WorldWideScience

Sample records for automatic image analysis

  1. Automatic dirt trail analysis in dermoscopy images.

    Science.gov (United States)

    Cheng, Beibei; Joe Stanley, R; Stoecker, William V; Osterwise, Christopher T P; Stricklin, Sherea M; Hinton, Kristen A; Moss, Randy H; Oliviero, Margaret; Rabinovitz, Harold S

    2013-02-01

    Basal cell carcinoma (BCC) is the most common cancer in the US. Dermatoscopes are devices used by physicians to facilitate the early detection of these cancers based on the identification of skin lesion structures often specific to BCCs. One new lesion structure, referred to as dirt trails, has the appearance of dark gray, brown or black dots and clods of varying sizes distributed in elongated clusters with indistinct borders, often appearing as curvilinear trails. In this research, we explore a dirt trail detection and analysis algorithm for extracting, measuring, and characterizing dirt trails based on size, distribution, and color in dermoscopic skin lesion images. These dirt trails are then used to automatically discriminate BCC from benign skin lesions. For an experimental data set of 35 BCC images with dirt trails and 79 benign lesion images, a neural network-based classifier achieved a 0.902 are under a receiver operating characteristic curve using a leave-one-out approach. Results obtained from this study show that automatic detection of dirt trails in dermoscopic images of BCC is feasible. This is important because of the large number of these skin cancers seen every year and the challenge of discovering these earlier with instrumentation. © 2011 John Wiley & Sons A/S.

  2. Application of automatic image analysis in wood science

    Science.gov (United States)

    Charles W. McMillin

    1982-01-01

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

  3. Towards automatic quantitative analysis of cardiac MR perfusion images

    NARCIS (Netherlands)

    Breeuwer, M.; Quist, M.; Spreeuwers, Lieuwe Jan; Paetsch, I.; Al-Saadi, N.; Nagel, E.

    2001-01-01

    Magnetic Resonance Imaging (MRI) is a powerful technique for imaging cardiovascular diseases. The introduction of cardiovascular MRI into clinical practice is however hampered by the lack of efficient and reliable automatic image analysis methods. This paper focuses on the automatic evaluation of

  4. Automatic analysis of microscopic images of red blood cell aggregates

    Science.gov (United States)

    Menichini, Pablo A.; Larese, Mónica G.; Riquelme, Bibiana D.

    2015-06-01

    Red blood cell aggregation is one of the most important factors in blood viscosity at stasis or at very low rates of flow. The basic structure of aggregates is a linear array of cell commonly termed as rouleaux. Enhanced or abnormal aggregation is seen in clinical conditions, such as diabetes and hypertension, producing alterations in the microcirculation, some of which can be analyzed through the characterization of aggregated cells. Frequently, image processing and analysis for the characterization of RBC aggregation were done manually or semi-automatically using interactive tools. We propose a system that processes images of RBC aggregation and automatically obtains the characterization and quantification of the different types of RBC aggregates. Present technique could be interesting to perform the adaptation as a routine used in hemorheological and Clinical Biochemistry Laboratories because this automatic method is rapid, efficient and economical, and at the same time independent of the user performing the analysis (repeatability of the analysis).

  5. Radiation dosimetry by automatic image analysis of dicentric chromosomes

    International Nuclear Information System (INIS)

    Bayley, R.; Carothers, A.; Farrow, S.; Gordon, J.; Ji, L.; Piper, J.; Rutovitz, D.; Stark, M.; Chen, X.; Wald, N.; Pittsburgh Univ., PA

    1991-01-01

    A system for scoring dicentric chromosomes by image analysis comprised fully automatic location of mitotic cells, automatic retrieval, focus and digitisation at high resolution, automatic rejection of nuclei and debris and detection and segmentation of chromosome clusters, automatic centromere location, and subsequent rapid interactive visual review of potential dicentric chromosomes to confirm positives and reject false positives. A calibration set of about 15000 cells was used to establish the quadratic dose response for 60 Co γ-irradiation. The dose-response function parameters were established by a maximum likelihood technique, and confidence limits in the dose response and in the corresponding inverse curve, of estimated dose for observed dicentric frequency, were established by Monte Carlo techniques. The system was validated in a blind trial by analysing a test comprising a total of about 8000 cells irradiated to 1 of 10 dose levels, and estimating the doses from the observed dicentric frequency. There was a close correspondence between the estimated and true doses. The overall sensitivity of the system in terms of the proportion of the total population of dicentrics present in the cells analysed that were detected by the system was measured to be about 40%. This implies that about 2.5 times more cells must be analysed by machine than by visual analysis. Taking this factor into account, the measured review time and false positive rates imply that analysis by the system of sufficient cells to provide the equivalent of a visual analysis of 500 cells would require about 1 h for operator review. (author). 20 refs.; 4 figs.; 5 tabs

  6. GANALYZER: A TOOL FOR AUTOMATIC GALAXY IMAGE ANALYSIS

    International Nuclear Information System (INIS)

    Shamir, Lior

    2011-01-01

    We describe Ganalyzer, a model-based tool that can automatically analyze and classify galaxy images. Ganalyzer works by separating the galaxy pixels from the background pixels, finding the center and radius of the galaxy, generating the radial intensity plot, and then computing the slopes of the peaks detected in the radial intensity plot to measure the spirality of the galaxy and determine its morphological class. Unlike algorithms that are based on machine learning, Ganalyzer is based on measuring the spirality of the galaxy, a task that is difficult to perform manually, and in many cases can provide a more accurate analysis compared to manual observation. Ganalyzer is simple to use, and can be easily embedded into other image analysis applications. Another advantage is its speed, which allows it to analyze ∼10,000,000 galaxy images in five days using a standard modern desktop computer. These capabilities can make Ganalyzer a useful tool in analyzing large data sets of galaxy images collected by autonomous sky surveys such as SDSS, LSST, or DES. The software is available for free download at http://vfacstaff.ltu.edu/lshamir/downloads/ganalyzer, and the data used in the experiment are available at http://vfacstaff.ltu.edu/lshamir/downloads/ganalyzer/GalaxyImages.zip.

  7. Ganalyzer: A Tool for Automatic Galaxy Image Analysis

    Science.gov (United States)

    Shamir, Lior

    2011-08-01

    We describe Ganalyzer, a model-based tool that can automatically analyze and classify galaxy images. Ganalyzer works by separating the galaxy pixels from the background pixels, finding the center and radius of the galaxy, generating the radial intensity plot, and then computing the slopes of the peaks detected in the radial intensity plot to measure the spirality of the galaxy and determine its morphological class. Unlike algorithms that are based on machine learning, Ganalyzer is based on measuring the spirality of the galaxy, a task that is difficult to perform manually, and in many cases can provide a more accurate analysis compared to manual observation. Ganalyzer is simple to use, and can be easily embedded into other image analysis applications. Another advantage is its speed, which allows it to analyze ~10,000,000 galaxy images in five days using a standard modern desktop computer. These capabilities can make Ganalyzer a useful tool in analyzing large data sets of galaxy images collected by autonomous sky surveys such as SDSS, LSST, or DES. The software is available for free download at http://vfacstaff.ltu.edu/lshamir/downloads/ganalyzer, and the data used in the experiment are available at http://vfacstaff.ltu.edu/lshamir/downloads/ganalyzer/GalaxyImages.zip.

  8. Automatic comic page image understanding based on edge segment analysis

    Science.gov (United States)

    Liu, Dong; Wang, Yongtao; Tang, Zhi; Li, Luyuan; Gao, Liangcai

    2013-12-01

    Comic page image understanding aims to analyse the layout of the comic page images by detecting the storyboards and identifying the reading order automatically. It is the key technique to produce the digital comic documents suitable for reading on mobile devices. In this paper, we propose a novel comic page image understanding method based on edge segment analysis. First, we propose an efficient edge point chaining method to extract Canny edge segments (i.e., contiguous chains of Canny edge points) from the input comic page image; second, we propose a top-down scheme to detect line segments within each obtained edge segment; third, we develop a novel method to detect the storyboards by selecting the border lines and further identify the reading order of these storyboards. The proposed method is performed on a data set consisting of 2000 comic page images from ten printed comic series. The experimental results demonstrate that the proposed method achieves satisfactory results on different comics and outperforms the existing methods.

  9. Automation of chromosomes analysis. Automatic system for image processing

    International Nuclear Information System (INIS)

    Le Go, R.; Cosnac, B. de; Spiwack, A.

    1975-01-01

    The A.S.T.I. is an automatic system relating to the fast conversational processing of all kinds of images (cells, chromosomes) converted to a numerical data set (120000 points, 16 grey levels stored in a MOS memory) through a fast D.O. analyzer. The system performs automatically the isolation of any individual image, the area and weighted area of which are computed. These results are directly displayed on the command panel and can be transferred to a mini-computer for further computations. A bright spot allows parts of an image to be picked out and the results to be displayed. This study is particularly directed towards automatic karyo-typing [fr

  10. Automatic telangiectasia analysis in dermoscopy images using adaptive critic design.

    Science.gov (United States)

    Cheng, B; Stanley, R J; Stoecker, W V; Hinton, K

    2012-11-01

    Telangiectasia, tiny skin vessels, are important dermoscopy structures used to discriminate basal cell carcinoma (BCC) from benign skin lesions. This research builds off of previously developed image analysis techniques to identify vessels automatically to discriminate benign lesions from BCCs. A biologically inspired reinforcement learning approach is investigated in an adaptive critic design framework to apply action-dependent heuristic dynamic programming (ADHDP) for discrimination based on computed features using different skin lesion contrast variations to promote the discrimination process. Lesion discrimination results for ADHDP are compared with multilayer perception backpropagation artificial neural networks. This study uses a data set of 498 dermoscopy skin lesion images of 263 BCCs and 226 competitive benign images as the input sets. This data set is extended from previous research [Cheng et al., Skin Research and Technology, 2011, 17: 278]. Experimental results yielded a diagnostic accuracy as high as 84.6% using the ADHDP approach, providing an 8.03% improvement over a standard multilayer perception method. We have chosen BCC detection rather than vessel detection as the endpoint. Although vessel detection is inherently easier, BCC detection has potential direct clinical applications. Small BCCs are detectable early by dermoscopy and potentially detectable by the automated methods described in this research. © 2011 John Wiley & Sons A/S.

  11. Automatic analysis of image quality control for Image Guided Radiation Therapy (IGRT) devices in external radiotherapy

    International Nuclear Information System (INIS)

    Torfeh, Tarraf

    2009-01-01

    On-board imagers mounted on a radiotherapy treatment machine are very effective devices that improve the geometric accuracy of radiation delivery. However, a precise and regular quality control program is required in order to achieve this objective. Our purpose consisted of developing software tools dedicated to an automatic image quality control of IGRT devices used in external radiotherapy: 2D-MV mode for measuring patient position during the treatment using high energy images, 2D-kV mode (low energy images) and 3D Cone Beam Computed Tomography (CBCT) MV or kV mode, used for patient positioning before treatment. Automated analysis of the Winston and Lutz test was also proposed. This test is used for the evaluation of the mechanical aspects of treatment machines on which additional constraints are carried out due to the on-board imagers additional weights. Finally, a technique of generating digital phantoms in order to assess the performance of the proposed software tools is described. Software tools dedicated to an automatic quality control of IGRT devices allow reducing by a factor of 100 the time spent by the medical physics team to analyze the results of controls while improving their accuracy by using objective and reproducible analysis and offering traceability through generating automatic monitoring reports and statistical studies. (author) [fr

  12. Automatic analysis of the micronucleus test in primary human lymphocytes using image analysis.

    Science.gov (United States)

    Frieauff, W; Martus, H J; Suter, W; Elhajouji, A

    2013-01-01

    The in vitro micronucleus test (MNT) is a well-established test for early screening of new chemical entities in industrial toxicology. For assessing the clastogenic or aneugenic potential of a test compound, micronucleus induction in cells has been shown repeatedly to be a sensitive and a specific parameter. Various automated systems to replace the tedious and time-consuming visual slide analysis procedure as well as flow cytometric approaches have been discussed. The ROBIAS (Robotic Image Analysis System) for both automatic cytotoxicity assessment and micronucleus detection in human lymphocytes was developed at Novartis where the assay has been used to validate positive results obtained in the MNT in TK6 cells, which serves as the primary screening system for genotoxicity profiling in early drug development. In addition, the in vitro MNT has become an accepted alternative to support clinical studies and will be used for regulatory purposes as well. The comparison of visual with automatic analysis results showed a high degree of concordance for 25 independent experiments conducted for the profiling of 12 compounds. For concentration series of cyclophosphamide and carbendazim, a very good correlation between automatic and visual analysis by two examiners could be established, both for the relative division index used as cytotoxicity parameter, as well as for micronuclei scoring in mono- and binucleated cells. Generally, false-positive micronucleus decisions could be controlled by fast and simple relocation of the automatically detected patterns. The possibility to analyse 24 slides within 65h by automatic analysis over the weekend and the high reproducibility of the results make automatic image processing a powerful tool for the micronucleus analysis in primary human lymphocytes. The automated slide analysis for the MNT in human lymphocytes complements the portfolio of image analysis applications on ROBIAS which is supporting various assays at Novartis.

  13. System for automatic x-ray-image analysis, measurement, and sorting of laser fusion targets

    International Nuclear Information System (INIS)

    Singleton, R.M.; Perkins, D.E.; Willenborg, D.L.

    1980-01-01

    This paper describes the Automatic X-Ray Image Analysis and Sorting (AXIAS) system which is designed to analyze and measure x-ray images of opaque hollow microspheres used as laser fusion targets. The x-ray images are first recorded on a high resolution film plate. The AXIAS system then digitizes and processes the images to accurately measure the target parameters and defects. The primary goals of the AXIAS system are: to provide extremely accurate and rapid measurements, to engineer a practical system for a routine production environment and to furnish the capability of automatically measuring an array of images for sorting and selection

  14. Semi-supervised learning based probabilistic latent semantic analysis for automatic image annotation

    Institute of Scientific and Technical Information of China (English)

    Tian Dongping

    2017-01-01

    In recent years, multimedia annotation problem has been attracting significant research attention in multimedia and computer vision areas, especially for automatic image annotation, whose purpose is to provide an efficient and effective searching environment for users to query their images more easily.In this paper, a semi-supervised learning based probabilistic latent semantic analysis ( PL-SA) model for automatic image annotation is presenred.Since it' s often hard to obtain or create la-beled images in large quantities while unlabeled ones are easier to collect, a transductive support vector machine ( TSVM) is exploited to enhance the quality of the training image data.Then, differ-ent image features with different magnitudes will result in different performance for automatic image annotation.To this end, a Gaussian normalization method is utilized to normalize different features extracted from effective image regions segmented by the normalized cuts algorithm so as to reserve the intrinsic content of images as complete as possible.Finally, a PLSA model with asymmetric mo-dalities is constructed based on the expectation maximization( EM) algorithm to predict a candidate set of annotations with confidence scores.Extensive experiments on the general-purpose Corel5k dataset demonstrate that the proposed model can significantly improve performance of traditional PL-SA for the task of automatic image annotation.

  15. Automatic computer aided analysis algorithms and system for adrenal tumors on CT images.

    Science.gov (United States)

    Chai, Hanchao; Guo, Yi; Wang, Yuanyuan; Zhou, Guohui

    2017-12-04

    The adrenal tumor will disturb the secreting function of adrenocortical cells, leading to many diseases. Different kinds of adrenal tumors require different therapeutic schedules. In the practical diagnosis, it highly relies on the doctor's experience to judge the tumor type by reading the hundreds of CT images. This paper proposed an automatic computer aided analysis method for adrenal tumors detection and classification. It consisted of the automatic segmentation algorithms, the feature extraction and the classification algorithms. These algorithms were then integrated into a system and conducted on the graphic interface by using MATLAB Graphic user interface (GUI). The accuracy of the automatic computer aided segmentation and classification reached 90% on 436 CT images. The experiments proved the stability and reliability of this automatic computer aided analytic system.

  16. Technical characterization by image analysis: an automatic method of mineralogical studies

    International Nuclear Information System (INIS)

    Oliveira, J.F. de

    1988-01-01

    The application of a modern method of image analysis fully automated for the study of grain size distribution modal assays, degree of liberation and mineralogical associations is discussed. The image analyser is interfaced with a scanning electron microscope and an energy dispersive X-rays analyser. The image generated by backscattered electrons is analysed automatically and the system has been used in accessment studies of applied mineralogy as well as in process control in the mining industry. (author) [pt

  17. Automatic morphometry of synaptic boutons of cultured cells using granulometric analysis of digital images

    NARCIS (Netherlands)

    Prodanov, D.P.; Heeroma, Joost; Marani, Enrico

    2006-01-01

    Numbers, linear density, and surface area of synaptic boutons can be important parameters in studies on synaptic plasticity in cultured neurons. We present a method for automatic identification and morphometry of boutons based on filtering of digital images using granulometric analysis. Cultures of

  18. Automatic analysis of digitized TV-images by a computer-driven optical microscope

    International Nuclear Information System (INIS)

    Rosa, G.; Di Bartolomeo, A.; Grella, G.; Romano, G.

    1997-01-01

    New methods of image analysis and three-dimensional pattern recognition were developed in order to perform the automatic scan of nuclear emulsion pellicles. An optical microscope, with a motorized stage, was equipped with a CCD camera and an image digitizer, and interfaced to a personal computer. Selected software routines inspired the design of a dedicated hardware processor. Fast operation, high efficiency and accuracy were achieved. First applications to high-energy physics experiments are reported. Further improvements are in progress, based on a high-resolution fast CCD camera and on programmable digital signal processors. Applications to other research fields are envisaged. (orig.)

  19. Porosity determination on pyrocarbon by means of automatic quantitative image analysis

    Energy Technology Data Exchange (ETDEWEB)

    Koizlik, K.; Uhlenbruck, U.; Delle, W.; Hoven, H.; Nickel, H.

    1976-05-01

    For a long time, the quantitative image analysis is well known as a method for quantifying the results of material investigation basing on ceramography. The development of the automatic image analyzers has made it a fast and elegant procedure for evaluation. Since 1975, it is used in IRW to determine easily and routinely the macroporosity and by this the density of the pyrocarbon coatings of nuclear fuel particles. This report describes the definition of measuring parameters, the measuring procedure, the mathematical calculations, and first experimental and mathematical results.

  20. Fully automatic algorithm for the analysis of vessels in the angiographic image of the eye fundus

    Directory of Open Access Journals (Sweden)

    Koprowski Robert

    2012-06-01

    Full Text Available Abstract Background The available scientific literature contains descriptions of manual, semi-automated and automated methods for analysing angiographic images. The presented algorithms segment vessels calculating their tortuosity or number in a given area. We describe a statistical analysis of the inclination of the vessels in the fundus as related to their distance from the center of the optic disc. Methods The paper presents an automated method for analysing vessels which are found in angiographic images of the eye using a Matlab implemented algorithm. It performs filtration and convolution operations with suggested masks. The result is an image containing information on the location of vessels and their inclination angle in relation to the center of the optic disc. This is a new approach to the analysis of vessels whose usefulness has been confirmed in the diagnosis of hypertension. Results The proposed algorithm analyzed and processed the images of the eye fundus using a classifier in the form of decision trees. It enabled the proper classification of healthy patients and those with hypertension. The result is a very good separation of healthy subjects from the hypertensive ones: sensitivity - 83%, specificity - 100%, accuracy - 96%. This confirms a practical usefulness of the proposed method. Conclusions This paper presents an algorithm for the automatic analysis of morphological parameters of the fundus vessels. Such an analysis is performed during fluorescein angiography of the eye. The presented algorithm automatically calculates the global statistical features connected with both tortuosity of vessels and their total area or their number.

  1. Automatic system for quantification and visualization of lung aeration on chest computed tomography images: the Lung Image System Analysis - LISA

    Energy Technology Data Exchange (ETDEWEB)

    Felix, John Hebert da Silva; Cortez, Paulo Cesar, E-mail: jhsfelix@gmail.co [Universidade Federal do Ceara (UFC), Fortaleza, CE (Brazil). Dept. de Engenharia de Teleinformatica; Holanda, Marcelo Alcantara [Universidade Federal do Ceara (UFC), Fortaleza, CE (Brazil). Hospital Universitario Walter Cantidio. Dept. de Medicina Clinica

    2010-12-15

    High Resolution Computed Tomography (HRCT) is the exam of choice for the diagnostic evaluation of lung parenchyma diseases. There is an increasing interest for computational systems able to automatically analyze the radiological densities of the lungs in CT images. The main objective of this study is to present a system for the automatic quantification and visualization of the lung aeration in HRCT images of different degrees of aeration, called Lung Image System Analysis (LISA). The secondary objective is to compare LISA to the Osiris system and also to specific algorithm lung segmentation (ALS), on the accuracy of the lungs segmentation. The LISA system automatically extracts the following image attributes: lungs perimeter, cross sectional area, volume, the radiological densities histograms, the mean lung density (MLD) in Hounsfield units (HU), the relative area of the lungs with voxels with density values lower than -950 HU (RA950) and the 15th percentile of the least density voxels (PERC15). Furthermore, LISA has a colored mask algorithm that applies pseudo-colors to the lung parenchyma according to the pre-defined radiological density chosen by the system user. The lungs segmentations of 102 images of 8 healthy volunteers and 141 images of 11 patients with Chronic Obstructive Pulmonary Disease (COPD) were compared on the accuracy and concordance among the three methods. The LISA was more effective on lungs segmentation than the other two methods. LISA's color mask tool improves the spatial visualization of the degrees of lung aeration and the various attributes of the image that can be extracted may help physicians and researchers to better assess lung aeration both quantitatively and qualitatively. LISA may have important clinical and research applications on the assessment of global and regional lung aeration and therefore deserves further developments and validation studies. (author)

  2. Automatic system for quantification and visualization of lung aeration on chest computed tomography images: the Lung Image System Analysis - LISA

    International Nuclear Information System (INIS)

    Felix, John Hebert da Silva; Cortez, Paulo Cesar; Holanda, Marcelo Alcantara

    2010-01-01

    High Resolution Computed Tomography (HRCT) is the exam of choice for the diagnostic evaluation of lung parenchyma diseases. There is an increasing interest for computational systems able to automatically analyze the radiological densities of the lungs in CT images. The main objective of this study is to present a system for the automatic quantification and visualization of the lung aeration in HRCT images of different degrees of aeration, called Lung Image System Analysis (LISA). The secondary objective is to compare LISA to the Osiris system and also to specific algorithm lung segmentation (ALS), on the accuracy of the lungs segmentation. The LISA system automatically extracts the following image attributes: lungs perimeter, cross sectional area, volume, the radiological densities histograms, the mean lung density (MLD) in Hounsfield units (HU), the relative area of the lungs with voxels with density values lower than -950 HU (RA950) and the 15th percentile of the least density voxels (PERC15). Furthermore, LISA has a colored mask algorithm that applies pseudo-colors to the lung parenchyma according to the pre-defined radiological density chosen by the system user. The lungs segmentations of 102 images of 8 healthy volunteers and 141 images of 11 patients with Chronic Obstructive Pulmonary Disease (COPD) were compared on the accuracy and concordance among the three methods. The LISA was more effective on lungs segmentation than the other two methods. LISA's color mask tool improves the spatial visualization of the degrees of lung aeration and the various attributes of the image that can be extracted may help physicians and researchers to better assess lung aeration both quantitatively and qualitatively. LISA may have important clinical and research applications on the assessment of global and regional lung aeration and therefore deserves further developments and validation studies. (author)

  3. Automatic extraction of soft tissues from 3D MRI head images using model driven analysis

    International Nuclear Information System (INIS)

    Jiang, Hao; Yamamoto, Shinji; Imao, Masanao.

    1995-01-01

    This paper presents an automatic extraction system (called TOPS-3D : Top Down Parallel Pattern Recognition System for 3D Images) of soft tissues from 3D MRI head images by using model driven analysis algorithm. As the construction of system TOPS we developed, two concepts have been considered in the design of system TOPS-3D. One is the system having a hierarchical structure of reasoning using model information in higher level, and the other is a parallel image processing structure used to extract plural candidate regions for a destination entity. The new points of system TOPS-3D are as follows. (1) The TOPS-3D is a three-dimensional image analysis system including 3D model construction and 3D image processing techniques. (2) A technique is proposed to increase connectivity between knowledge processing in higher level and image processing in lower level. The technique is realized by applying opening operation of mathematical morphology, in which a structural model function defined in higher level by knowledge representation is immediately used to the filter function of opening operation as image processing in lower level. The system TOPS-3D applied to 3D MRI head images consists of three levels. First and second levels are reasoning part, and third level is image processing part. In experiments, we applied 5 samples of 3D MRI head images with size 128 x 128 x 128 pixels to the system TOPS-3D to extract the regions of soft tissues such as cerebrum, cerebellum and brain stem. From the experimental results, the system is robust for variation of input data by using model information, and the position and shape of soft tissues are extracted corresponding to anatomical structure. (author)

  4. Fractal Analysis of Elastographic Images for Automatic Detection of Diffuse Diseases of Salivary Glands: Preliminary Results

    Directory of Open Access Journals (Sweden)

    Alexandru Florin Badea

    2013-01-01

    Full Text Available The geometry of some medical images of tissues, obtained by elastography and ultrasonography, is characterized in terms of complexity parameters such as the fractal dimension (FD. It is well known that in any image there are very subtle details that are not easily detectable by the human eye. However, in many cases like medical imaging diagnosis, these details are very important since they might contain some hidden information about the possible existence of certain pathological lesions like tissue degeneration, inflammation, or tumors. Therefore, an automatic method of analysis could be an expedient tool for physicians to give a faultless diagnosis. The fractal analysis is of great importance in relation to a quantitative evaluation of “real-time” elastography, a procedure considered to be operator dependent in the current clinical practice. Mathematical analysis reveals significant discrepancies among normal and pathological image patterns. The main objective of our work is to demonstrate the clinical utility of this procedure on an ultrasound image corresponding to a submandibular diffuse pathology.

  5. Automatic registration of multi-modal microscopy images for integrative analysis of prostate tissue sections

    International Nuclear Information System (INIS)

    Lippolis, Giuseppe; Edsjö, Anders; Helczynski, Leszek; Bjartell, Anders; Overgaard, Niels Chr

    2013-01-01

    Prostate cancer is one of the leading causes of cancer related deaths. For diagnosis, predicting the outcome of the disease, and for assessing potential new biomarkers, pathologists and researchers routinely analyze histological samples. Morphological and molecular information may be integrated by aligning microscopic histological images in a multiplex fashion. This process is usually time-consuming and results in intra- and inter-user variability. The aim of this study is to investigate the feasibility of using modern image analysis methods for automated alignment of microscopic images from differently stained adjacent paraffin sections from prostatic tissue specimens. Tissue samples, obtained from biopsy or radical prostatectomy, were sectioned and stained with either hematoxylin & eosin (H&E), immunohistochemistry for p63 and AMACR or Time Resolved Fluorescence (TRF) for androgen receptor (AR). Image pairs were aligned allowing for translation, rotation and scaling. The registration was performed automatically by first detecting landmarks in both images, using the scale invariant image transform (SIFT), followed by the well-known RANSAC protocol for finding point correspondences and finally aligned by Procrustes fit. The Registration results were evaluated using both visual and quantitative criteria as defined in the text. Three experiments were carried out. First, images of consecutive tissue sections stained with H&E and p63/AMACR were successfully aligned in 85 of 88 cases (96.6%). The failures occurred in 3 out of 13 cores with highly aggressive cancer (Gleason score ≥ 8). Second, TRF and H&E image pairs were aligned correctly in 103 out of 106 cases (97%). The third experiment considered the alignment of image pairs with the same staining (H&E) coming from a stack of 4 sections. The success rate for alignment dropped from 93.8% in adjacent sections to 22% for sections furthest away. The proposed method is both reliable and fast and therefore well suited

  6. Semi-automatic system for UV images analysis of historical musical instruments

    Science.gov (United States)

    Dondi, Piercarlo; Invernizzi, Claudia; Licchelli, Maurizio; Lombardi, Luca; Malagodi, Marco; Rovetta, Tommaso

    2015-06-01

    The selection of representative areas to be analyzed is a common problem in the study of Cultural Heritage items. UV fluorescence photography is an extensively used technique to highlight specific surface features which cannot be observed in visible light (e.g. restored parts or treated with different materials), and it proves to be very effective in the study of historical musical instruments. In this work we propose a new semi-automatic solution for selecting areas with the same perceived color (a simple clue of similar materials) on UV photos, using a specifically designed interactive tool. The proposed method works in two steps: (i) users select a small rectangular area of the image; (ii) program automatically highlights all the areas that have the same color of the selected input. The identification is made by the analysis of the image in HSV color model, the most similar to the human perception. The achievable result is more accurate than a manual selection, because it can detect also points that users do not recognize as similar due to perception illusion. The application has been developed following the rules of usability, and Human Computer Interface has been improved after a series of tests performed by expert and non-expert users. All the experiments were performed on UV imagery of the Stradivari violins collection stored by "Museo del Violino" in Cremona.

  7. Approaches to automatic parameter fitting in a microscopy image segmentation pipeline: An exploratory parameter space analysis.

    Science.gov (United States)

    Held, Christian; Nattkemper, Tim; Palmisano, Ralf; Wittenberg, Thomas

    2013-01-01

    Research and diagnosis in medicine and biology often require the assessment of a large amount of microscopy image data. Although on the one hand, digital pathology and new bioimaging technologies find their way into clinical practice and pharmaceutical research, some general methodological issues in automated image analysis are still open. In this study, we address the problem of fitting the parameters in a microscopy image segmentation pipeline. We propose to fit the parameters of the pipeline's modules with optimization algorithms, such as, genetic algorithms or coordinate descents, and show how visual exploration of the parameter space can help to identify sub-optimal parameter settings that need to be avoided. This is of significant help in the design of our automatic parameter fitting framework, which enables us to tune the pipeline for large sets of micrographs. The underlying parameter spaces pose a challenge for manual as well as automated parameter optimization, as the parameter spaces can show several local performance maxima. Hence, optimization strategies that are not able to jump out of local performance maxima, like the hill climbing algorithm, often result in a local maximum.

  8. Approaches to automatic parameter fitting in a microscopy image segmentation pipeline: An exploratory parameter space analysis

    Directory of Open Access Journals (Sweden)

    Christian Held

    2013-01-01

    Full Text Available Introduction: Research and diagnosis in medicine and biology often require the assessment of a large amount of microscopy image data. Although on the one hand, digital pathology and new bioimaging technologies find their way into clinical practice and pharmaceutical research, some general methodological issues in automated image analysis are still open. Methods: In this study, we address the problem of fitting the parameters in a microscopy image segmentation pipeline. We propose to fit the parameters of the pipeline′s modules with optimization algorithms, such as, genetic algorithms or coordinate descents, and show how visual exploration of the parameter space can help to identify sub-optimal parameter settings that need to be avoided. Results: This is of significant help in the design of our automatic parameter fitting framework, which enables us to tune the pipeline for large sets of micrographs. Conclusion: The underlying parameter spaces pose a challenge for manual as well as automated parameter optimization, as the parameter spaces can show several local performance maxima. Hence, optimization strategies that are not able to jump out of local performance maxima, like the hill climbing algorithm, often result in a local maximum.

  9. Automatic T1 bladder tumor detection by using wavelet analysis in cystoscopy images

    Science.gov (United States)

    Freitas, Nuno R.; Vieira, Pedro M.; Lima, Estevão; Lima, Carlos S.

    2018-02-01

    Correct classification of cystoscopy images depends on the interpreter’s experience. Bladder cancer is a common lesion that can only be confirmed by biopsying the tissue, therefore, the automatic identification of tumors plays a significant role in early stage diagnosis and its accuracy. To our best knowledge, the use of white light cystoscopy images for bladder tumor diagnosis has not been reported so far. In this paper, a texture analysis based approach is proposed for bladder tumor diagnosis presuming that tumors change in tissue texture. As is well accepted by the scientific community, texture information is more present in the medium to high frequency range which can be selected by using a discrete wavelet transform (DWT). Tumor enhancement can be improved by using automatic segmentation, since a mixing with normal tissue is avoided under ideal conditions. The segmentation module proposed in this paper takes advantage of the wavelet decomposition tree to discard poor texture information in such a way that both steps of the proposed algorithm segmentation and classification share the same focus on texture. Multilayer perceptron and a support vector machine with a stratified ten-fold cross-validation procedure were used for classification purposes by using the hue-saturation-value (HSV), red-green-blue, and CIELab color spaces. Performances of 91% in sensitivity and 92.9% in specificity were obtained regarding HSV color by using both preprocessing and classification steps based on the DWT. The proposed method can achieve good performance on identifying bladder tumor frames. These promising results open the path towards a deeper study regarding the applicability of this algorithm in computer aided diagnosis.

  10. Evaluation of ventricular dysfunction using semi-automatic longitudinal strain analysis of four-chamber cine MR imaging.

    Science.gov (United States)

    Kawakubo, Masateru; Nagao, Michinobu; Kumazawa, Seiji; Yamasaki, Yuzo; Chishaki, Akiko S; Nakamura, Yasuhiko; Honda, Hiroshi; Morishita, Junji

    2016-02-01

    The aim of this study was to evaluate ventricular dysfunction using the longitudinal strain analysis in 4-chamber (4CH) cine MR imaging, and to investigate the agreement between the semi-automatic and manual measurements in the analysis. Fifty-two consecutive patients with ischemic, or non-ischemic cardiomyopathy and repaired tetralogy of Fallot who underwent cardiac MR examination incorporating cine MR imaging were retrospectively enrolled. The LV and RV longitudinal strain values were obtained by semi-automatically and manually. Receiver operating characteristic (ROC) analysis was performed to determine the optimal cutoff of the minimum longitudinal strain value for the detection of patients with cardiac dysfunction. The correlations between manual and semi-automatic measurements for LV and RV walls were analyzed by Pearson coefficient analysis. ROC analysis demonstrated the optimal cut-off of the minimum longitudinal strain values (εL_min) for diagnoses the LV and RV dysfunction at a high accuracy (LV εL_min = -7.8 %: area under the curve, 0.89; sensitivity, 83 %; specificity, 91 %, RV εL_min = -15.7 %: area under the curve, 0.82; sensitivity, 92 %; specificity, 68 %). Excellent correlations between manual and semi-automatic measurements for LV and RV free wall were observed (LV, r = 0.97, p cine MR imaging can evaluate LV and RV dysfunction with simply and easy measurements. The strain analysis could have extensive application in cardiac imaging for various clinical cases.

  11. Automatic Generation of Algorithms for the Statistical Analysis of Planetary Nebulae Images

    Science.gov (United States)

    Fischer, Bernd

    2004-01-01

    which use numerical approximations even in cases where closed-form solutions exist. AutoBayes is implemented in Prolog and comprises approximately 75.000 lines of code. In this paper, we take one typical scientific data analysis problem-analyzing planetary nebulae images taken by the Hubble Space Telescope-and show how AutoBayes can be used to automate the implementation of the necessary anal- ysis programs. We initially follow the analysis described by Knuth and Hajian [KHO2] and use AutoBayes to derive code for the published models. We show the details of the code derivation process, including the symbolic computations and automatic integration of library procedures, and compare the results of the automatically generated and manually implemented code. We then go beyond the original analysis and use AutoBayes to derive code for a simple image segmentation procedure based on a mixture model which can be used to automate a manual preproceesing step. Finally, we combine the original approach with the simple segmentation which yields a more detailed analysis. This also demonstrates that AutoBayes makes it easy to combine different aspects of data analysis.

  12. ATMAD: robust image analysis for Automatic Tissue MicroArray De-arraying.

    Science.gov (United States)

    Nguyen, Hoai Nam; Paveau, Vincent; Cauchois, Cyril; Kervrann, Charles

    2018-04-19

    Over the last two decades, an innovative technology called Tissue Microarray (TMA), which combines multi-tissue and DNA microarray concepts, has been widely used in the field of histology. It consists of a collection of several (up to 1000 or more) tissue samples that are assembled onto a single support - typically a glass slide - according to a design grid (array) layout, in order to allow multiplex analysis by treating numerous samples under identical and standardized conditions. However, during the TMA manufacturing process, the sample positions can be highly distorted from the design grid due to the imprecision when assembling tissue samples and the deformation of the embedding waxes. Consequently, these distortions may lead to severe errors of (histological) assay results when the sample identities are mismatched between the design and its manufactured output. The development of a robust method for de-arraying TMA, which localizes and matches TMA samples with their design grid, is therefore crucial to overcome the bottleneck of this prominent technology. In this paper, we propose an Automatic, fast and robust TMA De-arraying (ATMAD) approach dedicated to images acquired with brightfield and fluorescence microscopes (or scanners). First, tissue samples are localized in the large image by applying a locally adaptive thresholding on the isotropic wavelet transform of the input TMA image. To reduce false detections, a parametric shape model is considered for segmenting ellipse-shaped objects at each detected position. Segmented objects that do not meet the size and the roundness criteria are discarded from the list of tissue samples before being matched with the design grid. Sample matching is performed by estimating the TMA grid deformation under the thin-plate model. Finally, thanks to the estimated deformation, the true tissue samples that were preliminary rejected in the early image processing step are recognized by running a second segmentation step. We

  13. An analysis of line-drawings based upon automatically inferred grammar and its application to chest x-ray images

    International Nuclear Information System (INIS)

    Nakayama, Akira; Yoshida, Yuuji; Fukumura, Teruo

    1984-01-01

    There is a technique using inferring grammer as image- structure analyzing technique. This technique involves a few problems if it is applied to naturally obtained images, as the practical grammatical technique for two-dimensional image is not established. The authors developed a technique which solved the above problems for the main purpose of the automated structure analysis of naturally obtained image. The first half of this paper describes on the automatic inference of line drawing generation grammar and the line drawing analysis based on that automatic inference. The second half of the paper reports on the actual analysis. The proposed technique is that to extract object line drawings out of the line drawings containing noise. The technique was evaluated for its effectiveness with an example of extracting rib center lines out of thin line chest X-ray images having practical scale and complexity. In this example, the total number of characteristic points (ends, branch points and intersections) composing line drawings per one image was 377, and the total number of line segments composing line drawings was 566 on average per sheet. The extraction ratio was 86.6 % which seemed to be proper when the complexity of input line drawings was considered. Further, the result was compared with the identified rib center lines with the automatic screening system AISCR-V3 for comparison with the conventional processing technique, and it was satisfactory when the versatility of this method was considered. (Wakatsuki, Y.)

  14. Automatic alignment of radionuclide images

    International Nuclear Information System (INIS)

    Barber, D.C.

    1982-01-01

    The variability of the position, dimensions and orientation of a radionuclide image within the field of view of a gamma camera hampers attempts to analyse the image numerically. This paper describes a method of using a set of training images of a particular type, in this case right lateral brain images, to define the likely variations in the position, dimensions and orientation for that type of image and to provide alignment data for a program that automatically aligns new images of the specified type to a standard position, size and orientation. Examples are given of the use of this method on three types of radionuclide image. (author)

  15. Automatic classification of retinal three-dimensional optical coherence tomography images using principal component analysis network with composite kernels.

    Science.gov (United States)

    Fang, Leyuan; Wang, Chong; Li, Shutao; Yan, Jun; Chen, Xiangdong; Rabbani, Hossein

    2017-11-01

    We present an automatic method, termed as the principal component analysis network with composite kernel (PCANet-CK), for the classification of three-dimensional (3-D) retinal optical coherence tomography (OCT) images. Specifically, the proposed PCANet-CK method first utilizes the PCANet to automatically learn features from each B-scan of the 3-D retinal OCT images. Then, multiple kernels are separately applied to a set of very important features of the B-scans and these kernels are fused together, which can jointly exploit the correlations among features of the 3-D OCT images. Finally, the fused (composite) kernel is incorporated into an extreme learning machine for the OCT image classification. We tested our proposed algorithm on two real 3-D spectral domain OCT (SD-OCT) datasets (of normal subjects and subjects with the macular edema and age-related macular degeneration), which demonstrated its effectiveness. (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).

  16. Development of automatic image analysis methods for high-throughput and high-content screening

    NARCIS (Netherlands)

    Di, Zi

    2013-01-01

    This thesis focuses on the development of image analysis methods for ultra-high content analysis of high-throughput screens where cellular phenotype responses to various genetic or chemical perturbations that are under investigation. Our primary goal is to deliver efficient and robust image analysis

  17. ACIR: automatic cochlea image registration

    Science.gov (United States)

    Al-Dhamari, Ibraheem; Bauer, Sabine; Paulus, Dietrich; Lissek, Friedrich; Jacob, Roland

    2017-02-01

    Efficient Cochlear Implant (CI) surgery requires prior knowledge of the cochlea's size and its characteristics. This information helps to select suitable implants for different patients. To get these measurements, a segmentation method of cochlea medical images is needed. An important pre-processing step for good cochlea segmentation involves efficient image registration. The cochlea's small size and complex structure, in addition to the different resolutions and head positions during imaging, reveals a big challenge for the automated registration of the different image modalities. In this paper, an Automatic Cochlea Image Registration (ACIR) method for multi- modal human cochlea images is proposed. This method is based on using small areas that have clear structures from both input images instead of registering the complete image. It uses the Adaptive Stochastic Gradient Descent Optimizer (ASGD) and Mattes's Mutual Information metric (MMI) to estimate 3D rigid transform parameters. The use of state of the art medical image registration optimizers published over the last two years are studied and compared quantitatively using the standard Dice Similarity Coefficient (DSC). ACIR requires only 4.86 seconds on average to align cochlea images automatically and to put all the modalities in the same spatial locations without human interference. The source code is based on the tool elastix and is provided for free as a 3D Slicer plugin. Another contribution of this work is a proposed public cochlea standard dataset which can be downloaded for free from a public XNAT server.

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

    Directory of Open Access Journals (Sweden)

    Kemal Akyol

    2016-01-01

    Full Text Available 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.

  19. Automatic analysis of quality of images from X-ray digital flat detectors

    International Nuclear Information System (INIS)

    Le Meur, Y.

    2009-04-01

    Since last decade, medical imaging has grown up with the development of new digital imaging techniques. In the field of X-ray radiography, new detectors replace progressively older techniques, based on film or x-ray intensifiers. These digital detectors offer a higher sensibility and reduced overall dimensions. This work has been prepared with Trixell, the world leading company in flat detectors for medical radiography. It deals with quality control on digital images stemming from these detectors. High quality standards of medical imaging impose a close analysis of the defects that can appear on the images. This work describes a complete process for quality analysis of such images. A particular focus is given on the detection task of the defects, thanks to methods well adapted to our context of spatially correlated defects in noise background. (author)

  20. Automatic neuron segmentation and neural network analysis method for phase contrast microscopy images.

    Science.gov (United States)

    Pang, Jincheng; Özkucur, Nurdan; Ren, Michael; Kaplan, David L; Levin, Michael; Miller, Eric L

    2015-11-01

    Phase Contrast Microscopy (PCM) is an important tool for the long term study of living cells. Unlike fluorescence methods which suffer from photobleaching of fluorophore or dye molecules, PCM image contrast is generated by the natural variations in optical index of refraction. Unfortunately, the same physical principles which allow for these studies give rise to complex artifacts in the raw PCM imagery. Of particular interest in this paper are neuron images where these image imperfections manifest in very different ways for the two structures of specific interest: cell bodies (somas) and dendrites. To address these challenges, we introduce a novel parametric image model using the level set framework and an associated variational approach which simultaneously restores and segments this class of images. Using this technique as the basis for an automated image analysis pipeline, results for both the synthetic and real images validate and demonstrate the advantages of our approach.

  1. CLG for Automatic Image Segmentation

    OpenAIRE

    Christo Ananth; S.Santhana Priya; S.Manisha; T.Ezhil Jothi; M.S.Ramasubhaeswari

    2017-01-01

    This paper proposes an automatic segmentation method which effectively combines Active Contour Model, Live Wire method and Graph Cut approach (CLG). The aim of Live wire method is to provide control to the user on segmentation process during execution. Active Contour Model provides a statistical model of object shape and appearance to a new image which are built during a training phase. In the graph cut technique, each pixel is represented as a node and the distance between those nodes is rep...

  2. Comparative Analysis between LDR and HDR Images for Automatic Fruit Recognition and Counting

    Directory of Open Access Journals (Sweden)

    Tatiana M. Pinho

    2017-01-01

    Full Text Available Precision agriculture is gaining an increasing interest in the current farming paradigm. This new production concept relies on the use of information technology (IT to provide a control and supervising structure that can lead to better management policies. In this framework, imaging techniques that provide visual information over the farming area play an important role in production status monitoring. As such, accurate representation of the gathered production images is a major concern, especially if those images are used in detection and classification tasks. Real scenes, observed in natural environment, present high dynamic ranges that cannot be represented by the common LDR (Low Dynamic Range devices. However, this issue can be handled by High Dynamic Range (HDR images since they have the ability to store luminance information similarly to the human visual system. In order to prove their advantage in image processing, a comparative analysis between LDR and HDR images, for fruits detection and counting, was carried out. The obtained results show that the use of HDR images improves the detection performance to more than 30% when compared to LDR.

  3. Interconnecting smartphone, image analysis server, and case report forms in clinical trials for automatic skin lesion tracking in clinical trials

    Science.gov (United States)

    Haak, Daniel; Doma, Aliaa; Gombert, Alexander; Deserno, Thomas M.

    2016-03-01

    Today, subject's medical data in controlled clinical trials is captured digitally in electronic case report forms (eCRFs). However, eCRFs only insufficiently support integration of subject's image data, although medical imaging is looming large in studies today. For bed-side image integration, we present a mobile application (App) that utilizes the smartphone-integrated camera. To ensure high image quality with this inexpensive consumer hardware, color reference cards are placed in the camera's field of view next to the lesion. The cards are used for automatic calibration of geometry, color, and contrast. In addition, a personalized code is read from the cards that allows subject identification. For data integration, the App is connected to an communication and image analysis server that also holds the code-study-subject relation. In a second system interconnection, web services are used to connect the smartphone with OpenClinica, an open-source, Food and Drug Administration (FDA)-approved electronic data capture (EDC) system in clinical trials. Once the photographs have been securely stored on the server, they are released automatically from the mobile device. The workflow of the system is demonstrated by an ongoing clinical trial, in which photographic documentation is frequently performed to measure the effect of wound incision management systems. All 205 images, which have been collected in the study so far, have been correctly identified and successfully integrated into the corresponding subject's eCRF. Using this system, manual steps for the study personnel are reduced, and, therefore, errors, latency and costs decreased. Our approach also increases data security and privacy.

  4. SU-C-201-04: Quantification of Perfusion Heterogeneity Based On Texture Analysis for Fully Automatic Detection of Ischemic Deficits From Myocardial Perfusion Imaging

    International Nuclear Information System (INIS)

    Fang, Y; Huang, H; Su, T

    2015-01-01

    Purpose: Texture-based quantification of image heterogeneity has been a popular topic for imaging studies in recent years. As previous studies mainly focus on oncological applications, we report our recent efforts of applying such techniques on cardiac perfusion imaging. A fully automated procedure has been developed to perform texture analysis for measuring the image heterogeneity. Clinical data were used to evaluate the preliminary performance of such methods. Methods: Myocardial perfusion images of Thallium-201 scans were collected from 293 patients with suspected coronary artery disease. Each subject underwent a Tl-201 scan and a percutaneous coronary intervention (PCI) within three months. The PCI Result was used as the gold standard of coronary ischemia of more than 70% stenosis. Each Tl-201 scan was spatially normalized to an image template for fully automatic segmentation of the LV. The segmented voxel intensities were then carried into the texture analysis with our open-source software Chang Gung Image Texture Analysis toolbox (CGITA). To evaluate the clinical performance of the image heterogeneity for detecting the coronary stenosis, receiver operating characteristic (ROC) analysis was used to compute the overall accuracy, sensitivity and specificity as well as the area under curve (AUC). Those indices were compared to those obtained from the commercially available semi-automatic software QPS. Results: With the fully automatic procedure to quantify heterogeneity from Tl-201 scans, we were able to achieve a good discrimination with good accuracy (74%), sensitivity (73%), specificity (77%) and AUC of 0.82. Such performance is similar to those obtained from the semi-automatic QPS software that gives a sensitivity of 71% and specificity of 77%. Conclusion: Based on fully automatic procedures of data processing, our preliminary data indicate that the image heterogeneity of myocardial perfusion imaging can provide useful information for automatic determination

  5. System for the automatic analysis of defects in X-ray imaging

    International Nuclear Information System (INIS)

    Favier, C.; Thomas, G.; Brebant, C.; Mogavero, R.

    1984-05-01

    A radiological device was developed to obtain direct digitized views. A set of algorithms has been developed and demonstrated for the automatic evaluation of weldings. Some results concerning electronuclear fuel pin weldings are presented [fr

  6. Automatic image processing as a means of safeguarding nuclear material

    International Nuclear Information System (INIS)

    Kahnmeyer, W.; Willuhn, K.; Uebel, W.

    1985-01-01

    Problems involved in computerized analysis of pictures taken by automatic film or video cameras in the context of international safeguards implementation are described. They include technical ones as well as the need to establish objective criteria for assessing image information. In the near future automatic image processing systems will be useful in verifying the identity and integrity of IAEA seals. (author)

  7. Automatic Contour Extraction from 2D Image

    Directory of Open Access Journals (Sweden)

    Panagiotis GIOANNIS

    2011-03-01

    Full Text Available Aim: To develop a method for automatic contour extraction from a 2D image. Material and Method: The method is divided in two basic parts where the user initially chooses the starting point and the threshold. Finally the method is applied to computed tomography of bone images. Results: An interesting method is developed which can lead to a successful boundary extraction of 2D images. Specifically data extracted from a computed tomography images can be used for 2D bone reconstruction. Conclusions: We believe that such an algorithm or part of it can be applied on several other applications for shape feature extraction in medical image analysis and generally at computer graphics.

  8. Automatic Cobb Angle Determination From Radiographic Images

    NARCIS (Netherlands)

    Sardjono, Tri Arief; Wilkinson, Michael H. F.; Veldhuizen, Albert G.; van Ooijen, Peter M. A.; Purnama, Ketut E.; Verkerke, Gijsbertus J.

    2013-01-01

    Study Design. Automatic measurement of Cobb angle in patients with scoliosis. Objective. To test the accuracy of an automatic Cobb angle determination method from frontal radiographical images. Summary of Background Data. Thirty-six frontal radiographical images of patients with scoliosis. Methods.

  9. Wavelet-Based Bayesian Methods for Image Analysis and Automatic Target Recognition

    National Research Council Canada - National Science Library

    Nowak, Robert

    2001-01-01

    .... We have developed two new techniques. First, we have develop a wavelet-based approach to image restoration and deconvolution problems using Bayesian image models and an alternating-maximation method...

  10. Operational Automatic Remote Sensing Image Understanding Systems: Beyond Geographic Object-Based and Object-Oriented Image Analysis (GEOBIA/GEOOIA. Part 1: Introduction

    Directory of Open Access Journals (Sweden)

    Andrea Baraldi

    2012-09-01

    Full Text Available According to existing literature and despite their commercial success, state-of-the-art two-stage non-iterative geographic object-based image analysis (GEOBIA systems and three-stage iterative geographic object-oriented image analysis (GEOOIA systems, where GEOOIA/GEOBIA, remain affected by a lack of productivity, general consensus and research. To outperform the degree of automation, accuracy, efficiency, robustness, scalability and timeliness of existing GEOBIA/GEOOIA systems in compliance with the Quality Assurance Framework for Earth Observation (QA4EO guidelines, this methodological work is split into two parts. The present first paper provides a multi-disciplinary Strengths, Weaknesses, Opportunities and Threats (SWOT analysis of the GEOBIA/GEOOIA approaches that augments similar analyses proposed in recent years. In line with constraints stemming from human vision, this SWOT analysis promotes a shift of learning paradigm in the pre-attentive vision first stage of a remote sensing (RS image understanding system (RS-IUS, from sub-symbolic statistical model-based (inductive image segmentation to symbolic physical model-based (deductive image preliminary classification. Hence, a symbolic deductive pre-attentive vision first stage accomplishes image sub-symbolic segmentation and image symbolic pre-classification simultaneously. In the second part of this work a novel hybrid (combined deductive and inductive RS-IUS architecture featuring a symbolic deductive pre-attentive vision first stage is proposed and discussed in terms of: (a computational theory (system design; (b information/knowledge representation; (c algorithm design; and (d implementation. As proof-of-concept of symbolic physical model-based pre-attentive vision first stage, the spectral knowledge-based, operational, near real-time Satellite Image Automatic Mapper™ (SIAM™ is selected from existing literature. To the best of these authors’ knowledge, this is the first time a

  11. Quantitative assessment of intermetallic phase precipitation in a super duplex stainless steel weld metal using automatic image analysis

    Energy Technology Data Exchange (ETDEWEB)

    Gregori, A. [AB Sandvik Steel, Sandviken (Sweden). R and D Centre; Nilsson, J.-O. [AB Sandvik Steel, R and D Centre, Sandviken (Sweden); Bonollo, F. [Univ. di Padova, DTGSI, Vicenza (Italy)

    1999-07-01

    The microstructure of weld metal of the type 25%Cr-10%Ni-4%Mo-0.28%N in both as-welded and isothermally heat treated (temperature range: 700-1050 C: time range: 10s-72h) conditions has been investigated. Multipass welding was performed in Ar+2%N{sub 2} atmosphere using GTAW. By means of the electron diffraction technique. {sigma}-phase and {chi}-phase were detected and investigated. {chi}-phase precipitated more readily than {sigma}-phase and was found to be a precursor to {sigma}-phase by providing suitable nucleation sites. Quantitative image analysis of ferrite and intermetallic phases was performed as well as manual point counting (ISO 9042). Automatic image analysis was found to be more accurate. The results were used to assess the TTT-diagram with respect to intermetallic phase formation. On the basis of these results a CCT-diagram was computed, considering the intermetallic phase formation described by an Avrami type equation and adopting the additivity rule. (orig.)

  12. Automatic analysis of online image data for law enforcement agencies by concept detection and instance search

    Science.gov (United States)

    de Boer, Maaike H. T.; Bouma, Henri; Kruithof, Maarten C.; ter Haar, Frank B.; Fischer, Noëlle M.; Hagendoorn, Laurens K.; Joosten, Bart; Raaijmakers, Stephan

    2017-10-01

    The information available on-line and off-line, from open as well as from private sources, is growing at an exponential rate and places an increasing demand on the limited resources of Law Enforcement Agencies (LEAs). The absence of appropriate tools and techniques to collect, process, and analyze the volumes of complex and heterogeneous data has created a severe information overload. If a solution is not found, the impact on law enforcement will be dramatic, e.g. because important evidence is missed or the investigation time is too long. Furthermore, there is an uneven level of capabilities to deal with the large volumes of complex and heterogeneous data that come from multiple open and private sources at national level across the EU, which hinders cooperation and information sharing. Consequently, there is a pertinent need to develop tools, systems and processes which expedite online investigations. In this paper, we describe a suite of analysis tools to identify and localize generic concepts, instances of objects and logos in images, which constitutes a significant portion of everyday law enforcement data. We describe how incremental learning based on only a few examples and large-scale indexing are addressed in both concept detection and instance search. Our search technology allows querying of the database by visual examples and by keywords. Our tools are packaged in a Docker container to guarantee easy deployment on a system and our tools exploit possibilities provided by open source toolboxes, contributing to the technical autonomy of LEAs.

  13. TU-F-17A-01: BEST IN PHYSICS (JOINT IMAGING-THERAPY) - An Automatic Toolkit for Efficient and Robust Analysis of 4D Respiratory Motion

    International Nuclear Information System (INIS)

    Wei, J; Yuan, A; Li, G

    2014-01-01

    Purpose: To provide an automatic image analysis toolkit to process thoracic 4-dimensional computed tomography (4DCT) and extract patient-specific motion information to facilitate investigational or clinical use of 4DCT. Methods: We developed an automatic toolkit in MATLAB to overcome the extra workload from the time dimension in 4DCT. This toolkit employs image/signal processing, computer vision, and machine learning methods to visualize, segment, register, and characterize lung 4DCT automatically or interactively. A fully-automated 3D lung segmentation algorithm was designed and 4D lung segmentation was achieved in batch mode. Voxel counting was used to calculate volume variations of the torso, lung and its air component, and local volume changes at the diaphragm and chest wall to characterize breathing pattern. Segmented lung volumes in 12 patients are compared with those from a treatment planning system (TPS). Voxel conversion was introduced from CT# to other physical parameters, such as gravity-induced pressure, to create a secondary 4D image. A demon algorithm was applied in deformable image registration and motion trajectories were extracted automatically. Calculated motion parameters were plotted with various templates. Machine learning algorithms, such as Naive Bayes and random forests, were implemented to study respiratory motion. This toolkit is complementary to and will be integrated with the Computational Environment for Radiotherapy Research (CERR). Results: The automatic 4D image/data processing toolkit provides a platform for analysis of 4D images and datasets. It processes 4D data automatically in batch mode and provides interactive visual verification for manual adjustments. The discrepancy in lung volume calculation between this and the TPS is <±2% and the time saving is by 1–2 orders of magnitude. Conclusion: A framework of 4D toolkit has been developed to analyze thoracic 4DCT automatically or interactively, facilitating both investigational

  14. TU-F-17A-01: BEST IN PHYSICS (JOINT IMAGING-THERAPY) - An Automatic Toolkit for Efficient and Robust Analysis of 4D Respiratory Motion

    Energy Technology Data Exchange (ETDEWEB)

    Wei, J [City College of New York, New York, NY (United States); Yuan, A; Li, G [Memorial Sloan Kettering Cancer Center, New York, NY (United States)

    2014-06-15

    Purpose: To provide an automatic image analysis toolkit to process thoracic 4-dimensional computed tomography (4DCT) and extract patient-specific motion information to facilitate investigational or clinical use of 4DCT. Methods: We developed an automatic toolkit in MATLAB to overcome the extra workload from the time dimension in 4DCT. This toolkit employs image/signal processing, computer vision, and machine learning methods to visualize, segment, register, and characterize lung 4DCT automatically or interactively. A fully-automated 3D lung segmentation algorithm was designed and 4D lung segmentation was achieved in batch mode. Voxel counting was used to calculate volume variations of the torso, lung and its air component, and local volume changes at the diaphragm and chest wall to characterize breathing pattern. Segmented lung volumes in 12 patients are compared with those from a treatment planning system (TPS). Voxel conversion was introduced from CT# to other physical parameters, such as gravity-induced pressure, to create a secondary 4D image. A demon algorithm was applied in deformable image registration and motion trajectories were extracted automatically. Calculated motion parameters were plotted with various templates. Machine learning algorithms, such as Naive Bayes and random forests, were implemented to study respiratory motion. This toolkit is complementary to and will be integrated with the Computational Environment for Radiotherapy Research (CERR). Results: The automatic 4D image/data processing toolkit provides a platform for analysis of 4D images and datasets. It processes 4D data automatically in batch mode and provides interactive visual verification for manual adjustments. The discrepancy in lung volume calculation between this and the TPS is <±2% and the time saving is by 1–2 orders of magnitude. Conclusion: A framework of 4D toolkit has been developed to analyze thoracic 4DCT automatically or interactively, facilitating both investigational

  15. Automatic Complexity Analysis

    DEFF Research Database (Denmark)

    Rosendahl, Mads

    1989-01-01

    One way to analyse programs is to to derive expressions for their computational behaviour. A time bound function (or worst-case complexity) gives an upper bound for the computation time as a function of the size of input. We describe a system to derive such time bounds automatically using abstract...

  16. Automatic analysis of ultrasonic data

    International Nuclear Information System (INIS)

    Horteur, P.; Colin, J.; Benoist, P.; Bonis, M.; Paradis, L.

    1986-10-01

    This paper describes an automatic and self-contained data processing system, transportable on site, able to perform images such as ''A. Scan'', ''B. Scan'', ... to present very quickly the results of the control. It can be used in the case of pressure vessel inspection [fr

  17. Automatic extraction of left ventricle in SPECT myocardial perfusion imaging

    International Nuclear Information System (INIS)

    Liu Li; Zhao Shujun; Yao Zhiming; Wang Daoyu

    1999-01-01

    An automatic method of extracting left ventricle from SPECT myocardial perfusion data was introduced. This method was based on the least square analysis of the positions of all short-axis slices pixels from the half sphere-cylinder myocardial model, and used a iterative reconstruction technique to automatically cut off the non-left ventricular tissue from the perfusion images. Thereby, this technique provided the bases for further quantitative analysis

  18. AUTOMATIC APPROACH TO VHR SATELLITE IMAGE CLASSIFICATION

    Directory of Open Access Journals (Sweden)

    P. Kupidura

    2016-06-01

    Full Text Available In this paper, we present a proposition of a fully automatic classification of VHR satellite images. Unlike the most widespread approaches: supervised classification, which requires prior defining of class signatures, or unsupervised classification, which must be followed by an interpretation of its results, the proposed method requires no human intervention except for the setting of the initial parameters. The presented approach bases on both spectral and textural analysis of the image and consists of 3 steps. The first step, the analysis of spectral data, relies on NDVI values. Its purpose is to distinguish between basic classes, such as water, vegetation and non-vegetation, which all differ significantly spectrally, thus they can be easily extracted basing on spectral analysis. The second step relies on granulometric maps. These are the product of local granulometric analysis of an image and present information on the texture of each pixel neighbourhood, depending on the texture grain. The purpose of texture analysis is to distinguish between different classes, spectrally similar, but yet of different texture, e.g. bare soil from a built-up area, or low vegetation from a wooded area. Due to the use of granulometric analysis, based on mathematical morphology opening and closing, the results are resistant to the border effect (qualifying borders of objects in an image as spaces of high texture, which affect other methods of texture analysis like GLCM statistics or fractal analysis. Therefore, the effectiveness of the analysis is relatively high. Several indices based on values of different granulometric maps have been developed to simplify the extraction of classes of different texture. The third and final step of the process relies on a vegetation index, based on near infrared and blue bands. Its purpose is to correct partially misclassified pixels. All the indices used in the classification model developed relate to reflectance values, so the

  19. Automatic Hierarchical Color Image Classification

    Directory of Open Access Journals (Sweden)

    Jing Huang

    2003-02-01

    Full Text Available Organizing images into semantic categories can be extremely useful for content-based image retrieval and image annotation. Grouping images into semantic classes is a difficult problem, however. Image classification attempts to solve this hard problem by using low-level image features. In this paper, we propose a method for hierarchical classification of images via supervised learning. This scheme relies on using a good low-level feature and subsequently performing feature-space reconfiguration using singular value decomposition to reduce noise and dimensionality. We use the training data to obtain a hierarchical classification tree that can be used to categorize new images. Our experimental results suggest that this scheme not only performs better than standard nearest-neighbor techniques, but also has both storage and computational advantages.

  20. Automatic emotional expression analysis from eye area

    Science.gov (United States)

    Akkoç, Betül; Arslan, Ahmet

    2015-02-01

    Eyes play an important role in expressing emotions in nonverbal communication. In the present study, emotional expression classification was performed based on the features that were automatically extracted from the eye area. Fırst, the face area and the eye area were automatically extracted from the captured image. Afterwards, the parameters to be used for the analysis through discrete wavelet transformation were obtained from the eye area. Using these parameters, emotional expression analysis was performed through artificial intelligence techniques. As the result of the experimental studies, 6 universal emotions consisting of expressions of happiness, sadness, surprise, disgust, anger and fear were classified at a success rate of 84% using artificial neural networks.

  1. Automatic system for detecting pornographic images

    Science.gov (United States)

    Ho, Kevin I. C.; Chen, Tung-Shou; Ho, Jun-Der

    2002-09-01

    Due to the dramatic growth of network and multimedia technology, people can more easily get variant information by using Internet. Unfortunately, it also makes the diffusion of illegal and harmful content much easier. So, it becomes an important topic for the Internet society to protect and safeguard Internet users from these content that may be encountered while surfing on the Net, especially children. Among these content, porno graphs cause more serious harm. Therefore, in this study, we propose an automatic system to detect still colour porno graphs. Starting from this result, we plan to develop an automatic system to search porno graphs or to filter porno graphs. Almost all the porno graphs possess one common characteristic that is the ratio of the size of skin region and non-skin region is high. Based on this characteristic, our system first converts the colour space from RGB colour space to HSV colour space so as to segment all the possible skin-colour regions from scene background. We also apply the texture analysis on the selected skin-colour regions to separate the skin regions from non-skin regions. Then, we try to group the adjacent pixels located in skin regions. If the ratio is over a given threshold, we can tell if the given image is a possible porno graph. Based on our experiment, less than 10% of non-porno graphs are classified as pornography, and over 80% of the most harmful porno graphs are classified correctly.

