Workspace definition for navigated control functional endoscopic sinus surgery

NASA Astrophysics Data System (ADS)

Gessat, Michael; Hofer, Mathias; Audette, Michael; Dietz, Andreas; Meixensberger, Jürgen; Stauß, Gero; Burgert, Oliver

2007-03-01

For the pre-operative definition of a surgical workspace for Navigated Control ® Functional Endoscopic Sinus Surgery (FESS), we developed a semi-automatic image processing system. Based on observations of surgeons using a manual system, we implemented a workflow-based engineering process that led us to the development of a system reducing time and workload spent during the workspace definition. The system uses a feature based on local curvature to align vertices of a polygonal outline along the bone structures defining the cavities of the inner nose. An anisotropic morphologic operator was developed solve problems arising from artifacts from noise and partial volume effects. We used time measurements and NASA's TLX questionnaire to evaluate our system.

Reliability and accuracy analysis of a new semiautomatic radiographic measurement software in adult scoliosis.

PubMed

Aubin, Carl-Eric; Bellefleur, Christian; Joncas, Julie; de Lanauze, Dominic; Kadoury, Samuel; Blanke, Kathy; Parent, Stefan; Labelle, Hubert

2011-05-20

Radiographic software measurement analysis in adult scoliosis. To assess the accuracy as well as the intra- and interobserver reliability of measuring different indices on preoperative adult scoliosis radiographs using a novel measurement software that includes a calibration procedure and semiautomatic features to facilitate the measurement process. Scoliosis requires a careful radiographic evaluation to assess the deformity. Manual and computer radiographic process measures have been studied extensively to determine the reliability and reproducibility in adolescent idiopathic scoliosis. Most studies rely on comparing given measurements, which are repeated by the same user or by an expert user. A given measure with a small intra- or interobserver error might be deemed as good repeatability, but all measurements might not be truly accurate because the ground-truth value is often unknown. Thorough accuracy assessment of radiographic measures is necessary to assess scoliotic deformities, compare these measures at different stages or to permit valid multicenter studies. Thirty-four sets of adult scoliosis digital radiographs were measured two times by three independent observers using a novel radiographic measurement software that includes semiautomatic features to facilitate the measurement process. Twenty different measures taken from the Spinal Deformity Study Group radiographic measurement manual were performed on the coronal and sagittal images. Intra- and intermeasurer reliability for each measure was assessed. The accuracy of the measurement software was also assessed using a physical spine model in six different scoliotic configurations as a true reference. The majority of the measures demonstrated good to excellent intra- and intermeasurer reliability, except for sacral obliquity. The standard variation of all the measures was very small: ≤ 4.2° for Cobb angles, ≤ 4.2° for the kyphosis, ≤ 5.7° for the lordosis, ≤ 3.9° for the pelvic angles, and ≤5.3° for the sacral angles. The variability in the linear measurements (distances) was <4 mm. The variance of the measures was 1.7 and 2.6 times greater, respectively, for the angular and linear measures between the inter- and intrameasurer reliability. The image quality positively influenced the intermeasurer reliability especially for the proximal thoracic Cobb angle, T10-L2 lordosis, sacral slope and L5 seating. The accuracy study revealed that on average the difference in the angular measures was < 2° for the Cobb angles, and < 4° for the other angles, except T2-T12 kyphosis (5.3°). The linear measures were all <3.5 mm difference on average. The majority of the measures, which were analyzed in this study demonstrated good to excellent reliability and accuracy. The novel semiautomatic measurement software can be recommended for use for clinical, research or multicenter study purposes.

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

NASA Astrophysics Data System (ADS)

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%.

Techniques on semiautomatic segmentation using the Adobe Photoshop

NASA Astrophysics Data System (ADS)

Park, Jin Seo; Chung, Min Suk; Hwang, Sung Bae

2005-04-01

The purpose of this research is to enable anybody to semiautomatically segment the anatomical structures in the MRIs, CTs, and other medical images on the personal computer. The segmented images are used for making three-dimensional images, which are helpful in medical education and research. To achieve this purpose, the following trials were performed. The entire body of a volunteer was MR scanned to make 557 MRIs, which were transferred to a personal computer. On Adobe Photoshop, contours of 19 anatomical structures in the MRIs were semiautomatically drawn using MAGNETIC LASSO TOOL; successively, manually corrected using either LASSO TOOL or DIRECT SELECTION TOOL to make 557 segmented images. In a likewise manner, 11 anatomical structures in the 8,500 anatomcial images were segmented. Also, 12 brain and 10 heart anatomical structures in anatomical images were segmented. Proper segmentation was verified by making and examining the coronal, sagittal, and three-dimensional images from the segmented images. During semiautomatic segmentation on Adobe Photoshop, suitable algorithm could be used, the extent of automatization could be regulated, convenient user interface could be used, and software bugs rarely occurred. The techniques of semiautomatic segmentation using Adobe Photoshop are expected to be widely used for segmentation of the anatomical structures in various medical images.

Geospatial mapping of Antarctic coastal oasis using geographic object-based image analysis and high resolution satellite imagery

NASA Astrophysics Data System (ADS)

Jawak, Shridhar D.; Luis, Alvarinho J.

2016-04-01

An accurate spatial mapping and characterization of land cover features in cryospheric regions is an essential procedure for many geoscientific studies. A novel semi-automated method was devised by coupling spectral index ratios (SIRs) and geographic object-based image analysis (OBIA) to extract cryospheric geospatial information from very high resolution WorldView 2 (WV-2) satellite imagery. The present study addresses development of multiple rule sets for OBIA-based classification of WV-2 imagery to accurately extract land cover features in the Larsemann Hills, east Antarctica. Multilevel segmentation process was applied to WV-2 image to generate different sizes of geographic image objects corresponding to various land cover features with respect to scale parameter. Several SIRs were applied to geographic objects at different segmentation levels to classify land mass, man-made features, snow/ice, and water bodies. We focus on water body class to identify water areas at the image level, considering their uneven appearance on landmass and ice. The results illustrated that synergetic usage of SIRs and OBIA can provide accurate means to identify land cover classes with an overall classification accuracy of ≍97%. In conclusion, our results suggest that OBIA is a powerful tool for carrying out automatic and semiautomatic analysis for most cryospheric remote-sensing applications, and the synergetic coupling with pixel-based SIRs is found to be a superior method for mining geospatial information.

Semi-automatic computerized approach to radiological quantification in rheumatoid arthritis

NASA Astrophysics Data System (ADS)

Steiner, Wolfgang; Schoeffmann, Sylvia; Prommegger, Andrea; Boegl, Karl; Klinger, Thomas; Peloschek, Philipp; Kainberger, Franz

2004-04-01

Rheumatoid Arthritis (RA) is a common systemic disease predominantly involving the joints. Precise diagnosis and follow-up therapy requires objective quantification. For this purpose, radiological analyses using standardized scoring systems are considered to be the most appropriate method. The aim of our study is to develop a semi-automatic image analysis software, especially applicable for scoring of joints in rheumatic disorders. The X-Ray RheumaCoach software delivers various scoring systems (Larsen-Score and Ratingen-Rau-Score) which can be applied by the scorer. In addition to the qualitative assessment of joints performed by the radiologist, a semi-automatic image analysis for joint detection and measurements of bone diameters and swollen tissue supports the image assessment process. More than 3000 radiographs from hands and feet of more than 200 RA patients were collected, analyzed, and statistically evaluated. Radiographs were quantified using conventional paper-based Larsen score and the X-Ray RheumaCoach software. The use of the software shortened the scoring time by about 25 percent and reduced the rate of erroneous scorings in all our studies. Compared to paper-based scoring methods, the X-Ray RheumaCoach software offers several advantages: (i) Structured data analysis and input that minimizes variance by standardization, (ii) faster and more precise calculation of sum scores and indices, (iii) permanent data storing and fast access to the software"s database, (iv) the possibility of cross-calculation to other scores, (v) semi-automatic assessment of images, and (vii) reliable documentation of results in the form of graphical printouts.

Semi-automatic ground truth generation using unsupervised clustering and limited manual labeling: Application to handwritten character recognition

PubMed Central

Vajda, Szilárd; Rangoni, Yves; Cecotti, Hubert

2015-01-01

For training supervised classifiers to recognize different patterns, large data collections with accurate labels are necessary. In this paper, we propose a generic, semi-automatic labeling technique for large handwritten character collections. In order to speed up the creation of a large scale ground truth, the method combines unsupervised clustering and minimal expert knowledge. To exploit the potential discriminant complementarities across features, each character is projected into five different feature spaces. After clustering the images in each feature space, the human expert labels the cluster centers. Each data point inherits the label of its cluster’s center. A majority (or unanimity) vote decides the label of each character image. The amount of human involvement (labeling) is strictly controlled by the number of clusters – produced by the chosen clustering approach. To test the efficiency of the proposed approach, we have compared, and evaluated three state-of-the art clustering methods (k-means, self-organizing maps, and growing neural gas) on the MNIST digit data set, and a Lampung Indonesian character data set, respectively. Considering a k-nn classifier, we show that labeling manually only 1.3% (MNIST), and 3.2% (Lampung) of the training data, provides the same range of performance than a completely labeled data set would. PMID:25870463

Semi-automatic segmentation of brain tumors using population and individual information.

PubMed

Wu, Yao; Yang, Wei; Jiang, Jun; Li, Shuanqian; Feng, Qianjin; Chen, Wufan

2013-08-01

Efficient segmentation of tumors in medical images is of great practical importance in early diagnosis and radiation plan. This paper proposes a novel semi-automatic segmentation method based on population and individual statistical information to segment brain tumors in magnetic resonance (MR) images. First, high-dimensional image features are extracted. Neighborhood components analysis is proposed to learn two optimal distance metrics, which contain population and patient-specific information, respectively. The probability of each pixel belonging to the foreground (tumor) and the background is estimated by the k-nearest neighborhood classifier under the learned optimal distance metrics. A cost function for segmentation is constructed through these probabilities and is optimized using graph cuts. Finally, some morphological operations are performed to improve the achieved segmentation results. Our dataset consists of 137 brain MR images, including 68 for training and 69 for testing. The proposed method overcomes segmentation difficulties caused by the uneven gray level distribution of the tumors and even can get satisfactory results if the tumors have fuzzy edges. Experimental results demonstrate that the proposed method is robust to brain tumor segmentation.

Building Facade Reconstruction by Fusing Terrestrial Laser Points and Images

PubMed Central

Pu, Shi; Vosselman, George

2009-01-01

Laser data and optical data have a complementary nature for three dimensional feature extraction. Efficient integration of the two data sources will lead to a more reliable and automated extraction of three dimensional features. This paper presents a semiautomatic building facade reconstruction approach, which efficiently combines information from terrestrial laser point clouds and close range images. A building facade's general structure is discovered and established using the planar features from laser data. Then strong lines in images are extracted using Canny extractor and Hough transformation, and compared with current model edges for necessary improvement. Finally, textures with optimal visibility are selected and applied according to accurate image orientations. Solutions to several challenge problems throughout the collaborated reconstruction, such as referencing between laser points and multiple images and automated texturing, are described. The limitations and remaining works of this approach are also discussed. PMID:22408539

Comparative study on the performance of textural image features for active contour segmentation.

PubMed

Moraru, Luminita; Moldovanu, Simona

2012-07-01

We present a computerized method for the semi-automatic detection of contours in ultrasound images. The novelty of our study is the introduction of a fast and efficient image function relating to parametric active contour models. This new function is a combination of the gray-level information and first-order statistical features, called standard deviation parameters. In a comprehensive study, the developed algorithm and the efficiency of segmentation were first tested for synthetic images. Tests were also performed on breast and liver ultrasound images. The proposed method was compared with the watershed approach to show its efficiency. The performance of the segmentation was estimated using the area error rate. Using the standard deviation textural feature and a 5×5 kernel, our curve evolution was able to produce results close to the minimal area error rate (namely 8.88% for breast images and 10.82% for liver images). The image resolution was evaluated using the contrast-to-gradient method. The experiments showed promising segmentation results.

Hi-fidelity multi-scale local processing for visually optimized far-infrared Herschel images

NASA Astrophysics Data System (ADS)

Li Causi, G.; Schisano, E.; Liu, S. J.; Molinari, S.; Di Giorgio, A.

2016-07-01

In the context of the "Hi-Gal" multi-band full-plane mapping program for the Galactic Plane, as imaged by the Herschel far-infrared satellite, we have developed a semi-automatic tool which produces high definition, high quality color maps optimized for visual perception of extended features, like bubbles and filaments, against the high background variations. We project the map tiles of three selected bands onto a 3-channel panorama, which spans the central 130 degrees of galactic longitude times 2.8 degrees of galactic latitude, at the pixel scale of 3.2", in cartesian galactic coordinates. Then we process this image piecewise, applying a custom multi-scale local stretching algorithm, enforced by a local multi-scale color balance. Finally, we apply an edge-preserving contrast enhancement to perform an artifact-free details sharpening. Thanks to this tool, we have thus produced a stunning giga-pixel color image of the far-infrared Galactic Plane that we made publicly available with the recent release of the Hi-Gal mosaics and compact source catalog.

Asteroid (21) Lutetia: Semi-Automatic Impact Craters Detection and Classification

NASA Astrophysics Data System (ADS)

Jenerowicz, M.; Banaszkiewicz, M.

2018-05-01

The need to develop an automated method, independent of lighting and surface conditions, for the identification and measurement of impact craters, as well as the creation of a reliable and efficient tool, has become a justification of our studies. This paper presents a methodology for the detection of impact craters based on their spectral and spatial features. The analysis aims at evaluation of the algorithm capabilities to determinate the spatial parameters of impact craters presented in a time series. In this way, time-consuming visual interpretation of images would be reduced to the special cases. The developed algorithm is tested on a set of OSIRIS high resolution images of asteroid Lutetia surface which is characterized by varied landforms and the abundance of craters created by collisions with smaller bodies of the solar system.The proposed methodology consists of three main steps: characterisation of objects of interest on limited set of data, semi-automatic extraction of impact craters performed for total set of data by applying the Mathematical Morphology image processing (Serra, 1988, Soille, 2003), and finally, creating libraries of spatial and spectral parameters for extracted impact craters, i.e. the coordinates of the crater center, semi-major and semi-minor axis, shadow length and cross-section. The overall accuracy of the proposed method is 98 %, the Kappa coefficient is 0.84, the correlation coefficient is ∼ 0.80, the omission error 24.11 %, the commission error 3.45 %. The obtained results show that methods based on Mathematical Morphology operators are effective also with a limited number of data and low-contrast images.

Pseudo colour visualization of fused multispectral laser scattering images for optical diagnosis of rheumatoid arthritis

NASA Astrophysics Data System (ADS)

Zabarylo, U.; Minet, O.

2010-01-01

Investigations on the application of optical procedures for the diagnosis of rheumatism using scattered light images are only at the beginning both in terms of new image-processing methods and subsequent clinical application. For semi-automatic diagnosis using laser light, the multispectral scattered light images are registered and overlapped to pseudo-coloured images, which depict diagnostically essential contents by visually highlighting pathological changes.

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

PubMed

Schäfer, Sebastian; Nylund, Kim; Sævik, Fredrik; Engjom, Trond; Mézl, Martin; Jiřík, Radovan; Dimcevski, Georg; Gilja, Odd Helge; Tönnies, Klaus

2015-08-01

This paper presents a system for correcting motion influences in time-dependent 2D contrast-enhanced ultrasound (CEUS) images to assess tissue perfusion characteristics. The system consists of a semi-automatic frame selection method to find images with out-of-plane motion as well as a method for automatic motion compensation. Translational and non-rigid motion compensation is applied by introducing a temporal continuity assumption. A study consisting of 40 clinical datasets was conducted to compare the perfusion with simulated perfusion using pharmacokinetic modeling. Overall, the proposed approach decreased the mean average difference between the measured perfusion and the pharmacokinetic model estimation. It was non-inferior for three out of four patient cohorts to a manual approach and reduced the analysis time by 41% compared to manual processing. Copyright © 2014 Elsevier Ltd. All rights reserved.

Semi-automatic medical image segmentation with adaptive local statistics in Conditional Random Fields framework.

PubMed

Hu, Yu-Chi J; Grossberg, Michael D; Mageras, Gikas S

2008-01-01

Planning radiotherapy and surgical procedures usually require onerous manual segmentation of anatomical structures from medical images. In this paper we present a semi-automatic and accurate segmentation method to dramatically reduce the time and effort required of expert users. This is accomplished by giving a user an intuitive graphical interface to indicate samples of target and non-target tissue by loosely drawing a few brush strokes on the image. We use these brush strokes to provide the statistical input for a Conditional Random Field (CRF) based segmentation. Since we extract purely statistical information from the user input, we eliminate the need of assumptions on boundary contrast previously used by many other methods, A new feature of our method is that the statistics on one image can be reused on related images without registration. To demonstrate this, we show that boundary statistics provided on a few 2D slices of volumetric medical data, can be propagated through the entire 3D stack of images without using the geometric correspondence between images. In addition, the image segmentation from the CRF can be formulated as a minimum s-t graph cut problem which has a solution that is both globally optimal and fast. The combination of a fast segmentation and minimal user input that is reusable, make this a powerful technique for the segmentation of medical images.

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

PubMed Central

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

Quantitative radiomic profiling of glioblastoma represents transcriptomic expression.

PubMed

Kong, Doo-Sik; Kim, Junhyung; Ryu, Gyuha; You, Hye-Jin; Sung, Joon Kyung; Han, Yong Hee; Shin, Hye-Mi; Lee, In-Hee; Kim, Sung-Tae; Park, Chul-Kee; Choi, Seung Hong; Choi, Jeong Won; Seol, Ho Jun; Lee, Jung-Il; Nam, Do-Hyun

2018-01-19

Quantitative imaging biomarkers have increasingly emerged in the field of research utilizing available imaging modalities. We aimed to identify good surrogate radiomic features that can represent genetic changes of tumors, thereby establishing noninvasive means for predicting treatment outcome. From May 2012 to June 2014, we retrospectively identified 65 patients with treatment-naïve glioblastoma with available clinical information from the Samsung Medical Center data registry. Preoperative MR imaging data were obtained for all 65 patients with primary glioblastoma. A total of 82 imaging features including first-order statistics, volume, and size features, were semi-automatically extracted from structural and physiologic images such as apparent diffusion coefficient and perfusion images. Using commercially available software, NordicICE, we performed quantitative imaging analysis and collected the dataset composed of radiophenotypic parameters. Unsupervised clustering methods revealed that the radiophenotypic dataset was composed of three clusters. Each cluster represented a distinct molecular classification of glioblastoma; classical type, proneural and neural types, and mesenchymal type. These clusters also reflected differential clinical outcomes. We found that extracted imaging signatures does not represent copy number variation and somatic mutation. Quantitative radiomic features provide a potential evidence to predict molecular phenotype and treatment outcome. Radiomic profiles represents transcriptomic phenotypes more well.

Correlation and registration of ERTS multispectral imagery. [by a digital processing technique

NASA Technical Reports Server (NTRS)

Bonrud, L. O.; Henrikson, P. J.

1974-01-01

Examples of automatic digital processing demonstrate the feasibility of registering one ERTS multispectral scanner (MSS) image with another obtained on a subsequent orbit, and automatic matching, correlation, and registration of MSS imagery with aerial photography (multisensor correlation) is demonstrated. Excellent correlation was obtained with patch sizes exceeding 16 pixels square. Qualities which lead to effective control point selection are distinctive features, good contrast, and constant feature characteristics. Results of the study indicate that more than 300 degrees of freedom are required to register two standard ERTS-1 MSS frames covering 100 by 100 nautical miles to an accuracy of 0.6 pixel mean radial displacement error. An automatic strip processing technique demonstrates 600 to 1200 degrees of freedom over a quater frame of ERTS imagery. Registration accuracies in the range of 0.3 pixel to 0.5 pixel mean radial error were confirmed by independent error analysis. Accuracies in the range of 0.5 pixel to 1.4 pixel mean radial error were demonstrated by semi-automatic registration over small geographic areas.

Semi-Automatic Extraction Algorithm for Images of the Ciliary Muscle

PubMed Central

Kao, Chiu-Yen; Richdale, Kathryn; Sinnott, Loraine T.; Ernst, Lauren E.; Bailey, Melissa D.

2011-01-01

Purpose To development and evaluate a semi-automatic algorithm for segmentation and morphological assessment of the dimensions of the ciliary muscle in Visante™ Anterior Segment Optical Coherence Tomography images. Methods Geometric distortions in Visante images analyzed as binary files were assessed by imaging an optical flat and human donor tissue. The appropriate pixel/mm conversion factor to use for air (n = 1) was estimated by imaging calibration spheres. A semi-automatic algorithm was developed to extract the dimensions of the ciliary muscle from Visante images. Measurements were also made manually using Visante software calipers. Interclass correlation coefficients (ICC) and Bland-Altman analyses were used to compare the methods. A multilevel model was fitted to estimate the variance of algorithm measurements that was due to differences within- and between-examiners in scleral spur selection versus biological variability. Results The optical flat and the human donor tissue were imaged and appeared without geometric distortions in binary file format. Bland-Altman analyses revealed that caliper measurements tended to underestimate ciliary muscle thickness at 3 mm posterior to the scleral spur in subjects with the thickest ciliary muscles (t = 3.6, p < 0.001). The percent variance due to within- or between-examiner differences in scleral spur selection was found to be small (6%) when compared to the variance due to biological difference across subjects (80%). Using the mean of measurements from three images achieved an estimated ICC of 0.85. Conclusions The semi-automatic algorithm successfully segmented the ciliary muscle for further measurement. Using the algorithm to follow the scleral curvature to locate more posterior measurements is critical to avoid underestimating thickness measurements. This semi-automatic algorithm will allow for repeatable, efficient, and masked ciliary muscle measurements in large datasets. PMID:21169877

Three-dimensional reconstruction from serial sections in PC-Windows platform by using 3D_Viewer.

PubMed

Xu, Yi-Hua; Lahvis, Garet; Edwards, Harlene; Pitot, Henry C

2004-11-01

Three-dimensional (3D) reconstruction from serial sections allows identification of objects of interest in 3D and clarifies the relationship among these objects. 3D_Viewer, developed in our laboratory for this purpose, has four major functions: image alignment, movie frame production, movie viewing, and shift-overlay image generation. Color images captured from serial sections were aligned; then the contours of objects of interest were highlighted in a semi-automatic manner. These 2D images were then automatically stacked at different viewing angles, and their composite images on a projected plane were recorded by an image transform-shift-overlay technique. These composition images are used in the object-rotation movie show. The design considerations of the program and the procedures used for 3D reconstruction from serial sections are described. This program, with a digital image-capture system, a semi-automatic contours highlight method, and an automatic image transform-shift-overlay technique, greatly speeds up the reconstruction process. Since images generated by 3D_Viewer are in a general graphic format, data sharing with others is easy. 3D_Viewer is written in MS Visual Basic 6, obtainable from our laboratory on request.

SABRE--A Novel Software Tool for Bibliographic Post-Processing.

ERIC Educational Resources Information Center

Burge, Cecil D.

1989-01-01

Describes the software architecture and application of SABRE (Semi-Automated Bibliographic Environment), which is one of the first products to provide a semi-automatic environment for relevancy ranking of citations obtained from searches of bibliographic databases. Features designed to meet the review, categorization, culling, and reporting needs…

Semi-automatic feedback using concurrence between mixture vectors for general databases

NASA Astrophysics Data System (ADS)

Larabi, Mohamed-Chaker; Richard, Noel; Colot, Olivier; Fernandez-Maloigne, Christine

2001-12-01

This paper describes how a query system can exploit the basic knowledge by employing semi-automatic relevance feedback to refine queries and runtimes. For general databases, it is often useless to call complex attributes, because we have not sufficient information about images in the database. Moreover, these images can be topologically very different from one to each other and an attribute that is powerful for a database category may be very powerless for the other categories. The idea is to use very simple features, such as color histogram, correlograms, Color Coherence Vectors (CCV), to fill out the signature vector. Then, a number of mixture vectors is prepared depending on the number of very distinctive categories in the database. Knowing that a mixture vector is a vector containing the weight of each attribute that will be used to compute a similarity distance. We post a query in the database using successively all the mixture vectors defined previously. We retain then the N first images for each vector in order to make a mapping using the following information: Is image I present in several mixture vectors results? What is its rank in the results? These informations allow us to switch the system on an unsupervised relevance feedback or user's feedback (supervised feedback).

