frequency transducer for contrast agent-specific intravascular imaging, here we describe signal processing approaches based on minimum variance (MV) beamforming and the phase coherence factor (PCF) for improving the spatial resolution and contrast-to-tissue ratio (CTR) in IVUS imaging. These approaches are examined through simulations, phantom studies, ex vivo studies in porcine arteries, and in vivo studies in chicken embryos. In phantom studies, PCF processing improved CTR by a mean of 4.2dB, while combined MV and PCF processing improved spatial resolution by 41.7%. Improvements of 2.2dB in CTR and 37.2% in resolution were observed in vivo. Applying these processing strategies can enhance image quality in conventional B-mode IVUS or in contrast-enhanced IVUS, where signal-to-noise ratio is relatively low and resolution is at a premium. Copyright © 2016 Elsevier B.V. All rights reserved.

CARES: Completely Automated Robust Edge Snapper for carotid ultrasound IMT measurement on a multi-institutional database of 300 images: a two stage system combining an intensity-based feature approach with first order absolute moments

NASA Astrophysics Data System (ADS)

Molinari, Filippo; Acharya, Rajendra; Zeng, Guang; Suri, Jasjit S.

2011-03-01

The carotid intima-media thickness (IMT) is the most used marker for the progression of atherosclerosis and onset of the cardiovascular diseases. Computer-aided measurements improve accuracy, but usually require user interaction. In this paper we characterized a new and completely automated technique for carotid segmentation and IMT measurement based on the merits of two previously developed techniques. We used an integrated approach of intelligent image feature extraction and line fitting for automatically locating the carotid artery in the image frame, followed by wall interfaces extraction based on Gaussian edge operator. We called our system - CARES. We validated the CARES on a multi-institutional database of 300 carotid ultrasound images. IMT measurement bias was 0.032 +/- 0.141 mm, better than other automated techniques and comparable to that of user-driven methodologies. Our novel approach of CARES processed 96% of the images leading to the figure of merit to be 95.7%. CARES ensured complete automation and high accuracy in IMT measurement; hence it could be a suitable clinical tool for processing of large datasets in multicenter studies involving atherosclerosis.pre-

MIA - A free and open source software for gray scale medical image analysis

PubMed Central

2013-01-01

Background Gray scale images make the bulk of data in bio-medical image analysis, and hence, the main focus of many image processing tasks lies in the processing of these monochrome images. With ever improving acquisition devices, spatial and temporal image resolution increases, and data sets become very large. Various image processing frameworks exists that make the development of new algorithms easy by using high level programming languages or visual programming. These frameworks are also accessable to researchers that have no background or little in software development because they take care of otherwise complex tasks. Specifically, the management of working memory is taken care of automatically, usually at the price of requiring more it. As a result, processing large data sets with these tools becomes increasingly difficult on work station class computers. One alternative to using these high level processing tools is the development of new algorithms in a languages like C++, that gives the developer full control over how memory is handled, but the resulting workflow for the prototyping of new algorithms is rather time intensive, and also not appropriate for a researcher with little or no knowledge in software development. Another alternative is in using command line tools that run image processing tasks, use the hard disk to store intermediate results, and provide automation by using shell scripts. Although not as convenient as, e.g. visual programming, this approach is still accessable to researchers without a background in computer science. However, only few tools exist that provide this kind of processing interface, they are usually quite task specific, and don’t provide an clear approach when one wants to shape a new command line tool from a prototype shell script. Results The proposed framework, MIA, provides a combination of command line tools, plug-ins, and libraries that make it possible to run image processing tasks interactively in a command shell and to prototype by using the according shell scripting language. Since the hard disk becomes the temporal storage memory management is usually a non-issue in the prototyping phase. By using string-based descriptions for filters, optimizers, and the likes, the transition from shell scripts to full fledged programs implemented in C++ is also made easy. In addition, its design based on atomic plug-ins and single tasks command line tools makes it easy to extend MIA, usually without the requirement to touch or recompile existing code. Conclusion In this article, we describe the general design of MIA, a general purpouse framework for gray scale image processing. We demonstrated the applicability of the software with example applications from three different research scenarios, namely motion compensation in myocardial perfusion imaging, the processing of high resolution image data that arises in virtual anthropology, and retrospective analysis of treatment outcome in orthognathic surgery. With MIA prototyping algorithms by using shell scripts that combine small, single-task command line tools is a viable alternative to the use of high level languages, an approach that is especially useful when large data sets need to be processed. PMID:24119305

MIA - A free and open source software for gray scale medical image analysis.

PubMed

Wollny, Gert; Kellman, Peter; Ledesma-Carbayo, María-Jesus; Skinner, Matthew M; Hublin, Jean-Jaques; Hierl, Thomas

2013-10-11

Gray scale images make the bulk of data in bio-medical image analysis, and hence, the main focus of many image processing tasks lies in the processing of these monochrome images. With ever improving acquisition devices, spatial and temporal image resolution increases, and data sets become very large.Various image processing frameworks exists that make the development of new algorithms easy by using high level programming languages or visual programming. These frameworks are also accessable to researchers that have no background or little in software development because they take care of otherwise complex tasks. Specifically, the management of working memory is taken care of automatically, usually at the price of requiring more it. As a result, processing large data sets with these tools becomes increasingly difficult on work station class computers.One alternative to using these high level processing tools is the development of new algorithms in a languages like C++, that gives the developer full control over how memory is handled, but the resulting workflow for the prototyping of new algorithms is rather time intensive, and also not appropriate for a researcher with little or no knowledge in software development.Another alternative is in using command line tools that run image processing tasks, use the hard disk to store intermediate results, and provide automation by using shell scripts. Although not as convenient as, e.g. visual programming, this approach is still accessable to researchers without a background in computer science. However, only few tools exist that provide this kind of processing interface, they are usually quite task specific, and don't provide an clear approach when one wants to shape a new command line tool from a prototype shell script. The proposed framework, MIA, provides a combination of command line tools, plug-ins, and libraries that make it possible to run image processing tasks interactively in a command shell and to prototype by using the according shell scripting language. Since the hard disk becomes the temporal storage memory management is usually a non-issue in the prototyping phase. By using string-based descriptions for filters, optimizers, and the likes, the transition from shell scripts to full fledged programs implemented in C++ is also made easy. In addition, its design based on atomic plug-ins and single tasks command line tools makes it easy to extend MIA, usually without the requirement to touch or recompile existing code. In this article, we describe the general design of MIA, a general purpouse framework for gray scale image processing. We demonstrated the applicability of the software with example applications from three different research scenarios, namely motion compensation in myocardial perfusion imaging, the processing of high resolution image data that arises in virtual anthropology, and retrospective analysis of treatment outcome in orthognathic surgery. With MIA prototyping algorithms by using shell scripts that combine small, single-task command line tools is a viable alternative to the use of high level languages, an approach that is especially useful when large data sets need to be processed.

Automation of 3D reconstruction of neural tissue from large volume of conventional serial section transmission electron micrographs.

PubMed

Mishchenko, Yuriy

2009-01-30

We describe an approach for automation of the process of reconstruction of neural tissue from serial section transmission electron micrographs. Such reconstructions require 3D segmentation of individual neuronal processes (axons and dendrites) performed in densely packed neuropil. We first detect neuronal cell profiles in each image in a stack of serial micrographs with multi-scale ridge detector. Short breaks in detected boundaries are interpolated using anisotropic contour completion formulated in fuzzy-logic framework. Detected profiles from adjacent sections are linked together based on cues such as shape similarity and image texture. Thus obtained 3D segmentation is validated by human operators in computer-guided proofreading process. Our approach makes possible reconstructions of neural tissue at final rate of about 5 microm3/manh, as determined primarily by the speed of proofreading. To date we have applied this approach to reconstruct few blocks of neural tissue from different regions of rat brain totaling over 1000microm3, and used these to evaluate reconstruction speed, quality, error rates, and presence of ambiguous locations in neuropil ssTEM imaging data.

MOPEX: a software package for astronomical image processing and visualization

NASA Astrophysics Data System (ADS)

Makovoz, David; Roby, Trey; Khan, Iffat; Booth, Hartley

2006-06-01

We present MOPEX - a software package for astronomical image processing and display. The package is a combination of command-line driven image processing software written in C/C++ with a Java-based GUI. The main image processing capabilities include creating mosaic images, image registration, background matching, point source extraction, as well as a number of minor image processing tasks. The combination of the image processing and display capabilities allows for much more intuitive and efficient way of performing image processing. The GUI allows for the control over the image processing and display to be closely intertwined. Parameter setting, validation, and specific processing options are entered by the user through a set of intuitive dialog boxes. Visualization feeds back into further processing by providing a prompt feedback of the processing results. The GUI also allows for further analysis by accessing and displaying data from existing image and catalog servers using a virtual observatory approach. Even though originally designed for the Spitzer Space Telescope mission, a lot of functionalities are of general usefulness and can be used for working with existing astronomical data and for new missions. The software used in the package has undergone intensive testing and benefited greatly from effective software reuse. The visualization part has been used for observation planning for both the Spitzer and Herschel Space Telescopes as part the tool Spot. The visualization capabilities of Spot have been enhanced and integrated with the image processing functionality of the command-line driven MOPEX. The image processing software is used in the Spitzer automated pipeline processing, which has been in operation for nearly 3 years. The image processing capabilities have also been tested in off-line processing by numerous astronomers at various institutions around the world. The package is multi-platform and includes automatic update capabilities. The software package has been developed by a small group of software developers and scientists at the Spitzer Science Center. It is available for distribution at the Spitzer Science Center web page.

Different binarization processes validated against manual counts of fluorescent bacterial cells.

PubMed

Tamminga, Gerrit G; Paulitsch-Fuchs, Astrid H; Jansen, Gijsbert J; Euverink, Gert-Jan W

2016-09-01

State of the art software methods (such as fixed value approaches or statistical approaches) to create a binary image of fluorescent bacterial cells are not as accurate and precise as they should be for counting bacteria and measuring their area. To overcome these bottlenecks, we introduce biological significance to obtain a binary image from a greyscale microscopic image. Using our biological significance approach we are able to automatically count about the same number of cells as an individual researcher would do by manual/visual counting. Using the fixed value or statistical approach to obtain a binary image leads to about 20% less cells in automatic counting. In our procedure we included the area measurements of the bacterial cells to determine the right parameters for background subtraction and threshold values. In an iterative process the threshold and background subtraction values were incremented until the number of particles smaller than a typical bacterial cell is less than the number of bacterial cells with a certain area. This research also shows that every image has a specific threshold with respect to the optical system, magnification and staining procedure as well as the exposure time. The biological significance approach shows that automatic counting can be performed with the same accuracy, precision and reproducibility as manual counting. The same approach can be used to count bacterial cells using different optical systems (Leica, Olympus and Navitar), magnification factors (200× and 400×), staining procedures (DNA (Propidium Iodide) and RNA (FISH)) and substrates (polycarbonate filter or glass). Copyright © 2016 Elsevier B.V. All rights reserved.

Fault Diagnosis of Rolling Bearing Based on Fast Nonlocal Means and Envelop Spectrum

PubMed Central

Lv, Yong; Zhu, Qinglin; Yuan, Rui

2015-01-01

The nonlocal means (NL-Means) method that has been widely used in the field of image processing in recent years effectively overcomes the limitations of the neighborhood filter and eliminates the artifact and edge problems caused by the traditional image denoising methods. Although NL-Means is very popular in the field of 2D image signal processing, it has not received enough attention in the field of 1D signal processing. This paper proposes a novel approach that diagnoses the fault of a rolling bearing based on fast NL-Means and the envelop spectrum. The parameters of the rolling bearing signals are optimized in the proposed method, which is the key contribution of this paper. This approach is applied to the fault diagnosis of rolling bearing, and the results have shown the efficiency at detecting roller bearing failures. PMID:25585105

Full 3D Microwave Tomography enhanced GPR surveys: a case study

NASA Astrophysics Data System (ADS)

Catapano, Ilaria; Soldovieri, Francesco; Affinito, Antonio; Hugenschmidt, Johannes

2014-05-01

Ground Penetrating Radar (GPR) systems are well assessed non-invasive diagnostic tools capable of providing high resolution images of the inner structure of the probed spatial region. Owing to this capability, GPR systems are nowadays more and more considered in the frame of civil engineering surveys since they may give information on constructive details as well as on the aging and risk factors affecting the healthiness of an infrastructure. In this frame, accurate, reliable and easily interpretable images of the probed scenarios are mandatory in order to support the management of maintenance works and assure the safety of structures. Such a requirement motivates the use of different and sophisticated data processing approaches in order to compare more than one image of the same scene, thus improving the reliability and objectiveness of the GPR survey results. Among GPR data processing procedures, Microwave Tomography approaches based on the Born approximation face the imaging as the solution of a linear inverse problem, which is solved by using the Truncated Singular Value Decomposition as a regularized inversion scheme [1]. So far, an approach exploiting a 2D scalar model of the scattering phenomenon have been adopted to process GPR data gathered along a single scan. In this case, 3D images are obtained by interpolating 2D reconstructions (this is referred commonly as pseudo 3D imaging). Such an imaging approach have provided valuable results in several real cases dealing with not only surveys for civil engineering but also archeological prospection, subservice monitoring, security surveys and so on [1-4]. These encouraging results have motivated the development of a full 3D Microwave Tomography approach capable of accounting for the vectorial nature of the wave propagation. The reconstruction capabilities of this novel approach have been assessed mainly against experimental data collected in laboratory controlled conditions. The obtained results corroborate that, the use of a full 3D scattering model allows an improved estimation of the objects shape and size with respect to pseudo 3D imaging [5]. In this communication, the performance offered by the full 3D imaging approach is investigated by using a dataset from infrastructure inspection. Since the collapse of a car park in Switzerland killing 7 firemen, "punching", where a pile remains upright but the ceiling carried by the pile falls down, is considered a serious problem The 3D tomography approach was applied to a dataset acquired in a car park in the vicinity of piles. Such datasets can be used for an assessment of the safety of such structures and can therefore be considered as relevant test cases for innovative data processing and inversion strategies. REFERENCES [1] F. Soldovieri, J. Hugenschmidt, R. Persico and G. Leone, "A linear inverse scattering algorithm for realistic GPR applications, Near Surf. Geophys., vol. 5, pp.29-42, 2007. [2] I. Catapano, L. Crocco R. Di Napoli, F. Soldovieri, A. Brancaccio, F. Pesando, A. Aiello, "Microwave tomography enhanced GPR surveys in Centaur's Domus, Regio VI of Pompeii, Italy", J. Geophys. Eng., vol.9, S92-S99, 2012. [3] I. Catapano, R. Di Napoli, F. Soldovieri, M. Bavusi, A. Loperte, J. Dumoulin, Structural monitoring via microwave tomography-enhanced GPR: the Montagnole test site, J. Geophys. Eng., vol. 9, S100-S107, 2012 [4] J. Hugenschmidt, A. Kalogeropoulos, F. Soldovieri, G. Prisco (2010) Processing Strategies for high-resolution GPR Concrete Inspections, NDT & E International, Volume 43, Issue 4: 334-342. [5] I. Catapano, A. Affinito, G. Gennarelli, F. di Maio, A. Loperte, F. Soldovieri, Full three-dimensional imaging via ground penetrating radar: assessment in controlled conditions and on field for archaeological prospecting, Appl. Phys. A: Materials Science and Processing, pp. 1-8, Article in Press

Software architecture for intelligent image processing using Prolog

NASA Astrophysics Data System (ADS)

Jones, Andrew C.; Batchelor, Bruce G.

1994-10-01

We describe a prototype system for interactive image processing using Prolog, implemented by the first author on an Apple Macintosh computer. This system is inspired by Prolog+, but differs from it in two particularly important respects. The first is that whereas Prolog+ assumes the availability of dedicated image processing hardware, with which the Prolog system communicates, our present system implements image processing functions in software using the C programming language. The second difference is that although our present system supports Prolog+ commands, these are implemented in terms of lower-level Prolog predicates which provide a more flexible approach to image manipulation. We discuss the impact of the Apple Macintosh operating system upon the implementation of the image-processing functions, and the interface between these functions and the Prolog system. We also explain how the Prolog+ commands have been implemented. The system described in this paper is a fairly early prototype, and we outline how we intend to develop the system, a task which is expedited by the extensible architecture we have implemented.

Accessible biometrics: A frustrated total internal reflection approach to imaging fingerprints.

PubMed

Smith, Nathan D; Sharp, James S

2017-05-01

Fingerprints are widely used as a means of identifying persons of interest because of the highly individual nature of the spatial distribution and types of features (or minuta) found on the surface of a finger. This individuality has led to their wide application in the comparison of fingerprints found at crime scenes with those taken from known offenders and suspects in custody. However, despite recent advances in machine vision technology and image processing techniques, fingerprint evidence is still widely being collected using outdated practices involving ink and paper - a process that can be both time consuming and expensive. Reduction of forensic service budgets increasingly requires that evidence be gathered and processed more rapidly and efficiently. However, many of the existing digital fingerprint acquisition devices have proven too expensive to roll out on a large scale. As a result new, low-cost imaging technologies are required to increase the quality and throughput of the processing of fingerprint evidence. Here we describe an inexpensive approach to digital fingerprint acquisition that is based upon frustrated total internal reflection imaging. The quality and resolution of the images produced are shown to be as good as those currently acquired using ink and paper based methods. The same imaging technique is also shown to be capable of imaging powdered fingerprints that have been lifted from a crime scene using adhesive tape or gel lifters. Copyright © 2017 The Chartered Society of Forensic Sciences. Published by Elsevier B.V. All rights reserved.

Some advances/results in monitoring road cracks from 2D pavement images within the scope of the collaborative FP7 TRIMM project

NASA Astrophysics Data System (ADS)

Baltazart, Vincent; Moliard, Jean-Marc; Amhaz, Rabih; Wright, Dean; Jethwa, Manish

2015-04-01

Monitoring road surface conditions is an important issue in many countries. Several projects have looked into this issue in recent years, including TRIMM 2011-2014. The objective of such projects has been to detect surface distresses, like cracking, raveling and water ponding, in order to plan effective road maintenance and to afford a better sustainability of the pavement. The monitoring of cracking conventionally focuses on open cracks on the surface of the pavement, as opposed to reflexive cracks embedded in the pavement materials. For monitoring surface condition, in situ human visual inspection has been gradually replaced by automatic image data collection at traffic speed. Off-line image processing techniques have been developed for monitoring surface condition in support of human visual control. Full automation of crack monitoring has been approached with caution, and depends on a proper manual assessment of the performance. This work firstly presents some aspects of the current state of monitoring that have been reported so far in the literature and in previous projects: imaging technology and image processing techniques. Then, the work presents the two image processing techniques that have been developed within the scope of the TRIMM project to automatically detect pavement cracking from images. The first technique is a heuristic approach (HA) based on the search for gradient within the image. It was originally developed to process pavement images from the French imaging device, Aigle-RN. The second technique, the Minimal Path Selection (MPS) method, has been developed within an ongoing PhD work at IFSTTAR. The proposed new technique provides a fine and accurate segmentation of the crack pattern along with the estimation of the crack width. HA has been assessed against the field data collection provided by Yotta and TRL with the imaging device Tempest 2. The performance assessment has been threefold: first it was performed against the reference data set including 130 km of pavement images over UK roads, second over a few selected short sections of contiguous pavement images, and finally over a few sample images as a case study. The performance of MPS has been assessed against an older image data base. Pixel-based PGT was available to provide the most sensitive performance assessment. MPS has shown its ability to provide a very accurate cracking pattern without reducing the image resolution on the segmented images. Thus, it allows measurement of the crack width; it is found to behave more robustly against the image texture and better matched for dealing with low contrast pavement images. The benchmarking of seven automatic segmentation techniques has been provided at both the pixel and the grid levels. The performance assessment includes three minimal path selection algorithms, namely MPS, Free Form Anisotropy (FFA), one geodesic contour with automatic selection of points of interests (GC-POI), HA, and two Markov-based methods. Among others, MPS approach reached the best performance at the pixel level while it is matched to the FFA approach at the grid level. Finally, the project has emphasized the need for a reliable ground truth data collection. Owing to its accuracy, MPS may serve as a reference benchmark for other methods to provide the automatic segmentation of pavement images at the pixel level and beyond. As a counterpart, MPS requires a reduction in the computing time. Keywords: cracking, automatic segmentation, image processing, pavement, surface distress, monitoring, DICE, performance

Developing tools for digital radar image data evaluation

NASA Technical Reports Server (NTRS)

Domik, G.; Leberl, F.; Raggam, J.

1986-01-01

The refinement of radar image analysis methods has led to a need for a systems approach to radar image processing software. Developments stimulated through satellite radar are combined with standard image processing techniques to create a user environment to manipulate and analyze airborne and satellite radar images. One aim is to create radar products for the user from the original data to enhance the ease of understanding the contents. The results are called secondary image products and derive from the original digital images. Another aim is to support interactive SAR image analysis. Software methods permit use of a digital height model to create ortho images, synthetic images, stereo-ortho images, radar maps or color combinations of different component products. Efforts are ongoing to integrate individual tools into a combined hardware/software environment for interactive radar image analysis.

Fast restoration approach for motion blurred image based on deconvolution under the blurring paths

NASA Astrophysics Data System (ADS)

Shi, Yu; Song, Jie; Hua, Xia

2015-12-01

For the real-time motion deblurring, it is of utmost importance to get a higher processing speed with about the same image quality. This paper presents a fast Richardson-Lucy motion deblurring approach to remove motion blur which rotates blurred image under blurring paths. Hence, the computational time is reduced sharply by using one-dimensional Fast Fourier Transform in one-dimensional Richardson-Lucy method. In order to obtain accurate transformational results, interpolation method is incorporated to fetch the gray values. Experiment results demonstrate that the proposed approach is efficient and effective to reduce motion blur under the blur paths.

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

PubMed

Anderson, Jeffrey R; Barrett, Steven F

2009-01-01

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

Research on fast Fourier transforms algorithm of huge remote sensing image technology with GPU and partitioning technology.

PubMed

Yang, Xue; Li, Xue-You; Li, Jia-Guo; Ma, Jun; Zhang, Li; Yang, Jan; Du, Quan-Ye

2014-02-01

Fast Fourier transforms (FFT) is a basic approach to remote sensing image processing. With the improvement of capacity of remote sensing image capture with the features of hyperspectrum, high spatial resolution and high temporal resolution, how to use FFT technology to efficiently process huge remote sensing image becomes the critical step and research hot spot of current image processing technology. FFT algorithm, one of the basic algorithms of image processing, can be used for stripe noise removal, image compression, image registration, etc. in processing remote sensing image. CUFFT function library is the FFT algorithm library based on CPU and FFTW. FFTW is a FFT algorithm developed based on CPU in PC platform, and is currently the fastest CPU based FFT algorithm function library. However there is a common problem that once the available memory or memory is less than the capacity of image, there will be out of memory or memory overflow when using the above two methods to realize image FFT arithmetic. To address this problem, a CPU and partitioning technology based Huge Remote Fast Fourier Transform (HRFFT) algorithm is proposed in this paper. By improving the FFT algorithm in CUFFT function library, the problem of out of memory and memory overflow is solved. Moreover, this method is proved rational by experiment combined with the CCD image of HJ-1A satellite. When applied to practical image processing, it improves effect of the image processing, speeds up the processing, which saves the time of computation and achieves sound result.

Dynamic Stimuli And Active Processing In Human Visual Perception

NASA Astrophysics Data System (ADS)

Haber, Ralph N.

1990-03-01

Theories of visual perception traditionally have considered a static retinal image to be the starting point for processing; and has considered processing both to be passive and a literal translation of that frozen, two dimensional, pictorial image. This paper considers five problem areas in the analysis of human visually guided locomotion, in which the traditional approach is contrasted to newer ones that utilize dynamic definitions of stimulation, and an active perceiver: (1) differentiation between object motion and self motion, and among the various kinds of self motion (e.g., eyes only, head only, whole body, and their combinations); (2) the sources and contents of visual information that guide movement; (3) the acquisition and performance of perceptual motor skills; (4) the nature of spatial representations, percepts, and the perceived layout of space; and (5) and why the retinal image is a poor starting point for perceptual processing. These newer approaches argue that stimuli must be considered as dynamic: humans process the systematic changes in patterned light when objects move and when they themselves move. Furthermore, the processing of visual stimuli must be active and interactive, so that perceivers can construct panoramic and stable percepts from an interaction of stimulus information and expectancies of what is contained in the visual environment. These developments all suggest a very different approach to the computational analyses of object location and identification, and of the visual guidance of locomotion.

Evaluating the effectiveness of treatment of corneal ulcers via computer-based automatic image analysis

NASA Astrophysics Data System (ADS)

Otoum, Nesreen A.; Edirisinghe, Eran A.; Dua, Harminder; Faraj, Lana

2012-06-01

Corneal Ulcers are a common eye disease that requires prompt treatment. Recently a number of treatment approaches have been introduced that have been proven to be very effective. Unfortunately, the monitoring process of the treatment procedure remains manual and hence time consuming and prone to human errors. In this research we propose an automatic image analysis based approach to measure the size of an ulcer and its subsequent further investigation to determine the effectiveness of any treatment process followed. In Ophthalmology an ulcer area is detected for further inspection via luminous excitation of a dye. Usually in the imaging systems utilised for this purpose (i.e. a slit lamp with an appropriate dye) the ulcer area is excited to be luminous green in colour as compared to rest of the cornea which appears blue/brown. In the proposed approach we analyse the image in the HVS colour space. Initially a pre-processing stage that carries out a local histogram equalisation is used to bring back detail in any over or under exposed areas. Secondly we deal with the removal of potential reflections from the affected areas by making use of image registration of two candidate corneal images based on the detected corneal areas. Thirdly the exact corneal boundary is detected by initially registering an ellipse to the candidate corneal boundary detected via edge detection and subsequently allowing the user to modify the boundary to overlap with the boundary of the ulcer being observed. Although this step makes the approach semi automatic, it removes the impact of breakages of the corneal boundary due to occlusion, noise, image quality degradations. The ratio between the ulcer area confined within the corneal area to the corneal area is used as a measure of comparison. We demonstrate the use of the proposed tool in the analysis of the effectiveness of a treatment procedure adopted for corneal ulcers in patients by comparing the variation of corneal size over time.

Intensity-Curvature Measurement Approaches for the Diagnosis of Magnetic Resonance Imaging Brain Tumors.

PubMed

Ciulla, Carlo; Veljanovski, Dimitar; Rechkoska Shikoska, Ustijana; Risteski, Filip A

2015-11-01

This research presents signal-image post-processing techniques called Intensity-Curvature Measurement Approaches with application to the diagnosis of human brain tumors detected through Magnetic Resonance Imaging (MRI). Post-processing of the MRI of the human brain encompasses the following model functions: (i) bivariate cubic polynomial, (ii) bivariate cubic Lagrange polynomial, (iii) monovariate sinc, and (iv) bivariate linear. The following Intensity-Curvature Measurement Approaches were used: (i) classic-curvature, (ii) signal resilient to interpolation, (iii) intensity-curvature measure and (iv) intensity-curvature functional. The results revealed that the classic-curvature, the signal resilient to interpolation and the intensity-curvature functional are able to add additional information useful to the diagnosis carried out with MRI. The contribution to the MRI diagnosis of our study are: (i) the enhanced gray level scale of the tumor mass and the well-behaved representation of the tumor provided through the signal resilient to interpolation, and (ii) the visually perceptible third dimension perpendicular to the image plane provided through the classic-curvature and the intensity-curvature functional.

Intensity-Curvature Measurement Approaches for the Diagnosis of Magnetic Resonance Imaging Brain Tumors

PubMed Central

Ciulla, Carlo; Veljanovski, Dimitar; Rechkoska Shikoska, Ustijana; Risteski, Filip A.

2015-01-01

This research presents signal-image post-processing techniques called Intensity-Curvature Measurement Approaches with application to the diagnosis of human brain tumors detected through Magnetic Resonance Imaging (MRI). Post-processing of the MRI of the human brain encompasses the following model functions: (i) bivariate cubic polynomial, (ii) bivariate cubic Lagrange polynomial, (iii) monovariate sinc, and (iv) bivariate linear. The following Intensity-Curvature Measurement Approaches were used: (i) classic-curvature, (ii) signal resilient to interpolation, (iii) intensity-curvature measure and (iv) intensity-curvature functional. The results revealed that the classic-curvature, the signal resilient to interpolation and the intensity-curvature functional are able to add additional information useful to the diagnosis carried out with MRI. The contribution to the MRI diagnosis of our study are: (i) the enhanced gray level scale of the tumor mass and the well-behaved representation of the tumor provided through the signal resilient to interpolation, and (ii) the visually perceptible third dimension perpendicular to the image plane provided through the classic-curvature and the intensity-curvature functional. PMID:26644943

A review of state-of-the-art speckle reduction techniques for optical coherence tomography fingertip scans

NASA Astrophysics Data System (ADS)

Darlow, Luke N.; Akhoury, Sharat S.; Connan, James

2015-02-01

Standard surface fingerprint scanners are vulnerable to counterfeiting attacks and also failure due to skin damage and distortion. Thus a high security and damage resistant means of fingerprint acquisition is needed, providing scope for new approaches and technologies. Optical Coherence Tomography (OCT) is a high resolution imaging technology that can be used to image the human fingertip and allow for the extraction of a subsurface fingerprint. Being robust toward spoofing and damage, the subsurface fingerprint is an attractive solution. However, the nature of the OCT scanning process induces speckle: a correlative and multiplicative noise. Six speckle reducing filters for the digital enhancement of OCT fingertip scans have been evaluated. The optimized Bayesian non-local means algorithm improved the structural similarity between processed and reference images by 34%, increased the signal-to-noise ratio, and yielded the most promising visual results. An adaptive wavelet approach, originally designed for ultrasound imaging, and a speckle reducing anisotropic diffusion approach also yielded promising results. A reformulation of these in future work, with an OCT-speckle specific model, may improve their performance.

Applications of process improvement techniques to improve workflow in abdominal imaging.

PubMed

Tamm, Eric Peter

2016-03-01

Major changes in the management and funding of healthcare are underway that will markedly change the way radiology studies will be reimbursed. The result will be the need to deliver radiology services in a highly efficient manner while maintaining quality. The science of process improvement provides a practical approach to improve the processes utilized in radiology. This article will address in a step-by-step manner how to implement process improvement techniques to improve workflow in abdominal imaging.

The Analysis of Image Segmentation Hierarchies with a Graph-based Knowledge Discovery System

NASA Technical Reports Server (NTRS)

Tilton, James C.; Cooke, diane J.; Ketkar, Nikhil; Aksoy, Selim

2008-01-01

Currently available pixel-based analysis techniques do not effectively extract the information content from the increasingly available high spatial resolution remotely sensed imagery data. A general consensus is that object-based image analysis (OBIA) is required to effectively analyze this type of data. OBIA is usually a two-stage process; image segmentation followed by an analysis of the segmented objects. We are exploring an approach to OBIA in which hierarchical image segmentations provided by the Recursive Hierarchical Segmentation (RHSEG) software developed at NASA GSFC are analyzed by the Subdue graph-based knowledge discovery system developed by a team at Washington State University. In this paper we discuss out initial approach to representing the RHSEG-produced hierarchical image segmentations in a graphical form understandable by Subdue, and provide results on real and simulated data. We also discuss planned improvements designed to more effectively and completely convey the hierarchical segmentation information to Subdue and to improve processing efficiency.

Automated cloud and shadow detection and filling using two-date Landsat imagery in the United States

USGS Publications Warehouse

Jin, Suming; Homer, Collin G.; Yang, Limin; Xian, George; Fry, Joyce; Danielson, Patrick; Townsend, Philip A.

2013-01-01

A simple, efficient, and practical approach for detecting cloud and shadow areas in satellite imagery and restoring them with clean pixel values has been developed. Cloud and shadow areas are detected using spectral information from the blue, shortwave infrared, and thermal infrared bands of Landsat Thematic Mapper or Enhanced Thematic Mapper Plus imagery from two dates (a target image and a reference image). These detected cloud and shadow areas are further refined using an integration process and a false shadow removal process according to the geometric relationship between cloud and shadow. Cloud and shadow filling is based on the concept of the Spectral Similarity Group (SSG), which uses the reference image to find similar alternative pixels in the target image to serve as replacement values for restored areas. Pixels are considered to belong to one SSG if the pixel values from Landsat bands 3, 4, and 5 in the reference image are within the same spectral ranges. This new approach was applied to five Landsat path/rows across different landscapes and seasons with various types of cloud patterns. Results show that almost all of the clouds were captured with minimal commission errors, and shadows were detected reasonably well. Among five test scenes, the lowest producer's accuracy of cloud detection was 93.9% and the lowest user's accuracy was 89%. The overall cloud and shadow detection accuracy ranged from 83.6% to 99.3%. The pixel-filling approach resulted in a new cloud-free image that appears seamless and spatially continuous despite differences in phenology between the target and reference images. Our methods offer a straightforward and robust approach for preparing images for the new 2011 National Land Cover Database production.

Novel SPECT Technologies and Approaches in Cardiac Imaging

PubMed Central

Slomka, Piotr; Hung, Guang-Uei; Germano, Guido; Berman, Daniel S.