  2. Automatic extraction analysis of the anatomical functional area for normal brain 18F-FDG PET imaging

    International Nuclear Information System (INIS)

    Guo Wanhua; Jiang Xufeng; Zhang Liying; Lu Zhongwei; Li Peiyong; Zhu Chengmo; Zhang Jiange; Pan Jiapu

    2003-01-01

    Using self-designed automatic extraction software of brain functional area, the grey scale distribution of 18 F-FDG imaging and the relationship between the 18 F-FDG accumulation of brain anatomic function area and the 18 F-FDG injected dose, the level of glucose, the age, etc., were studied. According to the Talairach coordinate system, after rotation, drift and plastic deformation, the 18 F-FDG PET imaging was registered into the Talairach coordinate atlas, and then the average gray value scale ratios between individual brain anatomic functional area and whole brain area was calculated. Further more the statistics of the relationship between the 18 F-FDG accumulation of every brain anatomic function area and the 18 F-FDG injected dose, the level of glucose and the age were tested by using multiple stepwise regression model. After images' registration, smoothing and extraction, main cerebral cortex of the 18 F-FDG PET brain imaging can be successfully localized and extracted, such as frontal lobe, parietal lobe, occipital lobe, temporal lobe, cerebellum, brain ventricle, thalamus and hippocampus. The average ratios to the inner reference of every brain anatomic functional area were 1.01 ± 0.15. By multiple stepwise regression with the exception of thalamus and hippocampus, the grey scale of all the brain functional area was negatively correlated to the ages, but with no correlation to blood sugar and dose in all areas. To the 18 F-FDG PET imaging, the brain functional area extraction program could automatically delineate most of the cerebral cortical area, and also successfully reflect the brain blood and metabolic study, but extraction of the more detailed area needs further investigation

  3. Some results of automatic processing of images

    International Nuclear Information System (INIS)

    Golenishchev, I.A.; Gracheva, T.N.; Khardikov, S.V.

    1975-01-01

    The problems of automatic deciphering of the radiographic picture the purpose of which is making a conclusion concerning the quality of the inspected product on the basis of the product defect images in the picture are considered. The methods of defect image recognition are listed, and the algorithms and the class features of defects are described. The results of deciphering of a small radiographic picture by means of the ''Minsk-22'' computer are presented. It is established that the sensitivity of the method of the automatic deciphering is close to that obtained for visual deciphering

  4. Automatic measurement of images on astrometric plates

    Science.gov (United States)

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

    1994-04-01

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

  5. Automatic terrain modeling using transfinite element analysis

    KAUST Repository

    Collier, Nathan; Calo, Victor M.

    2010-01-01

    An automatic procedure for modeling terrain is developed based on L2 projection-based interpolation of discrete terrain data onto transfinite function spaces. The function space is refined automatically by the use of image processing techniques

  6. Automatic detection of blurred images in UAV image sets

    Science.gov (United States)

    Sieberth, Till; Wackrow, Rene; Chandler, Jim H.

    2016-12-01

    Unmanned aerial vehicles (UAV) have become an interesting and active research topic for photogrammetry. Current research is based on images acquired by an UAV, which have a high ground resolution and good spectral and radiometrical resolution, due to the low flight altitudes combined with a high resolution camera. UAV image flights are also cost effective and have become attractive for many applications including, change detection in small scale areas. One of the main problems preventing full automation of data processing of UAV imagery is the degradation effect of blur caused by camera movement during image acquisition. This can be caused by the normal flight movement of the UAV as well as strong winds, turbulence or sudden operator inputs. This blur disturbs the visual analysis and interpretation of the data, causes errors and can degrade the accuracy in automatic photogrammetric processing algorithms. The detection and removal of these images is currently achieved manually, which is both time consuming and prone to error, particularly for large image-sets. To increase the quality of data processing an automated process is necessary, which must be both reliable and quick. This paper describes the development of an automatic filtering process, which is based upon the quantification of blur in an image. Images with known blur are processed digitally to determine a quantifiable measure of image blur. The algorithm is required to process UAV images fast and reliably to relieve the operator from detecting blurred images manually. The newly developed method makes it possible to detect blur caused by linear camera displacement and is based on human detection of blur. Humans detect blurred images best by comparing it to other images in order to establish whether an image is blurred or not. The developed algorithm simulates this procedure by creating an image for comparison using image processing. Creating internally a comparable image makes the method independent of

  7. Automatic Segmentation of Dermoscopic Images by Iterative Classification

    Directory of Open Access Journals (Sweden)

    Maciel Zortea

    2011-01-01

    Full Text Available Accurate detection of the borders of skin lesions is a vital first step for computer aided diagnostic systems. This paper presents a novel automatic approach to segmentation of skin lesions that is particularly suitable for analysis of dermoscopic images. Assumptions about the image acquisition, in particular, the approximate location and color, are used to derive an automatic rule to select small seed regions, likely to correspond to samples of skin and the lesion of interest. The seed regions are used as initial training samples, and the lesion segmentation problem is treated as binary classification problem. An iterative hybrid classification strategy, based on a weighted combination of estimated posteriors of a linear and quadratic classifier, is used to update both the automatically selected training samples and the segmentation, increasing reliability and final accuracy, especially for those challenging images, where the contrast between the background skin and lesion is low.

  8. Semi-automatic image analysis methodology for the segmentation of bubbles and drops in complex dispersions occurring in bioreactors

    Science.gov (United States)

    Taboada, B.; Vega-Alvarado, L.; Córdova-Aguilar, M. S.; Galindo, E.; Corkidi, G.

    2006-09-01

    Characterization of multiphase systems occurring in fermentation processes is a time-consuming and tedious process when manual methods are used. This work describes a new semi-automatic methodology for the on-line assessment of diameters of oil drops and air bubbles occurring in a complex simulated fermentation broth. High-quality digital images were obtained from the interior of a mechanically stirred tank. These images were pre-processed to find segments of edges belonging to the objects of interest. The contours of air bubbles and oil drops were then reconstructed using an improved Hough transform algorithm which was tested in two, three and four-phase simulated fermentation model systems. The results were compared against those obtained manually by a trained observer, showing no significant statistical differences. The method was able to reduce the total processing time for the measurements of bubbles and drops in different systems by 21-50% and the manual intervention time for the segmentation procedure by 80-100%.

  9. Contribution to automatic image recognition. Application to analysis of plain scenes of overlapping parts in robot technology

    International Nuclear Information System (INIS)

    Tan, Shengbiao

    1987-01-01

    A method for object modeling and overlapped object automatic recognition is presented. Our work is composed of three essential parts: image processing, object modeling, and evaluation of the implementation of the stated concepts. In the first part, we present a method of edge encoding which is based on a re-sampling of the data encoded according to Freeman, this method generates an isotropic, homogenous and very precise representation. The second part relates to object modeling. This important step makes much easier the recognition work. The new method proposed characterizes a model with two groups of information: the description group containing the primitives, the discrimination group containing data packs, called 'transition vectors'. Based on this original method of information organization, a 'relative learning' is able to select, to ignore and to update the information concerning the objects already learned, according to the new information to be included into the data base. The recognition is a two-pass process: the first pass determines very efficiently the presence of objects by making use of each object's particularities, and this hypothesis is either confirmed or rejected by the following fine verification pass. The last part describes in detail the experimentation results. We demonstrate the robustness of the algorithms with images in both poor lighting and overlapping objects conditions. The system, named SOFIA, has been installed into an industrial vision system series and works in real time. (author) [fr

  10. Automatic segmentation of MR brain images with a convolutional neural network

    NARCIS (Netherlands)

    Moeskops, P.; Viergever, M.A.; Mendrik, A.M.; de Vries, L.S.; Benders, M.J.N.L.; Išgum, I.

    2016-01-01

    Automatic segmentation in MR brain images is important for quantitative analysis in large-scale studies with images acquired at all ages. This paper presents a method for the automatic segmentation of MR brain images into a number of tissue classes using a convolutional neural network. To ensure

  11. Image-based automatic recognition of larvae

    Science.gov (United States)

    Sang, Ru; Yu, Guiying; Fan, Weijun; Guo, Tiantai

    2010-08-01

    As the main objects, imagoes have been researched in quarantine pest recognition in these days. However, pests in their larval stage are latent, and the larvae spread abroad much easily with the circulation of agricultural and forest products. It is presented in this paper that, as the new research objects, larvae are recognized by means of machine vision, image processing and pattern recognition. More visional information is reserved and the recognition rate is improved as color image segmentation is applied to images of larvae. Along with the characteristics of affine invariance, perspective invariance and brightness invariance, scale invariant feature transform (SIFT) is adopted for the feature extraction. The neural network algorithm is utilized for pattern recognition, and the automatic identification of larvae images is successfully achieved with satisfactory results.

  12. Automatic Vessel Segmentation on Retinal Images

    Institute of Scientific and Technical Information of China (English)

    Chun-Yuan Yu; Chia-Jen Chang; Yen-Ju Yao; Shyr-Shen Yu

    2014-01-01

    Several features of retinal vessels can be used to monitor the progression of diseases. Changes in vascular structures, for example, vessel caliber, branching angle, and tortuosity, are portents of many diseases such as diabetic retinopathy and arterial hyper-tension. This paper proposes an automatic retinal vessel segmentation method based on morphological closing and multi-scale line detection. First, an illumination correction is performed on the green band retinal image. Next, the morphological closing and subtraction processing are applied to obtain the crude retinal vessel image. Then, the multi-scale line detection is used to fine the vessel image. Finally, the binary vasculature is extracted by the Otsu algorithm. In this paper, for improving the drawbacks of multi-scale line detection, only the line detectors at 4 scales are used. The experimental results show that the accuracy is 0.939 for DRIVE (digital retinal images for vessel extraction) retinal database, which is much better than other methods.

  13. Automatic caption generation for news images.

    Science.gov (United States)

    Feng, Yansong; Lapata, Mirella

    2013-04-01

    This paper is concerned with the task of automatically generating captions for images, which is important for many image-related applications. Examples include video and image retrieval as well as the development of tools that aid visually impaired individuals to access pictorial information. Our approach leverages the vast resource of pictures available on the web and the fact that many of them are captioned and colocated with thematically related documents. Our model learns to create captions from a database of news articles, the pictures embedded in them, and their captions, and consists of two stages. Content selection identifies what the image and accompanying article are about, whereas surface realization determines how to verbalize the chosen content. We approximate content selection with a probabilistic image annotation model that suggests keywords for an image. The model postulates that images and their textual descriptions are generated by a shared set of latent variables (topics) and is trained on a weakly labeled dataset (which treats the captions and associated news articles as image labels). Inspired by recent work in summarization, we propose extractive and abstractive surface realization models. Experimental results show that it is viable to generate captions that are pertinent to the specific content of an image and its associated article, while permitting creativity in the description. Indeed, the output of our abstractive model compares favorably to handwritten captions and is often superior to extractive methods.

  14. High-throughput image analysis of tumor spheroids: a user-friendly software application to measure the size of spheroids automatically and accurately.

    Science.gov (United States)

    Chen, Wenjin; Wong, Chung; Vosburgh, Evan; Levine, Arnold J; Foran, David J; Xu, Eugenia Y

    2014-07-08

    The increasing number of applications of three-dimensional (3D) tumor spheroids as an in vitro model for drug discovery requires their adaptation to large-scale screening formats in every step of a drug screen, including large-scale image analysis. Currently there is no ready-to-use and free image analysis software to meet this large-scale format. Most existing methods involve manually drawing the length and width of the imaged 3D spheroids, which is a tedious and time-consuming process. This study presents a high-throughput image analysis software application - SpheroidSizer, which measures the major and minor axial length of the imaged 3D tumor spheroids automatically and accurately; calculates the volume of each individual 3D tumor spheroid; then outputs the results in two different forms in spreadsheets for easy manipulations in the subsequent data analysis. The main advantage of this software is its powerful image analysis application that is adapted for large numbers of images. It provides high-throughput computation and quality-control workflow. The estimated time to process 1,000 images is about 15 min on a minimally configured laptop, or around 1 min on a multi-core performance workstation. The graphical user interface (GUI) is also designed for easy quality control, and users can manually override the computer results. The key method used in this software is adapted from the active contour algorithm, also known as Snakes, which is especially suitable for images with uneven illumination and noisy background that often plagues automated imaging processing in high-throughput screens. The complimentary "Manual Initialize" and "Hand Draw" tools provide the flexibility to SpheroidSizer in dealing with various types of spheroids and diverse quality images. This high-throughput image analysis software remarkably reduces labor and speeds up the analysis process. Implementing this software is beneficial for 3D tumor spheroids to become a routine in vitro model

  15. Automatic differentiation algorithms in model analysis

    NARCIS (Netherlands)

    Huiskes, M.J.

    2002-01-01

    Title: Automatic differentiation algorithms in model analysis
    Author: M.J. Huiskes
    Date: 19 March, 2002

    In this thesis automatic differentiation algorithms and derivative-based methods

  16. Development of automatic extraction of the corpus callosum from magnetic resonance imaging of the head and examination of the early dementia objective diagnostic technique in feature analysis

    International Nuclear Information System (INIS)

    Kodama, Naoki; Kaneko, Tomoyuki

    2005-01-01

    We examined the objective diagnosis of dementia based on changes in the corpus callosum. We examined midsagittal head MR images of 17 early dementia patients (2 men and 15 women; mean age, 77.2±3.3 years) and 18 healthy elderly controls (2 men and 16 women; mean age, 73.8±6.5 years), 35 subjects altogether. First, the corpus callosum was automatically extracted from the MR images. Next, early dementia was compared with the healthy elderly individuals using 5 features of the straight-line methods, 5 features of the Run-Length Matrix, and 6 features of the Co-occurrence Matrix from the corpus callosum. Automatic extraction of the corpus callosum showed an accuracy rate of 84.1±3.7%. A statistically significant difference was found in 6 of the 16 features between early dementia patients and healthy elderly controls. Discriminant analysis using the 6 features demonstrated a sensitivity of 88.2% and specificity of 77.8%, with an overall accuracy of 82.9%. These results indicate that feature analysis based on changes in the corpus callosum can be used as an objective diagnostic technique for early dementia. (author)

  17. Automatic extraction of corpus callosum from midsagittal head MR image and examination of Alzheimer-type dementia objective diagnostic system in feature analysis

    International Nuclear Information System (INIS)

    Kaneko, Tomoyuki; Kodama, Naoki; Kaeriyama, Tomoharu; Fukumoto, Ichiro

    2004-01-01

    We studied the objective diagnosis of Alzheimer-type dementia based on changes in the corpus callosum. We examined midsagittal head MR images of 40 Alzheimer-type dementia patients (15 men and 25 women; mean age, 75.4±5.5 years) and 31 healthy elderly persons (10 men and 21 women; mean age, 73.4±7.5 years), 71 subjects altogether. First, the corpus callosum was automatically extracted from midsagittal head MR images. Next, Alzheimer-type dementia was compared with the healthy elderly individuals using the features of shape factor and six features of Co-occurrence Matrix from the corpus callosum. Automatic extraction of the corpus callosum succeeded in 64 of 71 individuals, for an extraction rate of 90.1%. A statistically significant difference was found in 7 of the 9 features between Alzheimer-type dementia patients and the healthy elderly adults. Discriminant analysis using the 7 features demonstrated a sensitivity rate of 82.4%, specificity of 89.3%, and overall accuracy of 85.5%. These results indicated the possibility of an objective diagnostic system for Alzheimer-type dementia using feature analysis based on change in the corpus callosum. (author)

  18. Analysis of image factors of x-ray films : study for the intelligent replenishment system of automatic film processor

    Energy Technology Data Exchange (ETDEWEB)

    Park, Sung Tae; Yoon, Chong Hyun; Park, Kwang BO; Auh, Yong Ho; Lee, Hyoung Jin; In, Kyung Hwan; Kim, Keon Chung [Asan Medical Center, Ulsan Univ. College of Medicine, Ulsan (Korea, Republic of)

    1998-06-01

    We analyzed image factors to determine the characteristic factors that need for intelligent replenishment system of the auto film processor. We processed the serial 300 sheets of radiographic films of chest phantom without replenishment of developing and fixation replenisher. We took the digital data by using film digitizer which scanned the films and automatically summed up the pixel values of the films. We analyzed characteristic curves, average gradients and relative speeds of individual film using densitometer and step densitometry. We also evaluated the pH of developer, fixer, and washer fluid with digital pH meter. Fixer residual rate and washing effect were measured by densitometer using the reagent methods. There was no significant reduction of the digital density numbers of the serial films without replenishment of developer and fixer. The average gradients were gradually decreased by 0.02 and relative speeds were also gradually decreased by 6.96% relative to initial standard step-densitometric measurement. The pHs of developer and fixer were reflected the inactivation of each fluid. The fixer residual rates and washing effects after processing each 25 sheets of films were in the normal range. We suggest that the digital data are not reliable due to limitation of the hardware and software of the film digitizer. We conclude that average gradient and relative speed which mean the film's contrast and sensitivity respectively are reliable factors for determining the need for the replenishment of the auto film processor. We need more study of simpler equations and programming for more intelligent replenishment system of the auto film processor.

  19. Image simulation for automatic license plate recognition

    Science.gov (United States)

    Bala, Raja; Zhao, Yonghui; Burry, Aaron; Kozitsky, Vladimir; Fillion, Claude; Saunders, Craig; Rodríguez-Serrano, José

    2012-01-01

    Automatic license plate recognition (ALPR) is an important capability for traffic surveillance applications, including toll monitoring and detection of different types of traffic violations. ALPR is a multi-stage process comprising plate localization, character segmentation, optical character recognition (OCR), and identification of originating jurisdiction (i.e. state or province). Training of an ALPR system for a new jurisdiction typically involves gathering vast amounts of license plate images and associated ground truth data, followed by iterative tuning and optimization of the ALPR algorithms. The substantial time and effort required to train and optimize the ALPR system can result in excessive operational cost and overhead. In this paper we propose a framework to create an artificial set of license plate images for accelerated training and optimization of ALPR algorithms. The framework comprises two steps: the synthesis of license plate images according to the design and layout for a jurisdiction of interest; and the modeling of imaging transformations and distortions typically encountered in the image capture process. Distortion parameters are estimated by measurements of real plate images. The simulation methodology is successfully demonstrated for training of OCR.

  20. Semi-automatic measures of activity in selected south polar regions of Mars using morphological image analysis

    Science.gov (United States)

    Aye, Klaus-Michael; Portyankina, Ganna; Pommerol, Antoine; Thomas, Nicolas

    The High Resolution Imaging Science Experiment (HiRISE) onboard Mars Reconnaissance Orbiter (MRO) has been used to monitor the seasonal evolution of several regions at high southern latitudes. Of particular interest have been jet-like activities that may result from the process described by Kieffer (2007), involving translucent CO2 ice. These jets are assumed to create fan-shaped ground features, as studied e.g. in Hansen et.al. (2010) and Portyankina et.al. (2010). In Thomas et.al. (2009), a small region of interest (ROI) inside the south polar Inca City region (81° S, 296° E) was defined for which the seasonal change of the number of fans was determined. This ROI was chosen for its strong visual variability in ground features. The mostly manual counting work showed, that the number of apparent fans increases monotonously for a considerable amount of time from the beginning of the spring time observations at Ls of 178° until approx. 230° , following the increase of available solar energy for the aforementioned processes of the Kieffer model. This fact indicates that the number of visual fan features can be used as an activity measure for the seasonal evolution of this area, in addition to commonly used evolution studies of surface reflectance. Motivated by these results, we would like to determine the fan count evolution for more south polar areas like Ithaca, Manhattan, Giza and others. To increase the reproducibility of the results by avoiding potential variability in fan shape recognition by human eye and to increase the production efficiency, efforts are being undertaken to automise the fan counting procedure. The techniques used, cleanly separated in different stages of the procedure, the difficulties for each stage and an overview of the tools used at each step will be presented. After showing a proof of concept in Aye et.al. (2010), for a ROI that is comparable to the one previously used for manual counting in Thomas et.al. (2009), we now will show

  1. Portable and Automatic Moessbauer Analysis

    International Nuclear Information System (INIS)

    Souza, P. A. de; Garg, V. K.; Klingelhoefer, G.; Gellert, R.; Guetlich, P.

    2002-01-01

    A portable Moessbauer spectrometer, developed for extraterrestrial applications, opens up new industrial applications of MBS. But for industrial applications, an available tool for fast data analysis is also required, and it should be easy to handle. The analysis of Moessbauer spectra and their parameters is a barrier for the popularity of this wide-applicable spectroscopic technique in industry. Based on experience, the analysis of a Moessbauer spectrum is time-consuming and requires the dedication of a specialist. However, the analysis of Moessbauer spectra, from the fitting to the identification of the sample phases, can be faster using by genetic algorithms, fuzzy logic and artificial neural networks. Industrial applications are very specific ones and the data analysis can be performed using these algorithms. In combination with an automatic analysis, the Moessbauer spectrometer can be used as a probe instrument which covers the main industrial needs for an on-line monitoring of its products, processes and case studies. Some of these real industrial applications will be discussed.

  2. Upgrade of the automatic analysis system in the TJ-II Thomson Scattering diagnostic: New image recognition classifier and fault condition detection

    NARCIS (Netherlands)

    Makili, L.; Vega, J.; Dormido-Canto, S.; Pastor, I.; Pereira, A.; Farias, G.; Portas, A.; Perez-Risco, D.; Rodriguez-Fernandez, M. C.; Busch, P.

    2010-01-01

    An automatic image classification system based on support vector machines (SVM) has been in operation for years in the TJ-II Thomson Scattering diagnostic. It recognizes five different types of images: CCD camera background, measurement of stray light without plasma or in a collapsed discharge,

  3. Towards an automatic tool for resolution evaluation of mammographic images

    Energy Technology Data Exchange (ETDEWEB)

    De Oliveira, J. E. E. [FUMEC, Av. Alfonso Pena 3880, CEP 30130-009 Belo Horizonte - MG (Brazil); Nogueira, M. S., E-mail: juliae@fumec.br [Centro de Desenvolvimento da Tecnologia Nuclear / CNEN, Pte. Antonio Carlos 6627, 31270-901, Belo Horizonte - MG (Brazil)

    2014-08-15

    Medical images are important for diagnosis purposes as they are related to patients medical history and pathology. Breast cancer represents a leading cause of death among women worldwide, and its early detection is the most effective method of reducing mortality. In a way to identify small structures with low density differences, a high image quality is required with the use of low doses of radiation. The analysis of the quality of the obtained image from a mammogram is performed from an image of a simulated breast and this is a fundamental key point for a program of quality control of mammography equipment s. In a control program of mammographic equipment s, besides the analysis of the quality of mammographic images, each element of the chain which composes the formation of the image is also analyzed: X-rays equipment s, radiographic films, and operating conditions. This control allows that an effective and efficient exam can be provided to the population and is within the standards of quality required for the early detection of breast cancer. However, according to the State Program of Quality Control in Mammography of Minas Gerais, Brazil, only 40% of the mammographies have provided a simulated image with a minimum level of quality, thus reinforcing the need for monitoring the images. The reduction of the morbidity and mortality indexes, with optimization and assurance of access to diagnosis and breast cancer treatment in the state of Minas Gerais, Brazil, may be the result of a mammographic exam which has a final image with good quality and which automatic evaluation is not subjective. The reason is that one has to consider the hypothesis that humans are subjective when performing the image analysis and that the evaluation of the image can be executed by a computer with objectivity. In 2007, in order to maintain the standard quality needed to mammography, the State Health Secretariat of Minas Gerais, Brazil, established a Program of Monthly Monitoring the

  4. Towards an automatic tool for resolution evaluation of mammographic images

    International Nuclear Information System (INIS)

    De Oliveira, J. E. E.; Nogueira, M. S.

    2014-08-01

    Medical images are important for diagnosis purposes as they are related to patients medical history and pathology. Breast cancer represents a leading cause of death among women worldwide, and its early detection is the most effective method of reducing mortality. In a way to identify small structures with low density differences, a high image quality is required with the use of low doses of radiation. The analysis of the quality of the obtained image from a mammogram is performed from an image of a simulated breast and this is a fundamental key point for a program of quality control of mammography equipment s. In a control program of mammographic equipment s, besides the analysis of the quality of mammographic images, each element of the chain which composes the formation of the image is also analyzed: X-rays equipment s, radiographic films, and operating conditions. This control allows that an effective and efficient exam can be provided to the population and is within the standards of quality required for the early detection of breast cancer. However, according to the State Program of Quality Control in Mammography of Minas Gerais, Brazil, only 40% of the mammographies have provided a simulated image with a minimum level of quality, thus reinforcing the need for monitoring the images. The reduction of the morbidity and mortality indexes, with optimization and assurance of access to diagnosis and breast cancer treatment in the state of Minas Gerais, Brazil, may be the result of a mammographic exam which has a final image with good quality and which automatic evaluation is not subjective. The reason is that one has to consider the hypothesis that humans are subjective when performing the image analysis and that the evaluation of the image can be executed by a computer with objectivity. In 2007, in order to maintain the standard quality needed to mammography, the State Health Secretariat of Minas Gerais, Brazil, established a Program of Monthly Monitoring the

  5. Development of automatic surveillance of animal behaviour and welfare using image analysis and machine learned segmentation technique.

    Science.gov (United States)

    Nilsson, M; Herlin, A H; Ardö, H; Guzhva, O; Åström, K; Bergsten, C

    2015-11-01

    In this paper the feasibility to extract the proportion of pigs located in different areas of a pig pen by advanced image analysis technique is explored and discussed for possible applications. For example, pigs generally locate themselves in the wet dunging area at high ambient temperatures in order to avoid heat stress, as wetting the body surface is the major path to dissipate the heat by evaporation. Thus, the portion of pigs in the dunging area and resting area, respectively, could be used as an indicator of failure of controlling the climate in the pig environment as pigs are not supposed to rest in the dunging area. The computer vision methodology utilizes a learning based segmentation approach using several features extracted from the image. The learning based approach applied is based on extended state-of-the-art features in combination with a structured prediction framework based on a logistic regression solver using elastic net regularization. In addition, the method is able to produce a probability per pixel rather than form a hard decision. This overcomes some of the limitations found in a setup using grey-scale information only. The pig pen is a difficult imaging environment because of challenging lighting conditions like shadows, poor lighting and poor contrast between pig and background. In order to test practical conditions, a pen containing nine young pigs was filmed from a top view perspective by an Axis M3006 camera with a resolution of 640 × 480 in three, 10-min sessions under different lighting conditions. The results indicate that a learning based method improves, in comparison with greyscale methods, the possibility to reliable identify proportions of pigs in different areas of the pen. Pigs with a changed behaviour (location) in the pen may indicate changed climate conditions. Changed individual behaviour may also indicate inferior health or acute illness.

  6. Upgrade of the automatic analysis system in the TJ-II Thomson Scattering diagnostic: New image recognition classifier and fault condition detection

    International Nuclear Information System (INIS)

    Makili, L.; Vega, J.; Dormido-Canto, S.; Pastor, I.; Pereira, A.; Farias, G.; Portas, A.; Perez-Risco, D.; Rodriguez-Fernandez, M.C.; Busch, P.

    2010-01-01

    An automatic image classification system based on support vector machines (SVM) has been in operation for years in the TJ-II Thomson Scattering diagnostic. It recognizes five different types of images: CCD camera background, measurement of stray light without plasma or in a collapsed discharge, image during ECH phase, image during NBI phase and image after reaching the cut off density during ECH heating. Each kind of image implies the execution of different application software. Due to the fact that the recognition system is based on a learning system and major modifications have been carried out in both the diagnostic (optics) and TJ-II plasmas (injected power), the classifier model is no longer valid. A new SVM model has been developed with the current conditions. Also, specific error conditions in the data acquisition process can automatically be detected and managed now. The recovering process has been automated, thereby avoiding the loss of data in ensuing discharges.

  7. Upgrade of the automatic analysis system in the TJ-II Thomson Scattering diagnostic: New image recognition classifier and fault condition detection

    Energy Technology Data Exchange (ETDEWEB)

    Makili, L. [Dpto. Informatica y Automatica - UNED, Madrid (Spain); Vega, J. [Asociacion EURATOM/CIEMAT para Fusion, Madrid (Spain); Dormido-Canto, S., E-mail: sebas@dia.uned.e [Dpto. Informatica y Automatica - UNED, Madrid (Spain); Pastor, I.; Pereira, A. [Asociacion EURATOM/CIEMAT para Fusion, Madrid (Spain); Farias, G. [Dpto. Informatica y Automatica - UNED, Madrid (Spain); Portas, A.; Perez-Risco, D.; Rodriguez-Fernandez, M.C. [Asociacion EURATOM/CIEMAT para Fusion, Madrid (Spain); Busch, P. [FOM Institut voor PlasmaFysica Rijnhuizen, Nieuwegein (Netherlands)

    2010-07-15

    An automatic image classification system based on support vector machines (SVM) has been in operation for years in the TJ-II Thomson Scattering diagnostic. It recognizes five different types of images: CCD camera background, measurement of stray light without plasma or in a collapsed discharge, image during ECH phase, image during NBI phase and image after reaching the cut off density during ECH heating. Each kind of image implies the execution of different application software. Due to the fact that the recognition system is based on a learning system and major modifications have been carried out in both the diagnostic (optics) and TJ-II plasmas (injected power), the classifier model is no longer valid. A new SVM model has been developed with the current conditions. Also, specific error conditions in the data acquisition process can automatically be detected and managed now. The recovering process has been automated, thereby avoiding the loss of data in ensuing discharges.

  8. Automatic cloud coverage assessment of Formosat-2 image

    Science.gov (United States)

    Hsu, Kuo-Hsien

    2011-11-01

    Formosat-2 satellite equips with the high-spatial-resolution (2m ground sampling distance) remote sensing instrument. It has been being operated on the daily-revisiting mission orbit by National Space organization (NSPO) of Taiwan since May 21 2004. NSPO has also serving as one of the ground receiving stations for daily processing the received Formosat- 2 images. The current cloud coverage assessment of Formosat-2 image for NSPO Image Processing System generally consists of two major steps. Firstly, an un-supervised K-means method is used for automatically estimating the cloud statistic of Formosat-2 image. Secondly, manual estimation of cloud coverage from Formosat-2 image is processed by manual examination. Apparently, a more accurate Automatic Cloud Coverage Assessment (ACCA) method certainly increases the efficiency of processing step 2 with a good prediction of cloud statistic. In this paper, mainly based on the research results from Chang et al, Irish, and Gotoh, we propose a modified Formosat-2 ACCA method which considered pre-processing and post-processing analysis. For pre-processing analysis, cloud statistic is determined by using un-supervised K-means classification, Sobel's method, Otsu's method, non-cloudy pixels reexamination, and cross-band filter method. Box-Counting fractal method is considered as a post-processing tool to double check the results of pre-processing analysis for increasing the efficiency of manual examination.

  9. Automatic landslide detection from LiDAR DTM derivatives by geographic-object-based image analysis based on open-source software

    Science.gov (United States)

    Knevels, Raphael; Leopold, Philip; Petschko, Helene

    2017-04-01

    With high-resolution airborne Light Detection and Ranging (LiDAR) data more commonly available, many studies have been performed to facilitate the detailed information on the earth surface and to analyse its limitation. Specifically in the field of natural hazards, digital terrain models (DTM) have been used to map hazardous processes such as landslides mainly by visual interpretation of LiDAR DTM derivatives. However, new approaches are striving towards automatic detection of landslides to speed up the process of generating landslide inventories. These studies usually use a combination of optical imagery and terrain data, and are designed in commercial software packages such as ESRI ArcGIS, Definiens eCognition, or MathWorks MATLAB. The objective of this study was to investigate the potential of open-source software for automatic landslide detection based only on high-resolution LiDAR DTM derivatives in a study area within the federal state of Burgenland, Austria. The study area is very prone to landslides which have been mapped with different methodologies in recent years. The free development environment R was used to integrate open-source geographic information system (GIS) software, such as SAGA (System for Automated Geoscientific Analyses), GRASS (Geographic Resources Analysis Support System), or TauDEM (Terrain Analysis Using Digital Elevation Models). The implemented geographic-object-based image analysis (GEOBIA) consisted of (1) derivation of land surface parameters, such as slope, surface roughness, curvature, or flow direction, (2) finding optimal scale parameter by the use of an objective function, (3) multi-scale segmentation, (4) classification of landslide parts (main scarp, body, flanks) by k-mean thresholding, (5) assessment of the classification performance using a pre-existing landslide inventory, and (6) post-processing analysis for the further use in landslide inventories. The results of the developed open-source approach demonstrated good

  10. ARCOCT: Automatic detection of lumen border in intravascular OCT images.

    Science.gov (United States)

    Cheimariotis, Grigorios-Aris; Chatzizisis, Yiannis S; Koutkias, Vassilis G; Toutouzas, Konstantinos; Giannopoulos, Andreas; Riga, Maria; Chouvarda, Ioanna; Antoniadis, Antonios P; Doulaverakis, Charalambos; Tsamboulatidis, Ioannis; Kompatsiaris, Ioannis; Giannoglou, George D; Maglaveras, Nicos

    2017-11-01

    Intravascular optical coherence tomography (OCT) is an invaluable tool for the detection of pathological features on the arterial wall and the investigation of post-stenting complications. Computational lumen border detection in OCT images is highly advantageous, since it may support rapid morphometric analysis. However, automatic detection is very challenging, since OCT images typically include various artifacts that impact image clarity, including features such as side branches and intraluminal blood presence. This paper presents ARCOCT, a segmentation method for fully-automatic detection of lumen border in OCT images. ARCOCT relies on multiple, consecutive processing steps, accounting for image preparation, contour extraction and refinement. In particular, for contour extraction ARCOCT employs the transformation of OCT images based on physical characteristics such as reflectivity and absorption of the tissue and, for contour refinement, local regression using weighted linear least squares and a 2nd degree polynomial model is employed to achieve artifact and small-branch correction as well as smoothness of the artery mesh. Our major focus was to achieve accurate contour delineation in the various types of OCT images, i.e., even in challenging cases with branches and artifacts. ARCOCT has been assessed in a dataset of 1812 images (308 from stented and 1504 from native segments) obtained from 20 patients. ARCOCT was compared against ground-truth manual segmentation performed by experts on the basis of various geometric features (e.g. area, perimeter, radius, diameter, centroid, etc.) and closed contour matching indicators (the Dice index, the Hausdorff distance and the undirected average distance), using standard statistical analysis methods. The proposed method was proven very efficient and close to the ground-truth, exhibiting non statistically-significant differences for most of the examined metrics. ARCOCT allows accurate and fully-automated lumen border

  11. The proportionator: unbiased stereological estimation using biased automatic image analysis and non-uniform probability proportional to size sampling

    DEFF Research Database (Denmark)

    Gardi, Jonathan Eyal; Nyengaard, Jens Randel; Gundersen, Hans Jørgen Gottlieb

    2008-01-01

    examined, which in turn leads to any of the known stereological estimates, including size distributions and spatial distributions. The unbiasedness is not a function of the assumed relation between the weight and the structure, which is in practice always a biased relation from a stereological (integral......, the desired number of fields are sampled automatically with probability proportional to the weight and presented to the expert observer. Using any known stereological probe and estimator, the correct count in these fields leads to a simple, unbiased estimate of the total amount of structure in the sections...... geometric) point of view. The efficiency of the proportionator depends, however, directly on this relation to be positive. The sampling and estimation procedure is simulated in sections with characteristics and various kinds of noises in possibly realistic ranges. In all cases examined, the proportionator...

  12. Comparison of screen-film combinations: results of a contrast detail study and interactive image quality analysis. Pt. 2. Linear assessment of grey scale ranges with interactive (automatic) image analysis

    International Nuclear Information System (INIS)

    Stamm, G.; Eichbaum, G.; Hagemann, G.

    1997-01-01

    The following three screen-film combinations were compared: (a) A combination of anticross-over film and UV-light emitting screens, (b) a combination of blue-light emitting screens and film, and (c) a conventional green fluorescing screen-film combination. Radiographs of a specially designed plexiglass phantom (0.2x0.2x0.12 m 3 ) with bar patterns of lead and plaster and of air, respectively were obtained using the following parameters: 12 pulse generator, 0.6 mm focus size, 4.7 mm aluminium prefilter, a grid with 40 lines/cm (12:1) and a focus-detector distance of 1.15 m. Image analysis was performed using an IBAS system and a Zeiss Kontron computer. Display conditions were the following: Display distance 0.12 m, a vario film objective 35/70 (Zeiss), a video camera tube with a Pb0 photocathode, 625 lines (Siemens Heimann), an IBAS image matrix of 512x512 pixels with a resolution of 7 lines/mm, the projected matrix area was 5000 μm 2 . Grey scale ranges were measured on a line perpendicular to the grouped bar patterns. The difference between the maximum and minimum density value served as signal. The spatial resolution of the detector system was measured when the signal value was three times higher than the standard deviation of the means of multiple density measurements. The results showed considerable advantages of the two new screen-film combinations as compared to the conventional screen-film combination. The result was contradictory to the findings with pure visual assessment of thresholds (part I) that had found no differences. The authors concluded that (automatic) interactive image analysis algorithms serve as an objective measure and are specifically advantageous when small differences in image quality are to be evaluated. (orig.) [de

  13. Automatic terrain modeling using transfinite element analysis

    KAUST Repository

    Collier, Nathan

    2010-05-31

    An automatic procedure for modeling terrain is developed based on L2 projection-based interpolation of discrete terrain data onto transfinite function spaces. The function space is refined automatically by the use of image processing techniques to detect regions of high error and the flexibility of the transfinite interpolation to add degrees of freedom to these areas. Examples are shown of a section of the Palo Duro Canyon in northern Texas.

  14. Automatic Thermal Infrared Panoramic Imaging Sensor

    National Research Council Canada - National Science Library

    Gutin, Mikhail; Tsui, Eddy K; Gutin, Olga; Wang, Xu-Ming; Gutin, Alexey

    2006-01-01

    .... Automatic detection, location, and tracking of targets outside protected area ensures maximum protection and at the same time reduces the workload on personnel, increases reliability and confidence...

  15. Operational Automatic Remote Sensing Image Understanding Systems: Beyond Geographic Object-Based and Object-Oriented Image Analysis (GEOBIA/GEOOIA. Part 2: Novel system Architecture, Information/Knowledge Representation, Algorithm Design and Implementation

    Directory of Open Access Journals (Sweden)

    Luigi Boschetti

    2012-09-01

    Full Text Available According to literature and despite their commercial success, state-of-the-art two-stage non-iterative geographic object-based image analysis (GEOBIA systems and three-stage iterative geographic object-oriented image analysis (GEOOIA systems, where GEOOIA/GEOBIA, remain affected by a lack of productivity, general consensus and research. To outperform the Quality Indexes of Operativeness (OQIs of existing GEOBIA/GEOOIA systems in compliance with the Quality Assurance Framework for Earth Observation (QA4EO guidelines, this methodological work is split into two parts. Based on an original multi-disciplinary Strengths, Weaknesses, Opportunities and Threats (SWOT analysis of the GEOBIA/GEOOIA approaches, the first part of this work promotes a shift of learning paradigm in the pre-attentive vision first stage of a remote sensing (RS image understanding system (RS-IUS, from sub-symbolic statistical model-based (inductive image segmentation to symbolic physical model-based (deductive image preliminary classification capable of accomplishing image sub-symbolic segmentation and image symbolic pre-classification simultaneously. In the present second part of this work, a novel hybrid (combined deductive and inductive RS-IUS architecture featuring a symbolic deductive pre-attentive vision first stage is proposed and discussed in terms of: (a computational theory (system design, (b information/knowledge representation, (c algorithm design and (d implementation. As proof-of-concept of symbolic physical model-based pre-attentive vision first stage, the spectral knowledge-based, operational, near real-time, multi-sensor, multi-resolution, application-independent Satellite Image Automatic Mapper™ (SIAM™ is selected from existing literature. To the best of these authors’ knowledge, this is the first time a symbolic syntactic inference system, like SIAM™, is made available to the RS community for operational use in a RS-IUS pre-attentive vision first stage

  16. Towards lung EIT image segmentation: automatic classification of lung tissue state from analysis of EIT monitored recruitment manoeuvres

    International Nuclear Information System (INIS)

    Grychtol, Bartłomiej; Wolf, Gerhard K; Arnold, John H; Adler, Andy

    2010-01-01

    There is emerging evidence that the ventilation strategy used in acute lung injury (ALI) makes a significant difference in outcome and that an inappropriate ventilation strategy may produce ventilator-associated lung injury. Most harmful during mechanical ventilation are lung overdistension and lung collapse or atelectasis. Electrical impedance tomography (EIT) as a non-invasive imaging technology may be helpful to identify lung areas at risk. Currently, no automated method is routinely available to identify lung areas that are overdistended, collapsed or ventilated appropriately. We propose a fuzzy logic-based algorithm to analyse EIT images obtained during stepwise changes of mean airway pressures during mechanical ventilation. The algorithm is tested on data from two published studies of stepwise inflation–deflation manoeuvres in an animal model of ALI using conventional and high-frequency oscillatory ventilation. The timing of lung opening and collapsing on segmented images obtained using the algorithm during an inflation–deflation manoeuvre is in agreement with well-known effects of surfactant administration and changes in shunt fraction. While the performance of the algorithm has not been verified against a gold standard, we feel that it presents an important first step in tackling this challenging and important problem

  17. Towards lung EIT image segmentation: automatic classification of lung tissue state from analysis of EIT monitored recruitment manoeuvres.

    Science.gov (United States)

    Grychtol, Bartłomiej; Wolf, Gerhard K; Adler, Andy; Arnold, John H

    2010-08-01

    There is emerging evidence that the ventilation strategy used in acute lung injury (ALI) makes a significant difference in outcome and that an inappropriate ventilation strategy may produce ventilator-associated lung injury. Most harmful during mechanical ventilation are lung overdistension and lung collapse or atelectasis. Electrical impedance tomography (EIT) as a non-invasive imaging technology may be helpful to identify lung areas at risk. Currently, no automated method is routinely available to identify lung areas that are overdistended, collapsed or ventilated appropriately. We propose a fuzzy logic-based algorithm to analyse EIT images obtained during stepwise changes of mean airway pressures during mechanical ventilation. The algorithm is tested on data from two published studies of stepwise inflation-deflation manoeuvres in an animal model of ALI using conventional and high-frequency oscillatory ventilation. The timing of lung opening and collapsing on segmented images obtained using the algorithm during an inflation-deflation manoeuvre is in agreement with well-known effects of surfactant administration and changes in shunt fraction. While the performance of the algorithm has not been verified against a gold standard, we feel that it presents an important first step in tackling this challenging and important problem.

  18. Upgrade of the Automatic Analysis System in the TJ-II Thomson Scattering Diagnostic: New Image Recognition Classifier and Fault Condition Detection

    Energy Technology Data Exchange (ETDEWEB)

    Makili, L.; Dormido-Canto, S. [UNED, Madrid (Spain); Vega, J.; Pastor, I.; Pereira, A.; Portas, A.; Perez-Risco, D.; Rodriguez-Fernandez, M. [Association EuratomCIEMAT para Fusion, Madrid (Spain); Busch, P. [FOM Instituut voor PlasmaFysica Rijnhuizen, Nieuwegein (Netherlands)

    2009-07-01

    Full text of publication follows: An automatic image classification system has been in operation for years in the TJ-II Thomson diagnostic. It recognizes five different types of images: CCD camera background, measurement of stray light without plasma or in a collapsed discharge, image during ECH phase, image during NBI phase and image after reaching the cut o density during ECH heating. Each kind of image implies the execution of different application software. Therefore, the classification system was developed to launch the corresponding software in an automatic way. The method to recognize the several classes was based on a learning system, in particular Support Vector Machines (SVM). Since the first implementation of the classifier, a relevant improvement has been accomplished in the diagnostic: a new notch filter is in operation, having a larger stray-light rejection at the ruby wavelength than the previous filter. On the other hand, its location in the optical system has been modified. As a consequence, the stray light pattern in the CCD image is located in a different position. In addition to these transformations, the power of neutral beams injected in the TJ-II plasma has been increased about a factor of 2. Due to the fact that the recognition system is based on a learning system and major modifications have been carried out in both the diagnostic (optics) and TJ-II plasmas (injected power), the classifier model is no longer valid. The creation of a new model (also based on SVM) under the present conditions has been necessary. Finally, specific error conditions in the data acquisition process can automatically be detected now. The recovering process can be automated, thereby avoiding the loss of data in ensuing discharges. (authors)

  19. Automatic discrimination of fine roots in minirhizotron images.

    Science.gov (United States)

    Zeng, Guang; Birchfield, Stanley T; Wells, Christina E

    2008-01-01

    Minirhizotrons provide detailed information on the production, life history and mortality of fine roots. However, manual processing of minirhizotron images is time-consuming, limiting the number and size of experiments that can reasonably be analysed. Previously, an algorithm was developed to automatically detect and measure individual roots in minirhizotron images. Here, species-specific root classifiers were developed to discriminate detected roots from bright background artifacts. Classifiers were developed from training images of peach (Prunus persica), freeman maple (Acer x freemanii) and sweetbay magnolia (Magnolia virginiana) using the Adaboost algorithm. True- and false-positive rates for classifiers were estimated using receiver operating characteristic curves. Classifiers gave true positive rates of 89-94% and false positive rates of 3-7% when applied to nontraining images of the species for which they were developed. The application of a classifier trained on one species to images from another species resulted in little or no reduction in accuracy. These results suggest that a single root classifier can be used to distinguish roots from background objects across multiple minirhizotron experiments. By incorporating root detection and discrimination algorithms into an open-source minirhizotron image analysis application, many analysis tasks that are currently performed by hand can be automated.

  20. Research of x-ray automatic image mosaic method

    Science.gov (United States)

    Liu, Bin; Chen, Shunan; Guo, Lianpeng; Xu, Wanpeng

    2013-10-01

    Image mosaic has widely applications value in the fields of medical image analysis, and it is a technology that carries on the spatial matching to a series of image which are overlapped with each other, and finally builds a seamless and high quality image which has high resolution and big eyeshot. In this paper, the method of grayscale cutting pseudo-color enhancement was firstly used to complete the mapping transformation from gray to the pseudo-color, and to extract SIFT features from the images. And then by making use of a similar measure of NCC (normalized cross correlation - Normalized cross-correlation), the method of RANSAC (Random Sample Consensus) was used to exclude the pseudofeature points right in order to complete the exact match of feature points. Finally, seamless mosaic and color fusion were completed by using wavelet multi-decomposition. The experiment shows that the method we used can effectively improve the precision and automation of the medical image mosaic, and provide an effective technical approach for automatic medical image mosaic.

  1. Semi-automatic motion compensation of contrast-enhanced ultrasound images from abdominal organs for perfusion analysis

    Czech Academy of Sciences Publication Activity Database

    Schafer, S.; Nylund, K.; Saevik, F.; Engjom, T.; Mézl, M.; Jiřík, Radovan; Dimcevski, G.; Gilja, O.H.; Tönnies, K.

    2015-01-01

    Roč. 63, AUG 1 (2015), s. 229-237 ISSN 0010-4825 R&D Projects: GA ČR GAP102/12/2380 Institutional support: RVO:68081731 Keywords : ultrasonography * motion analysis * motion compensation * registration * CEUS * contrast-enhanced ultrasound * perfusion * perfusion modeling Subject RIV: FS - Medical Facilities ; Equipment Impact factor: 1.521, year: 2015

  2. Automatic imitation: A meta-analysis.

    Science.gov (United States)

    Cracco, Emiel; Bardi, Lara; Desmet, Charlotte; Genschow, Oliver; Rigoni, Davide; De Coster, Lize; Radkova, Ina; Deschrijver, Eliane; Brass, Marcel

    2018-05-01

    Automatic imitation is the finding that movement execution is facilitated by compatible and impeded by incompatible observed movements. In the past 15 years, automatic imitation has been studied to understand the relation between perception and action in social interaction. Although research on this topic started in cognitive science, interest quickly spread to related disciplines such as social psychology, clinical psychology, and neuroscience. However, important theoretical questions have remained unanswered. Therefore, in the present meta-analysis, we evaluated seven key questions on automatic imitation. The results, based on 161 studies containing 226 experiments, revealed an overall effect size of g z = 0.95, 95% CI [0.88, 1.02]. Moderator analyses identified automatic imitation as a flexible, largely automatic process that is driven by movement and effector compatibility, but is also influenced by spatial compatibility. Automatic imitation was found to be stronger for forced choice tasks than for simple response tasks, for human agents than for nonhuman agents, and for goalless actions than for goal-directed actions. However, it was not modulated by more subtle factors such as animacy beliefs, motion profiles, or visual perspective. Finally, there was no evidence for a relation between automatic imitation and either empathy or autism. Among other things, these findings point toward actor-imitator similarity as a crucial modulator of automatic imitation and challenge the view that imitative tendencies are an indicator of social functioning. The current meta-analysis has important theoretical implications and sheds light on longstanding controversies in the literature on automatic imitation and related domains. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

  3. Automatic Road Pavement Assessment with Image Processing: Review and Comparison

    Directory of Open Access Journals (Sweden)

    Sylvie Chambon

    2011-01-01

    Full Text Available In the field of noninvasive sensing techniques for civil infrastructures monitoring, this paper addresses the problem of crack detection, in the surface of the French national roads, by automatic analysis of optical images. The first contribution is a state of the art of the image-processing tools applied to civil engineering. The second contribution is about fine-defect detection in pavement surface. The approach is based on a multi-scale extraction and a Markovian segmentation. Third, an evaluation and comparison protocol which has been designed for evaluating this difficult task—the road pavement crack detection—is introduced. Finally, the proposed method is validated, analysed, and compared to a detection approach based on morphological tools.

  4. Markov random field based automatic image alignment for electron tomography.

    Science.gov (United States)

    Amat, Fernando; Moussavi, Farshid; Comolli, Luis R; Elidan, Gal; Downing, Kenneth H; Horowitz, Mark

    2008-03-01

    We present a method for automatic full-precision alignment of the images in a tomographic tilt series. Full-precision automatic alignment of cryo electron microscopy images has remained a difficult challenge to date, due to the limited electron dose and low image contrast. These facts lead to poor signal to noise ratio (SNR) in the images, which causes automatic feature trackers to generate errors, even with high contrast gold particles as fiducial features. To enable fully automatic alignment for full-precision reconstructions, we frame the problem probabilistically as finding the most likely particle tracks given a set of noisy images, using contextual information to make the solution more robust to the noise in each image. To solve this maximum likelihood problem, we use Markov Random Fields (MRF) to establish the correspondence of features in alignment and robust optimization for projection model estimation. The resulting algorithm, called Robust Alignment and Projection Estimation for Tomographic Reconstruction, or RAPTOR, has not needed any manual intervention for the difficult datasets we have tried, and has provided sub-pixel alignment that is as good as the manual approach by an expert user. We are able to automatically map complete and partial marker trajectories and thus obtain highly accurate image alignment. Our method has been applied to challenging cryo electron tomographic datasets with low SNR from intact bacterial cells, as well as several plastic section and X-ray datasets.

  5. Automatic Cell Segmentation in Fluorescence Images of Confluent Cell Monolayers Using Multi-object Geometric Deformable Model

    OpenAIRE

    Yang, Zhen; Bogovic, John A.; Carass, Aaron; Ye, Mao; Searson, Peter C.; Prince, Jerry L.

    2013-01-01

    With the rapid development of microscopy for cell imaging, there is a strong and growing demand for image analysis software to quantitatively study cell morphology. Automatic cell segmentation is an important step in image analysis. Despite substantial progress, there is still a need to improve the accuracy, efficiency, and adaptability to different cell morphologies. In this paper, we propose a fully automatic method for segmenting cells in fluorescence images of confluent cell monolayers. T...

  6. Image processing. A system for the automatic sorting of chromosomes

    International Nuclear Information System (INIS)

    Najai, Amor

    1977-01-01

    The present paper deals with two aspects of the system: - an automata (specialized hardware) dedicated to image processing. Images are digitized, divided into sub-units and computations are carried out on their main parameters. - A software for the automatic recognition and sorting of chromosomes is implemented on a Multi-20 minicomputer, connected to the automata. (author) [fr

  7. Automatic food detection in egocentric images using artificial intelligence technology

    Science.gov (United States)

    Our objective was to develop an artificial intelligence (AI)-based algorithm which can automatically detect food items from images acquired by an egocentric wearable camera for dietary assessment. To study human diet and lifestyle, large sets of egocentric images were acquired using a wearable devic...

  8. Automatic segmentation of time-lapse microscopy images depicting a live Dharma embryo.

    Science.gov (United States)

    Zacharia, Eleni; Bondesson, Maria; Riu, Anne; Ducharme, Nicole A; Gustafsson, Jan-Åke; Kakadiaris, Ioannis A

    2011-01-01

    Biological inferences about the toxicity of chemicals reached during experiments on the zebrafish Dharma embryo can be greatly affected by the analysis of the time-lapse microscopy images depicting the embryo. Among the stages of image analysis, automatic and accurate segmentation of the Dharma embryo is the most crucial and challenging. In this paper, an accurate and automatic segmentation approach for the segmentation of the Dharma embryo data obtained by fluorescent time-lapse microscopy is proposed. Experiments performed in four stacks of 3D images over time have shown promising results.

  9. Automatic specular reflections removal for endoscopic images

    Science.gov (United States)

    Tan, Ke; Wang, Bin; Gao, Yuan

    2017-07-01

    Endoscopy imaging is utilized to provide a realistic view about the surfaces of organs inside the human body. Owing to the damp internal environment, these surfaces usually have a glossy appearance showing specular reflections. For many computer vision algorithms, the highlights created by specular reflections may become a significant source of error. In this paper, we present a novel method for restoration of the specular reflection regions from a single image. Specular restoration process starts with generating a substitute specular-free image with RPCA method. Then the specular removed image was obtained by taking the binary weighting template of highlight regions as the weighting for merging the original specular image and the substitute image. The modified template was furthermore discussed for the concealment of artificial effects in the edge of specular regions. Experimental results on the removal of the endoscopic image with specular reflections demonstrate the efficiency of the proposed method comparing to the existing methods.

  10. Automatic slice identification in 3D medical images with a ConvNet regressor

    NARCIS (Netherlands)

    de Vos, Bob D.; Viergever, Max A.; de Jong, Pim A.; Išgum, Ivana

    2016-01-01

    Identification of anatomical regions of interest is a prerequisite in many medical image analysis tasks. We propose a method that automatically identifies a slice of interest (SOI) in 3D images with a convolutional neural network (ConvNet) regressor. In 150 chest CT scans two reference slices were

  11. Microprocessors in automatic chemical analysis

    International Nuclear Information System (INIS)

    Goujon de Beauvivier, M.; Perez, J.-J.

    1979-01-01

    Application of microprocessors to programming and computing of solutions chemical analysis by a sequential technique is examined. Safety, performances reliability are compared to other methods. An example is given on uranium titration by spectrophotometry [fr

  12. Automatic analysis of charged particle spectra

    International Nuclear Information System (INIS)

    Seres, Z.; Kiss, A.

    1975-11-01

    A computer program system is developed for off-line automatic analysis of a series of charged particle spectra measured by solid-state detectors and collected on magnetic tapes. The procedure results in complete angular distributions for the excited levels of the final nucleus up to about 15 MeV. (orig.) [de

  13. Image analysis

    International Nuclear Information System (INIS)

    Berman, M.; Bischof, L.M.; Breen, E.J.; Peden, G.M.

    1994-01-01

    This paper provides an overview of modern image analysis techniques pertinent to materials science. The usual approach in image analysis contains two basic steps: first, the image is segmented into its constituent components (e.g. individual grains), and second, measurement and quantitative analysis are performed. Usually, the segmentation part of the process is the harder of the two. Consequently, much of the paper concentrates on this aspect, reviewing both fundamental segmentation tools (commonly found in commercial image analysis packages) and more advanced segmentation tools. There is also a review of the most widely used quantitative analysis methods for measuring the size, shape and spatial arrangements of objects. Many of the segmentation and analysis methods are demonstrated using complex real-world examples. Finally, there is a discussion of hardware and software issues. 42 refs., 17 figs

  14. Automatic crop row detection from UAV images

    DEFF Research Database (Denmark)

    Midtiby, Henrik; Rasmussen, Jesper

    are considered weeds. We have used a Sugar beet field as a case for evaluating the proposed crop detection method. The suggested image processing consists of: 1) locating vegetation regions in the image by thresholding the excess green image derived from the orig- inal image, 2) calculate the Hough transform......Images from Unmanned Aerial Vehicles can provide information about the weed distribution in fields. A direct way is to quantify the amount of vegetation present in different areas of the field. The limitation of this approach is that it includes both crops and weeds in the reported num- bers. To get...... of the segmented image 3) determine the dominating crop row direction by analysing output from the Hough transform and 4) use the found crop row direction to locate crop rows....

  15. Semi-Automatic Removal of Foreground Stars from Images of Galaxies

    Science.gov (United States)

    Frei, Zsolt

    1996-07-01

    A new procedure, designed to remove foreground stars from galaxy proviles is presented here. Although several programs exist for stellar and faint object photometry, none of them treat star removal from the images very carefully. I present my attempt to develop such a system, and briefly compare the performance of my software to one of the well-known stellar photometry packages, DAOPhot (Stetson 1987). Major steps in my procedure are: (1) automatic construction of an empirical 2D point spread function from well separated stars that are situated off the galaxy; (2) automatic identification of those peaks that are likely to be foreground stars, scaling the PSF and removing these stars, and patching residuals (in the automatically determined smallest possible area where residuals are truly significant); and (3) cosmetic fix of remaining degradations in the image. The algorithm and software presented here is significantly better for automatic removal of foreground stars from images of galaxies than DAOPhot or similar packages, since: (a) the most suitable stars are selected automatically from the image for the PSF fit; (b) after star-removal an intelligent and automatic procedure removes any possible residuals; (c) unlimited number of images can be cleaned in one run without any user interaction whatsoever. (SECTION: Computing and Data Analysis)

  16. AUTOMATIC MULTILEVEL IMAGE SEGMENTATION BASED ON FUZZY REASONING

    Directory of Open Access Journals (Sweden)

    Liang Tang

    2011-05-01

    Full Text Available An automatic multilevel image segmentation method based on sup-star fuzzy reasoning (SSFR is presented. Using the well-known sup-star fuzzy reasoning technique, the proposed algorithm combines the global statistical information implied in the histogram with the local information represented by the fuzzy sets of gray-levels, and aggregates all the gray-levels into several classes characterized by the local maximum values of the histogram. The presented method has the merits of determining the number of the segmentation classes automatically, and avoiding to calculating thresholds of segmentation. Emulating and real image segmentation experiments demonstrate that the SSFR is effective.

  17. Color image Segmentation using automatic thresholding techniques

    International Nuclear Information System (INIS)

    Harrabi, R.; Ben Braiek, E.

    2011-01-01

    In this paper, entropy and between-class variance based thresholding methods for color images segmentation are studied. The maximization of the between-class variance (MVI) and the entropy (ME) have been used as a criterion functions to determine an optimal threshold to segment images into nearly homogenous regions. Segmentation results from the two methods are validated and the segmentation sensitivity for the test data available is evaluated, and a comparative study between these methods in different color spaces is presented. The experimental results demonstrate the superiority of the MVI method for color image segmentation.

  18. Coherence measures in automatic time-migration velocity analysis

    International Nuclear Information System (INIS)

    Maciel, Jonathas S; Costa, Jessé C; Schleicher, Jörg

    2012-01-01

    Time-migration velocity analysis can be carried out automatically by evaluating the coherence of migrated seismic events in common-image gathers (CIGs). The performance of gradient methods for automatic time-migration velocity analysis depends on the coherence measures used as the objective function. We compare the results of four different coherence measures, being conventional semblance, differential semblance, an extended differential semblance using differences of more distant image traces and the product of the latter with conventional semblance. In our numerical experiments, the objective functions based on conventional semblance and on the product of conventional semblance with extended differential semblance provided the best velocity models, as evaluated by the flatness of the resulting CIGs. The method can be easily extended to anisotropic media. (paper)

  19. Automatic ultrasound image enhancement for 2D semi-automatic breast-lesion segmentation

    Science.gov (United States)

    Lu, Kongkuo; Hall, Christopher S.

    2014-03-01

    Breast cancer is the fastest growing cancer, accounting for 29%, of new cases in 2012, and second leading cause of cancer death among women in the United States and worldwide. Ultrasound (US) has been used as an indispensable tool for breast cancer detection/diagnosis and treatment. In computer-aided assistance, lesion segmentation is a preliminary but vital step, but the task is quite challenging in US images, due to imaging artifacts that complicate detection and measurement of the suspect lesions. The lesions usually present with poor boundary features and vary significantly in size, shape, and intensity distribution between cases. Automatic methods are highly application dependent while manual tracing methods are extremely time consuming and have a great deal of intra- and inter- observer variability. Semi-automatic approaches are designed to counterbalance the advantage and drawbacks of the automatic and manual methods. However, considerable user interaction might be necessary to ensure reasonable segmentation for a wide range of lesions. This work proposes an automatic enhancement approach to improve the boundary searching ability of the live wire method to reduce necessary user interaction while keeping the segmentation performance. Based on the results of segmentation of 50 2D breast lesions in US images, less user interaction is required to achieve desired accuracy, i.e. < 80%, when auto-enhancement is applied for live-wire segmentation.

  20. Automatic Detection of Vehicles Using Intensity Laser and Anaglyph Image

    Directory of Open Access Journals (Sweden)

    Hideo Araki

    2006-12-01

    Full Text Available In this work is presented a methodology to automatic car detection motion presents in digital aerial image on urban area using intensity, anaglyph and subtracting images. The anaglyph image is used to identify the motion cars on the expose take, because the cars provide red color due the not homology between objects. An implicit model was developed to provide a digital pixel value that has the specific propriety presented early, using the ratio between the RGB color of car object in the anaglyph image. The intensity image is used to decrease the false positive and to do the processing to work into roads and streets. The subtracting image is applied to decrease the false positives obtained due the markings road. The goal of this paper is automatically detect motion cars presents in digital aerial image in urban areas. The algorithm implemented applies normalization on the left and right images and later form the anaglyph with using the translation. The results show the applicability of proposed method and it potentiality on the automatic car detection and presented the performance of proposed methodology.

  1. Paediatric Automatic Phonological Analysis Tools (APAT).

    Science.gov (United States)

    Saraiva, Daniela; Lousada, Marisa; Hall, Andreia; Jesus, Luis M T

    2017-12-01

    To develop the pediatric Automatic Phonological Analysis Tools (APAT) and to estimate inter and intrajudge reliability, content validity, and concurrent validity. The APAT were constructed using Excel spreadsheets with formulas. The tools were presented to an expert panel for content validation. The corpus used in the Portuguese standardized test Teste Fonético-Fonológico - ALPE produced by 24 children with phonological delay or phonological disorder was recorded, transcribed, and then inserted into the APAT. Reliability and validity of APAT were analyzed. The APAT present strong inter- and intrajudge reliability (>97%). The content validity was also analyzed (ICC = 0.71), and concurrent validity revealed strong correlations between computerized and manual (traditional) methods. The development of these tools contributes to fill existing gaps in clinical practice and research, since previously there were no valid and reliable tools/instruments for automatic phonological analysis, which allowed the analysis of different corpora.