Semi-automatic segmentation of nonviable cardiac tissue using cine and delayed enhancement magnetic resonance images

NASA Astrophysics Data System (ADS)

O'Donnell, Thomas P.; Xu, Ning; Setser, Randolph M.; White, Richard D.

2003-05-01

Post myocardial infarction, the identification and assessment of non-viable (necrotic) tissues is necessary for effective development of intervention strategies and treatment plans. Delayed Enhancement Magnetic Resonance (DEMR) imaging is a technique whereby non-viable cardiac tissue appears with increased signal intensity. Radiologists typically acquire these images in conjunction with other functional modalities (e.g., MR Cine), and use domain knowledge and experience to isolate the non-viable tissues. In this paper, we present a technique for automatically segmenting these tissues given the delineation of myocardial borders in the DEMR and in the End-systolic and End-diastolic MR Cine images. Briefly, we obtain a set of segmentations furnished by an expert and employ an artificial intelligence technique, Support Vector Machines (SVMs), to "learn" the segmentations based on features culled from the images. Using those features we then allow the SVM to predict the segmentations the expert would provide on previously unseen images.

A methodology for the semi-automatic digital image analysis of fragmental impactites

NASA Astrophysics Data System (ADS)

Chanou, A.; Osinski, G. R.; Grieve, R. A. F.

2014-04-01

A semi-automated digital image analysis method is developed for the comparative textural study of impact melt-bearing breccias. This method uses the freeware software ImageJ developed by the National Institute of Health (NIH). Digital image analysis is performed on scans of hand samples (10-15 cm across), based on macroscopic interpretations of the rock components. All image processing and segmentation are done semi-automatically, with the least possible manual intervention. The areal fraction of components is estimated and modal abundances can be deduced, where the physical optical properties (e.g., contrast, color) of the samples allow it. Other parameters that can be measured include, for example, clast size, clast-preferred orientations, average box-counting dimension or fragment shape complexity, and nearest neighbor distances (NnD). This semi-automated method allows the analysis of a larger number of samples in a relatively short time. Textures, granulometry, and shape descriptors are of considerable importance in rock characterization. The methodology is used to determine the variations of the physical characteristics of some examples of fragmental impactites.

Text extraction from images in the wild using the Viola-Jones algorithm

NASA Astrophysics Data System (ADS)

Saabna, Raid M.; Zingboim, Eran

2018-04-01

Text Localization and extraction is an important issue in modern applications of computer vision. Applications such as reading and translating texts in the wild or from videos are among the many applications that can benefit results of this field. In this work, we adopt the well-known Viola-Jones algorithm to enable text extraction and localization from images in the wild. The Viola-Jones is an efficient, and a fast image-processing algorithm originally used for face detection. Based on some resemblance between text and face detection tasks in the wild, we have modified the viola-jones to detect regions of interest where text may be localized. In the proposed approach, some modification to the HAAR like features and a semi-automatic process of data set generating and manipulation were presented to train the algorithm. A process of sliding windows with different sizes have been used to scan the image for individual letters and letter clusters existence. A post processing step is used in order to combine the detected letters into words and to remove false positives. The novelty of the presented approach is using the strengths of a modified Viola-Jones algorithm to identify many different objects representing different letters and clusters of similar letters and later combine them into words of varying lengths. Impressive results were obtained on the ICDAR contest data sets.

Extraction of sandy bedforms features through geodesic morphometry

NASA Astrophysics Data System (ADS)

Debese, Nathalie; Jacq, Jean-José; Garlan, Thierry

2016-09-01

State-of-art echosounders reveal fine-scale details of mobile sandy bedforms, which are commonly found on continental shelfs. At present, their dynamics are still far from being completely understood. These bedforms are a serious threat to navigation security, anthropic structures and activities, placing emphasis on research breakthroughs. Bedform geometries and their dynamics are closely linked; therefore, one approach is to develop semi-automatic tools aiming at extracting their structural features from bathymetric datasets. Current approaches mimic manual processes or rely on morphological simplification of bedforms. The 1D and 2D approaches cannot address the wide ranges of both types and complexities of bedforms. In contrast, this work attempts to follow a 3D global semi-automatic approach based on a bathymetric TIN. The currently extracted primitives are the salient ridge and valley lines of the sand structures, i.e., waves and mega-ripples. The main difficulty is eliminating the ripples that are found to heavily overprint any observations. To this end, an anisotropic filter that is able to discard these structures while still enhancing the wave ridges is proposed. The second part of the work addresses the semi-automatic interactive extraction and 3D augmented display of the main lines structures. The proposed protocol also allows geoscientists to interactively insert topological constraints.

Semi-automatic measuring of arteriovenous relation as a possible silent brain infarction risk index in hypertensive patients.

PubMed

Vázquez Dorrego, X M; Manresa Domínguez, J M; Heras Tebar, A; Forés, R; Girona Marcé, A; Alzamora Sas, M T; Delgado Martínez, P; Riba-Llena, I; Ugarte Anduaga, J; Beristain Iraola, A; Barandiaran Martirena, I; Ruiz Bilbao, S M; Torán Monserrat, P

2016-11-01

To evaluate the usefulness of a semiautomatic measuring system of arteriovenous relation (RAV) from retinographic images of hypertensive patients in assessing their cardiovascular risk and silent brain ischemia (ICS) detection. Semi-automatic measurement of arterial and venous width were performed with the aid of Imedos software and conventional fundus examination from the analysis of retinal images belonging to the 976 patients integrated in the cohort Investigating Silent Strokes in Hypertensives: a magnetic resonance imaging study (ISSYS), group of hypertensive patients. All patients have been subjected to a cranial magnetic resonance imaging (RMN) to assess the presence or absence of brain silent infarct. Retinal images of 768 patients were studied. Among the clinical findings observed, association with ICS was only detected in patients with microaneurysms (OR 2.50; 95% CI: 1.05-5.98) or altered RAV (<0.666) (OR: 4.22; 95% CI: 2.56-6.96). In multivariate logistic regression analysis adjusted by age and sex, only altered RAV continued demonstrating as a risk factor (OR: 3.70; 95% CI: 2.21-6.18). The results show that the semiautomatic analysis of the retinal vasculature from retinal images has the potential to be considered as an important vascular risk factor in hypertensive population. Copyright © 2016 Sociedad Española de Oftalmología. Publicado por Elsevier España, S.L.U. All rights reserved.

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

PubMed

Chen, Xinyuan; Dai, Jianrong

2018-05-01

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

Fast Laser Holographic Interferometry For Wind Tunnels

NASA Technical Reports Server (NTRS)

Lee, George

1989-01-01

Proposed system makes holographic interferograms quickly in wind tunnels. Holograms reveal two-dimensional flows around airfoils and provide information on distributions of pressure, structures of wake and boundary layers, and density contours of flow fields. Holograms form quickly in thermoplastic plates in wind tunnel. Plates rigid and left in place so neither vibrations nor photgraphic-development process degrades accuracy of holograms. System processes and analyzes images quickly. Semiautomatic micro-computer-based desktop image-processing unit now undergoing development moves easily to wind tunnel, and its speed and memory adequate for flows about airfoils.

Semi-automatic mapping of fault rocks on a Digital Outcrop Model, Gole Larghe Fault Zone (Southern Alps, Italy)

NASA Astrophysics Data System (ADS)

Vho, Alice; Bistacchi, Andrea

2015-04-01

A quantitative analysis of fault-rock distribution is of paramount importance for studies of fault zone architecture, fault and earthquake mechanics, and fluid circulation along faults at depth. Here we present a semi-automatic workflow for fault-rock mapping on a Digital Outcrop Model (DOM). This workflow has been developed on a real case of study: the strike-slip Gole Larghe Fault Zone (GLFZ). It consists of a fault zone exhumed from ca. 10 km depth, hosted in granitoid rocks of Adamello batholith (Italian Southern Alps). Individual seismogenic slip surfaces generally show green cataclasites (cemented by the precipitation of epidote and K-feldspar from hydrothermal fluids) and more or less well preserved pseudotachylytes (black when well preserved, greenish to white when altered). First of all, a digital model for the outcrop is reconstructed with photogrammetric techniques, using a large number of high resolution digital photographs, processed with VisualSFM software. By using high resolution photographs the DOM can have a much higher resolution than with LIDAR surveys, up to 0.2 mm/pixel. Then, image processing is performed to map the fault-rock distribution with the ImageJ-Fiji package. Green cataclasites and epidote/K-feldspar veins can be quite easily separated from the host rock (tonalite) using spectral analysis. Particularly, band ratio and principal component analysis have been tested successfully. The mapping of black pseudotachylyte veins is more tricky because the differences between the pseudotachylyte and biotite spectral signature are not appreciable. For this reason we have tested different morphological processing tools aimed at identifying (and subtracting) the tiny biotite grains. We propose a solution based on binary images involving a combination of size and circularity thresholds. Comparing the results with manually segmented images, we noticed that major problems occur only when pseudotachylyte veins are very thin and discontinuous. After having tested and refined the image analysis processing for some typical images, we have recorded a macro with ImageJ-Fiji allowing to process all the images for a given DOM. As a result, the three different types of rocks can be semi-automatically mapped on large DOMs using a simple and efficient procedure. This allows to develop quantitative analyses of fault rock distribution and thickness, fault trace roughness/curvature and length, fault zone architecture, and alteration halos due to hydrothermal fluid-rock interaction. To improve our workflow, additional or different morphological operators could be integrated in our procedure to yield a better resolution on small and thin pseudotachylyte veins (e.g. perimeter/area ratio).

A system of regional agricultural land use mapping tested against small scale Apollo 9 color infrared photography of the Imperial Valley (California)

USGS Publications Warehouse

Johnson, Claude W.; Browden, Leonard W.; Pease, Robert W.

1969-01-01

Interpretation results of the small scale ClR photography of the Imperial Valley (California) taken on March 12, 1969 by the Apollo 9 earth orbiting satellite have shown that world wide agricultural land use mapping can be accomplished from satellite ClR imagery if sufficient a priori information is available for the region being mapped. Correlation of results with actual data is encouraging although the accuracy of identification of specific crops from the single image is poor. The poor results can be partly attributed to only one image taken during mid-season when the three major crops were reflecting approximately the same and their ClR image appears to indicate the same crop type. However, some incapacity can be attributed to lack of understanding of the subtle variations of visual and infrared color reflectance of vegetation and surrounding environment. Analysis of integrated color variations of the vegetation and background environment recorded on ClR imagery is discussed. Problems associated with the color variations may be overcome by development of a semi-automatic processing system which considers individual field units or cells. Design criteria for semi-automatic processing system are outlined.

Sentinel-2 for rapid operational landslide inventory mapping

NASA Astrophysics Data System (ADS)

Stumpf, André; Marc, Odin; Malet, Jean-Philippe; Michea, David

2017-04-01

Landslide inventory mapping after major triggering events such as heavy rainfalls or earthquakes is crucial for disaster response, the assessment of hazards, and the quantification of sediment budgets and empirical scaling laws. Numerous studies have already demonstrated the utility of very-high resolution satellite and aerial images for the elaboration of inventories based on semi-automatic methods or visual image interpretation. Nevertheless, such semi-automatic methods are rarely used in an operational context after major triggering events; this is partly due to access limitations on the required input datasets (i.e. VHR satellite images) and to the absence of dedicated services (i.e. processing chain) available for the landslide community. Several on-going initiatives allow to overcome these limitations. First, from a data perspective, the launch of the Sentinel-2 mission offers opportunities for the design of an operational service that can be deployed for landslide inventory mapping at any time and everywhere on the globe. Second, from an implementation perspective, the Geohazards Exploitation Platform (GEP) of the European Space Agency (ESA) allows the integration and diffusion of on-line processing algorithms in a high computing performance environment. Third, from a community perspective, the recently launched Landslide Pilot of the Committee on Earth Observation Satellites (CEOS), has targeted the take-off of such service as a main objective for the landslide community. Within this context, this study targets the development of a largely automatic, supervised image processing chain for landslide inventory mapping from bi-temporal (before and after a given event) Sentinel-2 optical images. The processing chain combines change detection methods, image segmentation, higher-level image features (e.g. texture, shape) and topographic variables. Based on a few representative examples provided by a human operator, a machine learning model is trained and subsequently used to distinguish newly triggered landslides from other landscape elements. The final map product is provided along with an uncertainty map that allows identifying areas which might require further considerations. The processing chain is tested for two recent and contrasted triggering events in New Zealand and Taiwan. A Mw 7.8 earthquake in New Zealand in November 2016 triggered tens of thousands of landslides in a complex environment, with important textural variations with elevations, due to vegetation change and snow cover. In contrast a large but unexceptional typhoon in July 2016 in Taiwan triggered a moderate amount of relatively small landslides in a lushly vegetated, more homogenous terrain. Based on the obtained results we discuss the potential and limitations of Sentinel-2 bi-temporal images and time-series for operational landslide inventory mapping This work is part of the General Studies Program (GSP) ALCANTARA of ESA.

Density estimation in aerial images of large crowds for automatic people counting

NASA Astrophysics Data System (ADS)

Herrmann, Christian; Metzler, Juergen

2013-05-01

Counting people is a common topic in the area of visual surveillance and crowd analysis. While many image-based solutions are designed to count only a few persons at the same time, like pedestrians entering a shop or watching an advertisement, there is hardly any solution for counting large crowds of several hundred persons or more. We addressed this problem previously by designing a semi-automatic system being able to count crowds consisting of hundreds or thousands of people based on aerial images of demonstrations or similar events. This system requires major user interaction to segment the image. Our principle aim is to reduce this manual interaction. To achieve this, we propose a new and automatic system. Besides counting the people in large crowds, the system yields the positions of people allowing a plausibility check by a human operator. In order to automatize the people counting system, we use crowd density estimation. The determination of crowd density is based on several features like edge intensity or spatial frequency. They indicate the density and discriminate between a crowd and other image regions like buildings, bushes or trees. We compare the performance of our automatic system to the previous semi-automatic system and to manual counting in images. By counting a test set of aerial images showing large crowds containing up to 12,000 people, the performance gain of our new system will be measured. By improving our previous system, we will increase the benefit of an image-based solution for counting people in large crowds.

Segmentation of neuroanatomy in magnetic resonance images

NASA Astrophysics Data System (ADS)

Simmons, Andrew; Arridge, Simon R.; Barker, G. J.; Tofts, Paul S.

1992-06-01

Segmentation in neurological magnetic resonance imaging (MRI) is necessary for feature extraction, volume measurement and for the three-dimensional display of neuroanatomy. Automated and semi-automated methods offer considerable advantages over manual methods because of their lack of subjectivity, their data reduction capabilities, and the time savings they give. We have used dual echo multi-slice spin-echo data sets which take advantage of the intrinsically multispectral nature of MRI. As a pre-processing step, a rf non-uniformity correction is applied and if the data is noisy the images are smoothed using a non-isotropic blurring method. Edge-based processing is used to identify the skin (the major outer contour) and the eyes. Edge-focusing has been used to significantly simplify edge images and thus allow simple postprocessing to pick out the brain contour in each slice of the data set. Edge- focusing is a technique which locates significant edges using a high degree of smoothing at a coarse level and tracks these edges to a fine level where the edges can be determined with high positional accuracy. Both 2-D and 3-D edge-detection methods have been compared. Once isolated, the brain is further processed to identify CSF, and, depending upon the MR pulse sequence used, the brain itself may be sub-divided into gray matter and white matter using semi-automatic contrast enhancement and clustering methods.

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

NASA Astrophysics Data System (ADS)

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.

Accurate analysis and visualization of cardiac (11)C-PIB uptake in amyloidosis with semiautomatic software.

PubMed

Kero, Tanja; Lindsjö, Lars; Sörensen, Jens; Lubberink, Mark

2016-08-01

(11)C-PIB PET is a promising non-invasive diagnostic tool for cardiac amyloidosis. Semiautomatic analysis of PET data is now available but it is not known how accurate these methods are for amyloid imaging. The aim of this study was to evaluate the feasibility of one semiautomatic software tool for analysis and visualization of (11)C-PIB left ventricular retention index (RI) in cardiac amyloidosis. Patients with systemic amyloidosis and cardiac involvement (n = 10) and healthy controls (n = 5) were investigated with dynamic (11)C-PIB PET. Two observers analyzed the PET studies with semiautomatic software to calculate the left ventricular RI of (11)C-PIB and to create parametric images. The mean RI at 15-25 min from the semiautomatic analysis was compared with RI based on manual analysis and showed comparable values (0.056 vs 0.054 min(-1) for amyloidosis patients and 0.024 vs 0.025 min(-1) in healthy controls; P = .78) and the correlation was excellent (r = 0.98). Inter-reader reproducibility also was excellent (intraclass correlation coefficient, ICC > 0.98). Parametric polarmaps and histograms made visual separation of amyloidosis patients and healthy controls fast and simple. Accurate semiautomatic analysis of cardiac (11)C-PIB RI in amyloidosis patients is feasible. Parametric polarmaps and histograms make visual interpretation fast and simple.

Comparison of hand and semiautomatic tracing methods for creating maxillofacial artificial organs using sequences of computed tomography (CT) and cone beam computed tomography (CBCT) images.

PubMed

Szabo, Bence T; Aksoy, Seçil; Repassy, Gabor; Csomo, Krisztian; Dobo-Nagy, Csaba; Orhan, Kaan

2017-06-09

The aim of this study was to compare the paranasal sinus volumes obtained by manual and semiautomatic imaging software programs using both CT and CBCT imaging. 121 computed tomography (CT) and 119 cone beam computed tomography (CBCT) examinations were selected from the databases of the authors' institutes. The Digital Imaging and Communications in Medicine (DICOM) images were imported into 3-dimensonal imaging software, in which hand mode and semiautomatic tracing methods were used to measure the volumes of both maxillary sinuses and the sphenoid sinus. The determined volumetric means were compared to previously published averages. Isometric CBCT-based volume determination results were closer to the real volume conditions, whereas the non-isometric CT-based volume measurements defined coherently lower volumes. By comparing the 2 volume measurement modes, the values gained from hand mode were closer to the literature data. Furthermore, CBCT-based image measurement results corresponded to the known averages. Our results suggest that CBCT images provide reliable volumetric information that can be depended on for artificial organ construction, and which may aid the guidance of the operator prior to or during the intervention.

Phantom Study Investigating the Accuracy of Manual and Automatic Image Fusion with the GE Logiq E9: Implications for use in Percutaneous Liver Interventions.

PubMed

Burgmans, Mark Christiaan; den Harder, J Michiel; Meershoek, Philippa; van den Berg, Nynke S; Chan, Shaun Xavier Ju Min; van Leeuwen, Fijs W B; van Erkel, Arian R

2017-06-01

To determine the accuracy of automatic and manual co-registration methods for image fusion of three-dimensional computed tomography (CT) with real-time ultrasonography (US) for image-guided liver interventions. CT images of a skills phantom with liver lesions were acquired and co-registered to US using GE Logiq E9 navigation software. Manual co-registration was compared to automatic and semiautomatic co-registration using an active tracker. Also, manual point registration was compared to plane registration with and without an additional translation point. Finally, comparison was made between manual and automatic selection of reference points. In each experiment, accuracy of the co-registration method was determined by measurement of the residual displacement in phantom lesions by two independent observers. Mean displacements for a superficial and deep liver lesion were comparable after manual and semiautomatic co-registration: 2.4 and 2.0 mm versus 2.0 and 2.5 mm, respectively. Both methods were significantly better than automatic co-registration: 5.9 and 5.2 mm residual displacement (p < 0.001; p < 0.01). The accuracy of manual point registration was higher than that of plane registration, the latter being heavily dependent on accurate matching of axial CT and US images by the operator. Automatic reference point selection resulted in significantly lower registration accuracy compared to manual point selection despite lower root-mean-square deviation (RMSD) values. The accuracy of manual and semiautomatic co-registration is better than that of automatic co-registration. For manual co-registration using a plane, choosing the correct plane orientation is an essential first step in the registration process. Automatic reference point selection based on RMSD values is error-prone.

Role of Gist and PHOG Features in Computer-Aided Diagnosis of Tuberculosis without Segmentation

PubMed Central

Chauhan, Arun; Chauhan, Devesh; Rout, Chittaranjan

2014-01-01

Purpose Effective diagnosis of tuberculosis (TB) relies on accurate interpretation of radiological patterns found in a chest radiograph (CXR). Lack of skilled radiologists and other resources, especially in developing countries, hinders its efficient diagnosis. Computer-aided diagnosis (CAD) methods provide second opinion to the radiologists for their findings and thereby assist in better diagnosis of cancer and other diseases including TB. However, existing CAD methods for TB are based on the extraction of textural features from manually or semi-automatically segmented CXRs. These methods are prone to errors and cannot be implemented in X-ray machines for automated classification. Methods Gabor, Gist, histogram of oriented gradients (HOG), and pyramid histogram of oriented gradients (PHOG) features extracted from the whole image can be implemented into existing X-ray machines to discriminate between TB and non-TB CXRs in an automated manner. Localized features were extracted for the above methods using various parameters, such as frequency range, blocks and region of interest. The performance of these features was evaluated against textural features. Two digital CXR image datasets (8-bit DA and 14-bit DB) were used for evaluating the performance of these features. Results Gist (accuracy 94.2% for DA, 86.0% for DB) and PHOG (accuracy 92.3% for DA, 92.0% for DB) features provided better results for both the datasets. These features were implemented to develop a MATLAB toolbox, TB-Xpredict, which is freely available for academic use at http://sourceforge.net/projects/tbxpredict/. This toolbox provides both automated training and prediction modules and does not require expertise in image processing for operation. Conclusion Since the features used in TB-Xpredict do not require segmentation, the toolbox can easily be implemented in X-ray machines. This toolbox can effectively be used for the mass screening of TB in high-burden areas with improved efficiency. PMID:25390291

Comparison and assessment of semi-automatic image segmentation in computed tomography scans for image-guided kidney surgery.

PubMed

Glisson, Courtenay L; Altamar, Hernan O; Herrell, S Duke; Clark, Peter; Galloway, Robert L

2011-11-01

Image segmentation is integral to implementing intraoperative guidance for kidney tumor resection. Results seen in computed tomography (CT) data are affected by target organ physiology as well as by the segmentation algorithm used. This work studies variables involved in using level set methods found in the Insight Toolkit to segment kidneys from CT scans and applies the results to an image guidance setting. A composite algorithm drawing on the strengths of multiple level set approaches was built using the Insight Toolkit. This algorithm requires image contrast state and seed points to be identified as input, and functions independently thereafter, selecting and altering method and variable choice as needed. Semi-automatic results were compared to expert hand segmentation results directly and by the use of the resultant surfaces for registration of intraoperative data. Direct comparison using the Dice metric showed average agreement of 0.93 between semi-automatic and hand segmentation results. Use of the segmented surfaces in closest point registration of intraoperative laser range scan data yielded average closest point distances of approximately 1 mm. Application of both inverse registration transforms from the previous step to all hand segmented image space points revealed that the distance variability introduced by registering to the semi-automatically segmented surface versus the hand segmented surface was typically less than 3 mm both near the tumor target and at distal points, including subsurface points. Use of the algorithm shortened user interaction time and provided results which were comparable to the gold standard of hand segmentation. Further, the use of the algorithm's resultant surfaces in image registration provided comparable transformations to surfaces produced by hand segmentation. These data support the applicability and utility of such an algorithm as part of an image guidance workflow.