2017-01-01

Recent novel approaches in myocardial perfusion single photon emission CT (SPECT) have been facilitated by new dedicated high-efficiency hardware with solid-state detectors and optimized collimators. New protocols include very low-dose (1 mSv) stress-only, two-position imaging to mitigate attenuation artifacts, and simultaneous dual-isotope imaging. Attenuation correction can be performed by specialized low-dose systems or by previously obtained CT coronary calcium scans. Hybrid protocols using CT angiography have been proposed. Image quality improvements have been demonstrated by novel reconstructions and motion correction. Fast SPECT acquisition facilitates dynamic flow and early function measurements. Image processing algorithms have become automated with virtually unsupervised extraction of quantitative imaging variables. This automation facilitates integration with clinical variables derived by machine learning to predict patient outcome or diagnosis. In this review, we describe new imaging protocols made possible by the new hardware developments. We also discuss several novel software approaches for the quantification and interpretation of myocardial perfusion SPECT scans. PMID:29034066

An Integrated Photogrammetric and Photoclinometric Approach for Pixel-Resolution 3d Modelling of Lunar Surface

NASA Astrophysics Data System (ADS)

Liu, W. C.; Wu, B.

2018-04-01

High-resolution 3D modelling of lunar surface is important for lunar scientific research and exploration missions. Photogrammetry is known for 3D mapping and modelling from a pair of stereo images based on dense image matching. However dense matching may fail in poorly textured areas and in situations when the image pair has large illumination differences. As a result, the actual achievable spatial resolution of the 3D model from photogrammetry is limited by the performance of dense image matching. On the other hand, photoclinometry (i.e., shape from shading) is characterised by its ability to recover pixel-wise surface shapes based on image intensity and imaging conditions such as illumination and viewing directions. More robust shape reconstruction through photoclinometry can be achieved by incorporating images acquired under different illumination conditions (i.e., photometric stereo). Introducing photoclinometry into photogrammetric processing can therefore effectively increase the achievable resolution of the mapping result while maintaining its overall accuracy. This research presents an integrated photogrammetric and photoclinometric approach for pixel-resolution 3D modelling of the lunar surface. First, photoclinometry is interacted with stereo image matching to create robust and spatially well distributed dense conjugate points. Then, based on the 3D point cloud derived from photogrammetric processing of the dense conjugate points, photoclinometry is further introduced to derive the 3D positions of the unmatched points and to refine the final point cloud. The approach is able to produce one 3D point for each image pixel within the overlapping area of the stereo pair so that to obtain pixel-resolution 3D models. Experiments using the Lunar Reconnaissance Orbiter Camera - Narrow Angle Camera (LROC NAC) images show the superior performances of the approach compared with traditional photogrammetric technique. The results and findings from this research contribute to optimal exploitation of image information for high-resolution 3D modelling of the lunar surface, which is of significance for the advancement of lunar and planetary mapping.

Multimodal imaging approach to monitor browning of adipose tissue in vivo.

PubMed

Chan, Xin Hui Derryn; Balasundaram, Ghayathri; Attia, Amalina Binte Ebrahim; Goggi, Julian L; Ramasamy, Boominathan; Han, Weiping; Olivo, Malini; Sugii, Shigeki

2018-06-01

The discovery that white adipocytes can undergo a browning process to become metabolically active beige cells has attracted significant interest in the fight against obesity. However, the study of adipose browning has been impeded by a lack of imaging tools that allow longitudinal and noninvasive monitoring of this process in vivo. Here, we report a preclinical imaging approach to detect development of beige adipocytes during adrenergic stimulation. In this approach, we expressed near-infrared fluorescent protein, iRFP720, driven under an uncoupling protein-1 ( Ucp1 ) promoter in mice by viral transduction, and used multispectral optoacoustic imaging technology with ultrasound tomography (MSOT-US) to assess adipose beiging during adrenergic stimulation. We observed increased photoacoustic signal at 720 nm, coupled with attenuated lipid signals in stimulated animals. As a proof of concept, we validated our approach against hybrid positron emission tomography combined with magnetic resonance (PET/MR) imaging modality, and quantified the extent of adipose browning by MRI-guided segmentation of 2-deoxy-2- 18 F-fluoro-d-glucose uptake signals. The browning extent detected by MSOT-US and PET/MR are well correlated with Ucp1 induction. Taken together, these systems offer great opportunities for preclinical screening aimed at identifying compounds that promote adipose browning and translation of these discoveries into clinical studies of humans. Copyright © 2018 Chan et al.

Cognitive issues in searching images with visual queries

NASA Astrophysics Data System (ADS)

Yu, ByungGu; Evens, Martha W.

1999-01-01

In this paper, we propose our image indexing technique and visual query processing technique. Our mental images are different from the actual retinal images and many things, such as personal interests, personal experiences, perceptual context, the characteristics of spatial objects, and so on, affect our spatial perception. These private differences are propagated into our mental images and so our visual queries become different from the real images that we want to find. This is a hard problem and few people have tried to work on it. In this paper, we survey the human mental imagery system, the human spatial perception, and discuss several kinds of visual queries. Also, we propose our own approach to visual query interpretation and processing.

Short-term solar flare prediction using image-case-based reasoning

NASA Astrophysics Data System (ADS)

Liu, Jin-Fu; Li, Fei; Zhang, Huai-Peng; Yu, Da-Ren

2017-10-01

Solar flares strongly influence space weather and human activities, and their prediction is highly complex. The existing solutions such as data based approaches and model based approaches have a common shortcoming which is the lack of human engagement in the forecasting process. An image-case-based reasoning method is introduced to achieve this goal. The image case library is composed of SOHO/MDI longitudinal magnetograms, the images from which exhibit the maximum horizontal gradient, the length of the neutral line and the number of singular points that are extracted for retrieving similar image cases. Genetic optimization algorithms are employed for optimizing the weight assignment for image features and the number of similar image cases retrieved. Similar image cases and prediction results derived by majority voting for these similar image cases are output and shown to the forecaster in order to integrate his/her experience with the final prediction results. Experimental results demonstrate that the case-based reasoning approach has slightly better performance than other methods, and is more efficient with forecasts improved by humans.

Hybrid cardiac imaging with MR-CAT scan: a feasibility study.

PubMed

Hillenbrand, C; Sandstede, J; Pabst, T; Hahn, D; Haase, A; Jakob, P M

2000-06-01

We demonstrate the feasibility of a new versatile hybrid imaging concept, the combined acquisition technique (CAT), for cardiac imaging. The cardiac CAT approach, which combines new methodology with existing technology, essentially integrates fast low-angle shot (FLASH) and echoplanar imaging (EPI) modules in a sequential fashion, whereby each acquisition module is employed with independently optimized imaging parameters. One important CAT sequence optimization feature is the ability to use different bandwidths for different acquisition modules. Twelve healthy subjects were imaged using three cardiac CAT acquisition strategies: a) CAT was used to reduce breath-hold duration times while maintaining constant spatial resolution; b) CAT was used to increase spatial resolution in a given breath-hold time; and c) single-heart beat CAT imaging was performed. The results obtained demonstrate the feasibility of cardiac imaging using the CAT approach and the potential of this technique to accelerate the imaging process with almost conserved image quality. Copyright 2000 Wiley-Liss, Inc.

Sensor fusion for synthetic vision

NASA Technical Reports Server (NTRS)

Pavel, M.; Larimer, J.; Ahumada, A.

1991-01-01

Display methodologies are explored for fusing images gathered by millimeter wave sensors with images rendered from an on-board terrain data base to facilitate visually guided flight and ground operations in low visibility conditions. An approach to fusion based on multiresolution image representation and processing is described which facilitates fusion of images differing in resolution within and between images. To investigate possible fusion methods, a workstation-based simulation environment is being developed.

Evidential Reasoning in Expert Systems for Image Analysis.

DTIC Science & Technology

1985-02-01

techniques to image analysis (IA). There is growing evidence that these techniques offer significant improvements in image analysis , particularly in the...2) to provide a common framework for analysis, (3) to structure the ER process for major expert-system tasks in image analysis , and (4) to identify...approaches to three important tasks for expert systems in the domain of image analysis . This segment concluded with an assessment of the strengths

[Research on spatially modulated Fourier transform imaging spectrometer data processing method].

PubMed

Huang, Min; Xiangli, Bin; Lü, Qun-Bo; Zhou, Jin-Song; Jing, Juan-Juan; Cui, Yan

2010-03-01

Fourier transform imaging spectrometer is a new technic, and has been developed very rapidly in nearly ten years. The data catched by Fourier transform imaging spectrometer is indirect data, can not be used by user, and need to be processed by various approaches, including data pretreatment, apodization, phase correction, FFT, and spectral radicalization calibration. No paper so far has been found roundly to introduce this method. In the present paper, the author will give an effective method to process the interfering data to spectral data, and with this method we can obtain good result.

A new scanning electron microscopy approach to image aerogels at the nanoscale

NASA Astrophysics Data System (ADS)

Solá, F.; Hurwitz, F.; Yang, J.

2011-04-01

A new scanning electron microscopy (SEM) technique to image poor electrically conductive aerogels is presented. The process can be performed by non-expert SEM users. We showed that negative charging effects on aerogels can be minimized significantly by inserting dry nitrogen gas close to the region of interest. The process involves the local recombination of accumulated negative charges with positive ions generated from ionization processes. This new technique made possible the acquisition of images of aerogels with pores down to approximately 3 nm in diameter using a positively biased Everhart-Thornley (ET) detector.

Designing a Virtual Item Bank Based on the Techniques of Image Processing

ERIC Educational Resources Information Center

Liao, Wen-Wei; Ho, Rong-Guey

2011-01-01

One of the major weaknesses of the item exposure rates of figural items in Intelligence Quotient (IQ) tests lies in its inaccuracies. In this study, a new approach is proposed and a useful test tool known as the Virtual Item Bank (VIB) is introduced. The VIB combine Automatic Item Generation theory and image processing theory with the concepts of…

Use of micro computed-tomography and 3D printing for reverse engineering of mouse embryo nasal capsule

NASA Astrophysics Data System (ADS)

Tesařová, M.; Zikmund, T.; Kaucká, M.; Adameyko, I.; Jaroš, J.; Paloušek, D.; Škaroupka, D.; Kaiser, J.

2016-03-01

Imaging of increasingly complex cartilage in vertebrate embryos is one of the key tasks of developmental biology. This is especially important to study shape-organizing processes during initial skeletal formation and growth. Advanced imaging techniques that are reflecting biological needs give a powerful impulse to push the boundaries of biological visualization. Recently, techniques for contrasting tissues and organs have improved considerably, extending traditional 2D imaging approaches to 3D . X-ray micro computed tomography (μCT), which allows 3D imaging of biological objects including their internal structures with a resolution in the micrometer range, in combination with contrasting techniques seems to be the most suitable approach for non-destructive imaging of embryonic developing cartilage. Despite there are many software-based ways for visualization of 3D data sets, having a real solid model of the studied object might give novel opportunities to fully understand the shape-organizing processes in the developing body. In this feasibility study we demonstrated the full procedure of creating a real 3D object of mouse embryo nasal capsule, i.e. the staining, the μCT scanning combined by the advanced data processing and the 3D printing.

a Clustering-Based Approach for Evaluation of EO Image Indexing

NASA Astrophysics Data System (ADS)

Bahmanyar, R.; Rigoll, G.; Datcu, M.

2013-09-01

The volume of Earth Observation data is increasing immensely in order of several Terabytes a day. Therefore, to explore and investigate the content of this huge amount of data, developing more sophisticated Content-Based Information Retrieval (CBIR) systems are highly demanded. These systems should be able to not only discover unknown structures behind the data, but also provide relevant results to the users' queries. Since in any retrieval system the images are processed based on a discrete set of their features (i.e., feature descriptors), study and assessment of the structure of feature space, build by different feature descriptors, is of high importance. In this paper, we introduce a clustering-based approach to study the content of image collections. In our approach, we claim that using both internal and external evaluation of clusters for different feature descriptors, helps to understand the structure of feature space. Moreover, the semantic understanding of users about the images also can be assessed. To validate the performance of our approach, we used an annotated Synthetic Aperture Radar (SAR) image collection. Quantitative results besides the visualization of feature space demonstrate the applicability of our approach.

Generalizing the Nonlocal-Means to Super-Resolution Reconstruction

DTIC Science & Technology

2008-12-12

Image Process., vol. 5, no. 6, pp. 996–1011, Jun. 1996. [7] A. J. Patti, M. I. Sezan, and M. A. Tekalp, “ Superresolution video reconstruction with...computationally efficient image superresolution algorithm,” IEEE Trans. Image Process., vol. 10, no. 4, pp. 573–583, Apr. 2001. [13] M. Elad and Y...pp. 21–36, May 2003. [18] S. Farsiu, D. Robinson, M. Elad, and P. Milanfar, “Robust shift and add approach to superresolution ,” in Proc. SPIE Conf

Cellular neural network-based hybrid approach toward automatic image registration

NASA Astrophysics Data System (ADS)

Arun, Pattathal VijayaKumar; Katiyar, Sunil Kumar

2013-01-01

Image registration is a key component of various image processing operations that involve the analysis of different image data sets. Automatic image registration domains have witnessed the application of many intelligent methodologies over the past decade; however, inability to properly model object shape as well as contextual information has limited the attainable accuracy. A framework for accurate feature shape modeling and adaptive resampling using advanced techniques such as vector machines, cellular neural network (CNN), scale invariant feature transform (SIFT), coreset, and cellular automata is proposed. CNN has been found to be effective in improving feature matching as well as resampling stages of registration and complexity of the approach has been considerably reduced using coreset optimization. The salient features of this work are cellular neural network approach-based SIFT feature point optimization, adaptive resampling, and intelligent object modelling. Developed methodology has been compared with contemporary methods using different statistical measures. Investigations over various satellite images revealed that considerable success was achieved with the approach. This system has dynamically used spectral and spatial information for representing contextual knowledge using CNN-prolog approach. This methodology is also illustrated to be effective in providing intelligent interpretation and adaptive resampling.

Using polynomials to simplify fixed pattern noise and photometric correction of logarithmic CMOS image sensors.

PubMed

Li, Jing; Mahmoodi, Alireza; Joseph, Dileepan

2015-10-16

An important class of complementary metal-oxide-semiconductor (CMOS) image sensors are those where pixel responses are monotonic nonlinear functions of light stimuli. This class includes various logarithmic architectures, which are easily capable of wide dynamic range imaging, at video rates, but which are vulnerable to image quality issues. To minimize fixed pattern noise (FPN) and maximize photometric accuracy, pixel responses must be calibrated and corrected due to mismatch and process variation during fabrication. Unlike literature approaches, which employ circuit-based models of varying complexity, this paper introduces a novel approach based on low-degree polynomials. Although each pixel may have a highly nonlinear response, an approximately-linear FPN calibration is possible by exploiting the monotonic nature of imaging. Moreover, FPN correction requires only arithmetic, and an optimal fixed-point implementation is readily derived, subject to a user-specified number of bits per pixel. Using a monotonic spline, involving cubic polynomials, photometric calibration is also possible without a circuit-based model, and fixed-point photometric correction requires only a look-up table. The approach is experimentally validated with a logarithmic CMOS image sensor and is compared to a leading approach from the literature. The novel approach proves effective and efficient.

Magnetic Resonance Fingerprinting - a promising new approach to obtain standardized imaging biomarkers from MRI.

PubMed

2015-04-01

Current routine MRI examinations rely on the acquisition of qualitative images that have a contrast "weighted" for a mixture of (magnetic) tissue properties. Recently, a novel approach was introduced, namely MR Fingerprinting (MRF) with a completely different approach to data acquisition, post-processing and visualization. Instead of using a repeated, serial acquisition of data for the characterization of individual parameters of interest, MRF uses a pseudo randomized acquisition that causes the signals from different tissues to have a unique signal evolution or 'fingerprint' that is simultaneously a function of the multiple material properties under investigation. The processing after acquisition involves a pattern recognition algorithm to match the fingerprints to a predefined dictionary of predicted signal evolutions. These can then be translated into quantitative maps of the magnetic parameters of interest. MR Fingerprinting (MRF) is a technique that could theoretically be applied to most traditional qualitative MRI methods and replaces them with acquisition of truly quantitative tissue measures. MRF is, thereby, expected to be much more accurate and reproducible than traditional MRI and should improve multi-center studies and significantly reduce reader bias when diagnostic imaging is performed. Key Points • MR fingerprinting (MRF) is a new approach to data acquisition, post-processing and visualization.• MRF provides highly accurate quantitative maps of T1, T2, proton density, diffusion.• MRF may offer multiparametric imaging with high reproducibility, and high potential for multicenter/ multivendor studies.

Imaging through water turbulence with a plenoptic sensor

NASA Astrophysics Data System (ADS)

Wu, Chensheng; Ko, Jonathan; Davis, Christopher C.

2016-09-01

A plenoptic sensor can be used to improve the image formation process in a conventional camera. Through this process, the conventional image is mapped to an image array that represents the image's photon paths along different angular directions. Therefore, it can be used to resolve imaging problems where severe distortion happens. Especially for objects observed at moderate range (10m to 200m) through turbulent water, the image can be twisted to be entirely unrecognizable and correction algorithms need to be applied. In this paper, we show how to use a plenoptic sensor to recover an unknown object in line of sight through significant water turbulence distortion. In general, our approach can be applied to both atmospheric turbulence and water turbulence conditions.

Symmetric Phase Only Filtering for Improved DPIV Data Processing

NASA Technical Reports Server (NTRS)

Wernet, Mark P.

2006-01-01

The standard approach in Digital Particle Image Velocimetry (DPIV) data processing is to use Fast Fourier Transforms to obtain the cross-correlation of two single exposure subregions, where the location of the cross-correlation peak is representative of the most probable particle displacement across the subregion. This standard DPIV processing technique is analogous to Matched Spatial Filtering, a technique commonly used in optical correlators to perform the crosscorrelation operation. Phase only filtering is a well known variation of Matched Spatial Filtering, which when used to process DPIV image data yields correlation peaks which are narrower and up to an order of magnitude larger than those obtained using traditional DPIV processing. In addition to possessing desirable correlation plane features, phase only filters also provide superior performance in the presence of DC noise in the correlation subregion. When DPIV image subregions contaminated with surface flare light or high background noise levels are processed using phase only filters, the correlation peak pertaining only to the particle displacement is readily detected above any signal stemming from the DC objects. Tedious image masking or background image subtraction are not required. Both theoretical and experimental analyses of the signal-to-noise ratio performance of the filter functions are presented. In addition, a new Symmetric Phase Only Filtering (SPOF) technique, which is a variation on the traditional phase only filtering technique, is described and demonstrated. The SPOF technique exceeds the performance of the traditionally accepted phase only filtering techniques and is easily implemented in standard DPIV FFT based correlation processing with no significant computational performance penalty. An "Automatic" SPOF algorithm is presented which determines when the SPOF is able to provide better signal to noise results than traditional PIV processing. The SPOF based optical correlation processing approach is presented as a new paradigm for more robust cross-correlation processing of low signal-to-noise ratio DPIV image data."

Segmentation of the glottal space from laryngeal images using the watershed transform.

PubMed

Osma-Ruiz, Víctor; Godino-Llorente, Juan I; Sáenz-Lechón, Nicolás; Fraile, Rubén

2008-04-01

The present work describes a new method for the automatic detection of the glottal space from laryngeal images obtained either with high speed or with conventional video cameras attached to a laryngoscope. The detection is based on the combination of several relevant techniques in the field of digital image processing. The image is segmented with a watershed transform followed by a region merging, while the final decision is taken using a simple linear predictor. This scheme has successfully segmented the glottal space in all the test images used. The method presented can be considered a generalist approach for the segmentation of the glottal space because, in contrast with other methods found in literature, this approach does not need either initialization or finding strict environmental conditions extracted from the images to be processed. Therefore, the main advantage is that the user does not have to outline the region of interest with a mouse click. In any case, some a priori knowledge about the glottal space is needed, but this a priori knowledge can be considered weak compared to the environmental conditions fixed in former works.

Associative architecture for image processing

NASA Astrophysics Data System (ADS)

Adar, Rutie; Akerib, Avidan

1997-09-01

This article presents a new generation in parallel processing architecture for real-time image processing. The approach is implemented in a real time image processor chip, called the XiumTM-2, based on combining a fully associative array which provides the parallel engine with a serial RISC core on the same die. The architecture is fully programmable and can be programmed to implement a wide range of color image processing, computer vision and media processing functions in real time. The associative part of the chip is based on patented pending methodology of Associative Computing Ltd. (ACL), which condenses 2048 associative processors, each of 128 'intelligent' bits. Each bit can be a processing bit or a memory bit. At only 33 MHz and 0.6 micron manufacturing technology process, the chip has a computational power of 3 billion ALU operations per second and 66 billion string search operations per second. The fully programmable nature of the XiumTM-2 chip enables developers to use ACL tools to write their own proprietary algorithms combined with existing image processing and analysis functions from ACL's extended set of libraries.

An enhanced approach for biomedical image restoration using image fusion techniques

NASA Astrophysics Data System (ADS)

Karam, Ghada Sabah; Abbas, Fatma Ismail; Abood, Ziad M.; Kadhim, Kadhim K.; Karam, Nada S.

2018-05-01

Biomedical image is generally noisy and little blur due to the physical mechanisms of the acquisition process, so one of the common degradations in biomedical image is their noise and poor contrast. The idea of biomedical image enhancement is to improve the quality of the image for early diagnosis. In this paper we are using Wavelet Transformation to remove the Gaussian noise from biomedical images: Positron Emission Tomography (PET) image and Radiography (Radio) image, in different color spaces (RGB, HSV, YCbCr), and we perform the fusion of the denoised images resulting from the above denoising techniques using add image method. Then some quantive performance metrics such as signal -to -noise ratio (SNR), peak signal-to-noise ratio (PSNR), and Mean Square Error (MSE), etc. are computed. Since this statistical measurement helps in the assessment of fidelity and image quality. The results showed that our approach can be applied of Image types of color spaces for biomedical images.

Tchebichef moment transform on image dithering for mobile applications

NASA Astrophysics Data System (ADS)

Ernawan, Ferda; Abu, Nur Azman; Rahmalan, Hidayah

2012-04-01

Currently, mobile image applications spend a lot of computing process to display images. A true color raw image contains billions of colors and it consumes high computational power in most mobile image applications. At the same time, mobile devices are only expected to be equipped with lower computing process and minimum storage space. Image dithering is a popular technique to reduce the numbers of bit per pixel at the expense of lower quality image displays. This paper proposes a novel approach on image dithering using 2x2 Tchebichef moment transform (TMT). TMT integrates a simple mathematical framework technique using matrices. TMT coefficients consist of real rational numbers. An image dithering based on TMT has the potential to provide better efficiency and simplicity. The preliminary experiment shows a promising result in term of error reconstructions and image visual textures.

Image Navigation and Registration Performance Assessment Evaluation Tools for GOES-R ABI and GLM

NASA Technical Reports Server (NTRS)

Houchin, Scott; Porter, Brian; Graybill, Justin; Slingerland, Philip

2017-01-01

The GOES-R Flight Project has developed an Image Navigation and Registration (INR) Performance Assessment Tool Set (IPATS) for measuring Advanced Baseline Imager (ABI) and Geostationary Lightning Mapper (GLM) INR performance metrics in the post-launch period for performance evaluation and long term monitoring. IPATS utilizes a modular algorithmic design to allow user selection of data processing sequences optimized for generation of each INR metric. This novel modular approach minimizes duplication of common processing elements, thereby maximizing code efficiency and speed. Fast processing is essential given the large number of sub-image registrations required to generate INR metrics for the many images produced over a 24 hour evaluation period. This paper describes the software design and implementation of IPATS and provides preliminary test results.

Unified modeling language and design of a case-based retrieval system in medical imaging.

PubMed

LeBozec, C; Jaulent, M C; Zapletal, E; Degoulet, P

1998-01-01

One goal of artificial intelligence research into case-based reasoning (CBR) systems is to develop approaches for designing useful and practical interactive case-based environments. Explaining each step of the design of the case-base and of the retrieval process is critical for the application of case-based systems to the real world. We describe herein our approach to the design of IDEM--Images and Diagnosis from Examples in Medicine--a medical image case-based retrieval system for pathologists. Our approach is based on the expressiveness of an object-oriented modeling language standard: the Unified Modeling Language (UML). We created a set of diagrams in UML notation illustrating the steps of the CBR methodology we used. The key aspect of this approach was selecting the relevant objects of the system according to user requirements and making visualization of cases and of the components of the case retrieval process. Further evaluation of the expressiveness of the design document is required but UML seems to be a promising formalism, improving the communication between the developers and users.

Virtual k -Space Modulation Optical Microscopy

NASA Astrophysics Data System (ADS)

Kuang, Cuifang; Ma, Ye; Zhou, Renjie; Zheng, Guoan; Fang, Yue; Xu, Yingke; Liu, Xu; So, Peter T. C.

2016-07-01

We report a novel superresolution microscopy approach for imaging fluorescence samples. The reported approach, termed virtual k -space modulation optical microscopy (VIKMOM), is able to improve the lateral resolution by a factor of 2, reduce the background level, improve the optical sectioning effect and correct for unknown optical aberrations. In the acquisition process of VIKMOM, we used a scanning confocal microscope setup with a 2D detector array to capture sample information at each scanned x -y position. In the recovery process of VIKMOM, we first modulated the captured data by virtual k -space coding and then employed a ptychography-inspired procedure to recover the sample information and correct for unknown optical aberrations. We demonstrated the performance of the reported approach by imaging fluorescent beads, fixed bovine pulmonary artery endothelial (BPAE) cells, and living human astrocytes (HA). As the VIKMOM approach is fully compatible with conventional confocal microscope setups, it may provide a turn-key solution for imaging biological samples with ˜100 nm lateral resolution, in two or three dimensions, with improved optical sectioning capabilities and aberration correcting.

Multifocus watermarking approach based on discrete cosine transform.

PubMed

Waheed, Safa Riyadh; Alkawaz, Mohammed Hazim; Rehman, Amjad; Almazyad, Abdulaziz S; Saba, Tanzila

2016-05-01

Image fusion process consolidates data and information from various images of same sight into a solitary image. Each of the source images might speak to a fractional perspective of the scene, and contains both "pertinent" and "immaterial" information. In this study, a new image fusion method is proposed utilizing the Discrete Cosine Transform (DCT) to join the source image into a solitary minimized image containing more exact depiction of the sight than any of the individual source images. In addition, the fused image comes out with most ideal quality image without bending appearance or loss of data. DCT algorithm is considered efficient in image fusion. The proposed scheme is performed in five steps: (1) RGB colour image (input image) is split into three channels R, G, and B for source images. (2) DCT algorithm is applied to each channel (R, G, and B). (3) The variance values are computed for the corresponding 8 × 8 blocks of each channel. (4) Each block of R of source images is compared with each other based on the variance value and then the block with maximum variance value is selected to be the block in the new image. This process is repeated for all channels of source images. (5) Inverse discrete cosine transform is applied on each fused channel to convert coefficient values to pixel values, and then combined all the channels to generate the fused image. The proposed technique can potentially solve the problem of unwanted side effects such as blurring or blocking artifacts by reducing the quality of the subsequent image in image fusion process. The proposed approach is evaluated using three measurement units: the average of Q(abf), standard deviation, and peak Signal Noise Rate. The experimental results of this proposed technique have shown good results as compared with older techniques. © 2016 Wiley Periodicals, Inc.

Near real-time skin deformation mapping

NASA Astrophysics Data System (ADS)

Kacenjar, Steve; Chen, Suzie; Jafri, Madiha; Wall, Brian; Pedersen, Richard; Bezozo, Richard

2013-02-01

A novel in vivo approach is described that provides large area mapping of the mechanical properties of the skin in human patients. Such information is important in the understanding of skin health, cosmetic surgery[1], aging, and impacts of sun exposure. Currently, several methods have been developed to estimate the local biomechanical properties of the skin, including the use of a physical biopsy of local areas of the skin (in vitro methods) [2, 3, and 4], and also the use of non-invasive methods (in vivo) [5, 6, and 7]. All such methods examine localized areas of the skin. Our approach examines the local elastic properties via the generation of field displacement maps of the skin created using time-sequence imaging [9] with 2D digital imaging correlation (DIC) [10]. In this approach, large areas of the skin are reviewed rapidly, and skin displacement maps are generated showing the contour maps of skin deformation. These maps are then used to precisely register skin images for purposes of diagnostic comparison. This paper reports on our mapping and registration approach, and demonstrates its ability to accurately measure the skin deformation through a described nulling interpolation process. The result of local translational DIC alignment is compared using this interpolation process. The effectiveness of the approach is reported in terms of residual RMS, image entropy measures, and differential segmented regional errors.

An Effective Palmprint Recognition Approach for Visible and Multispectral Sensor Images.

PubMed

Gumaei, Abdu; Sammouda, Rachid; Al-Salman, Abdul Malik; Alsanad, Ahmed

2018-05-15

Among several palmprint feature extraction methods the HOG-based method is attractive and performs well against changes in illumination and shadowing of palmprint images. However, it still lacks the robustness to extract the palmprint features at different rotation angles. To solve this problem, this paper presents a hybrid feature extraction method, named HOG-SGF that combines the histogram of oriented gradients (HOG) with a steerable Gaussian filter (SGF) to develop an effective palmprint recognition approach. The approach starts by processing all palmprint images by David Zhang's method to segment only the region of interests. Next, we extracted palmprint features based on the hybrid HOG-SGF feature extraction method. Then, an optimized auto-encoder (AE) was utilized to reduce the dimensionality of the extracted features. Finally, a fast and robust regularized extreme learning machine (RELM) was applied for the classification task. In the evaluation phase of the proposed approach, a number of experiments were conducted on three publicly available palmprint databases, namely MS-PolyU of multispectral palmprint images and CASIA and Tongji of contactless palmprint images. Experimentally, the results reveal that the proposed approach outperforms the existing state-of-the-art approaches even when a small number of training samples are used.

Diffusion processes in tumors: A nuclear medicine approach

NASA Astrophysics Data System (ADS)

Amaya, Helman

2016-07-01

The number of counts used in nuclear medicine imaging techniques, only provides physical information about the desintegration of the nucleus present in the the radiotracer molecules that were uptaken in a particular anatomical region, but that information is not a real metabolic information. For this reason a mathematical method was used to find a correlation between number of counts and 18F-FDG mass concentration. This correlation allows a better interpretation of the results obtained in the study of diffusive processes in an agar phantom, and based on it, an image from the PETCETIX DICOM sample image set from OsiriX-viewer software was processed. PET-CT gradient magnitude and Laplacian images could show direct information on diffusive processes for radiopharmaceuticals that enter into the cells by simple diffusion. In the case of the radiopharmaceutical 18F-FDG is necessary to include pharmacokinetic models, to make a correct interpretation of the gradient magnitude and Laplacian of counts images.

Measuring the complexity of design in real-time imaging software

NASA Astrophysics Data System (ADS)

Sangwan, Raghvinder S.; Vercellone-Smith, Pamela; Laplante, Phillip A.

2007-02-01

Due to the intricacies in the algorithms involved, the design of imaging software is considered to be more complex than non-image processing software (Sangwan et al, 2005). A recent investigation (Larsson and Laplante, 2006) examined the complexity of several image processing and non-image processing software packages along a wide variety of metrics, including those postulated by McCabe (1976), Chidamber and Kemerer (1994), and Martin (2003). This work found that it was not always possible to quantitatively compare the complexity between imaging applications and nonimage processing systems. Newer research and an accompanying tool (Structure 101, 2006), however, provides a greatly simplified approach to measuring software complexity. Therefore it may be possible to definitively quantify the complexity differences between imaging and non-imaging software, between imaging and real-time imaging software, and between software programs of the same application type. In this paper, we review prior results and describe the methodology for measuring complexity in imaging systems. We then apply a new complexity measurement methodology to several sets of imaging and non-imaging code in order to compare the complexity differences between the two types of applications. The benefit of such quantification is far reaching, for example, leading to more easily measured performance improvement and quality in real-time imaging code.

Raspberry Pi-powered imaging for plant phenotyping.

PubMed

Tovar, Jose C; Hoyer, J Steen; Lin, Andy; Tielking, Allison; Callen, Steven T; Elizabeth Castillo, S; Miller, Michael; Tessman, Monica; Fahlgren, Noah; Carrington, James C; Nusinow, Dmitri A; Gehan, Malia A

2018-03-01

Image-based phenomics is a powerful approach to capture and quantify plant diversity. However, commercial platforms that make consistent image acquisition easy are often cost-prohibitive. To make high-throughput phenotyping methods more accessible, low-cost microcomputers and cameras can be used to acquire plant image data. We used low-cost Raspberry Pi computers and cameras to manage and capture plant image data. Detailed here are three different applications of Raspberry Pi-controlled imaging platforms for seed and shoot imaging. Images obtained from each platform were suitable for extracting quantifiable plant traits (e.g., shape, area, height, color) en masse using open-source image processing software such as PlantCV. This protocol describes three low-cost platforms for image acquisition that are useful for quantifying plant diversity. When coupled with open-source image processing tools, these imaging platforms provide viable low-cost solutions for incorporating high-throughput phenomics into a wide range of research programs.

LCD motion blur reduction: a signal processing approach.