  2. Automatic DNA Diagnosis for 1D Gel Electrophoresis Images using Bio-image Processing Technique.

    Science.gov (United States)

    Intarapanich, Apichart; Kaewkamnerd, Saowaluck; Shaw, Philip J; Ukosakit, Kittipat; Tragoonrung, Somvong; Tongsima, Sissades

    2015-01-01

    DNA gel electrophoresis is a molecular biology technique for separating different sizes of DNA fragments. Applications of DNA gel electrophoresis include DNA fingerprinting (genetic diagnosis), size estimation of DNA, and DNA separation for Southern blotting. Accurate interpretation of DNA banding patterns from electrophoretic images can be laborious and error prone when a large number of bands are interrogated manually. Although many bio-imaging techniques have been proposed, none of them can fully automate the typing of DNA owing to the complexities of migration patterns typically obtained. We developed an image-processing tool that automatically calls genotypes from DNA gel electrophoresis images. The image processing workflow comprises three main steps: 1) lane segmentation, 2) extraction of DNA bands and 3) band genotyping classification. The tool was originally intended to facilitate large-scale genotyping analysis of sugarcane cultivars. We tested the proposed tool on 10 gel images (433 cultivars) obtained from polyacrylamide gel electrophoresis (PAGE) of PCR amplicons for detecting intron length polymorphisms (ILP) on one locus of the sugarcanes. These gel images demonstrated many challenges in automated lane/band segmentation in image processing including lane distortion, band deformity, high degree of noise in the background, and bands that are very close together (doublets). Using the proposed bio-imaging workflow, lanes and DNA bands contained within are properly segmented, even for adjacent bands with aberrant migration that cannot be separated by conventional techniques. The software, called GELect, automatically performs genotype calling on each lane by comparing with an all-banding reference, which was created by clustering the existing bands into the non-redundant set of reference bands. The automated genotype calling results were verified by independent manual typing by molecular biologists. This work presents an automated genotyping tool from DNA

  3. Automatic DNA Diagnosis for 1D Gel Electrophoresis Images using Bio-image Processing Technique

    Science.gov (United States)

    2015-01-01

    Background DNA gel electrophoresis is a molecular biology technique for separating different sizes of DNA fragments. Applications of DNA gel electrophoresis include DNA fingerprinting (genetic diagnosis), size estimation of DNA, and DNA separation for Southern blotting. Accurate interpretation of DNA banding patterns from electrophoretic images can be laborious and error prone when a large number of bands are interrogated manually. Although many bio-imaging techniques have been proposed, none of them can fully automate the typing of DNA owing to the complexities of migration patterns typically obtained. Results We developed an image-processing tool that automatically calls genotypes from DNA gel electrophoresis images. The image processing workflow comprises three main steps: 1) lane segmentation, 2) extraction of DNA bands and 3) band genotyping classification. The tool was originally intended to facilitate large-scale genotyping analysis of sugarcane cultivars. We tested the proposed tool on 10 gel images (433 cultivars) obtained from polyacrylamide gel electrophoresis (PAGE) of PCR amplicons for detecting intron length polymorphisms (ILP) on one locus of the sugarcanes. These gel images demonstrated many challenges in automated lane/band segmentation in image processing including lane distortion, band deformity, high degree of noise in the background, and bands that are very close together (doublets). Using the proposed bio-imaging workflow, lanes and DNA bands contained within are properly segmented, even for adjacent bands with aberrant migration that cannot be separated by conventional techniques. The software, called GELect, automatically performs genotype calling on each lane by comparing with an all-banding reference, which was created by clustering the existing bands into the non-redundant set of reference bands. The automated genotype calling results were verified by independent manual typing by molecular biologists. Conclusions This work presents an

  4. Automatic segmentation and disease classification using cardiac cine MR images

    NARCIS (Netherlands)

    Wolterink, Jelmer M.; Leiner, Tim; Viergever, Max A.; Išgum, Ivana

    2018-01-01

    Segmentation of the heart in cardiac cine MR is clinically used to quantify cardiac function. We propose a fully automatic method for segmentation and disease classification using cardiac cine MR images. A convolutional neural network (CNN) was designed to simultaneously segment the left ventricle

  5. Image-guided automatic triggering of a fractional CO2 laser in aesthetic procedures.

    Science.gov (United States)

    Wilczyński, Sławomir; Koprowski, Robert; Wiernek, Barbara K; Błońska-Fajfrowska, Barbara

    2016-09-01

    Laser procedures in dermatology and aesthetic medicine are associated with the need for manual laser triggering. This leads to pulse overlapping and side effects. Automatic laser triggering based on image analysis can provide a secure fit to each successive doses of radiation. A fractional CO2 laser was used in the study. 500 images of the human skin of healthy subjects were acquired. Automatic triggering was initiated by an application together with a camera which tracks and analyses the skin in visible light. The tracking algorithm uses the methods of image analysis to overlap images. After locating the characteristic points in analysed adjacent areas, the correspondence of graphs is found. The point coordinates derived from the images are the vertices of graphs with respect to which isomorphism is sought. When the correspondence of graphs is found, it is possible to overlap the neighbouring parts of the image. The proposed method of laser triggering owing to the automatic image fitting method allows for 100% repeatability. To meet this requirement, there must be at least 13 graph vertices obtained from the image. For this number of vertices, the time of analysis of a single image is less than 0.5s. The proposed method, applied in practice, may help reduce the number of side effects during dermatological laser procedures resulting from laser pulse overlapping. In addition, it reduces treatment time and enables to propose new techniques of treatment through controlled, precise laser pulse overlapping. Copyright © 2016 Elsevier Ltd. All rights reserved.

  6. Fully automatic registration and segmentation of first-pass myocardial perfusion MR image sequences.

    Science.gov (United States)

    Gupta, Vikas; Hendriks, Emile A; Milles, Julien; van der Geest, Rob J; Jerosch-Herold, Michael; Reiber, Johan H C; Lelieveldt, Boudewijn P F

    2010-11-01

    Derivation of diagnostically relevant parameters from first-pass myocardial perfusion magnetic resonance images involves the tedious and time-consuming manual segmentation of the myocardium in a large number of images. To reduce the manual interaction and expedite the perfusion analysis, we propose an automatic registration and segmentation method for the derivation of perfusion linked parameters. A complete automation was accomplished by first registering misaligned images using a method based on independent component analysis, and then using the registered data to automatically segment the myocardium with active appearance models. We used 18 perfusion studies (100 images per study) for validation in which the automatically obtained (AO) contours were compared with expert drawn contours on the basis of point-to-curve error, Dice index, and relative perfusion upslope in the myocardium. Visual inspection revealed successful segmentation in 15 out of 18 studies. Comparison of the AO contours with expert drawn contours yielded 2.23 ± 0.53 mm and 0.91 ± 0.02 as point-to-curve error and Dice index, respectively. The average difference between manually and automatically obtained relative upslope parameters was found to be statistically insignificant (P = .37). Moreover, the analysis time per slice was reduced from 20 minutes (manual) to 1.5 minutes (automatic). We proposed an automatic method that significantly reduced the time required for analysis of first-pass cardiac magnetic resonance perfusion images. The robustness and accuracy of the proposed method were demonstrated by the high spatial correspondence and statistically insignificant difference in perfusion parameters, when AO contours were compared with expert drawn contours. Copyright © 2010 AUR. Published by Elsevier Inc. All rights reserved.

  7. Automatic brightness control algorithms and their effect on fluoroscopic imaging

    International Nuclear Information System (INIS)

    Quinn, P.W.; Gagne, R.M.

    1989-01-01

    This paper reports a computer model used to investigate the effect on dose and image quality of three automatic brightness control (ABC) algorithms used in the imaging of barium during general-purpose fluoroscopy. A model incorporating all aspects of image formation - i.e., x- ray production, phantom attenuation, and energy absorption in the CSI phosphor - was driven according to each ABC algorithm as a function of patient thickness. The energy absorbed in the phosphor was kept constant, while the changes in exposure, integral dose, organ dose, and contrast were monitored

  8. Automatic Blastomere Recognition from a Single Embryo Image

    Directory of Open Access Journals (Sweden)

    Yun Tian

    2014-01-01

    Full Text Available The number of blastomeres of human day 3 embryos is one of the most important criteria for evaluating embryo viability. However, due to the transparency and overlap of blastomeres, it is a challenge to recognize blastomeres automatically using a single embryo image. This study proposes an approach based on least square curve fitting (LSCF for automatic blastomere recognition from a single image. First, combining edge detection, deletion of multiple connected points, and dilation and erosion, an effective preprocessing method was designed to obtain part of blastomere edges that were singly connected. Next, an automatic recognition method for blastomeres was proposed using least square circle fitting. This algorithm was tested on 381 embryo microscopic images obtained from the eight-cell period, and the results were compared with those provided by experts. Embryos were recognized with a 0 error rate occupancy of 21.59%, and the ratio of embryos in which the false recognition number was less than or equal to 2 was 83.16%. This experiment demonstrated that our method could efficiently and rapidly recognize the number of blastomeres from a single embryo image without the need to reconstruct the three-dimensional model of the blastomeres first; this method is simple and efficient.

  9. Automatic Methods in Image Processing and Their Relevance to Map-Making.

    Science.gov (United States)

    1981-02-11

    folding fre- quency = .5) and s is the "shaoing fac- tor" which controls the spatial frequency content of the signal; the signal band- width increases...ARIZONA UNIV TUCSON DIGITAL IAgE ANALYSIS LAB Iris 8/ 2AUTOMATIC METHOOS IN IMAGE PROCESSING AND THEIR RELEVANCE TO MA-.ETC~tl;FEB 1 S R HUNT DAA629

  10. Automatic segmentation of liver structure in CT images

    International Nuclear Information System (INIS)

    Bae, K.T.; Giger, M.L.; Chen, C.; Kahn, C.E. Jr.

    1993-01-01

    The segmentation and three-dimensional representation of the liver from a computed tomography (CT) scan is an important step in many medical applications, such as in the surgical planning for a living-donor liver transplant and in the automatic detection and documentation of pathological states. A method is being developed to automatically extract liver structure from abdominal CT scans using a priori information about liver morphology and digital image-processing techniques. Segmentation is performed sequentially image-by-image (slice-by-slice), starting with a reference image in which the liver occupies almost the entire right half of the abdomen cross section. Image processing techniques include gray-level thresholding, Gaussian smoothing, and eight-point connectivity tracking. For each case, the shape, size, and pixel density distribution of the liver are recorded for each CT image and used in the processing of other CT images. Extracted boundaries of the liver are smoothed using mathematical morphology techniques and B-splines. Computer-determined boundaries were compared with those drawn by a radiologist. The boundary descriptions from the two methods were in agreement, and the calculated areas were within 10%

  11. Development of automatic extraction method of left ventricular contours on long axis view MR cine images

    International Nuclear Information System (INIS)

    Utsunomiya, Shinichi; Iijima, Naoto; Yamasaki, Kazunari; Fujita, Akinori

    1995-01-01

    In the MRI cardiac function analysis, left ventricular volume curves and diagnosis parameters are obtained by extracting the left ventricular cavities as regions of interest (ROI) from long axis view MR cine images. The ROI extractions had to be done by manual operations, because automatization of the extraction is difficult. A long axis view left ventricular contour consists of a cardiac wall part and an aortic valve part. The above mentioned difficulty is due to the decline of contrast on the cardiac wall part, and the disappearance of edge on the aortic valve part. In this paper, we report a new automatic extraction method for long axis view MR cine images, which needs only 3 manually indicated points on the 1st image to extract all the contours from the total sequence of images. At first, candidate points of a contour are detected by edge detection. Then, selecting the best matched combination of candidate points by Dynamic Programming, the cardiac wall part is automatically extracted. The aortic valve part is manually extracted for the 1st image by indicating both the end points, and is automatically extracted for the rest of the images, by utilizing the aortic valve motion characteristics throughout a cardiac cycle. (author)

  12. Automatic correspondence detection in mammogram and breast tomosynthesis images

    Science.gov (United States)

    Ehrhardt, Jan; Krüger, Julia; Bischof, Arpad; Barkhausen, Jörg; Handels, Heinz

    2012-02-01

    Two-dimensional mammography is the major imaging modality in breast cancer detection. A disadvantage of mammography is the projective nature of this imaging technique. Tomosynthesis is an attractive modality with the potential to combine the high contrast and high resolution of digital mammography with the advantages of 3D imaging. In order to facilitate diagnostics and treatment in the current clinical work-flow, correspondences between tomosynthesis images and previous mammographic exams of the same women have to be determined. In this paper, we propose a method to detect correspondences in 2D mammograms and 3D tomosynthesis images automatically. In general, this 2D/3D correspondence problem is ill-posed, because a point in the 2D mammogram corresponds to a line in the 3D tomosynthesis image. The goal of our method is to detect the "most probable" 3D position in the tomosynthesis images corresponding to a selected point in the 2D mammogram. We present two alternative approaches to solve this 2D/3D correspondence problem: a 2D/3D registration method and a 2D/2D mapping between mammogram and tomosynthesis projection images with a following back projection. The advantages and limitations of both approaches are discussed and the performance of the methods is evaluated qualitatively and quantitatively using a software phantom and clinical breast image data. Although the proposed 2D/3D registration method can compensate for moderate breast deformations caused by different breast compressions, this approach is not suitable for clinical tomosynthesis data due to the limited resolution and blurring effects perpendicular to the direction of projection. The quantitative results show that the proposed 2D/2D mapping method is capable of detecting corresponding positions in mammograms and tomosynthesis images automatically for 61 out of 65 landmarks. The proposed method can facilitate diagnosis, visual inspection and comparison of 2D mammograms and 3D tomosynthesis images for

  13. Automatic anatomically selective image enhancement in digital chest radiography

    International Nuclear Information System (INIS)

    Sezan, M.I.; Minerbo, G.N.; Schaetzing, R.

    1989-01-01

    The authors develop a technique for automatic anatomically selective enhancement of digital chest radiographs. Anatomically selective enhancement is motivated by the desire to simultaneously meet the different enhancement requirements of the lung field and the mediastinum. A recent peak detection algorithm and a set of rules are applied to the image histogram to determine automatically a gray-level threshold between the lung field and mediastinum. The gray-level threshold facilitates anatomically selective gray-scale modification and/or unsharp masking. Further, in an attempt to suppress possible white-band or black-band artifacts due to unsharp masking at sharp edges, local-contrast adaptivity is incorporated into anatomically selective unsharp masking by designing an anatomy-sensitive emphasis parameter which varies asymmetrically with positive and negative values of the local image contrast

  14. Automatic coronary calcium scoring using noncontrast and contrast CT images

    Energy Technology Data Exchange (ETDEWEB)

    Yang, Guanyu, E-mail: yang.list@seu.edu.cn; Chen, Yang; Shu, Huazhong [Laboratory of Image Science and Technology, School of Computer Science and Engineering, Southeast University, No. 2, Si Pai Lou, Nanjing 210096 (China); Centre de Recherche en Information Biomédicale Sino-Français (LIA CRIBs), Nanjing 210096 (China); Key Laboratory of Computer Network and Information Integration, Southeast University, Ministry of Education, Nanjing 210096 (China); Ning, Xiufang; Sun, Qiaoyu [Laboratory of Image Science and Technology, School of Computer Science and Engineering, Southeast University, No. 2, Si Pai Lou, Nanjing 210096 (China); Key Laboratory of Computer Network and Information Integration, Southeast University, Ministry of Education, Nanjing 210096 (China); Coatrieux, Jean-Louis [INSERM-U1099, Rennes F-35000 (France); Labotatoire Traitement du Signal et de l’Image (LTSI), Université de Rennes 1, Campus de Beaulieu, Bat. 22, Rennes 35042 Cedex (France); Centre de Recherche en Information Biomédicale Sino-Français (LIA CRIBs), Nanjing 210096 (China)

    2016-05-15

    Purpose: Calcium scoring is widely used to assess the risk of coronary heart disease (CHD). Accurate coronary artery calcification detection in noncontrast CT image is a prerequisite step for coronary calcium scoring. Currently, calcified lesions in the coronary arteries are manually identified by radiologists in clinical practice. Thus, in this paper, a fully automatic calcium scoring method was developed to alleviate the work load of the radiologists or cardiologists. Methods: The challenge of automatic coronary calcification detection is to discriminate the calcification in the coronary arteries from the calcification in the other tissues. Since the anatomy of coronary arteries is difficult to be observed in the noncontrast CT images, the contrast CT image of the same patient is used to extract the regions of the aorta, heart, and coronary arteries. Then, a patient-specific region-of-interest (ROI) is generated in the noncontrast CT image according to the segmentation results in the contrast CT image. This patient-specific ROI focuses on the regions in the neighborhood of coronary arteries for calcification detection, which can eliminate the calcifications in the surrounding tissues. A support vector machine classifier is applied finally to refine the results by removing possible image noise. Furthermore, the calcified lesions in the noncontrast images belonging to the different main coronary arteries are identified automatically using the labeling results of the extracted coronary arteries. Results: Forty datasets from four different CT machine vendors were used to evaluate their algorithm, which were provided by the MICCAI 2014 Coronary Calcium Scoring (orCaScore) Challenge. The sensitivity and positive predictive value for the volume of detected calcifications are 0.989 and 0.948. Only one patient out of 40 patients had been assigned to the wrong risk category defined according to Agatston scores (0, 1–100, 101–300, >300) by comparing with the ground

  15. Image Analysis

    DEFF Research Database (Denmark)

    The 19th Scandinavian Conference on Image Analysis was held at the IT University of Copenhagen in Denmark during June 15-17, 2015. The SCIA conference series has been an ongoing biannual event for more than 30 years and over the years it has nurtured a world-class regional research and development...... area within the four participating Nordic countries. It is a regional meeting of the International Association for Pattern Recognition (IAPR). We would like to thank all authors who submitted works to this year’s SCIA, the invited speakers, and our Program Committee. In total 67 papers were submitted....... The topics of the accepted papers range from novel applications of vision systems, pattern recognition, machine learning, feature extraction, segmentation, 3D vision, to medical and biomedical image analysis. The papers originate from all the Scandinavian countries and several other European countries...

  16. Automatic anterior chamber angle assessment for HD-OCT images.

    Science.gov (United States)

    Tian, Jing; Marziliano, Pina; Baskaran, Mani; Wong, Hong-Tym; Aung, Tin

    2011-11-01

    Angle-closure glaucoma is a major blinding eye disease and could be detected by measuring the anterior chamber angle in the human eyes. High-definition OCT (Cirrus HD-OCT) is an emerging noninvasive, high-speed, and high-resolution imaging modality for the anterior segment of the eye. Here, we propose a novel algorithm which automatically detects a new landmark, Schwalbe's line, and measures the anterior chamber angle in the HD-OCT images. The distortion caused by refraction is corrected by dewarping the HD-OCT images, and three biometric measurements are defined to quantitatively assess the anterior chamber angle. The proposed algorithm was tested on 40 HD-OCT images of the eye and provided accurate measurements in about 1 second.

  17. Automatic UAV Image Geo-Registration by Matching UAV Images to Georeferenced Image Data

    Directory of Open Access Journals (Sweden)

    Xiangyu Zhuo

    2017-04-01

    Full Text Available Recent years have witnessed the fast development of UAVs (unmanned aerial vehicles. As an alternative to traditional image acquisition methods, UAVs bridge the gap between terrestrial and airborne photogrammetry and enable flexible acquisition of high resolution images. However, the georeferencing accuracy of UAVs is still limited by the low-performance on-board GNSS and INS. This paper investigates automatic geo-registration of an individual UAV image or UAV image blocks by matching the UAV image(s with a previously taken georeferenced image, such as an individual aerial or satellite image with a height map attached or an aerial orthophoto with a DSM (digital surface model attached. As the biggest challenge for matching UAV and aerial images is in the large differences in scale and rotation, we propose a novel feature matching method for nadir or slightly tilted images. The method is comprised of a dense feature detection scheme, a one-to-many matching strategy and a global geometric verification scheme. The proposed method is able to find thousands of valid matches in cases where SIFT and ASIFT fail. Those matches can be used to geo-register the whole UAV image block towards the reference image data. When the reference images offer high georeferencing accuracy, the UAV images can also be geolocalized in a global coordinate system. A series of experiments involving different scenarios was conducted to validate the proposed method. The results demonstrate that our approach achieves not only decimeter-level registration accuracy, but also comparable global accuracy as the reference images.

  18. Embryonic Heart Morphogenesis from Confocal Microscopy Imaging and Automatic Segmentation

    Directory of Open Access Journals (Sweden)

    Hongda Mao

    2013-01-01

    Full Text Available Embryonic heart morphogenesis (EHM is a complex and dynamic process where the heart transforms from a single tube into a four-chambered pump. This process is of great biological and clinical interest but is still poorly understood for two main reasons. On the one hand, the existing imaging modalities for investigating EHM suffered from either limited penetration depth or limited spatial resolution. On the other hand, current works typically adopted manual segmentation, which was tedious, subjective, and time consuming considering the complexity of developing heart geometry and the large size of images. In this paper, we propose to utilize confocal microscopy imaging with tissue optical immersion clearing technique to image the heart at different stages of development for EHM study. The imaging method is able to produce high spatial resolution images and achieve large penetration depth at the same time. Furthermore, we propose a novel convex active contour model for automatic image segmentation. The model has the ability to deal with intensity fall-off in depth which is characterized by confocal microscopy images. We acquired the images of embryonic quail hearts from day 6 to day 14 of incubation for EHM study. The experimental results were promising and provided us with an insight view of early heart growth pattern and also paved the road for data-driven heart growth modeling.

  19. Automatic analysis of trabecular bone structure from knee MRI

    DEFF Research Database (Denmark)

    Marques, Joselene; Granlund, Rabia; Lillholm, Martin

    2012-01-01

    We investigated the feasibility of quantifying osteoarthritis (OA) by analysis of the trabecular bone structure in low-field knee MRI. Generic texture features were extracted from the images and subsequently selected by sequential floating forward selection (SFFS), following a fully automatic......, uncommitted machine-learning based framework. Six different classifiers were evaluated in cross-validation schemes and the results showed that the presence of OA can be quantified by a bone structure marker. The performance of the developed marker reached a generalization area-under-the-ROC (AUC) of 0...

  20. Automatic food detection in egocentric images using artificial intelligence technology.

    Science.gov (United States)

    Jia, Wenyan; Li, Yuecheng; Qu, Ruowei; Baranowski, Thomas; Burke, Lora E; Zhang, Hong; Bai, Yicheng; Mancino, Juliet M; Xu, Guizhi; Mao, Zhi-Hong; Sun, Mingui

    2018-03-26

    To develop an artificial intelligence (AI)-based algorithm which can automatically detect food items from images acquired by an egocentric wearable camera for dietary assessment. To study human diet and lifestyle, large sets of egocentric images were acquired using a wearable device, called eButton, from free-living individuals. Three thousand nine hundred images containing real-world activities, which formed eButton data set 1, were manually selected from thirty subjects. eButton data set 2 contained 29 515 images acquired from a research participant in a week-long unrestricted recording. They included both food- and non-food-related real-life activities, such as dining at both home and restaurants, cooking, shopping, gardening, housekeeping chores, taking classes, gym exercise, etc. All images in these data sets were classified as food/non-food images based on their tags generated by a convolutional neural network. A cross data-set test was conducted on eButton data set 1. The overall accuracy of food detection was 91·5 and 86·4 %, respectively, when one-half of data set 1 was used for training and the other half for testing. For eButton data set 2, 74·0 % sensitivity and 87·0 % specificity were obtained if both 'food' and 'drink' were considered as food images. Alternatively, if only 'food' items were considered, the sensitivity and specificity reached 85·0 and 85·8 %, respectively. The AI technology can automatically detect foods from low-quality, wearable camera-acquired real-world egocentric images with reasonable accuracy, reducing both the burden of data processing and privacy concerns.

  1. Automatic macroscopic characterization of diesel sprays by means of a new image processing algorithm

    Science.gov (United States)

    Rubio-Gómez, Guillermo; Martínez-Martínez, S.; Rua-Mojica, Luis F.; Gómez-Gordo, Pablo; de la Garza, Oscar A.

    2018-05-01

    A novel algorithm is proposed for the automatic segmentation of diesel spray images and the calculation of their macroscopic parameters. The algorithm automatically detects each spray present in an image, and therefore it is able to work with diesel injectors with a different number of nozzle holes without any modification. The main characteristic of the algorithm is that it splits each spray into three different regions and then segments each one with an individually calculated binarization threshold. Each threshold level is calculated from the analysis of a representative luminosity profile of each region. This approach makes it robust to irregular light distribution along a single spray and between different sprays of an image. Once the sprays are segmented, the macroscopic parameters of each one are calculated. The algorithm is tested with two sets of diesel spray images taken under normal and irregular illumination setups.

  2. Automatic Solitary Lung Nodule Detection in Computed Tomography Images Slices

    Science.gov (United States)

    Sentana, I. W. B.; Jawas, N.; Asri, S. A.

    2018-01-01

    Lung nodule is an early indicator of some lung diseases, including lung cancer. In Computed Tomography (CT) based image, nodule is known as a shape that appears brighter than lung surrounding. This research aim to develop an application that automatically detect lung nodule in CT images. There are some steps in algorithm such as image acquisition and conversion, image binarization, lung segmentation, blob detection, and classification. Data acquisition is a step to taking image slice by slice from the original *.dicom format and then each image slices is converted into *.tif image format. Binarization that tailoring Otsu algorithm, than separated the background and foreground part of each image slices. After removing the background part, the next step is to segment part of the lung only so the nodule can localized easier. Once again Otsu algorithm is use to detect nodule blob in localized lung area. The final step is tailoring Support Vector Machine (SVM) to classify the nodule. The application has succeed detecting near round nodule with a certain threshold of size. Those detecting result shows drawback in part of thresholding size and shape of nodule that need to enhance in the next part of the research. The algorithm also cannot detect nodule that attached to wall and Lung Chanel, since it depend the searching only on colour differences.

  3. Automatic extraction of via in the CT image of PCB

    Science.gov (United States)

    Liu, Xifeng; Hu, Yuwei

    2018-04-01

    In modern industry, the nondestructive testing of printed circuit board (PCB) can prevent effectively the system failure and is becoming more and more important. In order to detect the via in the PCB base on the CT image automatically accurately and reliably, a novel algorithm for via extraction based on weighting stack combining the morphologic character of via is designed. Every slice data in the vertical direction of the PCB is superimposed to enhanced vias target. The OTSU algorithm is used to segment the slice image. OTSU algorithm of thresholding gray level images is efficient for separating an image into two classes where two types of fairly distinct classes exist in the image. Randomized Hough Transform was used to locate the region of via in the segmented binary image. Then the 3D reconstruction of via based on sequence slice images was done by volume rendering. The accuracy of via positioning and detecting from a CT images of PCB was demonstrated by proposed algorithm. It was found that the method is good in veracity and stability for detecting of via in three dimensional.

  4. Automatic anatomy recognition on CT images with pathology

    Science.gov (United States)

    Huang, Lidong; Udupa, Jayaram K.; Tong, Yubing; Odhner, Dewey; Torigian, Drew A.

    2016-03-01

    Body-wide anatomy recognition on CT images with pathology becomes crucial for quantifying body-wide disease burden. This, however, is a challenging problem because various diseases result in various abnormalities of objects such as shape and intensity patterns. We previously developed an automatic anatomy recognition (AAR) system [1] whose applicability was demonstrated on near normal diagnostic CT images in different body regions on 35 organs. The aim of this paper is to investigate strategies for adapting the previous AAR system to diagnostic CT images of patients with various pathologies as a first step toward automated body-wide disease quantification. The AAR approach consists of three main steps - model building, object recognition, and object delineation. In this paper, within the broader AAR framework, we describe a new strategy for object recognition to handle abnormal images. In the model building stage an optimal threshold interval is learned from near-normal training images for each object. This threshold is optimally tuned to the pathological manifestation of the object in the test image. Recognition is performed following a hierarchical representation of the objects. Experimental results for the abdominal body region based on 50 near-normal images used for model building and 20 abnormal images used for object recognition show that object localization accuracy within 2 voxels for liver and spleen and 3 voxels for kidney can be achieved with the new strategy.

  5. Automatic Image Alignment and Stitching of Medical Images with Seam Blending

    OpenAIRE

    Abhinav Kumar; Raja Sekhar Bandaru; B Madhusudan Rao; Saket Kulkarni; Nilesh Ghatpande

    2010-01-01

    This paper proposes an algorithm which automatically aligns and stitches the component medical images (fluoroscopic) with varying degrees of overlap into a single composite image. The alignment method is based on similarity measure between the component images. As applied here the technique is intensity based rather than feature based. It works well in domains where feature based methods have difficulty, yet more robust than traditional correlation. Component images are stitched together usin...

  6. BgCut: Automatic Ship Detection from UAV Images

    Directory of Open Access Journals (Sweden)

    Chao Xu

    2014-01-01

    foreground objects from sea automatically. First, a sea template library including images in different natural conditions is built to provide an initial template to the model. Then the background trimap is obtained by combing some templates matching with region growing algorithm. The output trimap initializes Grabcut background instead of manual intervention and the process of segmentation without iteration. The effectiveness of our proposed model is demonstrated by extensive experiments on a certain area of real UAV aerial images by an airborne Canon 5D Mark. The proposed algorithm is not only adaptive but also with good segmentation. Furthermore, the model in this paper can be well applied in the automated processing of industrial images for related researches.

  7. Automatic Microaneurysm Detection and Characterization Through Digital Color Fundus Images

    Energy Technology Data Exchange (ETDEWEB)

    Martins, Charles; Veras, Rodrigo; Ramalho, Geraldo; Medeiros, Fatima; Ushizima, Daniela

    2008-08-29

    Ocular fundus images can provide information about retinal, ophthalmic, and even systemic diseases such as diabetes. Microaneurysms (MAs) are the earliest sign of Diabetic Retinopathy, a frequently observed complication in both type 1 and type 2 diabetes. Robust detection of MAs in digital color fundus images is critical in the development of automated screening systems for this kind of disease. Automatic grading of these images is being considered by health boards so that the human grading task is reduced. In this paper we describe segmentation and the feature extraction methods for candidate MAs detection.We show that the candidate MAs detected with the methodology have been successfully classified by a MLP neural network (correct classification of 84percent).

  8. Sensitivity analysis and design optimization through automatic differentiation

    International Nuclear Information System (INIS)

    Hovland, Paul D; Norris, Boyana; Strout, Michelle Mills; Bhowmick, Sanjukta; Utke, Jean

    2005-01-01

    Automatic differentiation is a technique for transforming a program or subprogram that computes a function, including arbitrarily complex simulation codes, into one that computes the derivatives of that function. We describe the implementation and application of automatic differentiation tools. We highlight recent advances in the combinatorial algorithms and compiler technology that underlie successful implementation of automatic differentiation tools. We discuss applications of automatic differentiation in design optimization and sensitivity analysis. We also describe ongoing research in the design of language-independent source transformation infrastructures for automatic differentiation algorithms

  9. Automatic identification of corrosion damage using image processing techniques

    Energy Technology Data Exchange (ETDEWEB)

    Bento, Mariana P.; Ramalho, Geraldo L.B.; Medeiros, Fatima N.S. de; Ribeiro, Elvis S. [Universidade Federal do Ceara (UFC), Fortaleza, CE (Brazil); Medeiros, Luiz C.L. [Petroleo Brasileiro S.A. (PETROBRAS), Rio de Janeiro, RJ (Brazil)

    2009-07-01

    This paper proposes a Nondestructive Evaluation (NDE) method for atmospheric corrosion detection on metallic surfaces using digital images. In this study, the uniform corrosion is characterized by texture attributes extracted from co-occurrence matrix and the Self Organizing Mapping (SOM) clustering algorithm. We present a technique for automatic inspection of oil and gas storage tanks and pipelines of petrochemical industries without disturbing their properties and performance. Experimental results are promising and encourage the possibility of using this methodology in designing trustful and robust early failure detection systems. (author)

  10. Planning applications in image analysis

    Science.gov (United States)

    Boddy, Mark; White, Jim; Goldman, Robert; Short, Nick, Jr.

    1994-01-01

    We describe two interim results from an ongoing effort to automate the acquisition, analysis, archiving, and distribution of satellite earth science data. Both results are applications of Artificial Intelligence planning research to the automatic generation of processing steps for image analysis tasks. First, we have constructed a linear conditional planner (CPed), used to generate conditional processing plans. Second, we have extended an existing hierarchical planning system to make use of durations, resources, and deadlines, thus supporting the automatic generation of processing steps in time and resource-constrained environments.

  11. Automatic quantitative analysis of liver functions by a computer system

    International Nuclear Information System (INIS)

    Shinpo, Takako

    1984-01-01

    In the previous paper, we confirmed the clinical usefulness of hepatic clearance (hepatic blood flow), which is the hepatic uptake and blood disappearance rate coefficients. These were obtained by the initial slope index of each minute during a period of five frames of a hepatogram by injecting sup(99m)Tc-Sn-colloid 37 MBq. To analyze the information simply, rapidly and accurately, we developed a automatic quantitative analysis for liver functions. Information was obtained every quarter minute during a period of 60 frames of the sequential image. The sequential counts were measured for the heart, whole liver, both left lobe and right lobes using a computer connected to a scintillation camera. We measured the effective hepatic blood flow, from the disappearance rate multiplied by the percentage of hepatic uptake as follows, (liver counts)/(tatal counts of the field) Our method of analysis automatically recorded the reappearance graph of the disappearance curve and uptake curve on the basis of the heart and the whole liver, respectively; and computed using BASIC language. This method makes it possible to obtain the image of the initial uptake of sup(99m)Tc-Sn-colloid into the liver by a small dose of it. (author)

  12. Automatic tissue image segmentation based on image processing and deep learning

    Science.gov (United States)

    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.

  13. Comparison of liver volumetry on contrast-enhanced CT images: one semiautomatic and two automatic approaches.

    Science.gov (United States)

    Cai, Wei; He, Baochun; Fan, Yingfang; Fang, Chihua; Jia, Fucang

    2016-11-08

    This study was to evaluate the accuracy, consistency, and efficiency of three liver volumetry methods- one interactive method, an in-house-developed 3D medical Image Analysis (3DMIA) system, one automatic active shape model (ASM)-based segmentation, and one automatic probabilistic atlas (PA)-guided segmentation method on clinical contrast-enhanced CT images. Forty-two datasets, including 27 normal liver and 15 space-occupying liver lesion patients, were retrospectively included in this study. The three methods - one semiautomatic 3DMIA, one automatic ASM-based, and one automatic PA-based liver volumetry - achieved an accuracy with VD (volume difference) of -1.69%, -2.75%, and 3.06% in the normal group, respectively, and with VD of -3.20%, -3.35%, and 4.14% in the space-occupying lesion group, respectively. However, the three methods achieved an efficiency of 27.63 mins, 1.26 mins, 1.18 mins on average, respectively, compared with the manual volumetry, which took 43.98 mins. The high intraclass correlation coefficient between the three methods and the manual method indicated an excel-lent agreement on liver volumetry. Significant differences in segmentation time were observed between the three methods (3DMIA, ASM, and PA) and the manual volumetry (p volumetries (ASM and PA) and the semiautomatic volumetry (3DMIA) (p < 0.001). The semiautomatic interactive 3DMIA, automatic ASM-based, and automatic PA-based liver volum-etry agreed well with manual gold standard in both the normal liver group and the space-occupying lesion group. The ASM- and PA-based automatic segmentation have better efficiency in clinical use. © 2016 The Authors.

  14. Radiometric Normalization of Temporal Images Combining Automatic Detection of Pseudo-Invariant Features from the Distance and Similarity Spectral Measures, Density Scatterplot Analysis, and Robust Regression

    Directory of Open Access Journals (Sweden)

    Ana Paula Ferreira de Carvalho

    2013-05-01

    Full Text Available Radiometric precision is difficult to maintain in orbital images due to several factors (atmospheric conditions, Earth-sun distance, detector calibration, illumination, and viewing angles. These unwanted effects must be removed for radiometric consistency among temporal images, leaving only land-leaving radiances, for optimum change detection. A variety of relative radiometric correction techniques were developed for the correction or rectification of images, of the same area, through use of reference targets whose reflectance do not change significantly with time, i.e., pseudo-invariant features (PIFs. This paper proposes a new technique for radiometric normalization, which uses three sequential methods for an accurate PIFs selection: spectral measures of temporal data (spectral distance and similarity, density scatter plot analysis (ridge method, and robust regression. The spectral measures used are the spectral angle (Spectral Angle Mapper, SAM, spectral correlation (Spectral Correlation Mapper, SCM, and Euclidean distance. The spectral measures between the spectra at times t1 and t2 and are calculated for each pixel. After classification using threshold values, it is possible to define points with the same spectral behavior, including PIFs. The distance and similarity measures are complementary and can be calculated together. The ridge method uses a density plot generated from images acquired on different dates for the selection of PIFs. In a density plot, the invariant pixels, together, form a high-density ridge, while variant pixels (clouds and land cover changes are spread, having low density, facilitating its exclusion. Finally, the selected PIFs are subjected to a robust regression (M-estimate between pairs of temporal bands for the detection and elimination of outliers, and to obtain the optimal linear equation for a given set of target points. The robust regression is insensitive to outliers, i.e., observation that appears to deviate

  15. Automatic intra-modality brain image registration method

    International Nuclear Information System (INIS)

    Whitaker, J.M.; Ardekani, B.A.; Braun, M.

    1996-01-01

    Full text: Registration of 3D images of brain of the same or different subjects has potential importance in clinical diagnosis, treatment planning and neurological research. The broad aim of our work is to produce an automatic and robust intra-modality, brain image registration algorithm for intra-subject and inter-subject studies. Our algorithm is composed of two stages. Initial alignment is achieved by finding the values of nine transformation parameters (representing translation, rotation and scale) that minimise the nonoverlapping regions of the head. This is achieved by minimisation of the sum of the exclusive OR of two binary head images, produced using the head extraction procedure described by Ardekani et al. (J Comput Assist Tomogr, 19:613-623, 1995). The initial alignment successfully determines the scale parameters and gross translation and rotation parameters. Fine alignment uses an objective function described for inter-modality registration in Ardekani et al. (ibid.). The algorithm segments one of the images to be aligned into a set of connected components using K-means clustering. Registration is achieved by minimising the K-means variance of the segmentation induced in the other image. Similarity of images of the same modality makes the method attractive for intra-modality registration. A 3D MR image, with voxel dimensions, 2x2x6 mm, was misaligned. The registered image shows visually accurate registration. The average displacement of a pixel from its correct location was measured to be 3.3 mm. The algorithm was tested on intra-subject MR images and was found to produce good qualitative results. Using the data available, the algorithm produced promising qualitative results in intra-subject registration. Further work is necessary in its application to intersubject registration, due to large variability in brain structure between subjects. Clinical evaluation of the algorithm for selected applications is required

  16. Automatic Shadow Detection and Removal from a Single Image.

    Science.gov (United States)

    Khan, Salman H; Bennamoun, Mohammed; Sohel, Ferdous; Togneri, Roberto

    2016-03-01

    We present a framework to automatically detect and remove shadows in real world scenes from a single image. Previous works on shadow detection put a lot of effort in designing shadow variant and invariant hand-crafted features. In contrast, our framework automatically learns the most relevant features in a supervised manner using multiple convolutional deep neural networks (ConvNets). The features are learned at the super-pixel level and along the dominant boundaries in the image. The predicted posteriors based on the learned features are fed to a conditional random field model to generate smooth shadow masks. Using the detected shadow masks, we propose a Bayesian formulation to accurately extract shadow matte and subsequently remove shadows. The Bayesian formulation is based on a novel model which accurately models the shadow generation process in the umbra and penumbra regions. The model parameters are efficiently estimated using an iterative optimization procedure. Our proposed framework consistently performed better than the state-of-the-art on all major shadow databases collected under a variety of conditions.

  17. Automatic segmentation of the glenohumeral cartilages from magnetic resonance images

    International Nuclear Information System (INIS)

    Neubert, A.; Yang, Z.; Engstrom, C.; Xia, Y.; Strudwick, M. W.; Chandra, S. S.; Crozier, S.; Fripp, J.

    2016-01-01

    Purpose: Magnetic resonance (MR) imaging plays a key role in investigating early degenerative disorders and traumatic injuries of the glenohumeral cartilages. Subtle morphometric and biochemical changes of potential relevance to clinical diagnosis, treatment planning, and evaluation can be assessed from measurements derived from in vivo MR segmentation of the cartilages. However, segmentation of the glenohumeral cartilages, using approaches spanning manual to automated methods, is technically challenging, due to their thin, curved structure and overlapping intensities of surrounding tissues. Automatic segmentation of the glenohumeral cartilages from MR imaging is not at the same level compared to the weight-bearing knee and hip joint cartilages despite the potential applications with respect to clinical investigation of shoulder disorders. In this work, the authors present a fully automated segmentation method for the glenohumeral cartilages using MR images of healthy shoulders. Methods: The method involves automated segmentation of the humerus and scapula bones using 3D active shape models, the extraction of the expected bone–cartilage interface, and cartilage segmentation using a graph-based method. The cartilage segmentation uses localization, patient specific tissue estimation, and a model of the cartilage thickness variation. The accuracy of this method was experimentally validated using a leave-one-out scheme on a database of MR images acquired from 44 asymptomatic subjects with a true fast imaging with steady state precession sequence on a 3 T scanner (Siemens Trio) using a dedicated shoulder coil. The automated results were compared to manual segmentations from two experts (an experienced radiographer and an experienced musculoskeletal anatomist) using the Dice similarity coefficient (DSC) and mean absolute surface distance (MASD) metrics. Results: Accurate and precise bone segmentations were achieved with mean DSC of 0.98 and 0.93 for the humeral head

  18. Automatic segmentation of the glenohumeral cartilages from magnetic resonance images

    Energy Technology Data Exchange (ETDEWEB)

    Neubert, A., E-mail: ales.neubert@csiro.au [School of Information Technology and Electrical Engineering, University of Queensland, Brisbane 4072, Australia and The Australian E-Health Research Centre, CSIRO Health and Biosecurity, Brisbane 4029 (Australia); Yang, Z. [School of Information Technology and Electrical Engineering, University of Queensland, Brisbane 4072, Australia and Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190 (China); Engstrom, C. [School of Human Movement Studies, University of Queensland, Brisbane 4072 (Australia); Xia, Y.; Strudwick, M. W.; Chandra, S. S.; Crozier, S. [School of Information Technology and Electrical Engineering, University of Queensland, Brisbane 4072 (Australia); Fripp, J. [The Australian E-Health Research Centre, CSIRO Health and Biosecurity, Brisbane, 4029 (Australia)

    2016-10-15

    Purpose: Magnetic resonance (MR) imaging plays a key role in investigating early degenerative disorders and traumatic injuries of the glenohumeral cartilages. Subtle morphometric and biochemical changes of potential relevance to clinical diagnosis, treatment planning, and evaluation can be assessed from measurements derived from in vivo MR segmentation of the cartilages. However, segmentation of the glenohumeral cartilages, using approaches spanning manual to automated methods, is technically challenging, due to their thin, curved structure and overlapping intensities of surrounding tissues. Automatic segmentation of the glenohumeral cartilages from MR imaging is not at the same level compared to the weight-bearing knee and hip joint cartilages despite the potential applications with respect to clinical investigation of shoulder disorders. In this work, the authors present a fully automated segmentation method for the glenohumeral cartilages using MR images of healthy shoulders. Methods: The method involves automated segmentation of the humerus and scapula bones using 3D active shape models, the extraction of the expected bone–cartilage interface, and cartilage segmentation using a graph-based method. The cartilage segmentation uses localization, patient specific tissue estimation, and a model of the cartilage thickness variation. The accuracy of this method was experimentally validated using a leave-one-out scheme on a database of MR images acquired from 44 asymptomatic subjects with a true fast imaging with steady state precession sequence on a 3 T scanner (Siemens Trio) using a dedicated shoulder coil. The automated results were compared to manual segmentations from two experts (an experienced radiographer and an experienced musculoskeletal anatomist) using the Dice similarity coefficient (DSC) and mean absolute surface distance (MASD) metrics. Results: Accurate and precise bone segmentations were achieved with mean DSC of 0.98 and 0.93 for the humeral head

  19. Image Based Hair Segmentation Algorithm for the Application of Automatic Facial Caricature Synthesis

    Directory of Open Access Journals (Sweden)

    Yehu Shen

    2014-01-01

    Full Text Available Hair is a salient feature in human face region and are one of the important cues for face analysis. Accurate detection and presentation of hair region is one of the key components for automatic synthesis of human facial caricature. In this paper, an automatic hair detection algorithm for the application of automatic synthesis of facial caricature based on a single image is proposed. Firstly, hair regions in training images are labeled manually and then the hair position prior distributions and hair color likelihood distribution function are estimated from these labels efficiently. Secondly, the energy function of the test image is constructed according to the estimated prior distributions of hair location and hair color likelihood. This energy function is further optimized according to graph cuts technique and initial hair region is obtained. Finally, K-means algorithm and image postprocessing techniques are applied to the initial hair region so that the final hair region can be segmented precisely. Experimental results show that the average processing time for each image is about 280 ms and the average hair region detection accuracy is above 90%. The proposed algorithm is applied to a facial caricature synthesis system. Experiments proved that with our proposed hair segmentation algorithm the facial caricatures are vivid and satisfying.

  20. Fast and Automatic Ultrasound Simulation from CT Images

    Directory of Open Access Journals (Sweden)

    Weijian Cong

    2013-01-01

    Full Text Available Ultrasound is currently widely used in clinical diagnosis because of its fast and safe imaging principles. As the anatomical structures present in an ultrasound image are not as clear as CT or MRI. Physicians usually need advance clinical knowledge and experience to distinguish diseased tissues. Fast simulation of ultrasound provides a cost-effective way for the training and correlation of ultrasound and the anatomic structures. In this paper, a novel method is proposed for fast simulation of ultrasound from a CT image. A multiscale method is developed to enhance tubular structures so as to simulate the blood flow. The acoustic response of common tissues is generated by weighted integration of adjacent regions on the ultrasound propagation path in the CT image, from which parameters, including attenuation, reflection, scattering, and noise, are estimated simultaneously. The thin-plate spline interpolation method is employed to transform the simulation image between polar and rectangular coordinate systems. The Kaiser window function is utilized to produce integration and radial blurring effects of multiple transducer elements. Experimental results show that the developed method is very fast and effective, allowing realistic ultrasound to be fast generated. Given that the developed method is fully automatic, it can be utilized for ultrasound guided navigation in clinical practice and for training purpose.

  1. Automatic Image Segmentation Using Active Contours with Univariate Marginal Distribution

    Directory of Open Access Journals (Sweden)

    I. Cruz-Aceves

    2013-01-01

    Full Text Available This paper presents a novel automatic image segmentation method based on the theory of active contour models and estimation of distribution algorithms. The proposed method uses the univariate marginal distribution model to infer statistical dependencies between the control points on different active contours. These contours have been generated through an alignment process of reference shape priors, in order to increase the exploration and exploitation capabilities regarding different interactive segmentation techniques. This proposed method is applied in the segmentation of the hollow core in microscopic images of photonic crystal fibers and it is also used to segment the human heart and ventricular areas from datasets of computed tomography and magnetic resonance images, respectively. Moreover, to evaluate the performance of the medical image segmentations compared to regions outlined by experts, a set of similarity measures has been adopted. The experimental results suggest that the proposed image segmentation method outperforms the traditional active contour model and the interactive Tseng method in terms of segmentation accuracy and stability.

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

    Energy Technology Data Exchange (ETDEWEB)

    Qiu, J [Taishan Medical University, Taian, Shandong (China); Washington University in St Louis, St Louis, MO (United States); Li, H. Harlod; Zhang, T; Yang, D [Washington University in St Louis, St Louis, MO (United States); Ma, F [Taishan Medical University, Taian, Shandong (China)

    2015-06-15

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

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

    International Nuclear Information System (INIS)

    Qiu, J; Li, H. Harlod; Zhang, T; Yang, D; Ma, F

    2015-01-01

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

  4. AUTOMATIC BUILDING OUTLINING FROM MULTI-VIEW OBLIQUE IMAGES

    Directory of Open Access Journals (Sweden)

    J. Xiao

    2012-07-01

    Full Text Available Automatic building detection plays an important role in many applications. Multiple overlapped airborne images as well as lidar point clouds are among the most popular data sources used for this purpose. Multi-view overlapped oblique images bear both height and colour information, and additionally we explicitly have access to the vertical extent of objects, therefore we explore the usability of this data source solely to detect and outline buildings in this paper. The outline can then be used for further 3D modelling. In the previous work, building hypotheses are generated using a box model based on detected façades from four directions. In each viewing direction, façade edges extracted from images and height information by stereo matching from an image pair is used for the façade detection. Given that many façades were missing due to occlusion or lack of texture whilst building roofs can be viewed in most images, this work mainly focuses on improve the building box outline by adding roof information. Stereo matched point cloud generated from oblique images are combined with the features from images. Initial roof patches are located in the point cloud. Then AdaBoost is used to integrate geometric and radiometric attributes extracted from oblique image on grid pixel level with the aim to refine the roof area. Generalized contours of the roof pixels are taken as building outlines. The preliminary test has been done by training with five buildings and testing around sixty building clusters. The proposed method performs well concerning covering the irregular roofs as well as improve the sides location of slope roof buildings. Outline result comparing with cadastral map shows almost all above 70% completeness and correctness in an area-based assessment, as well as 20% to 40% improvement in correctness with respect to our previous work.

  5. Bio-EdIP: An automatic approach for in vitro cell confluence images quantification.

    Science.gov (United States)

    Cardona, Andrés; Ariza-Jiménez, Leandro; Uribe, Diego; Arroyave, Johanna C; Galeano, July; Cortés-Mancera, Fabian M

    2017-07-01

    Cell imaging is a widely-employed technique to analyze multiple biological processes. Therefore, simple, accurate and quantitative tools are needed to understand cellular events. For this purpose, Bio-EdIP was developed as a user-friendly tool to quantify confluence levels using cell culture images. The proposed algorithm combines a pre-processing step with subsequent stages that involve local processing techniques and a morphological reconstruction-based segmentation algorithm. Segmentation performance was assessed in three constructed image sets, comparing F-measure scores and AUC values (ROC analysis) for Bio-EdIP, its previous version and TScratch. Furthermore, segmentation results were compared with published algorithms using eight public benchmarks. Bio-EdIP automatically segmented cell-free regions from images of in vitro cell culture. Based on mean F-measure scores and ROC analysis, Bio-EdIP conserved a high performance regardless of image characteristics of the constructed dataset, when compared with its previous version and TScratch. Although acquisition quality of the public dataset affected Bio-EdIP segmentation, performance was better in two out of eight public sets. Bio-EdIP is a user-friendly interface, which is useful for the automatic analysis of confluence levels and cell growth processes using in vitro cell culture images. Here, we also presented new manually annotated data for algorithms evaluation. Copyright © 2017 Elsevier B.V. All rights reserved.

  6. AUTOMATIC ORIENTATION OF LARGE BLOCKS OF OBLIQUE IMAGES

    Directory of Open Access Journals (Sweden)

    E. Rupnik

    2013-05-01

    Full Text Available Nowadays, multi-camera platforms combining nadir and oblique cameras are experiencing a revival. Due to their advantages such as ease of interpretation, completeness through mitigation of occluding areas, as well as system accessibility, they have found their place in numerous civil applications. However, automatic post-processing of such imagery still remains a topic of research. Configuration of cameras poses a challenge on the traditional photogrammetric pipeline used in commercial software and manual measurements are inevitable. For large image blocks it is certainly an impediment. Within theoretical part of the work we review three common least square adjustment methods and recap on possible ways for a multi-camera system orientation. In the practical part we present an approach that successfully oriented a block of 550 images acquired with an imaging system composed of 5 cameras (Canon Eos 1D Mark III with different focal lengths. Oblique cameras are rotated in the four looking directions (forward, backward, left and right by 45° with respect to the nadir camera. The workflow relies only upon open-source software: a developed tool to analyse image connectivity and Apero to orient the image block. The benefits of the connectivity tool are twofold: in terms of computational time and success of Bundle Block Adjustment. It exploits the georeferenced information provided by the Applanix system in constraining feature point extraction to relevant images only, and guides the concatenation of images during the relative orientation. Ultimately an absolute transformation is performed resulting in mean re-projection residuals equal to 0.6 pix.

  7. Automatic selection of resting-state networks with functional magnetic resonance imaging

    Directory of Open Access Journals (Sweden)

    Silvia Francesca eStorti

    2013-05-01

    Full Text Available Functional magnetic resonance imaging (fMRI during a resting-state condition can reveal the co-activation of specific brain regions in distributed networks, called resting-state networks, which are selected by independent component analysis (ICA of the fMRI data. One of the major difficulties with component analysis is the automatic selection of the ICA features related to brain activity. In this study we describe a method designed to automatically select networks of potential functional relevance, specifically, those regions known to be involved in motor function, visual processing, executive functioning, auditory processing, memory, and the default-mode network. To do this, image analysis was based on probabilistic ICA as implemented in FSL software. After decomposition, the optimal number of components was selected by applying a novel algorithm which takes into account, for each component, Pearson's median coefficient of skewness of the spatial maps generated by FSL, followed by clustering, segmentation, and spectral analysis. To evaluate the performance of the approach, we investigated the resting-state networks in 25 subjects. For each subject, three resting-state scans were obtained with a Siemens Allegra 3 T scanner (NYU data set. Comparison of the visually and the automatically identified neuronal networks showed that the algorithm had high accuracy (first scan: 95%, second scan: 95%, third scan: 93% and precision (90%, 90%, 84%. The reproducibility of the networks for visual and automatic selection was very close: it was highly consistent in each subject for the default-mode network (≥ 92% and the occipital network, which includes the medial visual cortical areas (≥ 94%, and consistent for the attention network (≥ 80%, the right and/or left lateralized frontoparietal attention networks, and the temporal-motor network (≥ 80%. The automatic selection method may be used to detect neural networks and reduce subjectivity in ICA

  8. Automatic Hotspot and Sun Glint Detection in UAV Multispectral Images.

    Science.gov (United States)

    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.

  9. Image processing applied to automatic detection of defects during ultrasonic examination

    International Nuclear Information System (INIS)

    Moysan, J.

    1992-10-01

    This work is a study about image processing applied to ultrasonic BSCAN images which are obtained in the field of non destructive testing of weld. The goal is to define what image processing techniques can bring to ameliorate the exploitation of the data collected and, more precisely, what image processing can do to extract the meaningful echoes which enable to characterize and to size the defects. The report presents non destructive testing by ultrasounds in the nuclear field and it indicates specificities of the propagation of ultrasonic waves in austenitic weld. It gives a state of the art of the data processing applied to ultrasonic images in nondestructive evaluation. A new image analysis is then developed. It is based on a powerful tool, the co-occurrence matrix. This matrix enables to represent, in a whole representation, relations between amplitudes of couples of pixels. From the matrix analysis, a new complete and automatic method has been set down in order to define a threshold which separates echoes from noise. An automatic interpretation of the ultrasonic echoes is then possible. Complete validation has been done with standard pieces

  10. AUTOMATIC COREGISTRATION FOR MULTIVIEW SAR IMAGES IN URBAN AREAS

    Directory of Open Access Journals (Sweden)

    Y. Xiang

    2017-09-01

    Full Text Available Due to the high resolution property and the side-looking mechanism of SAR sensors, complex buildings structures make the registration of SAR images in urban areas becomes very hard. In order to solve the problem, an automatic and robust coregistration approach for multiview high resolution SAR images is proposed in the paper, which consists of three main modules. First, both the reference image and the sensed image are segmented into two parts, urban areas and nonurban areas. Urban areas caused by double or multiple scattering in a SAR image have a tendency to show higher local mean and local variance values compared with general homogeneous regions due to the complex structural information. Based on this criterion, building areas are extracted. After obtaining the target regions, L-shape structures are detected using the SAR phase congruency model and Hough transform. The double bounce scatterings formed by wall and ground are shown as strong L- or T-shapes, which are usually taken as the most reliable indicator for building detection. According to the assumption that buildings are rectangular and flat models, planimetric buildings are delineated using the L-shapes, then the reconstructed target areas are obtained. For the orignal areas and the reconstructed target areas, the SAR-SIFT matching algorithm is implemented. Finally, correct corresponding points are extracted by the fast sample consensus (FSC and the transformation model is also derived. The experimental results on a pair of multiview TerraSAR images with 1-m resolution show that the proposed approach gives a robust and precise registration performance, compared with the orignal SAR-SIFT method.

  11. Automatic Coregistration for Multiview SAR Images in Urban Areas

    Science.gov (United States)

    Xiang, Y.; Kang, W.; Wang, F.; You, H.

    2017-09-01

    Due to the high resolution property and the side-looking mechanism of SAR sensors, complex buildings structures make the registration of SAR images in urban areas becomes very hard. In order to solve the problem, an automatic and robust coregistration approach for multiview high resolution SAR images is proposed in the paper, which consists of three main modules. First, both the reference image and the sensed image are segmented into two parts, urban areas and nonurban areas. Urban areas caused by double or multiple scattering in a SAR image have a tendency to show higher local mean and local variance values compared with general homogeneous regions due to the complex structural information. Based on this criterion, building areas are extracted. After obtaining the target regions, L-shape structures are detected using the SAR phase congruency model and Hough transform. The double bounce scatterings formed by wall and ground are shown as strong L- or T-shapes, which are usually taken as the most reliable indicator for building detection. According to the assumption that buildings are rectangular and flat models, planimetric buildings are delineated using the L-shapes, then the reconstructed target areas are obtained. For the orignal areas and the reconstructed target areas, the SAR-SIFT matching algorithm is implemented. Finally, correct corresponding points are extracted by the fast sample consensus (FSC) and the transformation model is also derived. The experimental results on a pair of multiview TerraSAR images with 1-m resolution show that the proposed approach gives a robust and precise registration performance, compared with the orignal SAR-SIFT method.

  12. Simple Methods for Scanner Drift Normalization Validated for Automatic Segmentation of Knee Magnetic Resonance Imaging

    DEFF Research Database (Denmark)

    Dam, Erik Bjørnager

    2018-01-01

    Scanner drift is a well-known magnetic resonance imaging (MRI) artifact characterized by gradual signal degradation and scan intensity changes over time. In addition, hardware and software updates may imply abrupt changes in signal. The combined effects are particularly challenging for automatic...... image analysis methods used in longitudinal studies. The implication is increased measurement variation and a risk of bias in the estimations (e.g. in the volume change for a structure). We proposed two quite different approaches for scanner drift normalization and demonstrated the performance...... for segmentation of knee MRI using the fully automatic KneeIQ framework. The validation included a total of 1975 scans from both high-field and low-field MRI. The results demonstrated that the pre-processing method denoted Atlas Affine Normalization significantly removed scanner drift effects and ensured...

  13. Automatic fog detection for public safety by using camera images

    Science.gov (United States)

    Pagani, Giuliano Andrea; Roth, Martin; Wauben, Wiel

    2017-04-01

    Fog and reduced visibility have considerable impact on the performance of road, maritime, and aeronautical transportation networks. The impact ranges from minor delays to more serious congestions or unavailability of the infrastructure and can even lead to damage or loss of lives. Visibility is traditionally measured manually by meteorological observers using landmarks at known distances in the vicinity of the observation site. Nowadays, distributed cameras facilitate inspection of more locations from one remote monitoring center. The main idea is, however, still deriving the visibility or presence of fog by an operator judging the scenery and the presence of landmarks. Visibility sensors are also used, but they are rather costly and require regular maintenance. Moreover, observers, and in particular sensors, give only visibility information that is representative for a limited area. Hence the current density of visibility observations is insufficient to give detailed information on the presence of fog. Cameras are more and more deployed for surveillance and security reasons in cities and for monitoring traffic along main transportation ways. In addition to this primary use of cameras, we consider cameras as potential sensors to automatically identify low visibility conditions. The approach that we follow is to use machine learning techniques to determine the presence of fog and/or to make an estimation of the visibility. For that purpose a set of features are extracted from the camera images such as the number of edges, brightness, transmission of the image dark channel, fractal dimension. In addition to these image features, we also consider meteorological variables such as wind speed, temperature, relative humidity, and dew point as additional features to feed the machine learning model. The results obtained with a training and evaluation set consisting of 10-minute sampled images for two KNMI locations over a period of 1.5 years by using decision trees methods

  14. Evaluation of automatic image quality assessment in chest CT - A human cadaver study.

    Science.gov (United States)

    Franck, Caro; De Crop, An; De Roo, Bieke; Smeets, Peter; Vergauwen, Merel; Dewaele, Tom; Van Borsel, Mathias; Achten, Eric; Van Hoof, Tom; Bacher, Klaus

    2017-04-01

    The evaluation of clinical image quality (IQ) is important to optimize CT protocols and to keep patient doses as low as reasonably achievable. Considering the significant amount of effort needed for human observer studies, automatic IQ tools are a promising alternative. The purpose of this study was to evaluate automatic IQ assessment in chest CT using Thiel embalmed cadavers. Chest CT's of Thiel embalmed cadavers were acquired at different exposures. Clinical IQ was determined by performing a visual grading analysis. Physical-technical IQ (noise, contrast-to-noise and contrast-detail) was assessed in a Catphan phantom. Soft and sharp reconstructions were made with filtered back projection and two strengths of iterative reconstruction. In addition to the classical IQ metrics, an automatic algorithm was used to calculate image quality scores (IQs). To be able to compare datasets reconstructed with different kernels, the IQs values were normalized. Good correlations were found between IQs and the measured physical-technical image quality: noise (ρ=-1.00), contrast-to-noise (ρ=1.00) and contrast-detail (ρ=0.96). The correlation coefficients between IQs and the observed clinical image quality of soft and sharp reconstructions were 0.88 and 0.93, respectively. The automatic scoring algorithm is a promising tool for the evaluation of thoracic CT scans in daily clinical practice. It allows monitoring of the image quality of a chest protocol over time, without human intervention. Different reconstruction kernels can be compared after normalization of the IQs. Copyright © 2017 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

  15. Automatic airline baggage counting using 3D image segmentation

    Science.gov (United States)

    Yin, Deyu; Gao, Qingji; Luo, Qijun

    2017-06-01

    The baggage number needs to be checked automatically during baggage self-check-in. A fast airline baggage counting method is proposed in this paper using image segmentation based on height map which is projected by scanned baggage 3D point cloud. There is height drop in actual edge of baggage so that it can be detected by the edge detection operator. And then closed edge chains are formed from edge lines that is linked by morphological processing. Finally, the number of connected regions segmented by closed chains is taken as the baggage number. Multi-bag experiment that is performed on the condition of different placement modes proves the validity of the method.

  16. Automatic segmentation of blood vessels from retinal fundus images ...

    Indian Academy of Sciences (India)

    The retinal blood vessels were segmented through color space conversion and color channel extraction, image pre-processing, Gabor filtering, image postprocessing, feature construction through application of principal component analysis, k-means clustering and first level classification using Naïve–Bayes classification ...

  17. MatchGUI: A Graphical MATLAB-Based Tool for Automatic Image Co-Registration

    Science.gov (United States)

    Ansar, Adnan I.

    2011-01-01

    MatchGUI software, based on MATLAB, automatically matches two images and displays the match result by superimposing one image on the other. A slider bar allows focus to shift between the two images. There are tools for zoom, auto-crop to overlap region, and basic image markup. Given a pair of ortho-rectified images (focused primarily on Mars orbital imagery for now), this software automatically co-registers the imagery so that corresponding image pixels are aligned. MatchGUI requires minimal user input, and performs a registration over scale and inplane rotation fully automatically

  18. Extended morphological processing: a practical method for automatic spot detection of biological markers from microscopic images.

    Science.gov (United States)

    Kimori, Yoshitaka; Baba, Norio; Morone, Nobuhiro

    2010-07-08

    A reliable extraction technique for resolving multiple spots in light or electron microscopic images is essential in investigations of the spatial distribution and dynamics of specific proteins inside cells and tissues. Currently, automatic spot extraction and characterization in complex microscopic images poses many challenges to conventional image processing methods. A new method to extract closely located, small target spots from biological images is proposed. This method starts with a simple but practical operation based on the extended morphological top-hat transformation to subtract an uneven background. The core of our novel approach is the following: first, the original image is rotated in an arbitrary direction and each rotated image is opened with a single straight line-segment structuring element. Second, the opened images are unified and then subtracted from the original image. To evaluate these procedures, model images of simulated spots with closely located targets were created and the efficacy of our method was compared to that of conventional morphological filtering methods. The results showed the better performance of our method. The spots of real microscope images can be quantified to confirm that the method is applicable in a given practice. Our method achieved effective spot extraction under various image conditions, including aggregated target spots, poor signal-to-noise ratio, and large variations in the background intensity. Furthermore, it has no restrictions with respect to the shape of the extracted spots. The features of our method allow its broad application in biological and biomedical image information analysis.

  19. Automatic tumour volume delineation in respiratory-gated PET images

    International Nuclear Information System (INIS)

    Gubbi, Jayavardhana; Palaniswami, Marimuthu; Kanakatte, Aparna; Mani, Nallasamy; Kron, Tomas; Binns, David; Srinivasan, Bala

    2011-01-01

    Positron emission tomography (PET) is a state-of-the-art functional imaging technique used in the accurate detection of cancer. The main problem with the tumours present in the lungs is that they are non-stationary during each respiratory cycle. Tumours in the lungs can get displaced up to 2.5 cm during respiration. Accurate detection of the tumour enables avoiding the addition of extra margin around the tumour that is usually used during radiotherapy treatment planning. This paper presents a novel method to detect and track tumour in respiratory-gated PET images. The approach followed to achieve this task is to automatically delineate the tumour from the first frame using support vector machines. The resulting volume and position information from the first frame is used in tracking its motion in the subsequent frames with the help of level set (LS) deformable model. An excellent accuracy of 97% is obtained using wavelets and support vector machines. The volume calculated as a result of the machine learning (ML) stage is used as a constraint for deformable models and the tumour is tracked in the remaining seven phases of the respiratory cycle. As a result, the complete information about tumour movement during each respiratory cycle is available in relatively short time. The combination of the LS and ML approach accurately delineated the tumour volume from all frames, thereby providing a scope of using PET images towards planning an accurate and effective radiotherapy treatment for lung cancer.

  20. AUTOMATIC 3D MAPPING USING MULTIPLE UNCALIBRATED CLOSE RANGE IMAGES

    Directory of Open Access Journals (Sweden)

    M. Rafiei

    2013-09-01

    Full Text Available Automatic three-dimensions modeling of the real world is an important research topic in the geomatics and computer vision fields for many years. By development of commercial digital cameras and modern image processing techniques, close range photogrammetry is vastly utilized in many fields such as structure measurements, topographic surveying, architectural and archeological surveying, etc. A non-contact photogrammetry provides methods to determine 3D locations of objects from two-dimensional (2D images. Problem of estimating the locations of 3D points from multiple images, often involves simultaneously estimating both 3D geometry (structure and camera pose (motion, it is commonly known as structure from motion (SfM. In this research a step by step approach to generate the 3D point cloud of a scene is considered. After taking images with a camera, we should detect corresponding points in each two views. Here an efficient SIFT method is used for image matching for large baselines. After that, we must retrieve the camera motion and 3D position of the matched feature points up to a projective transformation (projective reconstruction. Lacking additional information on the camera or the scene makes the parallel lines to be unparalleled. The results of SfM computation are much more useful if a metric reconstruction is obtained. Therefor multiple views Euclidean reconstruction applied and discussed. To refine and achieve the precise 3D points we use more general and useful approach, namely bundle adjustment. At the end two real cases have been considered to reconstruct (an excavation and a tower.