Assessing the impact of graphical quality on automatic text recognition in digital maps

NASA Astrophysics Data System (ADS)

Chiang, Yao-Yi; Leyk, Stefan; Honarvar Nazari, Narges; Moghaddam, Sima; Tan, Tian Xiang

2016-08-01

Converting geographic features (e.g., place names) in map images into a vector format is the first step for incorporating cartographic information into a geographic information system (GIS). With the advancement in computational power and algorithm design, map processing systems have been considerably improved over the last decade. However, the fundamental map processing techniques such as color image segmentation, (map) layer separation, and object recognition are sensitive to minor variations in graphical properties of the input image (e.g., scanning resolution). As a result, most map processing results would not meet user expectations if the user does not "properly" scan the map of interest, pre-process the map image (e.g., using compression or not), and train the processing system, accordingly. These issues could slow down the further advancement of map processing techniques as such unsuccessful attempts create a discouraged user community, and less sophisticated tools would be perceived as more viable solutions. Thus, it is important to understand what kinds of maps are suitable for automatic map processing and what types of results and process-related errors can be expected. In this paper, we shed light on these questions by using a typical map processing task, text recognition, to discuss a number of map instances that vary in suitability for automatic processing. We also present an extensive experiment on a diverse set of scanned historical maps to provide measures of baseline performance of a standard text recognition tool under varying map conditions (graphical quality) and text representations (that can vary even within the same map sheet). Our experimental results help the user understand what to expect when a fully or semi-automatic map processing system is used to process a scanned map with certain (varying) graphical properties and complexities in map content.

Semi-automatic mapping of cultural heritage from airborne laser scanning using deep learning

NASA Astrophysics Data System (ADS)

Due Trier, Øivind; Salberg, Arnt-Børre; Holger Pilø, Lars; Tonning, Christer; Marius Johansen, Hans; Aarsten, Dagrun

2016-04-01

This paper proposes to use deep learning to improve semi-automatic mapping of cultural heritage from airborne laser scanning (ALS) data. Automatic detection methods, based on traditional pattern recognition, have been applied in a number of cultural heritage mapping projects in Norway for the past five years. Automatic detection of pits and heaps have been combined with visual interpretation of the ALS data for the mapping of deer hunting systems, iron production sites, grave mounds and charcoal kilns. However, the performance of the automatic detection methods varies substantially between ALS datasets. For the mapping of deer hunting systems on flat gravel and sand sediment deposits, the automatic detection results were almost perfect. However, some false detections appeared in the terrain outside of the sediment deposits. These could be explained by other pit-like landscape features, like parts of river courses, spaces between boulders, and modern terrain modifications. However, these were easy to spot during visual interpretation, and the number of missed individual pitfall traps was still low. For the mapping of grave mounds, the automatic method produced a large number of false detections, reducing the usefulness of the semi-automatic approach. The mound structure is a very common natural terrain feature, and the grave mounds are less distinct in shape than the pitfall traps. Still, applying automatic mound detection on an entire municipality did lead to a new discovery of an Iron Age grave field with more than 15 individual mounds. Automatic mound detection also proved to be useful for a detailed re-mapping of Norway's largest Iron Age grave yard, which contains almost 1000 individual graves. Combined pit and mound detection has been applied to the mapping of more than 1000 charcoal kilns that were used by an iron work 350-200 years ago. The majority of charcoal kilns were indirectly detected as either pits on the circumference, a central mound, or both. However, kilns with a flat interior and a shallow ditch along the circumference were often missed by the automatic detection method. The successfulness of automatic detection seems to depend on two factors: (1) the density of ALS ground hits on the cultural heritage structures being sought, and (2) to what extent these structures stand out from natural terrain structures. The first factor may, to some extent, be improved by using a higher number of ALS pulses per square meter. The second factor is difficult to change, and also highlights another challenge: how to make a general automatic method that is applicable in all types of terrain within a country. The mixed experience with traditional pattern recognition for semi-automatic mapping of cultural heritage led us to consider deep learning as an alternative approach. The main principle is that a general feature detector has been trained on a large image database. The feature detector is then tailored to a specific task by using a modest number of images of true and false examples of the features being sought. Results of using deep learning are compared with previous results using traditional pattern recognition.

Comparison between manual and semi-automatic segmentation of nasal cavity and paranasal sinuses from CT images.

PubMed

Tingelhoff, K; Moral, A I; Kunkel, M E; Rilk, M; Wagner, I; Eichhorn, K G; Wahl, F M; Bootz, F

2007-01-01

Segmentation of medical image data is getting more and more important over the last years. The results are used for diagnosis, surgical planning or workspace definition of robot-assisted systems. The purpose of this paper is to find out whether manual or semi-automatic segmentation is adequate for ENT surgical workflow or whether fully automatic segmentation of paranasal sinuses and nasal cavity is needed. We present a comparison of manual and semi-automatic segmentation of paranasal sinuses and the nasal cavity. Manual segmentation is performed by custom software whereas semi-automatic segmentation is realized by a commercial product (Amira). For this study we used a CT dataset of the paranasal sinuses which consists of 98 transversal slices, each 1.0 mm thick, with a resolution of 512 x 512 pixels. For the analysis of both segmentation procedures we used volume, extension (width, length and height), segmentation time and 3D-reconstruction. The segmentation time was reduced from 960 minutes with manual to 215 minutes with semi-automatic segmentation. We found highest variances segmenting nasal cavity. For the paranasal sinuses manual and semi-automatic volume differences are not significant. Dependent on the segmentation accuracy both approaches deliver useful results and could be used for e.g. robot-assisted systems. Nevertheless both procedures are not useful for everyday surgical workflow, because they take too much time. Fully automatic and reproducible segmentation algorithms are needed for segmentation of paranasal sinuses and nasal cavity.

Radiomics-based features for pattern recognition of lung cancer histopathology and metastases.

PubMed

Ferreira Junior, José Raniery; Koenigkam-Santos, Marcel; Cipriano, Federico Enrique Garcia; Fabro, Alexandre Todorovic; Azevedo-Marques, Paulo Mazzoncini de

2018-06-01

lung cancer is the leading cause of cancer-related deaths in the world, and its poor prognosis varies markedly according to tumor staging. Computed tomography (CT) is the imaging modality of choice for lung cancer evaluation, being used for diagnosis and clinical staging. Besides tumor stage, other features, like histopathological subtype, can also add prognostic information. In this work, radiomics-based CT features were used to predict lung cancer histopathology and metastases using machine learning models. local image datasets of confirmed primary malignant pulmonary tumors were retrospectively evaluated for testing and validation. CT images acquired with same protocol were semiautomatically segmented. Tumors were characterized by clinical features and computer attributes of intensity, histogram, texture, shape, and volume. Three machine learning classifiers used up to 100 selected features to perform the analysis. radiomics-based features yielded areas under the receiver operating characteristic curve of 0.89, 0.97, and 0.92 at testing and 0.75, 0.71, and 0.81 at validation for lymph nodal metastasis, distant metastasis, and histopathology pattern recognition, respectively. the radiomics characterization approach presented great potential to be used in a computational model to aid lung cancer histopathological subtype diagnosis as a "virtual biopsy" and metastatic prediction for therapy decision support without the necessity of a whole-body imaging scanning. Copyright © 2018 Elsevier B.V. All rights reserved.

Shortest-path constraints for 3D multiobject semiautomatic segmentation via clustering and Graph Cut.

PubMed

Kéchichian, Razmig; Valette, Sébastien; Desvignes, Michel; Prost, Rémy

2013-11-01

We derive shortest-path constraints from graph models of structure adjacency relations and introduce them in a joint centroidal Voronoi image clustering and Graph Cut multiobject semiautomatic segmentation framework. The vicinity prior model thus defined is a piecewise-constant model incurring multiple levels of penalization capturing the spatial configuration of structures in multiobject segmentation. Qualitative and quantitative analyses and comparison with a Potts prior-based approach and our previous contribution on synthetic, simulated, and real medical images show that the vicinity prior allows for the correct segmentation of distinct structures having identical intensity profiles and improves the precision of segmentation boundary placement while being fairly robust to clustering resolution. The clustering approach we take to simplify images prior to segmentation strikes a good balance between boundary adaptivity and cluster compactness criteria furthermore allowing to control the trade-off. Compared with a direct application of segmentation on voxels, the clustering step improves the overall runtime and memory footprint of the segmentation process up to an order of magnitude without compromising the quality of the result.

A software solution for recording circadian oscillator features in time-lapse live cell microscopy.

PubMed

Sage, Daniel; Unser, Michael; Salmon, Patrick; Dibner, Charna

2010-07-06

Fluorescent and bioluminescent time-lapse microscopy approaches have been successfully used to investigate molecular mechanisms underlying the mammalian circadian oscillator at the single cell level. However, most of the available software and common methods based on intensity-threshold segmentation and frame-to-frame tracking are not applicable in these experiments. This is due to cell movement and dramatic changes in the fluorescent/bioluminescent reporter protein during the circadian cycle, with the lowest expression level very close to the background intensity. At present, the standard approach to analyze data sets obtained from time lapse microscopy is either manual tracking or application of generic image-processing software/dedicated tracking software. To our knowledge, these existing software solutions for manual and automatic tracking have strong limitations in tracking individual cells if their plane shifts. In an attempt to improve existing methodology of time-lapse tracking of a large number of moving cells, we have developed a semi-automatic software package. It extracts the trajectory of the cells by tracking theirs displacements, makes the delineation of cell nucleus or whole cell, and finally yields measurements of various features, like reporter protein expression level or cell displacement. As an example, we present here single cell circadian pattern and motility analysis of NIH3T3 mouse fibroblasts expressing a fluorescent circadian reporter protein. Using Circadian Gene Express plugin, we performed fast and nonbiased analysis of large fluorescent time lapse microscopy datasets. Our software solution, Circadian Gene Express (CGE), is easy to use and allows precise and semi-automatic tracking of moving cells over longer period of time. In spite of significant circadian variations in protein expression with extremely low expression levels at the valley phase, CGE allows accurate and efficient recording of large number of cell parameters, including level of reporter protein expression, velocity, direction of movement, and others. CGE proves to be useful for the analysis of widefield fluorescent microscopy datasets, as well as for bioluminescence imaging. Moreover, it might be easily adaptable for confocal image analysis by manually choosing one of the focal planes of each z-stack of the various time points of a time series. CGE is a Java plugin for ImageJ; it is freely available at: http://bigwww.epfl.ch/sage/soft/circadian/.

A semi-automatic traffic sign detection, classification, and positioning system

NASA Astrophysics Data System (ADS)

Creusen, I. M.; Hazelhoff, L.; de With, P. H. N.

2012-01-01

The availability of large-scale databases containing street-level panoramic images offers the possibility to perform semi-automatic surveying of real-world objects such as traffic signs. These inventories can be performed significantly more efficiently than using conventional methods. Governmental agencies are interested in these inventories for maintenance and safety reasons. This paper introduces a complete semi-automatic traffic sign inventory system. The system consists of several components. First, a detection algorithm locates the 2D position of the traffic signs in the panoramic images. Second, a classification algorithm is used to identify the traffic sign. Third, the 3D position of the traffic sign is calculated using the GPS position of the photographs. Finally, the results are listed in a table for quick inspection and are also visualized in a web browser.

TU-A-9A-06: Semi-Automatic Segmentation of Skin Cancer in High-Frequency Ultrasound Images: Initial Comparison with Histology

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

Gao, Y; Li, X; Fishman, K

Purpose: In skin-cancer radiotherapy, the assessment of skin lesion is challenging, particularly with important features such as the depth and width hard to determine. The aim of this study is to develop interative segmentation method to delineate tumor boundary using high-frequency ultrasound images and to correlate the segmentation results with the histopathological tumor dimensions. Methods: We analyzed 6 patients who comprised a total of 10 skin lesions involving the face, scalp, and hand. The patient’s various skin lesions were scanned using a high-frequency ultrasound system (Episcan, LONGPORT, INC., PA, U.S.A), with a 30-MHz single-element transducer. The lateral resolution was 14.6more » micron and the axial resolution was 3.85 micron for the ultrasound image. Semiautomatic image segmentation was performed to extract the cancer region, using a robust statistics driven active contour algorithm. The corresponding histology images were also obtained after tumor resection and served as the reference standards in this study. Results: Eight out of the 10 lesions are successfully segmented. The ultrasound tumor delineation correlates well with the histology assessment, in all the measurements such as depth, size, and shape. The depths measured by the ultrasound have an average of 9.3% difference comparing with that in the histology images. The remaining 2 cases suffered from the situation of mismatching between pathology and ultrasound images. Conclusion: High-frequency ultrasound is a noninvasive, accurate and easy-accessible modality to image skin cancer. Our segmentation method, combined with high-frequency ultrasound technology, provides a promising tool to estimate the extent of the tumor to guide the radiotherapy procedure and monitor treatment response.« less

Dynamic deformable models for 3D MRI heart segmentation

NASA Astrophysics Data System (ADS)

Zhukov, Leonid; Bao, Zhaosheng; Gusikov, Igor; Wood, John; Breen, David E.

2002-05-01

Automated or semiautomated segmentation of medical images decreases interstudy variation, observer bias, and postprocessing time as well as providing clincally-relevant quantitative data. In this paper we present a new dynamic deformable modeling approach to 3D segmentation. It utilizes recently developed dynamic remeshing techniques and curvature estimation methods to produce high-quality meshes. The approach has been implemented in an interactive environment that allows a user to specify an initial model and identify key features in the data. These features act as hard constraints that the model must not pass through as it deforms. We have employed the method to perform semi-automatic segmentation of heart structures from cine MRI data.

Image-Based Modeling Techniques for Architectural Heritage 3d Digitalization: Limits and Potentialities

NASA Astrophysics Data System (ADS)

Santagati, C.; Inzerillo, L.; Di Paola, F.

2013-07-01

3D reconstruction from images has undergone a revolution in the last few years. Computer vision techniques use photographs from data set collection to rapidly build detailed 3D models. The simultaneous applications of different algorithms (MVS), the different techniques of image matching, feature extracting and mesh optimization are inside an active field of research in computer vision. The results are promising: the obtained models are beginning to challenge the precision of laser-based reconstructions. Among all the possibilities we can mainly distinguish desktop and web-based packages. Those last ones offer the opportunity to exploit the power of cloud computing in order to carry out a semi-automatic data processing, thus allowing the user to fulfill other tasks on its computer; whereas desktop systems employ too much processing time and hard heavy approaches. Computer vision researchers have explored many applications to verify the visual accuracy of 3D model but the approaches to verify metric accuracy are few and no one is on Autodesk 123D Catch applied on Architectural Heritage Documentation. Our approach to this challenging problem is to compare the 3Dmodels by Autodesk 123D Catch and 3D models by terrestrial LIDAR considering different object size, from the detail (capitals, moldings, bases) to large scale buildings for practitioner purpose.

Infective endocarditis detection through SPECT/CT images digital processing

NASA Astrophysics Data System (ADS)

Moreno, Albino; Valdés, Raquel; Jiménez, Luis; Vallejo, Enrique; Hernández, Salvador; Soto, Gabriel

2014-03-01

Infective endocarditis (IE) is a difficult-to-diagnose pathology, since its manifestation in patients is highly variable. In this work, it was proposed a semiautomatic algorithm based on SPECT images digital processing for the detection of IE using a CT images volume as a spatial reference. The heart/lung rate was calculated using the SPECT images information. There were no statistically significant differences between the heart/lung rates values of a group of patients diagnosed with IE (2.62+/-0.47) and a group of healthy or control subjects (2.84+/-0.68). However, it is necessary to increase the study sample of both the individuals diagnosed with IE and the control group subjects, as well as to improve the images quality.

VHR satellite imagery for humanitarian crisis management: a case study

NASA Astrophysics Data System (ADS)

Bitelli, Gabriele; Eleias, Magdalena; Franci, Francesca; Mandanici, Emanuele

2017-09-01

During the last years, remote sensing data along with GIS have been largely employed for supporting emergency management activities. In this context, the use of satellite images and derived map products has become more common also in the different phases of humanitarian crisis response. In this work very high resolution satellite imagery was processed to assess the evolution of Za'atari Refugee Camp, built in Jordan in 2012 by the UN Refugee Agency to host Syrian refugees. Multispectral satellite scenes of the Za'atari area were processed by means of object-based classifications. The main aim of the present work is the development of a semiautomated procedure for multi-temporal camp monitoring with particular reference to the dwellings detection. Whilst in the emergency mapping domain automation of feature extraction is widely investigated, in the field of humanitarian missions the information is often extracted by means of photointerpretation of the satellite data. This approach requires time for the interpretation; moreover, it is not reliable enough in complex situations, where features of interest are often small, heterogeneous and inconsistent. Therefore, the present paper discusses a methodology to obtain information for assisting humanitarian crisis management, using a semi-automatic classification approach applied to satellite imagery.

Semiautomatic tumor segmentation with multimodal images in a conditional random field framework.

PubMed

Hu, Yu-Chi; Grossberg, Michael; Mageras, Gikas

2016-04-01

Volumetric medical images of a single subject can be acquired using different imaging modalities, such as computed tomography, magnetic resonance imaging (MRI), and positron emission tomography. In this work, we present a semiautomatic segmentation algorithm that can leverage the synergies between different image modalities while integrating interactive human guidance. The algorithm provides a statistical segmentation framework partly automating the segmentation task while still maintaining critical human oversight. The statistical models presented are trained interactively using simple brush strokes to indicate tumor and nontumor tissues and using intermediate results within a patient's image study. To accomplish the segmentation, we construct the energy function in the conditional random field (CRF) framework. For each slice, the energy function is set using the estimated probabilities from both user brush stroke data and prior approved segmented slices within a patient study. The progressive segmentation is obtained using a graph-cut-based minimization. Although no similar semiautomated algorithm is currently available, we evaluated our method with an MRI data set from Medical Image Computing and Computer Assisted Intervention Society multimodal brain segmentation challenge (BRATS 2012 and 2013) against a similar fully automatic method based on CRF and a semiautomatic method based on grow-cut, and our method shows superior performance.

The Influence of Endmember Selection Method in Extracting Impervious Surface from Airborne Hyperspectral Imagery

NASA Astrophysics Data System (ADS)

Wang, J.; Feng, B.

2016-12-01

Impervious surface area (ISA) has long been studied as an important input into moisture flux models. In general, ISA impedes groundwater recharge, increases stormflow/flood frequency, and alters in-stream and riparian habitats. Urban area is recognized as one of the richest ISA environment. Urban ISA mapping assists flood prevention and urban planning. Hyperspectral imagery (HI), for its ability to detect subtle spectral signature, becomes an ideal candidate in urban ISA mapping. To map ISA from HI involves endmember (EM) selection. The high degree of spatial and spectral heterogeneity of urban environment puts great difficulty in this task: a compromise point is needed between the automatic degree and the good representativeness of the method. The study tested one manual and two semi-automatic EM selection strategies. The manual and the first semi-automatic methods have been widely used in EM selection. The second semi-automatic EM selection method is rather new and has been only proposed for moderate spatial resolution satellite. The manual method visually selected the EM candidates from eight landcover types in the original image. The first semi-automatic method chose the EM candidates using a threshold over the pixel purity index (PPI) map. The second semi-automatic method used the triangle shape of the HI scatter plot in the n-Dimension visualizer to identify the V-I-S (vegetation-impervious surface-soil) EM candidates: the pixels locate at the triangle points. The initial EM candidates from the three methods were further refined by three indexes (EM average RMSE, minimum average spectral angle, and count based EM selection) and generated three spectral libraries, which were used to classify the test image. Spectral angle mapper was applied. The accuracy reports for the classification results were generated. The overall accuracy are 85% for the manual method, 81% for the PPI method, and 87% for the V-I-S method. The V-I-S EM selection method performs best in this study. This fact proves the value of V-I-S EM selection method in not only moderate spatial resolution satellite image but also the more and more accessible high spatial resolution airborne image. This semi-automatic EM selection method can be adopted into a wide range of remote sensing images and provide ISA map for hydrology analysis.

Development and evaluation of a semiautomatic segmentation method for the estimation of LV parameters on cine MR images

NASA Astrophysics Data System (ADS)

Mazonakis, Michalis; Grinias, Elias; Pagonidis, Konstantin; Tziritas, George; Damilakis, John

2010-02-01

The purpose of this study was to develop and evaluate a semiautomatic method for left ventricular (LV) segmentation on cine MR images and subsequent estimation of cardiac parameters. The study group comprised cardiac MR examinations of 18 consecutive patients with known or suspected coronary artery disease. The new method allowed the automatic detection of the LV endocardial and epicardial boundaries on each short-axis cine MR image using a Bayesian flooding segmentation algorithm and weighted least-squares B-splines minimization. Manual editing of the automatic contours could be performed for unsatisfactory segmentation results. The end-diastolic volume (EDV), end-systolic volume (ESV), ejection fraction (EF) and LV mass estimated by the new method were compared with the reference values obtained by manually tracing the LV cavity borders. The reproducibility of the new method was determined using data from two independent observers. The mean number of endocardial and epicardial outlines not requiring any manual adjustment was more than 80% and 76% of the total contour number per study, respectively. The mean segmentation time including the required manual corrections was 2.3 ± 0.7 min per patient. LV volumes estimated by the semiautomatic method were significantly lower than those by manual tracing (P < 0.05), whereas no difference was found for EF and LV mass (P > 0.05). LV indices estimated by the two methods were well correlated (r >= 0.80). The mean difference between manual and semiautomatic method for estimating EDV, ESV, EF and LV mass was 6.1 ± 7.2 ml, 3.0 ± 5.2 ml, -0.6 ± 4.3% and -6.2 ± 12.2 g, respectively. The intraobserver and interobserver variability associated with the semiautomatic determination of LV indices was 0.5-1.2% and 0.8-3.9%, respectively. The estimation of LV parameters with the new semiautomatic segmentation method is technically feasible, highly reproducible and time effective.

CART V: recent advancements in computer-aided camouflage assessment

NASA Astrophysics Data System (ADS)

Müller, Thomas; Müller, Markus

2011-05-01

In order to facilitate systematic, computer aided improvements of camouflage and concealment assessment methods, the software system CART (Camouflage Assessment in Real-Time) was built up for the camouflage assessment of objects in multispectral image sequences (see contributions to SPIE 2007-2010 [1], [2], [3], [4]). It comprises a semi-automatic marking of target objects (ground truth generation) including their propagation over the image sequence and the evaluation via user-defined feature extractors as well as methods to assess the object's movement conspicuity. In this fifth part in an annual series at the SPIE conference in Orlando, this paper presents the enhancements over the recent year and addresses the camouflage assessment of static and moving objects in multispectral image data that can show noise or image artefacts. The presented methods fathom the correlations between image processing and camouflage assessment. A novel algorithm is presented based on template matching to assess the structural inconspicuity of an object objectively and quantitatively. The results can easily be combined with an MTI (moving target indication) based movement conspicuity assessment function in order to explore the influence of object movement to a camouflage effect in different environments. As the results show, the presented methods contribute to a significant benefit in the field of camouflage assessment.

A Semi-Automatic Method for Image Analysis of Edge Dynamics in Living Cells

PubMed Central

Huang, Lawrence; Helmke, Brian P.

2011-01-01

Spatial asymmetry of actin edge ruffling contributes to the process of cell polarization and directional migration, but mechanisms by which external cues control actin polymerization near cell edges remain unclear. We designed a quantitative image analysis strategy to measure the spatiotemporal distribution of actin edge ruffling. Time-lapse images of endothelial cells (ECs) expressing mRFP-actin were segmented using an active contour method. In intensity line profiles oriented normal to the cell edge, peak detection identified the angular distribution of polymerized actin within 1 µm of the cell edge, which was localized to lamellipodia and edge ruffles. Edge features associated with filopodia and peripheral stress fibers were removed. Circular statistical analysis enabled detection of cell polarity, indicated by a unimodal distribution of edge ruffles. To demonstrate the approach, we detected a rapid, nondirectional increase in edge ruffling in serum-stimulated ECs and a change in constitutive ruffling orientation in quiescent, nonpolarized ECs. Error analysis using simulated test images demonstrate robustness of the method to variations in image noise levels, edge ruffle arc length, and edge intensity gradient. These quantitative measurements of edge ruffling dynamics enable investigation at the cellular length scale of the underlying molecular mechanisms regulating actin assembly and cell polarization. PMID:21643526

A Semiautomatic Method for Multiple Sclerosis Lesion Segmentation on Dual-Echo MR Imaging: Application in a Multicenter Context.