PubMed

Har-Noy, Shay; Nguyen, Truong Q

2008-02-01

Liquid crystal displays (LCDs) have shown great promise in the consumer market for their use as both computer and television displays. Despite their many advantages, the inherent sample-and-hold nature of LCD image formation results in a phenomenon known as motion blur. In this work, we develop a method for motion blur reduction using the Richardson-Lucy deconvolution algorithm in concert with motion vector information from the scene. We further refine our approach by introducing a perceptual significance metric that allows us to weight the amount of processing performed on different regions in the image. In addition, we analyze the role of motion vector errors in the quality of our resulting image. Perceptual tests indicate that our algorithm reduces the amount of perceivable motion blur in LCDs.

PIZZARO: Forensic analysis and restoration of image and video data.

PubMed

Kamenicky, Jan; Bartos, Michal; Flusser, Jan; Mahdian, Babak; Kotera, Jan; Novozamsky, Adam; Saic, Stanislav; Sroubek, Filip; Sorel, Michal; Zita, Ales; Zitova, Barbara; Sima, Zdenek; Svarc, Petr; Horinek, Jan

2016-07-01

This paper introduces a set of methods for image and video forensic analysis. They were designed to help to assess image and video credibility and origin and to restore and increase image quality by diminishing unwanted blur, noise, and other possible artifacts. The motivation came from the best practices used in the criminal investigation utilizing images and/or videos. The determination of the image source, the verification of the image content, and image restoration were identified as the most important issues of which automation can facilitate criminalists work. Novel theoretical results complemented with existing approaches (LCD re-capture detection and denoising) were implemented in the PIZZARO software tool, which consists of the image processing functionality as well as of reporting and archiving functions to ensure the repeatability of image analysis procedures and thus fulfills formal aspects of the image/video analysis work. Comparison of new proposed methods with the state of the art approaches is shown. Real use cases are presented, which illustrate the functionality of the developed methods and demonstrate their applicability in different situations. The use cases as well as the method design were solved in tight cooperation of scientists from the Institute of Criminalistics, National Drug Headquarters of the Criminal Police and Investigation Service of the Police of the Czech Republic, and image processing experts from the Czech Academy of Sciences. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Simultaneous Denoising, Deconvolution, and Demixing of Calcium Imaging Data

PubMed Central

Pnevmatikakis, Eftychios A.; Soudry, Daniel; Gao, Yuanjun; Machado, Timothy A.; Merel, Josh; Pfau, David; Reardon, Thomas; Mu, Yu; Lacefield, Clay; Yang, Weijian; Ahrens, Misha; Bruno, Randy; Jessell, Thomas M.; Peterka, Darcy S.; Yuste, Rafael; Paninski, Liam

2016-01-01

SUMMARY We present a modular approach for analyzing calcium imaging recordings of large neuronal ensembles. Our goal is to simultaneously identify the locations of the neurons, demix spatially overlapping components, and denoise and deconvolve the spiking activity from the slow dynamics of the calcium indicator. Our approach relies on a constrained nonnegative matrix factorization that expresses the spatiotemporal fluorescence activity as the product of a spatial matrix that encodes the spatial footprint of each neuron in the optical field and a temporal matrix that characterizes the calcium concentration of each neuron over time. This framework is combined with a novel constrained deconvolution approach that extracts estimates of neural activity from fluorescence traces, to create a spatiotemporal processing algorithm that requires minimal parameter tuning. We demonstrate the general applicability of our method by applying it to in vitro and in vivo multineuronal imaging data, whole-brain light-sheet imaging data, and dendritic imaging data. PMID:26774160

A systematic approach to the interpretation of preoperative staging MRI for rectal cancer.

PubMed

Taylor, Fiona G M; Swift, Robert I; Blomqvist, Lennart; Brown, Gina

2008-12-01

The purpose of this article is to provide an aid to the systematic evaluation of MRI in staging rectal cancer. MRI has been shown to be an effective tool for the accurate preoperative staging of rectal cancer. In the Magnetic Resonance Imaging and Rectal Cancer European Equivalence Study (MERCURY), imaging workshops were held for participating radiologists to ensure standardization of scan acquisition techniques and interpretation of the images. In this article, we report how the information was obtained and give examples of the images and how they are interpreted, with the aim of providing a systematic approach to the reporting process.

Novel Applications of Radionuclide Imaging in Peripheral Vascular Disease

PubMed Central

Stacy, Mitchel R.; Sinusas, Albert J.

2015-01-01

Peripheral vascular disease (PVD) is a progressive atherosclerotic disease that leads to stenosis or occlusion of blood vessels supplying the lower extremities. Current diagnostic imaging techniques commonly focus on evaluation of anatomy or blood flow at the macrovascular level and do not permit assessment of the underlying pathophysiology associated with disease progression or treatment response. Molecular imaging with radionuclide-based approaches, such as PET and SPECT, can offer novel insight into PVD by providing non-invasive assessment of biological processes such as angiogenesis and atherosclerosis. This review discusses emerging radionuclide-based imaging approaches that have potential clinical applications in the evaluation of PVD progression and treatment. PMID:26590787

Characteristics of different frequency ranges in scanning electron microscope images

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

Sim, K. S., E-mail: kssim@mmu.edu.my; Nia, M. E.; Tan, T. L.

2015-07-22

We demonstrate a new approach to characterize the frequency range in general scanning electron microscope (SEM) images. First, pure frequency images are generated from low frequency to high frequency, and then, the magnification of each type of frequency image is implemented. By comparing the edge percentage of the SEM image to the self-generated frequency images, we can define the frequency ranges of the SEM images. Characterization of frequency ranges of SEM images benefits further processing and analysis of those SEM images, such as in noise filtering and contrast enhancement.

Image Registration of High-Resolution Uav Data: the New Hypare Algorithm

NASA Astrophysics Data System (ADS)

Bahr, T.; Jin, X.; Lasica, R.; Giessel, D.

2013-08-01

Unmanned aerial vehicles play an important role in the present-day civilian and military intelligence. Equipped with a variety of sensors, such as SAR imaging modes, E/O- and IR sensor technology, they are due to their agility suitable for many applications. Hence, the necessity arises to use fusion technologies and to develop them continuously. Here an exact image-to-image registration is essential. It serves as the basis for important image processing operations such as georeferencing, change detection, and data fusion. Therefore we developed the Hybrid Powered Auto-Registration Engine (HyPARE). HyPARE combines all available spatial reference information with a number of image registration approaches to improve the accuracy, performance, and automation of tie point generation and image registration. We demonstrate this approach by the registration of 39 still images from a high-resolution image stream, acquired with a Aeryon Photo3S™ camera on an Aeryon Scout micro-UAV™.

Medical image enhancement using resolution synthesis

NASA Astrophysics Data System (ADS)

Wong, Tak-Shing; Bouman, Charles A.; Thibault, Jean-Baptiste; Sauer, Ken D.

2011-03-01

We introduce a post-processing approach to improve the quality of CT reconstructed images. The scheme is adapted from the resolution-synthesis (RS)1 interpolation algorithm. In this approach, we consider the input image, scanned at a particular dose level, as a degraded version of a high quality image scanned at a high dose level. Image enhancement is achieved by predicting the high quality image by classification based linear regression. To improve the robustness of our scheme, we also apply the minimum description length principle to determine the optimal number of predictors to use in the scheme, and the ridge regression to regularize the design of the predictors. Experimental results show that our scheme is effective in reducing the noise in images reconstructed from filtered back projection without significant loss of image details. Alternatively, our scheme can also be applied to reduce dose while maintaining image quality at an acceptable level.

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.

Adaptive Microwave Staring Correlated Imaging for Targets Appearing in Discrete Clusters.

PubMed

Tian, Chao; Jiang, Zheng; Chen, Weidong; Wang, Dongjin

2017-10-21

Microwave staring correlated imaging (MSCI) can achieve ultra-high resolution in real aperture staring radar imaging using the correlated imaging process (CIP) under all-weather and all-day circumstances. The CIP must combine the received echo signal with the temporal-spatial stochastic radiation field. However, a precondition of the CIP is that the continuous imaging region must be discretized to a fine grid, and the measurement matrix should be accurately computed, which makes the imaging process highly complex when the MSCI system observes a wide area. This paper proposes an adaptive imaging approach for the targets in discrete clusters to reduce the complexity of the CIP. The approach is divided into two main stages. First, as discrete clustered targets are distributed in different range strips in the imaging region, the transmitters of the MSCI emit narrow-pulse waveforms to separate the echoes of the targets in different strips in the time domain; using spectral entropy, a modified method robust against noise is put forward to detect the echoes of the discrete clustered targets, based on which the strips with targets can be adaptively located. Second, in a strip with targets, the matched filter reconstruction algorithm is used to locate the regions with targets, and only the regions of interest are discretized to a fine grid; sparse recovery is used, and the band exclusion is used to maintain the non-correlation of the dictionary. Simulation results are presented to demonstrate that the proposed approach can accurately and adaptively locate the regions with targets and obtain high-quality reconstructed images.

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

PubMed Central

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

2011-01-01

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

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

PubMed

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

2011-01-01

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

Illustration Watermarking for Digital Images: An Investigation of Hierarchical Signal Inheritances for Nested Object-based Embedding

DTIC Science & Technology

2007-02-23

approach for signal-level watermark inheritance. 15. SUBJECT TERMS EOARD, Steganography , Image Fusion, Data Mining, Image ...in watermarking algorithms , a program interface and protocol has been de - veloped, which allows control of the embedding and retrieval processes by the...watermarks in an image . Watermarking algorithm (DLL) Watermarking editor (Delphi) - User marks all objects: ci - class information oi - object instance

Coastline detection with time series of SAR images

NASA Astrophysics Data System (ADS)

Ao, Dongyang; Dumitru, Octavian; Schwarz, Gottfried; Datcu, Mihai

2017-10-01

For maritime remote sensing, coastline detection is a vital task. With continuous coastline detection results from satellite image time series, the actual shoreline, the sea level, and environmental parameters can be observed to support coastal management and disaster warning. Established coastline detection methods are often based on SAR images and wellknown image processing approaches. These methods involve a lot of complicated data processing, which is a big challenge for remote sensing time series. Additionally, a number of SAR satellites operating with polarimetric capabilities have been launched in recent years, and many investigations of target characteristics in radar polarization have been performed. In this paper, a fast and efficient coastline detection method is proposed which comprises three steps. First, we calculate a modified correlation coefficient of two SAR images of different polarization. This coefficient differs from the traditional computation where normalization is needed. Through this modified approach, the separation between sea and land becomes more prominent. Second, we set a histogram-based threshold to distinguish between sea and land within the given image. The histogram is derived from the statistical distribution of the polarized SAR image pixel amplitudes. Third, we extract continuous coastlines using a Canny image edge detector that is rather immune to speckle noise. Finally, the individual coastlines derived from time series of .SAR images can be checked for changes.

Performance assessment of multi-frequency processing of ICU chest images for enhanced visualization of tubes and catheters

NASA Astrophysics Data System (ADS)

Wang, Xiaohui; Couwenhoven, Mary E.; Foos, David H.; Doran, James; Yankelevitz, David F.; Henschke, Claudia I.

2008-03-01

An image-processing method has been developed to improve the visibility of tube and catheter features in portable chest x-ray (CXR) images captured in the intensive care unit (ICU). The image-processing method is based on a multi-frequency approach, wherein the input image is decomposed into different spatial frequency bands, and those bands that contain the tube and catheter signals are individually enhanced by nonlinear boosting functions. Using a random sampling strategy, 50 cases were retrospectively selected for the study from a large database of portable CXR images that had been collected from multiple institutions over a two-year period. All images used in the study were captured using photo-stimulable, storage phosphor computed radiography (CR) systems. Each image was processed two ways. The images were processed with default image processing parameters such as those used in clinical settings (control). The 50 images were then separately processed using the new tube and catheter enhancement algorithm (test). Three board-certified radiologists participated in a reader study to assess differences in both detection-confidence performance and diagnostic efficiency between the control and test images. Images were evaluated on a diagnostic-quality, 3-megapixel monochrome monitor. Two scenarios were studied: the baseline scenario, representative of today's workflow (a single-control image presented with the window/level adjustments enabled) vs. the test scenario (a control/test image pair presented with a toggle enabled and the window/level settings disabled). The radiologists were asked to read the images in each scenario as they normally would for clinical diagnosis. Trend analysis indicates that the test scenario offers improved reading efficiency while providing as good or better detection capability compared to the baseline scenario.

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

PubMed

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

2017-11-01

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

Segmentation of stereo terrain images

NASA Astrophysics Data System (ADS)

George, Debra A.; Privitera, Claudio M.; Blackmon, Theodore T.; Zbinden, Eric; Stark, Lawrence W.

2000-06-01

We have studied four approaches to segmentation of images: three automatic ones using image processing algorithms and a fourth approach, human manual segmentation. We were motivated toward helping with an important NASA Mars rover mission task -- replacing laborious manual path planning with automatic navigation of the rover on the Mars terrain. The goal of the automatic segmentations was to identify an obstacle map on the Mars terrain to enable automatic path planning for the rover. The automatic segmentation was first explored with two different segmentation methods: one based on pixel luminance, and the other based on pixel altitude generated through stereo image processing. The third automatic segmentation was achieved by combining these two types of image segmentation. Human manual segmentation of Martian terrain images was used for evaluating the effectiveness of the combined automatic segmentation as well as for determining how different humans segment the same images. Comparisons between two different segmentations, manual or automatic, were measured using a similarity metric, SAB. Based on this metric, the combined automatic segmentation did fairly well in agreeing with the manual segmentation. This was a demonstration of a positive step towards automatically creating the accurate obstacle maps necessary for automatic path planning and rover navigation.

The artificial object detection and current velocity measurement using SAR ocean surface images

NASA Astrophysics Data System (ADS)

Alpatov, Boris; Strotov, Valery; Ershov, Maksim; Muraviev, Vadim; Feldman, Alexander; Smirnov, Sergey

2017-10-01

Due to the fact that water surface covers wide areas, remote sensing is the most appropriate way of getting information about ocean environment for vessel tracking, security purposes, ecological studies and others. Processing of synthetic aperture radar (SAR) images is extensively used for control and monitoring of the ocean surface. Image data can be acquired from Earth observation satellites, such as TerraSAR-X, ERS, and COSMO-SkyMed. Thus, SAR image processing can be used to solve many problems arising in this field of research. This paper discusses some of them including ship detection, oil pollution control and ocean currents mapping. Due to complexity of the problem several specialized algorithm are necessary to develop. The oil spill detection algorithm consists of the following main steps: image preprocessing, detection of dark areas, parameter extraction and classification. The ship detection algorithm consists of the following main steps: prescreening, land masking, image segmentation combined with parameter measurement, ship orientation estimation and object discrimination. The proposed approach to ocean currents mapping is based on Doppler's law. The results of computer modeling on real SAR images are presented. Based on these results it is concluded that the proposed approaches can be used in maritime applications.

Towards automated segmentation of cells and cell nuclei in nonlinear optical microscopy.

PubMed

Medyukhina, Anna; Meyer, Tobias; Schmitt, Michael; Romeike, Bernd F M; Dietzek, Benjamin; Popp, Jürgen

2012-11-01

Nonlinear optical (NLO) imaging techniques based e.g. on coherent anti-Stokes Raman scattering (CARS) or two photon excited fluorescence (TPEF) show great potential for biomedical imaging. In order to facilitate the diagnostic process based on NLO imaging, there is need for an automated calculation of quantitative values such as cell density, nucleus-to-cytoplasm ratio, average nuclear size. Extraction of these parameters is helpful for the histological assessment in general and specifically e.g. for the determination of tumor grades. This requires an accurate image segmentation and detection of locations and boundaries of cells and nuclei. Here we present an image processing approach for the detection of nuclei and cells in co-registered TPEF and CARS images. The algorithm developed utilizes the gray-scale information for the detection of the nuclei locations and the gradient information for the delineation of the nuclear and cellular boundaries. The approach reported is capable for an automated segmentation of cells and nuclei in multimodal TPEF-CARS images of human brain tumor samples. The results are important for the development of NLO microscopy into a clinically relevant diagnostic tool. Copyright © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Reducing noise component on medical images

NASA Astrophysics Data System (ADS)

Semenishchev, Evgeny; Voronin, Viacheslav; Dub, Vladimir; Balabaeva, Oksana

2018-04-01

Medical visualization and analysis of medical data is an actual direction. Medical images are used in microbiology, genetics, roentgenology, oncology, surgery, ophthalmology, etc. Initial data processing is a major step towards obtaining a good diagnostic result. The paper considers the approach allows an image filtering with preservation of objects borders. The algorithm proposed in this paper is based on sequential data processing. At the first stage, local areas are determined, for this purpose the method of threshold processing, as well as the classical ICI algorithm, is applied. The second stage uses a method based on based on two criteria, namely, L2 norm and the first order square difference. To preserve the boundaries of objects, we will process the transition boundary and local neighborhood the filtering algorithm with a fixed-coefficient. For example, reconstructed images of CT, x-ray, and microbiological studies are shown. The test images show the effectiveness of the proposed algorithm. This shows the applicability of analysis many medical imaging applications.

Correlation processing for correction of phase distortions in subaperture imaging.

PubMed

Tavh, B; Karaman, M

1999-01-01

Ultrasonic subaperture imaging combines synthetic aperture and phased array approaches and permits low-cost systems with improved image quality. In subaperture processing, a large array is synthesized using echo signals collected from a number of receive subapertures by multiple firings of a phased transmit subaperture. Tissue inhomogeneities and displacements in subaperture imaging may cause significant phase distortions on received echo signals. Correlation processing on reference echo signals can be used for correction of the phase distortions, for which the accuracy and robustness are critically limited by the signal correlation. In this study, we explore correlation processing techniques for adaptive subaperture imaging with phase correction for motion and tissue inhomogeneities. The proposed techniques use new subaperture data acquisition schemes to produce reference signal sets with improved signal correlation. The experimental test results were obtained using raw radio frequency (RF) data acquired from two different phantoms with 3.5 MHz, 128-element transducer array. The results show that phase distortions can effectively be compensated by the proposed techniques in real-time adaptive subaperture imaging.

Automatic detection of the inner ears in head CT images using deep convolutional neural networks

NASA Astrophysics Data System (ADS)

Zhang, Dongqing; Noble, Jack H.; Dawant, Benoit M.

2018-03-01

Cochlear implants (CIs) use electrode arrays that are surgically inserted into the cochlea to stimulate nerve endings to replace the natural electro-mechanical transduction mechanism and restore hearing for patients with profound hearing loss. Post-operatively, the CI needs to be programmed. Traditionally, this is done by an audiologist who is blind to the positions of the electrodes relative to the cochlea and relies on the patient's subjective response to stimuli. This is a trial-and-error process that can be frustratingly long (dozens of programming sessions are not unusual). To assist audiologists, we have proposed what we call IGCIP for image-guided cochlear implant programming. In IGCIP, we use image processing algorithms to segment the intra-cochlear anatomy in pre-operative CT images and to localize the electrode arrays in post-operative CTs. We have shown that programming strategies informed by image-derived information significantly improve hearing outcomes for both adults and pediatric populations. We are now aiming at deploying these techniques clinically, which requires full automation. One challenge we face is the lack of standard image acquisition protocols. The content of the image volumes we need to process thus varies greatly and visual inspection and labelling is currently required to initialize processing pipelines. In this work we propose a deep learning-based approach to automatically detect if a head CT volume contains two ears, one ear, or no ear. Our approach has been tested on a data set that contains over 2,000 CT volumes from 153 patients and we achieve an overall 95.97% classification accuracy.

Realizing parameterless automatic classification of remote sensing imagery using ontology engineering and cyberinfrastructure techniques

NASA Astrophysics Data System (ADS)

Sun, Ziheng; Fang, Hui; Di, Liping; Yue, Peng

2016-09-01

It was an untouchable dream for remote sensing experts to realize total automatic image classification without inputting any parameter values. Experts usually spend hours and hours on tuning the input parameters of classification algorithms in order to obtain the best results. With the rapid development of knowledge engineering and cyberinfrastructure, a lot of data processing and knowledge reasoning capabilities become online accessible, shareable and interoperable. Based on these recent improvements, this paper presents an idea of parameterless automatic classification which only requires an image and automatically outputs a labeled vector. No parameters and operations are needed from endpoint consumers. An approach is proposed to realize the idea. It adopts an ontology database to store the experiences of tuning values for classifiers. A sample database is used to record training samples of image segments. Geoprocessing Web services are used as functionality blocks to finish basic classification steps. Workflow technology is involved to turn the overall image classification into a total automatic process. A Web-based prototypical system named PACS (Parameterless Automatic Classification System) is implemented. A number of images are fed into the system for evaluation purposes. The results show that the approach could automatically classify remote sensing images and have a fairly good average accuracy. It is indicated that the classified results will be more accurate if the two databases have higher quality. Once the experiences and samples in the databases are accumulated as many as an expert has, the approach should be able to get the results with similar quality to that a human expert can get. Since the approach is total automatic and parameterless, it can not only relieve remote sensing workers from the heavy and time-consuming parameter tuning work, but also significantly shorten the waiting time for consumers and facilitate them to engage in image classification activities. Currently, the approach is used only on high resolution optical three-band remote sensing imagery. The feasibility using the approach on other kinds of remote sensing images or involving additional bands in classification will be studied in future.

Ross filter pairs for metal artefact reduction in x-ray tomography: a case study based on imaging and segmentation of metallic implants

NASA Astrophysics Data System (ADS)

Arhatari, Benedicta D.; Abbey, Brian

2018-01-01

Ross filter pairs have recently been demonstrated as a highly effective means of producing quasi-monoenergetic beams from polychromatic X-ray sources. They have found applications in both X-ray spectroscopy and for elemental separation in X-ray computed tomography (XCT). Here we explore whether they could be applied to the problem of metal artefact reduction (MAR) for applications in medical imaging. Metal artefacts are a common problem in X-ray imaging of metal implants embedded in bone and soft tissue. A number of data post-processing approaches to MAR have been proposed in the literature, however these can be time-consuming and sometimes have limited efficacy. Here we describe and demonstrate an alternative approach based on beam conditioning using Ross filter pairs. This approach obviates the need for any complex post-processing of the data and enables MAR and segmentation from the surrounding tissue by exploiting the absorption edge contrast of the implant.

Study on some useful Operators for Graph-theoretic Image Processing

NASA Astrophysics Data System (ADS)

Moghani, Ali; Nasiri, Parviz

2010-11-01

In this paper we describe a human perception based approach to pixel color segmentation which applied in color reconstruction by numerical method associated with graph-theoretic image processing algorithm typically in grayscale. Fuzzy sets defined on the Hue, Saturation and Value components of the HSV color space, provide a fuzzy logic model that aims to follow the human intuition of color classification.

New Windows based Color Morphological Operators for Biomedical Image Processing

NASA Astrophysics Data System (ADS)

Pastore, Juan; Bouchet, Agustina; Brun, Marcel; Ballarin, Virginia

2016-04-01

Morphological image processing is well known as an efficient methodology for image processing and computer vision. With the wide use of color in many areas, the interest on the color perception and processing has been growing rapidly. Many models have been proposed to extend morphological operators to the field of color images, dealing with some new problems not present previously in the binary and gray level contexts. These solutions usually deal with the lattice structure of the color space, or provide it with total orders, to be able to define basic operators with required properties. In this work we propose a new locally defined ordering, in the context of window based morphological operators, for the definition of erosions-like and dilation-like operators, which provides the same desired properties expected from color morphology, avoiding some of the drawbacks of the prior approaches. Experimental results show that the proposed color operators can be efficiently used for color image processing.

Digital processing of radiographic images

NASA Technical Reports Server (NTRS)

Bond, A. D.; Ramapriyan, H. K.

1973-01-01

Some techniques are presented and the software documentation for the digital enhancement of radiographs. Both image handling and image processing operations are considered. The image handling operations dealt with are: (1) conversion of format of data from packed to unpacked and vice versa; (2) automatic extraction of image data arrays; (3) transposition and 90 deg rotations of large data arrays; (4) translation of data arrays for registration; and (5) reduction of the dimensions of data arrays by integral factors. Both the frequency and the spatial domain approaches are presented for the design and implementation of the image processing operation. It is shown that spatial domain recursive implementation of filters is much faster than nonrecursive implementations using fast fourier transforms (FFT) for the cases of interest in this work. The recursive implementation of a class of matched filters for enhancing image signal to noise ratio is described. Test patterns are used to illustrate the filtering operations. The application of the techniques to radiographic images of metallic structures is demonstrated through several examples.

On-line monitoring of fluid bed granulation by photometric imaging.

PubMed

Soppela, Ira; Antikainen, Osmo; Sandler, Niklas; Yliruusi, Jouko

2014-11-01

This paper introduces and discusses a photometric surface imaging approach for on-line monitoring of fluid bed granulation. Five granule batches consisting of paracetamol and varying amounts of lactose and microcrystalline cellulose were manufactured with an instrumented fluid bed granulator. Photometric images and NIR spectra were continuously captured on-line and particle size information was extracted from them. Also key process parameters were recorded. The images provided direct real-time information on the growth, attrition and packing behaviour of the batches. Moreover, decreasing image brightness in the drying phase was found to indicate granule drying. The changes observed in the image data were also linked to the moisture and temperature profiles of the processes. Combined with complementary process analytical tools, photometric imaging opens up possibilities for improved real-time evaluation fluid bed granulation. Furthermore, images can give valuable insight into the behaviour of excipients or formulations during product development. Copyright © 2014 Elsevier B.V. All rights reserved.

Multispectral imaging approach for simplified non-invasive in-vivo evaluation of gingival erythema

NASA Astrophysics Data System (ADS)

Eckhard, Timo; Valero, Eva M.; Nieves, Juan L.; Gallegos-Rueda, José M.; Mesa, Francisco

2012-03-01

Erythema is a common visual sign of gingivitis. In this work, a new and simple low-cost image capture and analysis method for erythema assessment is proposed. The method is based on digital still images of gingivae and applied on a pixel-by-pixel basis. Multispectral images are acquired with a conventional digital camera and multiplexed LED illumination panels at 460nm and 630nm peak wavelength. An automatic work-flow segments teeth from gingiva regions in the images and creates a map of local blood oxygenation levels, which relates to the presence of erythema. The map is computed from the ratio of the two spectral images. An advantage of the proposed approach is that the whole process is easy to manage by dental health care professionals in clinical environment.

Client-side Medical Image Colorization in a Collaborative Environment.

PubMed

Virag, Ioan; Stoicu-Tivadar, Lăcrămioara; Crişan-Vida, Mihaela

2015-01-01

The paper presents an application related to collaborative medicine using a browser based medical visualization system with focus on the medical image colorization process and the underlying open source web development technologies involved. Browser based systems allow physicians to share medical data with their remotely located counterparts or medical students, assisting them during patient diagnosis, treatment monitoring, surgery planning or for educational purposes. This approach brings forth the advantage of ubiquity. The system can be accessed from a any device, in order to process the images, assuring the independence towards having a specific proprietary operating system. The current work starts with processing of DICOM (Digital Imaging and Communications in Medicine) files and ends with the rendering of the resulting bitmap images on a HTML5 (fifth revision of the HyperText Markup Language) canvas element. The application improves the image visualization emphasizing different tissue densities.

An efficient approach to integrated MeV ion imaging.

PubMed

Nikbakht, T; Kakuee, O; Solé, V A; Vosuoghi, Y; Lamehi-Rachti, M

2018-03-01

An ionoluminescence (IL) spectral imaging system, besides the common MeV ion imaging facilities such as µ-PIXE and µ-RBS, is implemented at the Van de Graaff laboratory of Tehran. A versatile processing software is required to handle the large amount of data concurrently collected in µ-IL and common MeV ion imaging measurements through the respective methodologies. The open-source freeware PyMca, with image processing and multivariate analysis capabilities, is employed to simultaneously process common MeV ion imaging and µ-IL data. Herein, the program was adapted to support the OM_DAQ listmode data format. The appropriate performance of the µ-IL data acquisition system is confirmed through a case study. Moreover, the capabilities of the software for simultaneous analysis of µ-PIXE and µ-RBS experimental data are presented. Copyright © 2017 Elsevier B.V. All rights reserved.

Cellular Neural Network for Real Time Image Processing

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

Vagliasindi, G.; Arena, P.; Fortuna, L.

2008-03-12

Since their introduction in 1988, Cellular Nonlinear Networks (CNNs) have found a key role as image processing instruments. Thanks to their structure they are able of processing individual pixels in a parallel way providing fast image processing capabilities that has been applied to a wide range of field among which nuclear fusion. In the last years, indeed, visible and infrared video cameras have become more and more important in tokamak fusion experiments for the twofold aim of understanding the physics and monitoring the safety of the operation. Examining the output of these cameras in real-time can provide significant information formore » plasma control and safety of the machines. The potentiality of CNNs can be exploited to this aim. To demonstrate the feasibility of the approach, CNN image processing has been applied to several tasks both at the Frascati Tokamak Upgrade (FTU) and the Joint European Torus (JET)« less

Finite slice analysis (FINA) of sliced and velocity mapped images on a Cartesian grid

NASA Astrophysics Data System (ADS)

Thompson, J. O. F.; Amarasinghe, C.; Foley, C. D.; Rombes, N.; Gao, Z.; Vogels, S. N.; van de Meerakker, S. Y. T.; Suits, A. G.

2017-08-01

Although time-sliced imaging yields improved signal-to-noise and resolution compared with unsliced velocity mapped ion images, for finite slice widths as encountered in real experiments there is a loss of resolution and recovered intensities for the slow fragments. Recently, we reported a new approach that permits correction of these effects for an arbitrarily sliced distribution of a 3D charged particle cloud. This finite slice analysis (FinA) method utilizes basis functions that model the out-of-plane contribution of a given velocity component to the image for sequential subtraction in a spherical polar coordinate system. However, the original approach suffers from a slow processing time due to the weighting procedure needed to accurately model the out-of-plane projection of an anisotropic angular distribution. To overcome this issue we present a variant of the method in which the FinA approach is performed in a cylindrical coordinate system (Cartesian in the image plane) rather than a spherical polar coordinate system. Dubbed C-FinA, we show how this method is applied in much the same manner. We compare this variant to the polar FinA method and find that the processing time (of a 510 × 510 pixel image) in its most extreme case improves by a factor of 100. We also show that although the resulting velocity resolution is not quite as high as the polar version, this new approach shows superior resolution for fine structure in the differential cross sections. We demonstrate the method on a range of experimental and synthetic data at different effective slice widths.

Highly curved image sensors: a practical approach for improved optical performance

NASA Astrophysics Data System (ADS)

Guenter, Brian; Joshi, Neel; Stoakley, Richard; Keefe, Andrew; Geary, Kevin; Freeman, Ryan; Hundley, Jake; Patterson, Pamela; Hammon, David; Herrera, Guillermo; Sherman, Elena; Nowak, Andrew; Schubert, Randall; Brewer, Peter; Yang, Louis; Mott, Russell; McKnight, Geoff

2017-06-01

The significant optical and size benefits of using a curved focal surface for imaging systems have been well studied yet never brought to market for lack of a high-quality, mass-producible, curved image sensor. In this work we demonstrate that commercial silicon CMOS image sensors can be thinned and formed into accurate, highly curved optical surfaces with undiminished functionality. Our key development is a pneumatic forming process that avoids rigid mechanical constraints and suppresses wrinkling instabilities. A combination of forming-mold design, pressure membrane elastic properties, and controlled friction forces enables us to gradually contact the die at the corners and smoothly press the sensor into a spherical shape. Allowing the die to slide into the concave target shape enables a threefold increase in the spherical curvature over prior approaches having mechanical constraints that resist deformation, and create a high-stress, stretch-dominated state. Our process creates a bridge between the high precision and low-cost but planar CMOS process, and ideal non-planar component shapes such as spherical imagers for improved optical systems. We demonstrate these curved sensors in prototype cameras with custom lenses, measuring exceptional resolution of 3220 line-widths per picture height at an aperture of f/1.2 and nearly 100% relative illumination across the field. Though we use a 1/2.3" format image sensor in this report, we also show this process is generally compatible with many state of the art imaging sensor formats. By example, we report photogrammetry test data for an APS-C sized silicon die formed to a 30$^\\circ$ subtended spherical angle. These gains in sharpness and relative illumination enable a new generation of ultra-high performance, manufacturable, digital imaging systems for scientific, industrial, and artistic use.

Art and Design Blogs: A Socially-Wise Approach to Creativity

ERIC Educational Resources Information Center

Budge, Kylie

2012-01-01

Many images of the "artist" or "designer" pervade the media and popular consciousness. Contemporary images of the artist and creativity that focus solely on the individual offer a very narrow depiction of the varying ways creativity occurs for artists and designers. These images do not capture the variety of creative processes and myriad ways…

Information theoretic analysis of edge detection in visual communication

NASA Astrophysics Data System (ADS)

Jiang, Bo; Rahman, Zia-ur

2010-08-01

Generally, the designs of digital image processing algorithms and image gathering devices remain separate. Consequently, the performance of digital image processing algorithms is evaluated without taking into account the artifacts introduced into the process by the image gathering process. However, experiments show that the image gathering process profoundly impacts the performance of digital image processing and the quality of the resulting images. Huck et al. proposed one definitive theoretic analysis of visual communication channels, where the different parts, such as image gathering, processing, and display, are assessed in an integrated manner using Shannon's information theory. In this paper, we perform an end-to-end information theory based system analysis to assess edge detection methods. We evaluate the performance of the different algorithms as a function of the characteristics of the scene, and the parameters, such as sampling, additive noise etc., that define the image gathering system. The edge detection algorithm is regarded to have high performance only if the information rate from the scene to the edge approaches the maximum possible. This goal can be achieved only by jointly optimizing all processes. People generally use subjective judgment to compare different edge detection methods. There is not a common tool that can be used to evaluate the performance of the different algorithms, and to give people a guide for selecting the best algorithm for a given system or scene. Our information-theoretic assessment becomes this new tool to which allows us to compare the different edge detection operators in a common environment.