  1. Automatic multiresolution age-related macular degeneration detection from fundus images

    Science.gov (United States)

    Garnier, Mickaël.; Hurtut, Thomas; Ben Tahar, Houssem; Cheriet, Farida

    2014-03-01

    Age-related Macular Degeneration (AMD) is a leading cause of legal blindness. As the disease progress, visual loss occurs rapidly, therefore early diagnosis is required for timely treatment. Automatic, fast and robust screening of this widespread disease should allow an early detection. Most of the automatic diagnosis methods in the literature are based on a complex segmentation of the drusen, targeting a specific symptom of the disease. In this paper, we present a preliminary study for AMD detection from color fundus photographs using a multiresolution texture analysis. We analyze the texture at several scales by using a wavelet decomposition in order to identify all the relevant texture patterns. Textural information is captured using both the sign and magnitude components of the completed model of Local Binary Patterns. An image is finally described with the textural pattern distributions of the wavelet coefficient images obtained at each level of decomposition. We use a Linear Discriminant Analysis for feature dimension reduction, to avoid the curse of dimensionality problem, and image classification. Experiments were conducted on a dataset containing 45 images (23 healthy and 22 diseased) of variable quality and captured by different cameras. Our method achieved a recognition rate of 93:3%, with a specificity of 95:5% and a sensitivity of 91:3%. This approach shows promising results at low costs that in agreement with medical experts as well as robustness to both image quality and fundus camera model.

  2. Quadrant Dynamic with Automatic Plateau Limit Histogram Equalization for Image Enhancement

    Directory of Open Access Journals (Sweden)

    P. Jagatheeswari

    2014-01-01

    Full Text Available The fundamental and important preprocessing stage in image processing is the image contrast enhancement technique. Histogram equalization is an effective contrast enhancement technique. In this paper, a histogram equalization based technique called quadrant dynamic with automatic plateau limit histogram equalization (QDAPLHE is introduced. In this method, a hybrid of dynamic and clipped histogram equalization methods are used to increase the brightness preservation and to reduce the overenhancement. Initially, the proposed QDAPLHE algorithm passes the input image through a median filter to remove the noises present in the image. Then the histogram of the filtered image is divided into four subhistograms while maintaining second separated point as the mean brightness. Then the clipping process is implemented by calculating automatically the plateau limit as the clipped level. The clipped portion of the histogram is modified to reduce the loss of image intensity value. Finally the clipped portion is redistributed uniformly to the entire dynamic range and the conventional histogram equalization is executed in each subhistogram independently. Based on the qualitative and the quantitative analysis, the QDAPLHE method outperforms some existing methods in literature.

  3. Semi-automatic ROI placement system for analysis of brain PET images based on elastic model. Application to diagnosis of Alzheimer's disease

    International Nuclear Information System (INIS)

    Ohyama, Masashi; Mishina, Masahiro; Kitamura, Shin; Katayama, Yasuo; Senda, Michio; Tanizaki, Naoki; Ishii, Kenji

    2000-01-01

    PET with 18F-fluorodeoxyglucose (FDG) is a useful technique to image cerebral glucose metabolism and to detect patients with Alzheimer's disease in the early stage, in which characteristic temporoparietal hypometabolism is visualized. We have developed a new system, in which the standard brain ROI atlas made of networks of segments is elastically transformed to match the subject brain images, so that standard ROIs defined on the segments are placed on the individual brain images and are used to measure radioactivity over each brain region. We applied this methods to Alzheimer's disease. This method was applied to the images of 10 normal subjects (ages 55 +/- 12) and 21 patients clinically diagnosed as Alzheimer's disease (age 61 +/- 10). The FDG uptake reflecting glucose metabolism was evaluated with SUV, i.e. decay corrected radioactivity divided by injected dose per body weight in (Bq/ml)/(Bq/g). The system worked all right in every subject including those with extensive hypometabolism. Alzheimer patients showed markedly lower in the parietal cortex (4.0-4.1). When the threshold value of FDG uptake in the parietal lobe was set as 5 (Bq/ml)/(Bq/g), we could discriminate the patients with Alzheimer's disease from the normal subjects. The sensitivity was 86% and the specificity was 90%. This system can assist diagnosis of FDG images and may be useful for treating data of a large number of subjects; e.g. when PET is applied to health screening. (author)

  4. Interactive vs. automatic ultrasound image segmentation methods for staging hepatic lipidosis.

    Science.gov (United States)

    Weijers, Gert; Starke, Alexander; Haudum, Alois; Thijssen, Johan M; Rehage, Jürgen; De Korte, Chris L

    2010-07-01

    The aim of this study was to test the hypothesis that automatic segmentation of vessels in ultrasound (US) images can produce similar or better results in grading fatty livers than interactive segmentation. A study was performed in postpartum dairy cows (N=151), as an animal model of human fatty liver disease, to test this hypothesis. Five transcutaneous and five intraoperative US liver images were acquired in each animal and a liverbiopsy was taken. In liver tissue samples, triacylglycerol (TAG) was measured by biochemical analysis and hepatic diseases other than hepatic lipidosis were excluded by histopathologic examination. Ultrasonic tissue characterization (UTC) parameters--Mean echo level, standard deviation (SD) of echo level, signal-to-noise ratio (SNR), residual attenuation coefficient (ResAtt) and axial and lateral speckle size--were derived using a computer-aided US (CAUS) protocol and software package. First, the liver tissue was interactively segmented by two observers. With increasing fat content, fewer hepatic vessels were visible in the ultrasound images and, therefore, a smaller proportion of the liver needed to be excluded from these images. Automatic-segmentation algorithms were implemented and it was investigated whether better results could be achieved than with the subjective and time-consuming interactive-segmentation procedure. The automatic-segmentation algorithms were based on both fixed and adaptive thresholding techniques in combination with a 'speckle'-shaped moving-window exclusion technique. All data were analyzed with and without postprocessing as contained in CAUS and with different automated-segmentation techniques. This enabled us to study the effect of the applied postprocessing steps on single and multiple linear regressions ofthe various UTC parameters with TAG. Improved correlations for all US parameters were found by using automatic-segmentation techniques. Stepwise multiple linear-regression formulas where derived and used

  5. Real-time automatic fiducial marker tracking in low contrast cine-MV images

    Energy Technology Data Exchange (ETDEWEB)

    Lin, Wei-Yang; Lin, Shu-Fang; Yang, Sheng-Chang; Liou, Shu-Cheng; Nath, Ravinder; Liu Wu [Department of Computer Science and Information Engineering, National Chung Cheng University, Taiwan, 62102 (China); Department of Therapeutic Radiology, Yale University School of Medicine, New Haven, Connecticut 06510-3220 (United States)

    2013-01-15

    Purpose: To develop a real-time automatic method for tracking implanted radiographic markers in low-contrast cine-MV patient images used in image-guided radiation therapy (IGRT). Methods: Intrafraction motion tracking using radiotherapy beam-line MV images have gained some attention recently in IGRT because no additional imaging dose is introduced. However, MV images have much lower contrast than kV images, therefore a robust and automatic algorithm for marker detection in MV images is a prerequisite. Previous marker detection methods are all based on template matching or its derivatives. Template matching needs to match object shape that changes significantly for different implantation and projection angle. While these methods require a large number of templates to cover various situations, they are often forced to use a smaller number of templates to reduce the computation load because their methods all require exhaustive search in the region of interest. The authors solve this problem by synergetic use of modern but well-tested computer vision and artificial intelligence techniques; specifically the authors detect implanted markers utilizing discriminant analysis for initialization and use mean-shift feature space analysis for sequential tracking. This novel approach avoids exhaustive search by exploiting the temporal correlation between consecutive frames and makes it possible to perform more sophisticated detection at the beginning to improve the accuracy, followed by ultrafast sequential tracking after the initialization. The method was evaluated and validated using 1149 cine-MV images from two prostate IGRT patients and compared with manual marker detection results from six researchers. The average of the manual detection results is considered as the ground truth for comparisons. Results: The average root-mean-square errors of our real-time automatic tracking method from the ground truth are 1.9 and 2.1 pixels for the two patients (0.26 mm/pixel). The

  6. Real-time automatic fiducial marker tracking in low contrast cine-MV images

    International Nuclear Information System (INIS)

    Lin, Wei-Yang; Lin, Shu-Fang; Yang, Sheng-Chang; Liou, Shu-Cheng; Nath, Ravinder; Liu Wu

    2013-01-01

    Purpose: To develop a real-time automatic method for tracking implanted radiographic markers in low-contrast cine-MV patient images used in image-guided radiation therapy (IGRT). Methods: Intrafraction motion tracking using radiotherapy beam-line MV images have gained some attention recently in IGRT because no additional imaging dose is introduced. However, MV images have much lower contrast than kV images, therefore a robust and automatic algorithm for marker detection in MV images is a prerequisite. Previous marker detection methods are all based on template matching or its derivatives. Template matching needs to match object shape that changes significantly for different implantation and projection angle. While these methods require a large number of templates to cover various situations, they are often forced to use a smaller number of templates to reduce the computation load because their methods all require exhaustive search in the region of interest. The authors solve this problem by synergetic use of modern but well-tested computer vision and artificial intelligence techniques; specifically the authors detect implanted markers utilizing discriminant analysis for initialization and use mean-shift feature space analysis for sequential tracking. This novel approach avoids exhaustive search by exploiting the temporal correlation between consecutive frames and makes it possible to perform more sophisticated detection at the beginning to improve the accuracy, followed by ultrafast sequential tracking after the initialization. The method was evaluated and validated using 1149 cine-MV images from two prostate IGRT patients and compared with manual marker detection results from six researchers. The average of the manual detection results is considered as the ground truth for comparisons. Results: The average root-mean-square errors of our real-time automatic tracking method from the ground truth are 1.9 and 2.1 pixels for the two patients (0.26 mm/pixel). The

  7. Automatic segmentation of dynamic neuroreceptor single-photon emission tomography images using fuzzy clustering

    International Nuclear Information System (INIS)

    Acton, P.D.; Pilowsky, L.S.; Kung, H.F.; Ell, P.J.

    1999-01-01

    The segmentation of medical images is one of the most important steps in the analysis and quantification of imaging data. However, partial volume artefacts make accurate tissue boundary definition difficult, particularly for images with lower resolution commonly used in nuclear medicine. In single-photon emission tomography (SPET) neuroreceptor studies, areas of specific binding are usually delineated by manually drawing regions of interest (ROIs), a time-consuming and subjective process. This paper applies the technique of fuzzy c-means clustering (FCM) to automatically segment dynamic neuroreceptor SPET images. Fuzzy clustering was tested using a realistic, computer-generated, dynamic SPET phantom derived from segmenting an MR image of an anthropomorphic brain phantom. Also, the utility of applying FCM to real clinical data was assessed by comparison against conventional ROI analysis of iodine-123 iodobenzamide (IBZM) binding to dopamine D 2 /D 3 receptors in the brains of humans. In addition, a further test of the methodology was assessed by applying FCM segmentation to [ 123 I]IDAM images (5-iodo-2-[[2-2-[(dimethylamino)methyl]phenyl]thio] benzyl alcohol) of serotonin transporters in non-human primates. In the simulated dynamic SPET phantom, over a wide range of counts and ratios of specific binding to background, FCM correlated very strongly with the true counts (correlation coefficient r 2 >0.99, P 123 I]IBZM data comparable with manual ROI analysis, with the binding ratios derived from both methods significantly correlated (r 2 =0.83, P<0.0001). Fuzzy clustering is a powerful tool for the automatic, unsupervised segmentation of dynamic neuroreceptor SPET images. Where other automated techniques fail completely, and manual ROI definition would be highly subjective, FCM is capable of segmenting noisy images in a robust and repeatable manner. (orig.)

  8. Spinal imaging and image analysis

    CERN Document Server

    Yao, Jianhua

    2015-01-01

    This book is instrumental to building a bridge between scientists and clinicians in the field of spine imaging by introducing state-of-the-art computational methods in the context of clinical applications.  Spine imaging via computed tomography, magnetic resonance imaging, and other radiologic imaging modalities, is essential for noninvasively visualizing and assessing spinal pathology. Computational methods support and enhance the physician’s ability to utilize these imaging techniques for diagnosis, non-invasive treatment, and intervention in clinical practice. Chapters cover a broad range of topics encompassing radiological imaging modalities, clinical imaging applications for common spine diseases, image processing, computer-aided diagnosis, quantitative analysis, data reconstruction and visualization, statistical modeling, image-guided spine intervention, and robotic surgery. This volume serves a broad audience as  contributions were written by both clinicians and researchers, which reflects the inte...

  9. Mirion--a software package for automatic processing of mass spectrometric images.

    Science.gov (United States)

    Paschke, C; Leisner, A; Hester, A; Maass, K; Guenther, S; Bouschen, W; Spengler, B

    2013-08-01

    Mass spectrometric imaging (MSI) techniques are of growing interest for the Life Sciences. In recent years, the development of new instruments employing ion sources that are tailored for spatial scanning allowed the acquisition of large data sets. A subsequent data processing, however, is still a bottleneck in the analytical process, as a manual data interpretation is impossible within a reasonable time frame. The transformation of mass spectrometric data into spatial distribution images of detected compounds turned out to be the most appropriate method to visualize the results of such scans, as humans are able to interpret images faster and easier than plain numbers. Image generation, thus, is a time-consuming and complex yet very efficient task. The free software package "Mirion," presented in this paper, allows the handling and analysis of data sets acquired by mass spectrometry imaging. Mirion can be used for image processing of MSI data obtained from many different sources, as it uses the HUPO-PSI-based standard data format imzML, which is implemented in the proprietary software of most of the mass spectrometer companies. Different graphical representations of the recorded data are available. Furthermore, automatic calculation and overlay of mass spectrometric images promotes direct comparison of different analytes for data evaluation. The program also includes tools for image processing and image analysis.

  10. Dynamic Analysis of a Pendulum Dynamic Automatic Balancer

    Directory of Open Access Journals (Sweden)

    Jin-Seung Sohn

    2007-01-01

    Full Text Available The automatic dynamic balancer is a device to reduce the vibration from unbalanced mass of rotors. Instead of considering prevailing ball automatic dynamic balancer, pendulum automatic dynamic balancer is analyzed. For the analysis of dynamic stability and behavior, the nonlinear equations of motion for a system are derived with respect to polar coordinates by the Lagrange's equations. The perturbation method is applied to investigate the dynamic behavior of the system around the equilibrium position. Based on the linearized equations, the dynamic stability of the system around the equilibrium positions is investigated by the eigenvalue analysis.

  11. Retinal Imaging and Image Analysis

    Science.gov (United States)

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

    2011-01-01

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

  12. Describing Old Czech Declension Patterns for Automatic Text Analysis

    Czech Academy of Sciences Publication Activity Database

    Jínová, P.; Lehečka, Boris; Oliva jr., Karel

    -, č. 13 (2014), s. 7-17 ISSN 1579-8372 Institutional support: RVO:68378092 Keywords : Old Czech morphology * declension patterns * automatic text analysis * i-stems * ja-stems Subject RIV: AI - Linguistics

  13. Automatic multimodal real-time tracking for image plane alignment in interventional Magnetic Resonance Imaging

    International Nuclear Information System (INIS)

    Neumann, Markus

    2014-01-01

    Interventional magnetic resonance imaging (MRI) aims at performing minimally invasive percutaneous interventions, such as tumor ablations and biopsies, under MRI guidance. During such interventions, the acquired MR image planes are typically aligned to the surgical instrument (needle) axis and to surrounding anatomical structures of interest in order to efficiently monitor the advancement in real-time of the instrument inside the patient's body. Object tracking inside the MRI is expected to facilitate and accelerate MR-guided interventions by allowing to automatically align the image planes to the surgical instrument. In this PhD thesis, an image-based work-flow is proposed and refined for automatic image plane alignment. An automatic tracking work-flow was developed, performing detection and tracking of a passive marker directly in clinical real-time images. This tracking work-flow is designed for fully automated image plane alignment, with minimization of tracking-dedicated time. Its main drawback is its inherent dependence on the slow clinical MRI update rate. First, the addition of motion estimation and prediction with a Kalman filter was investigated and improved the work-flow tracking performance. Second, a complementary optical sensor was used for multi-sensor tracking in order to decouple the tracking update rate from the MR image acquisition rate. Performance of the work-flow was evaluated with both computer simulations and experiments using an MR compatible test bed. Results show a high robustness of the multi-sensor tracking approach for dynamic image plane alignment, due to the combination of the individual strengths of each sensor. (author)

  14. Comparison of liver volumetry on contrast‐enhanced CT images: one semiautomatic and two automatic approaches

    Science.gov (United States)

    Cai, Wei; He, Baochun; Fang, Chihua

    2016-01-01

    This study was to evaluate the accuracy, consistency, and efficiency of three liver volumetry methods— one interactive method, an in‐house‐developed 3D medical Image Analysis (3DMIA) system, one automatic active shape model (ASM)‐based segmentation, and one automatic probabilistic atlas (PA)‐guided segmentation method on clinical contrast‐enhanced CT images. Forty‐two datasets, including 27 normal liver and 15 space‐occupying liver lesion patients, were retrospectively included in this study. The three methods — one semiautomatic 3DMIA, one automatic ASM‐based, and one automatic PA‐based liver volumetry — achieved an accuracy with VD (volume difference) of −1.69%,−2.75%, and 3.06% in the normal group, respectively, and with VD of −3.20%,−3.35%, and 4.14% in the space‐occupying lesion group, respectively. However, the three methods achieved an efficiency of 27.63 mins, 1.26 mins, 1.18 mins on average, respectively, compared with the manual volumetry, which took 43.98 mins. The high intraclass correlation coefficient between the three methods and the manual method indicated an excellent agreement on liver volumetry. Significant differences in segmentation time were observed between the three methods (3DMIA, ASM, and PA) and the manual volumetry (pvolumetries (ASM and PA) and the semiautomatic volumetry (3DMIA) (pvolumetry agreed well with manual gold standard in both the normal liver group and the space‐occupying lesion group. The ASM‐ and PA‐based automatic segmentation have better efficiency in clinical use. PACS number(s): 87.55.‐x PMID:27929487

  15. Analysis of carotid artery plaque and wall boundaries on CT images by using a semi-automatic method based on level set model

    International Nuclear Information System (INIS)

    Saba, Luca; Sannia, Stefano; Ledda, Giuseppe; Gao, Hao; Acharya, U.R.; Suri, Jasjit S.

    2012-01-01

    The purpose of this study was to evaluate the potentialities of a semi-automated technique in the detection and measurement of the carotid artery plaque. Twenty-two consecutive patients (18 males, 4 females; mean age 62 years) examined with MDCTA from January 2011 to March 2011 were included in this retrospective study. Carotid arteries are examined with a 16-multi-detector-row CT system, and for each patient, the most diseased carotid was selected. In the first phase, the carotid plaque was identified and one experienced radiologist manually traced the inner and outer boundaries by using polyline and radial distance method (PDM and RDM, respectively). In the second phase, the carotid inner and outer boundaries were traced with an automated algorithm: level-set-method (LSM). Data were compared by using Pearson rho correlation, Bland-Altman, and regression. A total of 715 slices were analyzed. The mean thickness of the plaque using the reference PDM was 1.86 mm whereas using the LSM-PDM was 1.96 mm; using the reference RDM was 2.06 mm whereas using the LSM-RDM was 2.03 mm. The correlation values between the references, the LSM, the PDM and the RDM were 0.8428, 0.9921, 0.745 and 0.6425. Bland-Altman demonstrated a very good agreement in particular with the RDM method. Results of our study indicate that LSM method can automatically measure the thickness of the plaque and that the best results are obtained with the RDM. Our results suggest that advanced computer-based algorithms can identify and trace the plaque boundaries like an experienced human reader. (orig.)

  16. Analysis of carotid artery plaque and wall boundaries on CT images by using a semi-automatic method based on level set model

    Energy Technology Data Exchange (ETDEWEB)

    Saba, Luca; Sannia, Stefano; Ledda, Giuseppe [University of Cagliari - Azienda Ospedaliero Universitaria di Cagliari, Department of Radiology, Monserrato, Cagliari (Italy); Gao, Hao [University of Strathclyde, Signal Processing Centre for Excellence in Signal and Image Processing, Department of Electronic and Electrical Engineering, Glasgow (United Kingdom); Acharya, U.R. [Ngee Ann Polytechnic University, Department of Electronics and Computer Engineering, Clementi (Singapore); Suri, Jasjit S. [Biomedical Technologies Inc., Denver, CO (United States); Idaho State University (Aff.), Pocatello, ID (United States)

    2012-11-15

    The purpose of this study was to evaluate the potentialities of a semi-automated technique in the detection and measurement of the carotid artery plaque. Twenty-two consecutive patients (18 males, 4 females; mean age 62 years) examined with MDCTA from January 2011 to March 2011 were included in this retrospective study. Carotid arteries are examined with a 16-multi-detector-row CT system, and for each patient, the most diseased carotid was selected. In the first phase, the carotid plaque was identified and one experienced radiologist manually traced the inner and outer boundaries by using polyline and radial distance method (PDM and RDM, respectively). In the second phase, the carotid inner and outer boundaries were traced with an automated algorithm: level-set-method (LSM). Data were compared by using Pearson rho correlation, Bland-Altman, and regression. A total of 715 slices were analyzed. The mean thickness of the plaque using the reference PDM was 1.86 mm whereas using the LSM-PDM was 1.96 mm; using the reference RDM was 2.06 mm whereas using the LSM-RDM was 2.03 mm. The correlation values between the references, the LSM, the PDM and the RDM were 0.8428, 0.9921, 0.745 and 0.6425. Bland-Altman demonstrated a very good agreement in particular with the RDM method. Results of our study indicate that LSM method can automatically measure the thickness of the plaque and that the best results are obtained with the RDM. Our results suggest that advanced computer-based algorithms can identify and trace the plaque boundaries like an experienced human reader. (orig.)

  17. Automatic detection of NIL defects using microscopy and image processing

    KAUST Repository

    Pietroy, David

    2013-12-01

    Nanoimprint Lithography (NIL) is a promising technology for low cost and large scale nanostructure fabrication. This technique is based on a contact molding-demolding process, that can produce number of defects such as incomplete filling, negative patterns, sticking. In this paper, microscopic imaging combined to a specific processing algorithm is used to detect numerically defects in printed patterns. Results obtained for 1D and 2D imprinted gratings with different microscopic image magnifications are presented. Results are independent on the device which captures the image (optical, confocal or electron microscope). The use of numerical images allows the possibility to automate the detection and to compute a statistical analysis of defects. This method provides a fast analysis of printed gratings and could be used to monitor the production of such structures. © 2013 Elsevier B.V. All rights reserved.

  18. Neural network for automatic analysis of motility data

    DEFF Research Database (Denmark)

    Jakobsen, Erik; Kruse-Andersen, S; Kolberg, Jens Godsk

    1994-01-01

    comparable. However, the neural network recognized pressure peaks clearly generated by muscular activity that had escaped detection by the conventional program. In conclusion, we believe that neurocomputing has potential advantages for automatic analysis of gastrointestinal motility data.......Continuous recording of intraluminal pressures for extended periods of time is currently regarded as a valuable method for detection of esophageal motor abnormalities. A subsequent automatic analysis of the resulting motility data relies on strict mathematical criteria for recognition of pressure...

  19. VISUAL PERCEPTION BASED AUTOMATIC RECOGNITION OF CELL MOSAICS IN HUMAN CORNEAL ENDOTHELIUMMICROSCOPY IMAGES

    Directory of Open Access Journals (Sweden)

    Yann Gavet

    2011-05-01

    Full Text Available The human corneal endothelium can be observed with two types of microscopes: classical optical microscope for ex-vivo imaging, and specular optical microscope for in-vivo imaging. The quality of the cornea is correlated to the endothelial cell density and morphometry. Automatic methods to analyze the human corneal endothelium images are still not totally efficient. Image analysis methods that focus only on cell contours do not give good results in presence of noise and of bad conditions of acquisition. More elaborated methods introduce regional informations in order to performthe cell contours completion, thus implementing the duality contour-region. Their good performance can be explained by their connections with several basic principles of human visual perception (Gestalt Theory and Marr's computational theory.

  20. Automatic segmentation of the choroid in enhanced depth imaging optical coherence tomography images.

    Science.gov (United States)

    Tian, Jing; Marziliano, Pina; Baskaran, Mani; Tun, Tin Aung; Aung, Tin

    2013-03-01

    Enhanced Depth Imaging (EDI) optical coherence tomography (OCT) provides high-definition cross-sectional images of the choroid in vivo, and hence is used in many clinical studies. However, the quantification of the choroid depends on the manual labelings of two boundaries, Bruch's membrane and the choroidal-scleral interface. This labeling process is tedious and subjective of inter-observer differences, hence, automatic segmentation of the choroid layer is highly desirable. In this paper, we present a fast and accurate algorithm that could segment the choroid automatically. Bruch's membrane is detected by searching the pixel with the biggest gradient value above the retinal pigment epithelium (RPE) and the choroidal-scleral interface is delineated by finding the shortest path of the graph formed by valley pixels using Dijkstra's algorithm. The experiments comparing automatic segmentation results with the manual labelings are conducted on 45 EDI-OCT images and the average of Dice's Coefficient is 90.5%, which shows good consistency of the algorithm with the manual labelings. The processing time for each image is about 1.25 seconds.

  1. AUTOMATIC INTERPRETATION OF HIGH RESOLUTION SAR IMAGES: FIRST RESULTS OF SAR IMAGE SIMULATION FOR SINGLE BUILDINGS

    Directory of Open Access Journals (Sweden)

    J. Tao

    2012-09-01

    Full Text Available Due to the all-weather data acquisition capabilities, high resolution space borne Synthetic Aperture Radar (SAR plays an important role in remote sensing applications like change detection. However, because of the complex geometric mapping of buildings in urban areas, SAR images are often hard to interpret. SAR simulation techniques ease the visual interpretation of SAR images, while fully automatic interpretation is still a challenge. This paper presents a method for supporting the interpretation of high resolution SAR images with simulated radar images using a LiDAR digital surface model (DSM. Line features are extracted from the simulated and real SAR images and used for matching. A single building model is generated from the DSM and used for building recognition in the SAR image. An application for the concept is presented for the city centre of Munich where the comparison of the simulation to the TerraSAR-X data shows a good similarity. Based on the result of simulation and matching, special features (e.g. like double bounce lines, shadow areas etc. can be automatically indicated in SAR image.

  2. Automatic analysis of macerals and reflectance; Analisis Automatico de Macerales y Reflectancia

    Energy Technology Data Exchange (ETDEWEB)

    Catalina, J.C.; Alarcon, D.; Gonzalez Prado, J.

    1998-12-01

    A new system has been developed to perform automatically macerals and reflectance analysis of single-seam bituminous coals, improving the interlaboratory accuracy of these types of analyses. The system follows the same steps as the manual method, requiring a human operator for preparation of coal samples and system startup; then, sample scanning, microscope focusing and field centre analysis are fully automatic. The main and most innovative idea of this approach is to coordinate an expert system with an image processing system, using both reflectance and morphological information. In this way, the system tries to reproduce the analysis procedure followed by a human expert in petrography. (Author)

  3. Automatic Cell Segmentation in Fluorescence Images of Confluent Cell Monolayers Using Multi-object Geometric Deformable Model.

    Science.gov (United States)

    Yang, Zhen; Bogovic, John A; Carass, Aaron; Ye, Mao; Searson, Peter C; Prince, Jerry L

    2013-03-13

    With the rapid development of microscopy for cell imaging, there is a strong and growing demand for image analysis software to quantitatively study cell morphology. Automatic cell segmentation is an important step in image analysis. Despite substantial progress, there is still a need to improve the accuracy, efficiency, and adaptability to different cell morphologies. In this paper, we propose a fully automatic method for segmenting cells in fluorescence images of confluent cell monolayers. This method addresses several challenges through a combination of ideas. 1) It realizes a fully automatic segmentation process by first detecting the cell nuclei as initial seeds and then using a multi-object geometric deformable model (MGDM) for final segmentation. 2) To deal with different defects in the fluorescence images, the cell junctions are enhanced by applying an order-statistic filter and principal curvature based image operator. 3) The final segmentation using MGDM promotes robust and accurate segmentation results, and guarantees no overlaps and gaps between neighboring cells. The automatic segmentation results are compared with manually delineated cells, and the average Dice coefficient over all distinguishable cells is 0.88.

  4. Automatic sample changer for neutron activation analysis at CDTN, Brazil

    International Nuclear Information System (INIS)

    Aimore Dutra Neto; Oliveira Pelaes, Ana Clara; Jacimovic, Radojko

    2018-01-01

    An automatic sample changer was recently developed and installed in the Neutron Activation Analysis (NAA) Laboratory. The certified reference material BCR-320R, Channel Sediment, was analysed in order to verify the reliability of the results obtained by NAA, k 0 -standardisation method, using this automatic system during the gamma-ray measurement step. The results were compared to those manually obtained. The values pointed out that the automatic sample changer is working properly. This changer will increase the productiveness of the neutron activation technique applied at Nuclear Technology Development Centre, CDTN/CNEN expanding its competitiveness as an analytical technique in relation to other techniques. (author)

  5. Automatic performance tuning of parallel and accelerated seismic imaging kernels

    KAUST Repository

    Haberdar, Hakan

    2014-01-01

    With the increased complexity and diversity of mainstream high performance computing systems, significant effort is required to tune parallel applications in order to achieve the best possible performance for each particular platform. This task becomes more and more challenging and requiring a larger set of skills. Automatic performance tuning is becoming a must for optimizing applications such as Reverse Time Migration (RTM) widely used in seismic imaging for oil and gas exploration. An empirical search based auto-tuning approach is applied to the MPI communication operations of the parallel isotropic and tilted transverse isotropic kernels. The application of auto-tuning using the Abstract Data and Communication Library improved the performance of the MPI communications as well as developer productivity by providing a higher level of abstraction. Keeping productivity in mind, we opted toward pragma based programming for accelerated computation on latest accelerated architectures such as GPUs using the fairly new OpenACC standard. The same auto-tuning approach is also applied to the OpenACC accelerated seismic code for optimizing the compute intensive kernel of the Reverse Time Migration application. The application of such technique resulted in an improved performance of the original code and its ability to adapt to different execution environments.

  6. Cost-benefit analysis of the ATM automatic deposit service

    Directory of Open Access Journals (Sweden)

    Ivica Županović

    2015-03-01

    Full Text Available Bankers and other financial experts have analyzed the value of automated teller machines (ATM in terms of growing consumer demand, rising costs of technology development, decreasing profitability and market share. This paper presents a step-by-step cost-benefit analysis of the ATM automatic deposit service. The first step is to determine user attitudes towards using ATM automatic deposit service by using the Technology Acceptance Model (TAM. The second step is to determine location priorities for ATMs that provide automatic deposit services using the Analytic Hierarchy Process (AHP model. The results of the previous steps enable a highly efficient application of cost-benefit analysis for evaluating costs and benefits of automatic deposit services. To understand fully the proposed procedure outside of theoretical terms, a real-world application of a case study is conducted.

  7. Fast automatic quantitative cell replication with fluorescent live cell imaging

    Directory of Open Access Journals (Sweden)

    Wang Ching-Wei

    2012-01-01

    Full Text Available Abstract Background live cell imaging is a useful tool to monitor cellular activities in living systems. It is often necessary in cancer research or experimental research to quantify the dividing capabilities of cells or the cell proliferation level when investigating manipulations of the cells or their environment. Manual quantification of fluorescence microscopic image is difficult because human is neither sensitive to fine differences in color intensity nor effective to count and average fluorescence level among cells. However, auto-quantification is not a straightforward problem to solve. As the sampling location of the microscopy changes, the amount of cells in individual microscopic images varies, which makes simple measurement methods such as the sum of stain intensity values or the total number of positive stain within each image inapplicable. Thus, automated quantification with robust cell segmentation techniques is required. Results An automated quantification system with robust cell segmentation technique are presented. The experimental results in application to monitor cellular replication activities show that the quantitative score is promising to represent the cell replication level, and scores for images from different cell replication groups are demonstrated to be statistically significantly different using ANOVA, LSD and Tukey HSD tests (p-value Conclusion A robust automated quantification method of live cell imaging is built to measure the cell replication level, providing a robust quantitative analysis system in fluorescent live cell imaging. In addition, the presented unsupervised entropy based cell segmentation for live cell images is demonstrated to be also applicable for nuclear segmentation of IHC tissue images.

  8. Automatic 2D segmentation of airways in thorax computed tomography images

    International Nuclear Information System (INIS)

    Cavalcante, Tarique da Silveira; Cortez, Paulo Cesar; Almeida, Thomaz Maia de; Felix, John Hebert da Silva; Holanda, Marcelo Alcantara

    2013-01-01

    Introduction: much of the world population is affected by pulmonary diseases, such as the bronchial asthma, bronchitis and bronchiectasis. The bronchial diagnosis is based on the airways state. In this sense, the automatic segmentation of the airways in Computed Tomography (CT) scans is a critical step in the aid to diagnosis of these diseases. Methods: this paper evaluates algorithms for airway automatic segmentation, using Neural Network Multilayer Perceptron (MLP) and Lung Densities Analysis (LDA) for detecting airways, along with Region Growing (RG), Active Contour Method (ACM) Balloon and Topology Adaptive to segment them. Results: we obtained results in three stages: comparative analysis of the detection algorithms MLP and LDA, with a gold standard acquired by three physicians with expertise in CT imaging of the chest; comparative analysis of segmentation algorithms ACM Balloon, ACM Topology Adaptive, MLP and RG; and evaluation of possible combinations between segmentation and detection algorithms, resulting in the complete method for automatic segmentation of the airways in 2D. Conclusion: the low incidence of false negative and the significant reduction of false positive, results in similarity coefficient and sensitivity exceeding 91% and 87% respectively, for a combination of algorithms with satisfactory segmentation quality. (author)

  9. Development of an automatic identification algorithm for antibiogram analysis

    OpenAIRE

    Costa, LFR; Eduardo Silva; Noronha, VT; Ivone Vaz-Moreira; Olga C Nunes; de Andrade, MM

    2015-01-01

    Routinely, diagnostic and microbiology laboratories perform antibiogram analysis which can present some difficulties leading to misreadings and intra and inter-reader deviations. An Automatic Identification Algorithm (AIA) has been proposed as a solution to overcome some issues associated with the disc diffusion method, which is the main goal of this work. ALA allows automatic scanning of inhibition zones obtained by antibiograms. More than 60 environmental isolates were tested using suscepti...

  10. Characterization of a sequential pipeline approach to automatic tissue segmentation from brain MR Images

    International Nuclear Information System (INIS)

    Hou, Zujun; Huang, Su

    2008-01-01

    Quantitative analysis of gray matter and white matter in brain magnetic resonance imaging (MRI) is valuable for neuroradiology and clinical practice. Submission of large collections of MRI scans to pipeline processing is increasingly important. We characterized this process and suggest several improvements. To investigate tissue segmentation from brain MR images through a sequential approach, a pipeline that consecutively executes denoising, skull/scalp removal, intensity inhomogeneity correction and intensity-based classification was developed. The denoising phase employs a 3D-extension of the Bayes-Shrink method. The inhomogeneity is corrected by an improvement of the Dawant et al.'s method with automatic generation of reference points. The N3 method has also been evaluated. Subsequently the brain tissue is segmented into cerebrospinal fluid, gray matter and white matter by a generalized Otsu thresholding technique. Intensive comparisons with other sequential or iterative methods have been carried out using simulated and real images. The sequential approach with judicious selection on the algorithm selection in each stage is not only advantageous in speed, but also can attain at least as accurate segmentation as iterative methods under a variety of noise or inhomogeneity levels. A sequential approach to tissue segmentation, which consecutively executes the wavelet shrinkage denoising, scalp/skull removal, inhomogeneity correction and intensity-based classification was developed to automatically segment the brain tissue into CSF, GM and WM from brain MR images. This approach is advantageous in several common applications, compared with other pipeline methods. (orig.)

  11. Retinal imaging and image analysis

    NARCIS (Netherlands)

    Abramoff, M.D.; Garvin, Mona K.; Sonka, Milan

    2010-01-01

    Many important eye diseases as well as systemic diseases manifest themselves in the retina. While a number of other anatomical structures contribute to the process of vision, this review focuses on retinal imaging and image analysis. Following a brief overview of the most prevalent causes of

  12. Automatic Detection of Inactive Solar Cell Cracks in Electroluminescence Images

    DEFF Research Database (Denmark)

    Spataru, Sergiu; Hacke, Peter; Sera, Dezso

    2017-01-01

    We propose an algorithm for automatic determination of the electroluminescence (EL) signal threshold level corresponding to inactive solar cell cracks, resulting from their disconnection from the electrical circuit of the cell. The method enables automatic quantification of the cell crack size an...

  13. Automatic Detection of Inactive Solar Cell Cracks in Electroluminescence Images

    DEFF Research Database (Denmark)

    Spataru, Sergiu; Hacke, Peter; Sera, Dezso

    2017-01-01

    We propose an algorithm for automatic determination of the electroluminescence (EL) signal threshold level corresponding to inactive solar cell cracks, resulting from their disconnection from the electrical circuit of the cell. The method enables automatic quantification of the cell crack size...

  14. Automatic detection of regions of interest in mammographic images

    Science.gov (United States)

    Cheng, Erkang; Ling, Haibin; Bakic, Predrag R.; Maidment, Andrew D. A.; Megalooikonomou, Vasileios

    2011-03-01

    This work is a part of our ongoing study aimed at comparing the topology of anatomical branching structures with the underlying image texture. Detection of regions of interest (ROIs) in clinical breast images serves as the first step in development of an automated system for image analysis and breast cancer diagnosis. In this paper, we have investigated machine learning approaches for the task of identifying ROIs with visible breast ductal trees in a given galactographic image. Specifically, we have developed boosting based framework using the AdaBoost algorithm in combination with Haar wavelet features for the ROI detection. Twenty-eight clinical galactograms with expert annotated ROIs were used for training. Positive samples were generated by resampling near the annotated ROIs, and negative samples were generated randomly by image decomposition. Each detected ROI candidate was given a confidences core. Candidate ROIs with spatial overlap were merged and their confidence scores combined. We have compared three strategies for elimination of false positives. The strategies differed in their approach to combining confidence scores by summation, averaging, or selecting the maximum score.. The strategies were compared based upon the spatial overlap with annotated ROIs. Using a 4-fold cross-validation with the annotated clinical galactographic images, the summation strategy showed the best performance with 75% detection rate. When combining the top two candidates, the selection of maximum score showed the best performance with 96% detection rate.

  15. Occupancy Analysis of Sports Arenas Using Thermal Imaging

    DEFF Research Database (Denmark)

    Gade, Rikke; Jørgensen, Anders; Moeslund, Thomas B.

    2012-01-01

    This paper presents a system for automatic analysis of the occupancy of sports arenas. By using a thermal camera for image capturing the number of persons and their location on the court are found without violating any privacy issues. The images are binarised with an automatic threshold method...

  16. Automatic registration of fused lidar/digital imagery (texel images) for three-dimensional image creation

    Science.gov (United States)

    Budge, Scott E.; Badamikar, Neeraj S.; Xie, Xuan

    2015-03-01

    Several photogrammetry-based methods have been proposed that the derive three-dimensional (3-D) information from digital images from different perspectives, and lidar-based methods have been proposed that merge lidar point clouds and texture the merged point clouds with digital imagery. Image registration alone has difficulty with smooth regions with low contrast, whereas point cloud merging alone has difficulty with outliers and a lack of proper convergence in the merging process. This paper presents a method to create 3-D images that uses the unique properties of texel images (pixel-fused lidar and digital imagery) to improve the quality and robustness of fused 3-D images. The proposed method uses both image processing and point-cloud merging to combine texel images in an iterative technique. Since the digital image pixels and the lidar 3-D points are fused at the sensor level, more accurate 3-D images are generated because registration of image data automatically improves the merging of the point clouds, and vice versa. Examples illustrate the value of this method over other methods. The proposed method also includes modifications for the situation where an estimate of position and attitude of the sensor is known, when obtained from low-cost global positioning systems and inertial measurement units sensors.

  17. Automatic segmentation of the left ventricle in a cardiac MR short axis image using blind morphological operation

    Science.gov (United States)

    Irshad, Mehreen; Muhammad, Nazeer; Sharif, Muhammad; Yasmeen, Mussarat

    2018-04-01

    Conventionally, cardiac MR image analysis is done manually. Automatic examination for analyzing images can replace the monotonous tasks of massive amounts of data to analyze the global and regional functions of the cardiac left ventricle (LV). This task is performed using MR images to calculate the analytic cardiac parameter like end-systolic volume, end-diastolic volume, ejection fraction, and myocardial mass, respectively. These analytic parameters depend upon genuine delineation of epicardial, endocardial, papillary muscle, and trabeculations contours. In this paper, we propose an automatic segmentation method using the sum of absolute differences technique to localize the left ventricle. Blind morphological operations are proposed to segment and detect the LV contours of the epicardium and endocardium, automatically. We test the benchmark Sunny Brook dataset for evaluation of the proposed work. Contours of epicardium and endocardium are compared quantitatively to determine contour's accuracy and observe high matching values. Similarity or overlapping of an automatic examination to the given ground truth analysis by an expert are observed with high accuracy as with an index value of 91.30% . The proposed method for automatic segmentation gives better performance relative to existing techniques in terms of accuracy.

  18. Automatized system of radioactive material analysis

    International Nuclear Information System (INIS)

    Pchelkin, V.A.; Sviderskij, M.F.; Litvinov, V.A.; Lavrikov, S.A.

    1979-01-01

    An automatized system has been developed for the identification of substance, element and isotope content of radioactive materials on the basis of data obtained for studying physical-chemical properties of substances (with the help of atomic-absorption spectrometers, infrared spectrometer, mass-spectrometer, derivatograph etc.). The system is based on the following principles: independent operation of each device; a possibility of increasing the number of physical instruments and devices; modular properties of engineering and computer means; modular properties and standardization of mathematical equipment, high reliability of the system; continuity of programming languages; a possibility of controlling the devices with the help of high-level language, typification of the system; simple and easy service; low cost. Block-diagram of the system is given

  19. Combining Stereo SECCHI COR2 and HI1 Images for Automatic CME Front Edge Tracking

    Science.gov (United States)

    Kirnosov, Vladimir; Chang, Lin-Ching; Pulkkinen, Antti

    2016-01-01

    COR2 coronagraph images are the most commonly used data for coronal mass ejection (CME) analysis among the various types of data provided by the STEREO (Solar Terrestrial Relations Observatory) SECCHI (Sun-Earth Connection Coronal and Heliospheric Investigation) suite of instruments. The field of view (FOV) in COR2 images covers 215 solar radii (Rs) that allow for tracking the front edge of a CME in its initial stage to forecast the lead-time of a CME and its chances of reaching the Earth. However, estimating the lead-time of a CME using COR2 images gives a larger lead-time, which may be associated with greater uncertainty. To reduce this uncertainty, CME front edge tracking should be continued beyond the FOV of COR2 images. Therefore, heliospheric imager (HI1) data that covers 1590 Rs FOV must be included. In this paper, we propose a novel automatic method that takes both COR2 and HI1 images into account and combine the results to track the front edges of a CME continuously. The method consists of two modules: pre-processing and tracking. The pre-processing module produces a set of segmented images, which contain the signature of a CME, for both COR2 and HI1 separately. In addition, the HI1 images are resized and padded, so that the center of the Sun is the central coordinate of the resized HI1 images. The resulting COR2 andHI1 image set is then fed into the tracking module to estimate the position angle (PA) and track the front edge of a CME. The detected front edge is then used to produce a height-time profile that is used to estimate the speed of a CME. The method was validated using 15 CME events observed in the period from January 1, 2008 to August 31, 2009. The results demonstrate that the proposed method is effective for CME front edge tracking in both COR2 and HI1 images. Using this method, the CME front edge can now be tracked automatically and continuously in a much larger range, i.e., from 2 to 90 Rs, for the first time. These improvement scan greatly

  20. Multispectral analysis of multimodal images

    Energy Technology Data Exchange (ETDEWEB)

    Kvinnsland, Yngve; Brekke, Njaal (Dept. of Surgical Sciences, Univ. of Bergen, Bergen (Norway)); Taxt, Torfinn M.; Gruener, Renate (Dept. of Biomedicine, Univ. of Bergen, Bergen (Norway))

    2009-02-15

    An increasing number of multimodal images represent a valuable increase in available image information, but at the same time it complicates the extraction of diagnostic information across the images. Multispectral analysis (MSA) has the potential to simplify this problem substantially as unlimited number of images can be combined, and tissue properties across the images can be extracted automatically. Materials and methods. We have developed a software solution for MSA containing two algorithms for unsupervised classification, an EM-algorithm finding multinormal class descriptions and the k-means clustering algorithm, and two for supervised classification, a Bayesian classifier using multinormal class descriptions and a kNN-algorithm. The software has an efficient user interface for the creation and manipulation of class descriptions, and it has proper tools for displaying the results. Results. The software has been tested on different sets of images. One application is to segment cross-sectional images of brain tissue (T1- and T2-weighted MR images) into its main normal tissues and brain tumors. Another interesting set of images are the perfusion maps and diffusion maps, derived images from raw MR images. The software returns segmentation that seem to be sensible. Discussion. The MSA software appears to be a valuable tool for image analysis with multimodal images at hand. It readily gives a segmentation of image volumes that visually seems to be sensible. However, to really learn how to use MSA, it will be necessary to gain more insight into what tissues the different segments contain, and the upcoming work will therefore be focused on examining the tissues through for example histological sections.

  1. Automatic localization of landmark sets in head CT images with regression forests for image registration initialization

    Science.gov (United States)

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

    2016-03-01

    Cochlear Implants (CIs) are electrode arrays that are surgically inserted into the cochlea. Individual contacts stimulate frequency-mapped nerve endings thus replacing the natural electro-mechanical transduction mechanism. CIs are programmed post-operatively by audiologists but this is currently done using behavioral tests without imaging information that permits relating electrode position to inner ear anatomy. We have recently developed a series of image processing steps that permit the segmentation of the inner ear anatomy and the localization of individual contacts. We have proposed a new programming strategy that uses this information and we have shown in a study with 68 participants that 78% of long term recipients preferred the programming parameters determined with this new strategy. A limiting factor to the large scale evaluation and deployment of our technique is the amount of user interaction still required in some of the steps used in our sequence of image processing algorithms. One such step is the rough registration of an atlas to target volumes prior to the use of automated intensity-based algorithms when the target volumes have very different fields of view and orientations. In this paper we propose a solution to this problem. It relies on a random forest-based approach to automatically localize a series of landmarks. Our results obtained from 83 images with 132 registration tasks show that automatic initialization of an intensity-based algorithm proves to be a reliable technique to replace the manual step.

  2. Analysis of engineering drawings and raster map images

    CERN Document Server

    Henderson, Thomas C

    2013-01-01

    Presents up-to-date methods and algorithms for the automated analysis of engineering drawings and digital cartographic maps Discusses automatic engineering drawing and map analysis techniques Covers detailed accounts of the use of unsupervised segmentation algorithms to map images

  3. Image fusion between whole body FDG PET images and whole body MRI images using a full-automatic mutual information-based multimodality image registration software

    International Nuclear Information System (INIS)

    Uchida, Yoshitaka; Nakano, Yoshitada; Fujibuchi, Toshiou; Isobe, Tomoko; Kazama, Toshiki; Ito, Hisao

    2006-01-01

    We attempted image fusion between whole body PET and whole body MRI of thirty patients using a full-automatic mutual information (MI) -based multimodality image registration software and evaluated accuracy of this method and impact of the coregistrated imaging on diagnostic accuracy. For 25 of 30 fused images in body area, translating gaps were within 6 mm in all axes and rotating gaps were within 2 degrees around all axes. In head and neck area, considerably much gaps caused by difference of head inclination at imaging occurred in 16 patients, however these gaps were able to decrease by fused separately. In 6 patients, diagnostic accuracy using PET/MRI fused images was superior compared by PET image alone. This work shows that whole body FDG PET images and whole body MRI images can be automatically fused using MI-based multimodality image registration software accurately and this technique can add useful information when evaluating FDG PET images. (author)

  4. Automatic size analysis of coated fuel particles

    International Nuclear Information System (INIS)

    Wallisch, K.; Koss, P.

    1977-01-01

    The determination of the diameter, coating thickness, and sphericity of coated fuel particles by conventional methods is very time consuming. Therefore, statistical data can only be obtained with limited accuracy. An alternative method is described that avoids these disadvantages by utilizing a fast optical data-collecting system of high accuracy. This system allows the determination of the diameter of particles in the range between 100 and 1500 μm, with an accuracy of better than +-2 μm and with a rate of 100 particles per second. The density and thickness of coating layers can be determined by comparing the data obtained before and after coating, taking into account the relative increase of weight. A special device allows the automatic determination of the sphericity of single particles as well as the distribution in a batch. This device measures 50 to 100 different diameters of each particle per second. An on-line computer stores the measured data and calculates all parameters required, e.g., number of particles measured, particle diameter, standard deviation, diameter limiting values, average particle volume, average particle surface area, and the distribution of sphericity in absolute and percent form

  5. Automatic analysis of signals during Eddy currents controls

    International Nuclear Information System (INIS)

    Chiron, D.

    1983-06-01

    A method and the corresponding instrument have been developed for automatic analysis of Eddy currents testing signals. This apparatus enables at the same time the analysis, every 2 milliseconds, of two signals at two different frequencies. It can be used either on line with an Eddy Current testing instrument or with a magnetic tape recorder [fr

  6. A new method for automatic tracking of facial landmarks in 3D motion captured images (4D).

    Science.gov (United States)

    Al-Anezi, T; Khambay, B; Peng, M J; O'Leary, E; Ju, X; Ayoub, A

    2013-01-01

    The aim of this study was to validate the automatic tracking of facial landmarks in 3D image sequences. 32 subjects (16 males and 16 females) aged 18-35 years were recruited. 23 anthropometric landmarks were marked on the face of each subject with non-permanent ink using a 0.5mm pen. The subjects were asked to perform three facial animations (maximal smile, lip purse and cheek puff) from rest position. Each animation was captured by the 3D imaging system. A single operator manually digitised the landmarks on the 3D facial models and their locations were compared with those of the automatically tracked ones. To investigate the accuracy of manual digitisation, the operator re-digitised the same set of 3D images of 10 subjects (5 male and 5 female) at 1 month interval. The discrepancies in x, y and z coordinates between the 3D position of the manual digitised landmarks and that of the automatic tracked facial landmarks were within 0.17mm. The mean distance between the manually digitised and the automatically tracked landmarks using the tracking software was within 0.55 mm. The automatic tracking of facial landmarks demonstrated satisfactory accuracy which would facilitate the analysis of the dynamic motion during facial animations. Copyright © 2012 International Association of Oral and Maxillofacial Surgeons. Published by Elsevier Ltd. All rights reserved.

  7. Automatic Detection of Microaneurysms in Color Fundus Images using a Local Radon Transform Method

    Directory of Open Access Journals (Sweden)

    Hamid Reza Pourreza

    2009-03-01

    Full Text Available Introduction: Diabetic retinopathy (DR is one of the most serious and most frequent eye diseases in the world and the most common cause of blindness in adults between 20 and 60 years of age. Following 15 years of diabetes, about 2% of the diabetic patients are blind and 10% suffer from vision impairment due to DR complications. This paper addresses the automatic detection of microaneurysms (MA in color fundus images, which plays a key role in computer-assisted early diagnosis of diabetic retinopathy. Materials and Methods: The algorithm can be divided into three main steps. The purpose of the first step or pre-processing is background normalization and contrast enhancement of the images. The second step aims to detect candidates, i.e., all patterns possibly corresponding to MA, which is achieved using a local radon transform, Then, features are extracted, which are used in the last step to automatically classify the candidates into real MA or other objects using the SVM method. A database of 100 annotated images was used to test the algorithm. The algorithm was compared to manually obtained gradings of these images. Results: The sensitivity of diagnosis for DR was 100%, with specificity of 90% and the sensitivity of precise MA localization was 97%, at an average number of 5 false positives per image. Discussion and Conclusion: Sensitivity and specificity of this algorithm make it one of the best methods in this field. Using the local radon transform in this algorithm eliminates the noise sensitivity for MA detection in retinal image analysis.

  8. Automatic cumulative sums contour detection of FBP-reconstructed multi-object nuclear medicine images.

    Science.gov (United States)

    Protonotarios, Nicholas E; Spyrou, George M; Kastis, George A

    2017-06-01

    The problem of determining the contours of objects in nuclear medicine images has been studied extensively in the past, however most of the analysis has focused on a single object as opposed to multiple objects. The aim of this work is to develop an automated method for determining the contour of multiple objects in positron emission tomography (PET) and single photon emission computed tomography (SPECT) filtered backprojection (FBP) reconstructed images. These contours can be used for computing body edges for attenuation correction in PET and SPECT, as well as for eliminating streak artifacts outside the objects, which could be useful in compressive sensing reconstruction. Contour detection has been accomplished by applying a modified cumulative sums (CUSUM) scheme in the sinogram. Our approach automatically detects all objects in the image, without requiring a priori knowledge of the number of distinct objects in the reconstructed image. This method has been tested in simulated phantoms, such as an image-quality (IQ) phantom and two digital multi-object phantoms, as well as a real NEMA phantom and a clinical thoracic study. For this purpose, a GE Discovery PET scanner was employed. The detected contours achieved root mean square accuracy of 1.14 pixels, 1.69 pixels and 3.28 pixels and a Hausdorff distance of 3.13, 3.12 and 4.50 pixels, for the simulated image-quality phantom PET study, the real NEMA phantom and the clinical thoracic study, respectively. These results correspond to a significant improvement over recent results obtained in similar studies. Furthermore, we obtained an optimal sub-pattern assignment (OSPA) localization error of 0.94 and 1.48, for the two-objects and three-objects simulated phantoms, respectively. Our method performs efficiently for sets of convex objects and hence it provides a robust tool for automatic contour determination with precise results. Copyright © 2017 Elsevier Ltd. All rights reserved.

  9. Automatic detection of radioactive fixations in oncology PET images

    International Nuclear Information System (INIS)

    Tomei-Le-Digarcher, Sandrine

    2009-01-01

    Therapeutic follow-up of patients with cancer is nowadays of main interest in research. Positron Emission Tomography (PET) appears to become a reference exam for monitoring treatment of cancers, particular in lymphoma. This PhD thus deals on the development of a computer aided detection (CAD) tool focused on hardly visible tumors for whole-body 3D PET images. To achieve such a goal, we proposed an approach based on the combination of two classifiers, the Linear Discriminant Analysis (LDA) and the Support Vector Machines, associated with wavelet image features. Each classifier gives a 3D score map quantifying the probability of its voxels to correspond to a tumor. We proposed a 3D evaluation strategy based on the use of simulated images giving the targeted tumor characteristic gold standard. Such database was developed in this PhD from hundred Monte Carlo simulations of the Zuba phantom. It includes hundred images presenting 375 spherical tumors of calibrated contrasts. Results of the CAD obtained from the binary detection maps are promising. They open the perspective of enriching the binary information generally given to the clinician with parametric indices quantifying the pertinence of each detected tumor. (author)

  10. Automatic plankton image classification combining multiple view features via multiple kernel learning.

    Science.gov (United States)

    Zheng, Haiyong; Wang, Ruchen; Yu, Zhibin; Wang, Nan; Gu, Zhaorui; Zheng, Bing

    2017-12-28

    Plankton, including phytoplankton and zooplankton, are the main source of food for organisms in the ocean and form the base of marine food chain. As the fundamental components of marine ecosystems, plankton is very sensitive to environment changes, and the study of plankton abundance and distribution is crucial, in order to understand environment changes and protect marine ecosystems. This study was carried out to develop an extensive applicable plankton classification system with high accuracy for the increasing number of various imaging devices. Literature shows that most plankton image classification systems were limited to only one specific imaging device and a relatively narrow taxonomic scope. The real practical system for automatic plankton classification is even non-existent and this study is partly to fill this gap. Inspired by the analysis of literature and development of technology, we focused on the requirements of practical application and proposed an automatic system for plankton image classification combining multiple view features via multiple kernel learning (MKL). For one thing, in order to describe the biomorphic characteristics of plankton more completely and comprehensively, we combined general features with robust features, especially by adding features like Inner-Distance Shape Context for morphological representation. For another, we divided all the features into different types from multiple views and feed them to multiple classifiers instead of only one by combining different kernel matrices computed from different types of features optimally via multiple kernel learning. Moreover, we also applied feature selection method to choose the optimal feature subsets from redundant features for satisfying different datasets from different imaging devices. We implemented our proposed classification system on three different datasets across more than 20 categories from phytoplankton to zooplankton. The experimental results validated that our system

  11. Profiling School Shooters: Automatic Text-Based Analysis

    Directory of Open Access Journals (Sweden)

    Yair eNeuman

    2015-06-01

    Full Text Available School shooters present a challenge to both forensic psychiatry and law enforcement agencies. The relatively small number of school shooters, their various charateristics, and the lack of in-depth analysis of all of the shooters prior to the shooting add complexity to our understanding of this problem. In this short paper, we introduce a new methodology for automatically profiling school shooters. The methodology involves automatic analysis of texts and the production of several measures relevant for the identification of the shooters. Comparing texts written by six school shooters to 6056 texts written by a comparison group of male subjects, we found that the shooters' texts scored significantly higher on the Narcissistic Personality dimension as well as on the Humilated and Revengeful dimensions. Using a ranking/priorization procedure, similar to the one used for the automatic identification of sexual predators, we provide support for the validity and relevance of the proposed methodology.

  12. Automatic measurement system for light element isotope analysis

    International Nuclear Information System (INIS)

    Satake, Hiroshi; Ikegami, Kouichi.

    1990-01-01

    The automatic measurement system for the light element isotope analysis was developed by installing the specially designed inlet system which was controlled by a computer. The microcomputer system contains specific interface boards for the inlet system and the mass spectrometer, Micromass 602 E. All the components of the inlet and the computer system installed are easily available in Japan. Ten samples can be automatically measured as a maximum of. About 160 minutes are required for 10 measurements of δ 18 O values of CO 2 . Thus four samples can be measured per an hour using this system, while usually three samples for an hour using the manual operation. The automatized analysis system clearly has an advantage over the conventional method. This paper describes the details of this automated system, such as apparatuses used, the control procedure and the correction for reliable measurement. (author)

  13. Automatic classification of minimally invasive instruments based on endoscopic image sequences

    Science.gov (United States)

    Speidel, Stefanie; Benzko, Julia; Krappe, Sebastian; Sudra, Gunther; Azad, Pedram; Müller-Stich, Beat Peter; Gutt, Carsten; Dillmann, Rüdiger

    2009-02-01

    Minimally invasive surgery is nowadays a frequently applied technique and can be regarded as a major breakthrough in surgery. The surgeon has to adopt special operation-techniques and deal with difficulties like the complex hand-eye coordination and restricted mobility. To alleviate these constraints we propose to enhance the surgeon's capabilities by providing a context-aware assistance using augmented reality techniques. To analyze the current situation for context-aware assistance, we need intraoperatively gained sensor data and a model of the intervention. A situation consists of information about the performed activity, the used instruments, the surgical objects, the anatomical structures and defines the state of an intervention for a given moment in time. The endoscopic images provide a rich source of information which can be used for an image-based analysis. Different visual cues are observed in order to perform an image-based analysis with the objective to gain as much information as possible about the current situation. An important visual cue is the automatic recognition of the instruments which appear in the scene. In this paper we present the classification of minimally invasive instruments using the endoscopic images. The instruments are not modified by markers. The system segments the instruments in the current image and recognizes the instrument type based on three-dimensional instrument models.

  14. Automatic analysis of ciliary beat frequency using optical flow

    Science.gov (United States)

    Figl, Michael; Lechner, Manuel; Werther, Tobias; Horak, Fritz; Hummel, Johann; Birkfellner, Wolfgang

    2012-02-01

    Ciliary beat frequency (CBF) can be a useful parameter for diagnosis of several diseases, as e.g. primary ciliary dyskinesia. (PCD). CBF computation is usually done using manual evaluation of high speed video sequences, a tedious, observer dependent, and not very accurate procedure. We used the OpenCV's pyramidal implementation of the Lukas-Kanade algorithm for optical flow computation and applied this to certain objects to follow the movements. The objects were chosen by their contrast applying the corner detection by Shi and Tomasi. Discrimination between background/noise and cilia by a frequency histogram allowed to compute the CBF. Frequency analysis was done using the Fourier transform in matlab. The correct number of Fourier summands was found by the slope in an approximation curve. The method showed to be usable to distinguish between healthy and diseased samples. However there remain difficulties in automatically identifying the cilia, and also in finding enough high contrast cilia in the image. Furthermore the some of the higher contrast cilia are lost (and sometimes found) by the method, an easy way to distinguish the correct sub-path of a point's path have yet to be found in the case where the slope methods doesn't work.

  15. Dual-model automatic detection of nerve-fibres in corneal confocal microscopy images.

    Science.gov (United States)

    Dabbah, M A; Graham, J; Petropoulos, I; Tavakoli, M; Malik, R A

    2010-01-01

    Corneal Confocal Microscopy (CCM) imaging is a non-invasive surrogate of detecting, quantifying and monitoring diabetic peripheral neuropathy. This paper presents an automated method for detecting nerve-fibres from CCM images using a dual-model detection algorithm and compares the performance to well-established texture and feature detection methods. The algorithm comprises two separate models, one for the background and another for the foreground (nerve-fibres), which work interactively. Our evaluation shows significant improvement (p approximately 0) in both error rate and signal-to-noise ratio of this model over the competitor methods. The automatic method is also evaluated in comparison with manual ground truth analysis in assessing diabetic neuropathy on the basis of nerve-fibre length, and shows a strong correlation (r = 0.92). Both analyses significantly separate diabetic patients from control subjects (p approximately 0).

  16. Image processing and pattern recognition with CVIPtools MATLAB toolbox: automatic creation of masks for veterinary thermographic images

    Science.gov (United States)

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

    2016-09-01

    CVIPtools is a software package for the exploration of computer vision and image processing developed in the Computer Vision and Image Processing Laboratory at Southern Illinois University Edwardsville. CVIPtools is available in three variants - a) CVIPtools Graphical User Interface, b) CVIPtools C library and c) CVIPtools MATLAB toolbox, which makes it accessible to a variety of different users. It offers students, faculty, researchers and any user a free and easy way to explore computer vision and image processing techniques. Many functions have been implemented and are updated on a regular basis, the library has reached a level of sophistication that makes it suitable for both educational and research purposes. In this paper, the detail list of the functions available in the CVIPtools MATLAB toolbox are presented and how these functions can be used in image analysis and computer vision applications. The CVIPtools MATLAB toolbox allows the user to gain practical experience to better understand underlying theoretical problems in image processing and pattern recognition. As an example application, the algorithm for the automatic creation of masks for veterinary thermographic images is presented.

  17. Automatic breast tissue density estimation scheme in digital mammography images

    Science.gov (United States)

    Menechelli, Renan C.; Pacheco, Ana Luisa V.; Schiabel, Homero

    2017-03-01

    Cases of breast cancer have increased substantially each year. However, radiologists are subject to subjectivity and failures of interpretation which may affect the final diagnosis in this examination. The high density features in breast tissue are important factors related to these failures. Thus, among many functions some CADx (Computer-Aided Diagnosis) schemes are classifying breasts according to the predominant density. In order to aid in such a procedure, this work attempts to describe automated software for classification and statistical information on the percentage change in breast tissue density, through analysis of sub regions (ROIs) from the whole mammography image. Once the breast is segmented, the image is divided into regions from which texture features are extracted. Then an artificial neural network MLP was used to categorize ROIs. Experienced radiologists have previously determined the ROIs density classification, which was the reference to the software evaluation. From tests results its average accuracy was 88.7% in ROIs classification, and 83.25% in the classification of the whole breast density in the 4 BI-RADS density classes - taking into account a set of 400 images. Furthermore, when considering only a simplified two classes division (high and low densities) the classifier accuracy reached 93.5%, with AUC = 0.95.

  18. Contribution to automatic image recognition applied to robot technology

    International Nuclear Information System (INIS)

    Juvin, Didier

    1983-01-01

    This paper describes a method for the analysis and interpretation of the images of objects located in a plain scene which is the environment of a robot. The first part covers the recovery of the contour of objects present in the image, and discusses a novel contour-following technique based on the line arborescence concept in combination with a 'cost function' giving a quantitative assessment of contour quality. We present heuristics for moderate-cost, minimum-time arborescence coverage, which is equivalent to following probable contour lines in the image. A contour segmentation technique, invariant in the translational and rotational modes, is presented next. The second part describes a recognition method based on the above invariant encoding: the algorithm performs a preliminary screening based on coarse data derived from segmentation, followed by a comparison of forms with probable identity through application of a distance specified in terms of the invariant encoding. The last part covers the outcome of the above investigations, which have found an industrial application in the vision system of a range of robots. The system is set up in a 16-bit microprocessor and operates in real time. (author) [fr

  19. Automatic Georeferencing of Aerial Images by Means of Topographic Database Information

    DEFF Research Database (Denmark)

    Høhle, Joachim

    The book includes a preface and four articles which deal with the automatic georeferencing of aerial images. The articles are the written contribution of an seminar, held at Aalborg University in October 2002. The georeferencing or orientation of aerial images is the first step in mapping tasks l...... like generation of orthoimages, updating of topographic map data bases and generation of digial terrain models.......The book includes a preface and four articles which deal with the automatic georeferencing of aerial images. The articles are the written contribution of an seminar, held at Aalborg University in October 2002. The georeferencing or orientation of aerial images is the first step in mapping tasks...