PubMed

Storelli, L; Pagani, E; Rocca, M A; Horsfield, M A; Gallo, A; Bisecco, A; Battaglini, M; De Stefano, N; Vrenken, H; Thomas, D L; Mancini, L; Ropele, S; Enzinger, C; Preziosa, P; Filippi, M

2016-07-21

The automatic segmentation of MS lesions could reduce time required for image processing together with inter- and intraoperator variability for research and clinical trials. A multicenter validation of a proposed semiautomatic method for hyperintense MS lesion segmentation on dual-echo MR imaging is presented. The classification technique used is based on a region-growing approach starting from manual lesion identification by an expert observer with a final segmentation-refinement step. The method was validated in a cohort of 52 patients with relapsing-remitting MS, with dual-echo images acquired in 6 different European centers. We found a mathematic expression that made the optimization of the method independent of the need for a training dataset. The automatic segmentation was in good agreement with the manual segmentation (dice similarity coefficient = 0.62 and root mean square error = 2 mL). Assessment of the segmentation errors showed no significant differences in algorithm performance between the different MR scanner manufacturers (P > .05). The method proved to be robust, and no center-specific training of the algorithm was required, offering the possibility for application in a clinical setting. Adoption of the method should lead to improved reliability and less operator time required for image analysis in research and clinical trials in MS. © 2016 American Society of Neuroradiology.

MR diffusion-weighted imaging-based subcutaneous tumour volumetry in a xenografted nude mouse model using 3D Slicer: an accurate and repeatable method

PubMed Central

Ma, Zelan; Chen, Xin; Huang, Yanqi; He, Lan; Liang, Cuishan; Liang, Changhong; Liu, Zaiyi

2015-01-01

Accurate and repeatable measurement of the gross tumour volume(GTV) of subcutaneous xenografts is crucial in the evaluation of anti-tumour therapy. Formula and image-based manual segmentation methods are commonly used for GTV measurement but are hindered by low accuracy and reproducibility. 3D Slicer is open-source software that provides semiautomatic segmentation for GTV measurements. In our study, subcutaneous GTVs from nude mouse xenografts were measured by semiautomatic segmentation with 3D Slicer based on morphological magnetic resonance imaging(mMRI) or diffusion-weighted imaging(DWI)(b = 0,20,800 s/mm2) . These GTVs were then compared with those obtained via the formula and image-based manual segmentation methods with ITK software using the true tumour volume as the standard reference. The effects of tumour size and shape on GTVs measurements were also investigated. Our results showed that, when compared with the true tumour volume, segmentation for DWI(P = 0.060–0.671) resulted in better accuracy than that mMRI(P < 0.001) and the formula method(P < 0.001). Furthermore, semiautomatic segmentation for DWI(intraclass correlation coefficient, ICC = 0.9999) resulted in higher reliability than manual segmentation(ICC = 0.9996–0.9998). Tumour size and shape had no effects on GTV measurement across all methods. Therefore, DWI-based semiautomatic segmentation, which is accurate and reproducible and also provides biological information, is the optimal GTV measurement method in the assessment of anti-tumour treatments. PMID:26489359

Validation of a semi-automatic protocol for the assessment of the tear meniscus central area based on open-source software

NASA Astrophysics Data System (ADS)

Pena-Verdeal, Hugo; Garcia-Resua, Carlos; Yebra-Pimentel, Eva; Giraldez, Maria J.

2017-08-01

Purpose: Different lower tear meniscus parameters can be clinical assessed on dry eye diagnosis. The aim of this study was to propose and analyse the variability of a semi-automatic method for measuring lower tear meniscus central area (TMCA) by using open source software. Material and methods: On a group of 105 subjects, one video of the lower tear meniscus after fluorescein instillation was generated by a digital camera attached to a slit-lamp. A short light beam (3x5 mm) with moderate illumination in the central portion of the meniscus (6 o'clock) was used. Images were extracted from each video by a masked observer. By using an open source software based on Java (NIH ImageJ), a further observer measured in a masked and randomized order the TMCA in the short light beam illuminated area by two methods: (1) manual method, where TMCA images was "manually" measured; (2) semi-automatic method, where TMCA images were transformed in an 8-bit-binary image, then holes inside this shape were filled and on the isolated shape, the area size was obtained. Finally, both measurements, manual and semi-automatic, were compared. Results: Paired t-test showed no statistical difference between both techniques results (p = 0.102). Pearson correlation between techniques show a significant positive near to perfect correlation (r = 0.99; p < 0.001). Conclusions: This study showed a useful tool to objectively measure the frontal central area of the meniscus in photography by free open source software.

Application of a semi-automatic cartilage segmentation method for biomechanical modeling of the knee joint.

PubMed

Liukkonen, Mimmi K; Mononen, Mika E; Tanska, Petri; Saarakkala, Simo; Nieminen, Miika T; Korhonen, Rami K

2017-10-01

Manual segmentation of articular cartilage from knee joint 3D magnetic resonance images (MRI) is a time consuming and laborious task. Thus, automatic methods are needed for faster and reproducible segmentations. In the present study, we developed a semi-automatic segmentation method based on radial intensity profiles to generate 3D geometries of knee joint cartilage which were then used in computational biomechanical models of the knee joint. Six healthy volunteers were imaged with a 3T MRI device and their knee cartilages were segmented both manually and semi-automatically. The values of cartilage thicknesses and volumes produced by these two methods were compared. Furthermore, the influences of possible geometrical differences on cartilage stresses and strains in the knee were evaluated with finite element modeling. The semi-automatic segmentation and 3D geometry construction of one knee joint (menisci, femoral and tibial cartilages) was approximately two times faster than with manual segmentation. Differences in cartilage thicknesses, volumes, contact pressures, stresses, and strains between segmentation methods in femoral and tibial cartilage were mostly insignificant (p > 0.05) and random, i.e. there were no systematic differences between the methods. In conclusion, the devised semi-automatic segmentation method is a quick and accurate way to determine cartilage geometries; it may become a valuable tool for biomechanical modeling applications with large patient groups.

Semi Automatic Ontology Instantiation in the domain of Risk Management

NASA Astrophysics Data System (ADS)

Makki, Jawad; Alquier, Anne-Marie; Prince, Violaine

One of the challenging tasks in the context of Ontological Engineering is to automatically or semi-automatically support the process of Ontology Learning and Ontology Population from semi-structured documents (texts). In this paper we describe a Semi-Automatic Ontology Instantiation method from natural language text, in the domain of Risk Management. This method is composed from three steps 1 ) Annotation with part-of-speech tags, 2) Semantic Relation Instances Extraction, 3) Ontology instantiation process. It's based on combined NLP techniques using human intervention between steps 2 and 3 for control and validation. Since it heavily relies on linguistic knowledge it is not domain dependent which is a good feature for portability between the different fields of risk management application. The proposed methodology uses the ontology of the PRIMA1 project (supported by the European community) as a Generic Domain Ontology and populates it via an available corpus. A first validation of the approach is done through an experiment with Chemical Fact Sheets from Environmental Protection Agency2.

Ventriculogram segmentation using boosted decision trees

NASA Astrophysics Data System (ADS)

McDonald, John A.; Sheehan, Florence H.

2004-05-01

Left ventricular status, reflected in ejection fraction or end systolic volume, is a powerful prognostic indicator in heart disease. Quantitative analysis of these and other parameters from ventriculograms (cine xrays of the left ventricle) is infrequently performed due to the labor required for manual segmentation. None of the many methods developed for automated segmentation has achieved clinical acceptance. We present a method for semi-automatic segmentation of ventriculograms based on a very accurate two-stage boosted decision-tree pixel classifier. The classifier determines which pixels are inside the ventricle at key ED (end-diastole) and ES (end-systole) frames. The test misclassification rate is about 1%. The classifier is semi-automatic, requiring a user to select 3 points in each frame: the endpoints of the aortic valve and the apex. The first classifier stage is 2 boosted decision-trees, trained using features such as gray-level statistics (e.g. median brightness) and image geometry (e.g. coordinates relative to user supplied 3 points). Second stage classifiers are trained using the same features as the first, plus the output of the first stage. Border pixels are determined from the segmented images using dilation and erosion. A curve is then fit to the border pixels, minimizing a penalty function that trades off fidelity to the border pixels with smoothness. ED and ES volumes, and ejection fraction are estimated from border curves using standard area-length formulas. On independent test data, the differences between automatic and manual volumes (and ejection fractions) are similar in size to the differences between two human observers.

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

NASA Astrophysics Data System (ADS)

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 results of these semi-automatically determined seasonal fan count evolutions for Inca City, Ithaca and Manhattan ROIs, compare these evolutionary patterns with each other and with surface reflectance evolutions of both HiRISE and CRISM for the same locations. References: Aye, K.-M. et. al. (2010), LPSC 2010, 2707 Hansen, C. et. al (2010) Icarus, 205, Issue 1, p. 283-295 Kieffer, H.H. (2007), JGR 112 Portyankina, G. et. al. (2010), Icarus, 205, Issue 1, p. 311-320 Thomas, N. et. Al. (2009), Vol. 4, EPSC2009-478

Instruments and Methodologies for the Underwater Tridimensional Digitization and Data Musealization

NASA Astrophysics Data System (ADS)

Repola, L.; Memmolo, R.; Signoretti, D.

2015-04-01

In the research started within the SINAPSIS project of the Università degli Studi Suor Orsola Benincasa an underwater stereoscopic scanning aimed at surveying of submerged archaeological sites, integrable to standard systems for geomorphological detection of the coast, has been developed. The project involves the construction of hardware consisting of an aluminum frame supporting a pair of GoPro Hero Black Edition cameras and software for the production of point clouds and the initial processing of data. The software has features for stereoscopic vision system calibration, reduction of noise and the of distortion of underwater captured images, searching for corresponding points of stereoscopic images using stereo-matching algorithms (dense and sparse), for points cloud generating and filtering. Only after various calibration and survey tests carried out during the excavations envisaged in the project, the mastery of methods for an efficient acquisition of data has been achieved. The current development of the system has allowed generation of portions of digital models of real submerged scenes. A semi-automatic procedure for global registration of partial models is under development as a useful aid for the study and musealization of sites.

Optimizing the 3D-reconstruction technique for serial block-face scanning electron microscopy.

PubMed

Wernitznig, Stefan; Sele, Mariella; Urschler, Martin; Zankel, Armin; Pölt, Peter; Rind, F Claire; Leitinger, Gerd

2016-05-01

Elucidating the anatomy of neuronal circuits and localizing the synaptic connections between neurons, can give us important insights in how the neuronal circuits work. We are using serial block-face scanning electron microscopy (SBEM) to investigate the anatomy of a collision detection circuit including the Lobula Giant Movement Detector (LGMD) neuron in the locust, Locusta migratoria. For this, thousands of serial electron micrographs are produced that allow us to trace the neuronal branching pattern. The reconstruction of neurons was previously done manually by drawing cell outlines of each cell in each image separately. This approach was very time consuming and troublesome. To make the process more efficient a new interactive software was developed. It uses the contrast between the neuron under investigation and its surrounding for semi-automatic segmentation. For segmentation the user sets starting regions manually and the algorithm automatically selects a volume within the neuron until the edges corresponding to the neuronal outline are reached. Internally the algorithm optimizes a 3D active contour segmentation model formulated as a cost function taking the SEM image edges into account. This reduced the reconstruction time, while staying close to the manual reference segmentation result. Our algorithm is easy to use for a fast segmentation process, unlike previous methods it does not require image training nor an extended computing capacity. Our semi-automatic segmentation algorithm led to a dramatic reduction in processing time for the 3D-reconstruction of identified neurons. Copyright © 2016 Elsevier B.V. All rights reserved.

Phantom Study Investigating the Accuracy of Manual and Automatic Image Fusion with the GE Logiq E9: Implications for use in Percutaneous Liver Interventions

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

Burgmans, Mark Christiaan, E-mail: m.c.burgmans@lumc.nl; Harder, J. Michiel den, E-mail: chiel.den.harder@gmail.com; Meershoek, Philippa, E-mail: P.Meershoek@lumc.nl

PurposeTo determine the accuracy of automatic and manual co-registration methods for image fusion of three-dimensional computed tomography (CT) with real-time ultrasonography (US) for image-guided liver interventions.Materials and MethodsCT images of a skills phantom with liver lesions were acquired and co-registered to US using GE Logiq E9 navigation software. Manual co-registration was compared to automatic and semiautomatic co-registration using an active tracker. Also, manual point registration was compared to plane registration with and without an additional translation point. Finally, comparison was made between manual and automatic selection of reference points. In each experiment, accuracy of the co-registration method was determined bymore » measurement of the residual displacement in phantom lesions by two independent observers.ResultsMean displacements for a superficial and deep liver lesion were comparable after manual and semiautomatic co-registration: 2.4 and 2.0 mm versus 2.0 and 2.5 mm, respectively. Both methods were significantly better than automatic co-registration: 5.9 and 5.2 mm residual displacement (p < 0.001; p < 0.01). The accuracy of manual point registration was higher than that of plane registration, the latter being heavily dependent on accurate matching of axial CT and US images by the operator. Automatic reference point selection resulted in significantly lower registration accuracy compared to manual point selection despite lower root-mean-square deviation (RMSD) values.ConclusionThe accuracy of manual and semiautomatic co-registration is better than that of automatic co-registration. For manual co-registration using a plane, choosing the correct plane orientation is an essential first step in the registration process. Automatic reference point selection based on RMSD values is error-prone.« less

Semi-automatic mapping of fault rocks on a Digital Outcrop Model, Gole Larghe Fault Zone (Southern Alps, Italy)

NASA Astrophysics Data System (ADS)

Mittempergher, Silvia; Vho, Alice; Bistacchi, Andrea

2016-04-01

A quantitative analysis of fault-rock distribution in outcrops of exhumed fault zones is of fundamental importance for studies of fault zone architecture, fault and earthquake mechanics, and fluid circulation. We present a semi-automatic workflow for fault-rock mapping on a Digital Outcrop Model (DOM), developed on the Gole Larghe Fault Zone (GLFZ), a well exposed strike-slip fault in the Adamello batholith (Italian Southern Alps). The GLFZ has been exhumed from ca. 8-10 km depth, and consists of hundreds of individual seismogenic slip surfaces lined by green cataclasites (crushed wall rocks cemented by the hydrothermal epidote and K-feldspar) and black pseudotachylytes (solidified frictional melts, considered as a marker for seismic slip). A digital model of selected outcrop exposures was reconstructed with photogrammetric techniques, using a large number of high resolution digital photographs processed with VisualSFM software. The resulting DOM has a resolution up to 0.2 mm/pixel. Most of the outcrop was imaged using images each one covering a 1 x 1 m2 area, while selected structural features, such as sidewall ripouts or stepovers, were covered with higher-resolution images covering 30 x 40 cm2 areas.Image processing algorithms were preliminarily tested using the ImageJ-Fiji package, then a workflow in Matlab was developed to process a large collection of images sequentially. Particularly in detailed 30 x 40 cm images, cataclasites and hydrothermal veins were successfully identified using spectral analysis in RGB and HSV color spaces. This allows mapping the network of cataclasites and veins which provided the pathway for hydrothermal fluid circulation, and also the volume of mineralization, since we are able to measure the thickness of cataclasites and veins on the outcrop surface. The spectral signature of pseudotachylyte veins is indistinguishable from that of biotite grains in the wall rock (tonalite), so we tested morphological analysis tools to discriminate them with respect to biotite. In higher resolution images this could be performed using circularity and size thresholds, however this could not be easily implemented in an automated procedure since the thresholds must be varied by the interpreter almost for each image. In 1 x 1 m images the resolution is generally too low to distinguish cataclasite and pseudotachylyte, so most of the time fault rocks were treated together. For this analysis we developed a fully automated workflow that, after applying noise correction, classification and skeletonization algorithms, returns labeled edge images of fault segments together with vector polylines associated to edge properties. Vector and edge properties represent a useful format to perform further quantitative analysis, for instance for classifying fault segments based on structural criteria, detect continuous fault traces, and detect the kind of termination of faults/fractures. This approach allows to collect statistically relevant datasets useful for further quantitative structural analysis.

Implementation and evaluation of a new workflow for registration and segmentation of pulmonary MRI data for regional lung perfusion assessment.

PubMed

Böttger, T; Grunewald, K; Schöbinger, M; Fink, C; Risse, F; Kauczor, H U; Meinzer, H P; Wolf, Ivo

2007-03-07

Recently it has been shown that regional lung perfusion can be assessed using time-resolved contrast-enhanced magnetic resonance (MR) imaging. Quantification of the perfusion images has been attempted, based on definition of small regions of interest (ROIs). Use of complete lung segmentations instead of ROIs could possibly increase quantification accuracy. Due to the low signal-to-noise ratio, automatic segmentation algorithms cannot be applied. On the other hand, manual segmentation of the lung tissue is very time consuming and can become inaccurate, as the borders of the lung to adjacent tissues are not always clearly visible. We propose a new workflow for semi-automatic segmentation of the lung from additionally acquired morphological HASTE MR images. First the lung is delineated semi-automatically in the HASTE image. Next the HASTE image is automatically registered with the perfusion images. Finally, the transformation resulting from the registration is used to align the lung segmentation from the morphological dataset with the perfusion images. We evaluated rigid, affine and locally elastic transformations, suitable optimizers and different implementations of mutual information (MI) metrics to determine the best possible registration algorithm. We located the shortcomings of the registration procedure and under which conditions automatic registration will succeed or fail. Segmentation results were evaluated using overlap and distance measures. Integration of the new workflow reduces the time needed for post-processing of the data, simplifies the perfusion quantification and reduces interobserver variability in the segmentation process. In addition, the matched morphological data set can be used to identify morphologic changes as the source for the perfusion abnormalities.

System for objective assessment of image differences in digital cinema

NASA Astrophysics Data System (ADS)

Fliegel, Karel; Krasula, Lukáš; Páta, Petr; Myslík, Jiří; Pecák, Josef; Jícha, Marek

2014-09-01

There is high demand for quick digitization and subsequent image restoration of archived film records. Digitization is very urgent in many cases because various invaluable pieces of cultural heritage are stored on aging media. Only selected records can be reconstructed perfectly using painstaking manual or semi-automatic procedures. This paper aims to answer the question what are the quality requirements on the restoration process in order to obtain acceptably close visual perception of the digitally restored film in comparison to the original analog film copy. This knowledge is very important to preserve the original artistic intention of the movie producers. Subjective experiment with artificially distorted images has been conducted in order to answer the question what is the visual impact of common image distortions in digital cinema. Typical color and contrast distortions were introduced and test images were presented to viewers using digital projector. Based on the outcome of this subjective evaluation a system for objective assessment of image distortions has been developed and its performance tested. The system utilizes calibrated digital single-lens reflex camera and subsequent analysis of suitable features of images captured from the projection screen. The evaluation of captured image data has been optimized in order to obtain predicted differences between the reference and distorted images while achieving high correlation with the results of subjective assessment. The system can be used to objectively determine the difference between analog film and digital cinema images on the projection screen.

Segmentation of whole cells and cell nuclei from 3-D optical microscope images using dynamic programming.

PubMed

McCullough, D P; Gudla, P R; Harris, B S; Collins, J A; Meaburn, K J; Nakaya, M A; Yamaguchi, T P; Misteli, T; Lockett, S J

2008-05-01

Communications between cells in large part drive tissue development and function, as well as disease-related processes such as tumorigenesis. Understanding the mechanistic bases of these processes necessitates quantifying specific molecules in adjacent cells or cell nuclei of intact tissue. However, a major restriction on such analyses is the lack of an efficient method that correctly segments each object (cell or nucleus) from 3-D images of an intact tissue specimen. We report a highly reliable and accurate semi-automatic algorithmic method for segmenting fluorescence-labeled cells or nuclei from 3-D tissue images. Segmentation begins with semi-automatic, 2-D object delineation in a user-selected plane, using dynamic programming (DP) to locate the border with an accumulated intensity per unit length greater that any other possible border around the same object. Then the two surfaces of the object in planes above and below the selected plane are found using an algorithm that combines DP and combinatorial searching. Following segmentation, any perceived errors can be interactively corrected. Segmentation accuracy is not significantly affected by intermittent labeling of object surfaces, diffuse surfaces, or spurious signals away from surfaces. The unique strength of the segmentation method was demonstrated on a variety of biological tissue samples where all cells, including irregularly shaped cells, were accurately segmented based on visual inspection.

State of the art survey on MRI brain tumor segmentation.

PubMed

Gordillo, Nelly; Montseny, Eduard; Sobrevilla, Pilar

2013-10-01

Brain tumor segmentation consists of separating the different tumor tissues (solid or active tumor, edema, and necrosis) from normal brain tissues: gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). In brain tumor studies, the existence of abnormal tissues may be easily detectable most of the time. However, accurate and reproducible segmentation and characterization of abnormalities are not straightforward. In the past, many researchers in the field of medical imaging and soft computing have made significant survey in the field of brain tumor segmentation. Both semiautomatic and fully automatic methods have been proposed. Clinical acceptance of segmentation techniques has depended on the simplicity of the segmentation, and the degree of user supervision. Interactive or semiautomatic methods are likely to remain dominant in practice for some time, especially in these applications where erroneous interpretations are unacceptable. This article presents an overview of the most relevant brain tumor segmentation methods, conducted after the acquisition of the image. Given the advantages of magnetic resonance imaging over other diagnostic imaging, this survey is focused on MRI brain tumor segmentation. Semiautomatic and fully automatic techniques are emphasized. Copyright © 2013 Elsevier Inc. All rights reserved.

Fast conjugate phase image reconstruction based on a Chebyshev approximation to correct for B0 field inhomogeneity and concomitant gradients.

PubMed

Chen, Weitian; Sica, Christopher T; Meyer, Craig H

2008-11-01

Off-resonance effects can cause image blurring in spiral scanning and various forms of image degradation in other MRI methods. Off-resonance effects can be caused by both B0 inhomogeneity and concomitant gradient fields. Previously developed off-resonance correction methods focus on the correction of a single source of off-resonance. This work introduces a computationally efficient method of correcting for B0 inhomogeneity and concomitant gradients simultaneously. The method is a fast alternative to conjugate phase reconstruction, with the off-resonance phase term approximated by Chebyshev polynomials. The proposed algorithm is well suited for semiautomatic off-resonance correction, which works well even with an inaccurate or low-resolution field map. The proposed algorithm is demonstrated using phantom and in vivo data sets acquired by spiral scanning. Semiautomatic off-resonance correction alone is shown to provide a moderate amount of correction for concomitant gradient field effects, in addition to B0 imhomogeneity effects. However, better correction is provided by the proposed combined method. The best results were produced using the semiautomatic version of the proposed combined method.

Quantitative analysis of the patellofemoral motion pattern using semi-automatic processing of 4D CT data.

PubMed

Forsberg, Daniel; Lindblom, Maria; Quick, Petter; Gauffin, Håkan

2016-09-01

To present a semi-automatic method with minimal user interaction for quantitative analysis of the patellofemoral motion pattern. 4D CT data capturing the patellofemoral motion pattern of a continuous flexion and extension were collected for five patients prone to patellar luxation both pre- and post-surgically. For the proposed method, an observer would place landmarks in a single 3D volume, which then are automatically propagated to the other volumes in a time sequence. From the landmarks in each volume, the measures patellar displacement, patellar tilt and angle between femur and tibia were computed. Evaluation of the observer variability showed the proposed semi-automatic method to be favorable over a fully manual counterpart, with an observer variability of approximately 1.5[Formula: see text] for the angle between femur and tibia, 1.5 mm for the patellar displacement, and 4.0[Formula: see text]-5.0[Formula: see text] for the patellar tilt. The proposed method showed that surgery reduced the patellar displacement and tilt at maximum extension with approximately 10-15 mm and 15[Formula: see text]-20[Formula: see text] for three patients but with less evident differences for two of the patients. A semi-automatic method suitable for quantification of the patellofemoral motion pattern as captured by 4D CT data has been presented. Its observer variability is on par with that of other methods but with the distinct advantage to support continuous motions during the image acquisition.

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

PubMed

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

2014-10-01

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

Application of snakes and dynamic programming optimisation technique in modeling of buildings in informal settlement areas

NASA Astrophysics Data System (ADS)

Rüther, Heinz; Martine, Hagai M.; Mtalo, E. G.