The vectorization of a ray tracing program for image generation

NASA Technical Reports Server (NTRS)

Plunkett, D. J.; Cychosz, J. M.; Bailey, M. J.

1984-01-01

Ray tracing is a widely used method for producing realistic computer generated images. Ray tracing involves firing an imaginary ray from a view point, through a point on an image plane, into a three dimensional scene. The intersections of the ray with the objects in the scene determines what is visible at the point on the image plane. This process must be repeated many times, once for each point (commonly called a pixel) in the image plane. A typical image contains more than a million pixels making this process computationally expensive. A traditional ray tracing program processes one ray at a time. In such a serial approach, as much as ninety percent of the execution time is spent computing the intersection of a ray with the surface in the scene. With the CYBER 205, many rays can be intersected with all the bodies im the scene with a single series of vector operations. Vectorization of this intersection process results in large decreases in computation time. The CADLAB's interest in ray tracing stems from the need to produce realistic images of mechanical parts. A high quality image of a part during the design process can increase the productivity of the designer by helping him visualize the results of his work. To be useful in the design process, these images must be produced in a reasonable amount of time. This discussion will explain how the ray tracing process was vectorized and gives examples of the images obtained.

Image smoothing and enhancement via min/max curvature flow

NASA Astrophysics Data System (ADS)

Malladi, Ravikanth; Sethian, James A.

1996-03-01

We present a class of PDE-based algorithms suitable for a wide range of image processing applications. The techniques are applicable to both salt-and-pepper gray-scale noise and full- image continuous noise present in black and white images, gray-scale images, texture images and color images. At the core, the techniques rely on a level set formulation of evolving curves and surfaces and the viscosity in profile evolution. Essentially, the method consists of moving the isointensity contours in an image under curvature dependent speed laws to achieve enhancement. Compared to existing techniques, our approach has several distinct advantages. First, it contains only one enhancement parameter, which in most cases is automatically chosen. Second, the scheme automatically stops smoothing at some optimal point; continued application of the scheme produces no further change. Third, the method is one of the fastest possible schemes based on a curvature-controlled approach.

Tracking multiple particles in fluorescence time-lapse microscopy images via probabilistic data association.

PubMed

Godinez, William J; Rohr, Karl

2015-02-01

Tracking subcellular structures as well as viral structures displayed as 'particles' in fluorescence microscopy images yields quantitative information on the underlying dynamical processes. We have developed an approach for tracking multiple fluorescent particles based on probabilistic data association. The approach combines a localization scheme that uses a bottom-up strategy based on the spot-enhancing filter as well as a top-down strategy based on an ellipsoidal sampling scheme that uses the Gaussian probability distributions computed by a Kalman filter. The localization scheme yields multiple measurements that are incorporated into the Kalman filter via a combined innovation, where the association probabilities are interpreted as weights calculated using an image likelihood. To track objects in close proximity, we compute the support of each image position relative to the neighboring objects of a tracked object and use this support to recalculate the weights. To cope with multiple motion models, we integrated the interacting multiple model algorithm. The approach has been successfully applied to synthetic 2-D and 3-D images as well as to real 2-D and 3-D microscopy images, and the performance has been quantified. In addition, the approach was successfully applied to the 2-D and 3-D image data of the recent Particle Tracking Challenge at the IEEE International Symposium on Biomedical Imaging (ISBI) 2012.

Processing ultrasound backscatter to monitor high-intensity focused ultrasound (HIFU) therapy

NASA Astrophysics Data System (ADS)

Kaczkowski, Peter J.; Anand, Ajay; Bailey, Michael R.

2005-09-01

The development of new noninvasive surgical methods such as HIFU for the treatment of cancer and internal bleeding requires simultaneous development of new sensing approaches to guide, monitor, and assess the therapy. Ultrasound imaging using echo amplitude has long been used to map tissue morphology for diagnostic interpretation by the clinician. New quantitative ultrasonic methods that rely on amplitude and phase processing for tissue characterization are being developed for monitoring of ablative therapy. We have been developing the use of full wave ultrasound backscattering for real-time temperature estimation, and to image changes in tissue backscatter spectrum as therapy progresses. Both approaches rely on differential processing of the backscatter signal in time, and precise measurement of phase differences. Noise and artifacts from motion and nonstationary speckle statistics are addressed by constraining inversions for tissue parameters with physical models. We present results of HIFU experiments with static point and scanned HIFU exposures in which temperature rise can be accurately mapped using a new heat transfer equation (HTE) model-constrained inverse approach. We also present results of a recently developed spectral imaging method that elucidates microbubble-mediated nonlinearity not visible as a change in backscatter amplitude. [Work supported by Army MRMC.

Integrated circuits for volumetric ultrasound imaging with 2-D CMUT arrays.

PubMed

Bhuyan, Anshuman; Choe, Jung Woo; Lee, Byung Chul; Wygant, Ira O; Nikoozadeh, Amin; Oralkan, Ömer; Khuri-Yakub, Butrus T

2013-12-01

Real-time volumetric ultrasound imaging systems require transmit and receive circuitry to generate ultrasound beams and process received echo signals. The complexity of building such a system is high due to requirement of the front-end electronics needing to be very close to the transducer. A large number of elements also need to be interfaced to the back-end system and image processing of a large dataset could affect the imaging volume rate. In this work, we present a 3-D imaging system using capacitive micromachined ultrasonic transducer (CMUT) technology that addresses many of the challenges in building such a system. We demonstrate two approaches in integrating the transducer and the front-end electronics. The transducer is a 5-MHz CMUT array with an 8 mm × 8 mm aperture size. The aperture consists of 1024 elements (32 × 32) with an element pitch of 250 μm. An integrated circuit (IC) consists of a transmit beamformer and receive circuitry to improve the noise performance of the overall system. The assembly was interfaced with an FPGA and a back-end system (comprising of a data acquisition system and PC). The FPGA provided the digital I/O signals for the IC and the back-end system was used to process the received RF echo data (from the IC) and reconstruct the volume image using a phased array imaging approach. Imaging experiments were performed using wire and spring targets, a ventricle model and a human prostrate. Real-time volumetric images were captured at 5 volumes per second and are presented in this paper.

Simultenious binary hash and features learning for image retrieval

NASA Astrophysics Data System (ADS)

Frantc, V. A.; Makov, S. V.; Voronin, V. V.; Marchuk, V. I.; Semenishchev, E. A.; Egiazarian, K. O.; Agaian, S.

2016-05-01

Content-based image retrieval systems have plenty of applications in modern world. The most important one is the image search by query image or by semantic description. Approaches to this problem are employed in personal photo-collection management systems, web-scale image search engines, medical systems, etc. Automatic analysis of large unlabeled image datasets is virtually impossible without satisfactory image-retrieval technique. It's the main reason why this kind of automatic image processing has attracted so much attention during recent years. Despite rather huge progress in the field, semantically meaningful image retrieval still remains a challenging task. The main issue here is the demand to provide reliable results in short amount of time. This paper addresses the problem by novel technique for simultaneous learning of global image features and binary hash codes. Our approach provide mapping of pixel-based image representation to hash-value space simultaneously trying to save as much of semantic image content as possible. We use deep learning methodology to generate image description with properties of similarity preservation and statistical independence. The main advantage of our approach in contrast to existing is ability to fine-tune retrieval procedure for very specific application which allow us to provide better results in comparison to general techniques. Presented in the paper framework for data- dependent image hashing is based on use two different kinds of neural networks: convolutional neural networks for image description and autoencoder for feature to hash space mapping. Experimental results confirmed that our approach has shown promising results in compare to other state-of-the-art methods.

An approach of point cloud denoising based on improved bilateral filtering

NASA Astrophysics Data System (ADS)

Zheng, Zeling; Jia, Songmin; Zhang, Guoliang; Li, Xiuzhi; Zhang, Xiangyin

2018-04-01

An omnidirectional mobile platform is designed for building point cloud based on an improved filtering algorithm which is employed to handle the depth image. First, the mobile platform can move flexibly and the control interface is convenient to control. Then, because the traditional bilateral filtering algorithm is time-consuming and inefficient, a novel method is proposed which called local bilateral filtering (LBF). LBF is applied to process depth image obtained by the Kinect sensor. The results show that the effect of removing noise is improved comparing with the bilateral filtering. In the condition of off-line, the color images and processed images are used to build point clouds. Finally, experimental results demonstrate that our method improves the speed of processing time of depth image and the effect of point cloud which has been built.

Quantitative approach for optimizing e-beam condition of photoresist inspection and measurement

NASA Astrophysics Data System (ADS)

Lin, Chia-Jen; Teng, Chia-Hao; Cheng, Po-Chung; Sato, Yoshishige; Huang, Shang-Chieh; Chen, Chu-En; Maruyama, Kotaro; Yamazaki, Yuichiro

2018-03-01

Severe process margin in advanced technology node of semiconductor device is controlled by e-beam metrology system and e-beam inspection system with scanning electron microscopy (SEM) image. By using SEM, larger area image with higher image quality is required to collect massive amount of data for metrology and to detect defect in a large area for inspection. Although photoresist is the one of the critical process in semiconductor device manufacturing, observing photoresist pattern by SEM image is crucial and troublesome especially in the case of large image. The charging effect by e-beam irradiation on photoresist pattern causes deterioration of image quality, and it affect CD variation on metrology system and causes difficulties to continue defect inspection in a long time for a large area. In this study, we established a quantitative approach for optimizing e-beam condition with "Die to Database" algorithm of NGR3500 on photoresist pattern to minimize charging effect. And we enhanced the performance of measurement and inspection on photoresist pattern by using optimized e-beam condition. NGR3500 is the geometry verification system based on "Die to Database" algorithm which compares SEM image with design data [1]. By comparing SEM image and design data, key performance indicator (KPI) of SEM image such as "Sharpness", "S/N", "Gray level variation in FOV", "Image shift" can be retrieved. These KPIs were analyzed with different e-beam conditions which consist of "Landing Energy", "Probe Current", "Scanning Speed" and "Scanning Method", and the best e-beam condition could be achieved with maximum image quality, maximum scanning speed and minimum image shift. On this quantitative approach of optimizing e-beam condition, we could observe dependency of SEM condition on photoresist charging. By using optimized e-beam condition, measurement could be continued on photoresist pattern over 24 hours stably. KPIs of SEM image proved image quality during measurement and inspection was stabled enough.

Brain tumor image segmentation using kernel dictionary learning.

PubMed

Jeon Lee; Seung-Jun Kim; Rong Chen; Herskovits, Edward H

2015-08-01

Automated brain tumor image segmentation with high accuracy and reproducibility holds a big potential to enhance the current clinical practice. Dictionary learning (DL) techniques have been applied successfully to various image processing tasks recently. In this work, kernel extensions of the DL approach are adopted. Both reconstructive and discriminative versions of the kernel DL technique are considered, which can efficiently incorporate multi-modal nonlinear feature mappings based on the kernel trick. Our novel discriminative kernel DL formulation allows joint learning of a task-driven kernel-based dictionary and a linear classifier using a K-SVD-type algorithm. The proposed approaches were tested using real brain magnetic resonance (MR) images of patients with high-grade glioma. The obtained preliminary performances are competitive with the state of the art. The discriminative kernel DL approach is seen to reduce computational burden without much sacrifice in performance.

Statistical processing of large image sequences.

PubMed

Khellah, F; Fieguth, P; Murray, M J; Allen, M

2005-01-01

The dynamic estimation of large-scale stochastic image sequences, as frequently encountered in remote sensing, is important in a variety of scientific applications. However, the size of such images makes conventional dynamic estimation methods, for example, the Kalman and related filters, impractical. In this paper, we present an approach that emulates the Kalman filter, but with considerably reduced computational and storage requirements. Our approach is illustrated in the context of a 512 x 512 image sequence of ocean surface temperature. The static estimation step, the primary contribution here, uses a mixture of stationary models to accurately mimic the effect of a nonstationary prior, simplifying both computational complexity and modeling. Our approach provides an efficient, stable, positive-definite model which is consistent with the given correlation structure. Thus, the methods of this paper may find application in modeling and single-frame estimation.

Automatic anatomy recognition in whole-body PET/CT images

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

Wang, Huiqian; Udupa, Jayaram K., E-mail: jay@mail.med.upenn.edu; Odhner, Dewey

Purpose: Whole-body positron emission tomography/computed tomography (PET/CT) has become a standard method of imaging patients with various disease conditions, especially cancer. Body-wide accurate quantification of disease burden in PET/CT images is important for characterizing lesions, staging disease, prognosticating patient outcome, planning treatment, and evaluating disease response to therapeutic interventions. However, body-wide anatomy recognition in PET/CT is a critical first step for accurately and automatically quantifying disease body-wide, body-region-wise, and organwise. This latter process, however, has remained a challenge due to the lower quality of the anatomic information portrayed in the CT component of this imaging modality and the paucity ofmore » anatomic details in the PET component. In this paper, the authors demonstrate the adaptation of a recently developed automatic anatomy recognition (AAR) methodology [Udupa et al., “Body-wide hierarchical fuzzy modeling, recognition, and delineation of anatomy in medical images,” Med. Image Anal. 18, 752–771 (2014)] to PET/CT images. Their goal was to test what level of object localization accuracy can be achieved on PET/CT compared to that achieved on diagnostic CT images. Methods: The authors advance the AAR approach in this work in three fronts: (i) from body-region-wise treatment in the work of Udupa et al. to whole body; (ii) from the use of image intensity in optimal object recognition in the work of Udupa et al. to intensity plus object-specific texture properties, and (iii) from the intramodality model-building-recognition strategy to the intermodality approach. The whole-body approach allows consideration of relationships among objects in different body regions, which was previously not possible. Consideration of object texture allows generalizing the previous optimal threshold-based fuzzy model recognition method from intensity images to any derived fuzzy membership image, and in the process, to bring performance to the level achieved on diagnostic CT and MR images in body-region-wise approaches. The intermodality approach fosters the use of already existing fuzzy models, previously created from diagnostic CT images, on PET/CT and other derived images, thus truly separating the modality-independent object assembly anatomy from modality-specific tissue property portrayal in the image. Results: Key ways of combining the above three basic ideas lead them to 15 different strategies for recognizing objects in PET/CT images. Utilizing 50 diagnostic CT image data sets from the thoracic and abdominal body regions and 16 whole-body PET/CT image data sets, the authors compare the recognition performance among these 15 strategies on 18 objects from the thorax, abdomen, and pelvis in object localization error and size estimation error. Particularly on texture membership images, object localization is within three voxels on whole-body low-dose CT images and 2 voxels on body-region-wise low-dose images of known true locations. Surprisingly, even on direct body-region-wise PET images, localization error within 3 voxels seems possible. Conclusions: The previous body-region-wise approach can be extended to whole-body torso with similar object localization performance. Combined use of image texture and intensity property yields the best object localization accuracy. In both body-region-wise and whole-body approaches, recognition performance on low-dose CT images reaches levels previously achieved on diagnostic CT images. The best object recognition strategy varies among objects; the proposed framework however allows employing a strategy that is optimal for each object.« less

An Imaging Sensor-Aided Vision Navigation Approach that Uses a Geo-Referenced Image Database.

PubMed

Li, Yan; Hu, Qingwu; Wu, Meng; Gao, Yang

2016-01-28

In determining position and attitude, vision navigation via real-time image processing of data collected from imaging sensors is advanced without a high-performance global positioning system (GPS) and an inertial measurement unit (IMU). Vision navigation is widely used in indoor navigation, far space navigation, and multiple sensor-integrated mobile mapping. This paper proposes a novel vision navigation approach aided by imaging sensors and that uses a high-accuracy geo-referenced image database (GRID) for high-precision navigation of multiple sensor platforms in environments with poor GPS. First, the framework of GRID-aided vision navigation is developed with sequence images from land-based mobile mapping systems that integrate multiple sensors. Second, a highly efficient GRID storage management model is established based on the linear index of a road segment for fast image searches and retrieval. Third, a robust image matching algorithm is presented to search and match a real-time image with the GRID. Subsequently, the image matched with the real-time scene is considered to calculate the 3D navigation parameter of multiple sensor platforms. Experimental results show that the proposed approach retrieves images efficiently and has navigation accuracies of 1.2 m in a plane and 1.8 m in height under GPS loss in 5 min and within 1500 m.

An Imaging Sensor-Aided Vision Navigation Approach that Uses a Geo-Referenced Image Database

PubMed Central

Li, Yan; Hu, Qingwu; Wu, Meng; Gao, Yang

2016-01-01

In determining position and attitude, vision navigation via real-time image processing of data collected from imaging sensors is advanced without a high-performance global positioning system (GPS) and an inertial measurement unit (IMU). Vision navigation is widely used in indoor navigation, far space navigation, and multiple sensor-integrated mobile mapping. This paper proposes a novel vision navigation approach aided by imaging sensors and that uses a high-accuracy geo-referenced image database (GRID) for high-precision navigation of multiple sensor platforms in environments with poor GPS. First, the framework of GRID-aided vision navigation is developed with sequence images from land-based mobile mapping systems that integrate multiple sensors. Second, a highly efficient GRID storage management model is established based on the linear index of a road segment for fast image searches and retrieval. Third, a robust image matching algorithm is presented to search and match a real-time image with the GRID. Subsequently, the image matched with the real-time scene is considered to calculate the 3D navigation parameter of multiple sensor platforms. Experimental results show that the proposed approach retrieves images efficiently and has navigation accuracies of 1.2 m in a plane and 1.8 m in height under GPS loss in 5 min and within 1500 m. PMID:26828496

SIproc: an open-source biomedical data processing platform for large hyperspectral images.

PubMed

Berisha, Sebastian; Chang, Shengyuan; Saki, Sam; Daeinejad, Davar; He, Ziqi; Mankar, Rupali; Mayerich, David

2017-04-10

There has recently been significant interest within the vibrational spectroscopy community to apply quantitative spectroscopic imaging techniques to histology and clinical diagnosis. However, many of the proposed methods require collecting spectroscopic images that have a similar region size and resolution to the corresponding histological images. Since spectroscopic images contain significantly more spectral samples than traditional histology, the resulting data sets can approach hundreds of gigabytes to terabytes in size. This makes them difficult to store and process, and the tools available to researchers for handling large spectroscopic data sets are limited. Fundamental mathematical tools, such as MATLAB, Octave, and SciPy, are extremely powerful but require that the data be stored in fast memory. This memory limitation becomes impractical for even modestly sized histological images, which can be hundreds of gigabytes in size. In this paper, we propose an open-source toolkit designed to perform out-of-core processing of hyperspectral images. By taking advantage of graphical processing unit (GPU) computing combined with adaptive data streaming, our software alleviates common workstation memory limitations while achieving better performance than existing applications.

Comparison of pre-processing techniques for fluorescence microscopy images of cells labeled for actin.

PubMed

Muralidhar, Gautam S; Channappayya, Sumohana S; Slater, John H; Blinka, Ellen M; Bovik, Alan C; Frey, Wolfgang; Markey, Mia K

2008-11-06

Automated analysis of fluorescence microscopy images of endothelial cells labeled for actin is important for quantifying changes in the actin cytoskeleton. The current manual approach is laborious and inefficient. The goal of our work is to develop automated image analysis methods, thereby increasing cell analysis throughput. In this study, we present preliminary results on comparing different algorithms for cell segmentation and image denoising.

Quantum image pseudocolor coding based on the density-stratified method

NASA Astrophysics Data System (ADS)

Jiang, Nan; Wu, Wenya; Wang, Luo; Zhao, Na

2015-05-01

Pseudocolor processing is a branch of image enhancement. It dyes grayscale images to color images to make the images more beautiful or to highlight some parts on the images. This paper proposes a quantum image pseudocolor coding scheme based on the density-stratified method which defines a colormap and changes the density value from gray to color parallel according to the colormap. Firstly, two data structures: quantum image GQIR and quantum colormap QCR are reviewed or proposed. Then, the quantum density-stratified algorithm is presented. Based on them, the quantum realization in the form of circuits is given. The main advantages of the quantum version for pseudocolor processing over the classical approach are that it needs less memory and can speed up the computation. Two kinds of examples help us to describe the scheme further. Finally, the future work are analyzed.

Segmentation and Image Analysis of Abnormal Lungs at CT: Current Approaches, Challenges, and Future Trends

PubMed Central

Mansoor, Awais; Foster, Brent; Xu, Ziyue; Papadakis, Georgios Z.; Folio, Les R.; Udupa, Jayaram K.; Mollura, Daniel J.

2015-01-01

The computer-based process of identifying the boundaries of lung from surrounding thoracic tissue on computed tomographic (CT) images, which is called segmentation, is a vital first step in radiologic pulmonary image analysis. Many algorithms and software platforms provide image segmentation routines for quantification of lung abnormalities; however, nearly all of the current image segmentation approaches apply well only if the lungs exhibit minimal or no pathologic conditions. When moderate to high amounts of disease or abnormalities with a challenging shape or appearance exist in the lungs, computer-aided detection systems may be highly likely to fail to depict those abnormal regions because of inaccurate segmentation methods. In particular, abnormalities such as pleural effusions, consolidations, and masses often cause inaccurate lung segmentation, which greatly limits the use of image processing methods in clinical and research contexts. In this review, a critical summary of the current methods for lung segmentation on CT images is provided, with special emphasis on the accuracy and performance of the methods in cases with abnormalities and cases with exemplary pathologic findings. The currently available segmentation methods can be divided into five major classes: (a) thresholding-based, (b) region-based, (c) shape-based, (d) neighboring anatomy–guided, and (e) machine learning–based methods. The feasibility of each class and its shortcomings are explained and illustrated with the most common lung abnormalities observed on CT images. In an overview, practical applications and evolving technologies combining the presented approaches for the practicing radiologist are detailed. ©RSNA, 2015 PMID:26172351

A new omni-directional multi-camera system for high resolution surveillance

NASA Astrophysics Data System (ADS)

Cogal, Omer; Akin, Abdulkadir; Seyid, Kerem; Popovic, Vladan; Schmid, Alexandre; Ott, Beat; Wellig, Peter; Leblebici, Yusuf

2014-05-01

Omni-directional high resolution surveillance has a wide application range in defense and security fields. Early systems used for this purpose are based on parabolic mirror or fisheye lens where distortion due to the nature of the optical elements cannot be avoided. Moreover, in such systems, the image resolution is limited to a single image sensor's image resolution. Recently, the Panoptic camera approach that mimics the eyes of flying insects using multiple imagers has been presented. This approach features a novel solution for constructing a spherically arranged wide FOV plenoptic imaging system where the omni-directional image quality is limited by low-end sensors. In this paper, an overview of current Panoptic camera designs is provided. New results for a very-high resolution visible spectrum imaging and recording system inspired from the Panoptic approach are presented. The GigaEye-1 system, with 44 single cameras and 22 FPGAs, is capable of recording omni-directional video in a 360°×100° FOV at 9.5 fps with a resolution over (17,700×4,650) pixels (82.3MP). Real-time video capturing capability is also verified at 30 fps for a resolution over (9,000×2,400) pixels (21.6MP). The next generation system with significantly higher resolution and real-time processing capacity, called GigaEye-2, is currently under development. The important capacity of GigaEye-1 opens the door to various post-processing techniques in surveillance domain such as large perimeter object tracking, very-high resolution depth map estimation and high dynamicrange imaging which are beyond standard stitching and panorama generation methods.

Single Anisotropic 3-D MR Image Upsampling via Overcomplete Dictionary Trained From In-Plane High Resolution Slices.

PubMed

Jia, Yuanyuan; He, Zhongshi; Gholipour, Ali; Warfield, Simon K

2016-11-01

In magnetic resonance (MR), hardware limitation, scanning time, and patient comfort often result in the acquisition of anisotropic 3-D MR images. Enhancing image resolution is desired but has been very challenging in medical image processing. Super resolution reconstruction based on sparse representation and overcomplete dictionary has been lately employed to address this problem; however, these methods require extra training sets, which may not be always available. This paper proposes a novel single anisotropic 3-D MR image upsampling method via sparse representation and overcomplete dictionary that is trained from in-plane high resolution slices to upsample in the out-of-plane dimensions. The proposed method, therefore, does not require extra training sets. Abundant experiments, conducted on simulated and clinical brain MR images, show that the proposed method is more accurate than classical interpolation. When compared to a recent upsampling method based on the nonlocal means approach, the proposed method did not show improved results at low upsampling factors with simulated images, but generated comparable results with much better computational efficiency in clinical cases. Therefore, the proposed approach can be efficiently implemented and routinely used to upsample MR images in the out-of-planes views for radiologic assessment and postacquisition processing.

Content Based Image Retrieval by Using Color Descriptor and Discrete Wavelet Transform.

PubMed

Ashraf, Rehan; Ahmed, Mudassar; Jabbar, Sohail; Khalid, Shehzad; Ahmad, Awais; Din, Sadia; Jeon, Gwangil

2018-01-25

Due to recent development in technology, the complexity of multimedia is significantly increased and the retrieval of similar multimedia content is a open research problem. Content-Based Image Retrieval (CBIR) is a process that provides a framework for image search and low-level visual features are commonly used to retrieve the images from the image database. The basic requirement in any image retrieval process is to sort the images with a close similarity in term of visually appearance. The color, shape and texture are the examples of low-level image features. The feature plays a significant role in image processing. The powerful representation of an image is known as feature vector and feature extraction techniques are applied to get features that will be useful in classifying and recognition of images. As features define the behavior of an image, they show its place in terms of storage taken, efficiency in classification and obviously in time consumption also. In this paper, we are going to discuss various types of features, feature extraction techniques and explaining in what scenario, which features extraction technique will be better. The effectiveness of the CBIR approach is fundamentally based on feature extraction. In image processing errands like object recognition and image retrieval feature descriptor is an immense among the most essential step. The main idea of CBIR is that it can search related images to an image passed as query from a dataset got by using distance metrics. The proposed method is explained for image retrieval constructed on YCbCr color with canny edge histogram and discrete wavelet transform. The combination of edge of histogram and discrete wavelet transform increase the performance of image retrieval framework for content based search. The execution of different wavelets is additionally contrasted with discover the suitability of specific wavelet work for image retrieval. The proposed algorithm is prepared and tried to implement for Wang image database. For Image Retrieval Purpose, Artificial Neural Networks (ANN) is used and applied on standard dataset in CBIR domain. The execution of the recommended descriptors is assessed by computing both Precision and Recall values and compared with different other proposed methods with demonstrate the predominance of our method. The efficiency and effectiveness of the proposed approach outperforms the existing research in term of average precision and recall values.

FRAP Analysis: Accounting for Bleaching during Image Capture

PubMed Central

Wu, Jun; Shekhar, Nandini; Lele, Pushkar P.; Lele, Tanmay P.

2012-01-01

The analysis of Fluorescence Recovery After Photobleaching (FRAP) experiments involves mathematical modeling of the fluorescence recovery process. An important feature of FRAP experiments that tends to be ignored in the modeling is that there can be a significant loss of fluorescence due to bleaching during image capture. In this paper, we explicitly include the effects of bleaching during image capture in the model for the recovery process, instead of correcting for the effects of bleaching using reference measurements. Using experimental examples, we demonstrate the usefulness of such an approach in FRAP analysis. PMID:22912750

Space/Frequency Conversions in Image Processing and Transmission.

DTIC Science & Technology

1981-11-01

particularly with respect to the signal-to- noise ratio of the processed outputs. Devejlmnnt 9i a 1megtg fO-g s *&t~i egM2&Y conversion image_ aEggMsinLg: One...slowiv, whil e tle spatial impulse r-on i Ix~v; t) is vairied rapidly Iit *I tat tern recognitiont steartcl operaitioti. Under thiese c’irc-umstances, 11...electronic) will he incapable of recording the image with good signal-to- noise ratio. In what follows, we consider two approaches to producing these

Automatic tissue image segmentation based on image processing and deep learning

NASA Astrophysics Data System (ADS)

Kong, Zhenglun; Luo, Junyi; Xu, Shengpu; Li, Ting

2018-02-01

Image segmentation plays an important role in multimodality imaging, especially in fusion structural images offered by CT, MRI with functional images collected by optical technologies or other novel imaging technologies. Plus, image segmentation also provides detailed structure description for quantitative visualization of treating light distribution in the human body when incorporated with 3D light transport simulation method. Here we used image enhancement, operators, and morphometry methods to extract the accurate contours of different tissues such as skull, cerebrospinal fluid (CSF), grey matter (GM) and white matter (WM) on 5 fMRI head image datasets. Then we utilized convolutional neural network to realize automatic segmentation of images in a deep learning way. We also introduced parallel computing. Such approaches greatly reduced the processing time compared to manual and semi-automatic segmentation and is of great importance in improving speed and accuracy as more and more samples being learned. Our results can be used as a criteria when diagnosing diseases such as cerebral atrophy, which is caused by pathological changes in gray matter or white matter. We demonstrated the great potential of such image processing and deep leaning combined automatic tissue image segmentation in personalized medicine, especially in monitoring, and treatments.

Time-to-impact sensors in robot vision applications based on the near-sensor image processing concept

NASA Astrophysics Data System (ADS)

Åström, Anders; Forchheimer, Robert

2012-03-01

Based on the Near-Sensor Image Processing (NSIP) concept and recent results concerning optical flow and Time-to- Impact (TTI) computation with this architecture, we show how these results can be used and extended for robot vision applications. The first case involves estimation of the tilt of an approaching planar surface. The second case concerns the use of two NSIP cameras to estimate absolute distance and speed similar to a stereo-matching system but without the need to do image correlations. Going back to a one-camera system, the third case deals with the problem to estimate the shape of the approaching surface. It is shown that the previously developed TTI method not only gives a very compact solution with respect to hardware complexity, but also surprisingly high performance.

Using Polynomials to Simplify Fixed Pattern Noise and Photometric Correction of Logarithmic CMOS Image Sensors

PubMed Central

Li, Jing; Mahmoodi, Alireza; Joseph, Dileepan

2015-01-01

An important class of complementary metal-oxide-semiconductor (CMOS) image sensors are those where pixel responses are monotonic nonlinear functions of light stimuli. This class includes various logarithmic architectures, which are easily capable of wide dynamic range imaging, at video rates, but which are vulnerable to image quality issues. To minimize fixed pattern noise (FPN) and maximize photometric accuracy, pixel responses must be calibrated and corrected due to mismatch and process variation during fabrication. Unlike literature approaches, which employ circuit-based models of varying complexity, this paper introduces a novel approach based on low-degree polynomials. Although each pixel may have a highly nonlinear response, an approximately-linear FPN calibration is possible by exploiting the monotonic nature of imaging. Moreover, FPN correction requires only arithmetic, and an optimal fixed-point implementation is readily derived, subject to a user-specified number of bits per pixel. Using a monotonic spline, involving cubic polynomials, photometric calibration is also possible without a circuit-based model, and fixed-point photometric correction requires only a look-up table. The approach is experimentally validated with a logarithmic CMOS image sensor and is compared to a leading approach from the literature. The novel approach proves effective and efficient. PMID:26501287

A data mining based approach to predict spatiotemporal changes in satellite images

NASA Astrophysics Data System (ADS)

Boulila, W.; Farah, I. R.; Ettabaa, K. Saheb; Solaiman, B.; Ghézala, H. Ben

2011-06-01

The interpretation of remotely sensed images in a spatiotemporal context is becoming a valuable research topic. However, the constant growth of data volume in remote sensing imaging makes reaching conclusions based on collected data a challenging task. Recently, data mining appears to be a promising research field leading to several interesting discoveries in various areas such as marketing, surveillance, fraud detection and scientific discovery. By integrating data mining and image interpretation techniques, accurate and relevant information (i.e. functional relation between observed parcels and a set of informational contents) can be automatically elicited. This study presents a new approach to predict spatiotemporal changes in satellite image databases. The proposed method exploits fuzzy sets and data mining concepts to build predictions and decisions for several remote sensing fields. It takes into account imperfections related to the spatiotemporal mining process in order to provide more accurate and reliable information about land cover changes in satellite images. The proposed approach is validated using SPOT images representing the Saint-Denis region, capital of Reunion Island. Results show good performances of the proposed framework in predicting change for the urban zone.

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

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

Sandoval, D; Mlady, G; Selwyn, R

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

Investigation into image quality difference between total variation and nonlinear sparsifying transform based compressed sensing

NASA Astrophysics Data System (ADS)

Dong, Jian; Kudo, Hiroyuki

2017-03-01

Compressed sensing (CS) is attracting growing concerns in sparse-view computed tomography (CT) image reconstruction. The most standard approach of CS is total variation (TV) minimization. However, images reconstructed by TV usually suffer from distortions, especially in reconstruction of practical CT images, in forms of patchy artifacts, improper serrate edges and loss of image textures. Most existing CS approaches including TV achieve image quality improvement by applying linear transforms to object image, but linear transforms usually fail to take discontinuities into account, such as edges and image textures, which is considered to be the key reason for image distortions. Actually, discussions on nonlinear filter based image processing has a long history, leading us to clarify that the nonlinear filters yield better results compared to linear filters in image processing task such as denoising. Median root prior was first utilized by Alenius as nonlinear transform in CT image reconstruction, with significant gains obtained. Subsequently, Zhang developed the application of nonlocal means-based CS. A fact is gradually becoming clear that the nonlinear transform based CS has superiority in improving image quality compared with the linear transform based CS. However, it has not been clearly concluded in any previous paper within the scope of our knowledge. In this work, we investigated the image quality differences between the conventional TV minimization and nonlinear sparsifying transform based CS, as well as image quality differences among different nonlinear sparisying transform based CSs in sparse-view CT image reconstruction. Additionally, we accelerated the implementation of nonlinear sparsifying transform based CS algorithm.