  20. A contextual image segmentation system using a priori information for automatic data classification in nuclear physics

    International Nuclear Information System (INIS)

    Benkirane, A.; Auger, G.; Chbihi, A.; Bloyet, D.; Plagnol, E.

    1994-01-01

    This paper presents an original approach to solve an automatic data classification problem by means of image processing techniques. The classification is achieved using image segmentation techniques for extracting the meaningful classes. Two types of information are merged for this purpose: the information contained in experimental images and a priori information derived from underlying physics (and adapted to image segmentation problem). This data fusion is widely used at different stages of the segmentation process. This approach yields interesting results in terms of segmentation performances, even in very noisy cases. Satisfactory classification results are obtained in cases where more ''classical'' automatic data classification methods fail. (authors). 25 refs., 14 figs., 1 append

  1. A contextual image segmentation system using a priori information for automatic data classification in nuclear physics

    Energy Technology Data Exchange (ETDEWEB)

    Benkirane, A; Auger, G; Chbihi, A [Grand Accelerateur National d` Ions Lourds (GANIL), 14 - Caen (France); Bloyet, D [Caen Univ., 14 (France); Plagnol, E [Paris-11 Univ., 91 - Orsay (France). Inst. de Physique Nucleaire

    1994-12-31

    This paper presents an original approach to solve an automatic data classification problem by means of image processing techniques. The classification is achieved using image segmentation techniques for extracting the meaningful classes. Two types of information are merged for this purpose: the information contained in experimental images and a priori information derived from underlying physics (and adapted to image segmentation problem). This data fusion is widely used at different stages of the segmentation process. This approach yields interesting results in terms of segmentation performances, even in very noisy cases. Satisfactory classification results are obtained in cases where more ``classical`` automatic data classification methods fail. (authors). 25 refs., 14 figs., 1 append.

  2. Social Signals, their function, and automatic analysis: A survey

    NARCIS (Netherlands)

    Vinciarelli, Alessandro; Pantic, Maja; Bourlard, Hervé; Pentland, Alex

    2008-01-01

    Social Signal Processing (SSP) aims at the analysis of social behaviour in both Human-Human and Human-Computer interactions. SSP revolves around automatic sensing and interpretation of social signals, complex aggregates of nonverbal behaviours through which individuals express their attitudes

  3. Automatic Online Lecture Highlighting Based on Multimedia Analysis

    Science.gov (United States)

    Che, Xiaoyin; Yang, Haojin; Meinel, Christoph

    2018-01-01

    Textbook highlighting is widely considered to be beneficial for students. In this paper, we propose a comprehensive solution to highlight the online lecture videos in both sentence- and segment-level, just as is done with paper books. The solution is based on automatic analysis of multimedia lecture materials, such as speeches, transcripts, and…

  4. Application of software technology to automatic test data analysis

    Science.gov (United States)

    Stagner, J. R.

    1991-01-01

    The verification process for a major software subsystem was partially automated as part of a feasibility demonstration. The methods employed are generally useful and applicable to other types of subsystems. The effort resulted in substantial savings in test engineer analysis time and offers a method for inclusion of automatic verification as a part of regression testing.

  5. Trends of Science Education Research: An Automatic Content Analysis

    Science.gov (United States)

    Chang, Yueh-Hsia; Chang, Chun-Yen; Tseng, Yuen-Hsien

    2010-01-01

    This study used scientometric methods to conduct an automatic content analysis on the development trends of science education research from the published articles in the four journals of "International Journal of Science Education, Journal of Research in Science Teaching, Research in Science Education, and Science Education" from 1990 to 2007. The…

  6. Automatic Text Analysis Based on Transition Phenomena of Word Occurrences

    Science.gov (United States)

    Pao, Miranda Lee

    1978-01-01

    Describes a method of selecting index terms directly from a word frequency list, an idea originally suggested by Goffman. Results of the analysis of word frequencies of two articles seem to indicate that the automated selection of index terms from a frequency list holds some promise for automatic indexing. (Author/MBR)

  7. A computer program for automatic gamma-ray spectra analysis

    International Nuclear Information System (INIS)

    Hiromura, Kazuyuki

    1975-01-01

    A computer program for automatic analysis of gamma-ray spectra obtained with a Ge(Li) detector is presented. The program includes a method by comparing the successive values of experimental data for the automatic peak finding and method of leastsquares for the peak fitting. The peak shape in the fitting routine is a 'modified Gaussian', which consists of two different Gaussians with the same height joined at the centroid. A quadratic form is chosen as a function representing the background. A maximum of four peaks can be treated in the fitting routine by the program. Some improvements in question are described. (auth.)

  8. Automatic movie skimming with general tempo analysis

    Science.gov (United States)

    Lee, Shih-Hung; Yeh, Chia-Hung; Kuo, C. C. J.

    2003-11-01

    Story units are extracted by general tempo analysis including tempos analysis including tempos of audio and visual information in this research. Although many schemes have been proposed to successfully segment video data into shots using basic low-level features, how to group shots into meaningful units called story units is still a challenging problem. By focusing on a certain type of video such as sport or news, we can explore models with the specific application domain knowledge. For movie contents, many heuristic rules based on audiovisual clues have been proposed with limited success. We propose a method to extract story units using general tempo analysis. Experimental results are given to demonstrate the feasibility and efficiency of the proposed technique.

  9. SU-E-J-15: Automatically Detect Patient Treatment Position and Orientation in KV Portal Images

    Energy Technology Data Exchange (ETDEWEB)

    Qiu, J [Washington University in St Louis, Taian, Shandong (China); Yang, D [Washington University School of Medicine, St Louis, MO (United States)

    2015-06-15

    Purpose: In the course of radiation therapy, the complex information processing workflow will Result in potential errors, such as incorrect or inaccurate patient setups. With automatic image check and patient identification, such errors could be effectively reduced. For this purpose, we developed a simple and rapid image processing method, to automatically detect the patient position and orientation in 2D portal images, so to allow automatic check of positions and orientations for patient daily RT treatments. Methods: Based on the principle of portal image formation, a set of whole body DRR images were reconstructed from multiple whole body CT volume datasets, and fused together to be used as the matching template. To identify the patient setup position and orientation shown in a 2D portal image, the 2D portal image was preprocessed (contrast enhancement, down-sampling and couch table detection), then matched to the template image so to identify the laterality (left or right), position, orientation and treatment site. Results: Five day’s clinical qualified portal images were gathered randomly, then were processed by the automatic detection and matching method without any additional information. The detection results were visually checked by physicists. 182 images were correct detection in a total of 200kV portal images. The correct rate was 91%. Conclusion: The proposed method can detect patient setup and orientation quickly and automatically. It only requires the image intensity information in KV portal images. This method can be useful in the framework of Electronic Chart Check (ECCK) to reduce the potential errors in workflow of radiation therapy and so to improve patient safety. In addition, the auto-detection results, as the patient treatment site position and patient orientation, could be useful to guide the sequential image processing procedures, e.g. verification of patient daily setup accuracy. This work was partially supported by research grant from

  10. SU-E-J-15: Automatically Detect Patient Treatment Position and Orientation in KV Portal Images

    International Nuclear Information System (INIS)

    Qiu, J; Yang, D

    2015-01-01

    Purpose: In the course of radiation therapy, the complex information processing workflow will Result in potential errors, such as incorrect or inaccurate patient setups. With automatic image check and patient identification, such errors could be effectively reduced. For this purpose, we developed a simple and rapid image processing method, to automatically detect the patient position and orientation in 2D portal images, so to allow automatic check of positions and orientations for patient daily RT treatments. Methods: Based on the principle of portal image formation, a set of whole body DRR images were reconstructed from multiple whole body CT volume datasets, and fused together to be used as the matching template. To identify the patient setup position and orientation shown in a 2D portal image, the 2D portal image was preprocessed (contrast enhancement, down-sampling and couch table detection), then matched to the template image so to identify the laterality (left or right), position, orientation and treatment site. Results: Five day’s clinical qualified portal images were gathered randomly, then were processed by the automatic detection and matching method without any additional information. The detection results were visually checked by physicists. 182 images were correct detection in a total of 200kV portal images. The correct rate was 91%. Conclusion: The proposed method can detect patient setup and orientation quickly and automatically. It only requires the image intensity information in KV portal images. This method can be useful in the framework of Electronic Chart Check (ECCK) to reduce the potential errors in workflow of radiation therapy and so to improve patient safety. In addition, the auto-detection results, as the patient treatment site position and patient orientation, could be useful to guide the sequential image processing procedures, e.g. verification of patient daily setup accuracy. This work was partially supported by research grant from

  11. Automatic circuit analysis based on mask information

    International Nuclear Information System (INIS)

    Preas, B.T.; Lindsay, B.W.; Gwyn, C.W.

    1976-01-01

    The Circuit Mask Translator (CMAT) code has been developed which converts integrated circuit mask information into a circuit schematic. Logical operations, pattern recognition, and special functions are used to identify and interconnect diodes, transistors, capacitors, and resistances. The circuit topology provided by the translator is compatible with the input required for a circuit analysis program

  12. Automatic detection of micro-aneurysms in retinal images based on curvelet transform and morphological operations

    Science.gov (United States)

    Mohammad Alipour, Shirin Hajeb; Rabbani, Hossein

    2013-09-01

    Diabetic retinopathy (DR) is one of the major complications of diabetes that changes the blood vessels of the retina and distorts patient vision that finally in high stages can lead to blindness. Micro-aneurysms (MAs) are one of the first pathologies associated with DR. The number and the location of MAs are very important in grading of DR. Early diagnosis of micro-aneurysms (MAs) can reduce the incidence of blindness. As MAs are tiny area of blood protruding from vessels in the retina and their size is about 25 to 100 microns, automatic detection of these tiny lesions is still challenging. MAs occurring in the macula can lead to visual loss. Also the position of a lesion such as MAs relative to the macula is a useful feature for analysis and classification of different stages of DR. Because MAs are more distinguishable in fundus fluorescin angiography (FFA) compared to color fundus images, we introduce a new method based on curvelet transform and morphological operations for MAs detection in FFA images. As vessels and MAs are the bright parts of FFA image, firstly extracted vessels by curvelet transform are removed from image. Then morphological operations are applied on resulted image for detecting MAs.

  13. Advances in image compression and automatic target recognition; Proceedings of the Meeting, Orlando, FL, Mar. 30, 31, 1989

    Science.gov (United States)

    Tescher, Andrew G. (Editor)

    1989-01-01

    Various papers on image compression and automatic target recognition are presented. Individual topics addressed include: target cluster detection in cluttered SAR imagery, model-based target recognition using laser radar imagery, Smart Sensor front-end processor for feature extraction of images, object attitude estimation and tracking from a single video sensor, symmetry detection in human vision, analysis of high resolution aerial images for object detection, obscured object recognition for an ATR application, neural networks for adaptive shape tracking, statistical mechanics and pattern recognition, detection of cylinders in aerial range images, moving object tracking using local windows, new transform method for image data compression, quad-tree product vector quantization of images, predictive trellis encoding of imagery, reduced generalized chain code for contour description, compact architecture for a real-time vision system, use of human visibility functions in segmentation coding, color texture analysis and synthesis using Gibbs random fields.

  14. Automatic nuclei segmentation in H&E stained breast cancer histopathology images.

    Directory of Open Access Journals (Sweden)

    Mitko Veta

    Full Text Available The introduction of fast digital slide scanners that provide whole slide images has led to a revival of interest in image analysis applications in pathology. Segmentation of cells and nuclei is an important first step towards automatic analysis of digitized microscopy images. We therefore developed an automated nuclei segmentation method that works with hematoxylin and eosin (H&E stained breast cancer histopathology images, which represent regions of whole digital slides. The procedure can be divided into four main steps: 1 pre-processing with color unmixing and morphological operators, 2 marker-controlled watershed segmentation at multiple scales and with different markers, 3 post-processing for rejection of false regions and 4 merging of the results from multiple scales. The procedure was developed on a set of 21 breast cancer cases (subset A and tested on a separate validation set of 18 cases (subset B. The evaluation was done in terms of both detection accuracy (sensitivity and positive predictive value and segmentation accuracy (Dice coefficient. The mean estimated sensitivity for subset A was 0.875 (±0.092 and for subset B 0.853 (±0.077. The mean estimated positive predictive value was 0.904 (±0.075 and 0.886 (±0.069 for subsets A and B, respectively. For both subsets, the distribution of the Dice coefficients had a high peak around 0.9, with the vast majority of segmentations having values larger than 0.8.

  15. Automatic nuclei segmentation in H&E stained breast cancer histopathology images.

    Science.gov (United States)

    Veta, Mitko; van Diest, Paul J; Kornegoor, Robert; Huisman, André; Viergever, Max A; Pluim, Josien P W

    2013-01-01

    The introduction of fast digital slide scanners that provide whole slide images has led to a revival of interest in image analysis applications in pathology. Segmentation of cells and nuclei is an important first step towards automatic analysis of digitized microscopy images. We therefore developed an automated nuclei segmentation method that works with hematoxylin and eosin (H&E) stained breast cancer histopathology images, which represent regions of whole digital slides. The procedure can be divided into four main steps: 1) pre-processing with color unmixing and morphological operators, 2) marker-controlled watershed segmentation at multiple scales and with different markers, 3) post-processing for rejection of false regions and 4) merging of the results from multiple scales. The procedure was developed on a set of 21 breast cancer cases (subset A) and tested on a separate validation set of 18 cases (subset B). The evaluation was done in terms of both detection accuracy (sensitivity and positive predictive value) and segmentation accuracy (Dice coefficient). The mean estimated sensitivity for subset A was 0.875 (±0.092) and for subset B 0.853 (±0.077). The mean estimated positive predictive value was 0.904 (±0.075) and 0.886 (±0.069) for subsets A and B, respectively. For both subsets, the distribution of the Dice coefficients had a high peak around 0.9, with the vast majority of segmentations having values larger than 0.8.

  16. A fast and automatic mosaic method for high-resolution satellite images

    Science.gov (United States)

    Chen, Hongshun; He, Hui; Xiao, Hongyu; Huang, Jing

    2015-12-01

    We proposed a fast and fully automatic mosaic method for high-resolution satellite images. First, the overlapped rectangle is computed according to geographical locations of the reference and mosaic images and feature points on both the reference and mosaic images are extracted by a scale-invariant feature transform (SIFT) algorithm only from the overlapped region. Then, the RANSAC method is used to match feature points of both images. Finally, the two images are fused into a seamlessly panoramic image by the simple linear weighted fusion method or other method. The proposed method is implemented in C++ language based on OpenCV and GDAL, and tested by Worldview-2 multispectral images with a spatial resolution of 2 meters. Results show that the proposed method can detect feature points efficiently and mosaic images automatically.

  17. Multispectral Image Analysis for Astaxanthin Coating Classification

    DEFF Research Database (Denmark)

    Ljungqvist, Martin Georg; Ersbøll, Bjarne Kjær; Nielsen, Michael Engelbrecht

    2012-01-01

    Industrial quality inspection using image analysis on astaxanthin coating in aquaculture feed pellets is of great importance for automatic production control. The pellets were divided into two groups: one with pellets coated using synthetic astaxanthin in fish oil and the other with pellets coated...

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

    Science.gov (United States)

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

  19. Automatic detection of NIL defects using microscopy and image processing

    KAUST Repository

    Pietroy, David; Gereige, Issam; Gourgon, Cé cile

    2013-01-01

    patterns, sticking. In this paper, microscopic imaging combined to a specific processing algorithm is used to detect numerically defects in printed patterns. Results obtained for 1D and 2D imprinted gratings with different microscopic image magnifications

  20. Automatic Delineation of On-Line Head-And-Neck Computed Tomography Images: Toward On-Line Adaptive Radiotherapy

    International Nuclear Information System (INIS)

    Zhang Tiezhi; Chi Yuwei; Meldolesi, Elisa; Yan Di

    2007-01-01

    Purpose: To develop and validate a fully automatic region-of-interest (ROI) delineation method for on-line adaptive radiotherapy. Methods and Materials: On-line adaptive radiotherapy requires a robust and automatic image segmentation method to delineate ROIs in on-line volumetric images. We have implemented an atlas-based image segmentation method to automatically delineate ROIs of head-and-neck helical computed tomography images. A total of 32 daily computed tomography images from 7 head-and-neck patients were delineated using this automatic image segmentation method. Manually drawn contours on the daily images were used as references in the evaluation of automatically delineated ROIs. Two methods were used in quantitative validation: (1) the dice similarity coefficient index, which indicates the overlapping ratio between the manually and automatically delineated ROIs; and (2) the distance transformation, which yields the distances between the manually and automatically delineated ROI surfaces. Results: Automatic segmentation showed agreement with manual contouring. For most ROIs, the dice similarity coefficient indexes were approximately 0.8. Similarly, the distance transformation evaluation results showed that the distances between the manually and automatically delineated ROI surfaces were mostly within 3 mm. The distances between two surfaces had a mean of 1 mm and standard deviation of <2 mm in most ROIs. Conclusion: With atlas-based image segmentation, it is feasible to automatically delineate ROIs on the head-and-neck helical computed tomography images in on-line adaptive treatments

  1. Automatic Segmenting Structures in MRI's Based on Texture Analysis and Fuzzy Logic

    Science.gov (United States)

    Kaur, Mandeep; Rattan, Munish; Singh, Pushpinder

    2017-12-01

    The purpose of this paper is to present the variational method for geometric contours which helps the level set function remain close to the sign distance function, therefor it remove the need of expensive re-initialization procedure and thus, level set method is applied on magnetic resonance images (MRI) to track the irregularities in them as medical imaging plays a substantial part in the treatment, therapy and diagnosis of various organs, tumors and various abnormalities. It favors the patient with more speedy and decisive disease controlling with lesser side effects. The geometrical shape, the tumor's size and tissue's abnormal growth can be calculated by the segmentation of that particular image. It is still a great challenge for the researchers to tackle with an automatic segmentation in the medical imaging. Based on the texture analysis, different images are processed by optimization of level set segmentation. Traditionally, optimization was manual for every image where each parameter is selected one after another. By applying fuzzy logic, the segmentation of image is correlated based on texture features, to make it automatic and more effective. There is no initialization of parameters and it works like an intelligent system. It segments the different MRI images without tuning the level set parameters and give optimized results for all MRI's.

  2. Automatic Matching of Large Scale Images and Terrestrial LIDAR Based on App Synergy of Mobile Phone

    Science.gov (United States)

    Xia, G.; Hu, C.

    2018-04-01

    The digitalization of Cultural Heritage based on ground laser scanning technology has been widely applied. High-precision scanning and high-resolution photography of cultural relics are the main methods of data acquisition. The reconstruction with the complete point cloud and high-resolution image requires the matching of image and point cloud, the acquisition of the homonym feature points, the data registration, etc. However, the one-to-one correspondence between image and corresponding point cloud depends on inefficient manual search. The effective classify and management of a large number of image and the matching of large image and corresponding point cloud will be the focus of the research. In this paper, we propose automatic matching of large scale images and terrestrial LiDAR based on APP synergy of mobile phone. Firstly, we develop an APP based on Android, take pictures and record related information of classification. Secondly, all the images are automatically grouped with the recorded information. Thirdly, the matching algorithm is used to match the global and local image. According to the one-to-one correspondence between the global image and the point cloud reflection intensity image, the automatic matching of the image and its corresponding laser radar point cloud is realized. Finally, the mapping relationship between global image, local image and intensity image is established according to homonym feature point. So we can establish the data structure of the global image, the local image in the global image, the local image corresponding point cloud, and carry on the visualization management and query of image.

  3. Automatic construction of 3D-ASM intensity models by simulating image acquisition: application to myocardial gated SPECT studies.

    Science.gov (United States)

    Tobon-Gomez, Catalina; Butakoff, Constantine; Aguade, Santiago; Sukno, Federico; Moragas, Gloria; Frangi, Alejandro F

    2008-11-01

    Active shape models bear a great promise for model-based medical image analysis. Their practical use, though, is undermined due to the need to train such models on large image databases. Automatic building of point distribution models (PDMs) has been successfully addressed and a number of autolandmarking techniques are currently available. However, the need for strategies to automatically build intensity models around each landmark has been largely overlooked in the literature. This work demonstrates the potential of creating intensity models automatically by simulating image generation. We show that it is possible to reuse a 3D PDM built from computed tomography (CT) to segment gated single photon emission computed tomography (gSPECT) studies. Training is performed on a realistic virtual population where image acquisition and formation have been modeled using the SIMIND Monte Carlo simulator and ASPIRE image reconstruction software, respectively. The dataset comprised 208 digital phantoms (4D-NCAT) and 20 clinical studies. The evaluation is accomplished by comparing point-to-surface and volume errors against a proper gold standard. Results show that gSPECT studies can be successfully segmented by models trained under this scheme with subvoxel accuracy. The accuracy in estimated LV function parameters, such as end diastolic volume, end systolic volume, and ejection fraction, ranged from 90.0% to 94.5% for the virtual population and from 87.0% to 89.5% for the clinical population.

  4. Anatomy-based automatic detection and segmentation of major vessels in thoracic CTA images

    International Nuclear Information System (INIS)

    Zou Xiaotao; Liang Jianming; Wolf, M.; Salganicoff, M.; Krishnan, A.; Nadich, D.P.

    2007-01-01

    Existing approaches for automated computerized detection of pulmonary embolism (PE) using computed tomography angiography (CTA) usually focus on segmental and sub-segmental emboli. The goal of our current research is to extend our existing approach to automated detection of central PE. In order to detect central emboli, the major vessels must be first identified and segmented automatically. This submission presents an anatomy-based method for automatic computerized detection and segmentation of aortas and main pulmonary arteries in CTA images. (orig.)

  5. Effectiveness of an Automatic Tracking Software in Underwater Motion Analysis

    Directory of Open Access Journals (Sweden)

    Fabrício A. Magalhaes

    2013-12-01

    Full Text Available Tracking of markers placed on anatomical landmarks is a common practice in sports science to perform the kinematic analysis that interests both athletes and coaches. Although different software programs have been developed to automatically track markers and/or features, none of them was specifically designed to analyze underwater motion. Hence, this study aimed to evaluate the effectiveness of a software developed for automatic tracking of underwater movements (DVP, based on the Kanade-Lucas-Tomasi feature tracker. Twenty-one video recordings of different aquatic exercises (n = 2940 markers’ positions were manually tracked to determine the markers’ center coordinates. Then, the videos were automatically tracked using DVP and a commercially available software (COM. Since tracking techniques may produce false targets, an operator was instructed to stop the automatic procedure and to correct the position of the cursor when the distance between the calculated marker’s coordinate and the reference one was higher than 4 pixels. The proportion of manual interventions required by the software was used as a measure of the degree of automation. Overall, manual interventions were 10.4% lower for DVP (7.4% than for COM (17.8%. Moreover, when examining the different exercise modes separately, the percentage of manual interventions was 5.6% to 29.3% lower for DVP than for COM. Similar results were observed when analyzing the type of marker rather than the type of exercise, with 9.9% less manual interventions for DVP than for COM. In conclusion, based on these results, the developed automatic tracking software presented can be used as a valid and useful tool for underwater motion analysis.

  6. Brain MR Image Restoration Using an Automatic Trilateral Filter With GPU-Based Acceleration.

    Science.gov (United States)

    Chang, Herng-Hua; Li, Cheng-Yuan; Gallogly, Audrey Haihong

    2018-02-01

    Noise reduction in brain magnetic resonance (MR) images has been a challenging and demanding task. This study develops a new trilateral filter that aims to achieve robust and efficient image restoration. Extended from the bilateral filter, the proposed algorithm contains one additional intensity similarity funct-ion, which compensates for the unique characteristics of noise in brain MR images. An entropy function adaptive to intensity variations is introduced to regulate the contributions of the weighting components. To hasten the computation, parallel computing based on the graphics processing unit (GPU) strategy is explored with emphasis on memory allocations and thread distributions. To automate the filtration, image texture feature analysis associated with machine learning is investigated. Among the 98 candidate features, the sequential forward floating selection scheme is employed to acquire the optimal texture features for regularization. Subsequently, a two-stage classifier that consists of support vector machines and artificial neural networks is established to predict the filter parameters for automation. A speedup gain of 757 was reached to process an entire MR image volume of 256 × 256 × 256 pixels, which completed within 0.5 s. Automatic restoration results revealed high accuracy with an ensemble average relative error of 0.53 ± 0.85% in terms of the peak signal-to-noise ratio. This self-regulating trilateral filter outperformed many state-of-the-art noise reduction methods both qualitatively and quantitatively. We believe that this new image restoration algorithm is of potential in many brain MR image processing applications that require expedition and automation.

  7. Automatic assessment of average diaphragm motion trajectory from 4DCT images through machine learning.

    Science.gov (United States)

    Li, Guang; Wei, Jie; Huang, Hailiang; Gaebler, Carl Philipp; Yuan, Amy; Deasy, Joseph O

    2015-12-01

    To automatically estimate average diaphragm motion trajectory (ADMT) based on four-dimensional computed tomography (4DCT), facilitating clinical assessment of respiratory motion and motion variation and retrospective motion study. We have developed an effective motion extraction approach and a machine-learning-based algorithm to estimate the ADMT. Eleven patients with 22 sets of 4DCT images (4DCT1 at simulation and 4DCT2 at treatment) were studied. After automatically segmenting the lungs, the differential volume-per-slice (dVPS) curves of the left and right lungs were calculated as a function of slice number for each phase with respective to the full-exhalation. After 5-slice moving average was performed, the discrete cosine transform (DCT) was applied to analyze the dVPS curves in frequency domain. The dimensionality of the spectrum data was reduced by using several lowest frequency coefficients ( f v ) to account for most of the spectrum energy (Σ f v 2 ). Multiple linear regression (MLR) method was then applied to determine the weights of these frequencies by fitting the ground truth-the measured ADMT, which are represented by three pivot points of the diaphragm on each side. The 'leave-one-out' cross validation method was employed to analyze the statistical performance of the prediction results in three image sets: 4DCT1, 4DCT2, and 4DCT1 + 4DCT2. Seven lowest frequencies in DCT domain were found to be sufficient to approximate the patient dVPS curves ( R = 91%-96% in MLR fitting). The mean error in the predicted ADMT using leave-one-out method was 0.3 ± 1.9 mm for the left-side diaphragm and 0.0 ± 1.4 mm for the right-side diaphragm. The prediction error is lower in 4DCT2 than 4DCT1, and is the lowest in 4DCT1 and 4DCT2 combined. This frequency-analysis-based machine learning technique was employed to predict the ADMT automatically with an acceptable error (0.2 ± 1.6 mm). This volumetric approach is not affected by the presence of the lung tumors

  8. Automatic segmentation of MR brain images of preterm infants using supervised classification.

    Science.gov (United States)

    Moeskops, Pim; Benders, Manon J N L; Chiţ, Sabina M; Kersbergen, Karina J; Groenendaal, Floris; de Vries, Linda S; Viergever, Max A; Išgum, Ivana

    2015-09-01

    Preterm birth is often associated with impaired brain development. The state and expected progression of preterm brain development can be evaluated using quantitative assessment of MR images. Such measurements require accurate segmentation of different tissue types in those images. This paper presents an algorithm for the automatic segmentation of unmyelinated white matter (WM), cortical grey matter (GM), and cerebrospinal fluid in the extracerebral space (CSF). The algorithm uses supervised voxel classification in three subsequent stages. In the first stage, voxels that can easily be assigned to one of the three tissue types are labelled. In the second stage, dedicated analysis of the remaining voxels is performed. The first and the second stages both use two-class classification for each tissue type separately. Possible inconsistencies that could result from these tissue-specific segmentation stages are resolved in the third stage, which performs multi-class classification. A set of T1- and T2-weighted images was analysed, but the optimised system performs automatic segmentation using a T2-weighted image only. We have investigated the performance of the algorithm when using training data randomly selected from completely annotated images as well as when using training data from only partially annotated images. The method was evaluated on images of preterm infants acquired at 30 and 40weeks postmenstrual age (PMA). When the method was trained using random selection from the completely annotated images, the average Dice coefficients were 0.95 for WM, 0.81 for GM, and 0.89 for CSF on an independent set of images acquired at 30weeks PMA. When the method was trained using only the partially annotated images, the average Dice coefficients were 0.95 for WM, 0.78 for GM and 0.87 for CSF for the images acquired at 30weeks PMA, and 0.92 for WM, 0.80 for GM and 0.85 for CSF for the images acquired at 40weeks PMA. Even though the segmentations obtained using training data

  9. A workflow for the automatic segmentation of organelles in electron microscopy image stacks

    Science.gov (United States)

    Perez, Alex J.; Seyedhosseini, Mojtaba; Deerinck, Thomas J.; Bushong, Eric A.; Panda, Satchidananda; Tasdizen, Tolga; Ellisman, Mark H.

    2014-01-01

    Electron microscopy (EM) facilitates analysis of the form, distribution, and functional status of key organelle systems in various pathological processes, including those associated with neurodegenerative disease. Such EM data often provide important new insights into the underlying disease mechanisms. The development of more accurate and efficient methods to quantify changes in subcellular microanatomy has already proven key to understanding the pathogenesis of Parkinson's and Alzheimer's diseases, as well as glaucoma. While our ability to acquire large volumes of 3D EM data is progressing rapidly, more advanced analysis tools are needed to assist in measuring precise three-dimensional morphologies of organelles within data sets that can include hundreds to thousands of whole cells. Although new imaging instrument throughputs can exceed teravoxels of data per day, image segmentation and analysis remain significant bottlenecks to achieving quantitative descriptions of whole cell structural organellomes. Here, we present a novel method for the automatic segmentation of organelles in 3D EM image stacks. Segmentations are generated using only 2D image information, making the method suitable for anisotropic imaging techniques such as serial block-face scanning electron microscopy (SBEM). Additionally, no assumptions about 3D organelle morphology are made, ensuring the method can be easily expanded to any number of structurally and functionally diverse organelles. Following the presentation of our algorithm, we validate its performance by assessing the segmentation accuracy of different organelle targets in an example SBEM dataset and demonstrate that it can be efficiently parallelized on supercomputing resources, resulting in a dramatic reduction in runtime. PMID:25426032

  10. Fully automatic algorithm for segmenting full human diaphragm in non-contrast CT Images

    Science.gov (United States)

    Karami, Elham; Gaede, Stewart; Lee, Ting-Yim; Samani, Abbas

    2015-03-01

    The diaphragm is a sheet of muscle which separates the thorax from the abdomen and it acts as the most important muscle of the respiratory system. As such, an accurate segmentation of the diaphragm, not only provides key information for functional analysis of the respiratory system, but also can be used for locating other abdominal organs such as the liver. However, diaphragm segmentation is extremely challenging in non-contrast CT images due to the diaphragm's similar appearance to other abdominal organs. In this paper, we present a fully automatic algorithm for diaphragm segmentation in non-contrast CT images. The method is mainly based on a priori knowledge about the human diaphragm anatomy. The diaphragm domes are in contact with the lungs and the heart while its circumference runs along the lumbar vertebrae of the spine as well as the inferior border of the ribs and sternum. As such, the diaphragm can be delineated by segmentation of these organs followed by connecting relevant parts of their outline properly. More specifically, the bottom surface of the lungs and heart, the spine borders and the ribs are delineated, leading to a set of scattered points which represent the diaphragm's geometry. Next, a B-spline filter is used to find the smoothest surface which pass through these points. This algorithm was tested on a noncontrast CT image of a lung cancer patient. The results indicate that there is an average Hausdorff distance of 2.96 mm between the automatic and manually segmented diaphragms which implies a favourable accuracy.

  11. MO-F-CAMPUS-J-02: Automatic Recognition of Patient Treatment Site in Portal Images Using Machine Learning

    International Nuclear Information System (INIS)

    Chang, X; Yang, D

    2015-01-01

    Purpose: To investigate the method to automatically recognize the treatment site in the X-Ray portal images. It could be useful to detect potential treatment errors, and to provide guidance to sequential tasks, e.g. automatically verify the patient daily setup. Methods: The portal images were exported from MOSAIQ as DICOM files, and were 1) processed with a threshold based intensity transformation algorithm to enhance contrast, and 2) where then down-sampled (from 1024×768 to 128×96) by using bi-cubic interpolation algorithm. An appearance-based vector space model (VSM) was used to rearrange the images into vectors. A principal component analysis (PCA) method was used to reduce the vector dimensions. A multi-class support vector machine (SVM), with radial basis function kernel, was used to build the treatment site recognition models. These models were then used to recognize the treatment sites in the portal image. Portal images of 120 patients were included in the study. The images were selected to cover six treatment sites: brain, head and neck, breast, lung, abdomen and pelvis. Each site had images of the twenty patients. Cross-validation experiments were performed to evaluate the performance. Results: MATLAB image processing Toolbox and scikit-learn (a machine learning library in python) were used to implement the proposed method. The average accuracies using the AP and RT images separately were 95% and 94% respectively. The average accuracy using AP and RT images together was 98%. Computation time was ∼0.16 seconds per patient with AP or RT image, ∼0.33 seconds per patient with both of AP and RT images. Conclusion: The proposed method of treatment site recognition is efficient and accurate. It is not sensitive to the differences of image intensity, size and positions of patients in the portal images. It could be useful for the patient safety assurance. The work was partially supported by a research grant from Varian Medical System

  12. MO-F-CAMPUS-J-02: Automatic Recognition of Patient Treatment Site in Portal Images Using Machine Learning

    Energy Technology Data Exchange (ETDEWEB)

    Chang, X; Yang, D [Washington University in St Louis, St Louis, MO (United States)

    2015-06-15

    Purpose: To investigate the method to automatically recognize the treatment site in the X-Ray portal images. It could be useful to detect potential treatment errors, and to provide guidance to sequential tasks, e.g. automatically verify the patient daily setup. Methods: The portal images were exported from MOSAIQ as DICOM files, and were 1) processed with a threshold based intensity transformation algorithm to enhance contrast, and 2) where then down-sampled (from 1024×768 to 128×96) by using bi-cubic interpolation algorithm. An appearance-based vector space model (VSM) was used to rearrange the images into vectors. A principal component analysis (PCA) method was used to reduce the vector dimensions. A multi-class support vector machine (SVM), with radial basis function kernel, was used to build the treatment site recognition models. These models were then used to recognize the treatment sites in the portal image. Portal images of 120 patients were included in the study. The images were selected to cover six treatment sites: brain, head and neck, breast, lung, abdomen and pelvis. Each site had images of the twenty patients. Cross-validation experiments were performed to evaluate the performance. Results: MATLAB image processing Toolbox and scikit-learn (a machine learning library in python) were used to implement the proposed method. The average accuracies using the AP and RT images separately were 95% and 94% respectively. The average accuracy using AP and RT images together was 98%. Computation time was ∼0.16 seconds per patient with AP or RT image, ∼0.33 seconds per patient with both of AP and RT images. Conclusion: The proposed method of treatment site recognition is efficient and accurate. It is not sensitive to the differences of image intensity, size and positions of patients in the portal images. It could be useful for the patient safety assurance. The work was partially supported by a research grant from Varian Medical System.

  13. Classification of scintigrams on the base of an automatic analysis

    International Nuclear Information System (INIS)

    Vidyukov, V.I.; Kasatkin, Yu.N.; Kal'nitskaya, E.F.; Mironov, S.P.; Rotenberg, E.M.

    1980-01-01

    The stages of drawing a discriminative system based on self-education for an automatic analysis of scintigrams have been considered. The results of the classification of 240 scintigrams of the liver into ''normal'', ''diffuse lesions'', ''focal lesions'' have been evaluated by medical experts and computer. The accuracy of the computerized classification was 91.7%, that of the experts-85%. The automatic analysis methods of scintigrams of the liver have been realized using the specialized MDS system of data processing. The quality of the discriminative system has been assessed on 125 scintigrams. The accuracy of the classification is equal to 89.6%. The employment of the self-education; methods permitted one to single out two subclasses depending on the severity of diffuse lesions

  14. Validated automatic segmentation of AMD pathology including drusen and geographic atrophy in SD-OCT images.

    Science.gov (United States)

    Chiu, Stephanie J; Izatt, Joseph A; O'Connell, Rachelle V; Winter, Katrina P; Toth, Cynthia A; Farsiu, Sina

    2012-01-05

    To automatically segment retinal spectral domain optical coherence tomography (SD-OCT) images of eyes with age-related macular degeneration (AMD) and various levels of image quality to advance the study of retinal pigment epithelium (RPE)+drusen complex (RPEDC) volume changes indicative of AMD progression. A general segmentation framework based on graph theory and dynamic programming was used to segment three retinal boundaries in SD-OCT images of eyes with drusen and geographic atrophy (GA). A validation study for eyes with nonneovascular AMD was conducted, forming subgroups based on scan quality and presence of GA. To test for accuracy, the layer thickness results from two certified graders were compared against automatic segmentation results for 220 B-scans across 20 patients. For reproducibility, automatic layer volumes were compared that were generated from 0° versus 90° scans in five volumes with drusen. The mean differences in the measured thicknesses of the total retina and RPEDC layers were 4.2 ± 2.8 and 3.2 ± 2.6 μm for automatic versus manual segmentation. When the 0° and 90° datasets were compared, the mean differences in the calculated total retina and RPEDC volumes were 0.28% ± 0.28% and 1.60% ± 1.57%, respectively. The average segmentation time per image was 1.7 seconds automatically versus 3.5 minutes manually. The automatic algorithm accurately and reproducibly segmented three retinal boundaries in images containing drusen and GA. This automatic approach can reduce time and labor costs and yield objective measurements that potentially reveal quantitative RPE changes in longitudinal clinical AMD studies. (ClinicalTrials.gov number, NCT00734487.).

  15. Analysis of Phonetic Transcriptions for Danish Automatic Speech Recognition

    DEFF Research Database (Denmark)

    Kirkedal, Andreas Søeborg

    2013-01-01

    Automatic speech recognition (ASR) relies on three resources: audio, orthographic transcriptions and a pronunciation dictionary. The dictionary or lexicon maps orthographic words to sequences of phones or phonemes that represent the pronunciation of the corresponding word. The quality of a speech....... The analysis indicates that transcribing e.g. stress or vowel duration has a negative impact on performance. The best performance is obtained with coarse phonetic annotation and improves performance 1% word error rate and 3.8% sentence error rate....

  16. Automatic Measurement of Fetal Brain Development from Magnetic Resonance Imaging: New Reference Data.

    Science.gov (United States)

    Link, Daphna; Braginsky, Michael B; Joskowicz, Leo; Ben Sira, Liat; Harel, Shaul; Many, Ariel; Tarrasch, Ricardo; Malinger, Gustavo; Artzi, Moran; Kapoor, Cassandra; Miller, Elka; Ben Bashat, Dafna

    2018-01-01

    Accurate fetal brain volume estimation is of paramount importance in evaluating fetal development. The aim of this study was to develop an automatic method for fetal brain segmentation from magnetic resonance imaging (MRI) data, and to create for the first time a normal volumetric growth chart based on a large cohort. A semi-automatic segmentation method based on Seeded Region Growing algorithm was developed and applied to MRI data of 199 typically developed fetuses between 18 and 37 weeks' gestation. The accuracy of the algorithm was tested against a sub-cohort of ground truth manual segmentations. A quadratic regression analysis was used to create normal growth charts. The sensitivity of the method to identify developmental disorders was demonstrated on 9 fetuses with intrauterine growth restriction (IUGR). The developed method showed high correlation with manual segmentation (r2 = 0.9183, p user independent, applicable with retrospective data, and is suggested for use in routine clinical practice. © 2017 S. Karger AG, Basel.

  17. Automatic relative RPC image model bias compensation through hierarchical image matching for improving DEM quality

    Science.gov (United States)

    Noh, Myoung-Jong; Howat, Ian M.

    2018-02-01

    The quality and efficiency of automated Digital Elevation Model (DEM) extraction from stereoscopic satellite imagery is critically dependent on the accuracy of the sensor model used for co-locating pixels between stereo-pair images. In the absence of ground control or manual tie point selection, errors in the sensor models must be compensated with increased matching search-spaces, increasing both the computation time and the likelihood of spurious matches. Here we present an algorithm for automatically determining and compensating the relative bias in Rational Polynomial Coefficients (RPCs) between stereo-pairs utilizing hierarchical, sub-pixel image matching in object space. We demonstrate the algorithm using a suite of image stereo-pairs from multiple satellites over a range stereo-photogrammetrically challenging polar terrains. Besides providing a validation of the effectiveness of the algorithm for improving DEM quality, experiments with prescribed sensor model errors yield insight into the dependence of DEM characteristics and quality on relative sensor model bias. This algorithm is included in the Surface Extraction through TIN-based Search-space Minimization (SETSM) DEM extraction software package, which is the primary software used for the U.S. National Science Foundation ArcticDEM and Reference Elevation Model of Antarctica (REMA) products.

  18. Comparison of human and automatic segmentations of kidneys from CT images

    International Nuclear Information System (INIS)

    Rao, Manjori; Stough, Joshua; Chi, Y.-Y.; Muller, Keith; Tracton, Gregg; Pizer, Stephen M.; Chaney, Edward L.

    2005-01-01

    Purpose: A controlled observer study was conducted to compare a method for automatic image segmentation with conventional user-guided segmentation of right and left kidneys from planning computerized tomographic (CT) images. Methods and materials: Deformable shape models called m-reps were used to automatically segment right and left kidneys from 12 target CT images, and the results were compared with careful manual segmentations performed by two human experts. M-rep models were trained based on manual segmentations from a collection of images that did not include the targets. Segmentation using m-reps began with interactive initialization to position the kidney model over the target kidney in the image data. Fully automatic segmentation proceeded through two stages at successively smaller spatial scales. At the first stage, a global similarity transformation of the kidney model was computed to position the model closer to the target kidney. The similarity transformation was followed by large-scale deformations based on principal geodesic analysis (PGA). During the second stage, the medial atoms comprising the m-rep model were deformed one by one. This procedure was iterated until no changes were observed. The transformations and deformations at both stages were driven by optimizing an objective function with two terms. One term penalized the currently deformed m-rep by an amount proportional to its deviation from the mean m-rep derived from PGA of the training segmentations. The second term computed a model-to-image match term based on the goodness of match of the trained intensity template for the currently deformed m-rep with the corresponding intensity data in the target image. Human and m-rep segmentations were compared using quantitative metrics provided in a toolset called Valmet. Metrics reported in this article include (1) percent volume overlap; (2) mean surface distance between two segmentations; and (3) maximum surface separation (Hausdorff distance

  19. Automatic measurement of axial length of human eye using three-dimensional magnetic resonance imaging

    International Nuclear Information System (INIS)

    Watanabe, Masaki; Kiryu, Tohru

    2011-01-01

    The measurement of axial length and the evaluation of three dimensional (3D) form of an eye are essential to evaluate the mechanism of myopia progression. We propose a method of automatic measurement of axial length including adjustment of the pulse sequence of short-term scan which could suppress influence of eyeblink, using a magnetic resonance imaging (MRI) which acquires 3D images noninvasively. Acquiring T 2 -weighted images with 3.0 tesla MRI device and eight-channel phased-array head coil, we extracted left and right eye ball images, and then reconstructed 3D volume. The surface coordinates were calculated from 3D volume, fitting the ellipsoid model coordinates with the surface coordinates, and measured the axial length automatically. Measuring twenty one subjects, we compared the automatically measured values of axial length with the manually measured ones, then confirmed significant elongation in the axial length of myopia compared with that of emmetropia. Furthermore, there were no significant differences (P<0.05) between the means of automatic measurements and the manual ones. Accordingly, the automatic measurement process of axial length could be a tool for the elucidation of the mechanism of myopia progression, which would be suitable for evaluating the axial length easily and noninvasively. (author)

  20. Semi-Automatic Image Labelling Using Depth Information

    Directory of Open Access Journals (Sweden)

    Mostafa Pordel

    2015-05-01

    Full Text Available Image labeling tools help to extract objects within images to be used as ground truth for learning and testing in object detection processes. The inputs for such tools are usually RGB images. However with new widely available low-cost sensors like Microsoft Kinect it is possible to use depth images in addition to RGB images. Despite many existing powerful tools for image labeling, there is a need for RGB-depth adapted tools. We present a new interactive labeling tool that partially automates image labeling, with two major contributions. First, the method extends the concept of image segmentation from RGB to RGB-depth using Fuzzy C-Means clustering, connected component labeling and superpixels, and generates bounding pixels to extract the desired objects. Second, it minimizes the interaction time needed for object extraction by doing an efficient segmentation in RGB-depth space. Very few clicks are needed for the entire procedure compared to existing, tools. When the desired object is the closest object to the camera, which is often the case in robotics applications, no clicks at all are required to accurately extract the object.

  1. Application of image recognition-based automatic hyphae detection in fungal keratitis.

    Science.gov (United States)

    Wu, Xuelian; Tao, Yuan; Qiu, Qingchen; Wu, Xinyi

    2018-03-01

    The purpose of this study is to evaluate the accuracy of two methods in diagnosis of fungal keratitis, whereby one method is automatic hyphae detection based on images recognition and the other method is corneal smear. We evaluate the sensitivity and specificity of the method in diagnosis of fungal keratitis, which is automatic hyphae detection based on image recognition. We analyze the consistency of clinical symptoms and the density of hyphae, and perform quantification using the method of automatic hyphae detection based on image recognition. In our study, 56 cases with fungal keratitis (just single eye) and 23 cases with bacterial keratitis were included. All cases underwent the routine inspection of slit lamp biomicroscopy, corneal smear examination, microorganism culture and the assessment of in vivo confocal microscopy images before starting medical treatment. Then, we recognize the hyphae images of in vivo confocal microscopy by using automatic hyphae detection based on image recognition to evaluate its sensitivity and specificity and compare with the method of corneal smear. The next step is to use the index of density to assess the severity of infection, and then find the correlation with the patients' clinical symptoms and evaluate consistency between them. The accuracy of this technology was superior to corneal smear examination (p hyphae detection of image recognition was 89.29%, and the specificity was 95.65%. The area under the ROC curve was 0.946. The correlation coefficient between the grading of the severity in the fungal keratitis by the automatic hyphae detection based on image recognition and the clinical grading is 0.87. The technology of automatic hyphae detection based on image recognition was with high sensitivity and specificity, able to identify fungal keratitis, which is better than the method of corneal smear examination. This technology has the advantages when compared with the conventional artificial identification of confocal

  2. Segmentation of Multi-Isotope Imaging Mass Spectrometry Data for Semi-Automatic Detection of Regions of Interest

    Science.gov (United States)

    Poczatek, J. Collin; Turck, Christoph W.; Lechene, Claude

    2012-01-01

    Multi-isotope imaging mass spectrometry (MIMS) associates secondary ion mass spectrometry (SIMS) with detection of several atomic masses, the use of stable isotopes as labels, and affiliated quantitative image-analysis software. By associating image and measure, MIMS allows one to obtain quantitative information about biological processes in sub-cellular domains. MIMS can be applied to a wide range of biomedical problems, in particular metabolism and cell fate [1], [2], [3]. In order to obtain morphologically pertinent data from MIMS images, we have to define regions of interest (ROIs). ROIs are drawn by hand, a tedious and time-consuming process. We have developed and successfully applied a support vector machine (SVM) for segmentation of MIMS images that allows fast, semi-automatic boundary detection of regions of interests. Using the SVM, high-quality ROIs (as compared to an expert's manual delineation) were obtained for 2 types of images derived from unrelated data sets. This automation simplifies, accelerates and improves the post-processing analysis of MIMS images. This approach has been integrated into “Open MIMS,” an ImageJ-plugin for comprehensive analysis of MIMS images that is available online at http://www.nrims.hms.harvard.edu/NRIMS_ImageJ.php. PMID:22347386

  3. Efficient and automatic image reduction framework for space debris detection based on GPU technology

    Science.gov (United States)

    Diprima, Francesco; Santoni, Fabio; Piergentili, Fabrizio; Fortunato, Vito; Abbattista, Cristoforo; Amoruso, Leonardo

    2018-04-01

    In the last years, the increasing number of space debris has triggered the need of a distributed monitoring system for the prevention of possible space collisions. Space surveillance based on ground telescope allows the monitoring of the traffic of the Resident Space Objects (RSOs) in the Earth orbit. This space debris surveillance has several applications such as orbit prediction and conjunction assessment. In this paper is proposed an optimized and performance-oriented pipeline for sources extraction intended to the automatic detection of space debris in optical data. The detection method is based on the morphological operations and Hough Transform for lines. Near real-time detection is obtained using General Purpose computing on Graphics Processing Units (GPGPU). The high degree of processing parallelism provided by GPGPU allows to split data analysis over thousands of threads in order to process big datasets with a limited computational time. The implementation has been tested on a large and heterogeneous images data set, containing both imaging satellites from different orbit ranges and multiple observation modes (i.e. sidereal and object tracking). These images were taken during an observation campaign performed from the EQUO (EQUatorial Observatory) observatory settled at the Broglio Space Center (BSC) in Kenya, which is part of the ASI-Sapienza Agreement.

  4. Automatic detection of kidney in 3D pediatric ultrasound images using deep neural networks

    Science.gov (United States)

    Tabrizi, Pooneh R.; Mansoor, Awais; Biggs, Elijah; Jago, James; Linguraru, Marius George

    2018-02-01

    Ultrasound (US) imaging is the routine and safe diagnostic modality for detecting pediatric urology problems, such as hydronephrosis in the kidney. Hydronephrosis is the swelling of one or both kidneys because of the build-up of urine. Early detection of hydronephrosis can lead to a substantial improvement in kidney health outcomes. Generally, US imaging is a challenging modality for the evaluation of pediatric kidneys with different shape, size, and texture characteristics. The aim of this study is to present an automatic detection method to help kidney analysis in pediatric 3DUS images. The method localizes the kidney based on its minimum volume oriented bounding box) using deep neural networks. Separate deep neural networks are trained to estimate the kidney position, orientation, and scale, making the method computationally efficient by avoiding full parameter training. The performance of the method was evaluated using a dataset of 45 kidneys (18 normal and 27 diseased kidneys diagnosed with hydronephrosis) through the leave-one-out cross validation method. Quantitative results show the proposed detection method could extract the kidney position, orientation, and scale ratio with root mean square values of 1.3 +/- 0.9 mm, 6.34 +/- 4.32 degrees, and 1.73 +/- 0.04, respectively. This method could be helpful in automating kidney segmentation for routine clinical evaluation.

  5. Automatic prostate MR image segmentation with sparse label propagation and domain-specific manifold regularization.

    Science.gov (United States)

    Liao, Shu; Gao, Yaozong; Shi, Yinghuan; Yousuf, Ambereen; Karademir, Ibrahim; Oto, Aytekin; Shen, Dinggang

    2013-01-01

    Automatic prostate segmentation in MR images plays an important role in prostate cancer diagnosis. However, there are two main challenges: (1) Large inter-subject prostate shape variations; (2) Inhomogeneous prostate appearance. To address these challenges, we propose a new hierarchical prostate MR segmentation method, with the main contributions lying in the following aspects: First, the most salient features are learnt from atlases based on a subclass discriminant analysis (SDA) method, which aims to find a discriminant feature subspace by simultaneously maximizing the inter-class distance and minimizing the intra-class variations. The projected features, instead of only voxel-wise intensity, will be served as anatomical signature of each voxel. Second, based on the projected features, a new multi-atlases sparse label fusion framework is proposed to estimate the prostate likelihood of each voxel in the target image from the coarse level. Third, a domain-specific semi-supervised manifold regularization method is proposed to incorporate the most reliable patient-specific information identified by the prostate likelihood map to refine the segmentation result from the fine level. Our method is evaluated on a T2 weighted prostate MR image dataset consisting of 66 patients and compared with two state-of-the-art segmentation methods. Experimental results show that our method consistently achieves the highest segmentation accuracies than other methods under comparison.

  6. Image analysis for material characterisation

    Science.gov (United States)

    Livens, Stefan

    In this thesis, a number of image analysis methods are presented as solutions to two applications concerning the characterisation of materials. Firstly, we deal with the characterisation of corrosion images, which is handled using a multiscale texture analysis method based on wavelets. We propose a feature transformation that deals with the problem of rotation invariance. Classification is performed with a Learning Vector Quantisation neural network and with combination of outputs. In an experiment, 86,2% of the images showing either pit formation or cracking, are correctly classified. Secondly, we develop an automatic system for the characterisation of silver halide microcrystals. These are flat crystals with a triangular or hexagonal base and a thickness in the 100 to 200 nm range. A light microscope is used to image them. A novel segmentation method is proposed, which allows to separate agglomerated crystals. For the measurement of shape, the ratio between the largest and the smallest radius yields the best results. The thickness measurement is based on the interference colours that appear for light reflected at the crystals. The mean colour of different thickness populations is determined, from which a calibration curve is derived. With this, the thickness of new populations can be determined accurately.

  7. Automatic solar image motion measurements. [electronic disk flux monitoring

    Science.gov (United States)

    Colgate, S. A.; Moore, E. P.

    1975-01-01

    The solar seeing image motion has been monitored electronically and absolutely with a 25 cm telescope at three sites along the ridge at the southern end of the Magdalena Mountains west of Socorro, New Mexico. The uncorrelated component of the variations of the optical flux from two points at opposite limbs of the solar disk was continually monitored in 3 frequencies centered at 0.3, 3 and 30 Hz. The frequency band of maximum signal centered at 3 Hz showed the average absolute value of image motion to be somewhat less than 2sec. The observer estimates of combined blurring and image motion were well correlated with electronically measured image motion, but the observer estimates gave a factor 2 larger value.

  8. Automatic Optimization of Hardware Accelerators for Image Processing

    OpenAIRE

    Reiche, Oliver; Häublein, Konrad; Reichenbach, Marc; Hannig, Frank; Teich, Jürgen; Fey, Dietmar

    2015-01-01

    In the domain of image processing, often real-time constraints are required. In particular, in safety-critical applications, such as X-ray computed tomography in medical imaging or advanced driver assistance systems in the automotive domain, timing is of utmost importance. A common approach to maintain real-time capabilities of compute-intensive applications is to offload those computations to dedicated accelerator hardware, such as Field Programmable Gate Arrays (FPGAs). Programming such arc...

  9. MR-based automatic delineation of volumes of interest in human brain PET images using probability maps

    DEFF Research Database (Denmark)

    Svarer, Claus; Madsen, Karina; Hasselbalch, Steen G.

    2005-01-01

    subjects' MR-images, where VOI sets have been defined manually. High-resolution structural MR-images and 5-HT(2A) receptor binding PET-images (in terms of (18)F-altanserin binding) from 10 healthy volunteers and 10 patients with mild cognitive impairment were included for the analysis. A template including...... 35 VOIs was manually delineated on the subjects' MR images. Through a warping algorithm template VOI sets defined from each individual were transferred to the other subjects MR-images and the voxel overlap was compared to the VOI set specifically drawn for that particular individual. Comparisons were...... delineation of the VOI set. The approach was also shown to work equally well in individuals with pronounced cerebral atrophy. Probability-map-based automatic delineation of VOIs is a fast, objective, reproducible, and safe way to assess regional brain values from PET or SPECT scans. In addition, the method...

  10. An image based system to automatically and objectivelly score the degreeof redness and scaling in psoriasi lesions

    DEFF Research Database (Denmark)

    Gomez, David Delgado; Ersbøll, Bjarne Kjær; Carstensen, Jens Michael

    2004-01-01

    In this work, a combined statistical and image analysis method to automatically evaluate the severity of scaling in psoriasis lesions is proposed. The method separates the different regions of the disease in the image and scores the degree of scaling based on the properties of these areas. The pr...... that the obtained scores are highly correlated with scores made by doctors. This and the fact that the obtained measures are continuous indicate the proposed method is a suitable tool to evaluate the lesion and to track the evolution of dermatological diseases....

  11. Automatic Detection of Changes on Mars Surface from High-Resolution Orbital Images

    Science.gov (United States)

    Sidiropoulos, Panagiotis; Muller, Jan-Peter

    2017-04-01

    Over the last 40 years Mars has been extensively mapped by several NASA and ESA orbital missions, generating a large image dataset comprised of approximately 500,000 high-resolution images (of citizen science can be employed for training and verification it is unsuitable for planetwide systematic change detection. In this work, we introduce a novel approach in planetary image change detection, which involves a batch-mode automatic change detection pipeline that identifies regions that have changed. This is tested in anger, on tens of thousands of high-resolution images over the MC11 quadrangle [5], acquired by CTX, HRSC, THEMIS-VIS and MOC-NA instruments [1]. We will present results which indicate a substantial level of activity in this region of Mars, including instances of dynamic natural phenomena that haven't been cataloged in the planetary science literature before. We will demonstrate the potential and usefulness of such an automatic approach in planetary science change detection. Acknowledgments: The research leading to these results has received funding from the STFC "MSSL Consolidated Grant" ST/K000977/1 and partial support from the European Union's Seventh Framework Programme (FP7/2007-2013) under iMars grant agreement n° 607379. References: [1] P. Sidiropoulos and J. - P. Muller (2015) On the status of orbital high-resolution repeat imaging of Mars for the observation of dynamic surface processes. Planetary and Space Science, 117: 207-222. [2] O. Aharonson, et al. (2003) Slope streak formation and dust deposition rates on Mars. Journal of Geophysical Research: Planets, 108(E12):5138 [3] A. McEwen, et al. (2011) Seasonal flows on warm martian slopes. Science, 333 (6043): 740-743. [4] S. Byrne, et al. (2009) Distribution of mid-latitude ground ice on mars from new impact craters. Science, 325(5948):1674-1676. [5] K. Gwinner, et al (2016) The High Resolution Stereo Camera (HRSC) of Mars Express and its approach to science analysis and mapping for Mars and

  12. Automatic measurement of cusps in 2.5D dental images

    Science.gov (United States)

    Wolf, Mattias; Paulus, Dietrich W.; Niemann, Heinrich

    1996-01-01

    Automatic reconstruction of occlusal surfaces of teeth is an application which might become more and more urgent due to the toxicity of amalgam. Modern dental chairside equipment is currently restricted to the production of inlays. The automatic reconstruction of the occlusal surface is presently not possible. For manufacturing an occlusal surface it is required to extract features from which it is possible to reconstruct destroyed teeth. In this paper, we demonstrate how intact upper molars can be automatically extracted in dental range and intensity images. After normalization of the 3D location, the sizes of the cusps are detected and the distances between them are calculated. In the presented approach, the detection of the upper molar is based on a knowledge-based segmentation which includes anatomic knowledge. After the segmentation of the interesting tooth the central fossa is calculated. The normalization of the spatial location is archieved by aligning the detected fossa with a reference axis. After searching the cusp tips in the range image the image is resized. The methods have been successfully tested on 60 images. The results have been compared with the results of a dentist's evaluation on a sample of 20 images. The results will be further used for automatic production of tooth inlays.

  13. Automatic delineation of brain regions on MRI and PET images from the pig.

    Science.gov (United States)

    Villadsen, Jonas; Hansen, Hanne D; Jørgensen, Louise M; Keller, Sune H; Andersen, Flemming L; Petersen, Ida N; Knudsen, Gitte M; Svarer, Claus

    2018-01-15

    The increasing use of the pig as a research model in neuroimaging requires standardized processing tools. For example, extraction of regional dynamic time series from brain PET images requires parcellation procedures that benefit from being automated. Manual inter-modality spatial normalization to a MRI atlas is operator-dependent, time-consuming, and can be inaccurate with lack of cortical radiotracer binding or skull uptake. A parcellated PET template that allows for automatic spatial normalization to PET images of any radiotracer. MRI and [ 11 C]Cimbi-36 PET scans obtained in sixteen pigs made the basis for the atlas. The high resolution MRI scans allowed for creation of an accurately averaged MRI template. By aligning the within-subject PET scans to their MRI counterparts, an averaged PET template was created in the same space. We developed an automatic procedure for spatial normalization of the averaged PET template to new PET images and hereby facilitated transfer of the atlas regional parcellation. Evaluation of the automatic spatial normalization procedure found the median voxel displacement to be 0.22±0.08mm using the MRI template with individual MRI images and 0.92±0.26mm using the PET template with individual [ 11 C]Cimbi-36 PET images. We tested the automatic procedure by assessing eleven PET radiotracers with different kinetics and spatial distributions by using perfusion-weighted images of early PET time frames. We here present an automatic procedure for accurate and reproducible spatial normalization and parcellation of pig PET images of any radiotracer with reasonable blood-brain barrier penetration. Copyright © 2017 Elsevier B.V. All rights reserved.

  14. Automatic segmentation of lumbar vertebrae in CT images

    Science.gov (United States)

    Kulkarni, Amruta; Raina, Akshita; Sharifi Sarabi, Mona; Ahn, Christine S.; Babayan, Diana; Gaonkar, Bilwaj; Macyszyn, Luke; Raghavendra, Cauligi

    2017-03-01

    Lower back pain is one of the most prevalent disorders in the developed/developing world. However, its etiology is poorly understood and treatment is often determined subjectively. In order to quantitatively study the emergence and evolution of back pain, it is necessary to develop consistently measurable markers for pathology. Imaging based measures offer one solution to this problem. The development of imaging based on quantitative biomarkers for the lower back necessitates automated techniques to acquire this data. While the problem of segmenting lumbar vertebrae has been addressed repeatedly in literature, the associated problem of computing relevant biomarkers on the basis of the segmentation has not been addressed thoroughly. In this paper, we propose a Random-Forest based approach that learns to segment vertebral bodies in CT images followed by a biomarker evaluation framework that extracts vertebral heights and widths from the segmentations obtained. Our dataset consists of 15 CT sagittal scans obtained from General Electric Healthcare. Our main approach is divided into three parts: the first stage is image pre-processing which is used to correct for variations in illumination across all the images followed by preparing the foreground and background objects from images; the next stage is Machine Learning using Random-Forests, which distinguishes the interest-point vectors between foreground or background; and the last step is image post-processing, which is crucial to refine the results of classifier. The Dice coefficient was used as a statistical validation metric to evaluate the performance of our segmentations with an average value of 0.725 for our dataset.

  15. Efficient Semi-Automatic 3D Segmentation for Neuron Tracing in Electron Microscopy Images

    Science.gov (United States)

    Jones, Cory; Liu, Ting; Cohan, Nathaniel Wood; Ellisman, Mark; Tasdizen, Tolga

    2015-01-01

    0.1. Background In the area of connectomics, there is a significant gap between the time required for data acquisition and dense reconstruction of the neural processes contained in the same dataset. Automatic methods are able to eliminate this timing gap, but the state-of-the-art accuracy so far is insufficient for use without user corrections. If completed naively, this process of correction can be tedious and time consuming. 0.2. New Method We present a new semi-automatic method that can be used to perform 3D segmentation of neurites in EM image stacks. It utilizes an automatic method that creates a hierarchical structure for recommended merges of superpixels. The user is then guided through each predicted region to quickly identify errors and establish correct links. 0.3. Results We tested our method on three datasets with both novice and expert users. Accuracy and timing were compared with published automatic, semi-automatic, and manual results. 0.4. Comparison with Existing Methods Post-automatic correction methods have also been used in [1] and [2]. These methods do not provide navigation or suggestions in the manner we present. Other semi-automatic methods require user input prior to the automatic segmentation such as [3] and [4] and are inherently different than our method. 0.5. Conclusion Using this method on the three datasets, novice users achieved accuracy exceeding state-of-the-art automatic results, and expert users achieved accuracy on par with full manual labeling but with a 70% time improvement when compared with other examples in publication. PMID:25769273

  16. In vitro motility evaluation of aggregated cancer cells by means of automatic image processing.

    Science.gov (United States)

    De Hauwer, C; Darro, F; Camby, I; Kiss, R; Van Ham, P; Decaesteker, C

    1999-05-01

    Set up of an automatic image processing based method that enables the motility of in vitro aggregated cells to be evaluated for a number of hours. Our biological model included the PC-3 human prostate cancer cell line growing as a monolayer on the bottom of Falcon plastic dishes containing conventional culture media. Our equipment consisted of an incubator, an inverted phase contrast microscope, a Charge Coupled Device (CCD) video camera, and a computer equipped with an image processing software developed in our laboratory. This computer-assisted microscope analysis of aggregated cells enables global cluster motility to be evaluated. This analysis also enables the trajectory of each cell to be isolated and parametrized within a given cluster or, indeed, the trajectories of individual cells outside a cluster. The results show that motility inside a PC-3 cluster is not restricted to slight motion due to cluster expansion, but rather consists of a marked cell movement within the cluster. The proposed equipment enables in vitro aggregated cell motility to be studied. This method can, therefore, be used in pharmacological studies in order to select anti-motility related compounds. The compounds selected by the equipment described could then be tested in vivo as potential anti-metastatic.