This paper presents a novel approach to semiautomatic building extraction in informal settlement areas from aerial photographs. The proposed approach uses a strategy of delineating buildings by optimising their approximate building contour position. Approximate building contours are derived automatically by locating elevation blobs in digital surface models. Building extraction is then effected by means of the snakes algorithm and the dynamic programming optimisation technique. With dynamic programming, the building contour optimisation problem is realized through a discrete multistage process and solved by the "time-delayed" algorithm, as developed in this work. The proposed building extraction approach is a semiautomatic process, with user-controlled operations linking fully automated subprocesses. Inputs into the proposed building extraction system are ortho-images and digital surface models, the latter being generated through image matching techniques. Buildings are modeled as "lumps" or elevation blobs in digital surface models, which are derived by altimetric thresholding of digital surface models. Initial windows for building extraction are provided by projecting the elevation blobs centre points onto an ortho-image. In the next step, approximate building contours are extracted from the ortho-image by region growing constrained by edges. Approximate building contours thus derived are inputs into the dynamic programming optimisation process in which final building contours are established. The proposed system is tested on two study areas: Marconi Beam in Cape Town, South Africa, and Manzese in Dar es Salaam, Tanzania. Sixty percent of buildings in the study areas have been extracted and verified and it is concluded that the proposed approach contributes meaningfully to the extraction of buildings in moderately complex and crowded informal settlement areas.

SU-E-J-107: Supervised Learning Model of Aligned Collagen for Human Breast Carcinoma Prognosis

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

Bredfeldt, J; Liu, Y; Conklin, M

Purpose: Our goal is to develop and apply a set of optical and computational tools to enable large-scale investigations of the interaction between collagen and tumor cells. Methods: We have built a novel imaging system for automating the capture of whole-slide second harmonic generation (SHG) images of collagen in registry with bright field (BF) images of hematoxylin and eosin stained tissue. To analyze our images, we have integrated a suite of supervised learning tools that semi-automatically model and score collagen interactions with tumor cells via a variety of metrics, a method we call Electronic Tumor Associated Collagen Signatures (eTACS). Thismore » group of tools first segments regions of epithelial cells and collagen fibers from BF and SHG images respectively. We then associate fibers with groups of epithelial cells and finally compute features based on the angle of interaction and density of the collagen surrounding the epithelial cell clusters. These features are then processed with a support vector machine to separate cancer patients into high and low risk groups. Results: We validated our model by showing that eTACS produces classifications that have statistically significant correlation with manual classifications. In addition, our system generated classification scores that accurately predicted breast cancer patient survival in a cohort of 196 patients. Feature rank analysis revealed that TACS positive fibers are more well aligned with each other, generally lower density, and terminate within or near groups of epithelial cells. Conclusion: We are working to apply our model to predict survival in larger cohorts of breast cancer patients with a diversity of breast cancer types, predict response to treatments such as COX2 inhibitors, and to study collagen architecture changes in other cancer types. In the future, our system may be used to provide metastatic potential information to cancer patients to augment existing clinical assays.« less

Feature-based and statistical methods for analyzing the Deepwater Horizon oil spill with AVIRIS imagery

USGS Publications Warehouse

Rand, R.S.; Clark, R.N.; Livo, K.E.

2011-01-01

The Deepwater Horizon oil spill covered a very large geographical area in the Gulf of Mexico creating potentially serious environmental impacts on both marine life and the coastal shorelines. Knowing the oil's areal extent and thickness as well as denoting different categories of the oil's physical state is important for assessing these impacts. High spectral resolution data in hyperspectral imagery (HSI) sensors such as Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) provide a valuable source of information that can be used for analysis by semi-automatic methods for tracking an oil spill's areal extent, oil thickness, and oil categories. However, the spectral behavior of oil in water is inherently a highly non-linear and variable phenomenon that changes depending on oil thickness and oil/water ratios. For certain oil thicknesses there are well-defined absorption features, whereas for very thin films sometimes there are almost no observable features. Feature-based imaging spectroscopy methods are particularly effective at classifying materials that exhibit specific well-defined spectral absorption features. Statistical methods are effective at classifying materials with spectra that exhibit a considerable amount of variability and that do not necessarily exhibit well-defined spectral absorption features. This study investigates feature-based and statistical methods for analyzing oil spills using hyperspectral imagery. The appropriate use of each approach is investigated and a combined feature-based and statistical method is proposed.

MRI histogram analysis enables objective and continuous classification of intervertebral disc degeneration.

PubMed

Waldenberg, Christian; Hebelka, Hanna; Brisby, Helena; Lagerstrand, Kerstin Magdalena

2018-05-01

Magnetic resonance imaging (MRI) is the best diagnostic imaging method for low back pain. However, the technique is currently not utilized in its full capacity, often failing to depict painful intervertebral discs (IVDs), potentially due to the rough degeneration classification system used clinically today. MR image histograms, which reflect the IVD heterogeneity, may offer sensitive imaging biomarkers for IVD degeneration classification. This study investigates the feasibility of using histogram analysis as means of objective and continuous grading of IVD degeneration. Forty-nine IVDs in ten low back pain patients (six males, 25-69 years) were examined with MRI (T2-weighted images and T2-maps). Each IVD was semi-automatically segmented on three mid-sagittal slices. Histogram features of the IVD were extracted from the defined regions of interest and correlated to Pfirrmann grade. Both T2-weighted images and T2-maps displayed similar histogram features. Histograms of well-hydrated IVDs displayed two separate peaks, representing annulus fibrosus and nucleus pulposus. Degenerated IVDs displayed decreased peak separation, where the separation was shown to correlate strongly with Pfirrmann grade (P < 0.05). In addition, some degenerated IVDs within the same Pfirrmann grade displayed diametrically different histogram appearances. Histogram features correlated well with IVD degeneration, suggesting that IVD histogram analysis is a suitable tool for objective and continuous IVD degeneration classification. As histogram analysis revealed IVD heterogeneity, it may be a clinical tool for characterization of regional IVD degeneration effects. To elucidate the usefulness of histogram analysis in patient management, IVD histogram features between asymptomatic and symptomatic individuals needs to be compared.

A semi-automatic method for positioning a femoral bone reconstruction for strict view generation.

PubMed

Milano, Federico; Ritacco, Lucas; Gomez, Adrian; Gonzalez Bernaldo de Quiros, Fernan; Risk, Marcelo

2010-01-01

In this paper we present a semi-automatic method for femoral bone positioning after 3D image reconstruction from Computed Tomography images. This serves as grounding for the definition of strict axial, longitudinal and anterior-posterior views, overcoming the problem of patient positioning biases in 2D femoral bone measuring methods. After the bone reconstruction is aligned to a standard reference frame, new tomographic slices can be generated, on which unbiased measures may be taken. This could allow not only accurate inter-patient comparisons but also intra-patient comparisons, i.e., comparisons of images of the same patient taken at different times. This method could enable medical doctors to diagnose and follow up several bone deformities more easily.

A semiautomatic CT-based ensemble segmentation of lung tumors: comparison with oncologists' delineations and with the surgical specimen.

PubMed

Rios Velazquez, Emmanuel; Aerts, Hugo J W L; Gu, Yuhua; Goldgof, Dmitry B; De Ruysscher, Dirk; Dekker, Andre; Korn, René; Gillies, Robert J; Lambin, Philippe

2012-11-01

To assess the clinical relevance of a semiautomatic CT-based ensemble segmentation method, by comparing it to pathology and to CT/PET manual delineations by five independent radiation oncologists in non-small cell lung cancer (NSCLC). For 20 NSCLC patients (stages Ib-IIIb) the primary tumor was delineated manually on CT/PET scans by five independent radiation oncologists and segmented using a CT based semi-automatic tool. Tumor volume and overlap fractions between manual and semiautomatic-segmented volumes were compared. All measurements were correlated with the maximal diameter on macroscopic examination of the surgical specimen. Imaging data are available on www.cancerdata.org. High overlap fractions were observed between the semi-automatically segmented volumes and the intersection (92.5±9.0, mean±SD) and union (94.2±6.8) of the manual delineations. No statistically significant differences in tumor volume were observed between the semiautomatic segmentation (71.4±83.2 cm(3), mean±SD) and manual delineations (81.9±94.1 cm(3); p=0.57). The maximal tumor diameter of the semiautomatic-segmented tumor correlated strongly with the macroscopic diameter of the primary tumor (r=0.96). Semiautomatic segmentation of the primary tumor on CT demonstrated high agreement with CT/PET manual delineations and strongly correlated with the macroscopic diameter considered as the "gold standard". This method may be used routinely in clinical practice and could be employed as a starting point for treatment planning, target definition in multi-center clinical trials or for high throughput data mining research. This method is particularly suitable for peripherally located tumors. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Fast conjugate phase image reconstruction based on a Chebyshev approximation to correct for B0 field inhomogeneity and concomitant gradients

PubMed Central

Chen, Weitian; Sica, Christopher T.; Meyer, Craig H.

2008-01-01

Off-resonance effects can cause image blurring in spiral scanning and various forms of image degradation in other MRI methods. Off-resonance effects can be caused by both B0 inhomogeneity and concomitant gradient fields. Previously developed off-resonance correction methods focus on the correction of a single source of off-resonance. This work introduces a computationally efficient method of correcting for B0 inhomogeneity and concomitant gradients simultaneously. The method is a fast alternative to conjugate phase reconstruction, with the off-resonance phase term approximated by Chebyshev polynomials. The proposed algorithm is well suited for semiautomatic off-resonance correction, which works well even with an inaccurate or low-resolution field map. The proposed algorithm is demonstrated using phantom and in vivo data sets acquired by spiral scanning. Semiautomatic off-resonance correction alone is shown to provide a moderate amount of correction for concomitant gradient field effects, in addition to B0 imhomogeneity effects. However, better correction is provided by the proposed combined method. The best results were produced using the semiautomatic version of the proposed combined method. PMID:18956462

Robust extraction of the aorta and pulmonary artery from 3D MDCT image data

NASA Astrophysics Data System (ADS)

Taeprasartsit, Pinyo; Higgins, William E.

2010-03-01

Accurate definition of the aorta and pulmonary artery from three-dimensional (3D) multi-detector CT (MDCT) images is important for pulmonary applications. This work presents robust methods for defining the aorta and pulmonary artery in the central chest. The methods work on both contrast enhanced and no-contrast 3D MDCT image data. The automatic methods use a common approach employing model fitting and selection and adaptive refinement. During the occasional event that more precise vascular extraction is desired or the method fails, we also have an alternate semi-automatic fail-safe method. The semi-automatic method extracts the vasculature by extending the medial axes into a user-guided direction. A ground-truth study over a series of 40 human 3D MDCT images demonstrates the efficacy, accuracy, robustness, and efficiency of the methods.

Semi-Automatic Segmentation Software for Quantitative Clinical Brain Glioblastoma Evaluation

PubMed Central

Zhu, Y; Young, G; Xue, Z; Huang, R; You, H; Setayesh, K; Hatabu, H; Cao, F; Wong, S.T.

2012-01-01

Rationale and Objectives Quantitative measurement provides essential information about disease progression and treatment response in patients with Glioblastoma multiforme (GBM). The goal of this paper is to present and validate a software pipeline for semi-automatic GBM segmentation, called AFINITI (Assisted Follow-up in NeuroImaging of Therapeutic Intervention), using clinical data from GBM patients. Materials and Methods Our software adopts the current state-of-the-art tumor segmentation algorithms and combines them into one clinically usable pipeline. Both the advantages of the traditional voxel-based and the deformable shape-based segmentation are embedded into the software pipeline. The former provides an automatic tumor segmentation scheme based on T1- and T2-weighted MR brain data, and the latter refines the segmentation results with minimal manual input. Results Twenty six clinical MR brain images of GBM patients were processed and compared with manual results. The results can be visualized using the embedded graphic user interface (GUI). Conclusion Validation results using clinical GBM data showed high correlation between the AFINITI results and manual annotation. Compared to the voxel-wise segmentation, AFINITI yielded more accurate results in segmenting the enhanced GBM from multimodality MRI data. The proposed pipeline could be used as additional information to interpret MR brain images in neuroradiology. PMID:22591720

Diagnostic accuracy of semi-automatic quantitative metrics as an alternative to expert reading of CT myocardial perfusion in the CORE320 study.

PubMed

Ostovaneh, Mohammad R; Vavere, Andrea L; Mehra, Vishal C; Kofoed, Klaus F; Matheson, Matthew B; Arbab-Zadeh, Armin; Fujisawa, Yasuko; Schuijf, Joanne D; Rochitte, Carlos E; Scholte, Arthur J; Kitagawa, Kakuya; Dewey, Marc; Cox, Christopher; DiCarli, Marcelo F; George, Richard T; Lima, Joao A C

To determine the diagnostic accuracy of semi-automatic quantitative metrics compared to expert reading for interpretation of computed tomography perfusion (CTP) imaging. The CORE320 multicenter diagnostic accuracy clinical study enrolled patients between 45 and 85 years of age who were clinically referred for invasive coronary angiography (ICA). Computed tomography angiography (CTA), CTP, single photon emission computed tomography (SPECT), and ICA images were interpreted manually in blinded core laboratories by two experienced readers. Additionally, eight quantitative CTP metrics as continuous values were computed semi-automatically from myocardial and blood attenuation and were combined using logistic regression to derive a final quantitative CTP metric score. For the reference standard, hemodynamically significant coronary artery disease (CAD) was defined as a quantitative ICA stenosis of 50% or greater and a corresponding perfusion defect by SPECT. Diagnostic accuracy was determined by area under the receiver operating characteristic curve (AUC). Of the total 377 included patients, 66% were male, median age was 62 (IQR: 56, 68) years, and 27% had prior myocardial infarction. In patient based analysis, the AUC (95% CI) for combined CTA-CTP expert reading and combined CTA-CTP semi-automatic quantitative metrics was 0.87(0.84-0.91) and 0.86 (0.83-0.9), respectively. In vessel based analyses the AUC's were 0.85 (0.82-0.88) and 0.84 (0.81-0.87), respectively. No significant difference in AUC was found between combined CTA-CTP expert reading and CTA-CTP semi-automatic quantitative metrics in patient based or vessel based analyses(p > 0.05 for all). Combined CTA-CTP semi-automatic quantitative metrics is as accurate as CTA-CTP expert reading to detect hemodynamically significant CAD. Copyright © 2018 Society of Cardiovascular Computed Tomography. Published by Elsevier Inc. All rights reserved.

Automatic tissue image segmentation based on image processing and deep learning

NASA Astrophysics Data System (ADS)

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

2018-02-01

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

A computerized MRI biomarker quantification scheme for a canine model of Duchenne muscular dystrophy

PubMed Central

Wang, Jiahui; Fan, Zheng; Vandenborne, Krista; Walter, Glenn; Shiloh-Malawsky, Yael; An, Hongyu; Kornegay, Joe N.; Styner, Martin A.

2015-01-01

Purpose Golden retriever muscular dystrophy (GRMD) is a widely used canine model of Duchenne muscular dystrophy (DMD). Recent studies have shown that magnetic resonance imaging (MRI) can be used to non-invasively detect consistent changes in both DMD and GRMD. In this paper, we propose a semi-automated system to quantify MRI biomarkers of GRMD. Methods Our system was applied to a database of 45 MRI scans from 8 normal and 10 GRMD dogs in a longitudinal natural history study. We first segmented six proximal pelvic limb muscles using two competing schemes: 1) standard, limited muscle range segmentation and 2) semi-automatic full muscle segmentation. We then performed pre-processing, including: intensity inhomogeneity correction, spatial registration of different image sequences, intensity calibration of T2-weighted (T2w) and T2-weighted fat suppressed (T2fs) images, and calculation of MRI biomarker maps. Finally, for each of the segmented muscles, we automatically measured MRI biomarkers of muscle volume and intensity statistics over MRI biomarker maps, and statistical image texture features. Results The muscle volume and the mean intensities in T2 value, fat, and water maps showed group differences between normal and GRMD dogs. For the statistical texture biomarkers, both the histogram and run-length matrix features showed obvious group differences between normal and GRMD dogs. The full muscle segmentation shows significantly less error and variability in the proposed biomarkers when compared to the standard, limited muscle range segmentation. Conclusion The experimental results demonstrated that this quantification tool can reliably quantify MRI biomarkers in GRMD dogs, suggesting that it would also be useful for quantifying disease progression and measuring therapeutic effect in DMD patients. PMID:23299128

Validation of a novel CARTOSEG™ segmentation module software for contrast-enhanced computed tomography-guided radiofrequency ablation in patients with atrial fibrillation.

PubMed

Imanli, Hasan; Bhatty, Shaun; Jeudy, Jean; Ghzally, Yousra; Ume, Kiddy; Vunnam, Rama; Itah, Refael; Amit, Mati; Duell, John; See, Vincent; Shorofsky, Stephen; Dickfeld, Timm M

2017-11-01

Visualization of left atrial (LA) anatomy using image integration modules has been associated with decreased radiation exposure and improved procedural outcome when used for guidance of pulmonary vein isolation (PVI) in atrial fibrillation (AF) ablation. We evaluated the CARTOSEG™ CT Segmentation Module (Biosense Webster, Inc.) that offers a new CT-specific semiautomatic reconstruction of the atrial endocardium. The CARTOSEG™ CT Segmentation Module software was assessed prospectively in 80 patients undergoing AF ablation. Using preprocedural contrast-enhanced computed tomography (CE-CT), cardiac chambers, coronary sinus (CS), and esophagus were semiautomatically segmented. Segmentation quality was assessed from 1 (poor) to 4 (excellent). The reconstructed structures were registered with the electroanatomic map (EAM). PVI was performed using the registered 3D images. Semiautomatic reconstruction of the heart chambers was successfully performed in all 80 patients with AF. CE-CT DICOM file import, semiautomatic segmentation of cardiac chambers, esophagus, and CS was performed in 185 ± 105, 18 ± 5, 119 ± 47, and 69 ± 19 seconds, respectively. Average segmentation quality was 3.9 ± 0.2, 3.8 ± 0.3, and 3.8 ± 0.2 for LA, esophagus, and CS, respectively. Registration accuracy between the EAM and CE-CT-derived segmentation was 4.2 ± 0.9 mm. Complications consisted of one perforation (1%) which required pericardiocentesis, one increased pericardial effusion treated conservatively (1%), and one early termination of ablation due to thrombus formation on the ablation sheath without TIA/stroke (1%). All targeted PVs (n  =  309) were successfully isolated. The novel CT- CARTOSEG™ CT Segmentation Module enables a rapid and reliable semiautomatic 3D reconstruction of cardiac chambers and adjacent anatomy, which facilitates successful and safe PVI. © 2017 Wiley Periodicals, Inc.

A correlative imaging based methodology for accurate quantitative assessment of bone formation in additive manufactured implants.

PubMed

Geng, Hua; Todd, Naomi M; Devlin-Mullin, Aine; Poologasundarampillai, Gowsihan; Kim, Taek Bo; Madi, Kamel; Cartmell, Sarah; Mitchell, Christopher A; Jones, Julian R; Lee, Peter D

2016-06-01

A correlative imaging methodology was developed to accurately quantify bone formation in the complex lattice structure of additive manufactured implants. Micro computed tomography (μCT) and histomorphometry were combined, integrating the best features from both, while demonstrating the limitations of each imaging modality. This semi-automatic methodology registered each modality using a coarse graining technique to speed the registration of 2D histology sections to high resolution 3D μCT datasets. Once registered, histomorphometric qualitative and quantitative bone descriptors were directly correlated to 3D quantitative bone descriptors, such as bone ingrowth and bone contact. The correlative imaging allowed the significant volumetric shrinkage of histology sections to be quantified for the first time (~15 %). This technique demonstrated the importance of location of the histological section, demonstrating that up to a 30 % offset can be introduced. The results were used to quantitatively demonstrate the effectiveness of 3D printed titanium lattice implants.

Tooth segmentation system with intelligent editing for cephalometric analysis

NASA Astrophysics Data System (ADS)

Chen, Shoupu

2015-03-01

Cephalometric analysis is the study of the dental and skeletal relationship in the head, and it is used as an assessment and planning tool for improved orthodontic treatment of a patient. Conventional cephalometric analysis identifies bony and soft-tissue landmarks in 2D cephalometric radiographs, in order to diagnose facial features and abnormalities prior to treatment, or to evaluate the progress of treatment. Recent studies in orthodontics indicate that there are persistent inaccuracies and inconsistencies in the results provided using conventional 2D cephalometric analysis. Obviously, plane geometry is inappropriate for analyzing anatomical volumes and their growth; only a 3D analysis is able to analyze the three-dimensional, anatomical maxillofacial complex, which requires computing inertia systems for individual or groups of digitally segmented teeth from an image volume of a patient's head. For the study of 3D cephalometric analysis, the current paper proposes a system for semi-automatically segmenting teeth from a cone beam computed tomography (CBCT) volume with two distinct features, including an intelligent user-input interface for automatic background seed generation, and a graphics processing unit (GPU) acceleration mechanism for three-dimensional GrowCut volume segmentation. Results show a satisfying average DICE score of 0.92, with the use of the proposed tooth segmentation system, by 15 novice users who segmented a randomly sampled tooth set. The average GrowCut processing time is around one second per tooth, excluding user interaction time.

D GIS for Flood Modelling in River Valleys

NASA Astrophysics Data System (ADS)

Tymkow, P.; Karpina, M.; Borkowski, A.

2016-06-01

The objective of this study is implementation of system architecture for collecting and analysing data as well as visualizing results for hydrodynamic modelling of flood flows in river valleys using remote sensing methods, tree-dimensional geometry of spatial objects and GPU multithread processing. The proposed solution includes: spatial data acquisition segment, data processing and transformation, mathematical modelling of flow phenomena and results visualization. Data acquisition segment was based on aerial laser scanning supplemented by images in visible range. Vector data creation was based on automatic and semiautomatic algorithms of DTM and 3D spatial features modelling. Algorithms for buildings and vegetation geometry modelling were proposed or adopted from literature. The implementation of the framework was designed as modular software using open specifications and partially reusing open source projects. The database structure for gathering and sharing vector data, including flood modelling results, was created using PostgreSQL. For the internal structure of feature classes of spatial objects in a database, the CityGML standard was used. For the hydrodynamic modelling the solutions of Navier-Stokes equations in two-dimensional version was implemented. Visualization of geospatial data and flow model results was transferred to the client side application. This gave the independence from server hardware platform. A real-world case in Poland, which is a part of Widawa River valley near Wroclaw city, was selected to demonstrate the applicability of proposed system.

Differentiation of Glioblastoma and Lymphoma Using Feature Extraction and Support Vector Machine.