Radiography for intensive care: participatory process analysis in a PACS-equipped and film/screen environment

NASA Astrophysics Data System (ADS)

Peer, Regina; Peer, Siegfried; Sander, Heike; Marsolek, Ingo; Koller, Wolfgang; Pappert, Dirk; Hierholzer, Johannes

2002-05-01

If new technology is introduced into medical practice it must prove to make a difference. However traditional approaches of outcome analysis failed to show a direct benefit of PACS on patient care and economical benefits are still in debate. A participatory process analysis was performed to compare workflow in a film based hospital and a PACS environment. This included direct observation of work processes, interview of involved staff, structural analysis and discussion of observations with staff members. After definition of common structures strong and weak workflow steps were evaluated. With a common workflow structure in both hospitals, benefits of PACS were revealed in workflow steps related to image reporting with simultaneous image access for ICU-physicians and radiologists, archiving of images as well as image and report distribution. However PACS alone is not able to cover the complete process of 'radiography for intensive care' from ordering of an image till provision of the final product equals image + report. Interference of electronic workflow with analogue process steps such as paper based ordering reduces the potential benefits of PACS. In this regard workflow modeling proved to be very helpful for the evaluation of complex work processes linking radiology and the ICU.

Dietary Assessment on a Mobile Phone Using Image Processing and Pattern Recognition Techniques: Algorithm Design and System Prototyping.

PubMed

Probst, Yasmine; Nguyen, Duc Thanh; Tran, Minh Khoi; Li, Wanqing

2015-07-27

Dietary assessment, while traditionally based on pen-and-paper, is rapidly moving towards automatic approaches. This study describes an Australian automatic food record method and its prototype for dietary assessment via the use of a mobile phone and techniques of image processing and pattern recognition. Common visual features including scale invariant feature transformation (SIFT), local binary patterns (LBP), and colour are used for describing food images. The popular bag-of-words (BoW) model is employed for recognizing the images taken by a mobile phone for dietary assessment. Technical details are provided together with discussions on the issues and future work.

Development and Current Status of Skull-Image Superimposition - Methodology and Instrumentation.

PubMed

Lan, Y

1992-12-01

This article presents a review of the literature and an evaluation on the development and application of skull-image superimposition technology - both instrumentation and methodology - contributed by a number of scholars since 1935. Along with a comparison of the methodologies involved in the two superimposition techniques - photographic and video - the author characterized the techniques in action and the recent advances in computer image superimposition processing technology. The major disadvantage of conventional approaches is its relying on subjective interpretation. Through painstaking comparison and analysis, computer image processing technology can make more conclusive identifications by direct testing and evaluating the various programmed indices. Copyright © 1992 Central Police University.

Time Series UAV Image-Based Point Clouds for Landslide Progression Evaluation Applications

PubMed Central

Moussa, Adel; El-Sheimy, Naser; Habib, Ayman

2017-01-01

Landslides are major and constantly changing threats to urban landscapes and infrastructure. It is essential to detect and capture landslide changes regularly. Traditional methods for monitoring landslides are time-consuming, costly, dangerous, and the quality and quantity of the data is sometimes unable to meet the necessary requirements of geotechnical projects. This motivates the development of more automatic and efficient remote sensing approaches for landslide progression evaluation. Automatic change detection involving low-altitude unmanned aerial vehicle image-based point clouds, although proven, is relatively unexplored, and little research has been done in terms of accounting for volumetric changes. In this study, a methodology for automatically deriving change displacement rates, in a horizontal direction based on comparisons between extracted landslide scarps from multiple time periods, has been developed. Compared with the iterative closest projected point (ICPP) registration method, the developed method takes full advantage of automated geometric measuring, leading to fast processing. The proposed approach easily processes a large number of images from different epochs and enables the creation of registered image-based point clouds without the use of extensive ground control point information or further processing such as interpretation and image correlation. The produced results are promising for use in the field of landslide research. PMID:29057847

Time Series UAV Image-Based Point Clouds for Landslide Progression Evaluation Applications.

PubMed

Al-Rawabdeh, Abdulla; Moussa, Adel; Foroutan, Marzieh; El-Sheimy, Naser; Habib, Ayman

2017-10-18

Landslides are major and constantly changing threats to urban landscapes and infrastructure. It is essential to detect and capture landslide changes regularly. Traditional methods for monitoring landslides are time-consuming, costly, dangerous, and the quality and quantity of the data is sometimes unable to meet the necessary requirements of geotechnical projects. This motivates the development of more automatic and efficient remote sensing approaches for landslide progression evaluation. Automatic change detection involving low-altitude unmanned aerial vehicle image-based point clouds, although proven, is relatively unexplored, and little research has been done in terms of accounting for volumetric changes. In this study, a methodology for automatically deriving change displacement rates, in a horizontal direction based on comparisons between extracted landslide scarps from multiple time periods, has been developed. Compared with the iterative closest projected point (ICPP) registration method, the developed method takes full advantage of automated geometric measuring, leading to fast processing. The proposed approach easily processes a large number of images from different epochs and enables the creation of registered image-based point clouds without the use of extensive ground control point information or further processing such as interpretation and image correlation. The produced results are promising for use in the field of landslide research.

Server-based Approach to Web Visualization of Integrated Three-dimensional Brain Imaging Data

PubMed Central

Poliakov, Andrew V.; Albright, Evan; Hinshaw, Kevin P.; Corina, David P.; Ojemann, George; Martin, Richard F.; Brinkley, James F.

2005-01-01

The authors describe a client-server approach to three-dimensional (3-D) visualization of neuroimaging data, which enables researchers to visualize, manipulate, and analyze large brain imaging datasets over the Internet. All computationally intensive tasks are done by a graphics server that loads and processes image volumes and 3-D models, renders 3-D scenes, and sends the renderings back to the client. The authors discuss the system architecture and implementation and give several examples of client applications that allow visualization and analysis of integrated language map data from single and multiple patients. PMID:15561787

Soft computing approach to 3D lung nodule segmentation in CT.

PubMed

Badura, P; Pietka, E

2014-10-01

This paper presents a novel, multilevel approach to the segmentation of various types of pulmonary nodules in computed tomography studies. It is based on two branches of computational intelligence: the fuzzy connectedness (FC) and the evolutionary computation. First, the image and auxiliary data are prepared for the 3D FC analysis during the first stage of an algorithm - the masks generation. Its main goal is to process some specific types of nodules connected to the pleura or vessels. It consists of some basic image processing operations as well as dedicated routines for the specific cases of nodules. The evolutionary computation is performed on the image and seed points in order to shorten the FC analysis and improve its accuracy. After the FC application, the remaining vessels are removed during the postprocessing stage. The method has been validated using the first dataset of studies acquired and described by the Lung Image Database Consortium (LIDC) and by its latest release - the LIDC-IDRI (Image Database Resource Initiative) database. Copyright © 2014 Elsevier Ltd. All rights reserved.

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

PubMed

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

2014-10-16

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

Cellular neural networks, the Navier-Stokes equation, and microarray image reconstruction.

PubMed

Zineddin, Bachar; Wang, Zidong; Liu, Xiaohui

2011-11-01

Although the last decade has witnessed a great deal of improvements achieved for the microarray technology, many major developments in all the main stages of this technology, including image processing, are still needed. Some hardware implementations of microarray image processing have been proposed in the literature and proved to be promising alternatives to the currently available software systems. However, the main drawback of those proposed approaches is the unsuitable addressing of the quantification of the gene spot in a realistic way without any assumption about the image surface. Our aim in this paper is to present a new image-reconstruction algorithm using the cellular neural network that solves the Navier-Stokes equation. This algorithm offers a robust method for estimating the background signal within the gene-spot region. The MATCNN toolbox for Matlab is used to test the proposed method. Quantitative comparisons are carried out, i.e., in terms of objective criteria, between our approach and some other available methods. It is shown that the proposed algorithm gives highly accurate and realistic measurements in a fully automated manner within a remarkably efficient time.

Automatic Crack Detection and Classification Method for Subway Tunnel Safety Monitoring

PubMed Central

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

2014-01-01

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

[Evaluating the maturity of IT-supported clinical imaging and diagnosis using the Digital Imaging Adoption Model : Are your clinical imaging processes ready for the digital era?

PubMed

Studzinski, J

2017-06-01

The Digital Imaging Adoption Model (DIAM) has been jointly developed by HIMSS Analytics and the European Society of Radiology (ESR). It helps evaluate the maturity of IT-supported processes in medical imaging, particularly in radiology. This eight-stage maturity model drives your organisational, strategic and tactical alignment towards imaging-IT planning. The key audience for the model comprises hospitals with imaging centers, as well as external imaging centers that collaborate with hospitals. The assessment focuses on different dimensions relevant to digital imaging, such as software infrastructure and usage, workflow security, clinical documentation and decision support, data exchange and analytical capabilities. With its standardised approach, it enables regional, national and international benchmarking. All DIAM participants receive a structured report that can be used as a basis for presenting, e.g. budget planning and investment decisions at management level.

Physics of fractional imaging in biomedicine.

PubMed

Sohail, Ayesha; Bég, O A; Li, Zhiwu; Celik, Sebahattin

2018-03-12

The mathematics of imaging is a growing field of research and is evolving rapidly parallel to evolution in the field of imaging. Imaging, which is a sub-field of biomedical engineering, considers novel approaches to visualize biological tissues with the general goal of improving health. "Medical imaging research provides improved diagnostic tools in clinical settings and supports the development of drugs and other therapies. The data acquisition and diagnostic interpretation with minimum error are the important technical aspects of medical imaging. The image quality and resolution are really important in portraying the internal aspects of patient's body. Although there are several user friendly resources for processing image features, such as enhancement, colour manipulation and compression, the development of new processing methods is still worthy of efforts. In this article we aim to present the role of fractional calculus in imaging with the aid of practical examples. Copyright © 2018 Elsevier Ltd. All rights reserved.

High-contrast multilayer imaging of biological organisms through dark-field digital refocusing.

PubMed

Faridian, Ahmad; Pedrini, Giancarlo; Osten, Wolfgang

2013-08-01

We have developed an imaging system to extract high contrast images from different layers of biological organisms. Utilizing a digital holographic approach, the system works without scanning through layers of the specimen. In dark-field illumination, scattered light has the main contribution in image formation, but in the case of coherent illumination, this creates a strong speckle noise that reduces the image quality. To remove this restriction, the specimen has been illuminated with various speckle-fields and a hologram has been recorded for each speckle-field. Each hologram has been analyzed separately and the corresponding intensity image has been reconstructed. The final image has been derived by averaging over the reconstructed images. A correlation approach has been utilized to determine the number of speckle-fields required to achieve a desired contrast and image quality. The reconstructed intensity images in different object layers are shown for different sea urchin larvae. Two multimedia files are attached to illustrate the process of digital focusing.

A Multimode Optical Imaging System for Preclinical Applications In Vivo: Technology Development, Multiscale Imaging, and Chemotherapy Assessment

PubMed Central

Hwang, Jae Youn; Wachsmann-Hogiu, Sebastian; Ramanujan, V. Krishnan; Ljubimova, Julia; Gross, Zeev; Gray, Harry B.; Medina-Kauwe, Lali K.; Farkas, Daniel L.

2012-01-01

Purpose Several established optical imaging approaches have been applied, usually in isolation, to preclinical studies; however, truly useful in vivo imaging may require a simultaneous combination of imaging modalities to examine dynamic characteristics of cells and tissues. We developed a new multimode optical imaging system designed to be application-versatile, yielding high sensitivity, and specificity molecular imaging. Procedures We integrated several optical imaging technologies, including fluorescence intensity, spectral, lifetime, intravital confocal, two-photon excitation, and bioluminescence, into a single system that enables functional multiscale imaging in animal models. Results The approach offers a comprehensive imaging platform for kinetic, quantitative, and environmental analysis of highly relevant information, with micro-to-macroscopic resolution. Applied to small animals in vivo, this provides superior monitoring of processes of interest, represented here by chemo-/nanoconstruct therapy assessment. Conclusions This new system is versatile and can be optimized for various applications, of which cancer detection and targeted treatment are emphasized here. PMID:21874388

Feature Matching of Historical Images Based on Geometry of Quadrilaterals

NASA Astrophysics Data System (ADS)

Maiwald, F.; Schneider, D.; Henze, F.; Münster, S.; Niebling, F.

2018-05-01

This contribution shows an approach to match historical images from the photo library of the Saxon State and University Library Dresden (SLUB) in the context of a historical three-dimensional city model of Dresden. In comparison to recent images, historical photography provides diverse factors which make an automatical image analysis (feature detection, feature matching and relative orientation of images) difficult. Due to e.g. film grain, dust particles or the digitalization process, historical images are often covered by noise interfering with the image signal needed for a robust feature matching. The presented approach uses quadrilaterals in image space as these are commonly available in man-made structures and façade images (windows, stones, claddings). It is explained how to generally detect quadrilaterals in images. Consequently, the properties of the quadrilaterals as well as the relationship to neighbouring quadrilaterals are used for the description and matching of feature points. The results show that most of the matches are robust and correct but still small in numbers.

In-line agglomeration degree estimation in fluidized bed pellet coating processes using visual imaging.

PubMed

Mehle, Andraž; Kitak, Domen; Podrekar, Gregor; Likar, Boštjan; Tomaževič, Dejan

2018-05-09

Agglomeration of pellets in fluidized bed coating processes is an undesirable phenomenon that affects the yield and quality of the product. In scope of PAT guidance, we present a system that utilizes visual imaging for in-line monitoring of the agglomeration degree. Seven pilot-scale Wurster coating processes were executed under various process conditions, providing a wide spectrum of process outcomes. Images of pellets were acquired during the coating processes in a contactless manner through an observation window of the coating apparatus. Efficient image analysis methods were developed for automatic recognition of discrete pellets and agglomerates in the acquired images. In-line obtained agglomeration degree trends revealed the agglomeration dynamics in distinct phases of the coating processes. We compared the in-line estimated agglomeration degree in the end point of each process to the results obtained by the off-line sieve analysis reference method. A strong positive correlation was obtained (coefficient of determination R 2 =0.99), confirming the feasibility of the approach. The in-line estimated agglomeration degree enables early detection of agglomeration and provides means for timely interventions to retain it in an acceptable range. Copyright © 2018 Elsevier B.V. All rights reserved.

A Multimodal Approach to Counselor Supervision.

ERIC Educational Resources Information Center

Ponterotto, Joseph G.; Zander, Toni A.

1984-01-01

Represents an initial effort to apply Lazarus's multimodal approach to a model of counselor supervision. Includes continuously monitoring the trainee's behavior, affect, sensations, images, cognitions, interpersonal functioning, and when appropriate, biological functioning (diet and drugs) in the supervisory process. (LLL)

Aortic annulus sizing using watershed transform and morphological approach for CT images

NASA Astrophysics Data System (ADS)

Mohammad, Norhasmira; Omar, Zaid; Sahrim, Mus'ab

2018-02-01

Aortic valve disease occurs due to calcification deposits on the area of leaflets within the human heart. It is progressive over time where it can affect the mechanism of the heart valve. To avoid the risk of surgery for vulnerable patients especially senior citizens, a new method has been introduced: Transcatheter Aortic Valve Implantation (TAVI), which places a synthetic catheter within the patient's valve. This entails a procedure of aortic annulus sizing, which requires manual measurement of the scanned images acquired from Computed Tomographic (CT) by experts. The step requires intensive efforts, though human error may still eventually lead to false measurement. In this research, image processing techniques are implemented onto cardiac CT images to achieve an automated and accurate measurement of the heart annulus. The image is first put through pre-processing for noise filtration and image enhancement. Then, a marker image is computed using the combination of opening and closing operations where the foreground image is marked as a feature while the background image is set to zero. Marker image is used to control the watershed transformation and also to prevent oversegmentation. This transformation has the advantage of fast computational and oversegmentation problems, which usually appear with the watershed transform can be solved with the introduction of marker image. Finally, the measurement of aortic annulus from the image data is obtained through morphological operations. Results affirm the approach's ability to achieve accurate annulus measurements compared to conventional techniques.

New method of contour image processing based on the formalism of spiral light beams

NASA Astrophysics Data System (ADS)

Volostnikov, Vladimir G.; Kishkin, S. A.; Kotova, S. P.

2013-07-01

The possibility of applying the mathematical formalism of spiral light beams to the problems of contour image recognition is theoretically studied. The advantages and disadvantages of the proposed approach are evaluated; the results of numerical modelling are presented.

Empirical projection-based basis-component decomposition method

NASA Astrophysics Data System (ADS)

Brendel, Bernhard; Roessl, Ewald; Schlomka, Jens-Peter; Proksa, Roland

2009-02-01

Advances in the development of semiconductor based, photon-counting x-ray detectors stimulate research in the domain of energy-resolving pre-clinical and clinical computed tomography (CT). For counting detectors acquiring x-ray attenuation in at least three different energy windows, an extended basis component decomposition can be performed in which in addition to the conventional approach of Alvarez and Macovski a third basis component is introduced, e.g., a gadolinium based CT contrast material. After the decomposition of the measured projection data into the basis component projections, conventional filtered-backprojection reconstruction is performed to obtain the basis-component images. In recent work, this basis component decomposition was obtained by maximizing the likelihood-function of the measurements. This procedure is time consuming and often unstable for excessively noisy data or low intrinsic energy resolution of the detector. Therefore, alternative procedures are of interest. Here, we introduce a generalization of the idea of empirical dual-energy processing published by Stenner et al. to multi-energy, photon-counting CT raw data. Instead of working in the image-domain, we use prior spectral knowledge about the acquisition system (tube spectra, bin sensitivities) to parameterize the line-integrals of the basis component decomposition directly in the projection domain. We compare this empirical approach with the maximum-likelihood (ML) approach considering image noise and image bias (artifacts) and see that only moderate noise increase is to be expected for small bias in the empirical approach. Given the drastic reduction of pre-processing time, the empirical approach is considered a viable alternative to the ML approach.

RootGraph: a graphic optimization tool for automated image analysis of plant roots

PubMed Central

Cai, Jinhai; Zeng, Zhanghui; Connor, Jason N.; Huang, Chun Yuan; Melino, Vanessa; Kumar, Pankaj; Miklavcic, Stanley J.

2015-01-01

This paper outlines a numerical scheme for accurate, detailed, and high-throughput image analysis of plant roots. In contrast to existing root image analysis tools that focus on root system-average traits, a novel, fully automated and robust approach for the detailed characterization of root traits, based on a graph optimization process is presented. The scheme, firstly, distinguishes primary roots from lateral roots and, secondly, quantifies a broad spectrum of root traits for each identified primary and lateral root. Thirdly, it associates lateral roots and their properties with the specific primary root from which the laterals emerge. The performance of this approach was evaluated through comparisons with other automated and semi-automated software solutions as well as against results based on manual measurements. The comparisons and subsequent application of the algorithm to an array of experimental data demonstrate that this method outperforms existing methods in terms of accuracy, robustness, and the ability to process root images under high-throughput conditions. PMID:26224880

An efficient multi-resolution GA approach to dental image alignment

NASA Astrophysics Data System (ADS)

Nassar, Diaa Eldin; Ogirala, Mythili; Adjeroh, Donald; Ammar, Hany

2006-02-01

Automating the process of postmortem identification of individuals using dental records is receiving an increased attention in forensic science, especially with the large volume of victims encountered in mass disasters. Dental radiograph alignment is a key step required for automating the dental identification process. In this paper, we address the problem of dental radiograph alignment using a Multi-Resolution Genetic Algorithm (MR-GA) approach. We use location and orientation information of edge points as features; we assume that affine transformations suffice to restore geometric discrepancies between two images of a tooth, we efficiently search the 6D space of affine parameters using GA progressively across multi-resolution image versions, and we use a Hausdorff distance measure to compute the similarity between a reference tooth and a query tooth subject to a possible alignment transform. Testing results based on 52 teeth-pair images suggest that our algorithm converges to reasonable solutions in more than 85% of the test cases, with most of the error in the remaining cases due to excessive misalignments.

Single-particle cryo-EM-Improved ab initio 3D reconstruction with SIMPLE/PRIME.

PubMed

Reboul, Cyril F; Eager, Michael; Elmlund, Dominika; Elmlund, Hans

2018-01-01

Cryogenic electron microscopy (cryo-EM) and single-particle analysis now enables the determination of high-resolution structures of macromolecular assemblies that have resisted X-ray crystallography and other approaches. We developed the SIMPLE open-source image-processing suite for analysing cryo-EM images of single-particles. A core component of SIMPLE is the probabilistic PRIME algorithm for identifying clusters of images in 2D and determine relative orientations of single-particle projections in 3D. Here, we extend our previous work on PRIME and introduce new stochastic optimization algorithms that improve the robustness of the approach. Our refined method for identification of homogeneous subsets of images in accurate register substantially improves the resolution of the cluster centers and of the ab initio 3D reconstructions derived from them. We now obtain maps with a resolution better than 10 Å by exclusively processing cluster centers. Excellent parallel code performance on over-the-counter laptops and CPU workstations is demonstrated. © 2017 The Protein Society.

A Model-Based Approach for Microvasculature Structure Distortion Correction in Two-Photon Fluorescence Microscopy Images

PubMed Central

Dao, Lam; Glancy, Brian; Lucotte, Bertrand; Chang, Lin-Ching; Balaban, Robert S; Hsu, Li-Yueh

2015-01-01

SUMMARY This paper investigates a post-processing approach to correct spatial distortion in two-photon fluorescence microscopy images for vascular network reconstruction. It is aimed at in vivo imaging of large field-of-view, deep-tissue studies of vascular structures. Based on simple geometric modeling of the object-of-interest, a distortion function is directly estimated from the image volume by deconvolution analysis. Such distortion function is then applied to sub volumes of the image stack to adaptively adjust for spatially varying distortion and reduce the image blurring through blind deconvolution. The proposed technique was first evaluated in phantom imaging of fluorescent microspheres that are comparable in size to the underlying capillary vascular structures. The effectiveness of restoring three-dimensional spherical geometry of the microspheres using the estimated distortion function was compared with empirically measured point-spread function. Next, the proposed approach was applied to in vivo vascular imaging of mouse skeletal muscle to reduce the image distortion of the capillary structures. We show that the proposed method effectively improve the image quality and reduce spatially varying distortion that occurs in large field-of-view deep-tissue vascular dataset. The proposed method will help in qualitative interpretation and quantitative analysis of vascular structures from fluorescence microscopy images. PMID:26224257

Frontal slab composite magnetic resonance neurography of the brachial plexus: implications for infraclavicular block approaches.

PubMed

Raphael, David T; McIntee, Diane; Tsuruda, Jay S; Colletti, Patrick; Tatevossian, Ray

2005-12-01

Magnetic resonance neurography (MRN) is an imaging method by which nerves can be selectively highlighted. Using commercial software, the authors explored a variety of approaches to develop a three-dimensional volume-rendered MRN image of the entire brachial plexus and used it to evaluate the accuracy of infraclavicular block approaches. With institutional review board approval, MRN of the brachial plexus was performed in 10 volunteer subjects. MRN imaging was performed on a GE 1.5-tesla magnetic resonance scanner (General Electric Healthcare Technologies, Waukesha, WI) using a phased array torso coil. Coronal STIR and T1 oblique sagittal sequences of the brachial plexus were obtained. Multiple software programs were explored for enhanced display and manipulation of the composite magnetic resonance images. The authors developed a frontal slab composite approach that allows single-frame reconstruction of a three-dimensional volume-rendered image of the entire brachial plexus. Automatic segmentation was supplemented by manual segmentation in nearly all cases. For each of three infraclavicular approaches (posteriorly directed needle below midclavicle, infracoracoid, or caudomedial to coracoid), the targeting error was measured as the distance from the MRN plexus midpoint to the approach-targeted site. Composite frontal slabs (coronal views), which are single-frame three-dimensional volume renderings from image-enhanced two-dimensional frontal view projections of the underlying coronal slices, were created. The targeting errors (mean +/- SD) for the approaches-midclavicle, infracoracoid, caudomedial to coracoid-were 0.43 +/- 0.67, 0.99 +/- 1.22, and 0.65 +/- 1.14 cm, respectively. Image-processed three-dimensional volume-rendered MNR scans, which allow visualization of the entire brachial plexus within a single composite image, have educational value in illustrating the complexity and individual variation of the plexus. Suggestions for improved guidance during infraclavicular block procedures are presented.

An Effective Palmprint Recognition Approach for Visible and Multispectral Sensor Images

PubMed Central

Sammouda, Rachid; Al-Salman, Abdul Malik; Alsanad, Ahmed

2018-01-01

Among several palmprint feature extraction methods the HOG-based method is attractive and performs well against changes in illumination and shadowing of palmprint images. However, it still lacks the robustness to extract the palmprint features at different rotation angles. To solve this problem, this paper presents a hybrid feature extraction method, named HOG-SGF that combines the histogram of oriented gradients (HOG) with a steerable Gaussian filter (SGF) to develop an effective palmprint recognition approach. The approach starts by processing all palmprint images by David Zhang’s method to segment only the region of interests. Next, we extracted palmprint features based on the hybrid HOG-SGF feature extraction method. Then, an optimized auto-encoder (AE) was utilized to reduce the dimensionality of the extracted features. Finally, a fast and robust regularized extreme learning machine (RELM) was applied for the classification task. In the evaluation phase of the proposed approach, a number of experiments were conducted on three publicly available palmprint databases, namely MS-PolyU of multispectral palmprint images and CASIA and Tongji of contactless palmprint images. Experimentally, the results reveal that the proposed approach outperforms the existing state-of-the-art approaches even when a small number of training samples are used. PMID:29762519

Kernel Regression Estimation of Fiber Orientation Mixtures in Diffusion MRI

PubMed Central

Cabeen, Ryan P.; Bastin, Mark E.; Laidlaw, David H.

2016-01-01

We present and evaluate a method for kernel regression estimation of fiber orientations and associated volume fractions for diffusion MR tractography and population-based atlas construction in clinical imaging studies of brain white matter. This is a model-based image processing technique in which representative fiber models are estimated from collections of component fiber models in model-valued image data. This extends prior work in nonparametric image processing and multi-compartment processing to provide computational tools for image interpolation, smoothing, and fusion with fiber orientation mixtures. In contrast to related work on multi-compartment processing, this approach is based on directional measures of divergence and includes data-adaptive extensions for model selection and bilateral filtering. This is useful for reconstructing complex anatomical features in clinical datasets analyzed with the ball-and-sticks model, and our framework’s data-adaptive extensions are potentially useful for general multi-compartment image processing. We experimentally evaluate our approach with both synthetic data from computational phantoms and in vivo clinical data from human subjects. With synthetic data experiments, we evaluate performance based on errors in fiber orientation, volume fraction, compartment count, and tractography-based connectivity. With in vivo data experiments, we first show improved scan-rescan reproducibility and reliability of quantitative fiber bundle metrics, including mean length, volume, streamline count, and mean volume fraction. We then demonstrate the creation of a multi-fiber tractography atlas from a population of 80 human subjects. In comparison to single tensor atlasing, our multi-fiber atlas shows more complete features of known fiber bundles and includes reconstructions of the lateral projections of the corpus callosum and complex fronto-parietal connections of the superior longitudinal fasciculus I, II, and III. PMID:26691524

Demonstration of a single-wavelength spectral-imaging-based Thai jasmine rice identification

NASA Astrophysics Data System (ADS)

Suwansukho, Kajpanya; Sumriddetchkajorn, Sarun; Buranasiri, Prathan

2011-07-01

A single-wavelength spectral-imaging-based Thai jasmine rice breed identification is demonstrated. Our nondestructive identification approach relies on a combination of fluorescent imaging and simple image processing techniques. Especially, we apply simple image thresholding, blob filtering, and image subtracting processes to either a 545 or a 575nm image in order to identify our desired Thai jasmine rice breed from others. Other key advantages include no waste product and fast identification time. In our demonstration, UVC light is used as our exciting light, a liquid crystal tunable optical filter is used as our wavelength seclector, and a digital camera with 640activepixels×480activepixels is used to capture the desired spectral image. Eight Thai rice breeds having similar size and shape are tested. Our experimental proof of concept shows that by suitably applying image thresholding, blob filtering, and image subtracting processes to the selected fluorescent image, the Thai jasmine rice breed can be identified with measured false acceptance rates of <22.9% and <25.7% for spectral images at 545 and 575nm wavelengths, respectively. A measured fast identification time is 25ms, showing high potential for real-time applications.

Brain extraction from normal and pathological images: A joint PCA/Image-Reconstruction approach.

PubMed

Han, Xu; Kwitt, Roland; Aylward, Stephen; Bakas, Spyridon; Menze, Bjoern; Asturias, Alexander; Vespa, Paul; Van Horn, John; Niethammer, Marc

2018-08-01

Brain extraction from 3D medical images is a common pre-processing step. A variety of approaches exist, but they are frequently only designed to perform brain extraction from images without strong pathologies. Extracting the brain from images exhibiting strong pathologies, for example, the presence of a brain tumor or of a traumatic brain injury (TBI), is challenging. In such cases, tissue appearance may substantially deviate from normal tissue appearance and hence violates algorithmic assumptions for standard approaches to brain extraction; consequently, the brain may not be correctly extracted. This paper proposes a brain extraction approach which can explicitly account for pathologies by jointly modeling normal tissue appearance and pathologies. Specifically, our model uses a three-part image decomposition: (1) normal tissue appearance is captured by principal component analysis (PCA), (2) pathologies are captured via a total variation term, and (3) the skull and surrounding tissue is captured by a sparsity term. Due to its convexity, the resulting decomposition model allows for efficient optimization. Decomposition and image registration steps are alternated to allow statistical modeling of normal tissue appearance in a fixed atlas coordinate system. As a beneficial side effect, the decomposition model allows for the identification of potentially pathological areas and the reconstruction of a quasi-normal image in atlas space. We demonstrate the effectiveness of our approach on four datasets: the publicly available IBSR and LPBA40 datasets which show normal image appearance, the BRATS dataset containing images with brain tumors, and a dataset containing clinical TBI images. We compare the performance with other popular brain extraction models: ROBEX, BEaST, MASS, BET, BSE and a recently proposed deep learning approach. Our model performs better than these competing approaches on all four datasets. Specifically, our model achieves the best median (97.11) and mean (96.88) Dice scores over all datasets. The two best performing competitors, ROBEX and MASS, achieve scores of 96.23/95.62 and 96.67/94.25 respectively. Hence, our approach is an effective method for high quality brain extraction for a wide variety of images. Copyright © 2018 Elsevier Inc. All rights reserved.

Modeling of polychromatic attenuation using computed tomography reconstructed images

NASA Technical Reports Server (NTRS)

Yan, C. H.; Whalen, R. T.; Beaupre, G. S.; Yen, S. Y.; Napel, S.

1999-01-01

This paper presents a procedure for estimating an accurate model of the CT imaging process including spectral effects. As raw projection data are typically unavailable to the end-user, we adopt a post-processing approach that utilizes the reconstructed images themselves. This approach includes errors from x-ray scatter and the nonidealities of the built-in soft tissue correction into the beam characteristics, which is crucial to beam hardening correction algorithms that are designed to be applied directly to CT reconstructed images. We formulate this approach as a quadratic programming problem and propose two different methods, dimension reduction and regularization, to overcome ill conditioning in the model. For the regularization method we use a statistical procedure, Cross Validation, to select the regularization parameter. We have constructed step-wedge phantoms to estimate the effective beam spectrum of a GE CT-I scanner. Using the derived spectrum, we computed the attenuation ratios for the wedge phantoms and found that the worst case modeling error is less than 3% of the corresponding attenuation ratio. We have also built two test (hybrid) phantoms to evaluate the effective spectrum. Based on these test phantoms, we have shown that the effective beam spectrum provides an accurate model for the CT imaging process. Last, we used a simple beam hardening correction experiment to demonstrate the effectiveness of the estimated beam profile for removing beam hardening artifacts. We hope that this estimation procedure will encourage more independent research on beam hardening corrections and will lead to the development of application-specific beam hardening correction algorithms.

Information, entropy, and fidelity in visual communication

NASA Astrophysics Data System (ADS)

Huck, Friedrich O.; Fales, Carl L.; Alter-Gartenberg, Rachel; Rahman, Zia-ur

1992-10-01

This paper presents an assessment of visual communication that integrates the critical limiting factors of image gathering an display with the digital processing that is used to code and restore images. The approach focuses on two mathematical criteria, information and fidelity, and on their relationships to the entropy of the encoded data and to the visual quality of the restored image.