  17. Automatic detection and classification of EOL-concrete and resulting recovered products by hyperspectral imaging

    Science.gov (United States)

    Palmieri, Roberta; Bonifazi, Giuseppe; Serranti, Silvia

    2014-05-01

    The recovery of materials from Demolition Waste (DW) represents one of the main target of the recycling industry and the its characterization is important in order to set up efficient sorting and/or quality control systems. End-Of-Life (EOL) concrete materials identification is necessary to maximize DW conversion into useful secondary raw materials, so it is fundamental to develop strategies for the implementation of an automatic recognition system of the recovered products. In this paper, HyperSpectral Imaging (HSI) technique was applied in order to detect DW composition. Hyperspectral images were acquired by a laboratory device equipped with a HSI sensing device working in the near infrared range (1000-1700 nm): NIR Spectral Camera™, embedding an ImSpector™ N17E (SPECIM Ltd, Finland). Acquired spectral data were analyzed adopting the PLS_Toolbox (Version 7.5, Eigenvector Research, Inc.) under Matlab® environment (Version 7.11.1, The Mathworks, Inc.), applying different chemometric methods: Principal Component Analysis (PCA) for exploratory data approach and Partial Least Square- Discriminant Analysis (PLS-DA) to build classification models. Results showed that it is possible to recognize DW materials, distinguishing recycled aggregates from contaminants (e.g. bricks, gypsum, plastics, wood, foam, etc.). The developed procedure is cheap, fast and non-destructive: it could be used to make some steps of the recycling process more efficient and less expensive.

  18. Computer Aided Solution for Automatic Segmenting and Measurements of Blood Leucocytes Using Static Microscope Images.

    Science.gov (United States)

    Abdulhay, Enas; Mohammed, Mazin Abed; Ibrahim, Dheyaa Ahmed; Arunkumar, N; Venkatraman, V

    2018-02-17

    Blood leucocytes segmentation in medical images is viewed as difficult process due to the variability of blood cells concerning their shape and size and the difficulty towards determining location of Blood Leucocytes. Physical analysis of blood tests to recognize leukocytes is tedious, time-consuming and liable to error because of the various morphological components of the cells. Segmentation of medical imagery has been considered as a difficult task because of complexity of images, and also due to the non-availability of leucocytes models which entirely captures the probable shapes in each structures and also incorporate cell overlapping, the expansive variety of the blood cells concerning their shape and size, various elements influencing the outer appearance of the blood leucocytes, and low Static Microscope Image disparity from extra issues outcoming about because of noise. We suggest a strategy towards segmentation of blood leucocytes using static microscope images which is a resultant of three prevailing systems of computer vision fiction: enhancing the image, Support vector machine for segmenting the image, and filtering out non ROI (region of interest) on the basis of Local binary patterns and texture features. Every one of these strategies are modified for blood leucocytes division issue, in this manner the subsequent techniques are very vigorous when compared with its individual segments. Eventually, we assess framework based by compare the outcome and manual division. The findings outcome from this study have shown a new approach that automatically segments the blood leucocytes and identify it from a static microscope images. Initially, the method uses a trainable segmentation procedure and trained support vector machine classifier to accurately identify the position of the ROI. After that, filtering out non ROI have proposed based on histogram analysis to avoid the non ROI and chose the right object. Finally, identify the blood leucocytes type using

  19. Automatic labeling and segmentation of vertebrae in CT images

    Science.gov (United States)

    Rasoulian, Abtin; Rohling, Robert N.; Abolmaesumi, Purang

    2014-03-01

    Labeling and segmentation of the spinal column from CT images is a pre-processing step for a range of image- guided interventions. State-of-the art techniques have focused either on image feature extraction or template matching for labeling of the vertebrae followed by segmentation of each vertebra. Recently, statistical multi- object models have been introduced to extract common statistical characteristics among several anatomies. In particular, we have created models for segmentation of the lumbar spine which are robust, accurate, and computationally tractable. In this paper, we reconstruct a statistical multi-vertebrae pose+shape model and utilize it in a novel framework for labeling and segmentation of the vertebra in a CT image. We validate our technique in terms of accuracy of the labeling and segmentation of CT images acquired from 56 subjects. The method correctly labels all vertebrae in 70% of patients and is only one level off for the remaining 30%. The mean distance error achieved for the segmentation is 2.1 +/- 0.7 mm.

  20. Plant phenomics: an overview of image acquisition technologies and image data analysis algorithms.

    Science.gov (United States)

    Perez-Sanz, Fernando; Navarro, Pedro J; Egea-Cortines, Marcos

    2017-11-01

    The study of phenomes or phenomics has been a central part of biology. The field of automatic phenotype acquisition technologies based on images has seen an important advance in the last years. As with other high-throughput technologies, it addresses a common set of problems, including data acquisition and analysis. In this review, we give an overview of the main systems developed to acquire images. We give an in-depth analysis of image processing with its major issues and the algorithms that are being used or emerging as useful to obtain data out of images in an automatic fashion. © The Author 2017. Published by Oxford University Press.

  1. Building an Image-Based System to automatically Score psoriasis

    DEFF Research Database (Denmark)

    G{'o}mez, D. Delgado; Carstensen, Jens Michael; Ersbøll, Bjarne Kjær

    2003-01-01

    Nowadays the medical tracking of dermatological diseases is imprecise. The main reason is the lack of suitable objective methods to evaluate the lesion. The severity of the disease is scored by doctors just through their visual examination. In this work, a system to take accurate images of dermat......Nowadays the medical tracking of dermatological diseases is imprecise. The main reason is the lack of suitable objective methods to evaluate the lesion. The severity of the disease is scored by doctors just through their visual examination. In this work, a system to take accurate images...

  2. Automatic segmentation and 3-dimensional display based on the knowledge of head MRI images

    International Nuclear Information System (INIS)

    Suzuki, Hidetomo; Toriwaki, Jun-ichiro.

    1987-01-01

    In this paper we present a procedure which automatically extracts soft tissues, such as subcutaneous fat, brain, and cerebral ventricle, from the multislice MRI images of head region, and displays their 3-dimensional images. Segmentation of soft tissues is done by use of an iterative thresholding. In order to select the optimum threshold value automatically, we introduce a measure to evaluate the goodness of segmentation into this procedure. When the measure satisfies given conditions, iteration of thresholding terminates, and the final result of segmentation is extracted by using the current threshold value. Since this procedure can execute segmentation and calculation of the goodness measure in each slice automatically, it remarkably decreases efforts of users. Moreover, the 3-dimensional display of the segmented tissues shows that this procedure can extract the shape of each soft tissue with reasonable precision for clinical use. (author)

  3. The One to Multiple Automatic High Accuracy Registration of Terrestrial LIDAR and Optical Images

    Science.gov (United States)

    Wang, Y.; Hu, C.; Xia, G.; Xue, H.

    2018-04-01

    The registration of ground laser point cloud and close-range image is the key content of high-precision 3D reconstruction of cultural relic object. In view of the requirement of high texture resolution in the field of cultural relic at present, The registration of point cloud and image data in object reconstruction will result in the problem of point cloud to multiple images. In the current commercial software, the two pairs of registration of the two kinds of data are realized by manually dividing point cloud data, manual matching point cloud and image data, manually selecting a two - dimensional point of the same name of the image and the point cloud, and the process not only greatly reduces the working efficiency, but also affects the precision of the registration of the two, and causes the problem of the color point cloud texture joint. In order to solve the above problems, this paper takes the whole object image as the intermediate data, and uses the matching technology to realize the automatic one-to-one correspondence between the point cloud and multiple images. The matching of point cloud center projection reflection intensity image and optical image is applied to realize the automatic matching of the same name feature points, and the Rodrigo matrix spatial similarity transformation model and weight selection iteration are used to realize the automatic registration of the two kinds of data with high accuracy. This method is expected to serve for the high precision and high efficiency automatic 3D reconstruction of cultural relic objects, which has certain scientific research value and practical significance.

  4. AUTOMATIC AND GENERIC MOSAICING OF MULTISENSOR IMAGES: AN APPLICATION TO PLEIADES HR

    Directory of Open Access Journals (Sweden)

    F. Bignalet-Cazalet

    2012-07-01

    Full Text Available In the early phase of the Pleiades program, the CNES (the French Space Agency specified and developed a fully automatic mosaicing processing unit, in order to generate satellite image mosaics under operational conditions. This tool can automatically put each input image in a common geometry, homogenize the radiometry, and generate orthomosaics using stitching lines. As the image quality commissioning phase of Pleiades1A is on-going, this mosaicing process is being tested for the first time under operational conditions. The French newly launched high resolution satellite can acquire adjacent images for French Civil and Defense User Ground Segments. This paper presents the very firsts results of mosaicing Pleiades1A images. Beyond Pleiades’ use, our mosaicing tool can process a significant variety of images, including other satellites and airborne acquisitions, using automatically-taken or external ground control points, offering time-based image superposition, and more. This paper also presents the design of the mosaicing tool and describes the processing workflow and the additional capabilities and applications.

  5. Automatic Video-based Analysis of Human Motion

    DEFF Research Database (Denmark)

    Fihl, Preben

    The human motion contains valuable information in many situations and people frequently perform an unconscious analysis of the motion of other people to understand their actions, intentions, and state of mind. An automatic analysis of human motion will facilitate many applications and thus has...... received great interest from both industry and research communities. The focus of this thesis is on video-based analysis of human motion and the thesis presents work within three overall topics, namely foreground segmentation, action recognition, and human pose estimation. Foreground segmentation is often...... the first important step in the analysis of human motion. By separating foreground from background the subsequent analysis can be focused and efficient. This thesis presents a robust background subtraction method that can be initialized with foreground objects in the scene and is capable of handling...

  6. Automatic registration of terrestrial point cloud using panoramic reflectance images

    NARCIS (Netherlands)

    Kang, Z.

    2008-01-01

    Much attention is paid to registration of terrestrial point clouds nowadays. Research is carried out towards improved efficiency and automation of the registration process. This paper reports a new approach for point clouds registration utilizing reflectance panoramic images. The approach follows a

  7. Automatic segmentation of blood vessels from retinal fundus images ...

    Indian Academy of Sciences (India)

    The retinal blood vessels were segmented through color space conversion and color channel .... Retinal blood vessel segmentation was also attempted through multi-scale operators. A few works in this ... fundus camera at 35 degrees field of view. The image ... vessel segmentation is available from two human observers.

  8. Diffeomorphic image registration with automatic time-step adjustment

    DEFF Research Database (Denmark)

    Pai, Akshay Sadananda Uppinakudru; Klein, S.; Sommer, Stefan Horst

    2015-01-01

    In this paper, we propose an automated Euler's time-step adjustment scheme for diffeomorphic image registration using stationary velocity fields (SVFs). The proposed variational problem aims at bounding the inverse consistency error by adaptively adjusting the number of Euler's step required to r...... accuracy as a fixed time-step scheme however at a much less computational cost....

  9. Effects of pose and image resolution on automatic face recognition

    NARCIS (Netherlands)

    Mahmood, Zahid; Ali, Tauseef; Khan, Samee U.

    The popularity of face recognition systems have increased due to their use in widespread applications. Driven by the enormous number of potential application domains, several algorithms have been proposed for face recognition. Face pose and image resolutions are among the two important factors that

  10. Construct Abstraction for Automatic Information Abstraction from Digital Images

    Science.gov (United States)

    2006-05-30

    objects and features and the names of objects of objects and features. For example, in Figure 15 the parts of the fish could be named the ‘mouth... fish -1 fish -2 fish -3 tennis shoe tennis racquet...of abstraction and generality. For example, an algorithm might usefully find a polygon ( blob ) in an image and calculate numbers such as the

  11. Multi-spectral Image Analysis for Astaxanthin Coating Classification

    DEFF Research Database (Denmark)

    Ljungqvist, Martin Georg; Ersbøll, Bjarne Kjær; Nielsen, Michael Engelbrecht

    2011-01-01

    Industrial quality inspection using image analysis on astaxanthin coating in aquaculture feed pellets is of great importance for automatic production control. In this study multi-spectral image analysis of pellets was performed using LDA, QDA, SNV and PCA on pixel level and mean value of pixels...

  12. Automatic segmentation of the bone and extraction of the bone-cartilage interface from magnetic resonance images of the knee

    International Nuclear Information System (INIS)

    Fripp, Jurgen; Crozier, Stuart; Warfield, Simon K; Ourselin, Sebastien

    2007-01-01

    The accurate segmentation of the articular cartilages from magnetic resonance (MR) images of the knee is important for clinical studies and drug trials into conditions like osteoarthritis. Currently, segmentations are obtained using time-consuming manual or semi-automatic algorithms which have high inter- and intra-observer variabilities. This paper presents an important step towards obtaining automatic and accurate segmentations of the cartilages, namely an approach to automatically segment the bones and extract the bone-cartilage interfaces (BCI) in the knee. The segmentation is performed using three-dimensional active shape models, which are initialized using an affine registration to an atlas. The BCI are then extracted using image information and prior knowledge about the likelihood of each point belonging to the interface. The accuracy and robustness of the approach was experimentally validated using an MR database of fat suppressed spoiled gradient recall images. The (femur, tibia, patella) bone segmentation had a median Dice similarity coefficient of (0.96, 0.96, 0.89) and an average point-to-surface error of 0.16 mm on the BCI. The extracted BCI had a median surface overlap of 0.94 with the real interface, demonstrating its usefulness for subsequent cartilage segmentation or quantitative analysis

  13. Automatic segmentation of the bone and extraction of the bone-cartilage interface from magnetic resonance images of the knee

    Energy Technology Data Exchange (ETDEWEB)

    Fripp, Jurgen [BioMedIA Lab, Autonomous Systems Laboratory, CSIRO ICT Centre, Level 20, 300 Adelaide street, Brisbane, QLD 4001 (Australia); Crozier, Stuart [School of Information Technology and Electrical Engineering, University of Queensland, St Lucia, QLD 4072 (Australia); Warfield, Simon K [Computational Radiology Laboratory, Harvard Medical School, Children' s Hospital Boston, 300 Longwood Avenue, Boston, MA 02115 (United States); Ourselin, Sebastien [BioMedIA Lab, Autonomous Systems Laboratory, CSIRO ICT Centre, Level 20, 300 Adelaide street, Brisbane, QLD 4001 (Australia)

    2007-03-21

    The accurate segmentation of the articular cartilages from magnetic resonance (MR) images of the knee is important for clinical studies and drug trials into conditions like osteoarthritis. Currently, segmentations are obtained using time-consuming manual or semi-automatic algorithms which have high inter- and intra-observer variabilities. This paper presents an important step towards obtaining automatic and accurate segmentations of the cartilages, namely an approach to automatically segment the bones and extract the bone-cartilage interfaces (BCI) in the knee. The segmentation is performed using three-dimensional active shape models, which are initialized using an affine registration to an atlas. The BCI are then extracted using image information and prior knowledge about the likelihood of each point belonging to the interface. The accuracy and robustness of the approach was experimentally validated using an MR database of fat suppressed spoiled gradient recall images. The (femur, tibia, patella) bone segmentation had a median Dice similarity coefficient of (0.96, 0.96, 0.89) and an average point-to-surface error of 0.16 mm on the BCI. The extracted BCI had a median surface overlap of 0.94 with the real interface, demonstrating its usefulness for subsequent cartilage segmentation or quantitative analysis.

  14. Automatic delineation of brain regions on MRI and PET images from the pig

    DEFF Research Database (Denmark)

    Villadsen, Jonas; Hansen, Hanne D; Jørgensen, Louise M

    2018-01-01

    : Manual inter-modality spatial normalization to a MRI atlas is operator-dependent, time-consuming, and can be inaccurate with lack of cortical radiotracer binding or skull uptake. NEW METHOD: A parcellated PET template that allows for automatic spatial normalization to PET images of any radiotracer....... RESULTS: MRI and [11C]Cimbi-36 PET scans obtained in sixteen pigs made the basis for the atlas. The high resolution MRI scans allowed for creation of an accurately averaged MRI template. By aligning the within-subject PET scans to their MRI counterparts, an averaged PET template was created in the same...... the MRI template with individual MRI images and 0.92±0.26mm using the PET template with individual [11C]Cimbi-36 PET images. We tested the automatic procedure by assessing eleven PET radiotracers with different kinetics and spatial distributions by using perfusion-weighted images of early PET time frames...

  15. DMET-analyzer: automatic analysis of Affymetrix DMET data.

    Science.gov (United States)

    Guzzi, Pietro Hiram; Agapito, Giuseppe; Di Martino, Maria Teresa; Arbitrio, Mariamena; Tassone, Pierfrancesco; Tagliaferri, Pierosandro; Cannataro, Mario

    2012-10-05

    Clinical Bioinformatics is currently growing and is based on the integration of clinical and omics data aiming at the development of personalized medicine. Thus the introduction of novel technologies able to investigate the relationship among clinical states and biological machineries may help the development of this field. For instance the Affymetrix DMET platform (drug metabolism enzymes and transporters) is able to study the relationship among the variation of the genome of patients and drug metabolism, detecting SNPs (Single Nucleotide Polymorphism) on genes related to drug metabolism. This may allow for instance to find genetic variants in patients which present different drug responses, in pharmacogenomics and clinical studies. Despite this, there is currently a lack in the development of open-source algorithms and tools for the analysis of DMET data. Existing software tools for DMET data generally allow only the preprocessing of binary data (e.g. the DMET-Console provided by Affymetrix) and simple data analysis operations, but do not allow to test the association of the presence of SNPs with the response to drugs. We developed DMET-Analyzer a tool for the automatic association analysis among the variation of the patient genomes and the clinical conditions of patients, i.e. the different response to drugs. The proposed system allows: (i) to automatize the workflow of analysis of DMET-SNP data avoiding the use of multiple tools; (ii) the automatic annotation of DMET-SNP data and the search in existing databases of SNPs (e.g. dbSNP), (iii) the association of SNP with pathway through the search in PharmaGKB, a major knowledge base for pharmacogenomic studies. DMET-Analyzer has a simple graphical user interface that allows users (doctors/biologists) to upload and analyse DMET files produced by Affymetrix DMET-Console in an interactive way. The effectiveness and easy use of DMET Analyzer is demonstrated through different case studies regarding the analysis of

  16. DMET-Analyzer: automatic analysis of Affymetrix DMET Data

    Directory of Open Access Journals (Sweden)

    Guzzi Pietro

    2012-10-01

    Full Text Available Abstract Background Clinical Bioinformatics is currently growing and is based on the integration of clinical and omics data aiming at the development of personalized medicine. Thus the introduction of novel technologies able to investigate the relationship among clinical states and biological machineries may help the development of this field. For instance the Affymetrix DMET platform (drug metabolism enzymes and transporters is able to study the relationship among the variation of the genome of patients and drug metabolism, detecting SNPs (Single Nucleotide Polymorphism on genes related to drug metabolism. This may allow for instance to find genetic variants in patients which present different drug responses, in pharmacogenomics and clinical studies. Despite this, there is currently a lack in the development of open-source algorithms and tools for the analysis of DMET data. Existing software tools for DMET data generally allow only the preprocessing of binary data (e.g. the DMET-Console provided by Affymetrix and simple data analysis operations, but do not allow to test the association of the presence of SNPs with the response to drugs. Results We developed DMET-Analyzer a tool for the automatic association analysis among the variation of the patient genomes and the clinical conditions of patients, i.e. the different response to drugs. The proposed system allows: (i to automatize the workflow of analysis of DMET-SNP data avoiding the use of multiple tools; (ii the automatic annotation of DMET-SNP data and the search in existing databases of SNPs (e.g. dbSNP, (iii the association of SNP with pathway through the search in PharmaGKB, a major knowledge base for pharmacogenomic studies. DMET-Analyzer has a simple graphical user interface that allows users (doctors/biologists to upload and analyse DMET files produced by Affymetrix DMET-Console in an interactive way. The effectiveness and easy use of DMET Analyzer is demonstrated through different

  17. Automatic Generation of Wide Dynamic Range Image without Pseudo-Edge Using Integration of Multi-Steps Exposure Images

    Science.gov (United States)

    Migiyama, Go; Sugimura, Atsuhiko; Osa, Atsushi; Miike, Hidetoshi

    Recently, digital cameras are offering technical advantages rapidly. However, the shot image is different from the sight image generated when that scenery is seen with the naked eye. There are blown-out highlights and crushed blacks in the image that photographed the scenery of wide dynamic range. The problems are hardly generated in the sight image. These are contributory cause of difference between the shot image and the sight image. Blown-out highlights and crushed blacks are caused by the difference of dynamic range between the image sensor installed in a digital camera such as CCD and CMOS and the human visual system. Dynamic range of the shot image is narrower than dynamic range of the sight image. In order to solve the problem, we propose an automatic method to decide an effective exposure range in superposition of edges. We integrate multi-step exposure images using the method. In addition, we try to erase pseudo-edges using the process to blend exposure values. Afterwards, we get a pseudo wide dynamic range image automatically.

  18. Automatic Description Generation from Images : A Survey of Models, Datasets, and Evaluation Measures

    NARCIS (Netherlands)

    Bernardi, Raffaella; Cakici, Ruket; Elliott, Desmond; Erdem, Aykut; Erdem, Erkut; Ikizler-Cinbis, Nazli; Keller, Frank; Muscat, Adrian; Plank, Barbara

    2016-01-01

    Automatic description generation from natural images is a challenging problem that has recently received a large amount of interest from the computer vision and natural language processing communities. In this survey, we classify the existing approaches based on how they conceptualize this problem,

  19. Semi-automatic construction of reference standards for evaluation of image registration

    NARCIS (Netherlands)

    Murphy, K.; Ginneken, van B.; Klein, S.; Staring, M.; Hoop, de B.J.; Viergever, M.A.; Pluim, J.P.W.

    2011-01-01

    Quantitative evaluation of image registration algorithms is a difficult and under-addressed issue due to the lack of a reference standard in most registration problems. In this work a method is presented whereby detailed reference standard data may be constructed in an efficient semi-automatic

  20. Automatic prostate localization on cone-beam CT scans for high precision image-guided radiotherapy

    NARCIS (Netherlands)

    Smitsmans, Monique H. P.; de Bois, Josien; Sonke, Jan-Jakob; Betgen, Anja; Zijp, Lambert J.; Jaffray, David A.; Lebesque, Joos V.; van Herk, Marcel

    2005-01-01

    PURPOSE: Previously, we developed an automatic three-dimensional gray-value registration (GR) method for fast prostate localization that could be used during online or offline image-guided radiotherapy. The method was tested on conventional computed tomography (CT) scans. In this study, the

  1. Interactive vs. automatic ultrasound image segmentation methods for staging hepatic lipidosis.

    NARCIS (Netherlands)

    Weijers, G.; Starke, A.; Haudum, A.; Thijssen, J.M.; Rehage, J.; Korte, C.L. de

    2010-01-01

    The aim of this study was to test the hypothesis that automatic segmentation of vessels in ultrasound (US) images can produce similar or better results in grading fatty livers than interactive segmentation. A study was performed in postpartum dairy cows (N=151), as an animal model of human fatty

  2. Color Image Segmentation Based on Different Color Space Models Using Automatic GrabCut

    Directory of Open Access Journals (Sweden)

    Dina Khattab

    2014-01-01

    Full Text Available This paper presents a comparative study using different color spaces to evaluate the performance of color image segmentation using the automatic GrabCut technique. GrabCut is considered as one of the semiautomatic image segmentation techniques, since it requires user interaction for the initialization of the segmentation process. The automation of the GrabCut technique is proposed as a modification of the original semiautomatic one in order to eliminate the user interaction. The automatic GrabCut utilizes the unsupervised Orchard and Bouman clustering technique for the initialization phase. Comparisons with the original GrabCut show the efficiency of the proposed automatic technique in terms of segmentation, quality, and accuracy. As no explicit color space is recommended for every segmentation problem, automatic GrabCut is applied with RGB, HSV, CMY, XYZ, and YUV color spaces. The comparative study and experimental results using different color images show that RGB color space is the best color space representation for the set of the images used.

  3. CAnat: An algorithm for the automatic segmentation of anatomy of medical images

    International Nuclear Information System (INIS)

    Caon, M.; Gobert, L.; Mariusz, B.

    2011-01-01

    Full text: To develop a method to automatically categorise organs and tissues displayed in medical images. Dosimetry calculations using Monte Carlo methods require a mathematical representation of human anatomy e.g. a voxel phantom. For a whole body, their construction involves processing several hundred images to identify each organ and tissue-the process is very time-consuming. This project is developing a Computational Anatomy (CAnat) algorithm to automatically recognise and classify the different tissue in a tomographic image. Methods The algorithm utilizes the Statistical Region Merging technique (SRM). The SRM depends on one estimated parameter. The parameter is a measure of statistical complexity of the image and can be automatically adjusted to suit individual image features. This allows for automatic tuning of coarseness of the overall segmentation as well as object specific selection for further tasks. CAnat is tested on two CT images selected to represent different anatomical complexities. In the mid-thigh image, tissues/. regions of interest are air, fat, muscle, bone marrow and compact bone. In the pelvic image, fat, urinary bladder and anus/colon, muscle, cancellous bone, and compact bone. Segmentation results were evaluated using the Jaccard index which is a measure of set agreement. An index of one indicates perfect agreement between CAnat and manual segmentation. The Jaccard indices for the mid-thigh CT were 0.99, 0.89, 0.97, 0.63 and 0.88, respectively and for the pelvic CT were 0.99, 0.81, 0.77, 0.93, 0.53, 0.76, respectively. Conclusion The high accuracy preliminary segmentation results demonstrate the feasibility of the CAnat algorithm.

  4. Automatic segmentation of cerebral MR images using artificial neural networks

    International Nuclear Information System (INIS)

    Alirezaie, J.; Jernigan, M.E.; Nahmias, C.

    1996-01-01

    In this paper we present an unsupervised clustering technique for multispectral segmentation of magnetic resonance (MR) images of the human brain. Our scheme utilizes the Self Organizing Feature Map (SOFM) artificial neural network for feature mapping and generates a set of codebook vectors. By extending the network with an additional layer the map will be classified and each tissue class will be labelled. An algorithm has been developed for extracting the cerebrum from the head scan prior to the segmentation. Extracting the cerebrum is performed by stripping away the skull pixels from the T2 image. Three tissue types of the brain: white matter, gray matter and cerebral spinal fluid (CSF) are segmented accurately. To compare the results with other conventional approaches we applied the c-means algorithm to the problem

  5. Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks

    Science.gov (United States)

    Cruz-Roa, Angel; Basavanhally, Ajay; González, Fabio; Gilmore, Hannah; Feldman, Michael; Ganesan, Shridar; Shih, Natalie; Tomaszewski, John; Madabhushi, Anant

    2014-03-01

    This paper presents a deep learning approach for automatic detection and visual analysis of invasive ductal carcinoma (IDC) tissue regions in whole slide images (WSI) of breast cancer (BCa). Deep learning approaches are learn-from-data methods involving computational modeling of the learning process. This approach is similar to how human brain works using different interpretation levels or layers of most representative and useful features resulting into a hierarchical learned representation. These methods have been shown to outpace traditional approaches of most challenging problems in several areas such as speech recognition and object detection. Invasive breast cancer detection is a time consuming and challenging task primarily because it involves a pathologist scanning large swathes of benign regions to ultimately identify the areas of malignancy. Precise delineation of IDC in WSI is crucial to the subsequent estimation of grading tumor aggressiveness and predicting patient outcome. DL approaches are particularly adept at handling these types of problems, especially if a large number of samples are available for training, which would also ensure the generalizability of the learned features and classifier. The DL framework in this paper extends a number of convolutional neural networks (CNN) for visual semantic analysis of tumor regions for diagnosis support. The CNN is trained over a large amount of image patches (tissue regions) from WSI to learn a hierarchical part-based representation. The method was evaluated over a WSI dataset from 162 patients diagnosed with IDC. 113 slides were selected for training and 49 slides were held out for independent testing. Ground truth for quantitative evaluation was provided via expert delineation of the region of cancer by an expert pathologist on the digitized slides. The experimental evaluation was designed to measure classifier accuracy in detecting IDC tissue regions in WSI. Our method yielded the best quantitative

  6. Rapid automatic keyword extraction for information retrieval and analysis

    Science.gov (United States)

    Rose, Stuart J [Richland, WA; Cowley,; E, Wendy [Richland, WA; Crow, Vernon L [Richland, WA; Cramer, Nicholas O [Richland, WA

    2012-03-06

    Methods and systems for rapid automatic keyword extraction for information retrieval and analysis. Embodiments can include parsing words in an individual document by delimiters, stop words, or both in order to identify candidate keywords. Word scores for each word within the candidate keywords are then calculated based on a function of co-occurrence degree, co-occurrence frequency, or both. Based on a function of the word scores for words within the candidate keyword, a keyword score is calculated for each of the candidate keywords. A portion of the candidate keywords are then extracted as keywords based, at least in part, on the candidate keywords having the highest keyword scores.

  7. ASTRA - an automatic system for transport analysis in a tokamak

    International Nuclear Information System (INIS)

    Pereverzev, G.V.; Yushmanov, P.N.; Dnestrovskii, A.Yu.; Polevoi, A.R.; Tarasjan, K.N.; Zakharov, L.E.

    1991-08-01

    The set of codes described here - ASTRA (Automatic System of Transport Analysis) - is a flexible and effective tool for the study of transport mechanisms in reactor-oriented facilities of the tokamak type. Flexibility is provided within the ASTRA system by a wide choice of standard relationships, functions and subroutines representing various transport coefficients, methods of auxiliary heating and other physical processes in the tokamak plasma, as well as by the possibility of pre-setting transport equations and variables for data output in a simple and conseptually transparent form. The transport code produced by the ASTRA system provides an adequate representation of the discharges for present experimental conditions. (orig.)

  8. Reliable clarity automatic-evaluation method for optical remote sensing images

    Science.gov (United States)

    Qin, Bangyong; Shang, Ren; Li, Shengyang; Hei, Baoqin; Liu, Zhiwen

    2015-10-01

    Image clarity, which reflects the sharpness degree at the edge of objects in images, is an important quality evaluate index for optical remote sensing images. Scholars at home and abroad have done a lot of work on estimation of image clarity. At present, common clarity-estimation methods for digital images mainly include frequency-domain function methods, statistical parametric methods, gradient function methods and edge acutance methods. Frequency-domain function method is an accurate clarity-measure approach. However, its calculation process is complicate and cannot be carried out automatically. Statistical parametric methods and gradient function methods are both sensitive to clarity of images, while their results are easy to be affected by the complex degree of images. Edge acutance method is an effective approach for clarity estimate, while it needs picking out the edges manually. Due to the limits in accuracy, consistent or automation, these existing methods are not applicable to quality evaluation of optical remote sensing images. In this article, a new clarity-evaluation method, which is based on the principle of edge acutance algorithm, is proposed. In the new method, edge detection algorithm and gradient search algorithm are adopted to automatically search the object edges in images. Moreover, The calculation algorithm for edge sharpness has been improved. The new method has been tested with several groups of optical remote sensing images. Compared with the existing automatic evaluation methods, the new method perform better both in accuracy and consistency. Thus, the new method is an effective clarity evaluation method for optical remote sensing images.

  9. A novel scheme for automatic nonrigid image registration using deformation invariant feature and geometric constraint

    Science.gov (United States)

    Deng, Zhipeng; Lei, Lin; Zhou, Shilin

    2015-10-01

    Automatic image registration is a vital yet challenging task, particularly for non-rigid deformation images which are more complicated and common in remote sensing images, such as distorted UAV (unmanned aerial vehicle) images or scanning imaging images caused by flutter. Traditional non-rigid image registration methods are based on the correctly matched corresponding landmarks, which usually needs artificial markers. It is a rather challenging task to locate the accurate position of the points and get accurate homonymy point sets. In this paper, we proposed an automatic non-rigid image registration algorithm which mainly consists of three steps: To begin with, we introduce an automatic feature point extraction method based on non-linear scale space and uniform distribution strategy to extract the points which are uniform distributed along the edge of the image. Next, we propose a hybrid point matching algorithm using DaLI (Deformation and Light Invariant) descriptor and local affine invariant geometric constraint based on triangulation which is constructed by K-nearest neighbor algorithm. Based on the accurate homonymy point sets, the two images are registrated by the model of TPS (Thin Plate Spline). Our method is demonstrated by three deliberately designed experiments. The first two experiments are designed to evaluate the distribution of point set and the correctly matching rate on synthetic data and real data respectively. The last experiment is designed on the non-rigid deformation remote sensing images and the three experimental results demonstrate the accuracy, robustness, and efficiency of the proposed algorithm compared with other traditional methods.

  10. eCTG: an automatic procedure to extract digital cardiotocographic signals from digital images.

    Science.gov (United States)

    Sbrollini, Agnese; Agostinelli, Angela; Marcantoni, Ilaria; Morettini, Micaela; Burattini, Luca; Di Nardo, Francesco; Fioretti, Sandro; Burattini, Laura

    2018-03-01

    Cardiotocography (CTG), consisting in the simultaneous recording of fetal heart rate (FHR) and maternal uterine contractions (UC), is a popular clinical test to assess fetal health status. Typically, CTG machines provide paper reports that are visually interpreted by clinicians. Consequently, visual CTG interpretation depends on clinician's experience and has a poor reproducibility. The lack of databases containing digital CTG signals has limited number and importance of retrospective studies finalized to set up procedures for automatic CTG analysis that could contrast visual CTG interpretation subjectivity. In order to help overcoming this problem, this study proposes an electronic procedure, termed eCTG, to extract digital CTG signals from digital CTG images, possibly obtainable by scanning paper CTG reports. eCTG was specifically designed to extract digital CTG signals from digital CTG images. It includes four main steps: pre-processing, Otsu's global thresholding, signal extraction and signal calibration. Its validation was performed by means of the "CTU-UHB Intrapartum Cardiotocography Database" by Physionet, that contains digital signals of 552 CTG recordings. Using MATLAB, each signal was plotted and saved as a digital image that was then submitted to eCTG. Digital CTG signals extracted by eCTG were eventually compared to corresponding signals directly available in the database. Comparison occurred in terms of signal similarity (evaluated by the correlation coefficient ρ, and the mean signal error MSE) and clinical features (including FHR baseline and variability; number, amplitude and duration of tachycardia, bradycardia, acceleration and deceleration episodes; number of early, variable, late and prolonged decelerations; and UC number, amplitude, duration and period). The value of ρ between eCTG and reference signals was 0.85 (P digital FHR and UC signals from digital CTG images. Copyright © 2018 Elsevier B.V. All rights reserved.

  11. Automatic Classification of Normal and Cancer Lung CT Images Using Multiscale AM-FM Features

    Directory of Open Access Journals (Sweden)

    Eman Magdy

    2015-01-01

    Full Text Available Computer-aided diagnostic (CAD systems provide fast and reliable diagnosis for medical images. In this paper, CAD system is proposed to analyze and automatically segment the lungs and classify each lung into normal or cancer. Using 70 different patients’ lung CT dataset, Wiener filtering on the original CT images is applied firstly as a preprocessing step. Secondly, we combine histogram analysis with thresholding and morphological operations to segment the lung regions and extract each lung separately. Amplitude-Modulation Frequency-Modulation (AM-FM method thirdly, has been used to extract features for ROIs. Then, the significant AM-FM features have been selected using Partial Least Squares Regression (PLSR for classification step. Finally, K-nearest neighbour (KNN, support vector machine (SVM, naïve Bayes, and linear classifiers have been used with the selected AM-FM features. The performance of each classifier in terms of accuracy, sensitivity, and specificity is evaluated. The results indicate that our proposed CAD system succeeded to differentiate between normal and cancer lungs and achieved 95% accuracy in case of the linear classifier.

  12. Effective System for Automatic Bundle Block Adjustment and Ortho Image Generation from Multi Sensor Satellite Imagery

    Science.gov (United States)

    Akilan, A.; Nagasubramanian, V.; Chaudhry, A.; Reddy, D. Rajesh; Sudheer Reddy, D.; Usha Devi, R.; Tirupati, T.; Radhadevi, P. V.; Varadan, G.

    2014-11-01

    Block Adjustment is a technique for large area mapping for images obtained from different remote sensingsatellites.The challenge in this process is to handle huge number of satellite imageries from different sources with different resolution and accuracies at the system level. This paper explains a system with various tools and techniques to effectively handle the end-to-end chain in large area mapping and production with good level of automation and the provisions for intuitive analysis of final results in 3D and 2D environment. In addition, the interface for using open source ortho and DEM references viz., ETM, SRTM etc. and displaying ESRI shapes for the image foot-prints are explained. Rigorous theory, mathematical modelling, workflow automation and sophisticated software engineering tools are included to ensure high photogrammetric accuracy and productivity. Major building blocks like Georeferencing, Geo-capturing and Geo-Modelling tools included in the block adjustment solution are explained in this paper. To provide optimal bundle block adjustment solution with high precision results, the system has been optimized in many stages to exploit the full utilization of hardware resources. The robustness of the system is ensured by handling failure in automatic procedure and saving the process state in every stage for subsequent restoration from the point of interruption. The results obtained from various stages of the system are presented in the paper.

  13. ASAP (Automatic Software for ASL Processing): A toolbox for processing Arterial Spin Labeling images.

    Science.gov (United States)

    Mato Abad, Virginia; García-Polo, Pablo; O'Daly, Owen; Hernández-Tamames, Juan Antonio; Zelaya, Fernando

    2016-04-01

    The method of Arterial Spin Labeling (ASL) has experienced a significant rise in its application to functional imaging, since it is the only technique capable of measuring blood perfusion in a truly non-invasive manner. Currently, there are no commercial packages for processing ASL data and there is no recognized standard for normalizing ASL data to a common frame of reference. This work describes a new Automated Software for ASL Processing (ASAP) that can automatically process several ASL datasets. ASAP includes functions for all stages of image pre-processing: quantification, skull-stripping, co-registration, partial volume correction and normalization. To assess the applicability and validity of the toolbox, this work shows its application in the study of hypoperfusion in a sample of healthy subjects at risk of progressing to Alzheimer's disease. ASAP requires limited user intervention, minimizing the possibility of random and systematic errors, and produces cerebral blood flow maps that are ready for statistical group analysis. The software is easy to operate and results in excellent quality of spatial normalization. The results found in this evaluation study are consistent with previous studies that find decreased perfusion in Alzheimer's patients in similar regions and demonstrate the applicability of ASAP. Copyright © 2015 Elsevier Inc. All rights reserved.

  14. Automatic analysis of attack data from distributed honeypot network

    Science.gov (United States)

    Safarik, Jakub; Voznak, MIroslav; Rezac, Filip; Partila, Pavol; Tomala, Karel

    2013-05-01

    There are many ways of getting real data about malicious activity in a network. One of them relies on masquerading monitoring servers as a production one. These servers are called honeypots and data about attacks on them brings us valuable information about actual attacks and techniques used by hackers. The article describes distributed topology of honeypots, which was developed with a strong orientation on monitoring of IP telephony traffic. IP telephony servers can be easily exposed to various types of attacks, and without protection, this situation can lead to loss of money and other unpleasant consequences. Using a distributed topology with honeypots placed in different geological locations and networks provides more valuable and independent results. With automatic system of gathering information from all honeypots, it is possible to work with all information on one centralized point. Communication between honeypots and centralized data store use secure SSH tunnels and server communicates only with authorized honeypots. The centralized server also automatically analyses data from each honeypot. Results of this analysis and also other statistical data about malicious activity are simply accessible through a built-in web server. All statistical and analysis reports serve as information basis for an algorithm which classifies different types of used VoIP attacks. The web interface then brings a tool for quick comparison and evaluation of actual attacks in all monitored networks. The article describes both, the honeypots nodes in distributed architecture, which monitor suspicious activity, and also methods and algorithms used on the server side for analysis of gathered data.

  15. Automatic particle-size analysis of HTGR nuclear fuel microspheres

    International Nuclear Information System (INIS)

    Mack, J.E.

    1977-01-01

    An automatic particle-size analyzer (PSA) has been developed at ORNL for measuring and counting samples of nuclear fuel microspheres in the diameter range of 300 to 1000 μm at rates in excess of 2000 particles per minute, requiring no sample preparation. A light blockage technique is used in conjunction with a particle singularizer. Each particle in the sample is sized, and the information is accumulated by a multi-channel pulse height analyzer. The data are then transferred automatically to a computer for calculation of mean diameter, standard deviation, kurtosis, and skewness of the distribution. Entering the sample weight and pre-coating data permits calculation of particle density and the mean coating thickness and density. Following this nondestructive analysis, the sample is collected and returned to the process line or used for further analysis. The device has potential as an on-line quality control device in processes dealing with spherical or near-spherical particles where rapid analysis is required for process control

  16. STUDY OF AUTOMATIC IMAGE RECTIFICATION AND REGISTRATION OF SCANNED HISTORICAL AERIAL PHOTOGRAPHS

    Directory of Open Access Journals (Sweden)

    H. R. Chen

    2016-06-01

    Full Text Available Historical aerial photographs directly provide good evidences of past times. The Research Center for Humanities and Social Sciences (RCHSS of Taiwan Academia Sinica has collected and scanned numerous historical maps and aerial images of Taiwan and China. Some maps or images have been geo-referenced manually, but most of historical aerial images have not been registered since there are no GPS or IMU data for orientation assisting in the past. In our research, we developed an automatic process of matching historical aerial images by SIFT (Scale Invariant Feature Transform for handling the great quantity of images by computer vision. SIFT is one of the most popular method of image feature extracting and matching. This algorithm extracts extreme values in scale space into invariant image features, which are robust to changing in rotation scale, noise, and illumination. We also use RANSAC (Random sample consensus to remove outliers, and obtain good conjugated points between photographs. Finally, we manually add control points for registration through least square adjustment based on collinear equation. In the future, we can use image feature points of more photographs to build control image database. Every new image will be treated as query image. If feature points of query image match the features in database, it means that the query image probably is overlapped with control images.With the updating of database, more and more query image can be matched and aligned automatically. Other research about multi-time period environmental changes can be investigated with those geo-referenced temporal spatial data.

  17. Independent component analysis for automatic note extraction from musical trills

    Science.gov (United States)

    Brown, Judith C.; Smaragdis, Paris

    2004-05-01

    The method of principal component analysis, which is based on second-order statistics (or linear independence), has long been used for redundancy reduction of audio data. The more recent technique of independent component analysis, enforcing much stricter statistical criteria based on higher-order statistical independence, is introduced and shown to be far superior in separating independent musical sources. This theory has been applied to piano trills and a database of trill rates was assembled from experiments with a computer-driven piano, recordings of a professional pianist, and commercially available compact disks. The method of independent component analysis has thus been shown to be an outstanding, effective means of automatically extracting interesting musical information from a sea of redundant data.

  18. Oncological image analysis.

    Science.gov (United States)

    Brady, Sir Michael; Highnam, Ralph; Irving, Benjamin; Schnabel, Julia A

    2016-10-01

    Cancer is one of the world's major healthcare challenges and, as such, an important application of medical image analysis. After a brief introduction to cancer, we summarise some of the major developments in oncological image analysis over the past 20 years, but concentrating those in the authors' laboratories, and then outline opportunities and challenges for the next decade. Copyright © 2016 Elsevier B.V. All rights reserved.

  19. Examination of the semi-automatic calculation technique of vegetation cover rate by digital camera images.

    Science.gov (United States)

    Takemine, S.; Rikimaru, A.; Takahashi, K.

    The rice is one of the staple foods in the world High quality rice production requires periodically collecting rice growth data to control the growth of rice The height of plant the number of stem the color of leaf is well known parameters to indicate rice growth Rice growth diagnosis method based on these parameters is used operationally in Japan although collecting these parameters by field survey needs a lot of labor and time Recently a laborsaving method for rice growth diagnosis is proposed which is based on vegetation cover rate of rice Vegetation cover rate of rice is calculated based on discriminating rice plant areas in a digital camera image which is photographed in nadir direction Discrimination of rice plant areas in the image was done by the automatic binarization processing However in the case of vegetation cover rate calculation method depending on the automatic binarization process there is a possibility to decrease vegetation cover rate against growth of rice In this paper a calculation method of vegetation cover rate was proposed which based on the automatic binarization process and referred to the growth hysteresis information For several images obtained by field survey during rice growing season vegetation cover rate was calculated by the conventional automatic binarization processing and the proposed method respectively And vegetation cover rate of both methods was compared with reference value obtained by visual interpretation As a result of comparison the accuracy of discriminating rice plant areas was increased by the proposed

  20. Evaluation of an automatic brain segmentation method developed for neonates on adult MR brain images

    Science.gov (United States)

    Moeskops, Pim; Viergever, Max A.; Benders, Manon J. N. L.; Išgum, Ivana

    2015-03-01

    Automatic brain tissue segmentation is of clinical relevance in images acquired at all ages. The literature presents a clear distinction between methods developed for MR images of infants, and methods developed for images of adults. The aim of this work is to evaluate a method developed for neonatal images in the segmentation of adult images. The evaluated method employs supervised voxel classification in subsequent stages, exploiting spatial and intensity information. Evaluation was performed using images available within the MRBrainS13 challenge. The obtained average Dice coefficients were 85.77% for grey matter, 88.66% for white matter, 81.08% for cerebrospinal fluid, 95.65% for cerebrum, and 96.92% for intracranial cavity, currently resulting in the best overall ranking. The possibility of applying the same method to neonatal as well as adult images can be of great value in cross-sectional studies that include a wide age range.

  1. Algorithm for automatic analysis of electro-oculographic data.

    Science.gov (United States)

    Pettersson, Kati; Jagadeesan, Sharman; Lukander, Kristian; Henelius, Andreas; Haeggström, Edward; Müller, Kiti

    2013-10-25

    Large amounts of electro-oculographic (EOG) data, recorded during electroencephalographic (EEG) measurements, go underutilized. We present an automatic, auto-calibrating algorithm that allows efficient analysis of such data sets. The auto-calibration is based on automatic threshold value estimation. Amplitude threshold values for saccades and blinks are determined based on features in the recorded signal. The performance of the developed algorithm was tested by analyzing 4854 saccades and 213 blinks recorded in two different conditions: a task where the eye movements were controlled (saccade task) and a task with free viewing (multitask). The results were compared with results from a video-oculography (VOG) device and manually scored blinks. The algorithm achieved 93% detection sensitivity for blinks with 4% false positive rate. The detection sensitivity for horizontal saccades was between 98% and 100%, and for oblique saccades between 95% and 100%. The classification sensitivity for horizontal and large oblique saccades (10 deg) was larger than 89%, and for vertical saccades larger than 82%. The duration and peak velocities of the detected horizontal saccades were similar to those in the literature. In the multitask measurement the detection sensitivity for saccades was 97% with a 6% false positive rate. The developed algorithm enables reliable analysis of EOG data recorded both during EEG and as a separate metrics.

  2. Automatic Data Logging and Quality Analysis System for Mobile Devices

    Directory of Open Access Journals (Sweden)

    Yong-Yi Fanjiang

    2017-01-01

    Full Text Available The testing phase of mobile device products includes two important test projects that must be completed before shipment: the field trial and the beta user trial. During the field trial, the product is certified based on its integration and stability with the local operator’s system, and, during the beta user trial, the product is certified by multiple users regarding its daily use, where the goal is to detect and solve early problems. In the traditional approach used to issue returns, testers must log into a web site, fill out a problem form, and then go through a browser or FTP to upload logs; however, this is inconvenient, and problems are reported slowly. Therefore, we propose an “automatic logging analysis system” (ALAS to construct a convenient test environment and, using a record analysis (log parser program, automate the parsing of log files and have questions automatically sent to the database by the system. Finally, the mean time between failures (MTBF is used to establish measurement indicators for the beta user trial.

  3. Performance evaluation of 2D and 3D deep learning approaches for automatic segmentation of multiple organs on CT images

    Science.gov (United States)

    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.

  4. Improvement in the performance of CAD for the Alzheimer-type dementia based on automatic extraction of temporal lobe from coronal MR images

    International Nuclear Information System (INIS)

    Kaeriyama, Tomoharu; Kodama, Naoki; Kaneko, Tomoyuki; Shimada, Tetsuo; Tanaka, Hiroyuki; Takeda, Ai; Fukumoto, Ichiro

    2004-01-01

    In this study, we extracted whole brain and temporal lobe images from MR images (26 healthy elderly controls and 34 Alzheimer-type dementia patients) by means of binarize, mask processing, template matching, Hough transformation, and boundary tracing etc. We assessed the extraction accuracy by comparing the extracted images to images extracts by a radiological technologist. The results of assessment by consistent rate; brain images 91.3±4.3%, right temporal lobe 83.3±6.9%, left temporal lobe 83.7±7.6%. Furthermore discriminant analysis using 6 textural features demonstrated sensitivity and specificity of 100% when the healthy elderly controls were compared to the Alzheimer-type dementia patients. Our research showed the possibility of automatic objective diagnosis of temporal lobe abnormalities by automatic extracted images of the temporal lobes. (author)

  5. Automatic Welding System of Aluminum Pipe by Monitoring Backside Image of Molten Pool Using Vision Sensor

    Science.gov (United States)

    Baskoro, Ario Sunar; Kabutomori, Masashi; Suga, Yasuo

    An automatic welding system using Tungsten Inert Gas (TIG) welding with vision sensor for welding of aluminum pipe was constructed. This research studies the intelligent welding process of aluminum alloy pipe 6063S-T5 in fixed position and moving welding torch with the AC welding machine. The monitoring system consists of a vision sensor using a charge-coupled device (CCD) camera to monitor backside image of molten pool. The captured image was processed to recognize the edge of molten pool by image processing algorithm. Neural network model for welding speed control were constructed to perform the process automatically. From the experimental results it shows the effectiveness of the control system confirmed by good detection of molten pool and sound weld of experimental result.

  6. Fast Automatic Airport Detection in Remote Sensing Images Using Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Fen Chen

    2018-03-01

    Full Text Available Fast and automatic detection of airports from remote sensing images is useful for many military and civilian applications. In this paper, a fast automatic detection method is proposed to detect airports from remote sensing images based on convolutional neural networks using the Faster R-CNN algorithm. This method first applies a convolutional neural network to generate candidate airport regions. Based on the features extracted from these proposals, it then uses another convolutional neural network to perform airport detection. By taking the typical elongated linear geometric shape of airports into consideration, some specific improvements to the method are proposed. These approaches successfully improve the quality of positive samples and achieve a better accuracy in the final detection results. Experimental results on an airport dataset, Landsat 8 images, and a Gaofen-1 satellite scene demonstrate the effectiveness and efficiency of the proposed method.

  7. Automatic detection of solar features in HSOS full-disk solar images using guided filter

    Science.gov (United States)

    Yuan, Fei; Lin, Jiaben; Guo, Jingjing; Wang, Gang; Tong, Liyue; Zhang, Xinwei; Wang, Bingxiang

    2018-02-01

    A procedure is introduced for the automatic detection of solar features using full-disk solar images from Huairou Solar Observing Station (HSOS), National Astronomical Observatories of China. In image preprocessing, median filter is applied to remove the noises. Guided filter is adopted to enhance the edges of solar features and restrain the solar limb darkening, which is first introduced into the astronomical target detection. Then specific features are detected by Otsu algorithm and further threshold processing technique. Compared with other automatic detection procedures, our procedure has some advantages such as real time and reliability as well as no need of local threshold. Also, it reduces the amount of computation largely, which is benefited from the efficient guided filter algorithm. The procedure has been tested on one month sequences (December 2013) of HSOS full-disk solar images and the result shows that the number of features detected by our procedure is well consistent with the manual one.

  8. AN AUTOMATIC OPTICAL AND SAR IMAGE REGISTRATION METHOD USING ITERATIVE MULTI-LEVEL AND REFINEMENT MODEL

    Directory of Open Access Journals (Sweden)

    C. Xu

    2016-06-01

    Full Text Available Automatic image registration is a vital yet challenging task, particularly for multi-sensor remote sensing images. Given the diversity of the data, it is unlikely that a single registration algorithm or a single image feature will work satisfactorily for all applications. Focusing on this issue, the mainly contribution of this paper is to propose an automatic optical-to-SAR image registration method using –level and refinement model: Firstly, a multi-level strategy of coarse-to-fine registration is presented, the visual saliency features is used to acquire coarse registration, and then specific area and line features are used to refine the registration result, after that, sub-pixel matching is applied using KNN Graph. Secondly, an iterative strategy that involves adaptive parameter adjustment for re-extracting and re-matching features is presented. Considering the fact that almost all feature-based registration methods rely on feature extraction results, the iterative strategy improve the robustness of feature matching. And all parameters can be automatically and adaptively adjusted in the iterative procedure. Thirdly, a uniform level set segmentation model for optical and SAR images is presented to segment conjugate features, and Voronoi diagram is introduced into Spectral Point Matching (VSPM to further enhance the matching accuracy between two sets of matching points. Experimental results show that the proposed method can effectively and robustly generate sufficient, reliable point pairs and provide accurate registration.

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

    Science.gov (United States)

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

    2014-10-01

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

  10. Automatic visual tracking and social behaviour analysis with multiple mice.

    Directory of Open Access Journals (Sweden)

    Luca Giancardo

    Full Text Available Social interactions are made of complex behavioural actions that might be found in all mammalians, including humans and rodents. Recently, mouse models are increasingly being used in preclinical research to understand the biological basis of social-related pathologies or abnormalities. However, reliable and flexible automatic systems able to precisely quantify social behavioural interactions of multiple mice are still missing. Here, we present a system built on two components. A module able to accurately track the position of multiple interacting mice from videos, regardless of their fur colour or light settings, and a module that automatically characterise social and non-social behaviours. The behavioural analysis is obtained by deriving a new set of specialised spatio-temporal features from the tracker output. These features are further employed by a learning-by-example classifier, which predicts for each frame and for each mouse in the cage one of the behaviours learnt from the examples given by the experimenters. The system is validated on an extensive set of experimental trials involving multiple mice in an open arena. In a first evaluation we compare the classifier output with the independent evaluation of two human graders, obtaining comparable results. Then, we show the applicability of our technique to multiple mice settings, using up to four interacting mice. The system is also compared with a solution recently proposed in the literature that, similarly to us, addresses the problem with a learning-by-examples approach. Finally, we further validated our automatic system to differentiate between C57B/6J (a commonly used reference inbred strain and BTBR T+tf/J (a mouse model for autism spectrum disorders. Overall, these data demonstrate the validity and effectiveness of this new machine learning system in the detection of social and non-social behaviours in multiple (>2 interacting mice, and its versatility to deal with different

  11. Gabor Analysis for Imaging

    DEFF Research Database (Denmark)

    Christensen, Ole; Feichtinger, Hans G.; Paukner, Stephan

    2015-01-01

    , it characterizes a function by its transform over phase space, which is the time–frequency plane (TF-plane) in a musical context or the location–wave-number domain in the context of image processing. Since the transition from the signal domain to the phase space domain introduces an enormous amount of data...... of the generalities relevant for an understanding of Gabor analysis of functions on Rd. We pay special attention to the case d = 2, which is the most important case for image processing and image analysis applications. The chapter is organized as follows. Section 2 presents central tools from functional analysis......, the application of Gabor expansions to image representation is considered in Sect. 6....

  12. Color Segmentation Approach of Infrared Thermography Camera Image for Automatic Fault Diagnosis

    International Nuclear Information System (INIS)

    Djoko Hari Nugroho; Ari Satmoko; Budhi Cynthia Dewi

    2007-01-01

    Predictive maintenance based on fault diagnosis becomes very important in current days to assure the availability and reliability of a system. The main purpose of this research is to configure a computer software for automatic fault diagnosis based on image model acquired from infrared thermography camera using color segmentation approach. This technique detects hot spots in equipment of the plants. Image acquired from camera is first converted to RGB (Red, Green, Blue) image model and then converted to CMYK (Cyan, Magenta, Yellow, Key for Black) image model. Assume that the yellow color in the image represented the hot spot in the equipment, the CMYK image model is then diagnosed using color segmentation model to estimate the fault. The software is configured utilizing Borland Delphi 7.0 computer programming language. The performance is then tested for 10 input infrared thermography images. The experimental result shows that the software capable to detect the faulty automatically with performance value of 80 % from 10 sheets of image input. (author)

  13. Visual mismatch negativity indicates automatic, task-independent detection of artistic image composition in abstract artworks.

    Science.gov (United States)

    Menzel, Claudia; Kovács, Gyula; Amado, Catarina; Hayn-Leichsenring, Gregor U; Redies, Christoph

    2018-05-06

    In complex abstract art, image composition (i.e., the artist's deliberate arrangement of pictorial elements) is an important aesthetic feature. We investigated whether the human brain detects image composition in abstract artworks automatically (i.e., independently of the experimental task). To this aim, we studied whether a group of 20 original artworks elicited a visual mismatch negativity when contrasted with a group of 20 images that were composed of the same pictorial elements as the originals, but in shuffled arrangements, which destroy artistic composition. We used a passive oddball paradigm with parallel electroencephalogram recordings to investigate the detection of image type-specific properties. We observed significant deviant-standard differences for the shuffled and original images, respectively. Furthermore, for both types of images, differences in amplitudes correlated with the behavioral ratings of the images. In conclusion, we show that the human brain can detect composition-related image properties in visual artworks in an automatic fashion. Copyright © 2018 Elsevier B.V. All rights reserved.

  14. Automatic Detection of Myocardial Boundaries in MR Cardio Perfusion Images

    NARCIS (Netherlands)

    Spreeuwers, Luuk; Breeuwer, Marcel

    2001-01-01

    Cardiovascular diseases often result in reduced blood perfusion of the myocardium (MC). Recent advances in MR allow fast recordingof contrast enhanced myocardial perfusion scans. For perfusion analysis the myocardial boundaries must be traced. Currently this is done manually. In this paper a method

  15. Automatic Fault Recognition of Photovoltaic Modules Based on Statistical Analysis of Uav Thermography

    Science.gov (United States)

    Kim, D.; Youn, J.; Kim, C.

    2017-08-01

    As a malfunctioning PV (Photovoltaic) cell has a higher temperature than adjacent normal cells, we can detect it easily with a thermal infrared sensor. However, it will be a time-consuming way to inspect large-scale PV power plants by a hand-held thermal infrared sensor. This paper presents an algorithm for automatically detecting defective PV panels using images captured with a thermal imaging camera from an UAV (unmanned aerial vehicle). The proposed algorithm uses statistical analysis of thermal intensity (surface temperature) characteristics of each PV module to verify the mean intensity and standard deviation of each panel as parameters for fault diagnosis. One of the characteristics of thermal infrared imaging is that the larger the distance between sensor and target, the lower the measured temperature of the object. Consequently, a global detection rule using the mean intensity of all panels in the fault detection algorithm is not applicable. Therefore, a local detection rule based on the mean intensity and standard deviation range was developed to detect defective PV modules from individual array automatically. The performance of the proposed algorithm was tested on three sample images; this verified a detection accuracy of defective panels of 97 % or higher. In addition, as the proposed algorithm can adjust the range of threshold values for judging malfunction at the array level, the local detection rule is considered better suited for highly sensitive fault detection compared to a global detection rule.

  16. AUTOMATIC FAULT RECOGNITION OF PHOTOVOLTAIC MODULES BASED ON STATISTICAL ANALYSIS OF UAV THERMOGRAPHY

    Directory of Open Access Journals (Sweden)

    D. Kim

    2017-08-01

    Full Text Available As a malfunctioning PV (Photovoltaic cell has a higher temperature than adjacent normal cells, we can detect it easily with a thermal infrared sensor. However, it will be a time-consuming way to inspect large-scale PV power plants by a hand-held thermal infrared sensor. This paper presents an algorithm for automatically detecting defective PV panels using images captured with a thermal imaging camera from an UAV (unmanned aerial vehicle. The proposed algorithm uses statistical analysis of thermal intensity (surface temperature characteristics of each PV module to verify the mean intensity and standard deviation of each panel as parameters for fault diagnosis. One of the characteristics of thermal infrared imaging is that the larger the distance between sensor and target, the lower the measured temperature of the object. Consequently, a global detection rule using the mean intensity of all panels in the fault detection algorithm is not applicable. Therefore, a local detection rule based on the mean intensity and standard deviation range was developed to detect defective PV modules from individual array automatically. The performance of the proposed algorithm was tested on three sample images; this verified a detection accuracy of defective panels of 97 % or higher. In addition, as the proposed algorithm can adjust the range of threshold values for judging malfunction at the array level, the local detection rule is considered better suited for highly sensitive fault detection compared to a global detection rule.

  17. A Method Based on Artificial Intelligence To Fully Automatize The Evaluation of Bovine Blastocyst Images.

    Science.gov (United States)

    Rocha, José Celso; Passalia, Felipe José; Matos, Felipe Delestro; Takahashi, Maria Beatriz; Ciniciato, Diego de Souza; Maserati, Marc Peter; Alves, Mayra Fernanda; de Almeida, Tamie Guibu; Cardoso, Bruna Lopes; Basso, Andrea Cristina; Nogueira, Marcelo Fábio Gouveia

    2017-08-09

    Morphological analysis is the standard method of assessing embryo quality; however, its inherent subjectivity tends to generate discrepancies among evaluators. Using genetic algorithms and artificial neural networks (ANNs), we developed a new method for embryo analysis that is more robust and reliable than standard methods. Bovine blastocysts produced in vitro were classified as grade 1 (excellent or good), 2 (fair), or 3 (poor) by three experienced embryologists according to the International Embryo Technology Society (IETS) standard. The images (n = 482) were subjected to automatic feature extraction, and the results were used as input for a supervised learning process. One part of the dataset (15%) was used for a blind test posterior to the fitting, for which the system had an accuracy of 76.4%. Interestingly, when the same embryologists evaluated a sub-sample (10%) of the dataset, there was only 54.0% agreement with the standard (mode for grades). However, when using the ANN to assess this sub-sample, there was 87.5% agreement with the modal values obtained by the evaluators. The presented methodology is covered by National Institute of Industrial Property (INPI) and World Intellectual Property Organization (WIPO) patents and is currently undergoing a commercial evaluation of its feasibility.

  18. Algorithm for automatic image dodging of unmanned aerial vehicle images using two-dimensional radiometric spatial attributes

    Science.gov (United States)

    Li, Wenzhuo; Sun, Kaimin; Li, Deren; Bai, Ting

    2016-07-01

    Unmanned aerial vehicle (UAV) remote sensing technology has come into wide use in recent years. The poor stability of the UAV platform, however, produces more inconsistencies in hue and illumination among UAV images than other more stable platforms. Image dodging is a process used to reduce these inconsistencies caused by different imaging conditions. We propose an algorithm for automatic image dodging of UAV images using two-dimensional radiometric spatial attributes. We use object-level image smoothing to smooth foreground objects in images and acquire an overall reference background image by relative radiometric correction. We apply the Contourlet transform to separate high- and low-frequency sections for every single image, and replace the low-frequency section with the low-frequency section extracted from the corresponding region in the overall reference background image. We apply the inverse Contourlet transform to reconstruct the final dodged images. In this process, a single image must be split into reasonable block sizes with overlaps due to large pixel size. Experimental mosaic results show that our proposed method reduces the uneven distribution of hue and illumination. Moreover, it effectively eliminates dark-bright interstrip effects caused by shadows and vignetting in UAV images while maximally protecting image texture information.

  19. Operation logic and functionality of automatic dose rate and image quality control of conventional fluoroscopy

    International Nuclear Information System (INIS)

    Lin, Pei-Jan Paul

    2009-01-01

    New generation of fluoroscopic imaging systems is equipped with spectral shaping filters complemented with sophisticated automatic dose rate and image quality control logic called ''fluoroscopy curve'' or ''trajectory''. Such fluoroscopy curves were implemented first on cardiovascular angiographic imaging systems and are now available on conventional fluoroscopy equipment. This study aims to investigate the control logic operations under the fluoroscopy mode and acquisition mode (equivalent to the legacy spot filming) of a conventional fluoroscopy system typically installed for upper-lower gastrointestinal examinations, interventional endoscopy laboratories, gastrointestinal laboratory, and pain clinics.

  20. Operation logic and functionality of automatic dose rate and image quality control of conventional fluoroscopy

    Energy Technology Data Exchange (ETDEWEB)

    Lin, Pei-Jan Paul [Department of Radiology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts 02115 (United States)

    2009-05-15

    New generation of fluoroscopic imaging systems is equipped with spectral shaping filters complemented with sophisticated automatic dose rate and image quality control logic called ''fluoroscopy curve'' or ''trajectory''. Such fluoroscopy curves were implemented first on cardiovascular angiographic imaging systems and are now available on conventional fluoroscopy equipment. This study aims to investigate the control logic operations under the fluoroscopy mode and acquisition mode (equivalent to the legacy spot filming) of a conventional fluoroscopy system typically installed for upper-lower gastrointestinal examinations, interventional endoscopy laboratories, gastrointestinal laboratory, and pain clinics.

  1. Automatic segmentation and 3D reconstruction of intravascular ultrasound images for a fast preliminar evaluation of vessel pathologies.