PubMed

Yang, Zhangjing; Feng, Piaopiao; Wen, Tian; Wan, Minghua; Hong, Xunning

2017-01-01

Differentiation of glioblastoma multiformes (GBMs) and lymphomas using multi-sequence magnetic resonance imaging (MRI) is an important task that is valuable for treatment planning. However, this task is a challenge because GBMs and lymphomas may have a similar appearance in MRI images. This similarity may lead to misclassification and could affect the treatment results. In this paper, we propose a semi-automatic method based on multi-sequence MRI to differentiate these two types of brain tumors. Our method consists of three steps: 1) the key slice is selected from 3D MRIs and region of interests (ROIs) are drawn around the tumor region; 2) different features are extracted based on prior clinical knowledge and validated using a t-test; and 3) features that are helpful for classification are used to build an original feature vector and a support vector machine is applied to perform classification. In total, 58 GBM cases and 37 lymphoma cases are used to validate our method. A leave-one-out crossvalidation strategy is adopted in our experiments. The global accuracy of our method was determined as 96.84%, which indicates that our method is effective for the differentiation of GBM and lymphoma and can be applied in clinical diagnosis. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Pulmonary lobar volumetry using novel volumetric computer-aided diagnosis and computed tomography

PubMed Central

Iwano, Shingo; Kitano, Mariko; Matsuo, Keiji; Kawakami, Kenichi; Koike, Wataru; Kishimoto, Mariko; Inoue, Tsutomu; Li, Yuanzhong; Naganawa, Shinji

2013-01-01

OBJECTIVES To compare the accuracy of pulmonary lobar volumetry using the conventional number of segments method and novel volumetric computer-aided diagnosis using 3D computed tomography images. METHODS We acquired 50 consecutive preoperative 3D computed tomography examinations for lung tumours reconstructed at 1-mm slice thicknesses. We calculated the lobar volume and the emphysematous lobar volume < −950 HU of each lobe using (i) the slice-by-slice method (reference standard), (ii) number of segments method, and (iii) semi-automatic and (iv) automatic computer-aided diagnosis. We determined Pearson correlation coefficients between the reference standard and the three other methods for lobar volumes and emphysematous lobar volumes. We also compared the relative errors among the three measurement methods. RESULTS Both semi-automatic and automatic computer-aided diagnosis results were more strongly correlated with the reference standard than the number of segments method. The correlation coefficients for automatic computer-aided diagnosis were slightly lower than those for semi-automatic computer-aided diagnosis because there was one outlier among 50 cases (2%) in the right upper lobe and two outliers among 50 cases (4%) in the other lobes. The number of segments method relative error was significantly greater than those for semi-automatic and automatic computer-aided diagnosis (P < 0.001). The computational time for automatic computer-aided diagnosis was 1/2 to 2/3 than that of semi-automatic computer-aided diagnosis. CONCLUSIONS A novel lobar volumetry computer-aided diagnosis system could more precisely measure lobar volumes than the conventional number of segments method. Because semi-automatic computer-aided diagnosis and automatic computer-aided diagnosis were complementary, in clinical use, it would be more practical to first measure volumes by automatic computer-aided diagnosis, and then use semi-automatic measurements if automatic computer-aided diagnosis failed. PMID:23526418

Left ventricular volume estimation in cardiac three-dimensional ultrasound: a semiautomatic border detection approach.

PubMed

van Stralen, Marijn; Bosch, Johan G; Voormolen, Marco M; van Burken, Gerard; Krenning, Boudewijn J; van Geuns, Robert-Jan M; Lancée, Charles T; de Jong, Nico; Reiber, Johan H C

2005-10-01

We propose a semiautomatic endocardial border detection method for three-dimensional (3D) time series of cardiac ultrasound (US) data based on pattern matching and dynamic programming, operating on two-dimensional (2D) slices of the 3D plus time data, for the estimation of full cycle left ventricular volume, with minimal user interaction. The presented method is generally applicable to 3D US data and evaluated on data acquired with the Fast Rotating Ultrasound (FRU-) Transducer, developed by Erasmus Medical Center (Rotterdam, the Netherlands), a conventional phased-array transducer, rotating at very high speed around its image axis. The detection is based on endocardial edge pattern matching using dynamic programming, which is constrained by a 3D plus time shape model. It is applied to an automatically selected subset of 2D images of the original data set, for typically 10 equidistant rotation angles and 16 cardiac phases (160 images). Initialization requires the drawing of four contours per patient manually. We evaluated this method on 14 patients against MRI end-diastole and end-systole volumes. Initialization requires the drawing of four contours per patient manually. We evaluated this method on 14 patients against MRI end-diastolic (ED) and end-systolic (ES) volumes. The semiautomatic border detection approach shows good correlations with MRI ED/ES volumes (r = 0.938) and low interobserver variability (y = 1.005x - 16.7, r = 0.943) over full-cycle volume estimations. It shows a high consistency in tracking the user-defined initial borders over space and time. We show that the ease of the acquisition using the FRU-transducer and the semiautomatic endocardial border detection method together can provide a way to quickly estimate the left ventricular volume over the full cardiac cycle using little user interaction.

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

PubMed Central

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 (p<0.001), as well as between the automatic volumetries (ASM and PA) and the semiautomatic volumetry (3DMIA) (p<0.001). The semiautomatic interactive 3DMIA, automatic ASM‐based, and automatic PA‐based liver volumetry 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

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

PubMed

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 < 0.001), as well as between the automatic 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.

A validation framework for brain tumor segmentation.

PubMed

Archip, Neculai; Jolesz, Ferenc A; Warfield, Simon K

2007-10-01

We introduce a validation framework for the segmentation of brain tumors from magnetic resonance (MR) images. A novel unsupervised semiautomatic brain tumor segmentation algorithm is also presented. The proposed framework consists of 1) T1-weighted MR images of patients with brain tumors, 2) segmentation of brain tumors performed by four independent experts, 3) segmentation of brain tumors generated by a semiautomatic algorithm, and 4) a software tool that estimates the performance of segmentation algorithms. We demonstrate the validation of the novel segmentation algorithm within the proposed framework. We show its performance and compare it with existent segmentation. The image datasets and software are available at http://www.brain-tumor-repository.org/. We present an Internet resource that provides access to MR brain tumor image data and segmentation that can be openly used by the research community. Its purpose is to encourage the development and evaluation of segmentation methods by providing raw test and image data, human expert segmentation results, and methods for comparing segmentation results.

Improved parameter extraction and classification for dynamic contrast enhanced MRI of prostate

NASA Astrophysics Data System (ADS)

Haq, Nandinee Fariah; Kozlowski, Piotr; Jones, Edward C.; Chang, Silvia D.; Goldenberg, S. Larry; Moradi, Mehdi

2014-03-01

Magnetic resonance imaging (MRI), particularly dynamic contrast enhanced (DCE) imaging, has shown great potential in prostate cancer diagnosis and prognosis. The time course of the DCE images provides measures of the contrast agent uptake kinetics. Also, using pharmacokinetic modelling, one can extract parameters from the DCE-MR images that characterize the tumor vascularization and can be used to detect cancer. A requirement for calculating the pharmacokinetic DCE parameters is estimating the Arterial Input Function (AIF). One needs an accurate segmentation of the cross section of the external femoral artery to obtain the AIF. In this work we report a semi-automatic method for segmentation of the cross section of the femoral artery, using circular Hough transform, in the sequence of DCE images. We also report a machine-learning framework to combine pharmacokinetic parameters with the model-free contrast agent uptake kinetic parameters extracted from the DCE time course into a nine-dimensional feature vector. This combination of features is used with random forest and with support vector machine classi cation for cancer detection. The MR data is obtained from patients prior to radical prostatectomy. After the surgery, wholemount histopathology analysis is performed and registered to the DCE-MR images as the diagnostic reference. We show that the use of a combination of pharmacokinetic parameters and the model-free empirical parameters extracted from the time course of DCE results in improved cancer detection compared to the use of each group of features separately. We also validate the proposed method for calculation of AIF based on comparison with the manual method.

PI2GIS: processing image to geographical information systems, a learning tool for QGIS

NASA Astrophysics Data System (ADS)

Correia, R.; Teodoro, A.; Duarte, L.

2017-10-01

To perform an accurate interpretation of remote sensing images, it is necessary to extract information using different image processing techniques. Nowadays, it became usual to use image processing plugins to add new capabilities/functionalities integrated in Geographical Information System (GIS) software. The aim of this work was to develop an open source application to automatically process and classify remote sensing images from a set of satellite input data. The application was integrated in a GIS software (QGIS), automating several image processing steps. The use of QGIS for this purpose is justified since it is easy and quick to develop new plugins, using Python language. This plugin is inspired in the Semi-Automatic Classification Plugin (SCP) developed by Luca Congedo. SCP allows the supervised classification of remote sensing images, the calculation of vegetation indices such as NDVI (Normalized Difference Vegetation Index) and EVI (Enhanced Vegetation Index) and other image processing operations. When analysing SCP, it was realized that a set of operations, that are very useful in teaching classes of remote sensing and image processing tasks, were lacking, such as the visualization of histograms, the application of filters, different image corrections, unsupervised classification and several environmental indices computation. The new set of operations included in the PI2GIS plugin can be divided into three groups: pre-processing, processing, and classification procedures. The application was tested consider an image from Landsat 8 OLI from a North area of Portugal.

High Accuracy 3D Processing of Satellite Imagery

NASA Technical Reports Server (NTRS)

Gruen, A.; Zhang, L.; Kocaman, S.

2007-01-01

Automatic DSM/DTM generation reproduces not only general features, but also detailed features of the terrain relief. Height accuracy of around 1 pixel in cooperative terrain. RMSE values of 1.3-1.5 m (1.0-2.0 pixels) for IKONOS and RMSE values of 2.9-4.6 m (0.5-1.0 pixels) for SPOT5 HRS. For 3D city modeling, the manual and semi-automatic feature extraction capability of SAT-PP provides a good basis. The tools of SAT-PP allowed the stereo-measurements of points on the roofs in order to generate a 3D city model with CCM The results show that building models with main roof structures can be successfully extracted by HRSI. As expected, with Quickbird more details are visible.

Fusion of pixel and object-based features for weed mapping using unmanned aerial vehicle imagery

NASA Astrophysics Data System (ADS)

Gao, Junfeng; Liao, Wenzhi; Nuyttens, David; Lootens, Peter; Vangeyte, Jürgen; Pižurica, Aleksandra; He, Yong; Pieters, Jan G.

2018-05-01

The developments in the use of unmanned aerial vehicles (UAVs) and advanced imaging sensors provide new opportunities for ultra-high resolution (e.g., less than a 10 cm ground sampling distance (GSD)) crop field monitoring and mapping in precision agriculture applications. In this study, we developed a strategy for inter- and intra-row weed detection in early season maize fields from aerial visual imagery. More specifically, the Hough transform algorithm (HT) was applied to the orthomosaicked images for inter-row weed detection. A semi-automatic Object-Based Image Analysis (OBIA) procedure was developed with Random Forests (RF) combined with feature selection techniques to classify soil, weeds and maize. Furthermore, the two binary weed masks generated from HT and OBIA were fused for accurate binary weed image. The developed RF classifier was evaluated by 5-fold cross validation, and it obtained an overall accuracy of 0.945, and Kappa value of 0.912. Finally, the relationship of detected weeds and their ground truth densities was quantified by a fitted linear model with a coefficient of determination of 0.895 and a root mean square error of 0.026. Besides, the importance of input features was evaluated, and it was found that the ratio of vegetation length and width was the most significant feature for the classification model. Overall, our approach can yield a satisfactory weed map, and we expect that the obtained accurate and timely weed map from UAV imagery will be applicable to realize site-specific weed management (SSWM) in early season crop fields for reducing spraying non-selective herbicides and costs.

Flexible methods for segmentation evaluation: results from CT-based luggage screening.

PubMed

Karimi, Seemeen; Jiang, Xiaoqian; Cosman, Pamela; Martz, Harry

2014-01-01

Imaging systems used in aviation security include segmentation algorithms in an automatic threat recognition pipeline. The segmentation algorithms evolve in response to emerging threats and changing performance requirements. Analysis of segmentation algorithms' behavior, including the nature of errors and feature recovery, facilitates their development. However, evaluation methods from the literature provide limited characterization of the segmentation algorithms. To develop segmentation evaluation methods that measure systematic errors such as oversegmentation and undersegmentation, outliers, and overall errors. The methods must measure feature recovery and allow us to prioritize segments. We developed two complementary evaluation methods using statistical techniques and information theory. We also created a semi-automatic method to define ground truth from 3D images. We applied our methods to evaluate five segmentation algorithms developed for CT luggage screening. We validated our methods with synthetic problems and an observer evaluation. Both methods selected the same best segmentation algorithm. Human evaluation confirmed the findings. The measurement of systematic errors and prioritization helped in understanding the behavior of each segmentation algorithm. Our evaluation methods allow us to measure and explain the accuracy of segmentation algorithms.

Comparison of thyroid segmentation techniques for 3D ultrasound

NASA Astrophysics Data System (ADS)

Wunderling, T.; Golla, B.; Poudel, P.; Arens, C.; Friebe, M.; Hansen, C.

2017-02-01

The segmentation of the thyroid in ultrasound images is a field of active research. The thyroid is a gland of the endocrine system and regulates several body functions. Measuring the volume of the thyroid is regular practice of diagnosing pathological changes. In this work, we compare three approaches for semi-automatic thyroid segmentation in freehand-tracked three-dimensional ultrasound images. The approaches are based on level set, graph cut and feature classification. For validation, sixteen 3D ultrasound records were created with ground truth segmentations, which we make publicly available. The properties analyzed are the Dice coefficient when compared against the ground truth reference and the effort of required interaction. Our results show that in terms of Dice coefficient, all algorithms perform similarly. For interaction, however, each algorithm has advantages over the other. The graph cut-based approach gives the practitioner direct influence on the final segmentation. Level set and feature classifier require less interaction, but offer less control over the result. All three compared methods show promising results for future work and provide several possible extensions.

Semiautomatic Assessment of the Terminal Ileum and Colon in Patients with Crohn Disease Using MRI (the VIGOR++ Project).

PubMed

Puylaert, Carl A J; Schüffler, Peter J; Naziroglu, Robiel E; Tielbeek, Jeroen A W; Li, Zhang; Makanyanga, Jesica C; Tutein Nolthenius, Charlotte J; Nio, C Yung; Pendsé, Douglas A; Menys, Alex; Ponsioen, Cyriel Y; Atkinson, David; Forbes, Alastair; Buhmann, Joachim M; Fuchs, Thomas J; Hatzakis, Haralambos; van Vliet, Lucas J; Stoker, Jaap; Taylor, Stuart A; Vos, Frans M

2018-02-07

The objective of this study was to develop and validate a predictive magnetic resonance imaging (MRI) activity score for ileocolonic Crohn disease activity based on both subjective and semiautomatic MRI features. An MRI activity score (the "virtual gastrointestinal tract [VIGOR]" score) was developed from 27 validated magnetic resonance enterography datasets, including subjective radiologist observation of mural T2 signal and semiautomatic measurements of bowel wall thickness, excess volume, and dynamic contrast enhancement (initial slope of increase). A second subjective score was developed based on only radiologist observations. For validation, two observers applied both scores and three existing scores to a prospective dataset of 106 patients (59 women, median age 33) with known Crohn disease, using the endoscopic Crohn's Disease Endoscopic Index of Severity (CDEIS) as a reference standard. The VIGOR score (17.1 × initial slope of increase + 0.2 × excess volume + 2.3 × mural T2) and other activity scores all had comparable correlation to the CDEIS scores (observer 1: r = 0.58 and 0.59, and observer 2: r = 0.34-0.40 and 0.43-0.51, respectively). The VIGOR score, however, improved interobserver agreement compared to the other activity scores (intraclass correlation coefficient = 0.81 vs 0.44-0.59). A diagnostic accuracy of 80%-81% was seen for the VIGOR score, similar to the other scores. The VIGOR score achieves comparable accuracy to conventional MRI activity scores, but with significantly improved reproducibility, favoring its use for disease monitoring and therapy evaluation. Copyright © 2018 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

Criminal Use of Assault Weapons and High-Capacity Semiautomatic Firearms: an Updated Examination of Local and National Sources.

PubMed

Koper, Christopher S; Johnson, William D; Nichols, Jordan L; Ayers, Ambrozine; Mullins, Natalie

2018-06-01

Policies restricting semiautomatic assault weapons and large-capacity ammunition magazines are intended to reduce gunshot victimizations by limiting the stock of semiautomatic firearms with large ammunition capacities and other military-style features conducive to criminal use. The federal government banned such weaponry from 1994 to 2004, and a few states currently impose similar restrictions. Recent debates concerning these weapons have highlighted their use in mass shootings, but there has been little examination of their use in gun crime more generally since the expiration of the federal ban. This study investigates current levels of criminal activity with assault weapons and other high-capacity semiautomatics in the USA using several local and national data sources including the following: (1) guns recovered by police in ten large cities, (2) guns reported by police to federal authorities for investigative tracing, (3) guns used in murders of police, and (4) guns used in mass murders. Results suggest assault weapons (primarily assault-type rifles) account for 2-12% of guns used in crime in general (most estimates suggest less than 7%) and 13-16% of guns used in murders of police. Assault weapons and other high-capacity semiautomatics together generally account for 22 to 36% of crime guns, with some estimates upwards of 40% for cases involving serious violence including murders of police. Assault weapons and other high-capacity semiautomatics appear to be used in a higher share of firearm mass murders (up to 57% in total), though data on this issue are very limited. Trend analyses also indicate that high-capacity semiautomatics have grown from 33 to 112% as a share of crime guns since the expiration of the federal ban-a trend that has coincided with recent growth in shootings nationwide. Further research seems warranted on how these weapons affect injuries and deaths from gun violence and how their regulation may impact public health.

Quantitative analysis of hyperpolarized 129Xe ventilation imaging in healthy volunteers and subjects with chronic obstructive pulmonary disease

PubMed Central

Virgincar, Rohan S.; Cleveland, Zackary I.; Kaushik, S. Sivaram; Freeman, Matthew S.; Nouls, John; Cofer, Gary P.; Martinez-Jimenez, Santiago; He, Mu; Kraft, Monica; Wolber, Jan; McAdams, H. Page; Driehuys, Bastiaan

2013-01-01

In this study, hyperpolarized (HP) 129Xe MR ventilation and 1H anatomical images were obtained from 3 subject groups: young healthy volunteers (HV), subjects with chronic obstructive pulmonary disease (COPD), and age-matched control subjects (AMC). Ventilation images were quantified by 2 methods: an expert reader-based ventilation defect score percentage (VDS%) and a semi-automatic segmentation-based ventilation defect percentage (VDP). Reader-based values were assigned by two experienced radiologists and resolved by consensus. In the semi-automatic analysis, 1H anatomical images and 129Xe ventilation images were both segmented following registration, to obtain the thoracic cavity volume (TCV) and ventilated volume (VV), respectively, which were then expressed as a ratio to obtain the VDP. Ventilation images were also characterized by generating signal intensity histograms from voxels within the TCV, and heterogeneity was analyzed using the coefficient of variation (CV). The reader-based VDS% correlated strongly with the semi-automatically generated VDP (r = 0.97, p < 0.0001), and with CV (r = 0.82, p < 0.0001). Both 129Xe ventilation defect scoring metrics readily separated the 3 groups from one another and correlated significantly with FEV1 (VDS%: r = -0.78, p = 0.0002; VDP: r = -0.79, p = 0.0003; CV: r = -0.66, p = 0.0059) and other pulmonary function tests. In the healthy subject groups (HV and AMC), the prevalence of ventilation defects also increased with age (VDS%: r = 0.61, p = 0.0002; VDP: r = 0.63, p = 0.0002). Moreover, ventilation histograms and their associated CVs distinguished between COPD subjects with similar ventilation defect scores but visibly different ventilation patterns. PMID:23065808

Pattern classification approach to characterizing solitary pulmonary nodules imaged on high-resolution computed tomography

NASA Astrophysics Data System (ADS)

McNitt-Gray, Michael F.; Hart, Eric M.; Goldin, Jonathan G.; Yao, Chih-Wei; Aberle, Denise R.

1996-04-01

The purpose of our study was to characterize solitary pulmonary nodules (SPN) as benign or malignant based on pattern classification techniques using size, shape, density and texture features extracted from HRCT images. HRCT images of patients with a SPN are acquired, routed through a PACS and displayed on a thoracic radiology workstation. Using the original data, the SPN is semiautomatically contoured using a nodule/background threshold. The contour is used to calculate size and several shape parameters, including compactness and bending energy. Pixels within the interior of the contour are used to calculate several features including: (1) nodule density-related features, such as representative Hounsfield number and moment of inertia, and (2) texture measures based on the spatial gray level dependence matrix and fractal dimension. The true diagnosis of the SPN is established by histology from biopsy or, in the case of some benign nodules, extended follow-up. Multi-dimensional analyses of the features are then performed to determine which features can discriminate between benign and malignant nodules. When a sufficient number of cases are obtained two pattern classifiers, a linear discriminator and a neural network, are trained and tested using a select subset of features. Preliminary data from nine (9) nodule cases have been obtained and several features extracted. While the representative CT number is a reasonably good indicator, it is an inconclusive predictor of SPN diagnosis when considered by itself. Separation between benign and malignant nodules improves when other features, such as the distribution of density as measured by moment of inertia, are included in the analysis. Software has been developed and preliminary results have been obtained which show that individual features may not be sufficient to discriminate between benign and malignant nodules. However, combinations of these features may be able to discriminate between these two classes. With additional cases and more features, we will be able to perform a feature selection procedure and ultimately to train and test pattern classifiers in this discrimination task.

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

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

Fang, Y; Huang, H; Su, T

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 PCImore » 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 of the myocardial ischemia.« less

Object oriented classification of high resolution data for inventory of horticultural crops

NASA Astrophysics Data System (ADS)

Hebbar, R.; Ravishankar, H. M.; Trivedi, S.; Subramoniam, S. R.; Uday, R.; Dadhwal, V. K.

2014-11-01

High resolution satellite images are associated with large variance and thus, per pixel classifiers often result in poor accuracy especially in delineation of horticultural crops. In this context, object oriented techniques are powerful and promising methods for classification. In the present study, a semi-automatic object oriented feature extraction model has been used for delineation of horticultural fruit and plantation crops using Erdas Objective Imagine. Multi-resolution data from Resourcesat LISS-IV and Cartosat-1 have been used as source data in the feature extraction model. Spectral and textural information along with NDVI were used as inputs for generation of Spectral Feature Probability (SFP) layers using sample training pixels. The SFP layers were then converted into raster objects using threshold and clump function resulting in pixel probability layer. A set of raster and vector operators was employed in the subsequent steps for generating thematic layer in the vector format. This semi-automatic feature extraction model was employed for classification of major fruit and plantations crops viz., mango, banana, citrus, coffee and coconut grown under different agro-climatic conditions. In general, the classification accuracy of about 75-80 per cent was achieved for these crops using object based classification alone and the same was further improved using minimal visual editing of misclassified areas. A comparison of on-screen visual interpretation with object oriented approach showed good agreement. It was observed that old and mature plantations were classified more accurately while young and recently planted ones (3 years or less) showed poor classification accuracy due to mixed spectral signature, wider spacing and poor stands of plantations. The results indicated the potential use of object oriented approach for classification of high resolution data for delineation of horticultural fruit and plantation crops. The present methodology is applicable at local levels and future development is focused on up-scaling the methodology for generation of fruit and plantation crop maps at regional and national level which is important for creation of database for overall horticultural crop development.

Automatic analysis of microscopic images of red blood cell aggregates

NASA Astrophysics Data System (ADS)

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).

Semi-automatic brain tumor segmentation by constrained MRFs using structural trajectories.

PubMed

Zhao, Liang; Wu, Wei; Corso, Jason J

2013-01-01

Quantifying volume and growth of a brain tumor is a primary prognostic measure and hence has received much attention in the medical imaging community. Most methods have sought a fully automatic segmentation, but the variability in shape and appearance of brain tumor has limited their success and further adoption in the clinic. In reaction, we present a semi-automatic brain tumor segmentation framework for multi-channel magnetic resonance (MR) images. This framework does not require prior model construction and only requires manual labels on one automatically selected slice. All other slices are labeled by an iterative multi-label Markov random field optimization with hard constraints. Structural trajectories-the medical image analog to optical flow and 3D image over-segmentation are used to capture pixel correspondences between consecutive slices for pixel labeling. We show robustness and effectiveness through an evaluation on the 2012 MICCAI BRATS Challenge Dataset; our results indicate superior performance to baselines and demonstrate the utility of the constrained MRF formulation.

Computer-based route-definition system for peripheral bronchoscopy.

PubMed

Graham, Michael W; Gibbs, Jason D; Higgins, William E

2012-04-01

Multi-detector computed tomography (MDCT) scanners produce high-resolution images of the chest. Given a patient's MDCT scan, a physician can use an image-guided intervention system to first plan and later perform bronchoscopy to diagnostic sites situated deep in the lung periphery. An accurate definition of complete routes through the airway tree leading to the diagnostic sites, however, is vital for avoiding navigation errors during image-guided bronchoscopy. We present a system for the robust definition of complete airway routes suitable for image-guided bronchoscopy. The system incorporates both automatic and semiautomatic MDCT analysis methods for this purpose. Using an intuitive graphical user interface, the user invokes automatic analysis on a patient's MDCT scan to produce a series of preliminary routes. Next, the user visually inspects each route and quickly corrects the observed route defects using the built-in semiautomatic methods. Application of the system to a human study for the planning and guidance of peripheral bronchoscopy demonstrates the efficacy of the system.

A median-Gaussian filtering framework for Moiré pattern noise removal from X-ray microscopy image.