Information, entropy and fidelity in visual communication

NASA Technical Reports Server (NTRS)

Huck, Friedrich O.; Fales, Carl L.; Alter-Gartenberg, Rachel; Rahman, Zia-Ur

1992-01-01

This paper presents an assessment of visual communication that integrates the critical limiting factors of image gathering and display with the digital processing that is used to code and restore images. The approach focuses on two mathematical criteria, information and fidelity, and on their relationships to the entropy of the encoded data and to the visual quality of the restored image.

An efficient method for facial component detection in thermal images

NASA Astrophysics Data System (ADS)

Paul, Michael; Blanik, Nikolai; Blazek, Vladimir; Leonhardt, Steffen

2015-04-01

A method to detect certain regions in thermal images of human faces is presented. In this approach, the following steps are necessary to locate the periorbital and the nose regions: First, the face is segmented from the background by thresholding and morphological filtering. Subsequently, a search region within the face, around its center of mass, is evaluated. Automatically computed temperature thresholds are used per subject and image or image sequence to generate binary images, in which the periorbital regions are located by integral projections. Then, the located positions are used to approximate the nose position. It is possible to track features in the located regions. Therefore, these regions are interesting for different applications like human-machine interaction, biometrics and biomedical imaging. The method is easy to implement and does not rely on any training images or templates. Furthermore, the approach saves processing resources due to simple computations and restricted search regions.

Light on body image treatment: acceptance through mindfulness.

PubMed

Stewart, Tiffany M

2004-11-01

The treatment of body image has to be multifaceted and should be directed toward the treatment of the whole individual-body, mind, and spirit-with an ultimate culmination of acceptance and compassion for the self. This article presents information on a mindful approach to the treatment of body image as it pertains to concerns with body size and shape. This approach fosters the idea that the treatment process should be one of observation, nonjudgment, neutrality, and acceptance. To this end, this article will depict the conceptualization of body image treatment from a mindful perspective, in which mindfulness serves as the foundation on which the multiple facets of treatment are built. The core components of body image treatment (i.e., cognitive, perceptual, behavioral, and emotional), in the context of mindfulness, are discussed as they relate to the treatment of body image disturbance. This article may be viewed as a theoretical overview of a new treatment concept for body image disturbance.

A Novel Defect Inspection Method for Semiconductor Wafer Based on Magneto-Optic Imaging

NASA Astrophysics Data System (ADS)

Pan, Z.; Chen, L.; Li, W.; Zhang, G.; Wu, P.

2013-03-01

The defects of semiconductor wafer may be generated from the manufacturing processes. A novel defect inspection method of semiconductor wafer is presented in this paper. The method is based on magneto-optic imaging, which involves inducing eddy current into the wafer under test, and detecting the magnetic flux associated with eddy current distribution in the wafer by exploiting the Faraday rotation effect. The magneto-optic image being generated may contain some noises that degrade the overall image quality, therefore, in this paper, in order to remove the unwanted noise present in the magneto-optic image, the image enhancement approach using multi-scale wavelet is presented, and the image segmentation approach based on the integration of watershed algorithm and clustering strategy is given. The experimental results show that many types of defects in wafer such as hole and scratch etc. can be detected by the method proposed in this paper.

White blood cell segmentation by circle detection using electromagnetism-like optimization.

PubMed

Cuevas, Erik; Oliva, Diego; Díaz, Margarita; Zaldivar, Daniel; Pérez-Cisneros, Marco; Pajares, Gonzalo

2013-01-01

Medical imaging is a relevant field of application of image processing algorithms. In particular, the analysis of white blood cell (WBC) images has engaged researchers from fields of medicine and computer vision alike. Since WBCs can be approximated by a quasicircular form, a circular detector algorithm may be successfully applied. This paper presents an algorithm for the automatic detection of white blood cells embedded into complicated and cluttered smear images that considers the complete process as a circle detection problem. The approach is based on a nature-inspired technique called the electromagnetism-like optimization (EMO) algorithm which is a heuristic method that follows electromagnetism principles for solving complex optimization problems. The proposed approach uses an objective function which measures the resemblance of a candidate circle to an actual WBC. Guided by the values of such objective function, the set of encoded candidate circles are evolved by using EMO, so that they can fit into the actual blood cells contained in the edge map of the image. Experimental results from blood cell images with a varying range of complexity are included to validate the efficiency of the proposed technique regarding detection, robustness, and stability.

Fast segmentation of satellite images using SLIC, WebGL and Google Earth Engine

NASA Astrophysics Data System (ADS)

Donchyts, Gennadii; Baart, Fedor; Gorelick, Noel; Eisemann, Elmar; van de Giesen, Nick

2017-04-01

Google Earth Engine (GEE) is a parallel geospatial processing platform, which harmonizes access to petabytes of freely available satellite images. It provides a very rich API, allowing development of dedicated algorithms to extract useful geospatial information from these images. At the same time, modern GPUs provide thousands of computing cores, which are mostly not utilized in this context. In the last years, WebGL became a popular and well-supported API, allowing fast image processing directly in web browsers. In this work, we will evaluate the applicability of WebGL to enable fast segmentation of satellite images. A new implementation of a Simple Linear Iterative Clustering (SLIC) algorithm using GPU shaders will be presented. SLIC is a simple and efficient method to decompose an image in visually homogeneous regions. It adapts a k-means clustering approach to generate superpixels efficiently. While this approach will be hard to scale, due to a significant amount of data to be transferred to the client, it should significantly improve exploratory possibilities and simplify development of dedicated algorithms for geoscience applications. Our prototype implementation will be used to improve surface water detection of the reservoirs using multispectral satellite imagery.

Unveiling molecular events in the brain by noninvasive imaging.

PubMed

Klohs, Jan; Rudin, Markus

2011-10-01

Neuroimaging allows researchers and clinicians to noninvasively assess structure and function of the brain. With the advances of imaging modalities such as magnetic resonance, nuclear, and optical imaging; the design of target-specific probes; and/or the introduction of reporter gene assays, these technologies are now capable of visualizing cellular and molecular processes in vivo. Undoubtedly, the system biological character of molecular neuroimaging, which allows for the study of molecular events in the intact organism, will enhance our understanding of physiology and pathophysiology of the brain and improve our ability to diagnose and treat diseases more specifically. Technical/scientific challenges to be faced are the development of highly sensitive imaging modalities, the design of specific imaging probe molecules capable of penetrating the CNS and reporting on endogenous cellular and molecular processes, and the development of tools for extracting quantitative, biologically relevant information from imaging data. Today, molecular neuroimaging is still an experimental approach with limited clinical impact; this is expected to change within the next decade. This article provides an overview of molecular neuroimaging approaches with a focus on rodent studies documenting the exploratory state of the field. Concepts are illustrated by discussing applications related to the pathophysiology of Alzheimer's disease.

White Blood Cell Segmentation by Circle Detection Using Electromagnetism-Like Optimization

PubMed Central

Oliva, Diego; Díaz, Margarita; Zaldivar, Daniel; Pérez-Cisneros, Marco; Pajares, Gonzalo

2013-01-01

Medical imaging is a relevant field of application of image processing algorithms. In particular, the analysis of white blood cell (WBC) images has engaged researchers from fields of medicine and computer vision alike. Since WBCs can be approximated by a quasicircular form, a circular detector algorithm may be successfully applied. This paper presents an algorithm for the automatic detection of white blood cells embedded into complicated and cluttered smear images that considers the complete process as a circle detection problem. The approach is based on a nature-inspired technique called the electromagnetism-like optimization (EMO) algorithm which is a heuristic method that follows electromagnetism principles for solving complex optimization problems. The proposed approach uses an objective function which measures the resemblance of a candidate circle to an actual WBC. Guided by the values of such objective function, the set of encoded candidate circles are evolved by using EMO, so that they can fit into the actual blood cells contained in the edge map of the image. Experimental results from blood cell images with a varying range of complexity are included to validate the efficiency of the proposed technique regarding detection, robustness, and stability. PMID:23476713

Pulse compression favourable aperiodic infrared imaging approach for non-destructive testing and evaluation of bio-materials

NASA Astrophysics Data System (ADS)

Mulaveesala, Ravibabu; Dua, Geetika; Arora, Vanita; Siddiqui, Juned A.; Muniyappa, Amarnath

2017-05-01

In recent years, aperiodic, transient pulse compression favourable infrared imaging methodologies demonstrated as reliable, quantitative, remote characterization and evaluation techniques for testing and evaluation of various biomaterials. This present work demonstrates a pulse compression favourable aperiodic thermal wave imaging technique, frequency modulated thermal wave imaging technique for bone diagnostics, especially by considering the bone with tissue, skin and muscle over layers. In order to find the capabilities of the proposed frequency modulated thermal wave imaging technique to detect the density variations in a multi layered skin-fat-muscle-bone structure, finite element modeling and simulation studies have been carried out. Further, frequency and time domain post processing approaches have been adopted on the temporal temperature data in order to improve the detection capabilities of frequency modulated thermal wave imaging.

3D Power Line Extraction from Multiple Aerial Images.

PubMed

Oh, Jaehong; Lee, Changno

2017-09-29

Power lines are cables that carry electrical power from a power plant to an electrical substation. They must be connected between the tower structures in such a way that ensures minimum tension and sufficient clearance from the ground. Power lines can stretch and sag with the changing weather, eventually exceeding the planned tolerances. The excessive sags can then cause serious accidents, while hindering the durability of the power lines. We used photogrammetric techniques with a low-cost drone to achieve efficient 3D mapping of power lines that are often difficult to approach. Unlike the conventional image-to-object space approach, we used the object-to-image space approach using cubic grid points. We processed four strips of aerial images to automatically extract the power line points in the object space. Experimental results showed that the approach could successfully extract the positions of the power line points for power line generation and sag measurement with the elevation accuracy of a few centimeters.

3D Power Line Extraction from Multiple Aerial Images

PubMed Central

Lee, Changno

2017-01-01

Power lines are cables that carry electrical power from a power plant to an electrical substation. They must be connected between the tower structures in such a way that ensures minimum tension and sufficient clearance from the ground. Power lines can stretch and sag with the changing weather, eventually exceeding the planned tolerances. The excessive sags can then cause serious accidents, while hindering the durability of the power lines. We used photogrammetric techniques with a low-cost drone to achieve efficient 3D mapping of power lines that are often difficult to approach. Unlike the conventional image-to-object space approach, we used the object-to-image space approach using cubic grid points. We processed four strips of aerial images to automatically extract the power line points in the object space. Experimental results showed that the approach could successfully extract the positions of the power line points for power line generation and sag measurement with the elevation accuracy of a few centimeters. PMID:28961204

Automatic screening and classification of diabetic retinopathy and maculopathy using fuzzy image processing.

PubMed

Rahim, Sarni Suhaila; Palade, Vasile; Shuttleworth, James; Jayne, Chrisina

2016-12-01

Digital retinal imaging is a challenging screening method for which effective, robust and cost-effective approaches are still to be developed. Regular screening for diabetic retinopathy and diabetic maculopathy diseases is necessary in order to identify the group at risk of visual impairment. This paper presents a novel automatic detection of diabetic retinopathy and maculopathy in eye fundus images by employing fuzzy image processing techniques. The paper first introduces the existing systems for diabetic retinopathy screening, with an emphasis on the maculopathy detection methods. The proposed medical decision support system consists of four parts, namely: image acquisition, image preprocessing including four retinal structures localisation, feature extraction and the classification of diabetic retinopathy and maculopathy. A combination of fuzzy image processing techniques, the Circular Hough Transform and several feature extraction methods are implemented in the proposed system. The paper also presents a novel technique for the macula region localisation in order to detect the maculopathy. In addition to the proposed detection system, the paper highlights a novel online dataset and it presents the dataset collection, the expert diagnosis process and the advantages of our online database compared to other public eye fundus image databases for diabetic retinopathy purposes.

Architecture of the parallel hierarchical network for fast image recognition

NASA Astrophysics Data System (ADS)

Timchenko, Leonid; Wójcik, Waldemar; Kokriatskaia, Natalia; Kutaev, Yuriy; Ivasyuk, Igor; Kotyra, Andrzej; Smailova, Saule

2016-09-01

Multistage integration of visual information in the brain allows humans to respond quickly to most significant stimuli while maintaining their ability to recognize small details in the image. Implementation of this principle in technical systems can lead to more efficient processing procedures. The multistage approach to image processing includes main types of cortical multistage convergence. The input images are mapped into a flexible hierarchy that reflects complexity of image data. Procedures of the temporal image decomposition and hierarchy formation are described in mathematical expressions. The multistage system highlights spatial regularities, which are passed through a number of transformational levels to generate a coded representation of the image that encapsulates a structure on different hierarchical levels in the image. At each processing stage a single output result is computed to allow a quick response of the system. The result is presented as an activity pattern, which can be compared with previously computed patterns on the basis of the closest match. With regard to the forecasting method, its idea lies in the following. In the results synchronization block, network-processed data arrive to the database where a sample of most correlated data is drawn using service parameters of the parallel-hierarchical network.

Incorporating Edge Information into Best Merge Region-Growing Segmentation

NASA Technical Reports Server (NTRS)

Tilton, James C.; Pasolli, Edoardo

2014-01-01

We have previously developed a best merge region-growing approach that integrates nonadjacent region object aggregation with the neighboring region merge process usually employed in region growing segmentation approaches. This approach has been named HSeg, because it provides a hierarchical set of image segmentation results. Up to this point, HSeg considered only global region feature information in the region growing decision process. We present here three new versions of HSeg that include local edge information into the region growing decision process at different levels of rigor. We then compare the effectiveness and processing times of these new versions HSeg with each other and with the original version of HSeg.

Electronic Photography at the NASA Langley Research Center

NASA Technical Reports Server (NTRS)

Holm, Jack; Judge, Nancianne

1995-01-01

An electronic photography facility has been established in the Imaging & Photographic Technology Section, Visual Imaging Branch, at the NASA Langley Research Center (LaRC). The purpose of this facility is to provide the LaRC community with access to digital imaging technology. In particular, capabilities have been established for image scanning, direct image capture, optimized image processing for storage, image enhancement, and optimized device dependent image processing for output. Unique approaches include: evaluation and extraction of the entire film information content through scanning; standardization of image file tone reproduction characteristics for optimal bit utilization and viewing; education of digital imaging personnel on the effects of sampling and quantization to minimize image processing related information loss; investigation of the use of small kernel optimal filters for image restoration; characterization of a large array of output devices and development of image processing protocols for standardized output. Currently, the laboratory has a large collection of digital image files which contain essentially all the information present on the original films. These files are stored at 8-bits per color, but the initial image processing was done at higher bit depths and/or resolutions so that the full 8-bits are used in the stored files. The tone reproduction of these files has also been optimized so the available levels are distributed according to visual perceptibility. Look up tables are available which modify these files for standardized output on various devices, although color reproduction has been allowed to float to some extent to allow for full utilization of output device gamut.

Cell segmentation in phase contrast microscopy images via semi-supervised classification over optics-related features.

PubMed

Su, Hang; Yin, Zhaozheng; Huh, Seungil; Kanade, Takeo

2013-10-01

Phase-contrast microscopy is one of the most common and convenient imaging modalities to observe long-term multi-cellular processes, which generates images by the interference of lights passing through transparent specimens and background medium with different retarded phases. Despite many years of study, computer-aided phase contrast microscopy analysis on cell behavior is challenged by image qualities and artifacts caused by phase contrast optics. Addressing the unsolved challenges, the authors propose (1) a phase contrast microscopy image restoration method that produces phase retardation features, which are intrinsic features of phase contrast microscopy, and (2) a semi-supervised learning based algorithm for cell segmentation, which is a fundamental task for various cell behavior analysis. Specifically, the image formation process of phase contrast microscopy images is first computationally modeled with a dictionary of diffraction patterns; as a result, each pixel of a phase contrast microscopy image is represented by a linear combination of the bases, which we call phase retardation features. Images are then partitioned into phase-homogeneous atoms by clustering neighboring pixels with similar phase retardation features. Consequently, cell segmentation is performed via a semi-supervised classification technique over the phase-homogeneous atoms. Experiments demonstrate that the proposed approach produces quality segmentation of individual cells and outperforms previous approaches. Copyright © 2013 Elsevier B.V. All rights reserved.

Smartphone-based quantitative measurements on holographic sensors.

PubMed

Khalili Moghaddam, Gita; Lowe, Christopher Robin

2017-01-01

The research reported herein integrates a generic holographic sensor platform and a smartphone-based colour quantification algorithm in order to standardise and improve the determination of the concentration of analytes of interest. The utility of this approach has been exemplified by analysing the replay colour of the captured image of a holographic pH sensor in near real-time. Personalised image encryption followed by a wavelet-based image compression method were applied to secure the image transfer across a bandwidth-limited network to the cloud. The decrypted and decompressed image was processed through four principal steps: Recognition of the hologram in the image with a complex background using a template-based approach, conversion of device-dependent RGB values to device-independent CIEXYZ values using a polynomial model of the camera and computation of the CIEL*a*b* values, use of the colour coordinates of the captured image to segment the image, select the appropriate colour descriptors and, ultimately, locate the region of interest (ROI), i.e. the hologram in this case, and finally, application of a machine learning-based algorithm to correlate the colour coordinates of the ROI to the analyte concentration. Integrating holographic sensors and the colour image processing algorithm potentially offers a cost-effective platform for the remote monitoring of analytes in real time in readily accessible body fluids by minimally trained individuals.

Smartphone-based quantitative measurements on holographic sensors

PubMed Central

Khalili Moghaddam, Gita

2017-01-01

The research reported herein integrates a generic holographic sensor platform and a smartphone-based colour quantification algorithm in order to standardise and improve the determination of the concentration of analytes of interest. The utility of this approach has been exemplified by analysing the replay colour of the captured image of a holographic pH sensor in near real-time. Personalised image encryption followed by a wavelet-based image compression method were applied to secure the image transfer across a bandwidth-limited network to the cloud. The decrypted and decompressed image was processed through four principal steps: Recognition of the hologram in the image with a complex background using a template-based approach, conversion of device-dependent RGB values to device-independent CIEXYZ values using a polynomial model of the camera and computation of the CIEL*a*b* values, use of the colour coordinates of the captured image to segment the image, select the appropriate colour descriptors and, ultimately, locate the region of interest (ROI), i.e. the hologram in this case, and finally, application of a machine learning-based algorithm to correlate the colour coordinates of the ROI to the analyte concentration. Integrating holographic sensors and the colour image processing algorithm potentially offers a cost-effective platform for the remote monitoring of analytes in real time in readily accessible body fluids by minimally trained individuals. PMID:29141008

Medical image integrity control and forensics based on watermarking--approximating local modifications and identifying global image alterations.

PubMed

Huang, H; Coatrieux, G; Shu, H Z; Luo, L M; Roux, Ch

2011-01-01

In this paper we present a medical image integrity verification system that not only allows detecting and approximating malevolent local image alterations (e.g. removal or addition of findings) but is also capable to identify the nature of global image processing applied to the image (e.g. lossy compression, filtering …). For that purpose, we propose an image signature derived from the geometric moments of pixel blocks. Such a signature is computed over regions of interest of the image and then watermarked in regions of non interest. Image integrity analysis is conducted by comparing embedded and recomputed signatures. If any, local modifications are approximated through the determination of the parameters of the nearest generalized 2D Gaussian. Image moments are taken as image features and serve as inputs to one classifier we learned to discriminate the type of global image processing. Experimental results with both local and global modifications illustrate the overall performances of our approach.

High-resolution imaging of cellular processes across textured surfaces using an indexed-matched elastomer.

PubMed

Ravasio, Andrea; Vaishnavi, Sree; Ladoux, Benoit; Viasnoff, Virgile

2015-03-01

Understanding and controlling how cells interact with the microenvironment has emerged as a prominent field in bioengineering, stem cell research and in the development of the next generation of in vitro assays as well as organs on a chip. Changing the local rheology or the nanotextured surface of substrates has proved an efficient approach to improve cell lineage differentiation, to control cell migration properties and to understand environmental sensing processes. However, introducing substrate surface textures often alters the ability to image cells with high precision, compromising our understanding of molecular mechanisms at stake in environmental sensing. In this paper, we demonstrate how nano/microstructured surfaces can be molded from an elastomeric material with a refractive index matched to the cell culture medium. Once made biocompatible, contrast imaging (differential interference contrast, phase contrast) and high-resolution fluorescence imaging of subcellular structures can be implemented through the textured surface using an inverted microscope. Simultaneous traction force measurements by micropost deflection were also performed, demonstrating the potential of our approach to study cell-environment interactions, sensing processes and cellular force generation with unprecedented resolution. Copyright © 2014 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Global high-frequency source imaging accounting for complexity in Green's functions

NASA Astrophysics Data System (ADS)

Lambert, V.; Zhan, Z.

2017-12-01

The general characterization of earthquake source processes at long periods has seen great success via seismic finite fault inversion/modeling. Complementary techniques, such as seismic back-projection, extend the capabilities of source imaging to higher frequencies and reveal finer details of the rupture process. However, such high frequency methods are limited by the implicit assumption of simple Green's functions, which restricts the use of global arrays and introduces artifacts (e.g., sweeping effects, depth/water phases) that require careful attention. This motivates the implementation of an imaging technique that considers the potential complexity of Green's functions at high frequencies. We propose an alternative inversion approach based on the modest assumption that the path effects contributing to signals within high-coherency subarrays share a similar form. Under this assumption, we develop a method that can combine multiple high-coherency subarrays to invert for a sparse set of subevents. By accounting for potential variability in the Green's functions among subarrays, our method allows for the utilization of heterogeneous global networks for robust high resolution imaging of the complex rupture process. The approach also provides a consistent framework for examining frequency-dependent radiation across a broad frequency spectrum.

Do High Dynamic Range threatments improve the results of Structure from Motion approaches in Geomorphology?

NASA Astrophysics Data System (ADS)

Gómez-Gutiérrez, Álvaro; Juan de Sanjosé-Blasco, José; Schnabel, Susanne; de Matías-Bejarano, Javier; Pulido-Fernández, Manuel; Berenguer-Sempere, Fernando

2015-04-01

In this work, the hypothesis of improving 3D models obtained with Structure from Motion (SfM) approaches using images pre-processed by High Dynamic Range (HDR) techniques is tested. Photographs of the Veleta Rock Glacier in Spain were captured with different exposure values (EV0, EV+1 and EV-1), two focal lengths (35 and 100 mm) and under different weather conditions for the years 2008, 2009, 2011, 2012 and 2014. HDR images were produced using the different EV steps within Fusion F.1 software. Point clouds were generated using commercial and free available SfM software: Agisoft Photoscan and 123D Catch. Models Obtained using pre-processed images and non-preprocessed images were compared in a 3D environment with a benchmark 3D model obtained by means of a Terrestrial Laser Scanner (TLS). A total of 40 point clouds were produced, georeferenced and compared. Results indicated that for Agisoft Photoscan software differences in the accuracy between models obtained with pre-processed and non-preprocessed images were not significant from a statistical viewpoint. However, in the case of the free available software 123D Catch, models obtained using images pre-processed by HDR techniques presented a higher point density and were more accurate. This tendency was observed along the 5 studied years and under different capture conditions. More work should be done in the near future to corroborate whether the results of similar software packages can be improved by HDR techniques (e.g. ARC3D, Bundler and PMVS2, CMP SfM, Photosynth and VisualSFM).

Matrix decomposition graphics processing unit solver for Poisson image editing

NASA Astrophysics Data System (ADS)

Lei, Zhao; Wei, Li

2012-10-01

In recent years, gradient-domain methods have been widely discussed in the image processing field, including seamless cloning and image stitching. These algorithms are commonly carried out by solving a large sparse linear system: the Poisson equation. However, solving the Poisson equation is a computational and memory intensive task which makes it not suitable for real-time image editing. A new matrix decomposition graphics processing unit (GPU) solver (MDGS) is proposed to settle the problem. A matrix decomposition method is used to distribute the work among GPU threads, so that MDGS will take full advantage of the computing power of current GPUs. Additionally, MDGS is a hybrid solver (combines both the direct and iterative techniques) and has two-level architecture. These enable MDGS to generate identical solutions with those of the common Poisson methods and achieve high convergence rate in most cases. This approach is advantageous in terms of parallelizability, enabling real-time image processing, low memory-taken and extensive applications.

Diffusion processes in tumors: A nuclear medicine approach

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

Amaya, Helman, E-mail: haamayae@unal.edu.co

The number of counts used in nuclear medicine imaging techniques, only provides physical information about the desintegration of the nucleus present in the the radiotracer molecules that were uptaken in a particular anatomical region, but that information is not a real metabolic information. For this reason a mathematical method was used to find a correlation between number of counts and {sup 18}F-FDG mass concentration. This correlation allows a better interpretation of the results obtained in the study of diffusive processes in an agar phantom, and based on it, an image from the PETCETIX DICOM sample image set from OsiriX-viewer softwaremore » was processed. PET-CT gradient magnitude and Laplacian images could show direct information on diffusive processes for radiopharmaceuticals that enter into the cells by simple diffusion. In the case of the radiopharmaceutical {sup 18}F-FDG is necessary to include pharmacokinetic models, to make a correct interpretation of the gradient magnitude and Laplacian of counts images.« less

Field-based rice classification in Wuhua county through integration of multi-temporal Sentinel-1A and Landsat-8 OLI data

NASA Astrophysics Data System (ADS)

Yang, Huijin; Pan, Bin; Wu, Wenfu; Tai, Jianhao

2018-07-01

Rice is one of the most important cereals in the world. With the change of agricultural land, it is urgently necessary to update information about rice planting areas. This study aims to map rice planting areas with a field-based approach through the integration of multi-temporal Sentinel-1A and Landsat-8 OLI data in Wuhua County of South China where has many basins and mountains. This paper, using multi-temporal SAR and optical images, proposes a methodology for the identification of rice-planting areas. This methodology mainly consists of SSM applied to time series SAR images for the calculation of a similarity measure, image segmentation process applied to the pan-sharpened optical image for the searching of homogenous objects, and the integration of SAR and optical data for the elimination of some speckles. The study compares the per-pixel approach with the per-field approach and the results show that the highest accuracy (91.38%) based on the field-based approach is 1.18% slightly higher than that based on the pixel-based approach for VH polarization, which is brought by eliminating speckle noise through comparing the rice maps of these two approaches. Therefore, the integration of Sentinel-1A and Landsat-8 OLI images with a field-based approach has great potential for mapping rice or other crops' areas.

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.

Performance evaluation of 2D and 3D deep learning approaches for automatic segmentation of multiple organs on CT images

NASA Astrophysics Data System (ADS)

Zhou, Xiangrong; Yamada, Kazuma; Kojima, Takuya; Takayama, Ryosuke; Wang, Song; Zhou, Xinxin; Hara, Takeshi; Fujita, Hiroshi

2018-02-01

The purpose of this study is to evaluate and compare the performance of modern deep learning techniques for automatically recognizing and segmenting multiple organ regions on 3D CT images. CT image segmentation is one of the important task in medical image analysis and is still very challenging. Deep learning approaches have demonstrated the capability of scene recognition and semantic segmentation on nature images and have been used to address segmentation problems of medical images. Although several works showed promising results of CT image segmentation by using deep learning approaches, there is no comprehensive evaluation of segmentation performance of the deep learning on segmenting multiple organs on different portions of CT scans. In this paper, we evaluated and compared the segmentation performance of two different deep learning approaches that used 2D- and 3D deep convolutional neural networks (CNN) without- and with a pre-processing step. A conventional approach that presents the state-of-the-art performance of CT image segmentation without deep learning was also used for comparison. A dataset that includes 240 CT images scanned on different portions of human bodies was used for performance evaluation. The maximum number of 17 types of organ regions in each CT scan were segmented automatically and compared to the human annotations by using ratio of intersection over union (IU) as the criterion. The experimental results demonstrated the IUs of the segmentation results had a mean value of 79% and 67% by averaging 17 types of organs that segmented by a 3D- and 2D deep CNN, respectively. All the results of the deep learning approaches showed a better accuracy and robustness than the conventional segmentation method that used probabilistic atlas and graph-cut methods. The effectiveness and the usefulness of deep learning approaches were demonstrated for solving multiple organs segmentation problem on 3D CT images.

On the importance of FIB-SEM specific segmentation algorithms for porous media

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

Salzer, Martin, E-mail: martin.salzer@uni-ulm.de; Thiele, Simon, E-mail: simon.thiele@imtek.uni-freiburg.de; Zengerle, Roland, E-mail: zengerle@imtek.uni-freiburg.de

2014-09-15

A new algorithmic approach to segmentation of highly porous three dimensional image data gained by focused ion beam tomography is described which extends the key-principle of local threshold backpropagation described in Salzer et al. (2012). The technique of focused ion beam tomography has shown to be capable of imaging the microstructure of functional materials. In order to perform a quantitative analysis on the corresponding microstructure a segmentation task needs to be performed. However, algorithmic segmentation of images obtained with focused ion beam tomography is a challenging problem for highly porous materials if filling the pore phase, e.g. with epoxy resin,more » is difficult. The gray intensities of individual voxels are not sufficient to determine the phase represented by them and usual thresholding methods are not applicable. We thus propose a new approach to segmentation that pays respect to the specifics of the imaging process of focused ion beam tomography. As an application of our approach, the segmentation of three dimensional images for a cathode material used in polymer electrolyte membrane fuel cells is discussed. We show that our approach preserves significantly more of the original nanostructure than a thresholding approach. - Highlights: • We describe a new approach to the segmentation of FIB-SEM images of porous media. • The first and last occurrences of structures are detected by analysing the z-profiles. • The algorithm is validated by comparing it to a manual segmentation. • The new approach shows significantly less artifacts than a thresholding approach. • A structural analysis also shows improved results for the obtained microstructure.« less

Comparison of ring artifact removal methods using flat panel detector based CT images

PubMed Central

2011-01-01

Background Ring artifacts are the concentric rings superimposed on the tomographic images often caused by the defective and insufficient calibrated detector elements as well as by the damaged scintillator crystals of the flat panel detector. It may be also generated by objects attenuating X-rays very differently in different projection direction. Ring artifact reduction techniques so far reported in the literature can be broadly classified into two groups. One category of the approaches is based on the sinogram processing also known as the pre-processing techniques and the other category of techniques perform processing on the 2-D reconstructed images, recognized as the post-processing techniques in the literature. The strength and weakness of these categories of approaches are yet to be explored from a common platform. Method In this paper, a comparative study of the two categories of ring artifact reduction techniques basically designed for the multi-slice CT instruments is presented from a common platform. For comparison, two representative algorithms from each of the two categories are selected from the published literature. A very recently reported state-of-the-art sinogram domain ring artifact correction method that classifies the ring artifacts according to their strength and then corrects the artifacts using class adaptive correction schemes is also included in this comparative study. The first sinogram domain correction method uses a wavelet based technique to detect the corrupted pixels and then using a simple linear interpolation technique estimates the responses of the bad pixels. The second sinogram based correction method performs all the filtering operations in the transform domain, i.e., in the wavelet and Fourier domain. On the other hand, the two post-processing based correction techniques actually operate on the polar transform domain of the reconstructed CT images. The first method extracts the ring artifact template vector using a homogeneity test and then corrects the CT images by subtracting the artifact template vector from the uncorrected images. The second post-processing based correction technique performs median and mean filtering on the reconstructed images to produce the corrected images. Results The performances of the comparing algorithms have been tested by using both quantitative and perceptual measures. For quantitative analysis, two different numerical performance indices are chosen. On the other hand, different types of artifact patterns, e.g., single/band ring, artifacts from defective and mis-calibrated detector elements, rings in highly structural object and also in hard object, rings from different flat-panel detectors are analyzed to perceptually investigate the strength and weakness of the five methods. An investigation has been also carried out to compare the efficacy of these algorithms in correcting the volume images from a cone beam CT with the parameters determined from one particular slice. Finally, the capability of each correction technique in retaining the image information (e.g., small object at the iso-center) accurately in the corrected CT image has been also tested. Conclusions The results show that the performances of the algorithms are limited and none is fully suitable for correcting different types of ring artifacts without introducing processing distortion to the image structure. To achieve the diagnostic quality of the corrected slices a combination of the two approaches (sinogram- and post-processing) can be used. Also the comparing methods are not suitable for correcting the volume images from a cone beam flat-panel detector based CT. PMID:21846411

Self-Calibrated In-Process Photogrammetry for Large Raw Part Measurement and Alignment before Machining

PubMed Central

Mendikute, Alberto; Zatarain, Mikel; Bertelsen, Álvaro; Leizea, Ibai

2017-01-01

Photogrammetry methods are being used more and more as a 3D technique for large scale metrology applications in industry. Optical targets are placed on an object and images are taken around it, where measuring traceability is provided by precise off-process pre-calibrated digital cameras and scale bars. According to the 2D target image coordinates, target 3D coordinates and camera views are jointly computed. One of the applications of photogrammetry is the measurement of raw part surfaces prior to its machining. For this application, post-process bundle adjustment has usually been adopted for computing the 3D scene. With that approach, a high computation time is observed, leading in practice to time consuming and user dependent iterative review and re-processing procedures until an adequate set of images is taken, limiting its potential for fast, easy-to-use, and precise measurements. In this paper, a new efficient procedure is presented for solving the bundle adjustment problem in portable photogrammetry. In-process bundle computing capability is demonstrated on a consumer grade desktop PC, enabling quasi real time 2D image and 3D scene computing. Additionally, a method for the self-calibration of camera and lens distortion has been integrated into the in-process approach due to its potential for highest precision when using low cost non-specialized digital cameras. Measurement traceability is set only by scale bars available in the measuring scene, avoiding the uncertainty contribution of off-process camera calibration procedures or the use of special purpose calibration artifacts. The developed self-calibrated in-process photogrammetry has been evaluated both in a pilot case scenario and in industrial scenarios for raw part measurement, showing a total in-process computing time typically below 1 s per image up to a maximum of 2 s during the last stages of the computed industrial scenes, along with a relative precision of 1/10,000 (e.g., 0.1 mm error in 1 m) with an error RMS below 0.2 pixels at image plane, ranging at the same performance reported for portable photogrammetry with precise off-process pre-calibrated cameras. PMID:28891946

Self-Calibrated In-Process Photogrammetry for Large Raw Part Measurement and Alignment before Machining.