    Science.gov (United States)

    Sanz-Requena, Roberto; Moratal, David; García-Sánchez, Diego Ramón; Bodí, Vicente; Rieta, José Joaquín; Sanchis, Juan Manuel

    2007-03-01

    Intravascular ultrasound (IVUS) imaging is used along with X-ray coronary angiography to detect vessel pathologies. Manual analysis of IVUS images is slow and time-consuming and it is not feasible for clinical purposes. A semi-automated method is proposed to generate 3D reconstructions from IVUS video sequences, so that a fast diagnose can be easily done, quantifying plaque length and severity as well as plaque volume of the vessels under study. The methodology described in this work has four steps: a pre-processing of IVUS images, a segmentation of media-adventitia contour, a detection of intima and plaque and a 3D reconstruction of the vessel. Preprocessing is intended to remove noise from the images without blurring the edges. Segmentation of media-adventitia contour is achieved using active contours (snakes). In particular, we use the gradient vector flow (GVF) as external force for the snakes. The detection of lumen border is obtained taking into account gray-level information of the inner part of the previously detected contours. A knowledge-based approach is used to determine which level of gray corresponds statistically to the different regions of interest: intima, plaque and lumen. The catheter region is automatically discarded. An estimate of plaque type is also given. Finally, 3D reconstruction of all detected regions is made. The suitability of this methodology has been verified for the analysis and visualization of plaque length, stenosis severity, automatic detection of the most problematic regions, calculus of plaque volumes and a preliminary estimation of plaque type obtaining for automatic measures of lumen and vessel area an average error smaller than 1mm(2) (equivalent aproximately to 10% of the average measure), for calculus of plaque and lumen volume errors smaller than 0.5mm(3) (equivalent approximately to 20% of the average measure) and for plaque type estimates a mismatch of less than 8% in the analysed frames.

  2. Automated image analysis of uterine cervical images

    Science.gov (United States)

    Li, Wenjing; Gu, Jia; Ferris, Daron; Poirson, Allen

    2007-03-01

    Cervical Cancer is the second most common cancer among women worldwide and the leading cause of cancer mortality of women in developing countries. If detected early and treated adequately, cervical cancer can be virtually prevented. Cervical precursor lesions and invasive cancer exhibit certain morphologic features that can be identified during a visual inspection exam. Digital imaging technologies allow us to assist the physician with a Computer-Aided Diagnosis (CAD) system. In colposcopy, epithelium that turns white after application of acetic acid is called acetowhite epithelium. Acetowhite epithelium is one of the major diagnostic features observed in detecting cancer and pre-cancerous regions. Automatic extraction of acetowhite regions from cervical images has been a challenging task due to specular reflection, various illumination conditions, and most importantly, large intra-patient variation. This paper presents a multi-step acetowhite region detection system to analyze the acetowhite lesions in cervical images automatically. First, the system calibrates the color of the cervical images to be independent of screening devices. Second, the anatomy of the uterine cervix is analyzed in terms of cervix region, external os region, columnar region, and squamous region. Third, the squamous region is further analyzed and subregions based on three levels of acetowhite are identified. The extracted acetowhite regions are accompanied by color scores to indicate the different levels of acetowhite. The system has been evaluated by 40 human subjects' data and demonstrates high correlation with experts' annotations.

  3. Comparison of automatic and visual methods used for image segmentation in Endodontics: a microCT study.

    Science.gov (United States)

    Queiroz, Polyane Mazucatto; Rovaris, Karla; Santaella, Gustavo Machado; Haiter-Neto, Francisco; Freitas, Deborah Queiroz

    2017-01-01

    To calculate root canal volume and surface area in microCT images, an image segmentation by selecting threshold values is required, which can be determined by visual or automatic methods. Visual determination is influenced by the operator's visual acuity, while the automatic method is done entirely by computer algorithms. To compare between visual and automatic segmentation, and to determine the influence of the operator's visual acuity on the reproducibility of root canal volume and area measurements. Images from 31 extracted human anterior teeth were scanned with a μCT scanner. Three experienced examiners performed visual image segmentation, and threshold values were recorded. Automatic segmentation was done using the "Automatic Threshold Tool" available in the dedicated software provided by the scanner's manufacturer. Volume and area measurements were performed using the threshold values determined both visually and automatically. The paired Student's t-test showed no significant difference between visual and automatic segmentation methods regarding root canal volume measurements (p=0.93) and root canal surface (p=0.79). Although visual and automatic segmentation methods can be used to determine the threshold and calculate root canal volume and surface, the automatic method may be the most suitable for ensuring the reproducibility of threshold determination.

  4. Image analysis for ophthalmological diagnosis image processing of Corvis ST images using Matlab

    CERN Document Server

    Koprowski, Robert

    2016-01-01

    This monograph focuses on the use of analysis and processing methods for images from the Corvis® ST tonometer. The presented analysis is associated with the quantitative, repeatable and fully automatic evaluation of the response of the eye, eyeball and cornea to an air-puff. All the described algorithms were practically implemented in MATLAB®. The monograph also describes and provides the full source code designed to perform the discussed calculations. As a result, this monograph is intended for scientists, graduate students and students of computer science and bioengineering as well as doctors wishing to expand their knowledge of modern diagnostic methods assisted by various image analysis and processing methods.

  5. The Digital Image Processing And Quantitative Analysis In Microscopic Image Characterization

    International Nuclear Information System (INIS)

    Ardisasmita, M. Syamsa

    2000-01-01

    Many electron microscopes although have produced digital images, but not all of them are equipped with a supporting unit to process and analyse image data quantitatively. Generally the analysis of image has to be made visually and the measurement is realized manually. The development of mathematical method for geometric analysis and pattern recognition, allows automatic microscopic image analysis with computer. Image processing program can be used for image texture and structure periodic analysis by the application of Fourier transform. Because the development of composite materials. Fourier analysis in frequency domain become important for measure the crystallography orientation. The periodic structure analysis and crystal orientation are the key to understand many material properties like mechanical strength. stress, heat conductivity, resistance, capacitance and other material electric and magnetic properties. In this paper will be shown the application of digital image processing in microscopic image characterization and analysis in microscopic image

  6. Automatic media-adventitia IVUS image segmentation based on sparse representation framework and dynamic directional active contour model.

    Science.gov (United States)

    Zakeri, Fahimeh Sadat; Setarehdan, Seyed Kamaledin; Norouzi, Somayye

    2017-10-01

    Segmentation of the arterial wall boundaries from intravascular ultrasound images is an important image processing task in order to quantify arterial wall characteristics such as shape, area, thickness and eccentricity. Since manual segmentation of these boundaries is a laborious and time consuming procedure, many researchers attempted to develop (semi-) automatic segmentation techniques as a powerful tool for educational and clinical purposes in the past but as yet there is no any clinically approved method in the market. This paper presents a deterministic-statistical strategy for automatic media-adventitia border detection by a fourfold algorithm. First, a smoothed initial contour is extracted based on the classification in the sparse representation framework which is combined with the dynamic directional convolution vector field. Next, an active contour model is utilized for the propagation of the initial contour toward the interested borders. Finally, the extracted contour is refined in the leakage, side branch openings and calcification regions based on the image texture patterns. The performance of the proposed algorithm is evaluated by comparing the results to those manually traced borders by an expert on 312 different IVUS images obtained from four different patients. The statistical analysis of the results demonstrates the efficiency of the proposed method in the media-adventitia border detection with enough consistency in the leakage and calcification regions. Copyright © 2017 Elsevier Ltd. All rights reserved.

  7. Automatic segmentation of male pelvic anatomy on computed tomography images: a comparison with multiple observers in the context of a multicentre clinical trial.

    Science.gov (United States)

    Geraghty, John P; Grogan, Garry; Ebert, Martin A

    2013-04-30

    This study investigates the variation in segmentation of several pelvic anatomical structures on computed tomography (CT) between multiple observers and a commercial automatic segmentation method, in the context of quality assurance and evaluation during a multicentre clinical trial. CT scans of two prostate cancer patients ('benchmarking cases'), one high risk (HR) and one intermediate risk (IR), were sent to multiple radiotherapy centres for segmentation of prostate, rectum and bladder structures according to the TROG 03.04 "RADAR" trial protocol definitions. The same structures were automatically segmented using iPlan software for the same two patients, allowing structures defined by automatic segmentation to be quantitatively compared with those defined by multiple observers. A sample of twenty trial patient datasets were also used to automatically generate anatomical structures for quantitative comparison with structures defined by individual observers for the same datasets. There was considerable agreement amongst all observers and automatic segmentation of the benchmarking cases for bladder (mean spatial variations segmenting a prostate with considerably more volume (mean +113.3%) than that automatically segmented. Similar results were seen across the twenty sample datasets, with disagreement between iPlan and observers dominant at the prostatic apex and superior part of the rectum, which is consistent with observations made during quality assurance reviews during the trial. This study has demonstrated quantitative analysis for comparison of multi-observer segmentation studies. For automatic segmentation algorithms based on image-registration as in iPlan, it is apparent that agreement between observer and automatic segmentation will be a function of patient-specific image characteristics, particularly for anatomy with poor contrast definition. For this reason, it is suggested that automatic registration based on transformation of a single reference dataset

  8. A new robust markerless method for automatic image-to-patient registration in image-guided neurosurgery system.

    Science.gov (United States)

    Liu, Yinlong; Song, Zhijian; Wang, Manning

    2017-12-01

    Compared with the traditional point-based registration in the image-guided neurosurgery system, surface-based registration is preferable because it does not use fiducial markers before image scanning and does not require image acquisition dedicated for navigation purposes. However, most existing surface-based registration methods must include a manual step for coarse registration, which increases the registration time and elicits some inconvenience and uncertainty. A new automatic surface-based registration method is proposed, which applies 3D surface feature description and matching algorithm to obtain point correspondences for coarse registration and uses the iterative closest point (ICP) algorithm in the last step to obtain an image-to-patient registration. Both phantom and clinical data were used to execute automatic registrations and target registration error (TRE) calculated to verify the practicality and robustness of the proposed method. In phantom experiments, the registration accuracy was stable across different downsampling resolutions (18-26 mm) and different support radii (2-6 mm). In clinical experiments, the mean TREs of two patients by registering full head surfaces were 1.30 mm and 1.85 mm. This study introduced a new robust automatic surface-based registration method based on 3D feature matching. The method achieved sufficient registration accuracy with different real-world surface regions in phantom and clinical experiments.

  9. Digital image analysis

    DEFF Research Database (Denmark)

    Riber-Hansen, Rikke; Vainer, Ben; Steiniche, Torben

    2012-01-01

    Digital image analysis (DIA) is increasingly implemented in histopathological research to facilitate truly quantitative measurements, decrease inter-observer variation and reduce hands-on time. Originally, efforts were made to enable DIA to reproduce manually obtained results on histological slides...... reproducibility, application of stereology-based quantitative measurements, time consumption, optimization of histological slides, regions of interest selection and recent developments in staining and imaging techniques....

  10. A prototype distributed object-oriented architecture for image-based automatic laser alignment

    International Nuclear Information System (INIS)

    Stout, E.A.; Kamm, V.J.M.; Spann, J.M.; Van Arsdall, P.J.

    1996-01-01

    Designing a computer control system for the National Ignition Facility (NIF) is a complex undertaking because of the system's large size and its distributed nature. The controls team is addressing that complexity by adopting the object-oriented programming paradigm, designing reusable software frameworks, and using the Common Object Request Broker Architecture (CORBA) for distribution. A prototype system for image-based automatic laser alignment has been developed to evaluate and gain experience with CORBA and OOP in a small distributed system. The prototype is also important in evaluating alignment concepts, image processing techniques, speed and accuracy of automatic alignment objectives for the NIF, and control hardware for aligment devices. The prototype system has met its inital objectives and provides a basis for continued development

  11. Semi-automatic watershed medical image segmentation methods for customized cancer radiation treatment planning simulation

    International Nuclear Information System (INIS)

    Kum Oyeon; Kim Hye Kyung; Max, N.

    2007-01-01

    A cancer radiation treatment planning simulation requires image segmentation to define the gross tumor volume, clinical target volume, and planning target volume. Manual segmentation, which is usual in clinical settings, depends on the operator's experience and may, in addition, change for every trial by the same operator. To overcome this difficulty, we developed semi-automatic watershed medical image segmentation tools using both the top-down watershed algorithm in the insight segmentation and registration toolkit (ITK) and Vincent-Soille's bottom-up watershed algorithm with region merging. We applied our algorithms to segment two- and three-dimensional head phantom CT data and to find pixel (or voxel) numbers for each segmented area, which are needed for radiation treatment optimization. A semi-automatic method is useful to avoid errors incurred by both human and machine sources, and provide clear and visible information for pedagogical purpose. (orig.)

  12. AUTOMATIC GLOBAL REGISTRATION BETWEEN AIRBORNE LIDAR DATA AND REMOTE SENSING IMAGE BASED ON STRAIGHT LINE FEATURES

    Directory of Open Access Journals (Sweden)

    Z. Q. Liu

    2018-04-01

    Full Text Available An automatic global registration approach for point clouds and remote sensing image based on straight line features is proposed which is insensitive to rotational and scale transformation. First, the building ridge lines and contour lines in point clouds are automatically detected as registration primitives by integrating region growth and topology identification. Second, the collinear condition equation is selected as registration transformation function which is based on rotation matrix described by unit quaternion. The similarity measure is established according to the distance between the corresponding straight line features from point clouds and the image in the same reference coordinate system. Finally, an iterative Hough transform is adopted to simultaneously estimate the parameters and obtain correspondence between registration primitives. Experimental results prove the proposed method is valid and the spectral information is useful for the following classification processing.

  13. Role of Artificial Intelligence Techniques (Automatic Classifiers) in Molecular Imaging Modalities in Neurodegenerative Diseases.

    Science.gov (United States)

    Cascianelli, Silvia; Scialpi, Michele; Amici, Serena; Forini, Nevio; Minestrini, Matteo; Fravolini, Mario Luca; Sinzinger, Helmut; Schillaci, Orazio; Palumbo, Barbara

    2017-01-01

    Artificial Intelligence (AI) is a very active Computer Science research field aiming to develop systems that mimic human intelligence and is helpful in many human activities, including Medicine. In this review we presented some examples of the exploiting of AI techniques, in particular automatic classifiers such as Artificial Neural Network (ANN), Support Vector Machine (SVM), Classification Tree (ClT) and ensemble methods like Random Forest (RF), able to analyze findings obtained by positron emission tomography (PET) or single-photon emission tomography (SPECT) scans of patients with Neurodegenerative Diseases, in particular Alzheimer's Disease. We also focused our attention on techniques applied in order to preprocess data and reduce their dimensionality via feature selection or projection in a more representative domain (Principal Component Analysis - PCA - or Partial Least Squares - PLS - are examples of such methods); this is a crucial step while dealing with medical data, since it is necessary to compress patient information and retain only the most useful in order to discriminate subjects into normal and pathological classes. Main literature papers on the application of these techniques to classify patients with neurodegenerative disease extracting data from molecular imaging modalities are reported, showing that the increasing development of computer aided diagnosis systems is very promising to contribute to the diagnostic process.

  14. An intelligent support system for automatic detection of cerebral vascular accidents from brain CT images.

    Science.gov (United States)

    Hajimani, Elmira; Ruano, M G; Ruano, A E

    2017-07-01

    This paper presents a Radial Basis Functions Neural Network (RBFNN) based detection system, for automatic identification of Cerebral Vascular Accidents (CVA) through analysis of Computed Tomographic (CT) images. For the design of a neural network classifier, a Multi Objective Genetic Algorithm (MOGA) framework is used to determine the architecture of the classifier, its corresponding parameters and input features by maximizing the classification precision, while ensuring generalization. This approach considers a large number of input features, comprising first and second order pixel intensity statistics, as well as symmetry/asymmetry information with respect to the ideal mid-sagittal line. Values of specificity of 98% and sensitivity of 98% were obtained, at pixel level, by an ensemble of non-dominated models generated by MOGA, in a set of 150 CT slices (1,867,602pixels), marked by a NeuroRadiologist. This approach also compares favorably at a lesion level with three other published solutions, in terms of specificity (86% compared with 84%), degree of coincidence of marked lesions (89% compared with 77%) and classification accuracy rate (96% compared with 88%). Copyright © 2017. Published by Elsevier B.V.

  15. Deep learning for automatic localization, identification, and segmentation of vertebral bodies in volumetric MR images

    Science.gov (United States)

    Suzani, Amin; Rasoulian, Abtin; Seitel, Alexander; Fels, Sidney; Rohling, Robert N.; Abolmaesumi, Purang

    2015-03-01

    This paper proposes an automatic method for vertebra localization, labeling, and segmentation in multi-slice Magnetic Resonance (MR) images. Prior work in this area on MR images mostly requires user interaction while our method is fully automatic. Cubic intensity-based features are extracted from image voxels. A deep learning approach is used for simultaneous localization and identification of vertebrae. The localized points are refined by local thresholding in the region of the detected vertebral column. Thereafter, a statistical multi-vertebrae model is initialized on the localized vertebrae. An iterative Expectation Maximization technique is used to register the vertebral body of the model to the image edges and obtain a segmentation of the lumbar vertebral bodies. The method is evaluated by applying to nine volumetric MR images of the spine. The results demonstrate 100% vertebra identification and a mean surface error of below 2.8 mm for 3D segmentation. Computation time is less than three minutes per high-resolution volumetric image.

  16. Automatic lung tumor segmentation on PET/CT images using fuzzy Markov random field model.

    Science.gov (United States)

    Guo, Yu; Feng, Yuanming; Sun, Jian; Zhang, Ning; Lin, Wang; Sa, Yu; Wang, Ping

    2014-01-01

    The combination of positron emission tomography (PET) and CT images provides complementary functional and anatomical information of human tissues and it has been used for better tumor volume definition of lung cancer. This paper proposed a robust method for automatic lung tumor segmentation on PET/CT images. The new method is based on fuzzy Markov random field (MRF) model. The combination of PET and CT image information is achieved by using a proper joint posterior probability distribution of observed features in the fuzzy MRF model which performs better than the commonly used Gaussian joint distribution. In this study, the PET and CT simulation images of 7 non-small cell lung cancer (NSCLC) patients were used to evaluate the proposed method. Tumor segmentations with the proposed method and manual method by an experienced radiation oncologist on the fused images were performed, respectively. Segmentation results obtained with the two methods were similar and Dice's similarity coefficient (DSC) was 0.85 ± 0.013. It has been shown that effective and automatic segmentations can be achieved with this method for lung tumors which locate near other organs with similar intensities in PET and CT images, such as when the tumors extend into chest wall or mediastinum.

  17. Automatic Lung Tumor Segmentation on PET/CT Images Using Fuzzy Markov Random Field Model

    Directory of Open Access Journals (Sweden)

    Yu Guo

    2014-01-01

    Full Text Available The combination of positron emission tomography (PET and CT images provides complementary functional and anatomical information of human tissues and it has been used for better tumor volume definition of lung cancer. This paper proposed a robust method for automatic lung tumor segmentation on PET/CT images. The new method is based on fuzzy Markov random field (MRF model. The combination of PET and CT image information is achieved by using a proper joint posterior probability distribution of observed features in the fuzzy MRF model which performs better than the commonly used Gaussian joint distribution. In this study, the PET and CT simulation images of 7 non-small cell lung cancer (NSCLC patients were used to evaluate the proposed method. Tumor segmentations with the proposed method and manual method by an experienced radiation oncologist on the fused images were performed, respectively. Segmentation results obtained with the two methods were similar and Dice’s similarity coefficient (DSC was 0.85 ± 0.013. It has been shown that effective and automatic segmentations can be achieved with this method for lung tumors which locate near other organs with similar intensities in PET and CT images, such as when the tumors extend into chest wall or mediastinum.

  18. An automatic system for segmentation, matching, anatomical labeling and measurement of airways from CT images

    DEFF Research Database (Denmark)

    Petersen, Jens; Feragen, Aasa; Owen, Megan

    segmental branches, and longitudinal matching of airway branches in repeated scans of the same subject. Methods and Materials: The segmentation process begins from an automatically detected seed point in the trachea. The airway centerline tree is then constructed by iteratively adding locally optimal paths...... differences. Results: The segmentation method has been used on 9711 low dose CT images from the Danish Lung Cancer Screening Trial (DLCST). Manual inspection of thumbnail images revealed gross errors in a total of 44 images. 29 were missing branches at the lobar level and only 15 had obvious false positives...... measurements to segments matched in multiple images of the same subject using image registration was observed to increase their reproducibility. The anatomical branch labeling tool was validated on a subset of 20 subjects, 5 of each category: asymptomatic, mild, moderate and severe COPD. The average inter...

  19. Automatic Centerline Extraction of Coverd Roads by Surrounding Objects from High Resolution Satellite Images

    Science.gov (United States)

    Kamangir, H.; Momeni, M.; Satari, M.

    2017-09-01

    This paper presents an automatic method to extract road centerline networks from high and very high resolution satellite images. The present paper addresses the automated extraction roads covered with multiple natural and artificial objects such as trees, vehicles and either shadows of buildings or trees. In order to have a precise road extraction, this method implements three stages including: classification of images based on maximum likelihood algorithm to categorize images into interested classes, modification process on classified images by connected component and morphological operators to extract pixels of desired objects by removing undesirable pixels of each class, and finally line extraction based on RANSAC algorithm. In order to evaluate performance of the proposed method, the generated results are compared with ground truth road map as a reference. The evaluation performance of the proposed method using representative test images show completeness values ranging between 77% and 93%.

  20. Multi-atlas-based automatic 3D segmentation for prostate brachytherapy in transrectal ultrasound images

    Science.gov (United States)

    Nouranian, Saman; Mahdavi, S. Sara; Spadinger, Ingrid; Morris, William J.; Salcudean, S. E.; Abolmaesumi, P.

    2013-03-01

    One of the commonly used treatment methods for early-stage prostate cancer is brachytherapy. The standard of care for planning this procedure is segmentation of contours from transrectal ultrasound (TRUS) images, which closely follow the prostate boundary. This process is currently performed either manually or using semi-automatic techniques. This paper introduces a fully automatic segmentation algorithm which uses a priori knowledge of contours in a reference data set of TRUS volumes. A non-parametric deformable registration method is employed to transform the atlas prostate contours to a target image coordinates. All atlas images are sorted based on their registration results and the highest ranked registration results are selected for decision fusion. A Simultaneous Truth and Performance Level Estimation algorithm is utilized to fuse labels from registered atlases and produce a segmented target volume. In this experiment, 50 patient TRUS volumes are obtained and a leave-one-out study on TRUS volumes is reported. We also compare our results with a state-of-the-art semi-automatic prostate segmentation method that has been clinically used for planning prostate brachytherapy procedures and we show comparable accuracy and precision within clinically acceptable runtime.

  1. Automatic non-proliferative diabetic retinopathy screening system based on color fundus image.

    Science.gov (United States)

    Xiao, Zhitao; Zhang, Xinpeng; Geng, Lei; Zhang, Fang; Wu, Jun; Tong, Jun; Ogunbona, Philip O; Shan, Chunyan

    2017-10-26

    Non-proliferative diabetic retinopathy is the early stage of diabetic retinopathy. Automatic detection of non-proliferative diabetic retinopathy is significant for clinical diagnosis, early screening and course progression of patients. This paper introduces the design and implementation of an automatic system for screening non-proliferative diabetic retinopathy based on color fundus images. Firstly, the fundus structures, including blood vessels, optic disc and macula, are extracted and located, respectively. In particular, a new optic disc localization method using parabolic fitting is proposed based on the physiological structure characteristics of optic disc and blood vessels. Then, early lesions, such as microaneurysms, hemorrhages and hard exudates, are detected based on their respective characteristics. An equivalent optical model simulating human eyes is designed based on the anatomical structure of retina. Main structures and early lesions are reconstructed in the 3D space for better visualization. Finally, the severity of each image is evaluated based on the international criteria of diabetic retinopathy. The system has been tested on public databases and images from hospitals. Experimental results demonstrate that the proposed system achieves high accuracy for main structures and early lesions detection. The results of severity classification for non-proliferative diabetic retinopathy are also accurate and suitable. Our system can assist ophthalmologists for clinical diagnosis, automatic screening and course progression of patients.

  2. Automatic Gleason grading of prostate cancer using quantitative phase imaging and machine learning

    Science.gov (United States)

    Nguyen, Tan H.; Sridharan, Shamira; Macias, Virgilia; Kajdacsy-Balla, Andre; Melamed, Jonathan; Do, Minh N.; Popescu, Gabriel

    2017-03-01

    We present an approach for automatic diagnosis of tissue biopsies. Our methodology consists of a quantitative phase imaging tissue scanner and machine learning algorithms to process these data. We illustrate the performance by automatic Gleason grading of prostate specimens. The imaging system operates on the principle of interferometry and, as a result, reports on the nanoscale architecture of the unlabeled specimen. We use these data to train a random forest classifier to learn textural behaviors of prostate samples and classify each pixel in the image into different classes. Automatic diagnosis results were computed from the segmented regions. By combining morphological features with quantitative information from the glands and stroma, logistic regression was used to discriminate regions with Gleason grade 3 versus grade 4 cancer in prostatectomy tissue. The overall accuracy of this classification derived from a receiver operating curve was 82%, which is in the range of human error when interobserver variability is considered. We anticipate that our approach will provide a clinically objective and quantitative metric for Gleason grading, allowing us to corroborate results across instruments and laboratories and feed the computer algorithms for improved accuracy.

  3. Automatic adjustment of display window (gray-level condition) for MR images using neural networks

    International Nuclear Information System (INIS)

    Ohhashi, Akinami; Nambu, Kyojiro.

    1992-01-01

    We have developed a system to automatically adjust the display window width and level (WWL) for MR images using neural networks. There were three main points in the development of our system as follows: 1) We defined an index for the clarity of a displayed image, and called 'EW'. EW is a quantitative measure of the clarity of an image displayed in a certain WWL, and can be derived from the difference between gray-level with the WWL adjusted by a human expert and with a certain WWL. 2) We extracted a group of six features from a gray-level histogram of a displayed image. We designed two neural networks which are able to learn the relationship between these features and the desired output (teaching signal), 'EQ', which is normalized to 0 to 1.0 from EW. Two neural networks were used to share the patterns to be learned; one learns a variety of patterns with less accuracy, and the other learns similar patterns with accuracy. Learning was performed using a back-propagation method. As a result, the neural networks after learning are able to provide a quantitative measure, 'Q', of the clarity of images displayed in the designated WWL. 3) Using the 'Hill climbing' method, we have been able to determine the best possible WWL for a displaying image. We have tested this technique for MR brain images. The results show that this system can adjust WWL comparable to that adjusted by a human expert for the majority of test images. The neural network is effective for the automatic adjustment of the display window for MR images. We are now studying the application of this method to MR images of another regions. (author)

  4. Single Point Vulnerability Analysis of Automatic Seismic Trip System

    Energy Technology Data Exchange (ETDEWEB)

    Oh, Seo Bin; Chung, Soon Il; Lee, Yong Suk [FNC Technology Co., Yongin (Korea, Republic of); Choi, Byung Pil [KHNP CRI, Daejeon (Korea, Republic of)

    2016-10-15

    Single Point Vulnerability (SPV) analysis is a process used to identify individual equipment whose failure alone will result in a reactor trip, turbine generator failure, or power reduction of more than 50%. Automatic Seismic Trip System (ASTS) is a newly installed system to ensure the safety of plant when earthquake occurs. Since this system directly shuts down the reactor, the failure or malfunction of its system component can cause a reactor trip more frequently than other systems. Therefore, an SPV analysis of ASTS is necessary to maintain its essential performance. To analyze SPV for ASTS, failure mode and effect analysis (FMEA) and fault tree analysis (FTA) was performed. In this study, FMEA and FTA methods were performed to select SPV equipment of ASTS. D/O, D/I, A/I card, seismic sensor, and trip relay had an effect on the reactor trip but their single failure will not cause reactor trip. In conclusion, ASTS is excluded as SPV. These results can be utilized as the basis data for ways to enhance facility reliability such as design modification and improvement of preventive maintenance procedure.

  5. Single Point Vulnerability Analysis of Automatic Seismic Trip System

    International Nuclear Information System (INIS)

    Oh, Seo Bin; Chung, Soon Il; Lee, Yong Suk; Choi, Byung Pil

    2016-01-01

    Single Point Vulnerability (SPV) analysis is a process used to identify individual equipment whose failure alone will result in a reactor trip, turbine generator failure, or power reduction of more than 50%. Automatic Seismic Trip System (ASTS) is a newly installed system to ensure the safety of plant when earthquake occurs. Since this system directly shuts down the reactor, the failure or malfunction of its system component can cause a reactor trip more frequently than other systems. Therefore, an SPV analysis of ASTS is necessary to maintain its essential performance. To analyze SPV for ASTS, failure mode and effect analysis (FMEA) and fault tree analysis (FTA) was performed. In this study, FMEA and FTA methods were performed to select SPV equipment of ASTS. D/O, D/I, A/I card, seismic sensor, and trip relay had an effect on the reactor trip but their single failure will not cause reactor trip. In conclusion, ASTS is excluded as SPV. These results can be utilized as the basis data for ways to enhance facility reliability such as design modification and improvement of preventive maintenance procedure

  6. Automatic Beam Path Analysis of Laser Wakefield Particle Acceleration Data

    Energy Technology Data Exchange (ETDEWEB)

    Rubel, Oliver; Geddes, Cameron G.R.; Cormier-Michel, Estelle; Wu, Kesheng; Prabhat,; Weber, Gunther H.; Ushizima, Daniela M.; Messmer, Peter; Hagen, Hans; Hamann, Bernd; Bethel, E. Wes

    2009-10-19

    Numerical simulations of laser wakefield particle accelerators play a key role in the understanding of the complex acceleration process and in the design of expensive experimental facilities. As the size and complexity of simulation output grows, an increasingly acute challenge is the practical need for computational techniques that aid in scientific knowledge discovery. To that end, we present a set of data-understanding algorithms that work in concert in a pipeline fashion to automatically locate and analyze high energy particle bunches undergoing acceleration in very large simulation datasets. These techniques work cooperatively by first identifying features of interest in individual timesteps, then integrating features across timesteps, and based on the information derived perform analysis of temporally dynamic features. This combination of techniques supports accurate detection of particle beams enabling a deeper level of scientific understanding of physical phenomena than hasbeen possible before. By combining efficient data analysis algorithms and state-of-the-art data management we enable high-performance analysis of extremely large particle datasets in 3D. We demonstrate the usefulness of our methods for a variety of 2D and 3D datasets and discuss the performance of our analysis pipeline.

  7. A method for the automatic separation of the images of galaxies and stars from measurements made with the COSMOS machine

    International Nuclear Information System (INIS)

    MacGillivray, H.T.; Martin, R.; Pratt, N.M.; Reddish, V.C.; Seddon, H.; Alexander, L.W.G.; Walker, G.S.; Williams, P.R.

    1976-01-01

    A method has been developed which allows the computer to distinguish automatically between the images of galaxies and those of stars from measurements made with the COSMOS automatic plate-measuring machine at the Royal Observatory, Edinburgh. Results have indicated that a 90 to 95 per cent separation between galaxies and stars is possible. (author)

  8. Feeding People's Curiosity: Leveraging the Cloud for Automatic Dissemination of Mars Images

    Science.gov (United States)

    Knight, David; Powell, Mark

    2013-01-01

    Smartphones and tablets have made wireless computing ubiquitous, and users expect instant, on-demand access to information. The Mars Science Laboratory (MSL) operations software suite, MSL InterfaCE (MSLICE), employs a different back-end image processing architecture compared to that of the Mars Exploration Rovers (MER) in order to better satisfy modern consumer-driven usage patterns and to offer greater server-side flexibility. Cloud services are a centerpiece of the server-side architecture that allows new image data to be delivered automatically to both scientists using MSLICE and the general public through the MSL website (http://mars.jpl.nasa.gov/msl/).

  9. Deformable meshes for medical image segmentation accurate automatic segmentation of anatomical structures

    CERN Document Server

    Kainmueller, Dagmar

    2014-01-01

    ? Segmentation of anatomical structures in medical image data is an essential task in clinical practice. Dagmar Kainmueller introduces methods for accurate fully automatic segmentation of anatomical structures in 3D medical image data. The author's core methodological contribution is a novel deformation model that overcomes limitations of state-of-the-art Deformable Surface approaches, hence allowing for accurate segmentation of tip- and ridge-shaped features of anatomical structures. As for practical contributions, she proposes application-specific segmentation pipelines for a range of anatom

  10. APPLICATION OF PRINCIPAL COMPONENT ANALYSIS TO RELAXOGRAPHIC IMAGES

    International Nuclear Information System (INIS)

    STOYANOVA, R.S.; OCHS, M.F.; BROWN, T.R.; ROONEY, W.D.; LI, X.; LEE, J.H.; SPRINGER, C.S.

    1999-01-01

    Standard analysis methods for processing inversion recovery MR images traditionally have used single pixel techniques. In these techniques each pixel is independently fit to an exponential recovery, and spatial correlations in the data set are ignored. By analyzing the image as a complete dataset, improved error analysis and automatic segmentation can be achieved. Here, the authors apply principal component analysis (PCA) to a series of relaxographic images. This procedure decomposes the 3-dimensional data set into three separate images and corresponding recovery times. They attribute the 3 images to be spatial representations of gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) content

  11. Automatic Derivation of Statistical Data Analysis Algorithms: Planetary Nebulae and Beyond

    Science.gov (United States)

    Fischer, Bernd; Hajian, Arsen; Knuth, Kevin; Schumann, Johann

    2004-04-01

    AUTOBAYES is a fully automatic program synthesis system for the data analysis domain. Its input is a declarative problem description in form of a statistical model; its output is documented and optimized C/C++ code. The synthesis process relies on the combination of three key techniques. Bayesian networks are used as a compact internal representation mechanism which enables problem decompositions and guides the algorithm derivation. Program schemas are used as independently composable building blocks for the algorithm construction; they can encapsulate advanced algorithms and data structures. A symbolic-algebraic system is used to find closed-form solutions for problems and emerging subproblems. In this paper, we describe the application of AUTOBAYES to the analysis of planetary nebulae images taken by the Hubble Space Telescope. We explain the system architecture, and present in detail the automatic derivation of the scientists' original analysis as well as a refined analysis using clustering models. This study demonstrates that AUTOBAYES is now mature enough so that it can be applied to realistic scientific data analysis tasks.

  12. Automatic settlement analysis of single-layer armour layers

    NARCIS (Netherlands)

    Hofland, B.; van gent, Marcel

    2016-01-01

    A method to quantify, analyse, and present the settlement of single-layer concrete armour layers of coastal structures is presented. The use of the image processing technique for settlement analysis is discussed based on various modelling
    studies performed over the years. The accuracy of the

  13. Automatic Registration Method for Fusion of ZY-1-02C Satellite Images

    Directory of Open Access Journals (Sweden)

    Qi Chen

    2013-12-01

    Full Text Available Automatic image registration (AIR has been widely studied in the fields of medical imaging, computer vision, and remote sensing. In various cases, such as image fusion, high registration accuracy should be achieved to meet application requirements. For satellite images, the large image size and unstable positioning accuracy resulting from the limited manufacturing technology of charge-coupled device, focal plane distortion, and unrecorded spacecraft jitter lead to difficulty in obtaining agreeable corresponding points for registration using only area-based matching or feature-based matching. In this situation, a coarse-to-fine matching strategy integrating two types of algorithms is proven feasible and effective. In this paper, an AIR method for application to the fusion of ZY-1-02C satellite imagery is proposed. First, the images are geometrically corrected. Coarse matching, based on scale invariant feature transform, is performed for the subsampled corrected images, and a rough global estimation is made with the matching results. Harris feature points are then extracted, and the coordinates of the corresponding points are calculated according to the global estimation results. Precise matching is conducted, based on normalized cross correlation and least squares matching. As complex image distortion cannot be precisely estimated, a local estimation using the structure of triangulated irregular network is applied to eliminate the false matches. Finally, image resampling is conducted, based on local affine transformation, to achieve high-precision registration. Experiments with ZY-1-02C datasets demonstrate that the accuracy of the proposed method meets the requirements of fusion application, and its efficiency is also suitable for the commercial operation of the automatic satellite data process system.

  14. Automatic detection of diabetic retinopathy features in ultra-wide field retinal images

    Science.gov (United States)

    Levenkova, Anastasia; Sowmya, Arcot; Kalloniatis, Michael; Ly, Angelica; Ho, Arthur

    2017-03-01

    Diabetic retinopathy (DR) is a major cause of irreversible vision loss. DR screening relies on retinal clinical signs (features). Opportunities for computer-aided DR feature detection have emerged with the development of Ultra-WideField (UWF) digital scanning laser technology. UWF imaging covers 82% greater retinal area (200°), against 45° in conventional cameras3 , allowing more clinically relevant retinopathy to be detected4 . UWF images also provide a high resolution of 3078 x 2702 pixels. Currently DR screening uses 7 overlapping conventional fundus images, and the UWF images provide similar results1,4. However, in 40% of cases, more retinopathy was found outside the 7-field ETDRS) fields by UWF and in 10% of cases, retinopathy was reclassified as more severe4 . This is because UWF imaging allows examination of both the central retina and more peripheral regions, with the latter implicated in DR6 . We have developed an algorithm for automatic recognition of DR features, including bright (cotton wool spots and exudates) and dark lesions (microaneurysms and blot, dot and flame haemorrhages) in UWF images. The algorithm extracts features from grayscale (green "red-free" laser light) and colour-composite UWF images, including intensity, Histogram-of-Gradient and Local binary patterns. Pixel-based classification is performed with three different classifiers. The main contribution is the automatic detection of DR features in the peripheral retina. The method is evaluated by leave-one-out cross-validation on 25 UWF retinal images with 167 bright lesions, and 61 other images with 1089 dark lesions. The SVM classifier performs best with AUC of 94.4% / 95.31% for bright / dark lesions.

  15. Adaptive and automatic red blood cell counting method based on microscopic hyperspectral imaging technology

    Science.gov (United States)

    Liu, Xi; Zhou, Mei; Qiu, Song; Sun, Li; Liu, Hongying; Li, Qingli; Wang, Yiting

    2017-12-01

    Red blood cell counting, as a routine examination, plays an important role in medical diagnoses. Although automated hematology analyzers are widely used, manual microscopic examination by a hematologist or pathologist is still unavoidable, which is time-consuming and error-prone. This paper proposes a full-automatic red blood cell counting method which is based on microscopic hyperspectral imaging of blood smears and combines spatial and spectral information to achieve high precision. The acquired hyperspectral image data of the blood smear in the visible and near-infrared spectral range are firstly preprocessed, and then a quadratic blind linear unmixing algorithm is used to get endmember abundance images. Based on mathematical morphological operation and an adaptive Otsu’s method, a binaryzation process is performed on the abundance images. Finally, the connected component labeling algorithm with magnification-based parameter setting is applied to automatically select the binary images of red blood cell cytoplasm. Experimental results show that the proposed method can perform well and has potential for clinical applications.

  16. Automatic adventitious respiratory sound analysis: A systematic review.

    Science.gov (United States)

    Pramono, Renard Xaviero Adhi; Bowyer, Stuart; Rodriguez-Villegas, Esther

    2017-01-01

    Automatic detection or classification of adventitious sounds is useful to assist physicians in diagnosing or monitoring diseases such as asthma, Chronic Obstructive Pulmonary Disease (COPD), and pneumonia. While computerised respiratory sound analysis, specifically for the detection or classification of adventitious sounds, has recently been the focus of an increasing number of studies, a standardised approach and comparison has not been well established. To provide a review of existing algorithms for the detection or classification of adventitious respiratory sounds. This systematic review provides a complete summary of methods used in the literature to give a baseline for future works. A systematic review of English articles published between 1938 and 2016, searched using the Scopus (1938-2016) and IEEExplore (1984-2016) databases. Additional articles were further obtained by references listed in the articles found. Search terms included adventitious sound detection, adventitious sound classification, abnormal respiratory sound detection, abnormal respiratory sound classification, wheeze detection, wheeze classification, crackle detection, crackle classification, rhonchi detection, rhonchi classification, stridor detection, stridor classification, pleural rub detection, pleural rub classification, squawk detection, and squawk classification. Only articles were included that focused on adventitious sound detection or classification, based on respiratory sounds, with performance reported and sufficient information provided to be approximately repeated. Investigators extracted data about the adventitious sound type analysed, approach and level of analysis, instrumentation or data source, location of sensor, amount of data obtained, data management, features, methods, and performance achieved. A total of 77 reports from the literature were included in this review. 55 (71.43%) of the studies focused on wheeze, 40 (51.95%) on crackle, 9 (11.69%) on stridor, 9 (11

  17. CIE L*a*b*: comparison of digital images obtained photographically by manual and automatic modes

    Directory of Open Access Journals (Sweden)

    Fabiana Takatsui

    2012-12-01

    Full Text Available The aim of this study was to analyze the color alterations performed by the CIE L*a*b* system in the digital imaging of shade guide tabs, which were obtained photographically according to the automatic and manual modes. This study also sought to examine the observers' agreement in quantifying the coordinates. Four Vita Lumin Vaccum shade guide tabs were used: A3.5, B1, B3 and C4. An EOS Canon digital camera was used to record the digital images of the shade tabs, and the images were processed using Adobe Photoshop software. A total of 80 observations (five replicates of each shade according to two observers in two modes, specifically, automatic and manual were obtained, leading to color values of L*, a* and b*. The color difference (ΔE between the modes was calculated and classified as either clinically acceptable or unacceptable. The results indicated that there was agreement between the two observers in obtaining the L*, a* and b* values related to all guides. However, the B1, B3, and C4 shade tabs had ΔE values classified as clinically acceptable (ΔE = 0.44, ΔE = 2.04 and ΔE = 2.69, respectively. The A3.5 shade tab had a ΔE value classified as clinically unacceptable (ΔE = 4.17, as it presented higher values for luminosity in the automatic mode (L* = 54.0 than in the manual mode (L* = 50.6. It was concluded that the B1, B3 and C4 shade tabs can be used at any of the modes in digital camera (manual or automatic, which was a different finding from that observed for the A3.5 shade tab.

  18. [Automatic Extraction and Analysis of Dosimetry Data in Radiotherapy Plans].

    Science.gov (United States)

    Song, Wei; Zhao, Di; Lu, Hong; Zhang, Biyun; Ma, Jun; Yu, Dahai

    To improve the efficiency and accuracy of extraction and analysis of dosimetry data in radiotherapy plans for a batch of patients. With the interface function provided in Matlab platform, a program was written to extract the dosimetry data exported from treatment planning system in DICOM RT format and exported the dose-volume data to an Excel file with the SPSS compatible format. This method was compared with manual operation for 14 gastric carcinoma patients to validate the efficiency and accuracy. The output Excel data were compatible with SPSS in format, the dosimetry data error for PTV dose interval of 90%-98%, PTV dose interval of 99%-106% and all OARs were -3.48E-5 ± 3.01E-5, -1.11E-3 ± 7.68E-4, -7.85E-5 ± 9.91E-5 respectively. Compared with manual operation, the time required was reduced from 5.3 h to 0.19 h and input error was reduced from 0.002 to 0. The automatic extraction of dosimetry data in DICOM RT format for batch patients, the SPSS compatible data exportation, quick analysis were achieved in this paper. The efficiency of clinical researches based on dosimetry data analysis of large number of patients will be improved with this methods.

  19. Towards the automatic detection and analysis of sunspot rotation

    Science.gov (United States)

    Brown, Daniel S.; Walker, Andrew P.

    2016-10-01

    Torsional rotation of sunspots have been noted by many authors over the past century. Sunspots have been observed to rotate up to the order of 200 degrees over 8-10 days, and these have often been linked with eruptive behaviour such as solar flares and coronal mass ejections. However, most studies in the literature are case studies or small-number studies which suffer from selection bias. In order to better understand sunspot rotation and its impact on the corona, unbiased large-sample statistical studies are required (including both rotating and non-rotating sunspots). While this can be done manually, a better approach is to automate the detection and analysis of rotating sunspots using robust methods with well characterised uncertainties. The SDO/HMI instrument provide long-duration, high-resolution and high-cadence continuum observations suitable for extracting a large number of examples of rotating sunspots. This presentation will outline the analysis of SDI/HMI data to determine the rotation (and non-rotation) profiles of sunspots for the complete duration of their transit across the solar disk, along with how this can be extended to automatically identify sunspots and initiate their analysis.

  20. Automatic and improved radiometric correction of Landsat imagery using reference values from MODIS surface reflectance images

    Science.gov (United States)

    Pons, X.; Pesquer, L.; Cristóbal, J.; González-Guerrero, O.

    2014-12-01

    Radiometric correction is a prerequisite for generating high-quality scientific data, making it possible to discriminate between product artefacts and real changes in Earth processes as well as accurately produce land cover maps and detect changes. This work contributes to the automatic generation of surface reflectance products for Landsat satellite series. Surface reflectances are generated by a new approach developed from a previous simplified radiometric (atmospheric + topographic) correction model. The proposed model keeps the core of the old model (incidence angles and cast-shadows through a digital elevation model [DEM], Earth-Sun distance, etc.) and adds new characteristics to enhance and automatize ground reflectance retrieval. The new model includes the following new features: (1) A fitting model based on reference values from pseudoinvariant areas that have been automatically extracted from existing reflectance products (Terra MODIS MOD09GA) that were selected also automatically by applying quality criteria that include a geostatistical pattern model. This guarantees the consistency of the internal and external series, making it unnecessary to provide extra atmospheric data for the acquisition date and time, dark objects or dense vegetation. (2) A spatial model for atmospheric optical depth that uses detailed DEM and MODTRAN simulations. (3) It is designed so that large time-series of images can be processed automatically to produce consistent Landsat surface reflectance time-series. (4) The approach can handle most images, acquired now or in the past, regardless of the processing system, with the exception of those with extremely high cloud coverage. The new methodology has been successfully applied to a series of near 300 images of the same area including MSS, TM and ETM+ imagery as well as to different formats and processing systems (LPGS and NLAPS from the USGS; CEOS from ESA) for different degrees of cloud coverage (up to 60%) and SLC

  1. Automatic block-matching registration to improve lung tumor localization during image-guided radiotherapy

    Science.gov (United States)

    Robertson, Scott Patrick

    To improve relatively poor outcomes for locally-advanced lung cancer patients, many current efforts are dedicated to minimizing uncertainties in radiotherapy. This enables the isotoxic delivery of escalated tumor doses, leading to better local tumor control. The current dissertation specifically addresses inter-fractional uncertainties resulting from patient setup variability. An automatic block-matching registration (BMR) algorithm is implemented and evaluated for the purpose of directly localizing advanced-stage lung tumors during image-guided radiation therapy. In this algorithm, small image sub-volumes, termed "blocks", are automatically identified on the tumor surface in an initial planning computed tomography (CT) image. Each block is independently and automatically registered to daily images acquired immediately prior to each treatment fraction. To improve the accuracy and robustness of BMR, this algorithm incorporates multi-resolution pyramid registration, regularization with a median filter, and a new multiple-candidate-registrations technique. The result of block-matching is a sparse displacement vector field that models local tissue deformations near the tumor surface. The distribution of displacement vectors is aggregated to obtain the final tumor registration, corresponding to the treatment couch shift for patient setup correction. Compared to existing rigid and deformable registration algorithms, the final BMR algorithm significantly improves the overlap between target volumes from the planning CT and registered daily images. Furthermore, BMR results in the smallest treatment margins for the given study population. However, despite these improvements, large residual target localization errors were noted, indicating that purely rigid couch shifts cannot correct for all sources of inter-fractional variability. Further reductions in treatment uncertainties may require the combination of high-quality target localization and adaptive radiotherapy.

  2. Ultrasound image-based thyroid nodule automatic segmentation using convolutional neural networks.

    Science.gov (United States)

    Ma, Jinlian; Wu, Fa; Jiang, Tian'an; Zhao, Qiyu; Kong, Dexing

    2017-11-01

    Delineation of thyroid nodule boundaries from ultrasound images plays an important role in calculation of clinical indices and diagnosis of thyroid diseases. However, it is challenging for accurate and automatic segmentation of thyroid nodules because of their heterogeneous appearance and components similar to the background. In this study, we employ a deep convolutional neural network (CNN) to automatically segment thyroid nodules from ultrasound images. Our CNN-based method formulates a thyroid nodule segmentation problem as a patch classification task, where the relationship among patches is ignored. Specifically, the CNN used image patches from images of normal thyroids and thyroid nodules as inputs and then generated the segmentation probability maps as outputs. A multi-view strategy is used to improve the performance of the CNN-based model. Additionally, we compared the performance of our approach with that of the commonly used segmentation methods on the same dataset. The experimental results suggest that our proposed method outperforms prior methods on thyroid nodule segmentation. Moreover, the results show that the CNN-based model is able to delineate multiple nodules in thyroid ultrasound images accurately and effectively. In detail, our CNN-based model can achieve an average of the overlap metric, dice ratio, true positive rate, false positive rate, and modified Hausdorff distance as [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text] on overall folds, respectively. Our proposed method is fully automatic without any user interaction. Quantitative results also indicate that our method is so efficient and accurate that it can be good enough to replace the time-consuming and tedious manual segmentation approach, demonstrating the potential clinical applications.

  3. Performance Analysis of the SIFT Operator for Automatic Feature Extraction and Matching in Photogrammetric Applications

    Directory of Open Access Journals (Sweden)

    Francesco Nex

    2009-05-01

    Full Text Available In the photogrammetry field, interest in region detectors, which are widely used in Computer Vision, is quickly increasing due to the availability of new techniques. Images acquired by Mobile Mapping Technology, Oblique Photogrammetric Cameras or Unmanned Aerial Vehicles do not observe normal acquisition conditions. Feature extraction and matching techniques, which are traditionally used in photogrammetry, are usually inefficient for these applications as they are unable to provide reliable results under extreme geometrical conditions (convergent taking geometry, strong affine transformations, etc. and for bad-textured images. A performance analysis of the SIFT technique in aerial and close-range photogrammetric applications is presented in this paper. The goal is to establish the suitability of the SIFT technique for automatic tie point extraction and approximate DSM (Digital Surface Model generation. First, the performances of the SIFT operator have been compared with those provided by feature extraction and matching techniques used in photogrammetry. All these techniques have been implemented by the authors and validated on aerial and terrestrial images. Moreover, an auto-adaptive version of the SIFT operator has been developed, in order to improve the performances of the SIFT detector in relation to the texture of the images. The Auto-Adaptive SIFT operator (A2 SIFT has been validated on several aerial images, with particular attention to large scale aerial images acquired using mini-UAV systems.

  4. Automatic labeling of MR brain images through extensible learning and atlas forests.

    Science.gov (United States)

    Xu, Lijun; Liu, Hong; Song, Enmin; Yan, Meng; Jin, Renchao; Hung, Chih-Cheng

    2017-12-01

    Multiatlas-based method is extensively used in MR brain images segmentation because of its simplicity and robustness. This method provides excellent accuracy although it is time consuming and limited in terms of obtaining information about new atlases. In this study, an automatic labeling of MR brain images through extensible learning and atlas forest is presented to address these limitations. We propose an extensible learning model which allows the multiatlas-based framework capable of managing the datasets with numerous atlases or dynamic atlas datasets and simultaneously ensure the accuracy of automatic labeling. Two new strategies are used to reduce the time and space complexity and improve the efficiency of the automatic labeling of brain MR images. First, atlases are encoded to atlas forests through random forest technology to reduce the time consumed for cross-registration between atlases and target image, and a scatter spatial vector is designed to eliminate errors caused by inaccurate registration. Second, an atlas selection method based on the extensible learning model is used to select atlases for target image without traversing the entire dataset and then obtain the accurate labeling. The labeling results of the proposed method were evaluated in three public datasets, namely, IBSR, LONI LPBA40, and ADNI. With the proposed method, the dice coefficient metric values on the three datasets were 84.17 ± 4.61%, 83.25 ± 4.29%, and 81.88 ± 4.53% which were 5% higher than those of the conventional method, respectively. The efficiency of the extensible learning model was evaluated by state-of-the-art methods for labeling of MR brain images. Experimental results showed that the proposed method could achieve accurate labeling for MR brain images without traversing the entire datasets. In the proposed multiatlas-based method, extensible learning and atlas forests were applied to control the automatic labeling of brain anatomies on large atlas datasets or dynamic

  5. A Novel, Automatic Quality Control Scheme for Real Time Image Transmission

    Directory of Open Access Journals (Sweden)

    S. Ramachandran

    2002-01-01

    Full Text Available A novel scheme to compute energy on-the-fly and thereby control the quality of the image frames dynamically is presented along with its FPGA implementation. This scheme is suitable for incorporation in image compression systems such as video encoders. In this new scheme, processing is automatically stopped when the desired quality is achieved for the image being processed by using a concept called pruning. Pruning also increases the processing speed by a factor of more than two when compared to the conventional method of processing without pruning. An MPEG-2 encoder implemented using this scheme is capable of processing good quality monochrome and color images of sizes up to 1024 × 768 pixels at the rate of 42 and 28 frames per second, respectively, with a compression ratio of over 17:1. The encoder is also capable of working in the fixed pruning level mode with user programmable features.

  6. A comparative study of automatic image segmentation algorithms for target tracking in MR-IGRT.

    Science.gov (United States)

    Feng, Yuan; Kawrakow, Iwan; Olsen, Jeff; Parikh, Parag J; Noel, Camille; Wooten, Omar; Du, Dongsu; Mutic, Sasa; Hu, Yanle

    2016-03-01

    On-board magnetic resonance (MR) image guidance during radiation therapy offers the potential for more accurate treatment delivery. To utilize the real-time image information, a crucial prerequisite is the ability to successfully segment and track regions of interest (ROI). The purpose of this work is to evaluate the performance of different segmentation algorithms using motion images (4 frames per second) acquired using a MR image-guided radiotherapy (MR-IGRT) system. Manual contours of the kidney, bladder, duodenum, and a liver tumor by an experienced radiation oncologist were used as the ground truth for performance evaluation. Besides the manual segmentation, images were automatically segmented using thresholding, fuzzy k-means (FKM), k-harmonic means (KHM), and reaction-diffusion level set evolution (RD-LSE) algorithms, as well as the tissue tracking algorithm provided by the ViewRay treatment planning and delivery system (VR-TPDS). The performance of the five algorithms was evaluated quantitatively by comparing with the manual segmentation using the Dice coefficient and target registration error (TRE) measured as the distance between the centroid of the manual ROI and the centroid of the automatically segmented ROI. All methods were able to successfully segment the bladder and the kidney, but only FKM, KHM, and VR-TPDS were able to segment the liver tumor and the duodenum. The performance of the thresholding, FKM, KHM, and RD-LSE algorithms degraded as the local image contrast decreased, whereas the performance of the VP-TPDS method was nearly independent of local image contrast due to the reference registration algorithm. For segmenting high-contrast images (i.e., kidney), the thresholding method provided the best speed (<1 ms) with a satisfying accuracy (Dice=0.95). When the image contrast was low, the VR-TPDS method had the best automatic contour. Results suggest an image quality determination procedure before segmentation and a combination of different

  7. Automatic Depth Extraction from 2D Images Using a Cluster-Based Learning Framework.

    Science.gov (United States)

    Herrera, Jose L; Del-Blanco, Carlos R; Garcia, Narciso

    2018-07-01

    There has been a significant increase in the availability of 3D players and displays in the last years. Nonetheless, the amount of 3D content has not experimented an increment of such magnitude. To alleviate this problem, many algorithms for converting images and videos from 2D to 3D have been proposed. Here, we present an automatic learning-based 2D-3D image conversion approach, based on the key hypothesis that color images with similar structure likely present a similar depth structure. The presented algorithm estimates the depth of a color query image using the prior knowledge provided by a repository of color + depth images. The algorithm clusters this database attending to their structural similarity, and then creates a representative of each color-depth image cluster that will be used as prior depth map. The selection of the appropriate prior depth map corresponding to one given color query image is accomplished by comparing the structural similarity in the color domain between the query image and the database. The comparison is based on a K-Nearest Neighbor framework that uses a learning procedure to build an adaptive combination of image feature descriptors. The best correspondences determine the cluster, and in turn the associated prior depth map. Finally, this prior estimation is enhanced through a segmentation-guided filtering that obtains the final depth map estimation. This approach has been tested using two publicly available databases, and compared with several state-of-the-art algorithms in order to prove its efficiency.

  8. Automatic luminous reflections detector using global threshold with increased luminosity contrast in images

    Science.gov (United States)

    Silva, Ricardo Petri; Naozuka, Gustavo Taiji; Mastelini, Saulo Martiello; Felinto, Alan Salvany

    2018-01-01

    The incidence of luminous reflections (LR) in captured images can interfere with the color of the affected regions. These regions tend to oversaturate, becoming whitish and, consequently, losing the original color information of the scene. Decision processes that employ images acquired from digital cameras can be impaired by the LR incidence. Such applications include real-time video surgeries, facial, and ocular recognition. This work proposes an algorithm called contrast enhancement of potential LR regions, which is a preprocessing to increase the contrast of potential LR regions, in order to improve the performance of automatic LR detectors. In addition, three automatic detectors were compared with and without the employment of our preprocessing method. The first one is a technique already consolidated in the literature called the Chang-Tseng threshold. We propose two automatic detectors called adapted histogram peak and global threshold. We employed four performance metrics to evaluate the detectors, namely, accuracy, precision, exactitude, and root mean square error. The exactitude metric is developed by this work. Thus, a manually defined reference model was created. The global threshold detector combined with our preprocessing method presented the best results, with an average exactitude rate of 82.47%.

  9. Generating Impact Maps from Automatically Detected Bomb Craters in Aerial Wartime Images Using Marked Point Processes

    Science.gov (United States)

    Kruse, Christian; Rottensteiner, Franz; Hoberg, Thorsten; Ziems, Marcel; Rebke, Julia; Heipke, Christian

    2018-04-01

    The aftermath of wartime attacks is often felt long after the war ended, as numerous unexploded bombs may still exist in the ground. Typically, such areas are documented in so-called impact maps which are based on the detection of bomb craters. This paper proposes a method for the automatic detection of bomb craters in aerial wartime images that were taken during the Second World War. The object model for the bomb craters is represented by ellipses. A probabilistic approach based on marked point processes determines the most likely configuration of objects within the scene. Adding and removing new objects to and from the current configuration, respectively, changing their positions and modifying the ellipse parameters randomly creates new object configurations. Each configuration is evaluated using an energy function. High gradient magnitudes along the border of the ellipse are favored and overlapping ellipses are penalized. Reversible Jump Markov Chain Monte Carlo sampling in combination with simulated annealing provides the global energy optimum, which describes the conformance with a predefined model. For generating the impact map a probability map is defined which is created from the automatic detections via kernel density estimation. By setting a threshold, areas around the detections are classified as contaminated or uncontaminated sites, respectively. Our results show the general potential of the method for the automatic detection of bomb craters and its automated generation of an impact map in a heterogeneous image stock.

  10. Image processing. A system for the automatic sorting of chromosomes; Traitement d'images - Applications au classement des chromosomes

    Energy Technology Data Exchange (ETDEWEB)

    Najai, Amor

    1977-05-27

    The present paper deals with two aspects of the system: - an automata (specialized hardware) dedicated to image processing. Images are digitized, divided into sub-units and computations are carried out on their main parameters. - A software for the automatic recognition and sorting of chromosomes is implemented on a Multi-20 minicomputer, connected to the automata. (author) [French] Nous decrivons un systeme automatique de classification de chromosomes. Il se compose de: - l'A.S.T.I., Automate Specialise de Traitement d'Images permettant de numeriser celles-ci, d'isoler des sous-images, d'effectuer des calculs sur leurs parametres principaux. - Un programme de reconnaissance et de classification automatique des chromosomes implante sur un mini-ordinateur MULTI-20, couple a l'A.S.T.I. (auteur)

  11. Automatic classification and detection of clinically relevant images for diabetic retinopathy

    Science.gov (United States)

    Xu, Xinyu; Li, Baoxin

    2008-03-01

    We proposed a novel approach to automatic classification of Diabetic Retinopathy (DR) images and retrieval of clinically-relevant DR images from a database. Given a query image, our approach first classifies the image into one of the three categories: microaneurysm (MA), neovascularization (NV) and normal, and then it retrieves DR images that are clinically-relevant to the query image from an archival image database. In the classification stage, the query DR images are classified by the Multi-class Multiple-Instance Learning (McMIL) approach, where images are viewed as bags, each of which contains a number of instances corresponding to non-overlapping blocks, and each block is characterized by low-level features including color, texture, histogram of edge directions, and shape. McMIL first learns a collection of instance prototypes for each class that maximizes the Diverse Density function using Expectation- Maximization algorithm. A nonlinear mapping is then defined using the instance prototypes and maps every bag to a point in a new multi-class bag feature space. Finally a multi-class Support Vector Machine is trained in the multi-class bag feature space. In the retrieval stage, we retrieve images from the archival database who bear the same label with the query image, and who are the top K nearest neighbors of the query image in terms of similarity in the multi-class bag feature space. The classification approach achieves high classification accuracy, and the retrieval of clinically-relevant images not only facilitates utilization of the vast amount of hidden diagnostic knowledge in the database, but also improves the efficiency and accuracy of DR lesion diagnosis and assessment.

  12. A complete software application for automatic registration of x-ray mammography and magnetic resonance images

    Energy Technology Data Exchange (ETDEWEB)

    Solves-Llorens, J. A.; Rupérez, M. J., E-mail: mjruperez@labhuman.i3bh.es; Monserrat, C. [LabHuman, Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia (Spain); Feliu, E.; García, M. [Hospital Clínica Benidorm, Avda. Alfonso Puchades, 8, 03501 Benidorm (Alicante) (Spain); Lloret, M. [Hospital Universitari y Politècnic La Fe, Bulevar Sur, 46026 Valencia (Spain)

    2014-08-15

    Purpose: This work presents a complete and automatic software application to aid radiologists in breast cancer diagnosis. The application is a fully automated method that performs a complete registration of magnetic resonance (MR) images and x-ray (XR) images in both directions (from MR to XR and from XR to MR) and for both x-ray mammograms, craniocaudal (CC), and mediolateral oblique (MLO). This new approximation allows radiologists to mark points in the MR images and, without any manual intervention, it provides their corresponding points in both types of XR mammograms and vice versa. Methods: The application automatically segments magnetic resonance images and x-ray images using the C-Means method and the Otsu method, respectively. It compresses the magnetic resonance images in both directions, CC and MLO, using a biomechanical model of the breast that distinguishes the specific biomechanical behavior of each one of its three tissues (skin, fat, and glandular tissue) separately. It makes a projection of both compressions and registers them with the original XR images using affine transformations and nonrigid registration methods. Results: The application has been validated by two expert radiologists. This was carried out through a quantitative validation on 14 data sets in which the Euclidean distance between points marked by the radiologists and the corresponding points obtained by the application were measured. The results showed a mean error of 4.2 ± 1.9 mm for the MRI to CC registration, 4.8 ± 1.3 mm for the MRI to MLO registration, and 4.1 ± 1.3 mm for the CC and MLO to MRI registration. Conclusions: A complete software application that automatically registers XR and MR images of the breast has been implemented. The application permits radiologists to estimate the position of a lesion that is suspected of being a tumor in an imaging modality based on its position in another different modality with a clinically acceptable error. The results show that the

  13. A complete software application for automatic registration of x-ray mammography and magnetic resonance images

    International Nuclear Information System (INIS)

    Solves-Llorens, J. A.; Rupérez, M. J.; Monserrat, C.; Feliu, E.; García, M.; Lloret, M.