PubMed

Wei, Zhouping; Wang, Jian; Nichol, Helen; Wiebe, Sheldon; Chapman, Dean

2012-02-01

Moiré pattern noise in Scanning Transmission X-ray Microscopy (STXM) imaging introduces significant errors in qualitative and quantitative image analysis. Due to the complex origin of the noise, it is difficult to avoid Moiré pattern noise during the image data acquisition stage. In this paper, we introduce a post-processing method for filtering Moiré pattern noise from STXM images. This method includes a semi-automatic detection of the spectral peaks in the Fourier amplitude spectrum by using a local median filter, and elimination of the spectral noise peaks using a Gaussian notch filter. The proposed median-Gaussian filtering framework shows good results for STXM images with the size of power of two, if such parameters as threshold, sizes of the median and Gaussian filters, and size of the low frequency window, have been properly selected. Copyright © 2011 Elsevier Ltd. All rights reserved.

Automated tumor volumetry using computer-aided image segmentation.

PubMed

Gaonkar, Bilwaj; Macyszyn, Luke; Bilello, Michel; Sadaghiani, Mohammed Salehi; Akbari, Hamed; Atthiah, Mark A; Ali, Zarina S; Da, Xiao; Zhan, Yiqang; O'Rourke, Donald; Grady, Sean M; Davatzikos, Christos

2015-05-01

Accurate segmentation of brain tumors, and quantification of tumor volume, is important for diagnosis, monitoring, and planning therapeutic intervention. Manual segmentation is not widely used because of time constraints. Previous efforts have mainly produced methods that are tailored to a particular type of tumor or acquisition protocol and have mostly failed to produce a method that functions on different tumor types and is robust to changes in scanning parameters, resolution, and image quality, thereby limiting their clinical value. Herein, we present a semiautomatic method for tumor segmentation that is fast, accurate, and robust to a wide variation in image quality and resolution. A semiautomatic segmentation method based on the geodesic distance transform was developed and validated by using it to segment 54 brain tumors. Glioblastomas, meningiomas, and brain metastases were segmented. Qualitative validation was based on physician ratings provided by three clinical experts. Quantitative validation was based on comparing semiautomatic and manual segmentations. Tumor segmentations obtained using manual and automatic methods were compared quantitatively using the Dice measure of overlap. Subjective evaluation was performed by having human experts rate the computerized segmentations on a 0-5 rating scale where 5 indicated perfect segmentation. The proposed method addresses a significant, unmet need in the field of neuro-oncology. Specifically, this method enables clinicians to obtain accurate and reproducible tumor volumes without the need for manual segmentation. Copyright © 2015 AUR. Published by Elsevier Inc. All rights reserved.

Automated Tumor Volumetry Using Computer-Aided Image Segmentation

PubMed Central

Bilello, Michel; Sadaghiani, Mohammed Salehi; Akbari, Hamed; Atthiah, Mark A.; Ali, Zarina S.; Da, Xiao; Zhan, Yiqang; O'Rourke, Donald; Grady, Sean M.; Davatzikos, Christos

2015-01-01

Rationale and Objectives Accurate segmentation of brain tumors, and quantification of tumor volume, is important for diagnosis, monitoring, and planning therapeutic intervention. Manual segmentation is not widely used because of time constraints. Previous efforts have mainly produced methods that are tailored to a particular type of tumor or acquisition protocol and have mostly failed to produce a method that functions on different tumor types and is robust to changes in scanning parameters, resolution, and image quality, thereby limiting their clinical value. Herein, we present a semiautomatic method for tumor segmentation that is fast, accurate, and robust to a wide variation in image quality and resolution. Materials and Methods A semiautomatic segmentation method based on the geodesic distance transform was developed and validated by using it to segment 54 brain tumors. Glioblastomas, meningiomas, and brain metastases were segmented. Qualitative validation was based on physician ratings provided by three clinical experts. Quantitative validation was based on comparing semiautomatic and manual segmentations. Results Tumor segmentations obtained using manual and automatic methods were compared quantitatively using the Dice measure of overlap. Subjective evaluation was performed by having human experts rate the computerized segmentations on a 0–5 rating scale where 5 indicated perfect segmentation. Conclusions The proposed method addresses a significant, unmet need in the field of neuro-oncology. Specifically, this method enables clinicians to obtain accurate and reproducible tumor volumes without the need for manual segmentation. PMID:25770633

Semi Automated Land Cover Layer Updating Process Utilizing Spectral Analysis and GIS Data Fusion

NASA Astrophysics Data System (ADS)

Cohen, L.; Keinan, E.; Yaniv, M.; Tal, Y.; Felus, A.; Regev, R.

2018-04-01

Technological improvements made in recent years of mass data gathering and analyzing, influenced the traditional methods of updating and forming of the national topographic database. It has brought a significant increase in the number of use cases and detailed geo information demands. Processes which its purpose is to alternate traditional data collection methods developed in many National Mapping and Cadaster Agencies. There has been significant progress in semi-automated methodologies aiming to facilitate updating of a topographic national geodatabase. Implementation of those is expected to allow a considerable reduction of updating costs and operation times. Our previous activity has focused on building automatic extraction (Keinan, Zilberstein et al, 2015). Before semiautomatic updating method, it was common that interpreter identification has to be as detailed as possible to hold most reliable database eventually. When using semi-automatic updating methodologies, the ability to insert human insights based knowledge is limited. Therefore, our motivations were to reduce the created gap by allowing end-users to add their data inputs to the basic geometric database. In this article, we will present a simple Land cover database updating method which combines insights extracted from the analyzed image, and a given spatial data of vector layers. The main stages of the advanced practice are multispectral image segmentation and supervised classification together with given vector data geometric fusion while maintaining the principle of low shape editorial work to be done. All coding was done utilizing open source software components.

Localization accuracy from automatic and semi-automatic rigid registration of locally-advanced lung cancer targets during image-guided radiation therapy

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

Robertson, Scott P.; Weiss, Elisabeth; Hugo, Geoffrey D.

2012-01-15

Purpose: To evaluate localization accuracy resulting from rigid registration of locally-advanced lung cancer targets using fully automatic and semi-automatic protocols for image-guided radiation therapy. Methods: Seventeen lung cancer patients, fourteen also presenting with involved lymph nodes, received computed tomography (CT) scans once per week throughout treatment under active breathing control. A physician contoured both lung and lymph node targets for all weekly scans. Various automatic and semi-automatic rigid registration techniques were then performed for both individual and simultaneous alignments of the primary gross tumor volume (GTV{sub P}) and involved lymph nodes (GTV{sub LN}) to simulate the localization process in image-guidedmore » radiation therapy. Techniques included ''standard'' (direct registration of weekly images to a planning CT), ''seeded'' (manual prealignment of targets to guide standard registration), ''transitive-based'' (alignment of pretreatment and planning CTs through one or more intermediate images), and ''rereferenced'' (designation of a new reference image for registration). Localization error (LE) was assessed as the residual centroid and border distances between targets from planning and weekly CTs after registration. Results: Initial bony alignment resulted in centroid LE of 7.3 {+-} 5.4 mm and 5.4 {+-} 3.4 mm for the GTV{sub P} and GTV{sub LN}, respectively. Compared to bony alignment, transitive-based and seeded registrations significantly reduced GTV{sub P} centroid LE to 4.7 {+-} 3.7 mm (p = 0.011) and 4.3 {+-} 2.5 mm (p < 1 x 10{sup -3}), respectively, but the smallest GTV{sub P} LE of 2.4 {+-} 2.1 mm was provided by rereferenced registration (p < 1 x 10{sup -6}). Standard registration significantly reduced GTV{sub LN} centroid LE to 3.2 {+-} 2.5 mm (p < 1 x 10{sup -3}) compared to bony alignment, with little additional gain offered by the other registration techniques. For simultaneous target alignment, centroid LE as low as 3.9 {+-} 2.7 mm and 3.8 {+-} 2.3 mm were achieved for the GTV{sub P} and GTV{sub LN}, respectively, using rereferenced registration. Conclusions: Target shape, volume, and configuration changes during radiation therapy limited the accuracy of standard rigid registration for image-guided localization in locally-advanced lung cancer. Significant error reductions were possible using other rigid registration techniques, with LE approaching the lower limit imposed by interfraction target variability throughout treatment.« less

Context-sensitive extraction of tree crown objects in urban areas using VHR satellite images

NASA Astrophysics Data System (ADS)

Ardila, Juan P.; Bijker, Wietske; Tolpekin, Valentyn A.; Stein, Alfred

2012-04-01

Municipalities need accurate and updated inventories of urban vegetation in order to manage green resources and estimate their return on investment in urban forestry activities. Earlier studies have shown that semi-automatic tree detection using remote sensing is a challenging task. This study aims to develop a reproducible geographic object-based image analysis (GEOBIA) methodology to locate and delineate tree crowns in urban areas using high resolution imagery. We propose a GEOBIA approach that considers the spectral, spatial and contextual characteristics of tree objects in the urban space. The study presents classification rules that exploit object features at multiple segmentation scales modifying the labeling and shape of image-objects. The GEOBIA methodology was implemented on QuickBird images acquired over the cities of Enschede and Delft (The Netherlands), resulting in an identification rate of 70% and 82% respectively. False negative errors concentrated on small trees and false positive errors in private gardens. The quality of crown boundaries was acceptable, with an overall delineation error <0.24 outside of gardens and backyards.

Semi-automatic image personalization tool for variable text insertion and replacement

NASA Astrophysics Data System (ADS)

Ding, Hengzhou; Bala, Raja; Fan, Zhigang; Eschbach, Reiner; Bouman, Charles A.; Allebach, Jan P.

2010-02-01

Image personalization is a widely used technique in personalized marketing,1 in which a vendor attempts to promote new products or retain customers by sending marketing collateral that is tailored to the customers' demographics, needs, and interests. With current solutions of which we are aware such as XMPie,2 DirectSmile,3 and AlphaPicture,4 in order to produce this tailored marketing collateral, image templates need to be created manually by graphic designers, involving complex grid manipulation and detailed geometric adjustments. As a matter of fact, the image template design is highly manual, skill-demanding and costly, and essentially the bottleneck for image personalization. We present a semi-automatic image personalization tool for designing image templates. Two scenarios are considered: text insertion and text replacement, with the text replacement option not offered in current solutions. The graphical user interface (GUI) of the tool is described in detail. Unlike current solutions, the tool renders the text in 3-D, which allows easy adjustment of the text. In particular, the tool has been implemented in Java, which introduces flexible deployment and eliminates the need for any special software or know-how on the part of the end user.

SU-E-QI-16: Reproducibility of Computed Tomography Quantitative Structural Features Using the FDA Thoracic Phantom Image Database

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

Budzevich, M; Grove, O; Balagurunathan, Y

Purpose: To assess the reproducibility of quantitative structural features using images from the computed tomography thoracic FDA phantom database under different scanning conditions. Methods: Development of quantitative image features to describe lesion shape and size, beyond conventional RECIST measures, is an evolving area of research in need of benchmarking standards. Gavrielides et al. (2010) scanned a FDA-developed thoracic phantom with nodules of various Hounsfield units (HU) values, shapes and sizes close to vascular structures using several scanners and varying scanning conditions/parameters; these images are in the public domain. We tested six structural features, namely, Convexity, Perimeter, Major Axis, Minor Axis,more » Extent Mean and Eccentricity, to characterize lung nodules. Convexity measures lesion irregularity referenced to a convex surface. Previously, we showed it to have prognostic value in lung adenocarcinoma. The above metrics and RECIST measures were evaluated on three spiculated (8mm/-300HU, 12mm/+30HU and 15mm/+30HU) and two non-spiculated (8mm/+100HU and 10mm/+100HU) nodules (from layout 2) imaged at three different mAs values: 25, 100 and 200 mAs; on a Phillips scanner (16-slice Mx8000-IDT; 3mm slice thickness). The nodules were segmented semi-automatically using a commercial software tool; the same HU range was used for all nodules. Results: Analysis showed convexity having the lowest maximum coefficient of variation (MCV): 1.1% and 0.6% for spiculated and non-spiculated nodules, respectively, much lower compared to RECIST Major and Minor axes whose MCV were 10.1% and 13.4% for spiculated, and 1.9% and 2.3% for non-spiculated nodules, respectively, across the various mAs. MCVs were consistently larger for speculated nodules. In general, the dependence of structural features on mAs (noise) was low. Conclusion: The FDA phantom CT database may be used for benchmarking of structural features for various scanners and scanning conditions; we used only a small fraction of available data. Our feature convexity outperformed other structural features including RECIST measures.« less

Recognition and inference of crevice processing on digitized paintings

NASA Astrophysics Data System (ADS)

Karuppiah, S. P.; Srivatsa, S. K.

2013-03-01

This paper is designed to detect and removal of cracks on digitized paintings. The cracks are detected by threshold. Afterwards, the thin dark brush strokes which have been misidentified as cracks are removed using Median radial basis function neural network on hue and saturation data, Semi-automatic procedure based on region growing. Finally, crack is filled using wiener filter. The paper is well designed in such a way that most of the cracks on digitized paintings have identified and removed. The paper % of betterment is 90%. This paper helps us to perform not only on digitized paintings but also the medical images and bmp images. This paper is implemented by Mat Lab.

Multiple sclerosis lesion segmentation using an automatic multimodal graph cuts.

PubMed

García-Lorenzo, Daniel; Lecoeur, Jeremy; Arnold, Douglas L; Collins, D Louis; Barillot, Christian

2009-01-01

Graph Cuts have been shown as a powerful interactive segmentation technique in several medical domains. We propose to automate the Graph Cuts in order to automatically segment Multiple Sclerosis (MS) lesions in MRI. We replace the manual interaction with a robust EM-based approach in order to discriminate between MS lesions and the Normal Appearing Brain Tissues (NABT). Evaluation is performed in synthetic and real images showing good agreement between the automatic segmentation and the target segmentation. We compare our algorithm with the state of the art techniques and with several manual segmentations. An advantage of our algorithm over previously published ones is the possibility to semi-automatically improve the segmentation due to the Graph Cuts interactive feature.

Rendering-based video-CT registration with physical constraints for image-guided endoscopic sinus surgery

NASA Astrophysics Data System (ADS)

Otake, Y.; Leonard, S.; Reiter, A.; Rajan, P.; Siewerdsen, J. H.; Ishii, M.; Taylor, R. H.; Hager, G. D.

2015-03-01

We present a system for registering the coordinate frame of an endoscope to pre- or intra- operatively acquired CT data based on optimizing the similarity metric between an endoscopic image and an image predicted via rendering of CT. Our method is robust and semi-automatic because it takes account of physical constraints, specifically, collisions between the endoscope and the anatomy, to initialize and constrain the search. The proposed optimization method is based on a stochastic optimization algorithm that evaluates a large number of similarity metric functions in parallel on a graphics processing unit. Images from a cadaver and a patient were used for evaluation. The registration error was 0.83 mm and 1.97 mm for cadaver and patient images respectively. The average registration time for 60 trials was 4.4 seconds. The patient study demonstrated robustness of the proposed algorithm against a moderate anatomical deformation.

Application of the 3D slicer chest imaging platform segmentation algorithm for large lung nodule delineation

PubMed Central

Parmar, Chintan; Blezek, Daniel; Estepar, Raul San Jose; Pieper, Steve; Kim, John; Aerts, Hugo J. W. L.

2017-01-01

Purpose Accurate segmentation of lung nodules is crucial in the development of imaging biomarkers for predicting malignancy of the nodules. Manual segmentation is time consuming and affected by inter-observer variability. We evaluated the robustness and accuracy of a publically available semiautomatic segmentation algorithm that is implemented in the 3D Slicer Chest Imaging Platform (CIP) and compared it with the performance of manual segmentation. Methods CT images of 354 manually segmented nodules were downloaded from the LIDC database. Four radiologists performed the manual segmentation and assessed various nodule characteristics. The semiautomatic CIP segmentation was initialized using the centroid of the manual segmentations, thereby generating four contours for each nodule. The robustness of both segmentation methods was assessed using the region of uncertainty (δ) and Dice similarity index (DSI). The robustness of the segmentation methods was compared using the Wilcoxon-signed rank test (pWilcoxon<0.05). The Dice similarity index (DSIAgree) between the manual and CIP segmentations was computed to estimate the accuracy of the semiautomatic contours. Results The median computational time of the CIP segmentation was 10 s. The median CIP and manually segmented volumes were 477 ml and 309 ml, respectively. CIP segmentations were significantly more robust than manual segmentations (median δCIP = 14ml, median dsiCIP = 99% vs. median δmanual = 222ml, median dsimanual = 82%) with pWilcoxon~10−16. The agreement between CIP and manual segmentations had a median DSIAgree of 60%. While 13% (47/354) of the nodules did not require any manual adjustment, minor to substantial manual adjustments were needed for 87% (305/354) of the nodules. CIP segmentations were observed to perform poorly (median DSIAgree≈50%) for non-/sub-solid nodules with subtle appearances and poorly defined boundaries. Conclusion Semi-automatic CIP segmentation can potentially reduce the physician workload for 13% of nodules owing to its computational efficiency and superior stability compared to manual segmentation. Although manual adjustment is needed for many cases, CIP segmentation provides a preliminary contour for physicians as a starting point. PMID:28594880

Semiautomatic segmentation of liver metastases on volumetric CT images

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

Yan, Jiayong; Schwartz, Lawrence H.; Zhao, Binsheng, E-mail: bz2166@cumc.columbia.edu

2015-11-15

Purpose: Accurate segmentation and quantification of liver metastases on CT images are critical to surgery/radiation treatment planning and therapy response assessment. To date, there are no reliable methods to perform such segmentation automatically. In this work, the authors present a method for semiautomatic delineation of liver metastases on contrast-enhanced volumetric CT images. Methods: The first step is to manually place a seed region-of-interest (ROI) in the lesion on an image. This ROI will (1) serve as an internal marker and (2) assist in automatically identifying an external marker. With these two markers, lesion contour on the image can be accuratelymore » delineated using traditional watershed transformation. Density information will then be extracted from the segmented 2D lesion and help determine the 3D connected object that is a candidate of the lesion volume. The authors have developed a robust strategy to automatically determine internal and external markers for marker-controlled watershed segmentation. By manually placing a seed region-of-interest in the lesion to be delineated on a reference image, the method can automatically determine dual threshold values to approximately separate the lesion from its surrounding structures and refine the thresholds from the segmented lesion for the accurate segmentation of the lesion volume. This method was applied to 69 liver metastases (1.1–10.3 cm in diameter) from a total of 15 patients. An independent radiologist manually delineated all lesions and the resultant lesion volumes served as the “gold standard” for validation of the method’s accuracy. Results: The algorithm received a median overlap, overestimation ratio, and underestimation ratio of 82.3%, 6.0%, and 11.5%, respectively, and a median average boundary distance of 1.2 mm. Conclusions: Preliminary results have shown that volumes of liver metastases on contrast-enhanced CT images can be accurately estimated by a semiautomatic segmentation method.« less

Automatical and accurate segmentation of cerebral tissues in fMRI dataset with combination of image processing and deep learning

NASA Astrophysics Data System (ADS)

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

2018-02-01

Image segmentation plays an important role in medical science. One application is multimodality imaging, especially the fusion of structural imaging with functional imaging, which includes CT, MRI and new types of imaging technology such as optical imaging to obtain functional images. The fusion process require precisely extracted structural information, in order to register the image to it. Here we used image enhancement, 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 deep learning way. Such approach 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. The contours of the borders of different tissues on all images were accurately extracted and 3D visualized. This can be used in low-level light therapy and optical simulation software such as MCVM. We obtained a precise three-dimensional distribution of brain, which offered doctors and researchers quantitative volume data and detailed morphological characterization for personal precise medicine of Cerebral atrophy/expansion. We hope this technique can bring convenience to visualization medical and personalized medicine.

Semi-automatic, octave-spanning optical frequency counter.

PubMed

Liu, Tze-An; Shu, Ren-Huei; Peng, Jin-Long

2008-07-07

This work presents and demonstrates a semi-automatic optical frequency counter with octave-spanning counting capability using two fiber laser combs operated at different repetition rates. Monochromators are utilized to provide an approximate frequency of the laser under measurement to determine the mode number difference between the two laser combs. The exact mode number of the beating comb line is obtained from the mode number difference and the measured beat frequencies. The entire measurement process, except the frequency stabilization of the laser combs and the optimization of the beat signal-to-noise ratio, is controlled by a computer running a semi-automatic optical frequency counter.

Flexible methods for segmentation evaluation: Results from CT-based luggage screening

PubMed Central

Karimi, Seemeen; Jiang, Xiaoqian; Cosman, Pamela; Martz, Harry

2017-01-01

BACKGROUND Imaging systems used in aviation security include segmentation algorithms in an automatic threat recognition pipeline. The segmentation algorithms evolve in response to emerging threats and changing performance requirements. Analysis of segmentation algorithms’ behavior, including the nature of errors and feature recovery, facilitates their development. However, evaluation methods from the literature provide limited characterization of the segmentation algorithms. OBJECTIVE To develop segmentation evaluation methods that measure systematic errors such as oversegmentation and undersegmentation, outliers, and overall errors. The methods must measure feature recovery and allow us to prioritize segments. METHODS We developed two complementary evaluation methods using statistical techniques and information theory. We also created a semi-automatic method to define ground truth from 3D images. We applied our methods to evaluate five segmentation algorithms developed for CT luggage screening. We validated our methods with synthetic problems and an observer evaluation. RESULTS Both methods selected the same best segmentation algorithm. Human evaluation confirmed the findings. The measurement of systematic errors and prioritization helped in understanding the behavior of each segmentation algorithm. CONCLUSIONS Our evaluation methods allow us to measure and explain the accuracy of segmentation algorithms. PMID:24699346

P04.19 Recommendations for computation of textural measures obtained from 3D brain tumor MRIs: A robustness analysis points out the need for standardization.

PubMed Central

Molina, D.; Pérez-Beteta, J.; Martínez-González, A.; Velásquez, C.; Martino, J.; Luque, B.; Revert, A.; Herruzo, I.; Arana, E.; Pérez-García, V. M.

2017-01-01

Abstract Introduction: Textural analysis refers to a variety of mathematical methods used to quantify the spatial variations in grey levels within images. In brain tumors, textural features have a great potential as imaging biomarkers having been shown to correlate with survival, tumor grade, tumor type, etc. However, these measures should be reproducible under dynamic range and matrix size changes for their clinical use. Our aim is to study this robustness in brain tumors with 3D magnetic resonance imaging, not previously reported in the literature. Materials and methods: 3D T1-weighted images of 20 patients with glioblastoma (64.80 ± 9.12 years-old) obtained from a 3T scanner were analyzed. Tumors were segmented using an in-house semi-automatic 3D procedure. A set of 16 3D textural features of the most common types (co-occurrence and run-length matrices) were selected, providing regional (run-length based measures) and local information (co-ocurrence matrices) on the tumor heterogeneity. Feature robustness was assessed by means of the coefficient of variation (CV) under both dynamic range (16, 32 and 64 gray levels) and/or matrix size (256x256 and 432x432) changes. Results: None of the textural features considered were robust under dynamic range changes. The textural co-occurrence matrix feature Entropy was the only textural feature robust (CV < 10%) under spatial resolution changes. Conclusions: In general, textural measures of three-dimensional brain tumor images are neither robust under dynamic range nor under matrix size changes. Thus, it becomes mandatory to fix standards for image rescaling after acquisition before the textural features are computed if they are to be used as imaging biomarkers. For T1-weighted images a dynamic range of 16 grey levels and a matrix size of 256x256 (and isotropic voxel) is found to provide reliable and comparable results and is feasible with current MRI scanners. The implications of this work go beyond the specific tumor type and MRI sequence studied here and pose the need for standardization in textural feature calculation of oncological images. FUNDING: James S. Mc. Donnell Foundation (USA) 21st Century Science Initiative in Mathematical and Complex Systems Approaches for Brain Cancer [Collaborative award 220020450 and planning grant 220020420], MINECO/FEDER [MTM2015-71200-R], JCCM [PEII-2014-031-P].