PubMed

Mendikute, Alberto; Yagüe-Fabra, José A; Zatarain, Mikel; Bertelsen, Álvaro; Leizea, Ibai

2017-09-09

Photogrammetry methods are being used more and more as a 3D technique for large scale metrology applications in industry. Optical targets are placed on an object and images are taken around it, where measuring traceability is provided by precise off-process pre-calibrated digital cameras and scale bars. According to the 2D target image coordinates, target 3D coordinates and camera views are jointly computed. One of the applications of photogrammetry is the measurement of raw part surfaces prior to its machining. For this application, post-process bundle adjustment has usually been adopted for computing the 3D scene. With that approach, a high computation time is observed, leading in practice to time consuming and user dependent iterative review and re-processing procedures until an adequate set of images is taken, limiting its potential for fast, easy-to-use, and precise measurements. In this paper, a new efficient procedure is presented for solving the bundle adjustment problem in portable photogrammetry. In-process bundle computing capability is demonstrated on a consumer grade desktop PC, enabling quasi real time 2D image and 3D scene computing. Additionally, a method for the self-calibration of camera and lens distortion has been integrated into the in-process approach due to its potential for highest precision when using low cost non-specialized digital cameras. Measurement traceability is set only by scale bars available in the measuring scene, avoiding the uncertainty contribution of off-process camera calibration procedures or the use of special purpose calibration artifacts. The developed self-calibrated in-process photogrammetry has been evaluated both in a pilot case scenario and in industrial scenarios for raw part measurement, showing a total in-process computing time typically below 1 s per image up to a maximum of 2 s during the last stages of the computed industrial scenes, along with a relative precision of 1/10,000 (e.g. 0.1 mm error in 1 m) with an error RMS below 0.2 pixels at image plane, ranging at the same performance reported for portable photogrammetry with precise off-process pre-calibrated cameras.

Microscopy mineral image enhancement based on improved adaptive threshold in nonsubsampled shearlet transform domain

NASA Astrophysics Data System (ADS)

Li, Liangliang; Si, Yujuan; Jia, Zhenhong

2018-03-01

In this paper, a novel microscopy mineral image enhancement method based on adaptive threshold in non-subsampled shearlet transform (NSST) domain is proposed. First, the image is decomposed into one low-frequency sub-band and several high-frequency sub-bands. Second, the gamma correction is applied to process the low-frequency sub-band coefficients, and the improved adaptive threshold is adopted to suppress the noise of the high-frequency sub-bands coefficients. Third, the processed coefficients are reconstructed with the inverse NSST. Finally, the unsharp filter is used to enhance the details of the reconstructed image. Experimental results on various microscopy mineral images demonstrated that the proposed approach has a better enhancement effect in terms of objective metric and subjective metric.

Automated reconstruction of standing posture panoramas from multi-sector long limb x-ray images

NASA Astrophysics Data System (ADS)

Miller, Linzey; Trier, Caroline; Ben-Zikri, Yehuda K.; Linte, Cristian A.

2016-03-01

Due to the digital X-ray imaging system's limited field of view, several individual sector images are required to capture the posture of an individual in standing position. These images are then "stitched together" to reconstruct the standing posture. We have created an image processing application that automates the stitching, therefore minimizing user input, optimizing workflow, and reducing human error. The application begins with pre-processing the input images by removing artifacts, filtering out isolated noisy regions, and amplifying a seamless bone edge. The resulting binary images are then registered together using a rigid-body intensity based registration algorithm. The identified registration transformations are then used to map the original sector images into the panorama image. Our method focuses primarily on the use of the anatomical content of the images to generate the panoramas as opposed to using external markers employed to aid with the alignment process. Currently, results show robust edge detection prior to registration and we have tested our approach by comparing the resulting automatically-stitched panoramas to the manually stitched panoramas in terms of registration parameters, target registration error of homologous markers, and the homogeneity of the digitally subtracted automatically- and manually-stitched images using 26 patient datasets.

Automatic localization of endoscope in intraoperative CT image: A simple approach to augmented reality guidance in laparoscopic surgery.

PubMed

Bernhardt, Sylvain; Nicolau, Stéphane A; Agnus, Vincent; Soler, Luc; Doignon, Christophe; Marescaux, Jacques

2016-05-01

The use of augmented reality in minimally invasive surgery has been the subject of much research for more than a decade. The endoscopic view of the surgical scene is typically augmented with a 3D model extracted from a preoperative acquisition. However, the organs of interest often present major changes in shape and location because of the pneumoperitoneum and patient displacement. There have been numerous attempts to compensate for this distortion between the pre- and intraoperative states. Some have attempted to recover the visible surface of the organ through image analysis and register it to the preoperative data, but this has proven insufficiently robust and may be problematic with large organs. A second approach is to introduce an intraoperative 3D imaging system as a transition. Hybrid operating rooms are becoming more and more popular, so this seems to be a viable solution, but current techniques require yet another external and constraining piece of apparatus such as an optical tracking system to determine the relationship between the intraoperative images and the endoscopic view. In this article, we propose a new approach to automatically register the reconstruction from an intraoperative CT acquisition with the static endoscopic view, by locating the endoscope tip in the volume data. We first describe our method to localize the endoscope orientation in the intraoperative image using standard image processing algorithms. Secondly, we highlight that the axis of the endoscope needs a specific calibration process to ensure proper registration accuracy. In the last section, we present quantitative and qualitative results proving the feasibility and the clinical potential of our approach. Copyright © 2016 Elsevier B.V. All rights reserved.

View synthesis using parallax invariance

NASA Astrophysics Data System (ADS)

Dornaika, Fadi

2001-06-01

View synthesis becomes a focus of attention of both the computer vision and computer graphics communities. It consists of creating novel images of a scene as it would appear from novel viewpoints. View synthesis can be used in a wide variety of applications such as video compression, graphics generation, virtual reality and entertainment. This paper addresses the following problem. Given a dense disparity map between two reference images, we would like to synthesize a novel view of the same scene associated with a novel viewpoint. Most of the existing work is relying on building a set of 3D meshes which are then projected onto the new image (the rendering process is performed using texture mapping). The advantages of our view synthesis approach are as follows. First, the novel view is specified by a rotation and a translation which are the most natural way to express the virtual location of the camera. Second, the approach is able to synthesize highly realistic images whose viewing position is significantly far away from the reference viewpoints. Third, the approach is able to handle the visibility problem during the synthesis process. Our developed framework has two main steps. The first step (analysis step) consists of computing the homography at infinity, the epipoles, and thus the parallax field associated with the reference images. The second step (synthesis step) consists of warping the reference image into a new one, which is based on the invariance of the computed parallax field. The analysis step is working directly on the reference views, and only need to be performed once. Examples of synthesizing novel views using either feature correspondences or dense disparity map have demonstrated the feasibility of the proposed approach.

Image segmentation for uranium isotopic analysis by SIMS: Combined adaptive thresholding and marker controlled watershed approach

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

Willingham, David G.; Naes, Benjamin E.; Heasler, Patrick G.

A novel approach to particle identification and particle isotope ratio determination has been developed for nuclear safeguard applications. This particle search approach combines an adaptive thresholding algorithm and marker-controlled watershed segmentation (MCWS) transform, which improves the secondary ion mass spectrometry (SIMS) isotopic analysis of uranium containing particle populations for nuclear safeguards applications. The Niblack assisted MCWS approach (a.k.a. SEEKER) developed for this work has improved the identification of isotopically unique uranium particles under conditions that have historically presented significant challenges for SIMS image data processing techniques. Particles obtained from five NIST uranium certified reference materials (CRM U129A, U015, U150, U500more » and U850) were successfully identified in regions of SIMS image data 1) where a high variability in image intensity existed, 2) where particles were touching or were in close proximity to one another and/or 3) where the magnitude of ion signal for a given region was count limited. Analysis of the isotopic distributions of uranium containing particles identified by SEEKER showed four distinct, accurately identified 235U enrichment distributions, corresponding to the NIST certified 235U/238U isotope ratios for CRM U129A/U015 (not statistically differentiated), U150, U500 and U850. Additionally, comparison of the minor uranium isotope (234U, 235U and 236U) atom percent values verified that, even in the absence of high precision isotope ratio measurements, SEEKER could be used to segment isotopically unique uranium particles from SIMS image data. Although demonstrated specifically for SIMS analysis of uranium containing particles for nuclear safeguards, SEEKER has application in addressing a broad set of image processing challenges.« less

A Review of Algorithms for Segmentation of Optical Coherence Tomography from Retina

PubMed Central

Kafieh, Raheleh; Rabbani, Hossein; Kermani, Saeed

2013-01-01

Optical coherence tomography (OCT) is a recently established imaging technique to describe different information about the internal structures of an object and to image various aspects of biological tissues. OCT image segmentation is mostly introduced on retinal OCT to localize the intra-retinal boundaries. Here, we review some of the important image segmentation methods for processing retinal OCT images. We may classify the OCT segmentation approaches into five distinct groups according to the image domain subjected to the segmentation algorithm. Current researches in OCT segmentation are mostly based on improving the accuracy and precision, and on reducing the required processing time. There is no doubt that current 3-D imaging modalities are now moving the research projects toward volume segmentation along with 3-D rendering and visualization. It is also important to develop robust methods capable of dealing with pathologic cases in OCT imaging. PMID:24083137

Satellite image analysis using neural networks

NASA Technical Reports Server (NTRS)

Sheldon, Roger A.

1990-01-01

The tremendous backlog of unanalyzed satellite data necessitates the development of improved methods for data cataloging and analysis. Ford Aerospace has developed an image analysis system, SIANN (Satellite Image Analysis using Neural Networks) that integrates the technologies necessary to satisfy NASA's science data analysis requirements for the next generation of satellites. SIANN will enable scientists to train a neural network to recognize image data containing scenes of interest and then rapidly search data archives for all such images. The approach combines conventional image processing technology with recent advances in neural networks to provide improved classification capabilities. SIANN allows users to proceed through a four step process of image classification: filtering and enhancement, creation of neural network training data via application of feature extraction algorithms, configuring and training a neural network model, and classification of images by application of the trained neural network. A prototype experimentation testbed was completed and applied to climatological data.

Clustering approach for unsupervised segmentation of malarial Plasmodium vivax parasite

NASA Astrophysics Data System (ADS)

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

2017-10-01

Malaria is a global health problem, particularly in Africa and south Asia where it causes countless deaths and morbidity cases. Efficient control and prompt of this disease require early detection and accurate diagnosis due to the large number of cases reported yearly. To achieve this aim, this paper proposes an image segmentation approach via unsupervised pixel segmentation of malaria parasite to automate the diagnosis of malaria. In this study, a modified clustering algorithm namely enhanced k-means (EKM) clustering, is proposed for malaria image segmentation. In the proposed EKM clustering, the concept of variance and a new version of transferring process for clustered members are used to assist the assignation of data to the proper centre during the process of clustering, so that good segmented malaria image can be generated. The effectiveness of the proposed EKM clustering has been analyzed qualitatively and quantitatively by comparing this algorithm with two popular image segmentation techniques namely Otsu's thresholding and k-means clustering. The experimental results show that the proposed EKM clustering has successfully segmented 100 malaria images of P. vivax species with segmentation accuracy, sensitivity and specificity of 99.20%, 87.53% and 99.58%, respectively. Hence, the proposed EKM clustering can be considered as an image segmentation tool for segmenting the malaria images.

Crowdsourcing scoring of immunohistochemistry images: Evaluating Performance of the Crowd and an Automated Computational Method

NASA Astrophysics Data System (ADS)

Irshad, Humayun; Oh, Eun-Yeong; Schmolze, Daniel; Quintana, Liza M.; Collins, Laura; Tamimi, Rulla M.; Beck, Andrew H.

2017-02-01

The assessment of protein expression in immunohistochemistry (IHC) images provides important diagnostic, prognostic and predictive information for guiding cancer diagnosis and therapy. Manual scoring of IHC images represents a logistical challenge, as the process is labor intensive and time consuming. Since the last decade, computational methods have been developed to enable the application of quantitative methods for the analysis and interpretation of protein expression in IHC images. These methods have not yet replaced manual scoring for the assessment of IHC in the majority of diagnostic laboratories and in many large-scale research studies. An alternative approach is crowdsourcing the quantification of IHC images to an undefined crowd. The aim of this study is to quantify IHC images for labeling of ER status with two different crowdsourcing approaches, image-labeling and nuclei-labeling, and compare their performance with automated methods. Crowdsourcing- derived scores obtained greater concordance with the pathologist interpretations for both image-labeling and nuclei-labeling tasks (83% and 87%), as compared to the pathologist concordance achieved by the automated method (81%) on 5,338 TMA images from 1,853 breast cancer patients. This analysis shows that crowdsourcing the scoring of protein expression in IHC images is a promising new approach for large scale cancer molecular pathology studies.

Ontology-based malaria parasite stage and species identification from peripheral blood smear images.

PubMed

Makkapati, Vishnu V; Rao, Raghuveer M

2011-01-01

The diagnosis and treatment of malaria infection requires detecting the presence of the malaria parasite in the patient as well as identification of the parasite species. We present an image processing-based approach to detect parasites in microscope images of a blood smear and an ontology-based classification of the stage of the parasite for identifying the species of infection. This approach is patterned after the diagnosis approach adopted by a pathologist for visual examination, and hence, is expected to deliver similar results. We formulate several rules based on the morphology of the basic components of a parasite, namely, chromatin dot(s) and cytoplasm, to identify the parasite stage and species. Numerical results are presented for data taken from various patients. A sensitivity of 88% and a specificity of 95% is reported by evaluation of the scheme on 55 images.

Deep learning approach to bacterial colony classification.

PubMed

Zieliński, Bartosz; Plichta, Anna; Misztal, Krzysztof; Spurek, Przemysław; Brzychczy-Włoch, Monika; Ochońska, Dorota

2017-01-01

In microbiology it is diagnostically useful to recognize various genera and species of bacteria. It can be achieved using computer-aided methods, which make the recognition processes more automatic and thus significantly reduce the time necessary for the classification. Moreover, in case of diagnostic uncertainty (the misleading similarity in shape or structure of bacterial cells), such methods can minimize the risk of incorrect recognition. In this article, we apply the state of the art method for texture analysis to classify genera and species of bacteria. This method uses deep Convolutional Neural Networks to obtain image descriptors, which are then encoded and classified with Support Vector Machine or Random Forest. To evaluate this approach and to make it comparable with other approaches, we provide a new dataset of images. DIBaS dataset (Digital Image of Bacterial Species) contains 660 images with 33 different genera and species of bacteria.

Observer efficiency in discrimination tasks simulating malignant and benign breast lesions imaged with ultrasound

PubMed Central

Abbey, Craig K.; Zemp, Roger J.; Liu, Jie; Lindfors, Karen K.; Insana, Michael F.

2009-01-01

We investigate and extend the ideal observer methodology developed by Smith and Wagner to detection and discrimination tasks related to breast sonography. We provide a numerical approach for evaluating the ideal observer acting on radio-frequency (RF) frame data, which involves inversion of large nonstationary covariance matrices, and we describe a power-series approach to computing this inverse. Considering a truncated power series suggests that the RF data be Wiener-filtered before forming the final envelope image. We have compared human performance for Wiener-filtered and conventional B-mode envelope images using psychophysical studies for 5 tasks related to breast cancer classification. We find significant improvements in visual detection and discrimination efficiency in four of these five tasks. We also use the Smith-Wagner approach to distinguish between human and processing inefficiencies, and find that generally the principle limitation comes from the information lost in computing the final envelope image. PMID:16468454

Low-complexity image processing for real-time detection of neonatal clonic seizures.

PubMed

Ntonfo, Guy Mathurin Kouamou; Ferrari, Gianluigi; Raheli, Riccardo; Pisani, Francesco

2012-05-01

In this paper, we consider a novel low-complexity real-time image-processing-based approach to the detection of neonatal clonic seizures. Our approach is based on the extraction, from a video of a newborn, of an average luminance signal representative of the body movements. Since clonic seizures are characterized by periodic movements of parts of the body (e.g., the limbs), by evaluating the periodicity of the extracted average luminance signal it is possible to detect the presence of a clonic seizure. The periodicity is investigated, through a hybrid autocorrelation-Yin estimation technique, on a per-window basis, where a time window is defined as a sequence of consecutive video frames. While processing is first carried out on a single window basis, we extend our approach to interlaced windows. The performance of the proposed detection algorithm is investigated, in terms of sensitivity and specificity, through receiver operating characteristic curves, considering video recordings of newborns affected by neonatal seizures.

Analysis of simulated image sequences from sensors for restricted-visibility operations

NASA Technical Reports Server (NTRS)

Kasturi, Rangachar

1991-01-01

A real time model of the visible output from a 94 GHz sensor, based on a radiometric simulation of the sensor, was developed. A sequence of images as seen from an aircraft as it approaches for landing was simulated using this model. Thirty frames from this sequence of 200 x 200 pixel images were analyzed to identify and track objects in the image using the Cantata image processing package within the visual programming environment provided by the Khoros software system. The image analysis operations are described.

Object segmentation using graph cuts and active contours in a pyramidal framework

NASA Astrophysics Data System (ADS)

Subudhi, Priyambada; Mukhopadhyay, Susanta

2018-03-01

Graph cuts and active contours are two very popular interactive object segmentation techniques in the field of computer vision and image processing. However, both these approaches have their own well-known limitations. Graph cut methods perform efficiently giving global optimal segmentation result for smaller images. However, for larger images, huge graphs need to be constructed which not only takes an unacceptable amount of memory but also increases the time required for segmentation to a great extent. On the other hand, in case of active contours, initial contour selection plays an important role in the accuracy of the segmentation. So a proper selection of initial contour may improve the complexity as well as the accuracy of the result. In this paper, we have tried to combine these two approaches to overcome their above-mentioned drawbacks and develop a fast technique of object segmentation. Here, we have used a pyramidal framework and applied the mincut/maxflow algorithm on the lowest resolution image with the least number of seed points possible which will be very fast due to the smaller size of the image. Then, the obtained segmentation contour is super-sampled and and worked as the initial contour for the next higher resolution image. As the initial contour is very close to the actual contour, so fewer number of iterations will be required for the convergence of the contour. The process is repeated for all the high-resolution images and experimental results show that our approach is faster as well as memory efficient as compare to both graph cut or active contour segmentation alone.

New Approach to Image Aerogels by Scanning Electron Microscopy

NASA Astrophysics Data System (ADS)

Solá, Francisco; Hurwitz, Frances; Yang, Jijing

2011-03-01

A new scanning electron microscopy (SEM) technique to image poor electrically conductive aerogels is presented. The process can be performed by non-expert SEM users. We showed that negative charging effects on aerogels can be minimized significantly by inserting dry nitrogen gas close to the region of interest. The process involves the local recombination of accumulated negative charges with positive ions generated from ionization processes. This new technique made possible the acquisition of images of aerogels with pores down to approximately 3nm in diameter using a positively biased Everhart-Thornley (E-T) detector. Well-founded concepts based on known models will also be presented with the aim to explain the results qualitatively.

Flame analysis using image processing techniques

NASA Astrophysics Data System (ADS)

Her Jie, Albert Chang; Zamli, Ahmad Faizal Ahmad; Zulazlan Shah Zulkifli, Ahmad; Yee, Joanne Lim Mun; Lim, Mooktzeng

2018-04-01

This paper presents image processing techniques with the use of fuzzy logic and neural network approach to perform flame analysis. Flame diagnostic is important in the industry to extract relevant information from flame images. Experiment test is carried out in a model industrial burner with different flow rates. Flame features such as luminous and spectral parameters are extracted using image processing and Fast Fourier Transform (FFT). Flame images are acquired using FLIR infrared camera. Non-linearities such as thermal acoustic oscillations and background noise affect the stability of flame. Flame velocity is one of the important characteristics that determines stability of flame. In this paper, an image processing method is proposed to determine flame velocity. Power spectral density (PSD) graph is a good tool for vibration analysis where flame stability can be approximated. However, a more intelligent diagnostic system is needed to automatically determine flame stability. In this paper, flame features of different flow rates are compared and analyzed. The selected flame features are used as inputs to the proposed fuzzy inference system to determine flame stability. Neural network is used to test the performance of the fuzzy inference system.

Determination of localization accuracy based on experimentally acquired image sets: applications to single molecule microscopy

PubMed Central

Tahmasbi, Amir; Ward, E. Sally; Ober, Raimund J.

2015-01-01

Fluorescence microscopy is a photon-limited imaging modality that allows the study of subcellular objects and processes with high specificity. The best possible accuracy (standard deviation) with which an object of interest can be localized when imaged using a fluorescence microscope is typically calculated using the Cramér-Rao lower bound, that is, the inverse of the Fisher information. However, the current approach for the calculation of the best possible localization accuracy relies on an analytical expression for the image of the object. This can pose practical challenges since it is often difficult to find appropriate analytical models for the images of general objects. In this study, we instead develop an approach that directly uses an experimentally collected image set to calculate the best possible localization accuracy for a general subcellular object. In this approach, we fit splines, i.e. smoothly connected piecewise polynomials, to the experimentally collected image set to provide a continuous model of the object, which can then be used for the calculation of the best possible localization accuracy. Due to its practical importance, we investigate in detail the application of the proposed approach in single molecule fluorescence microscopy. In this case, the object of interest is a point source and, therefore, the acquired image set pertains to an experimental point spread function. PMID:25837101

TANGO: a generic tool for high-throughput 3D image analysis for studying nuclear organization.

PubMed

Ollion, Jean; Cochennec, Julien; Loll, François; Escudé, Christophe; Boudier, Thomas

2013-07-15

The cell nucleus is a highly organized cellular organelle that contains the genetic material. The study of nuclear architecture has become an important field of cellular biology. Extracting quantitative data from 3D fluorescence imaging helps understand the functions of different nuclear compartments. However, such approaches are limited by the requirement for processing and analyzing large sets of images. Here, we describe Tools for Analysis of Nuclear Genome Organization (TANGO), an image analysis tool dedicated to the study of nuclear architecture. TANGO is a coherent framework allowing biologists to perform the complete analysis process of 3D fluorescence images by combining two environments: ImageJ (http://imagej.nih.gov/ij/) for image processing and quantitative analysis and R (http://cran.r-project.org) for statistical processing of measurement results. It includes an intuitive user interface providing the means to precisely build a segmentation procedure and set-up analyses, without possessing programming skills. TANGO is a versatile tool able to process large sets of images, allowing quantitative study of nuclear organization. TANGO is composed of two programs: (i) an ImageJ plug-in and (ii) a package (rtango) for R. They are both free and open source, available (http://biophysique.mnhn.fr/tango) for Linux, Microsoft Windows and Macintosh OSX. Distribution is under the GPL v.2 licence. thomas.boudier@snv.jussieu.fr Supplementary data are available at Bioinformatics online.

Particle Morphology Analysis of Biomass Material Based on Improved Image Processing Method

PubMed Central

Lu, Zhaolin

2017-01-01

Particle morphology, including size and shape, is an important factor that significantly influences the physical and chemical properties of biomass material. Based on image processing technology, a method was developed to process sample images, measure particle dimensions, and analyse the particle size and shape distributions of knife-milled wheat straw, which had been preclassified into five nominal size groups using mechanical sieving approach. Considering the great variation of particle size from micrometer to millimeter, the powders greater than 250 μm were photographed by a flatbed scanner without zoom function, and the others were photographed using a scanning electron microscopy (SEM) with high-image resolution. Actual imaging tests confirmed the excellent effect of backscattered electron (BSE) imaging mode of SEM. Particle aggregation is an important factor that affects the recognition accuracy of the image processing method. In sample preparation, the singulated arrangement and ultrasonic dispersion methods were used to separate powders into particles that were larger and smaller than the nominal size of 250 μm. In addition, an image segmentation algorithm based on particle geometrical information was proposed to recognise the finer clustered powders. Experimental results demonstrated that the improved image processing method was suitable to analyse the particle size and shape distributions of ground biomass materials and solve the size inconsistencies in sieving analysis. PMID:28298925

Wavelength feature mapping as a proxy to mineral chemistry for investigating geologic systems: An example from the Rodalquilar epithermal system

NASA Astrophysics Data System (ADS)

van der Meer, Freek; Kopačková, Veronika; Koucká, Lucie; van der Werff, Harald M. A.; van Ruitenbeek, Frank J. A.; Bakker, Wim H.

2018-02-01

The final product of a geologic remote sensing data analysis using multi spectral and hyperspectral images is a mineral (abundance) map. Multispectral data, such as ASTER, Landsat, SPOT, Sentinel-2, typically allow to determine qualitative estimates of what minerals are in a pixel, while hyperspectral data allow to quantify this. As input to most image classification or spectral processing approach, endmembers are required. An alternative approach to classification is to derive absorption feature characteristics such as the wavelength position of the deepest absorption, depth of the absorption and symmetry of the absorption feature from hyperspectral data. Two approaches are presented, tested and compared in this paper: the 'Wavelength Mapper' and the 'QuanTools'. Although these algorithms use a different mathematical solution to derive absorption feature wavelength and depth, and use different image post-processing, the results are consistent, comparable and reproducible. The wavelength images can be directly linked to mineral type and abundance, but more importantly also to mineral chemical composition and subtle changes thereof. This in turn allows to interpret hyperspectral data in terms of mineral chemistry changes which is a proxy to pressure-temperature of formation of minerals. We show the case of the Rodalquilar epithermal system of the southern Spanish Gabo de Gata volcanic area using HyMAP airborne hyperspectral images.

Automatic Hotspot and Sun Glint Detection in UAV Multispectral Images

PubMed Central

Ortega-Terol, Damian; Ballesteros, Rocio

2017-01-01

Last advances in sensors, photogrammetry and computer vision have led to high-automation levels of 3D reconstruction processes for generating dense models and multispectral orthoimages from Unmanned Aerial Vehicle (UAV) images. However, these cartographic products are sometimes blurred and degraded due to sun reflection effects which reduce the image contrast and colour fidelity in photogrammetry and the quality of radiometric values in remote sensing applications. This paper proposes an automatic approach for detecting sun reflections problems (hotspot and sun glint) in multispectral images acquired with an Unmanned Aerial Vehicle (UAV), based on a photogrammetric strategy included in a flight planning and control software developed by the authors. In particular, two main consequences are derived from the approach developed: (i) different areas of the images can be excluded since they contain sun reflection problems; (ii) the cartographic products obtained (e.g., digital terrain model, orthoimages) and the agronomical parameters computed (e.g., normalized vegetation index-NVDI) are improved since radiometric defects in pixels are not considered. Finally, an accuracy assessment was performed in order to analyse the error in the detection process, getting errors around 10 pixels for a ground sample distance (GSD) of 5 cm which is perfectly valid for agricultural applications. This error confirms that the precision in the detection of sun reflections can be guaranteed using this approach and the current low-cost UAV technology. PMID:29036930

Automatic Hotspot and Sun Glint Detection in UAV Multispectral Images.

PubMed

Ortega-Terol, Damian; Hernandez-Lopez, David; Ballesteros, Rocio; Gonzalez-Aguilera, Diego

2017-10-15

Last advances in sensors, photogrammetry and computer vision have led to high-automation levels of 3D reconstruction processes for generating dense models and multispectral orthoimages from Unmanned Aerial Vehicle (UAV) images. However, these cartographic products are sometimes blurred and degraded due to sun reflection effects which reduce the image contrast and colour fidelity in photogrammetry and the quality of radiometric values in remote sensing applications. This paper proposes an automatic approach for detecting sun reflections problems (hotspot and sun glint) in multispectral images acquired with an Unmanned Aerial Vehicle (UAV), based on a photogrammetric strategy included in a flight planning and control software developed by the authors. In particular, two main consequences are derived from the approach developed: (i) different areas of the images can be excluded since they contain sun reflection problems; (ii) the cartographic products obtained (e.g., digital terrain model, orthoimages) and the agronomical parameters computed (e.g., normalized vegetation index-NVDI) are improved since radiometric defects in pixels are not considered. Finally, an accuracy assessment was performed in order to analyse the error in the detection process, getting errors around 10 pixels for a ground sample distance (GSD) of 5 cm which is perfectly valid for agricultural applications. This error confirms that the precision in the detection of sun reflections can be guaranteed using this approach and the current low-cost UAV technology.

Online image classification under monotonic decision boundary constraint

NASA Astrophysics Data System (ADS)

Lu, Cheng; Allebach, Jan; Wagner, Jerry; Pitta, Brandi; Larson, David; Guo, Yandong

2015-01-01

Image classification is a prerequisite for copy quality enhancement in all-in-one (AIO) device that comprises a printer and scanner, and which can be used to scan, copy and print. Different processing pipelines are provided in an AIO printer. Each of the processing pipelines is designed specifically for one type of input image to achieve the optimal output image quality. A typical approach to this problem is to apply Support Vector Machine to classify the input image and feed it to its corresponding processing pipeline. The online training SVM can help users to improve the performance of classification as input images accumulate. At the same time, we want to make quick decision on the input image to speed up the classification which means sometimes the AIO device does not need to scan the entire image to make a final decision. These two constraints, online SVM and quick decision, raise questions regarding: 1) what features are suitable for classification; 2) how we should control the decision boundary in online SVM training. This paper will discuss the compatibility of online SVM and quick decision capability.

Near Real-Time Image Reconstruction

NASA Astrophysics Data System (ADS)

Denker, C.; Yang, G.; Wang, H.

2001-08-01

In recent years, post-facto image-processing algorithms have been developed to achieve diffraction-limited observations of the solar surface. We present a combination of frame selection, speckle-masking imaging, and parallel computing which provides real-time, diffraction-limited, 256×256 pixel images at a 1-minute cadence. Our approach to achieve diffraction limited observations is complementary to adaptive optics (AO). At the moment, AO is limited by the fact that it corrects wavefront abberations only for a field of view comparable to the isoplanatic patch. This limitation does not apply to speckle-masking imaging. However, speckle-masking imaging relies on short-exposure images which limits its spectroscopic applications. The parallel processing of the data is performed on a Beowulf-class computer which utilizes off-the-shelf, mass-market technologies to provide high computational performance for scientific calculations and applications at low cost. Beowulf computers have a great potential, not only for image reconstruction, but for any kind of complex data reduction. Immediate access to high-level data products and direct visualization of dynamic processes on the Sun are two of the advantages to be gained.

Edge detection - Image-plane versus digital processing

NASA Technical Reports Server (NTRS)

Huck, Friedrich O.; Fales, Carl L.; Park, Stephen K.; Triplett, Judith A.

1987-01-01

To optimize edge detection with the familiar Laplacian-of-Gaussian operator, it has become common to implement this operator with a large digital convolution mask followed by some interpolation of the processed data to determine the zero crossings that locate edges. It is generally recognized that this large mask causes substantial blurring of fine detail. It is shown that the spatial detail can be improved by a factor of about four with either the Wiener-Laplacian-of-Gaussian filter or an image-plane processor. The Wiener-Laplacian-of-Gaussian filter minimizes the image-gathering degradations if the scene statistics are at least approximately known and also serves as an interpolator to determine the desired zero crossings directly. The image-plane processor forms the Laplacian-of-Gaussian response by properly combining the optical design of the image-gathering system with a minimal three-by-three lateral-inhibitory processing mask. This approach, which is suggested by Marr's model of early processing in human vision, also reduces data processing by about two orders of magnitude and data transmission by up to an order of magnitude.

Feature and Intensity Based Medical Image Registration Using Particle Swarm Optimization.

PubMed

Abdel-Basset, Mohamed; Fakhry, Ahmed E; El-Henawy, Ibrahim; Qiu, Tie; Sangaiah, Arun Kumar

2017-11-03

Image registration is an important aspect in medical image analysis, and kinds use in a variety of medical applications. Examples include diagnosis, pre/post surgery guidance, comparing/merging/integrating images from multi-modal like Magnetic Resonance Imaging (MRI), and Computed Tomography (CT). Whether registering images across modalities for a single patient or registering across patients for a single modality, registration is an effective way to combine information from different images into a normalized frame for reference. Registered datasets can be used for providing information relating to the structure, function, and pathology of the organ or individual being imaged. In this paper a hybrid approach for medical images registration has been developed. It employs a modified Mutual Information (MI) as a similarity metric and Particle Swarm Optimization (PSO) method. Computation of mutual information is modified using a weighted linear combination of image intensity and image gradient vector flow (GVF) intensity. In this manner, statistical as well as spatial image information is included into the image registration process. Maximization of the modified mutual information is effected using the versatile Particle Swarm Optimization which is developed easily with adjusted less parameter. The developed approach has been tested and verified successfully on a number of medical image data sets that include images with missing parts, noise contamination, and/or of different modalities (CT, MRI). The registration results indicate the proposed model as accurate and effective, and show the posture contribution in inclusion of both statistical and spatial image data to the developed approach.