    2014-01-01

    Purpose: This work presents a complete and automatic software application to aid radiologists in breast cancer diagnosis. The application is a fully automated method that performs a complete registration of magnetic resonance (MR) images and x-ray (XR) images in both directions (from MR to XR and from XR to MR) and for both x-ray mammograms, craniocaudal (CC), and mediolateral oblique (MLO). This new approximation allows radiologists to mark points in the MR images and, without any manual intervention, it provides their corresponding points in both types of XR mammograms and vice versa. Methods: The application automatically segments magnetic resonance images and x-ray images using the C-Means method and the Otsu method, respectively. It compresses the magnetic resonance images in both directions, CC and MLO, using a biomechanical model of the breast that distinguishes the specific biomechanical behavior of each one of its three tissues (skin, fat, and glandular tissue) separately. It makes a projection of both compressions and registers them with the original XR images using affine transformations and nonrigid registration methods. Results: The application has been validated by two expert radiologists. This was carried out through a quantitative validation on 14 data sets in which the Euclidean distance between points marked by the radiologists and the corresponding points obtained by the application were measured. The results showed a mean error of 4.2 ± 1.9 mm for the MRI to CC registration, 4.8 ± 1.3 mm for the MRI to MLO registration, and 4.1 ± 1.3 mm for the CC and MLO to MRI registration. Conclusions: A complete software application that automatically registers XR and MR images of the breast has been implemented. The application permits radiologists to estimate the position of a lesion that is suspected of being a tumor in an imaging modality based on its position in another different modality with a clinically acceptable error. The results show that the

  14. A comparative study of automatic image segmentation algorithms for target tracking in MR‐IGRT

    Science.gov (United States)

    Feng, Yuan; Kawrakow, Iwan; Olsen, Jeff; Parikh, Parag J.; Noel, Camille; Wooten, Omar; Du, Dongsu; Mutic, Sasa

    2016-01-01

    On‐board magnetic resonance (MR) image guidance during radiation therapy offers the potential for more accurate treatment delivery. To utilize the real‐time image information, a crucial prerequisite is the ability to successfully segment and track regions of interest (ROI). The purpose of this work is to evaluate the performance of different segmentation algorithms using motion images (4 frames per second) acquired using a MR image‐guided radiotherapy (MR‐IGRT) system. Manual contours of the kidney, bladder, duodenum, and a liver tumor by an experienced radiation oncologist were used as the ground truth for performance evaluation. Besides the manual segmentation, images were automatically segmented using thresholding, fuzzy k‐means (FKM), k‐harmonic means (KHM), and reaction‐diffusion level set evolution (RD‐LSE) algorithms, as well as the tissue tracking algorithm provided by the ViewRay treatment planning and delivery system (VR‐TPDS). The performance of the five algorithms was evaluated quantitatively by comparing with the manual segmentation using the Dice coefficient and target registration error (TRE) measured as the distance between the centroid of the manual ROI and the centroid of the automatically segmented ROI. All methods were able to successfully segment the bladder and the kidney, but only FKM, KHM, and VR‐TPDS were able to segment the liver tumor and the duodenum. The performance of the thresholding, FKM, KHM, and RD‐LSE algorithms degraded as the local image contrast decreased, whereas the performance of the VP‐TPDS method was nearly independent of local image contrast due to the reference registration algorithm. For segmenting high‐contrast images (i.e., kidney), the thresholding method provided the best speed (<1 ms) with a satisfying accuracy (Dice=0.95). When the image contrast was low, the VR‐TPDS method had the best automatic contour. Results suggest an image quality determination procedure before segmentation and

  15. Computationally Efficient Automatic Coast Mode Target Tracking Based on Occlusion Awareness in Infrared Images.

    Science.gov (United States)

    Kim, Sohyun; Jang, Gwang-Il; Kim, Sungho; Kim, Junmo

    2018-03-27

    This paper proposes the automatic coast mode tracking of centroid trackers for infrared images to overcome the target occlusion status. The centroid tracking method, using only the brightness information of an image, is still widely used in infrared imaging tracking systems because it is difficult to extract meaningful features from infrared images. However, centroid trackers are likely to lose the track because they are highly vulnerable to screened status by the clutter or background. Coast mode, one of the tracking modes, maintains the servo slew rate with the tracking rate right before the loss of track. The proposed automatic coast mode tracking method makes decisions regarding entering coast mode by the prediction of target occlusion and tries to re-lock the target and resume the tracking after blind time. This algorithm comprises three steps. The first step is the prediction process of the occlusion by checking both matters which have target-likelihood brightness and which may screen the target despite different brightness. The second step is the process making inertial tracking commands to the servo. The last step is the process of re-locking a target based on the target modeling of histogram ratio. The effectiveness of the proposed algorithm is addressed by presenting experimental results based on computer simulation with various test imagery sequences compared to published tracking algorithms. The proposed algorithm is tested under a real environment with a naval electro-optical tracking system (EOTS) and airborne EO/IR system.

  16. Computationally Efficient Automatic Coast Mode Target Tracking Based on Occlusion Awareness in Infrared Images

    Directory of Open Access Journals (Sweden)

    Sohyun Kim

    2018-03-01

    Full Text Available This paper proposes the automatic coast mode tracking of centroid trackers for infrared images to overcome the target occlusion status. The centroid tracking method, using only the brightness information of an image, is still widely used in infrared imaging tracking systems because it is difficult to extract meaningful features from infrared images. However, centroid trackers are likely to lose the track because they are highly vulnerable to screened status by the clutter or background. Coast mode, one of the tracking modes, maintains the servo slew rate with the tracking rate right before the loss of track. The proposed automatic coast mode tracking method makes decisions regarding entering coast mode by the prediction of target occlusion and tries to re-lock the target and resume the tracking after blind time. This algorithm comprises three steps. The first step is the prediction process of the occlusion by checking both matters which have target-likelihood brightness and which may screen the target despite different brightness. The second step is the process making inertial tracking commands to the servo. The last step is the process of re-locking a target based on the target modeling of histogram ratio. The effectiveness of the proposed algorithm is addressed by presenting experimental results based on computer simulation with various test imagery sequences compared to published tracking algorithms. The proposed algorithm is tested under a real environment with a naval electro-optical tracking system (EOTS and airborne EO/IR system.

  17. Development of automatic navigation measuring system using template-matching software in image guided neurosurgery

    International Nuclear Information System (INIS)

    Watanabe, Yohei; Hayashi, Yuichiro; Fujii, Masazumi; Wakabayashi, Toshihiko; Kimura, Miyuki; Tsuzaka, Masatoshi; Sugiura, Akihiro

    2010-01-01

    An image-guided neurosurgery and neuronavigation system based on magnetic resonance imaging has been used as an indispensable tool for resection of brain tumors. Therefore, accuracy of the neuronavigation system, provided by periodic quality assurance (QA), is essential for image-guided neurosurgery. Two types of accuracy index, fiducial registration error (FRE) and target registration error (TRE), have been used to evaluate navigation accuracy. FRE shows navigation accuracy on points that have been registered. On the other hand, TRE shows navigation accuracy on points such as tumor, skin, and fiducial markers. This study shows that TRE is more reliable than FRE. However, calculation of TRE is a time-consuming, subjective task. Software for QA was developed to compute TRE. This software calculates TRE automatically by an image processing technique, such as automatic template matching. TRE was calculated by the software and compared with the results obtained by manual calculation. Using the software made it possible to achieve a reliable QA system. (author)

  18. Automatic lung segmentation in functional SPECT images using active shape models trained on reference lung shapes from CT.

    Science.gov (United States)

    Cheimariotis, Grigorios-Aris; Al-Mashat, Mariam; Haris, Kostas; Aletras, Anthony H; Jögi, Jonas; Bajc, Marika; Maglaveras, Nicolaos; Heiberg, Einar

    2018-02-01

    Image segmentation is an essential step in quantifying the extent of reduced or absent lung function. The aim of this study is to develop and validate a new tool for automatic segmentation of lungs in ventilation and perfusion SPECT images and compare automatic and manual SPECT lung segmentations with reference computed tomography (CT) volumes. A total of 77 subjects (69 patients with obstructive lung disease, and 8 subjects without apparent perfusion of ventilation loss) performed low-dose CT followed by ventilation/perfusion (V/P) SPECT examination in a hybrid gamma camera system. In the training phase, lung shapes from the 57 anatomical low-dose CT images were used to construct two active shape models (right lung and left lung) which were then used for image segmentation. The algorithm was validated in 20 patients, comparing its results to reference delineation of corresponding CT images, and by comparing automatic segmentation to manual delineations in SPECT images. The Dice coefficient between automatic SPECT delineations and manual SPECT delineations were 0.83 ± 0.04% for the right and 0.82 ± 0.05% for the left lung. There was statistically significant difference between reference volumes from CT and automatic delineations for the right (R = 0.53, p = 0.02) and left lung (R = 0.69, p automatic quantification of wide range of measurements.

  19. Automatic analysis of the 2015 Gorkha earthquake aftershock sequence.

    Science.gov (United States)

    Baillard, C.; Lyon-Caen, H.; Bollinger, L.; Rietbrock, A.; Letort, J.; Adhikari, L. B.

    2016-12-01

    The Mw 7.8 Gorkha earthquake, that partially ruptured the Main Himalayan Thrust North of Kathmandu on the 25th April 2015, was the largest and most catastrophic earthquake striking Nepal since the great M8.4 1934 earthquake. This mainshock was followed by multiple aftershocks, among them, two notable events that occurred on the 12th May with magnitudes of 7.3 Mw and 6.3 Mw. Due to these recent events it became essential for the authorities and for the scientific community to better evaluate the seismic risk in the region through a detailed analysis of the earthquake catalog, amongst others, the spatio-temporal distribution of the Gorkha aftershock sequence. Here we complement this first study by doing a microseismic study using seismic data coming from the eastern part of the Nepalese Seismological Center network associated to one broadband station in Everest. Our primary goal is to deliver an accurate catalog of the aftershock sequence. Due to the exceptional number of events detected we performed an automatic picking/locating procedure which can be splitted in 4 steps: 1) Coarse picking of the onsets using a classical STA/LTA picker, 2) phase association of picked onsets to detect and declare seismic events, 3) Kurtosis pick refinement around theoretical arrival times to increase picking and location accuracy and, 4) local magnitude calculation based amplitude of waveforms. This procedure is time efficient ( 1 sec/event), reduces considerably the location uncertainties ( 2 to 5 km errors) and increases the number of events detected compared to manual processing. Indeed, the automatic detection rate is 10 times higher than the manual detection rate. By comparing to the USGS catalog we were able to give a new attenuation law to compute local magnitudes in the region. A detailed analysis of the seismicity shows a clear migration toward the east of the region and a sudden decrease of seismicity 100 km east of Kathmandu which may reveal the presence of a tectonic

  20. Automatic Semiconductor Wafer Image Segmentation for Defect Detection Using Multilevel Thresholding

    Directory of Open Access Journals (Sweden)

    Saad N.H.

    2016-01-01

    Full Text Available Quality control is one of important process in semiconductor manufacturing. A lot of issues trying to be solved in semiconductor manufacturing industry regarding the rate of production with respect to time. In most semiconductor assemblies, a lot of wafers from various processes in semiconductor wafer manufacturing need to be inspected manually using human experts and this process required full concentration of the operators. This human inspection procedure, however, is time consuming and highly subjective. In order to overcome this problem, implementation of machine vision will be the best solution. This paper presents automatic defect segmentation of semiconductor wafer image based on multilevel thresholding algorithm which can be further adopted in machine vision system. In this work, the defect image which is in RGB image at first is converted to the gray scale image. Median filtering then is implemented to enhance the gray scale image. Then the modified multilevel thresholding algorithm is performed to the enhanced image. The algorithm worked in three main stages which are determination of the peak location of the histogram, segmentation the histogram between the peak and determination of first global minimum of histogram that correspond to the threshold value of the image. The proposed approach is being evaluated using defected wafer images. The experimental results shown that it can be used to segment the defect correctly and outperformed other thresholding technique such as Otsu and iterative thresholding.

  1. Automatic Detection of Storm Damages Using High-Altitude Photogrammetric Imaging

    Science.gov (United States)

    Litkey, P.; Nurminen, K.; Honkavaara, E.

    2013-05-01

    The risks of storms that cause damage in forests are increasing due to climate change. Quickly detecting fallen trees, assessing the amount of fallen trees and efficiently collecting them are of great importance for economic and environmental reasons. Visually detecting and delineating storm damage is a laborious and error-prone process; thus, it is important to develop cost-efficient and highly automated methods. Objective of our research project is to investigate and develop a reliable and efficient method for automatic storm damage detection, which is based on airborne imagery that is collected after a storm. The requirements for the method are the before-storm and after-storm surface models. A difference surface is calculated using two DSMs and the locations where significant changes have appeared are automatically detected. In our previous research we used four-year old airborne laser scanning surface model as the before-storm surface. The after-storm DSM was provided from the photogrammetric images using the Next Generation Automatic Terrain Extraction (NGATE) algorithm of Socet Set software. We obtained 100% accuracy in detection of major storm damages. In this investigation we will further evaluate the sensitivity of the storm-damage detection process. We will investigate the potential of national airborne photography, that is collected at no-leaf season, to automatically produce a before-storm DSM using image matching. We will also compare impact of the terrain extraction algorithm to the results. Our results will also promote the potential of national open source data sets in the management of natural disasters.

  2. Automatic anatomy recognition in whole-body PET/CT images

    International Nuclear Information System (INIS)

    Wang, Huiqian; Udupa, Jayaram K.; Odhner, Dewey; Tong, Yubing; Torigian, Drew A.; Zhao, Liming

    2016-01-01

    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 of 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

  3. Automatic anatomy recognition in whole-body PET/CT images

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Huiqian [College of Optoelectronic Engineering, Chongqing University, Chongqing 400044, China and Medical Image Processing Group Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania 19104 (United States); Udupa, Jayaram K., E-mail: jay@mail.med.upenn.edu; Odhner, Dewey; Tong, Yubing; Torigian, Drew A. [Medical Image Processing Group Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania 19104 (United States); Zhao, Liming [Medical Image Processing Group Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania 19104 and Research Center of Intelligent System and Robotics, Chongqing University of Posts and Telecommunications, Chongqing 400065 (China)

    2016-01-15

    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 of 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

  4. AN AUTOMATIC PROCEDURE FOR COMBINING DIGITAL IMAGES AND LASER SCANNER DATA

    Directory of Open Access Journals (Sweden)

    W. Moussa

    2012-07-01

    Full Text Available Besides improving both the geometry and the visual quality of the model, the integration of close-range photogrammetry and terrestrial laser scanning techniques directs at filling gaps in laser scanner point clouds to avoid modeling errors, reconstructing more details in higher resolution and recovering simple structures with less geometric details. Thus, within this paper a flexible approach for the automatic combination of digital images and laser scanner data is presented. Our approach comprises two methods for data fusion. The first method starts by a marker-free registration of digital images based on a point-based environment model (PEM of a scene which stores the 3D laser scanner point clouds associated with intensity and RGB values. The PEM allows the extraction of accurate control information for the direct computation of absolute camera orientations with redundant information by means of accurate space resection methods. In order to use the computed relations between the digital images and the laser scanner data, an extended Helmert (seven-parameter transformation is introduced and its parameters are estimated. Precedent to that, in the second method, the local relative orientation parameters of the camera images are calculated by means of an optimized Structure and Motion (SaM reconstruction method. Then, using the determined transformation parameters results in having absolute oriented images in relation to the laser scanner data. With the resulting absolute orientations we have employed robust dense image reconstruction algorithms to create oriented dense image point clouds, which are automatically combined with the laser scanner data to form a complete detailed representation of a scene. Examples of different data sets are shown and experimental results demonstrate the effectiveness of the presented procedures.

  5. Automatic Tracking Of Remote Sensing Precipitation Data Using Genetic Algorithm Image Registration Based Automatic Morphing: September 1999 Storm Floyd Case Study

    Science.gov (United States)

    Chiu, L.; Vongsaard, J.; El-Ghazawi, T.; Weinman, J.; Yang, R.; Kafatos, M.

    U Due to the poor temporal sampling by satellites, data gaps exist in satellite derived time series of precipitation. This poses a challenge for assimilating rain- fall data into forecast models. To yield a continuous time series, the classic image processing technique of digital image morphing has been used. However, the digital morphing technique was applied manually and that is time consuming. In order to avoid human intervention in the process, an automatic procedure for image morphing is needed for real-time operations. For this purpose, Genetic Algorithm Based Image Registration Automatic Morphing (GRAM) model was developed and tested in this paper. Specifically, automatic morphing technique was integrated with Genetic Algo- rithm and Feature Based Image Metamorphosis technique to fill in data gaps between satellite coverage. The technique was tested using NOWRAD data which are gener- ated from the network of NEXRAD radars. Time series of NOWRAD data from storm Floyd that occurred at the US eastern region on September 16, 1999 for 00:00, 01:00, 02:00,03:00, and 04:00am were used. The GRAM technique was applied to data col- lected at 00:00 and 04:00am. These images were also manually morphed. Images at 01:00, 02:00 and 03:00am were interpolated from the GRAM and manual morphing and compared with the original NOWRAD rainrates. The results show that the GRAM technique outperforms manual morphing. The correlation coefficients between the im- ages generated using manual morphing are 0.905, 0.900, and 0.905 for the images at 01:00, 02:00,and 03:00 am, while the corresponding correlation coefficients are 0.946, 0.911, and 0.913, respectively, based on the GRAM technique. Index terms ­ Remote Sensing, Image Registration, Hydrology, Genetic Algorithm, Morphing, NEXRAD

  6. NEAR REAL-TIME AUTOMATIC MARINE VESSEL DETECTION ON OPTICAL SATELLITE IMAGES

    Directory of Open Access Journals (Sweden)

    G. Máttyus

    2013-05-01

    Full Text Available Vessel monitoring and surveillance is important for maritime safety and security, environment protection and border control. Ship monitoring systems based on Synthetic-aperture Radar (SAR satellite images are operational. On SAR images the ships made of metal with sharp edges appear as bright dots and edges, therefore they can be well distinguished from the water. Since the radar is independent from the sun light and can acquire images also by cloudy weather and rain, it provides a reliable service. Vessel detection from spaceborne optical images (VDSOI can extend the SAR based systems by providing more frequent revisit times and overcoming some drawbacks of the SAR images (e.g. lower spatial resolution, difficult human interpretation. Optical satellite images (OSI can have a higher spatial resolution thus enabling the detection of smaller vessels and enhancing the vessel type classification. The human interpretation of an optical image is also easier than as of SAR image. In this paper I present a rapid automatic vessel detection method which uses pattern recognition methods, originally developed in the computer vision field. In the first step I train a binary classifier from image samples of vessels and background. The classifier uses simple features which can be calculated very fast. For the detection the classifier is slided along the image in various directions and scales. The detector has a cascade structure which rejects most of the background in the early stages which leads to faster execution. The detections are grouped together to avoid multiple detections. Finally the position, size(i.e. length and width and heading of the vessels is extracted from the contours of the vessel. The presented method is parallelized, thus it runs fast (in minutes for 16000 × 16000 pixels image on a multicore computer, enabling near real-time applications, e.g. one hour from image acquisition to end user.

  7. Near Real-Time Automatic Marine Vessel Detection on Optical Satellite Images

    Science.gov (United States)

    Máttyus, G.

    2013-05-01

    Vessel monitoring and surveillance is important for maritime safety and security, environment protection and border control. Ship monitoring systems based on Synthetic-aperture Radar (SAR) satellite images are operational. On SAR images the ships made of metal with sharp edges appear as bright dots and edges, therefore they can be well distinguished from the water. Since the radar is independent from the sun light and can acquire images also by cloudy weather and rain, it provides a reliable service. Vessel detection from spaceborne optical images (VDSOI) can extend the SAR based systems by providing more frequent revisit times and overcoming some drawbacks of the SAR images (e.g. lower spatial resolution, difficult human interpretation). Optical satellite images (OSI) can have a higher spatial resolution thus enabling the detection of smaller vessels and enhancing the vessel type classification. The human interpretation of an optical image is also easier than as of SAR image. In this paper I present a rapid automatic vessel detection method which uses pattern recognition methods, originally developed in the computer vision field. In the first step I train a binary classifier from image samples of vessels and background. The classifier uses simple features which can be calculated very fast. For the detection the classifier is slided along the image in various directions and scales. The detector has a cascade structure which rejects most of the background in the early stages which leads to faster execution. The detections are grouped together to avoid multiple detections. Finally the position, size(i.e. length and width) and heading of the vessels is extracted from the contours of the vessel. The presented method is parallelized, thus it runs fast (in minutes for 16000 × 16000 pixels image) on a multicore computer, enabling near real-time applications, e.g. one hour from image acquisition to end user.

  8. A Review of Automatic Methods Based on Image Processing Techniques for Tuberculosis Detection from Microscopic Sputum Smear Images.

    Science.gov (United States)

    Panicker, Rani Oomman; Soman, Biju; Saini, Gagan; Rajan, Jeny

    2016-01-01

    Tuberculosis (TB) is an infectious disease caused by the bacteria Mycobacterium tuberculosis. It primarily affects the lungs, but it can also affect other parts of the body. TB remains one of the leading causes of death in developing countries, and its recent resurgences in both developed and developing countries warrant global attention. The number of deaths due to TB is very high (as per the WHO report, 1.5 million died in 2013), although most are preventable if diagnosed early and treated. There are many tools for TB detection, but the most widely used one is sputum smear microscopy. It is done manually and is often time consuming; a laboratory technician is expected to spend at least 15 min per slide, limiting the number of slides that can be screened. Many countries, including India, have a dearth of properly trained technicians, and they often fail to detect TB cases due to the stress of a heavy workload. Automatic methods are generally considered as a solution to this problem. Attempts have been made to develop automatic approaches to identify TB bacteria from microscopic sputum smear images. In this paper, we provide a review of automatic methods based on image processing techniques published between 1998 and 2014. The review shows that the accuracy of algorithms for the automatic detection of TB increased significantly over the years and gladly acknowledges that commercial products based on published works also started appearing in the market. This review could be useful to researchers and practitioners working in the field of TB automation, providing a comprehensive and accessible overview of methods of this field of research.

  9. Using image processing technology and mathematical algorithm in the automatic selection of vocal cord opening and closing images from the larynx endoscopy video.

    Science.gov (United States)

    Kuo, Chung-Feng Jeffrey; Chu, Yueng-Hsiang; Wang, Po-Chun; Lai, Chun-Yu; Chu, Wen-Lin; Leu, Yi-Shing; Wang, Hsing-Won

    2013-12-01

    The human larynx is an important organ for voice production and respiratory mechanisms. The vocal cord is approximated for voice production and open for breathing. The videolaryngoscope is widely used for vocal cord examination. At present, physicians usually diagnose vocal cord diseases by manually selecting the image of the vocal cord opening to the largest extent (abduction), thus maximally exposing the vocal cord lesion. On the other hand, the severity of diseases such as vocal palsy, atrophic vocal cord is largely dependent on the vocal cord closing to the smallest extent (adduction). Therefore, diseases can be assessed by the image of the vocal cord opening to the largest extent, and the seriousness of breathy voice is closely correlated to the gap between vocal cords when closing to the smallest extent. The aim of the study was to design an automatic vocal cord image selection system to improve the conventional selection process by physicians and enhance diagnosis efficiency. Also, due to the unwanted fuzzy images resulting from examination process caused by human factors as well as the non-vocal cord images, texture analysis is added in this study to measure image entropy to establish a screening and elimination system to effectively enhance the accuracy of selecting the image of the vocal cord closing to the smallest extent. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

  10. Trends of Science Education Research: An Automatic Content Analysis

    Science.gov (United States)

    Chang, Yueh-Hsia; Chang, Chun-Yen; Tseng, Yuen-Hsien

    2010-08-01

    This study used scientometric methods to conduct an automatic content analysis on the development trends of science education research from the published articles in the four journals of International Journal of Science Education, Journal of Research in Science Teaching, Research in Science Education, and Science Education from 1990 to 2007. The multi-stage clustering technique was employed to investigate with what topics, to what development trends, and from whose contribution that the journal publications constructed as a science education research field. This study found that the research topic of Conceptual Change & Concept Mapping was the most studied topic, although the number of publications has slightly declined in the 2000's. The studies in the themes of Professional Development, Nature of Science and Socio-Scientific Issues, and Conceptual Chang and Analogy were found to be gaining attention over the years. This study also found that, embedded in the most cited references, the supporting disciplines and theories of science education research are constructivist learning, cognitive psychology, pedagogy, and philosophy of science.

  11. Automatic analysis of gamma spectra using a desk computer

    International Nuclear Information System (INIS)

    Rocca, H.C.

    1976-10-01

    A code for the analysis of gamma spectra obtained with a Ge(Li) detector was developed for use with a desk computer (Hewlett-Packard Model 9810 A). The process is performed in a totally automatic way, data are conveniently smoothed and the background is generated by a convolutive equation. A calibration of the equipment with well-known standard sources gives the necessary data for adjusting a third degree equation by minimun squares, relating the energy with the peak position. Criteria are given for determining if certain groups of values constitute or not a peak or if it is a double line. All the peaks are adjusted to a gaussian curve and if necessary decomposed in their components. Data entry is by punched tape, ASCII Code. An alf-numeric printer provides (a) the position of the peak and its energy, (b) its resolution if it is larger than expected, (c) the area of the peak with its statistic error determined by the method of Wasson. As option, the complete spectra with the determined background can be plotted. (author) [es

  12. An automatic registration method for frameless stereotaxy, image guided surgery, and enhanced reality visualization

    International Nuclear Information System (INIS)

    Grimson, W.E.L.; Lozano-Perez, T.; White, S.J.; Wells, W.M. III; Kikinis, R.

    1996-01-01

    There is a need for frameless guidance systems to help surgeons plan the exact location for incisions, to define the margins of tumors, and to precisely identify locations of neighboring critical structures. The authors have developed an automatic technique for registering clinical data, such as segmented magnetic resonance imaging (MRI) or computed tomography (CT) reconstructions, with any view of the patient on the operating table. They demonstrate on the specific example of neurosurgery. The method enables a visual mix of live video of the patient and the segmented three-dimensional (3-D) MRI or CT model. This supports enhanced reality techniques for planning and guiding neurosurgical procedures and allows them to interactively view extracranial or intracranial structures nonintrusively. Extensions of the method include image guided biopsies, focused therapeutic procedures, and clinical studies involving change detection over time sequences of images

  13. An automatic method to determine cutoff frequency based on image power spectrum

    International Nuclear Information System (INIS)

    Beis, J.S.; Vancouver Hospital and Health Sciences Center, British Columbia; Celler, A.; Barney, J.S.

    1995-01-01

    The authors present an algorithm for automatically choosing filter cutoff frequency (F c ) using the power spectrum of the projections. The method is based on the assumption that the expectation of the image power spectrum is the sum of the expectation of the blurred object power spectrum (dominant at low frequencies) plus a constant value due to Poisson noise. By considering the discrete components of the noise-dominated high-frequency spectrum as a Gaussian distribution N(μ,σ), the Student t-test determines F c as the highest frequency for which the image frequency components are unlikely to be drawn from N (μ,σ). The method is general and can be applied to any filter. In this work, the authors tested the approach using the Metz restoration filter on simulated, phantom, and patient data with good results. Quantitative performance of the technique was evaluated by plotting recovery coefficient (RC) versus NMSE of reconstructed images

  14. Developing an Intelligent Automatic Appendix Extraction Method from Ultrasonography Based on Fuzzy ART and Image Processing

    Directory of Open Access Journals (Sweden)

    Kwang Baek Kim

    2015-01-01

    Full Text Available Ultrasound examination (US does a key role in the diagnosis and management of the patients with clinically suspected appendicitis which is the most common abdominal surgical emergency. Among the various sonographic findings of appendicitis, outer diameter of the appendix is most important. Therefore, clear delineation of the appendix on US images is essential. In this paper, we propose a new intelligent method to extract appendix automatically from abdominal sonographic images as a basic building block of developing such an intelligent tool for medical practitioners. Knowing that the appendix is located at the lower organ area below the bottom fascia line, we conduct a series of image processing techniques to find the fascia line correctly. And then we apply fuzzy ART learning algorithm to the organ area in order to extract appendix accurately. The experiment verifies that the proposed method is highly accurate (successful in 38 out of 40 cases in extracting appendix.

  15. Automatic segmentation of male pelvic anatomy on computed tomography images: a comparison with multiple observers in the context of a multicentre clinical trial

    International Nuclear Information System (INIS)

    Geraghty, John P; Grogan, Garry; Ebert, Martin A

    2013-01-01

    This study investigates the variation in segmentation of several pelvic anatomical structures on computed tomography (CT) between multiple observers and a commercial automatic segmentation method, in the context of quality assurance and evaluation during a multicentre clinical trial. CT scans of two prostate cancer patients (‘benchmarking cases’), one high risk (HR) and one intermediate risk (IR), were sent to multiple radiotherapy centres for segmentation of prostate, rectum and bladder structures according to the TROG 03.04 “RADAR” trial protocol definitions. The same structures were automatically segmented using iPlan software for the same two patients, allowing structures defined by automatic segmentation to be quantitatively compared with those defined by multiple observers. A sample of twenty trial patient datasets were also used to automatically generate anatomical structures for quantitative comparison with structures defined by individual observers for the same datasets. There was considerable agreement amongst all observers and automatic segmentation of the benchmarking cases for bladder (mean spatial variations < 0.4 cm across the majority of image slices). Although there was some variation in interpretation of the superior-inferior (cranio-caudal) extent of rectum, human-observer contours were typically within a mean 0.6 cm of automatically-defined contours. Prostate structures were more consistent for the HR case than the IR case with all human observers segmenting a prostate with considerably more volume (mean +113.3%) than that automatically segmented. Similar results were seen across the twenty sample datasets, with disagreement between iPlan and observers dominant at the prostatic apex and superior part of the rectum, which is consistent with observations made during quality assurance reviews during the trial. This study has demonstrated quantitative analysis for comparison of multi-observer segmentation studies. For automatic segmentation

  16. Automatic screening and classification of diabetic retinopathy and maculopathy using fuzzy image processing.

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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.

  18. Automatic classification of ovarian cancer types from cytological images using deep convolutional neural networks.

    Science.gov (United States)

    Wu, Miao; Yan, Chuanbo; Liu, Huiqiang; Liu, Qian

    2018-06-29

    Ovarian cancer is one of the most common gynecologic malignancies. Accurate classification of ovarian cancer types (serous carcinoma, mucous carcinoma, endometrioid carcinoma, transparent cell carcinoma) is an essential part in the different diagnosis. Computer-aided diagnosis (CADx) can provide useful advice for pathologists to determine the diagnosis correctly. In our study, we employed a Deep Convolutional Neural Networks (DCNN) based on AlexNet to automatically classify the different types of ovarian cancers from cytological images. The DCNN consists of five convolutional layers, three max pooling layers, and two full reconnect layers. Then we trained the model by two group input data separately, one was original image data and the other one was augmented image data including image enhancement and image rotation. The testing results are obtained by the method of 10-fold cross-validation, showing that the accuracy of classification models has been improved from 72.76 to 78.20% by using augmented images as training data. The developed scheme was useful for classifying ovarian cancers from cytological images. © 2018 The Author(s).

  19. A semi-automatic method for extracting thin line structures in images as rooted tree network

    Energy Technology Data Exchange (ETDEWEB)

    Brazzini, Jacopo [Los Alamos National Laboratory; Dillard, Scott [Los Alamos National Laboratory; Soille, Pierre [EC - JRC

    2010-01-01

    This paper addresses the problem of semi-automatic extraction of line networks in digital images - e.g., road or hydrographic networks in satellite images, blood vessels in medical images, robust. For that purpose, we improve a generic method derived from morphological and hydrological concepts and consisting in minimum cost path estimation and flow simulation. While this approach fully exploits the local contrast and shape of the network, as well as its arborescent nature, we further incorporate local directional information about the structures in the image. Namely, an appropriate anisotropic metric is designed by using both the characteristic features of the target network and the eigen-decomposition of the gradient structure tensor of the image. Following, the geodesic propagation from a given seed with this metric is combined with hydrological operators for overland flow simulation to extract the line network. The algorithm is demonstrated for the extraction of blood vessels in a retina image and of a river network in a satellite image.

  20. DAF: differential ACE filtering image quality assessment by automatic color equalization

    Science.gov (United States)

    Ouni, S.; Chambah, M.; Saint-Jean, C.; Rizzi, A.

    2008-01-01

    Ideally, a quality assessment system would perceive and measure image or video impairments just like a human being. But in reality, objective quality metrics do not necessarily correlate well with perceived quality [1]. Plus, some measures assume that there exists a reference in the form of an "original" to compare to, which prevents their usage in digital restoration field, where often there is no reference to compare to. That is why subjective evaluation is the most used and most efficient approach up to now. But subjective assessment is expensive, time consuming and does not respond, hence, to the economic requirements [2,3]. Thus, reliable automatic methods for visual quality assessment are needed in the field of digital film restoration. The ACE method, for Automatic Color Equalization [4,6], is an algorithm for digital images unsupervised enhancement. It is based on a new computational approach that tries to model the perceptual response of our vision system merging the Gray World and White Patch equalization mechanisms in a global and local way. Like our vision system ACE is able to adapt to widely varying lighting conditions, and to extract visual information from the environment efficaciously. Moreover ACE can be run in an unsupervised manner. Hence it is very useful as a digital film restoration tool since no a priori information is available. In this paper we deepen the investigation of using the ACE algorithm as a basis for a reference free image quality evaluation. This new metric called DAF for Differential ACE Filtering [7] is an objective quality measure that can be used in several image restoration and image quality assessment systems. In this paper, we compare on different image databases, the results obtained with DAF and with some subjective image quality assessments (Mean Opinion Score MOS as measure of perceived image quality). We study also the correlation between objective measure and MOS. In our experiments, we have used for the first image

  1. AUTOMATIC TEXTURE RECONSTRUCTION OF 3D CITY MODEL FROM OBLIQUE IMAGES

    Directory of Open Access Journals (Sweden)

    J. Kang

    2016-06-01

    Full Text Available In recent years, the photorealistic 3D city models are increasingly important in various geospatial applications related to virtual city tourism, 3D GIS, urban planning, real-estate management. Besides the acquisition of high-precision 3D geometric data, texture reconstruction is also a crucial step for generating high-quality and visually realistic 3D models. However, most of the texture reconstruction approaches are probably leading to texture fragmentation and memory inefficiency. In this paper, we introduce an automatic framework of texture reconstruction to generate textures from oblique images for photorealistic visualization. Our approach include three major steps as follows: mesh parameterization, texture atlas generation and texture blending. Firstly, mesh parameterization procedure referring to mesh segmentation and mesh unfolding is performed to reduce geometric distortion in the process of mapping 2D texture to 3D model. Secondly, in the texture atlas generation step, the texture of each segmented region in texture domain is reconstructed from all visible images with exterior orientation and interior orientation parameters. Thirdly, to avoid color discontinuities at boundaries between texture regions, the final texture map is generated by blending texture maps from several corresponding images. We evaluated our texture reconstruction framework on a dataset of a city. The resulting mesh model can get textured by created texture without resampling. Experiment results show that our method can effectively mitigate the occurrence of texture fragmentation. It is demonstrated that the proposed framework is effective and useful for automatic texture reconstruction of 3D city model.

  2. Automatic Diabetic Macular Edema Detection in Fundus Images Using Publicly Available Datasets

    Energy Technology Data Exchange (ETDEWEB)

    Giancardo, Luca [ORNL; Meriaudeau, Fabrice [ORNL; Karnowski, Thomas Paul [ORNL; Li, Yaquin [University of Tennessee, Knoxville (UTK); Garg, Seema [University of North Carolina; Tobin Jr, Kenneth William [ORNL; Chaum, Edward [University of Tennessee, Knoxville (UTK)

    2011-01-01

    Diabetic macular edema (DME) is a common vision threatening complication of diabetic retinopathy. In a large scale screening environment DME can be assessed by detecting exudates (a type of bright lesions) in fundus images. In this work, we introduce a new methodology for diagnosis of DME using a novel set of features based on colour, wavelet decomposition and automatic lesion segmentation. These features are employed to train a classifier able to automatically diagnose DME. We present a new publicly available dataset with ground-truth data containing 169 patients from various ethnic groups and levels of DME. This and other two publicly available datasets are employed to evaluate our algorithm. We are able to achieve diagnosis performance comparable to retina experts on the MESSIDOR (an independently labelled dataset with 1200 images) with cross-dataset testing. Our algorithm is robust to segmentation uncertainties, does not need ground truth at lesion level, and is very fast, generating a diagnosis on an average of 4.4 seconds per image on an 2.6 GHz platform with an unoptimised Matlab implementation.

  3. Automatic determination of the size of elliptical nanoparticles from AFM images

    International Nuclear Information System (INIS)

    Sedlář, Jiří; Zitová, Barbara; Kopeček, Jaromír; Flusser, Jan; Todorciuc, Tatiana; Kratochvílová, Irena

    2013-01-01

    The objective of this work was to develop an accurate method for automatic determination of the size of elliptical nanoparticles from atomic force microscopy (AFM) images that would yield results consistent with results of manual measurements by experts. The proposed method was applied on phenylpyridyldiketopyrrolopyrrole (PPDP), a granular organic material with a wide scale of application and highly sensitive particle-size properties. A PPDP layer consists of similarly sized elliptical particles (c. 100 nm × 50 nm) and its properties can be estimated from the average length and width of the particles. The developed method is based on segmentation of salient particles by the watershed transform and approximation of their shapes by ellipses computed by image moments; it estimates the lengths and widths of the particles by the major and minor axes, respectively, of the corresponding ellipses. Its results proved to be consistent with results of manual measurements by a trained expert. The comparison showed that the developed method could be used in practice for precise automatic measurement of PPDP particles in AFM images

  4. Effects of image compression and degradation on an automatic diabetic retinopathy screening algorithm

    Science.gov (United States)

    Agurto, C.; Barriga, S.; Murray, V.; Pattichis, M.; Soliz, P.

    2010-03-01

    Diabetic retinopathy (DR) is one of the leading causes of blindness among adult Americans. Automatic methods for detection of the disease have been developed in recent years, most of them addressing the segmentation of bright and red lesions. In this paper we present an automatic DR screening system that does approach the problem through the segmentation of features. The algorithm determines non-diseased retinal images from those with pathology based on textural features obtained using multiscale Amplitude Modulation-Frequency Modulation (AM-FM) decompositions. The decomposition is represented as features that are the inputs to a classifier. The algorithm achieves 0.88 area under the ROC curve (AROC) for a set of 280 images from the MESSIDOR database. The algorithm is then used to analyze the effects of image compression and degradation, which will be present in most actual clinical or screening environments. Results show that the algorithm is insensitive to illumination variations, but high rates of compression and large blurring effects degrade its performance.

  5. Automatic localization of IASLC-defined mediastinal lymph node stations on CT images using fuzzy models

    Science.gov (United States)

    Matsumoto, Monica M. S.; Beig, Niha G.; Udupa, Jayaram K.; Archer, Steven; Torigian, Drew A.

    2014-03-01

    Lung cancer is associated with the highest cancer mortality rates among men and women in the United States. The accurate and precise identification of the lymph node stations on computed tomography (CT) images is important for staging disease and potentially for prognosticating outcome in patients with lung cancer, as well as for pretreatment planning and response assessment purposes. To facilitate a standard means of referring to lymph nodes, the International Association for the Study of Lung Cancer (IASLC) has recently proposed a definition of the different lymph node stations and zones in the thorax. However, nodal station identification is typically performed manually by visual assessment in clinical radiology. This approach leaves room for error due to the subjective and potentially ambiguous nature of visual interpretation, and is labor intensive. We present a method of automatically recognizing the mediastinal IASLC-defined lymph node stations by modifying a hierarchical fuzzy modeling approach previously developed for body-wide automatic anatomy recognition (AAR) in medical imagery. Our AAR-lymph node (AAR-LN) system follows the AAR methodology and consists of two steps. In the first step, the various lymph node stations are manually delineated on a set of CT images following the IASLC definitions. These delineations are then used to build a fuzzy hierarchical model of the nodal stations which are considered as 3D objects. In the second step, the stations are automatically located on any given CT image of the thorax by using the hierarchical fuzzy model and object recognition algorithms. Based on 23 data sets used for model building, 22 independent data sets for testing, and 10 lymph node stations, a mean localization accuracy of within 1-6 voxels has been achieved by the AAR-LN system.

  6. Automatic adventitious respiratory sound analysis: A systematic review.

    Directory of Open Access Journals (Sweden)

    Renard Xaviero Adhi Pramono

    Full Text Available Automatic detection or classification of adventitious sounds is useful to assist physicians in diagnosing or monitoring diseases such as asthma, Chronic Obstructive Pulmonary Disease (COPD, and pneumonia. While computerised respiratory sound analysis, specifically for the detection or classification of adventitious sounds, has recently been the focus of an increasing number of studies, a standardised approach and comparison has not been well established.To provide a review of existing algorithms for the detection or classification of adventitious respiratory sounds. This systematic review provides a complete summary of methods used in the literature to give a baseline for future works.A systematic review of English articles published between 1938 and 2016, searched using the Scopus (1938-2016 and IEEExplore (1984-2016 databases. Additional articles were further obtained by references listed in the articles found. Search terms included adventitious sound detection, adventitious sound classification, abnormal respiratory sound detection, abnormal respiratory sound classification, wheeze detection, wheeze classification, crackle detection, crackle classification, rhonchi detection, rhonchi classification, stridor detection, stridor classification, pleural rub detection, pleural rub classification, squawk detection, and squawk classification.Only articles were included that focused on adventitious sound detection or classification, based on respiratory sounds, with performance reported and sufficient information provided to be approximately repeated.Investigators extracted data about the adventitious sound type analysed, approach and level of analysis, instrumentation or data source, location of sensor, amount of data obtained, data management, features, methods, and performance achieved.A total of 77 reports from the literature were included in this review. 55 (71.43% of the studies focused on wheeze, 40 (51.95% on crackle, 9 (11.69% on stridor, 9

  7. Automatic medical image annotation and keyword-based image retrieval using relevance feedback.

    Science.gov (United States)

    Ko, Byoung Chul; Lee, JiHyeon; Nam, Jae-Yeal

    2012-08-01

    This paper presents novel multiple keywords annotation for medical images, keyword-based medical image retrieval, and relevance feedback method for image retrieval for enhancing image retrieval performance. For semantic keyword annotation, this study proposes a novel medical image classification method combining local wavelet-based center symmetric-local binary patterns with random forests. For keyword-based image retrieval, our retrieval system use the confidence score that is assigned to each annotated keyword by combining probabilities of random forests with predefined body relation graph. To overcome the limitation of keyword-based image retrieval, we combine our image retrieval system with relevance feedback mechanism based on visual feature and pattern classifier. Compared with other annotation and relevance feedback algorithms, the proposed method shows both improved annotation performance and accurate retrieval results.

  8. Construction of automatic photographic system for after-glow colour images (AGCI)

    International Nuclear Information System (INIS)

    Kawamura, Kousei; Hashimoto, Tetsuo.

    1995-01-01

    An automatic photographic system of the after-glow colour images (AGCI), which give very useful information related to crystal defects and impurities in white inorganic materials, has been developed. The present system consists of a combination of a photographic part installed in a dark bag with a control personal computer through an interface board. Thus, the photographic procedure of the successive and clear AGCIs could be accomplished from the direct contact of a colour film with an X-rays irradiated rock slices for desired exposure periods and interval times. By using this system, some AGCIs of ammonite fossil showed interesting changes of orange patterns due to structural fossil calcite. (author)

  9. An image-based automatic recognition method for the flowering stage of maize

    Science.gov (United States)

    Yu, Zhenghong; Zhou, Huabing; Li, Cuina

    2018-03-01

    In this paper, we proposed an image-based approach for automatic recognizing the flowering stage of maize. A modified HOG/SVM detection framework is first adopted to detect the ears of maize. Then, we use low-rank matrix recovery technology to precisely extract the ears at pixel level. At last, a new feature called color gradient histogram, as an indicator, is proposed to determine the flowering stage. Comparing experiment has been carried out to testify the validity of our method and the results indicate that our method can meet the demand for practical observation.

  10. Wide-Field Imaging Telescope-0 (WIT0) with automatic observing system

    Science.gov (United States)

    Ji, Tae-Geun; Byeon, Seoyeon; Lee, Hye-In; Park, Woojin; Lee, Sang-Yun; Hwang, Sungyong; Choi, Changsu; Gibson, Coyne Andrew; Kuehne, John W.; Prochaska, Travis; Marshall, Jennifer L.; Im, Myungshin; Pak, Soojong

    2018-01-01

    We introduce Wide-Field Imaging Telescope-0 (WIT0), with an automatic observing system. It is developed for monitoring the variabilities of many sources at a time, e.g. young stellar objects and active galactic nuclei. It can also find the locations of transient sources such as a supernova or gamma-ray bursts. In 2017 February, we installed the wide-field 10-inch telescope (Takahashi CCA-250) as a piggyback system on the 30-inch telescope at the McDonald Observatory in Texas, US. The 10-inch telescope has a 2.35 × 2.35 deg field-of-view with a 4k × 4k CCD Camera (FLI ML16803). To improve the observational efficiency of the system, we developed a new automatic observing software, KAOS30 (KHU Automatic Observing Software for McDonald 30-inch telescope), which was developed by Visual C++ on the basis of a windows operating system. The software consists of four control packages: the Telescope Control Package (TCP), the Data Acquisition Package (DAP), the Auto Focus Package (AFP), and the Script Mode Package (SMP). Since it also supports the instruments that are using the ASCOM driver, the additional hardware installations become quite simplified. We commissioned KAOS30 in 2017 August and are in the process of testing. Based on the WIT0 experiences, we will extend KAOS30 to control multiple telescopes in future projects.

  11. Image sequence analysis

    CERN Document Server

    1981-01-01

    The processing of image sequences has a broad spectrum of important applica­ tions including target tracking, robot navigation, bandwidth compression of TV conferencing video signals, studying the motion of biological cells using microcinematography, cloud tracking, and highway traffic monitoring. Image sequence processing involves a large amount of data. However, because of the progress in computer, LSI, and VLSI technologies, we have now reached a stage when many useful processing tasks can be done in a reasonable amount of time. As a result, research and development activities in image sequence analysis have recently been growing at a rapid pace. An IEEE Computer Society Workshop on Computer Analysis of Time-Varying Imagery was held in Philadelphia, April 5-6, 1979. A related special issue of the IEEE Transactions on Pattern Anal­ ysis and Machine Intelligence was published in November 1980. The IEEE Com­ puter magazine has also published a special issue on the subject in 1981. The purpose of this book ...

  12. Development of an automatic prompt gamma-ray activation analysis system

    International Nuclear Information System (INIS)

    Osawa, Takahito

    2013-01-01

    An automatic prompt gamma-ray activation analysis system was developed and installed at the Japan Research Reactor No. 3 Modified (JRR-3M). The main control software, referred to as AutoPGA, was developed using LabVIEW 2011 and the hand-made program can control all functions of the analytical system. The core of the new system is an automatic sample exchanger and measurement system with several additional automatic control functions integrated into the system. Up to fourteen samples can be automatically measured by the system. (author)

  13. Automatic Pedestrian Crossing Detection and Impairment Analysis Based on Mobile Mapping System

    Science.gov (United States)

    Liu, X.; Zhang, Y.; Li, Q.

    2017-09-01

    Pedestrian crossing, as an important part of transportation infrastructures, serves to secure pedestrians' lives and possessions and keep traffic flow in order. As a prominent feature in the street scene, detection of pedestrian crossing contributes to 3D road marking reconstruction and diminishing the adverse impact of outliers in 3D street scene reconstruction. Since pedestrian crossing is subject to wearing and tearing from heavy traffic flow, it is of great imperative to monitor its status quo. On this account, an approach of automatic pedestrian crossing detection using images from vehicle-based Mobile Mapping System is put forward and its defilement and impairment are analyzed in this paper. Firstly, pedestrian crossing classifier is trained with low recall rate. Then initial detections are refined by utilizing projection filtering, contour information analysis, and monocular vision. Finally, a pedestrian crossing detection and analysis system with high recall rate, precision and robustness will be achieved. This system works for pedestrian crossing detection under different situations and light conditions. It can recognize defiled and impaired crossings automatically in the meanwhile, which facilitates monitoring and maintenance of traffic facilities, so as to reduce potential traffic safety problems and secure lives and property.

  14. AUTOMATIC PEDESTRIAN CROSSING DETECTION AND IMPAIRMENT ANALYSIS BASED ON MOBILE MAPPING SYSTEM

    Directory of Open Access Journals (Sweden)

    X. Liu

    2017-09-01

    Full Text Available Pedestrian crossing, as an important part of transportation infrastructures, serves to secure pedestrians’ lives and possessions and keep traffic flow in order. As a prominent feature in the street scene, detection of pedestrian crossing contributes to 3D road marking reconstruction and diminishing the adverse impact of outliers in 3D street scene reconstruction. Since pedestrian crossing is subject to wearing and tearing from heavy traffic flow, it is of great imperative to monitor its status quo. On this account, an approach of automatic pedestrian crossing detection using images from vehicle-based Mobile Mapping System is put forward and its defilement and impairment are analyzed in this paper. Firstly, pedestrian crossing classifier is trained with low recall rate. Then initial detections are refined by utilizing projection filtering, contour information analysis, and monocular vision. Finally, a pedestrian crossing detection and analysis system with high recall rate, precision and robustness will be achieved. This system works for pedestrian crossing detection under different situations and light conditions. It can recognize defiled and impaired crossings automatically in the meanwhile, which facilitates monitoring and maintenance of traffic facilities, so as to reduce potential traffic safety problems and secure lives and property.

  15. CURRENT STATE ANALYSIS OF AUTOMATIC BLOCK SYSTEM DEVICES, METHODS OF ITS SERVICE AND MONITORING

    Directory of Open Access Journals (Sweden)

    A. M. Beznarytnyy

    2014-01-01

    Full Text Available Purpose. Development of formalized description of automatic block system of numerical code based on the analysis of characteristic failures of automatic block system and procedure of its maintenance. Methodology. For this research a theoretical and analytical methods have been used. Findings. Typical failures of the automatic block systems were analyzed, as well as basic reasons of failure occur were found out. It was determined that majority of failures occurs due to defects of the maintenance system. Advantages and disadvantages of the current service technology of automatic block system were analyzed. Works that can be automatized by means of technical diagnostics were found out. Formal description of the numerical code of automatic block system as a graph in the state space of the system was carried out. Originality. The state graph of the numerical code of automatic block system that takes into account gradual transition from the serviceable condition to the loss of efficiency was offered. That allows selecting diagnostic information according to attributes and increasing the effectiveness of recovery operations in the case of a malfunction. Practical value. The obtained results of analysis and proposed the state graph can be used as the basis for the development of new means of diagnosing devices for automatic block system, which in turn will improve the efficiency and service of automatic block system devices in general.

  16. Automatic cortical surface reconstruction of high-resolution T1 echo planar imaging data.

    Science.gov (United States)

    Renvall, Ville; Witzel, Thomas; Wald, Lawrence L; Polimeni, Jonathan R

    2016-07-01

    Echo planar imaging (EPI) is the method of choice for the majority of functional magnetic resonance imaging (fMRI), yet EPI is prone to geometric distortions and thus misaligns with conventional anatomical reference data. The poor geometric correspondence between functional and anatomical data can lead to severe misplacements and corruption of detected activation patterns. However, recent advances in imaging technology have provided EPI data with increasing quality and resolution. Here we present a framework for deriving cortical surface reconstructions directly from high-resolution EPI-based reference images that provide anatomical models exactly geometric distortion-matched to the functional data. Anatomical EPI data with 1mm isotropic voxel size were acquired using a fast multiple inversion recovery time EPI sequence (MI-EPI) at 7T, from which quantitative T1 maps were calculated. Using these T1 maps, volumetric data mimicking the tissue contrast of standard anatomical data were synthesized using the Bloch equations, and these T1-weighted data were automatically processed using FreeSurfer. The spatial alignment between T2(⁎)-weighted EPI data and the synthetic T1-weighted anatomical MI-EPI-based images was improved compared to the conventional anatomical reference. In particular, the alignment near the regions vulnerable to distortion due to magnetic susceptibility differences was improved, and sampling of the adjacent tissue classes outside of the cortex was reduced when using cortical surface reconstructions derived directly from the MI-EPI reference. The MI-EPI method therefore produces high-quality anatomical data that can be automatically segmented with standard software, providing cortical surface reconstructions that are geometrically matched to the BOLD fMRI data. Copyright © 2016 Elsevier Inc. All rights reserved.

  17. Poster - Thur Eve - 65: Optimization of an automatic image contouring system for radiation therapy.

    Science.gov (United States)

    Hamilton, T; Nedialkov, N; Wierzbicki, M

    2012-07-01

    Intensity modulated radiation therapy (IMRT) is an advanced technique used to concentrate the prescribed dose in the tumour while minimizing exposure to healthy tissues. Success in IMRT is greatly dependent upon the localization of the target volume and normal tissue, thus accurate contouring is crucial. In this paper, we describe an automated atlas-based image contouring system and our approach for improving the system by performing a full-scale optimization of registration parameters using high-performance computing. To achieve this, we use manually pre-contoured CT images of ten head and neck patients. For any parameter set, each patient data is registered with the remaining patients. Accuracy of the resulting contours is determined automatically by comparing their overlap with manually defined targets using Dice's similarity coefficient (DSC). This allows us to compare all permutations of the image registration parameter sets and input data to investigate their impact on final contour accuracy. Investigating the parameter space required 27,000 image registrations and 216,000 DSC computations. To perform these registrations we introduced a large cluster of high-performance computers and developed a parallel testing harness. The metrics collected from the tests show a wide range of performance, indicating that parameter selection is crucial in our contouring system. By selecting an optimized parameter set, we increased the mean overlap of the automatically contoured regions of interest by 50% and reduced registration time by 50% compared to the original parameters. Our findings illustrate that full-scale optimization is an effective method for improving the performance of the automated image contouring system. © 2012 American Association of Physicists in Medicine.

  18. An automatic extraction algorithm of three dimensional shape of brain parenchyma from MR images

    International Nuclear Information System (INIS)

    Matozaki, Takeshi

    2000-01-01

    For the simulation of surgical operations, the extraction of the selected region using MR images is useful. However, this segmentation requires a high level of skill and experience from the technicians. We have developed an unique automatic extraction algorithm for extracting three dimensional brain parenchyma using MR head images. It is named the ''three dimensional gray scale clumsy painter method''. In this method, a template having the shape of a pseudo-circle, a so called clumsy painter (CP), moves along the contour of the selected region and extracts the region surrounded by the contour. This method has advantages compared with the morphological filtering and the region growing method. Previously, this method was applied to binary images, but there were some problems in that the results of the extractions were varied by the value of the threshold level. We introduced gray level information of images to decide the threshold, and depend upon the change of image density between the brain parenchyma and CSF. We decided the threshold level by the vector of a map of templates, and changed the map according to the change of image density. As a result, the over extracted ratio was improved by 36%, and the under extracted ratio was improved by 20%. (author)

  19. Automatic registration of imaging mass spectrometry data to the Allen Brain Atlas transcriptome

    Science.gov (United States)

    Abdelmoula, Walid M.; Carreira, Ricardo J.; Shyti, Reinald; Balluff, Benjamin; Tolner, Else; van den Maagdenberg, Arn M. J. M.; Lelieveldt, B. P. F.; McDonnell, Liam; Dijkstra, Jouke

    2014-03-01

    Imaging Mass Spectrometry (IMS) is an emerging molecular imaging technology that provides spatially resolved information on biomolecular structures; each image pixel effectively represents a molecular mass spectrum. By combining the histological images and IMS-images, neuroanatomical structures can be distinguished based on their biomolecular features as opposed to morphological features. The combination of IMS data with spatially resolved gene expression maps of the mouse brain, as provided by the Allen Mouse Brain atlas, would enable comparative studies of spatial metabolic and gene expression patterns in life-sciences research and biomarker discovery. As such, it would be highly desirable to spatially register IMS slices to the Allen Brain Atlas (ABA). In this paper, we propose a multi-step automatic registration pipeline to register ABA histology to IMS- images. Key novelty of the method is the selection of the best reference section from the ABA, based on pre-processed histology sections. First, we extracted a hippocampus-specific geometrical feature from the given experimental histological section to initially localize it among the ABA sections. Then, feature-based linear registration is applied to the initially localized section and its two neighbors in the ABA to select the most similar reference section. A non-rigid registration yields a one-to-one mapping of the experimental IMS slice to the ABA. The pipeline was applied on 6 coronal sections from two mouse brains, showing high anatomical correspondence, demonstrating the feasibility of complementing biomolecule distributions from individual mice with the genome-wide ABA transcriptome.

  20. Automatic detection of end-diastole and end-systole from echocardiography images using manifold learning

    International Nuclear Information System (INIS)

    Gifani, Parisa; Behnam, Hamid; Shalbaf, Ahmad; Sani, Zahra Alizadeh

    2010-01-01

    The automatic detection of end-diastole and end-systole frames of echocardiography images is the first step for calculation of the ejection fraction, stroke volume and some other features related to heart motion abnormalities. In this paper, the manifold learning algorithm is applied on 2D echocardiography images to find out the relationship between the frames of one cycle of heart motion. By this approach the nonlinear embedded information in sequential images is represented in a two-dimensional manifold by the LLE algorithm and each image is depicted by a point on reconstructed manifold. There are three dense regions on the manifold which correspond to the three phases of cardiac cycle ('isovolumetric contraction', 'isovolumetric relaxation', 'reduced filling'), wherein there is no prominent change in ventricular volume. By the fact that the end-systolic and end-diastolic frames are in isovolumic phases of the cardiac cycle, the dense regions can be used to find these frames. By calculating the distance between consecutive points in the manifold, the isovolumic frames are mapped on the three minimums of the distance diagrams which were used to select the corresponding images. The minimum correlation between these images leads to detection of end-systole and end-diastole frames. The results on six healthy volunteers have been validated by an experienced echo cardiologist and depict the usefulness of the presented method

  1. Automatic segmentation of MRI head images by 3-D region growing method which utilizes edge information

    International Nuclear Information System (INIS)

    Jiang, Hao; Suzuki, Hidetomo; Toriwaki, Jun-ichiro

    1991-01-01

    This paper presents a 3-D segmentation method that automatically extracts soft tissue from multi-sliced MRI head images. MRI produces a sequence of two-dimensional (2-D) images which contains three-dimensional (3-D) information of organs. To utilize such information we need effective algorithms to treat 3-D digital images and to extract organs and tissues of interest. We developed a method to extract the brain from MRI images which uses a region growing procedure and integrates information of uniformity of gray levels and information of the presence of edge segments in the local area around the pixel of interest. First we generate a kernel region which is a part of brain tissue by simple thresholding. Then we grow the region by means of a region growing algorithm under the control of 3-D edge existence to obtain the region of the brain. Our method is rather simple because it uses basic 3-D image processing techniques like spatial difference. It is robust for variation of gray levels inside a tissue since it also refers to the edge information in the process of region growing. Therefore, the method is flexible enough to be applicable to the segmentation of other images including soft tissues which have complicated shapes and fluctuation in gray levels. (author)

  2. Semi-automatic geographic atrophy segmentation for SD-OCT images.

    Science.gov (United States)

    Chen, Qiang; de Sisternes, Luis; Leng, Theodore; Zheng, Luoluo; Kutzscher, Lauren; Rubin, Daniel L

    2013-01-01

    Geographic atrophy (GA) is a condition that is associated with retinal thinning and loss of the retinal pigment epithelium (RPE) layer. It appears in advanced stages of non-exudative age-related macular degeneration (AMD) and can lead to vision loss. We present a semi-automated GA segmentation algorithm for spectral-domain optical coherence tomography (SD-OCT) images. The method first identifies and segments a surface between the RPE and the choroid to generate retinal projection images in which the projection region is restricted to a sub-volume of the retina where the presence of GA can be identified. Subsequently, a geometric active contour model is employed to automatically detect and segment the extent of GA in the projection images. Two image data sets, consisting on 55 SD-OCT scans from twelve eyes in eight patients with GA and 56 SD-OCT scans from 56 eyes in 56 patients with GA, respectively, were utilized to qualitatively and quantitatively evaluate the proposed GA segmentation method. Experimental results suggest that the proposed algorithm can achieve high segmentation accuracy. The mean GA overlap ratios between our proposed method and outlines drawn in the SD-OCT scans, our method and outlines drawn in the fundus auto-fluorescence (FAF) images, and the commercial software (Carl Zeiss Meditec proprietary software, Cirrus version 6.0) and outlines drawn in FAF images were 72.60%, 65.88% and 59.83%, respectively.

  3. A rapid automatic analyzer and its methodology for effective bentonite content based on image recognition technology

    Directory of Open Access Journals (Sweden)

    Wei Long

    2016-09-01

    Full Text Available Fast and accurate determination of effective bentonite content in used clay bonded sand is very important for selecting the correct mixing ratio and mixing process to obtain high-performance molding sand. Currently, the effective bentonite content is determined by testing the ethylene blue absorbed in used clay bonded sand, which is usually a manual operation with some disadvantages including complicated process, long testing time and low accuracy. A rapid automatic analyzer of the effective bentonite content in used clay bonded sand was developed based on image recognition technology. The instrument consists of auto stirring, auto liquid removal, auto titration, step-rotation and image acquisition components, and processor. The principle of the image recognition method is first to decompose the color images into three-channel gray images based on the photosensitive degree difference of the light blue and dark blue in the three channels of red, green and blue, then to make the gray values subtraction calculation and gray level transformation of the gray images, and finally, to extract the outer circle light blue halo and the inner circle blue spot and calculate their area ratio. The titration process can be judged to reach the end-point while the area ratio is higher than the setting value.

  4. Automatic registration of Iphone images to LASER point clouds of the urban structures using shape features

    Directory of Open Access Journals (Sweden)

    B. Sirmacek

    2013-10-01

    Full Text Available Fusion of 3D airborne laser (LIDAR data and terrestrial optical imagery can be applied in 3D urban modeling and model up-dating. The most challenging aspect of the fusion procedure is registering the terrestrial optical images on the LIDAR point clouds. In this article, we propose an approach for registering these two different data from different sensor sources. As we use iPhone camera images which are taken in front of the interested urban structure by the application user and the high resolution LIDAR point clouds of the acquired by an airborne laser sensor. After finding the photo capturing position and orientation from the iPhone photograph metafile, we automatically select the area of interest in the point cloud and transform it into a range image which has only grayscale intensity levels according to the distance from the image acquisition position. We benefit from local features for registering the iPhone image to the generated range image. In this article, we have applied the registration process based on local feature extraction and graph matching. Finally, the registration result is used for facade texture mapping on the 3D building surface mesh which is generated from the LIDAR point cloud. Our experimental results indicate possible usage of the proposed algorithm framework for 3D urban map updating and enhancing purposes.

  5. Multisource Images Analysis Using Collaborative Clustering

    Directory of Open Access Journals (Sweden)

    Pierre Gançarski

    2008-04-01

    Full Text Available The development of very high-resolution (VHR satellite imagery has produced a huge amount of data. The multiplication of satellites which embed different types of sensors provides a lot of heterogeneous images. Consequently, the image analyst has often many different images available, representing the same area of the Earth surface. These images can be from different dates, produced by different sensors, or even at different resolutions. The lack of machine learning tools using all these representations in an overall process constraints to a sequential analysis of these various images. In order to use all the information available simultaneously, we propose a framework where different algorithms can use different views of the scene. Each one works on a different remotely sensed image and, thus, produces different and useful information. These algorithms work together in a collaborative way through an automatic and mutual refinement of their results, so that all the results have almost the same number of clusters, which are statistically similar. Finally, a unique result is produced, representing a consensus among the information obtained by each clustering method on its own image. The unified result and the complementarity of the single results (i.e., the agreement between the clustering methods as well as the disagreement lead to a better understanding of the scene. The experiments carried out on multispectral remote sensing images have shown that this method is efficient to extract relevant information and to improve the scene understanding.

  6. The analysis of slag and silicate samples with a fully automatic sequential x-ray spectrometer

    International Nuclear Information System (INIS)

    Austen, C.E.

    1976-01-01

    The application of a fully automatic Philips PW 1220 X-ray spectrometer to the analysis of slag and silicate materials is described. The controlling software, written in BASIC and the operational instructions for the automatic spectrometer as applied in this report are available on request

  7. An Imaging And Graphics Workstation For Image Sequence Analysis

    Science.gov (United States)

    Mostafavi, Hassan

    1990-01-01

    This paper describes an application-specific engineering workstation designed and developed to analyze imagery sequences from a variety of sources. The system combines the software and hardware environment of the modern graphic-oriented workstations with the digital image acquisition, processing and display techniques. The objective is to achieve automation and high throughput for many data reduction tasks involving metric studies of image sequences. The applications of such an automated data reduction tool include analysis of the trajectory and attitude of aircraft, missile, stores and other flying objects in various flight regimes including launch and separation as well as regular flight maneuvers. The workstation can also be used in an on-line or off-line mode to study three-dimensional motion of aircraft models in simulated flight conditions such as wind tunnels. The system's key features are: 1) Acquisition and storage of image sequences by digitizing real-time video or frames from a film strip; 2) computer-controlled movie loop playback, slow motion and freeze frame display combined with digital image sharpening, noise reduction, contrast enhancement and interactive image magnification; 3) multiple leading edge tracking in addition to object centroids at up to 60 fields per second from both live input video or a stored image sequence; 4) automatic and manual field-of-view and spatial calibration; 5) image sequence data base generation and management, including the measurement data products; 6) off-line analysis software for trajectory plotting and statistical analysis; 7) model-based estimation and tracking of object attitude angles; and 8) interface to a variety of video players and film transport sub-systems.

  8. Physical correction model for automatic correction of intensity non-uniformity in magnetic resonance imaging

    Directory of Open Access Journals (Sweden)

    Stefan Leger

    2017-10-01

    Conclusion: The proposed PCM algorithm led to a significantly improved image quality compared to the originally acquired images, suggesting that it is applicable to the correction of MRI data. Thus it may help to reduce intensity non-uniformity which is an important step for advanced image analysis.

  9. Background approximation in automatic qualitative X-ray-fluorescent analysis

    International Nuclear Information System (INIS)

    Jordanov, J.; Tsanov, T.; Stefanov, R.; Jordanov, N.; Paunov, M.

    1982-01-01

    An empirical method of finding the dependence of the background intensity (Isub(bg) on the wavelength is proposed, based on the approximation of the experimentally found values for the background in the course of an automatic qualitative X-ray fluorescent analysis with pre-set curve. It is assumed that the dependence I(lambda) will be well approximated by a curve of the type Isub(bg)=(lambda-lambda sub(o)sup(fsub(1)(lambda))exp[fsub(2)(lambda)] where fsub(1) (lambda) and f 2 (lambda) are linear functions with respect to the sought parameters. This assumption was checked out on a ''pure'' starch background, in which it is not known beforehand which points belong to the background. It was assumed that the dependence I(lambda) can be found from all minima in the spectrum. Three types of minima has been distinguished: 1. the lowest point between two well-solved X-ray lines; 2. a minimum obtained as a result of statistical flu