Segmentation-free image processing and analysis of precipitate shapes in 2D and 3D

NASA Astrophysics Data System (ADS)

Bales, Ben; Pollock, Tresa; Petzold, Linda

2017-06-01

Segmentation based image analysis techniques are routinely employed for quantitative analysis of complex microstructures containing two or more phases. The primary advantage of these approaches is that spatial information on the distribution of phases is retained, enabling subjective judgements of the quality of the segmentation and subsequent analysis process. The downside is that computing micrograph segmentations with data from morphologically complex microstructures gathered with error-prone detectors is challenging and, if no special care is taken, the artifacts of the segmentation will make any subsequent analysis and conclusions uncertain. In this paper we demonstrate, using a two phase nickel-base superalloy microstructure as a model system, a new methodology for analysis of precipitate shapes using a segmentation-free approach based on the histogram of oriented gradients feature descriptor, a classic tool in image analysis. The benefits of this methodology for analysis of microstructure in two and three-dimensions are demonstrated.

Evaluation of clinical image processing algorithms used in digital mammography.

PubMed

Zanca, Federica; Jacobs, Jurgen; Van Ongeval, Chantal; Claus, Filip; Celis, Valerie; Geniets, Catherine; Provost, Veerle; Pauwels, Herman; Marchal, Guy; Bosmans, Hilde

2009-03-01

Screening is the only proven approach to reduce the mortality of breast cancer, but significant numbers of breast cancers remain undetected even when all quality assurance guidelines are implemented. With the increasing adoption of digital mammography systems, image processing may be a key factor in the imaging chain. Although to our knowledge statistically significant effects of manufacturer-recommended image processings have not been previously demonstrated, the subjective experience of our radiologists, that the apparent image quality can vary considerably between different algorithms, motivated this study. This article addresses the impact of five such algorithms on the detection of clusters of microcalcifications. A database of unprocessed (raw) images of 200 normal digital mammograms, acquired with the Siemens Novation DR, was collected retrospectively. Realistic simulated microcalcification clusters were inserted in half of the unprocessed images. All unprocessed images were subsequently processed with five manufacturer-recommended image processing algorithms (Agfa Musica 1, IMS Raffaello Mammo 1.2, Sectra Mamea AB Sigmoid, Siemens OPVIEW v2, and Siemens OPVIEW v1). Four breast imaging radiologists were asked to locate and score the clusters in each image on a five point rating scale. The free-response data were analyzed by the jackknife free-response receiver operating characteristic (JAFROC) method and, for comparison, also with the receiver operating characteristic (ROC) method. JAFROC analysis revealed highly significant differences between the image processings (F = 8.51, p < 0.0001), suggesting that image processing strongly impacts the detectability of clusters. Siemens OPVIEW2 and Siemens OPVIEW1 yielded the highest and lowest performances, respectively. ROC analysis of the data also revealed significant differences between the processing but at lower significance (F = 3.47, p = 0.0305) than JAFROC. Both statistical analysis methods revealed that the same six pairs of modalities were significantly different, but the JAFROC confidence intervals were about 32% smaller than ROC confidence intervals. This study shows that image processing has a significant impact on the detection of microcalcifications in digital mammograms. Objective measurements, such as described here, should be used by the manufacturers to select the optimal image processing algorithm.

Color features as an approach for the automated screening of Salmonella strain

NASA Astrophysics Data System (ADS)

Trujillo, Alejandra Serrano; González, Viridiana Contreras; Andrade Rincón, Saulo E.; Palafox, Luis E.

2016-11-01

We present the implementation of a feature extraction approach for the automated screening of Salmonella sp., a task visually carried out by a microbiologist, where the resulting color characteristics of the culture media plate indicate the presence of this strain. The screening of Salmonella sp. is based on the inoculation and incubation of a sample on an agar plate, allowing the isolation of this strain, if present. This process uses three media: Xylose lysine deoxycholate, Salmonella Shigella, and Brilliant Green agar plates, which exhibit specific color characteristics over the colonies and over the surrounding medium for a presumed positive interpretation. Under a controlled illumination environment, images of plates are captured and the characteristics found over each agar are processed separately. Each agar is analyzed using statistical descriptors for texture, to determine the presence of colonies, followed by the extraction of color features. A comparison among the color features seen over the three media, according to the FDA Bacteriological Analytical Manual, determines the presence of Salmonella sp. on a given sample. The implemented process proves that the task addressed can be accomplished under an image processing approach, leading to the future validation and automation of additional screening processes.

Development of Advanced Signal Processing and Source Imaging Methods for Superparamagnetic Relaxometry

PubMed Central

Huang, Ming-Xiong; Anderson, Bill; Huang, Charles W.; Kunde, Gerd J.; Vreeland, Erika C.; Huang, Jeffrey W.; Matlashov, Andrei N.; Karaulanov, Todor; Nettles, Christopher P.; Gomez, Andrew; Minser, Kayla; Weldon, Caroline; Paciotti, Giulio; Harsh, Michael; Lee, Roland R.; Flynn, Edward R.

2017-01-01

Superparamagnetic Relaxometry (SPMR) is a highly sensitive technique for the in vivo detection of tumor cells and may improve early stage detection of cancers. SPMR employs superparamagnetic iron oxide nanoparticles (SPION). After a brief magnetizing pulse is used to align the SPION, SPMR measures the time decay of SPION using Super-conducting Quantum Interference Device (SQUID) sensors. Substantial research has been carried out in developing the SQUID hardware and in improving the properties of the SPION. However, little research has been done in the pre-processing of sensor signals and post-processing source modeling in SPMR. In the present study, we illustrate new pre-processing tools that were developed to: 1) remove trials contaminated with artifacts, 2) evaluate and ensure that a single decay process associated with bounded SPION exists in the data, 3) automatically detect and correct flux jumps, and 4) accurately fit the sensor signals with different decay models. Furthermore, we developed an automated approach based on multi-start dipole imaging technique to obtain the locations and magnitudes of multiple magnetic sources, without initial guesses from the users. A regularization process was implemented to solve the ambiguity issue related to the SPMR source variables. A procedure based on reduced chi-square cost-function was introduced to objectively obtain the adequate number of dipoles that describe the data. The new pre-processing tools and multi-start source imaging approach have been successfully evaluated using phantom data. In conclusion, these tools and multi-start source modeling approach substantially enhance the accuracy and sensitivity in detecting and localizing sources from the SPMR signals. Furthermore, multi-start approach with regularization provided robust and accurate solutions for a poor SNR condition similar to the SPMR detection sensitivity in the order of 1000 cells. We believe such algorithms will help establishing the industrial standards for SPMR when applying the technique in pre-clinical and clinical settings. PMID:28072579

Image Processing for Planetary Limb/Terminator Extraction

NASA Technical Reports Server (NTRS)

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

1995-01-01

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

Recursive search method for the image elements of functionally defined surfaces

NASA Astrophysics Data System (ADS)

Vyatkin, S. I.

2017-05-01

This paper touches upon the synthesis of high-quality images in real time and the technique for specifying three-dimensional objects on the basis of perturbation functions. The recursive search method for the image elements of functionally defined objects with the use of graphics processing units is proposed. The advantages of such an approach over the frame-buffer visualization method are shown.

Image classification of unlabeled malaria parasites in red blood cells.

PubMed

Zheng Zhang; Ong, L L Sharon; Kong Fang; Matthew, Athul; Dauwels, Justin; Ming Dao; Asada, Harry

2016-08-01

This paper presents a method to detect unlabeled malaria parasites in red blood cells. The current "gold standard" for malaria diagnosis is microscopic examination of thick blood smear, a time consuming process requiring extensive training. Our goal is to develop an automate process to identify malaria infected red blood cells. Major issues in automated analysis of microscopy images of unstained blood smears include overlapping cells and oddly shaped cells. Our approach creates robust templates to detect infected and uninfected red cells. Histogram of Oriented Gradients (HOGs) features are extracted from templates and used to train a classifier offline. Next, the ViolaJones object detection framework is applied to detect infected and uninfected red cells and the image background. Results show our approach out-performs classification approaches with PCA features by 50% and cell detection algorithms applying Hough transforms by 24%. Majority of related work are designed to automatically detect stained parasites in blood smears where the cells are fixed. Although it is more challenging to design algorithms for unstained parasites, our methods will allow analysis of parasite progression in live cells under different drug treatments.

Development of an optical microscopy system for automated bubble cloud analysis.

PubMed

Wesley, Daniel J; Brittle, Stuart A; Toolan, Daniel T W

2016-08-01

Recently, the number of uses of bubbles has begun to increase dramatically, with medicine, biofuel production, and wastewater treatment just some of the industries taking advantage of bubble properties, such as high mass transfer. As a result, more and more focus is being placed on the understanding and control of bubble formation processes and there are currently numerous techniques utilized to facilitate this understanding. Acoustic bubble sizing (ABS) and laser scattering techniques are able to provide information regarding bubble size and size distribution with minimal data processing, a major advantage over current optical-based direct imaging approaches. This paper demonstrates how direct bubble-imaging methods can be improved upon to yield high levels of automation and thus data comparable to ABS and laser scattering. We also discuss the added benefits of the direct imaging approaches and how it is possible to obtain considerable additional information above and beyond that which ABS and laser scattering can supply. This work could easily be exploited by both industrial-scale operations and small-scale laboratory studies, as this straightforward and cost-effective approach is highly transferrable and intuitive to use.

Applying compressive sensing to TEM video: A substantial frame rate increase on any camera

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

Stevens, Andrew; Kovarik, Libor; Abellan, Patricia

One of the main limitations of imaging at high spatial and temporal resolution during in-situ transmission electron microscopy (TEM) experiments is the frame rate of the camera being used to image the dynamic process. While the recent development of direct detectors has provided the hardware to achieve frame rates approaching 0.1 ms, the cameras are expensive and must replace existing detectors. In this paper, we examine the use of coded aperture compressive sensing (CS) methods to increase the frame rate of any camera with simple, low-cost hardware modifications. The coded aperture approach allows multiple sub-frames to be coded and integratedmore » into a single camera frame during the acquisition process, and then extracted upon readout using statistical CS inversion. Here we describe the background of CS and statistical methods in depth and simulate the frame rates and efficiencies for in-situ TEM experiments. Depending on the resolution and signal/noise of the image, it should be possible to increase the speed of any camera by more than an order of magnitude using this approach.« less

Applying compressive sensing to TEM video: A substantial frame rate increase on any camera

DOE PAGES

Stevens, Andrew; Kovarik, Libor; Abellan, Patricia; ...

2015-08-13

One of the main limitations of imaging at high spatial and temporal resolution during in-situ transmission electron microscopy (TEM) experiments is the frame rate of the camera being used to image the dynamic process. While the recent development of direct detectors has provided the hardware to achieve frame rates approaching 0.1 ms, the cameras are expensive and must replace existing detectors. In this paper, we examine the use of coded aperture compressive sensing (CS) methods to increase the frame rate of any camera with simple, low-cost hardware modifications. The coded aperture approach allows multiple sub-frames to be coded and integratedmore » into a single camera frame during the acquisition process, and then extracted upon readout using statistical CS inversion. Here we describe the background of CS and statistical methods in depth and simulate the frame rates and efficiencies for in-situ TEM experiments. Depending on the resolution and signal/noise of the image, it should be possible to increase the speed of any camera by more than an order of magnitude using this approach.« less

Comparison between visible/ NIR spectroscopy and hyperspectral imaging for detecting surface contaminants on poultry carcasses

USDA-ARS?s Scientific Manuscript database

The U. S. Department of Agriculture, Agricultural Research Service has been developing a method and system to detect fecal contamination on processed poultry carcasses with hyperspectral and multispectral imaging systems. The patented method utilizes a three step approach to contaminant detection. S...

Robust nuclei segmentation in cyto-histopathological images using statistical level set approach with topology preserving constraint

NASA Astrophysics Data System (ADS)

Taheri, Shaghayegh; Fevens, Thomas; Bui, Tien D.

2017-02-01

Computerized assessments for diagnosis or malignancy grading of cyto-histopathological specimens have drawn increased attention in the field of digital pathology. Automatic segmentation of cell nuclei is a fundamental step in such automated systems. Despite considerable research, nuclei segmentation is still a challenging task due noise, nonuniform illumination, and most importantly, in 2D projection images, overlapping and touching nuclei. In most published approaches, nuclei refinement is a post-processing step after segmentation, which usually refers to the task of detaching the aggregated nuclei or merging the over-segmented nuclei. In this work, we present a novel segmentation technique which effectively addresses the problem of individually segmenting touching or overlapping cell nuclei during the segmentation process. The proposed framework is a region-based segmentation method, which consists of three major modules: i) the image is passed through a color deconvolution step to extract the desired stains; ii) then the generalized fast radial symmetry transform is applied to the image followed by non-maxima suppression to specify the initial seed points for nuclei, and their corresponding GFRS ellipses which are interpreted as the initial nuclei borders for segmentation; iii) finally, these nuclei border initial curves are evolved through the use of a statistical level-set approach along with topology preserving criteria for segmentation and separation of nuclei at the same time. The proposed method is evaluated using Hematoxylin and Eosin, and fluorescent stained images, performing qualitative and quantitative analysis, showing that the method outperforms thresholding and watershed segmentation approaches.

Robust Statistical Fusion of Image Labels

PubMed Central

Landman, Bennett A.; Asman, Andrew J.; Scoggins, Andrew G.; Bogovic, John A.; Xing, Fangxu; Prince, Jerry L.

2011-01-01

Image labeling and parcellation (i.e. assigning structure to a collection of voxels) are critical tasks for the assessment of volumetric and morphometric features in medical imaging data. The process of image labeling is inherently error prone as images are corrupted by noise and artifacts. Even expert interpretations are subject to subjectivity and the precision of the individual raters. Hence, all labels must be considered imperfect with some degree of inherent variability. One may seek multiple independent assessments to both reduce this variability and quantify the degree of uncertainty. Existing techniques have exploited maximum a posteriori statistics to combine data from multiple raters and simultaneously estimate rater reliabilities. Although quite successful, wide-scale application has been hampered by unstable estimation with practical datasets, for example, with label sets with small or thin objects to be labeled or with partial or limited datasets. As well, these approaches have required each rater to generate a complete dataset, which is often impossible given both human foibles and the typical turnover rate of raters in a research or clinical environment. Herein, we propose a robust approach to improve estimation performance with small anatomical structures, allow for missing data, account for repeated label sets, and utilize training/catch trial data. With this approach, numerous raters can label small, overlapping portions of a large dataset, and rater heterogeneity can be robustly controlled while simultaneously estimating a single, reliable label set and characterizing uncertainty. The proposed approach enables many individuals to collaborate in the construction of large datasets for labeling tasks (e.g., human parallel processing) and reduces the otherwise detrimental impact of rater unavailability. PMID:22010145

Alignment of the measurement scale mark during immersion hydrometer calibration using an image processing system.

PubMed

Peña-Perez, Luis Manuel; Pedraza-Ortega, Jesus Carlos; Ramos-Arreguin, Juan Manuel; Arriaga, Saul Tovar; Fernandez, Marco Antonio Aceves; Becerra, Luis Omar; Hurtado, Efren Gorrostieta; Vargas-Soto, Jose Emilio

2013-10-24

The present work presents an improved method to align the measurement scale mark in an immersion hydrometer calibration system of CENAM, the National Metrology Institute (NMI) of Mexico, The proposed method uses a vision system to align the scale mark of the hydrometer to the surface of the liquid where it is immersed by implementing image processing algorithms. This approach reduces the variability in the apparent mass determination during the hydrostatic weighing in the calibration process, therefore decreasing the relative uncertainty of calibration.

Alignment of the Measurement Scale Mark during Immersion Hydrometer Calibration Using an Image Processing System

PubMed Central

Peña-Perez, Luis Manuel; Pedraza-Ortega, Jesus Carlos; Ramos-Arreguin, Juan Manuel; Arriaga, Saul Tovar; Fernandez, Marco Antonio Aceves; Becerra, Luis Omar; Hurtado, Efren Gorrostieta; Vargas-Soto, Jose Emilio

2013-01-01

The present work presents an improved method to align the measurement scale mark in an immersion hydrometer calibration system of CENAM, the National Metrology Institute (NMI) of Mexico, The proposed method uses a vision system to align the scale mark of the hydrometer to the surface of the liquid where it is immersed by implementing image processing algorithms. This approach reduces the variability in the apparent mass determination during the hydrostatic weighing in the calibration process, therefore decreasing the relative uncertainty of calibration. PMID:24284770

Photonics and bioinspiration

NASA Astrophysics Data System (ADS)

Lewis, Keith

2014-10-01

Biological systems exploiting light have benefitted from thousands of years of genetic evolution and can provide insight to support the development of new approaches for imaging, image processing and communication. For example, biological vision systems can provide significant diversity, yet are able to function with only a minimal degree of neural processing. Examples will be described underlying the processes used to support the development of new concepts for photonic systems, ranging from uncooled bolometers and tunable filters, to asymmetric free-space optical communication systems and new forms of camera capable of simultaneously providing spectral and polarimetric diversity.

Reconstructing Face Image from the Thermal Infrared Spectrum to the Visible Spectrum †

PubMed Central

Kresnaraman, Brahmastro; Deguchi, Daisuke; Takahashi, Tomokazu; Mekada, Yoshito; Ide, Ichiro; Murase, Hiroshi

2016-01-01

During the night or in poorly lit areas, thermal cameras are a better choice instead of normal cameras for security surveillance because they do not rely on illumination. A thermal camera is able to detect a person within its view, but identification from only thermal information is not an easy task. The purpose of this paper is to reconstruct the face image of a person from the thermal spectrum to the visible spectrum. After the reconstruction, further image processing can be employed, including identification/recognition. Concretely, we propose a two-step thermal-to-visible-spectrum reconstruction method based on Canonical Correlation Analysis (CCA). The reconstruction is done by utilizing the relationship between images in both thermal infrared and visible spectra obtained by CCA. The whole image is processed in the first step while the second step processes patches in an image. Results show that the proposed method gives satisfying results with the two-step approach and outperforms comparative methods in both quality and recognition evaluations. PMID:27110781

Conceptual design of the CZMIL data processing system (DPS): algorithms and software for fusing lidar, hyperspectral data, and digital images

NASA Astrophysics Data System (ADS)

Park, Joong Yong; Tuell, Grady

2010-04-01

The Data Processing System (DPS) of the Coastal Zone Mapping and Imaging Lidar (CZMIL) has been designed to automatically produce a number of novel environmental products through the fusion of Lidar, spectrometer, and camera data in a single software package. These new products significantly transcend use of the system as a bathymeter, and support use of CZMIL as a complete coastal and benthic mapping tool. The DPS provides a spinning globe capability for accessing data files; automated generation of combined topographic and bathymetric point clouds; a fully-integrated manual editor and data analysis tool; automated generation of orthophoto mosaics; automated generation of reflectance data cubes from the imaging spectrometer; a coupled air-ocean spectral optimization model producing images of chlorophyll and CDOM concentrations; and a fusion based capability to produce images and classifications of the shallow water seafloor. Adopting a multitasking approach, we expect to achieve computation of the point clouds, DEMs, and reflectance images at a 1:1 processing to acquisition ratio.

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

NASA Astrophysics Data System (ADS)

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

2009-05-01

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

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 to remove blurred signal from an image. 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. Copyright © 1998 Elsevier Inc. All rights reserved.

Automatic building identification under bomb damage conditions

NASA Astrophysics Data System (ADS)

Woodley, Robert; Noll, Warren; Barker, Joseph; Wunsch, Donald C., II

2009-05-01

Given the vast amount of image intelligence utilized in support of planning and executing military operations, a passive automated image processing capability for target identification is urgently required. Furthermore, transmitting large image streams from remote locations would quickly use available band width (BW) precipitating the need for processing to occur at the sensor location. This paper addresses the problem of automatic target recognition for battle damage assessment (BDA). We utilize an Adaptive Resonance Theory approach to cluster templates of target buildings. The results show that the network successfully classifies targets from non-targets in a virtual test bed environment.

Active point out-of-plane ultrasound calibration

NASA Astrophysics Data System (ADS)

Cheng, Alexis; Guo, Xiaoyu; Zhang, Haichong K.; Kang, Hyunjae; Etienne-Cummings, Ralph; Boctor, Emad M.

2015-03-01

Image-guided surgery systems are often used to provide surgeons with informational support. Due to several unique advantages such as ease of use, real-time image acquisition, and no ionizing radiation, ultrasound is a common intraoperative medical imaging modality used in image-guided surgery systems. To perform advanced forms of guidance with ultrasound, such as virtual image overlays or automated robotic actuation, an ultrasound calibration process must be performed. This process recovers the rigid body transformation between a tracked marker attached to the transducer and the ultrasound image. Point-based phantoms are considered to be accurate, but their calibration framework assumes that the point is in the image plane. In this work, we present the use of an active point phantom and a calibration framework that accounts for the elevational uncertainty of the point. Given the lateral and axial position of the point in the ultrasound image, we approximate a circle in the axial-elevational plane with a radius equal to the axial position. The standard approach transforms all of the imaged points to be a single physical point. In our approach, we minimize the distances between the circular subsets of each image, with them ideally intersecting at a single point. We simulated in noiseless and noisy cases, presenting results on out-of-plane estimation errors, calibration estimation errors, and point reconstruction precision. We also performed an experiment using a robot arm as the tracker, resulting in a point reconstruction precision of 0.64mm.

Voyager 1 Jupiter Southern Hemisphere Movie

NASA Technical Reports Server (NTRS)

2000-01-01

This movie shows a portion of Jupiter in the southern hemisphere over 17Jupiter days. Above the white belt, notice the series of atmospheric vortices headed west. Even these early approach frames show wild dynamics in the roiling environment south of the white belt. Notice the small tumbling white cloud near the center.

As Voyager 1 approached Jupiter in 1979, it took images of the planet at regular intervals. This sequence is made from 17 images taken once every Jupiter rotation period (about 10 hours). These images were acquired in the Blue filter around Feb. 1, 1979. The spacecraft was about 37 million kilometers from Jupiter at that time.

This time-lapse movie was produced at JPL by the Image Processing Laboratory in 1979.

Vision, healing brush, and fiber bundles

NASA Astrophysics Data System (ADS)

Georgiev, Todor

2005-03-01

The Healing Brush is a tool introduced for the first time in Adobe Photoshop (2002) that removes defects in images by seamless cloning (gradient domain fusion). The Healing Brush algorithms are built on a new mathematical approach that uses Fibre Bundles and Connections to model the representation of images in the visual system. Our mathematical results are derived from first principles of human vision, related to adaptation transforms of von Kries type and Retinex theory. In this paper we present the new result of Healing in arbitrary color space. In addition to supporting image repair and seamless cloning, our approach also produces the exact solution to the problem of high dynamic range compression of17 and can be applied to other image processing algorithms.

Novel Molecular Imaging Approaches to Abdominal Aortic Aneurysm Risk Stratification

PubMed Central

Toczek, Jakub; Meadows, Judith L.; Sadeghi, Mehran M.

2015-01-01

Selection of patients for abdominal aortic aneurysm (AAA) repair is currently based on aneurysm size, growth rate and symptoms. Molecular imaging of biological processes associated with aneurysm growth and rupture, e.g., inflammation and matrix remodeling, could improve patient risk stratification and lead to a reduction in AAA morbidity and mortality. 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) and ultrasmall superparamagnetic particles of iron oxide (USPIO) magnetic resonance imaging are two novel approaches to AAA imaging evaluated in clinical trials. A variety of other tracers, including those that target inflammatory cells and proteolytic enzymes (e.g., integrin αvβ3 and matrix metalloproteinases), have proven effective in preclinical models of AAA and show great potential for clinical translation. PMID:26763279

Image processing for navigation on a mobile embedded platform

NASA Astrophysics Data System (ADS)

Preuss, Thomas; Gentsch, Lars; Rambow, Mark

2006-02-01

Mobile computing devices such as PDAs or cellular phones may act as "Personal Multimedia Exchanges", but they are limited in their processing power as well as in their connectivity. Sensors as well as cellular phones and PDAs are able to gather multimedia data, e. g. images, but leak computing power to process that data on their own. Therefore, it is necessary, that these devices connect to devices with more performance, which provide e.g. image processing services. In this paper, a generic approach is presented that connects different kinds of clients with each other and allows them to interact with more powerful devices. This architecture, called BOSPORUS, represents a communication framework for dynamic peer-to-peer computing. Each peer offers and uses services in this network and communicates loosely coupled and asynchronously with the others. These features make BOSPORUS a service oriented network architecture (SONA). A mobile embedded system, which uses external services for image processing based on the BOSPORUS Framework is shown as an application of the BOSPORUS framework.

Dietary Assessment on a Mobile Phone Using Image Processing and Pattern Recognition Techniques: Algorithm Design and System Prototyping

PubMed Central

Probst, Yasmine; Nguyen, Duc Thanh; Tran, Minh Khoi; Li, Wanqing

2015-01-01

Dietary assessment, while traditionally based on pen-and-paper, is rapidly moving towards automatic approaches. This study describes an Australian automatic food record method and its prototype for dietary assessment via the use of a mobile phone and techniques of image processing and pattern recognition. Common visual features including scale invariant feature transformation (SIFT), local binary patterns (LBP), and colour are used for describing food images. The popular bag-of-words (BoW) model is employed for recognizing the images taken by a mobile phone for dietary assessment. Technical details are provided together with discussions on the issues and future work. PMID:26225994

Optical head tracking for functional magnetic resonance imaging using structured light.

PubMed

Zaremba, Andrei A; MacFarlane, Duncan L; Tseng, Wei-Che; Stark, Andrew J; Briggs, Richard W; Gopinath, Kaundinya S; Cheshkov, Sergey; White, Keith D

2008-07-01

An accurate motion-tracking technique is needed to compensate for subject motion during functional magnetic resonance imaging (fMRI) procedures. Here, a novel approach to motion metrology is discussed. A structured light pattern specifically coded for digital signal processing is positioned onto a fiduciary of the patient. As the patient undergoes spatial transformations in 6 DoF (degrees of freedom), a high-resolution CCD camera captures successive images for analysis on a computing platform. A high-speed image processing algorithm is used to calculate spatial transformations in a time frame commensurate with patient movements (10-100 ms) and with a precision of at least 0.5 microm for translations and 0.1 deg for rotations.

Application of snakes and dynamic programming optimisation technique in modeling of buildings in informal settlement areas

NASA Astrophysics Data System (ADS)

Rüther, Heinz; Martine, Hagai M.; Mtalo, E. G.

This paper presents a novel approach to semiautomatic building extraction in informal settlement areas from aerial photographs. The proposed approach uses a strategy of delineating buildings by optimising their approximate building contour position. Approximate building contours are derived automatically by locating elevation blobs in digital surface models. Building extraction is then effected by means of the snakes algorithm and the dynamic programming optimisation technique. With dynamic programming, the building contour optimisation problem is realized through a discrete multistage process and solved by the "time-delayed" algorithm, as developed in this work. The proposed building extraction approach is a semiautomatic process, with user-controlled operations linking fully automated subprocesses. Inputs into the proposed building extraction system are ortho-images and digital surface models, the latter being generated through image matching techniques. Buildings are modeled as "lumps" or elevation blobs in digital surface models, which are derived by altimetric thresholding of digital surface models. Initial windows for building extraction are provided by projecting the elevation blobs centre points onto an ortho-image. In the next step, approximate building contours are extracted from the ortho-image by region growing constrained by edges. Approximate building contours thus derived are inputs into the dynamic programming optimisation process in which final building contours are established. The proposed system is tested on two study areas: Marconi Beam in Cape Town, South Africa, and Manzese in Dar es Salaam, Tanzania. Sixty percent of buildings in the study areas have been extracted and verified and it is concluded that the proposed approach contributes meaningfully to the extraction of buildings in moderately complex and crowded informal settlement areas.

Alpha-fetoprotein-targeted reporter gene expression imaging in hepatocellular carcinoma.

PubMed

Kim, Kwang Il; Chung, Hye Kyung; Park, Ju Hui; Lee, Yong Jin; Kang, Joo Hyun

2016-07-21

Hepatocellular carcinoma (HCC) is one of the most common cancers in Eastern Asia, and its incidence is increasing globally. Numerous experimental models have been developed to better our understanding of the pathogenic mechanism of HCC and to evaluate novel therapeutic approaches. Molecular imaging is a convenient and up-to-date biomedical tool that enables the visualization, characterization and quantification of biologic processes in a living subject. Molecular imaging based on reporter gene expression, in particular, can elucidate tumor-specific events or processes by acquiring images of a reporter gene's expression driven by tumor-specific enhancers/promoters. In this review, we discuss the advantages and disadvantages of various experimental HCC mouse models and we present in vivo images of tumor-specific reporter gene expression driven by an alpha-fetoprotein (AFP) enhancer/promoter system in a mouse model of HCC. The current mouse models of HCC development are established by xenograft, carcinogen induction and genetic engineering, representing the spectrum of tumor-inducing factors and tumor locations. The imaging analysis approach of reporter genes driven by AFP enhancer/promoter is presented for these different HCC mouse models. Such molecular imaging can provide longitudinal information about carcinogenesis and tumor progression. We expect that clinical application of AFP-targeted reporter gene expression imaging systems will be useful for the detection of AFP-expressing HCC tumors and screening of increased/decreased AFP levels due to disease or drug treatment.

Alpha-fetoprotein-targeted reporter gene expression imaging in hepatocellular carcinoma

PubMed Central

Kim, Kwang Il; Chung, Hye Kyung; Park, Ju Hui; Lee, Yong Jin; Kang, Joo Hyun

2016-01-01

Hepatocellular carcinoma (HCC) is one of the most common cancers in Eastern Asia, and its incidence is increasing globally. Numerous experimental models have been developed to better our understanding of the pathogenic mechanism of HCC and to evaluate novel therapeutic approaches. Molecular imaging is a convenient and up-to-date biomedical tool that enables the visualization, characterization and quantification of biologic processes in a living subject. Molecular imaging based on reporter gene expression, in particular, can elucidate tumor-specific events or processes by acquiring images of a reporter gene’s expression driven by tumor-specific enhancers/promoters. In this review, we discuss the advantages and disadvantages of various experimental HCC mouse models and we present in vivo images of tumor-specific reporter gene expression driven by an alpha-fetoprotein (AFP) enhancer/promoter system in a mouse model of HCC. The current mouse models of HCC development are established by xenograft, carcinogen induction and genetic engineering, representing the spectrum of tumor-inducing factors and tumor locations. The imaging analysis approach of reporter genes driven by AFP enhancer/promoter is presented for these different HCC mouse models. Such molecular imaging can provide longitudinal information about carcinogenesis and tumor progression. We expect that clinical application of AFP-targeted reporter gene expression imaging systems will be useful for the detection of AFP-expressing HCC tumors and screening of increased/decreased AFP levels due to disease or drug treatment. PMID:27468205

Self-organizing maps: a versatile tool for the automatic analysis of untargeted imaging datasets.

PubMed

Franceschi, Pietro; Wehrens, Ron

2014-04-01

MS-based imaging approaches allow for location-specific identification of chemical components in biological samples, opening up possibilities of much more detailed understanding of biological processes and mechanisms. Data analysis, however, is challenging, mainly because of the sheer size of such datasets. This article presents a novel approach based on self-organizing maps, extending previous work in order to be able to handle the large number of variables present in high-resolution mass spectra. The key idea is to generate prototype images, representing spatial distributions of ions, rather than prototypical mass spectra. This allows for a two-stage approach, first generating typical spatial distributions and associated m/z bins, and later analyzing the interesting bins in more detail using accurate masses. The possibilities and advantages of the new approach are illustrated on an in-house dataset of apple slices. © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

NeuroSeg: automated cell detection and segmentation for in vivo two-photon Ca2+ imaging data.

PubMed

Guan, Jiangheng; Li, Jingcheng; Liang, Shanshan; Li, Ruijie; Li, Xingyi; Shi, Xiaozhe; Huang, Ciyu; Zhang, Jianxiong; Pan, Junxia; Jia, Hongbo; Zhang, Le; Chen, Xiaowei; Liao, Xiang

2018-01-01

Two-photon Ca 2+ imaging has become a popular approach for monitoring neuronal population activity with cellular or subcellular resolution in vivo. This approach allows for the recording of hundreds to thousands of neurons per animal and thus leads to a large amount of data to be processed. In particular, manually drawing regions of interest is the most time-consuming aspect of data analysis. However, the development of automated image analysis pipelines, which will be essential for dealing with the likely future deluge of imaging data, remains a major challenge. To address this issue, we developed NeuroSeg, an open-source MATLAB program that can facilitate the accurate and efficient segmentation of neurons in two-photon Ca 2+ imaging data. We proposed an approach using a generalized Laplacian of Gaussian filter to detect cells and weighting-based segmentation to separate individual cells from the background. We tested this approach on an in vivo two-photon Ca 2+ imaging dataset obtained from mouse cortical neurons with differently sized view fields. We show that this approach exhibits superior performance for cell detection and segmentation compared with the existing published tools. In addition, we integrated the previously reported, activity-based segmentation into our approach and found that this combined method was even more promising. The NeuroSeg software, including source code and graphical user interface, is freely available and will be a useful tool for in vivo brain activity mapping.