ViCAR: An Adaptive and Landmark-Free Registration of Time Lapse Image Data from Microfluidics Experiments

PubMed Central

Hattab, Georges; Schlüter, Jan-Philip; Becker, Anke; Nattkemper, Tim W.

2017-01-01

In order to understand gene function in bacterial life cycles, time lapse bioimaging is applied in combination with different marker protocols in so called microfluidics chambers (i.e., a multi-well plate). In one experiment, a series of T images is recorded for one visual field, with a pixel resolution of 60 nm/px. Any (semi-)automatic analysis of the data is hampered by a strong image noise, low contrast and, last but not least, considerable irregular shifts during the acquisition. Image registration corrects such shifts enabling next steps of the analysis (e.g., feature extraction or tracking). Image alignment faces two obstacles in this microscopic context: (a) highly dynamic structural changes in the sample (i.e., colony growth) and (b) an individual data set-specific sample environment which makes the application of landmarks-based alignments almost impossible. We present a computational image registration solution, we refer to as ViCAR: (Vi)sual (C)ues based (A)daptive (R)egistration, for such microfluidics experiments, consisting of (1) the detection of particular polygons (outlined and segmented ones, referred to as visual cues), (2) the adaptive retrieval of three coordinates throughout different sets of frames, and finally (3) an image registration based on the relation of these points correcting both rotation and translation. We tested ViCAR with different data sets and have found that it provides an effective spatial alignment thereby paving the way to extract temporal features pertinent to each resulting bacterial colony. By using ViCAR, we achieved an image registration with 99.9% of image closeness, based on the average rmsd of 4.10−2 pixels, and superior results compared to a state of the art algorithm. PMID:28620411

Estimation of terracing characteristics from airborne laser scanning data

NASA Astrophysics Data System (ADS)

Kokalj, Žiga

2015-04-01

Agricultural terraces are a fundamental morphological form of the Slovenian landscape. They are present in all of its diverse geographical regions, from Mediterranean and Dinaric hills and plateaus, Alpine mountains and plains, to Pannonian hills. New systematic research based on mapping aerial orthophotos and historical maps revealed previously unrecorded distribution and extent of terracing. However, the extensive overgrowing of the Slovenian countryside in the past century, when forest cover has grown from 40% to more than 60%, hid many of the terraces under a thick forest canopy. This is especially true for the higher and more remote areas where unfavourable natural conditions have coupled with depopulation processes. In such conditions, the only reasonable technique to observe cultural terraces and other remains of past human activities over large areas is airborne laser scanning. With the country-wide airborne lidar data becoming available, many new possibilities for discovery as well as quantitative analyses are becoming available. We explored manual and semiautomatic approaches to obtain terracing characteristics around representative villages of diverse landscape types. Individual terraces can be described with several attributes, such as riser slope gradient, riser height, tread area, length and width, ratio of length and width, altitude, location of the terrace in the thermal band, distance to the settlement, number and type of trees, distance between trees, and number of vineyard rows. Such characteristics can be derived manually, which can be painstakingly slow, but with relative precisions reaching the order of centimetres and decimetres, or semiautomatically, which is much faster, but with worse precision levels, mainly due to various outliers and errors in processing. The success of attribute derivation is highly dependent on raw lidar data acquisition parameters and processing. Manual interpretation has a distinct advantage of the possibility to explore and manipulate the raw data, i.e. the lidar point cloud, where relevant features that could be removed in the filtering process can still be traced and their exact extents discernible. However, this is only possible for specific and very detailed analyses, while much more of the work has to be done with already processed raster elevation data. Processing has to be tailored specifically with terracing in mind, otherwise typical characteristics, such as riser slope gradient and thread edges can be distorted. We also investigated the role different elevation model visualizations have on the manual interpretation of terraced landscapes and which visualizations can benefit semiautomatic processing.

Technique of semiautomatic surface reconstruction of the visible Korean human data using commercial software.

PubMed

Park, Jin Seo; Shin, Dong Sun; Chung, Min Suk; Hwang, Sung Bae; Chung, Jinoh

2007-11-01

This article describes the technique of semiautomatic surface reconstruction of anatomic structures using widely available commercial software. This technique would enable researchers to promptly and objectively perform surface reconstruction, creating three-dimensional anatomic images without any assistance from computer engineers. To develop the technique, we used data from the Visible Korean Human project, which produced digitalized photographic serial images of an entire cadaver. We selected 114 anatomic structures (skin [1], bones [32], knee joint structures [7], muscles [60], arteries [7], and nerves [7]) from the 976 anatomic images which were generated from the left lower limb of the cadaver. Using Adobe Photoshop, the selected anatomic structures in each serial image were outlined, creating a segmented image. The Photoshop files were then converted into Adobe Illustrator files to prepare isolated segmented images, so that the contours of the structure could be viewed independent of the surrounding anatomy. Using Alias Maya, these isolated segmented images were then stacked to construct a contour image. Gaps between the contour lines were filled with surfaces, and three-dimensional surface reconstruction could be visualized with Rhinoceros. Surface imperfections were then corrected to complete the three-dimensional images in Alias Maya. We believe that the three-dimensional anatomic images created by these methods will have widespread application in both medical education and research. 2007 Wiley-Liss, Inc

Automatic spatiotemporal matching of detected pleural thickenings

NASA Astrophysics Data System (ADS)

Chaisaowong, Kraisorn; Keller, Simon Kai; Kraus, Thomas

2014-01-01

Pleural thickenings can be found in asbestos exposed patient's lung. Non-invasive diagnosis including CT imaging can detect aggressive malignant pleural mesothelioma in its early stage. In order to create a quantitative documentation of automatic detected pleural thickenings over time, the differences in volume and thickness of the detected thickenings have to be calculated. Physicians usually estimate the change of each thickening via visual comparison which provides neither quantitative nor qualitative measures. In this work, automatic spatiotemporal matching techniques of the detected pleural thickenings at two points of time based on the semi-automatic registration have been developed, implemented, and tested so that the same thickening can be compared fully automatically. As result, the application of the mapping technique using the principal components analysis turns out to be advantageous than the feature-based mapping using centroid and mean Hounsfield Units of each thickening, since the resulting sensitivity was improved to 98.46% from 42.19%, while the accuracy of feature-based mapping is only slightly higher (84.38% to 76.19%).

Denoising and 4D visualization of OCT images

PubMed Central

Gargesha, Madhusudhana; Jenkins, Michael W.; Rollins, Andrew M.; Wilson, David L.

2009-01-01

We are using Optical Coherence Tomography (OCT) to image structure and function of the developing embryonic heart in avian models. Fast OCT imaging produces very large 3D (2D + time) and 4D (3D volumes + time) data sets, which greatly challenge ones ability to visualize results. Noise in OCT images poses additional challenges. We created an algorithm with a quick, data set specific optimization for reduction of both shot and speckle noise and applied it to 3D visualization and image segmentation in OCT. When compared to baseline algorithms (median, Wiener, orthogonal wavelet, basic non-orthogonal wavelet), a panel of experts judged the new algorithm to give much improved volume renderings concerning both noise and 3D visualization. Specifically, the algorithm provided a better visualization of the myocardial and endocardial surfaces, and the interaction of the embryonic heart tube with surrounding tissue. Quantitative evaluation using an image quality figure of merit also indicated superiority of the new algorithm. Noise reduction aided semi-automatic 2D image segmentation, as quantitatively evaluated using a contour distance measure with respect to an expert segmented contour. In conclusion, the noise reduction algorithm should be quite useful for visualization and quantitative measurements (e.g., heart volume, stroke volume, contraction velocity, etc.) in OCT embryo images. With its semi-automatic, data set specific optimization, we believe that the algorithm can be applied to OCT images from other applications. PMID:18679509

Evaluation of new data processing algorithms for planar gated ventriculography (MUGA)

PubMed Central

Fair, Joanna R.; Telepak, Robert J.

2009-01-01

Before implementing one of two new LVEF radionuclide gated ventriculogram (MUGA) systems, the results from 312 consecutive parallel patient studies were evaluated. Each gamma‐camera acquisition was simultaneously processed by semi‐automatic Medasys Pinnacle and by fully automatic and semiautomatic Philips nuclear medicine computer systems. The Philips systems yielded LVEF results within ±5LVEF percentage points of the Medasys system in fewer than half of the studies. The remaining values were higher or lower than those from the long‐used Medasys system. These differences might have changed cancer patient chemotherapy clinical decisions. As a result, our institution elected not to implement either new system. PACS: 87.57.U‐ Nuclear medicine imaging

Population based MRI and DTI templates of the adult ferret brain and tools for voxelwise analysis.

PubMed

Hutchinson, E B; Schwerin, S C; Radomski, K L; Sadeghi, N; Jenkins, J; Komlosh, M E; Irfanoglu, M O; Juliano, S L; Pierpaoli, C

2017-05-15

Non-invasive imaging has the potential to play a crucial role in the characterization and translation of experimental animal models to investigate human brain development and disorders, especially when employed to study animal models that more accurately represent features of human neuroanatomy. The purpose of this study was to build and make available MRI and DTI templates and analysis tools for the ferret brain as the ferret is a well-suited species for pre-clinical MRI studies with folded cortical surface, relatively high white matter volume and body dimensions that allow imaging with pre-clinical MRI scanners. Four ferret brain templates were built in this study - in-vivo MRI and DTI and ex-vivo MRI and DTI - using brain images across many ferrets and region of interest (ROI) masks corresponding to established ferret neuroanatomy were generated by semi-automatic and manual segmentation. The templates and ROI masks were used to create a web-based ferret brain viewing software for browsing the MRI and DTI volumes with annotations based on the ROI masks. A second objective of this study was to provide a careful description of the imaging methods used for acquisition, processing, registration and template building and to demonstrate several voxelwise analysis methods including Jacobian analysis of morphometry differences between the female and male brain and bias-free identification of DTI abnormalities in an injured ferret brain. The templates, tools and methodological optimization presented in this study are intended to advance non-invasive imaging approaches for human-similar animal species that will enable the use of pre-clinical MRI studies for understanding and treating brain disorders. Published by Elsevier Inc.

A deep learning approach for real time prostate segmentation in freehand ultrasound guided biopsy.

PubMed

Anas, Emran Mohammad Abu; Mousavi, Parvin; Abolmaesumi, Purang

2018-06-01

Targeted prostate biopsy, incorporating multi-parametric magnetic resonance imaging (mp-MRI) and its registration with ultrasound, is currently the state-of-the-art in prostate cancer diagnosis. The registration process in most targeted biopsy systems today relies heavily on accurate segmentation of ultrasound images. Automatic or semi-automatic segmentation is typically performed offline prior to the start of the biopsy procedure. In this paper, we present a deep neural network based real-time prostate segmentation technique during the biopsy procedure, hence paving the way for dynamic registration of mp-MRI and ultrasound data. In addition to using convolutional networks for extracting spatial features, the proposed approach employs recurrent networks to exploit the temporal information among a series of ultrasound images. One of the key contributions in the architecture is to use residual convolution in the recurrent networks to improve optimization. We also exploit recurrent connections within and across different layers of the deep networks to maximize the utilization of the temporal information. Furthermore, we perform dense and sparse sampling of the input ultrasound sequence to make the network robust to ultrasound artifacts. Our architecture is trained on 2,238 labeled transrectal ultrasound images, with an additional 637 and 1,017 unseen images used for validation and testing, respectively. We obtain a mean Dice similarity coefficient of 93%, a mean surface distance error of 1.10 mm and a mean Hausdorff distance error of 3.0 mm. A comparison of the reported results with those of a state-of-the-art technique indicates statistically significant improvement achieved by the proposed approach. Copyright © 2018 Elsevier B.V. All rights reserved.

Prostate segmentation by feature enhancement using domain knowledge and adaptive region based operations

NASA Astrophysics Data System (ADS)

Nanayakkara, Nuwan D.; Samarabandu, Jagath; Fenster, Aaron

2006-04-01

Estimation of prostate location and volume is essential in determining a dose plan for ultrasound-guided brachytherapy, a common prostate cancer treatment. However, manual segmentation is difficult, time consuming and prone to variability. In this paper, we present a semi-automatic discrete dynamic contour (DDC) model based image segmentation algorithm, which effectively combines a multi-resolution model refinement procedure together with the domain knowledge of the image class. The segmentation begins on a low-resolution image by defining a closed DDC model by the user. This contour model is then deformed progressively towards higher resolution images. We use a combination of a domain knowledge based fuzzy inference system (FIS) and a set of adaptive region based operators to enhance the edges of interest and to govern the model refinement using a DDC model. The automatic vertex relocation process, embedded into the algorithm, relocates deviated contour points back onto the actual prostate boundary, eliminating the need of user interaction after initialization. The accuracy of the prostate boundary produced by the proposed algorithm was evaluated by comparing it with a manually outlined contour by an expert observer. We used this algorithm to segment the prostate boundary in 114 2D transrectal ultrasound (TRUS) images of six patients scheduled for brachytherapy. The mean distance between the contours produced by the proposed algorithm and the manual outlines was 2.70 ± 0.51 pixels (0.54 ± 0.10 mm). We also showed that the algorithm is insensitive to variations of the initial model and parameter values, thus increasing the accuracy and reproducibility of the resulting boundaries in the presence of noise and artefacts.

Statistical colour models: an automated digital image analysis method for quantification of histological biomarkers.

PubMed

Shu, Jie; Dolman, G E; Duan, Jiang; Qiu, Guoping; Ilyas, Mohammad

2016-04-27

Colour is the most important feature used in quantitative immunohistochemistry (IHC) image analysis; IHC is used to provide information relating to aetiology and to confirm malignancy. Statistical modelling is a technique widely used for colour detection in computer vision. We have developed a statistical model of colour detection applicable to detection of stain colour in digital IHC images. Model was first trained by massive colour pixels collected semi-automatically. To speed up the training and detection processes, we removed luminance channel, Y channel of YCbCr colour space and chose 128 histogram bins which is the optimal number. A maximum likelihood classifier is used to classify pixels in digital slides into positively or negatively stained pixels automatically. The model-based tool was developed within ImageJ to quantify targets identified using IHC and histochemistry. The purpose of evaluation was to compare the computer model with human evaluation. Several large datasets were prepared and obtained from human oesophageal cancer, colon cancer and liver cirrhosis with different colour stains. Experimental results have demonstrated the model-based tool achieves more accurate results than colour deconvolution and CMYK model in the detection of brown colour, and is comparable to colour deconvolution in the detection of pink colour. We have also demostrated the proposed model has little inter-dataset variations. A robust and effective statistical model is introduced in this paper. The model-based interactive tool in ImageJ, which can create a visual representation of the statistical model and detect a specified colour automatically, is easy to use and available freely at http://rsb.info.nih.gov/ij/plugins/ihc-toolbox/index.html . Testing to the tool by different users showed only minor inter-observer variations in results.

Mutual information-based feature selection for radiomics

NASA Astrophysics Data System (ADS)

Oubel, Estanislao; Beaumont, Hubert; Iannessi, Antoine

2016-03-01

Background The extraction and analysis of image features (radiomics) is a promising field in the precision medicine era, with applications to prognosis, prediction, and response to treatment quantification. In this work, we present a mutual information - based method for quantifying reproducibility of features, a necessary step for qualification before their inclusion in big data systems. Materials and Methods Ten patients with Non-Small Cell Lung Cancer (NSCLC) lesions were followed over time (7 time points in average) with Computed Tomography (CT). Five observers segmented lesions by using a semi-automatic method and 27 features describing shape and intensity distribution were extracted. Inter-observer reproducibility was assessed by computing the multi-information (MI) of feature changes over time, and the variability of global extrema. Results The highest MI values were obtained for volume-based features (VBF). The lesion mass (M), surface to volume ratio (SVR) and volume (V) presented statistically significant higher values of MI than the rest of features. Within the same VBF group, SVR showed also the lowest variability of extrema. The correlation coefficient (CC) of feature values was unable to make a difference between features. Conclusions MI allowed to discriminate three features (M, SVR, and V) from the rest in a statistically significant manner. This result is consistent with the order obtained when sorting features by increasing values of extrema variability. MI is a promising alternative for selecting features to be considered as surrogate biomarkers in a precision medicine context.

Computer Vision Assisted Virtual Reality Calibration

NASA Technical Reports Server (NTRS)

Kim, W.

1999-01-01

A computer vision assisted semi-automatic virtual reality (VR) calibration technology has been developed that can accurately match a virtual environment of graphically simulated three-dimensional (3-D) models to the video images of the real task environment.

An independent software system for the analysis of dynamic MR images.

PubMed

Torheim, G; Lombardi, M; Rinck, P A

1997-01-01

A computer system for the manual, semi-automatic, and automatic analysis of dynamic MR images was to be developed on UNIX and personal computer platforms. The system was to offer an integrated and standardized way of performing both image processing and analysis that was independent of the MR unit used. The system consists of modules that are easily adaptable to special needs. Data from MR units or other diagnostic imaging equipment in techniques such as CT, ultrasonography, or nuclear medicine can be processed through the ACR-NEMA/DICOM standard file formats. A full set of functions is available, among them cine-loop visual analysis, and generation of time-intensity curves. Parameters such as cross-correlation coefficients, area under the curve, peak/maximum intensity, wash-in and wash-out slopes, time to peak, and relative signal intensity/contrast enhancement can be calculated. Other parameters can be extracted by fitting functions like the gamma-variate function. Region-of-interest data and parametric values can easily be exported. The system has been successfully tested in animal and patient examinations.

Diffusion kurtosis imaging can efficiently assess the glioma grade and cellular proliferation.

PubMed

Jiang, Rifeng; Jiang, Jingjing; Zhao, Lingyun; Zhang, Jiaxuan; Zhang, Shun; Yao, Yihao; Yang, Shiqi; Shi, Jingjing; Shen, Nanxi; Su, Changliang; Zhang, Ju; Zhu, Wenzhen

2015-12-08

Conventional diffusion imaging techniques are not sufficiently accurate for evaluating glioma grade and cellular proliferation, which are critical for guiding glioma treatment. Diffusion kurtosis imaging (DKI), an advanced non-Gaussian diffusion imaging technique, has shown potential in grading glioma; however, its applications in this tumor have not been fully elucidated. In this study, DKI and diffusion weighted imaging (DWI) were performed on 74 consecutive patients with histopathologically confirmed glioma. The kurtosis and conventional diffusion metric values of the tumor were semi-automatically obtained. The relationships of these metrics with the glioma grade and Ki-67 expression were evaluated. The diagnostic efficiency of these metrics in grading was further compared. It was demonstrated that compared with the conventional diffusion metrics, the kurtosis metrics were more promising imaging markers in distinguishing high-grade from low-grade gliomas and distinguishing among grade II, III and IV gliomas; the kurtosis metrics also showed great potential in the prediction of Ki-67 expression. To our best knowledge, we are the first to reveal the ability of DKI to assess the cellular proliferation of gliomas, and to employ the semi-automatic method for the accurate measurement of gliomas. These results could have a significant impact on the diagnosis and subsequent therapy of glioma.

KEGGParser: parsing and editing KEGG pathway maps in Matlab.

PubMed

Arakelyan, Arsen; Nersisyan, Lilit

2013-02-15

KEGG pathway database is a collection of manually drawn pathway maps accompanied with KGML format files intended for use in automatic analysis. KGML files, however, do not contain the required information for complete reproduction of all the events indicated in the static image of a pathway map. Several parsers and editors of KEGG pathways exist for processing KGML files. We introduce KEGGParser-a MATLAB based tool for KEGG pathway parsing, semiautomatic fixing, editing, visualization and analysis in MATLAB environment. It also works with Scilab. The source code is available at http://www.mathworks.com/matlabcentral/fileexchange/37561.

Does semi-automatic bone-fragment segmentation improve the reproducibility of the Letournel acetabular fracture classification?

PubMed

Boudissa, M; Orfeuvre, B; Chabanas, M; Tonetti, J

2017-09-01

The Letournel classification of acetabular fracture shows poor reproducibility in inexperienced observers, despite the introduction of 3D imaging. We therefore developed a method of semi-automatic segmentation based on CT data. The present prospective study aimed to assess: (1) whether semi-automatic bone-fragment segmentation increased the rate of correct classification; (2) if so, in which fracture types; and (3) feasibility using the open-source itksnap 3.0 software package without incurring extra cost for users. Semi-automatic segmentation of acetabular fractures significantly increases the rate of correct classification by orthopedic surgery residents. Twelve orthopedic surgery residents classified 23 acetabular fractures. Six used conventional 3D reconstructions provided by the center's radiology department (conventional group) and 6 others used reconstructions obtained by semi-automatic segmentation using the open-source itksnap 3.0 software package (segmentation group). Bone fragments were identified by specific colors. Correct classification rates were compared between groups on Chi 2 test. Assessment was repeated 2 weeks later, to determine intra-observer reproducibility. Correct classification rates were significantly higher in the "segmentation" group: 114/138 (83%) versus 71/138 (52%); P<0.0001. The difference was greater for simple (36/36 (100%) versus 17/36 (47%); P<0.0001) than complex fractures (79/102 (77%) versus 54/102 (53%); P=0.0004). Mean segmentation time per fracture was 27±3min [range, 21-35min]. The segmentation group showed excellent intra-observer correlation coefficients, overall (ICC=0.88), and for simple (ICC=0.92) and complex fractures (ICC=0.84). Semi-automatic segmentation, identifying the various bone fragments, was effective in increasing the rate of correct acetabular fracture classification on the Letournel system by orthopedic surgery residents. It may be considered for routine use in education and training. III: prospective case-control study of a diagnostic procedure. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

Web Based Semi-automatic Scientific Validation of Models of the Corona and Inner Heliosphere

NASA Astrophysics Data System (ADS)

MacNeice, P. J.; Chulaki, A.; Taktakishvili, A.; Kuznetsova, M. M.

2013-12-01

Validation is a critical step in preparing models of the corona and inner heliosphere for future roles supporting either or both the scientific research community and the operational space weather forecasting community. Validation of forecasting quality tends to focus on a short list of key features in the model solutions, with an unchanging order of priority. Scientific validation exposes a much larger range of physical processes and features, and as the models evolve to better represent features of interest, the research community tends to shift its focus to other areas which are less well understood and modeled. Given the more comprehensive and dynamic nature of scientific validation, and the limited resources available to the community to pursue this, it is imperative that the community establish a semi-automated process which engages the model developers directly into an ongoing and evolving validation process. In this presentation we describe the ongoing design and develpment of a web based facility to enable this type of validation of models of the corona and inner heliosphere, on the growing list of model results being generated, and on strategies we have been developing to account for model results that incorporate adaptively refined numerical grids.

Assessment of local pulse wave velocity distribution in mice using k-t BLAST PC-CMR with semi-automatic area segmentation.

PubMed

Herold, Volker; Herz, Stefan; Winter, Patrick; Gutjahr, Fabian Tobias; Andelovic, Kristina; Bauer, Wolfgang Rudolf; Jakob, Peter Michael

2017-10-16

Local aortic pulse wave velocity (PWV) is a measure for vascular stiffness and has a predictive value for cardiovascular events. Ultra high field CMR scanners allow the quantification of local PWV in mice, however these systems are yet unable to monitor the distribution of local elasticities. In the present study we provide a new accelerated method to quantify local aortic PWV in mice with phase-contrast cardiovascular magnetic resonance imaging (PC-CMR) at 17.6 T. Based on a k-t BLAST (Broad-use Linear Acquisition Speed-up Technique) undersampling scheme, total measurement time could be reduced by a factor of 6. The fast data acquisition enables to quantify the local PWV at several locations along the aortic blood vessel based on the evaluation of local temporal changes in blood flow and vessel cross sectional area. To speed up post processing and to eliminate operator bias, we introduce a new semi-automatic segmentation algorithm to quantify cross-sectional areas of the aortic vessel. The new methods were applied in 10 eight-month-old mice (4 C57BL/6J-mice and 6 ApoE (-/-) -mice) at 12 adjacent locations along the abdominal aorta. Accelerated data acquisition and semi-automatic post-processing delivered reliable measures for the local PWV, similiar to those obtained with full data sampling and manual segmentation. No statistically significant differences of the mean values could be detected for the different measurement approaches. Mean PWV values were elevated for the ApoE (-/-) -group compared to the C57BL/6J-group (3.5 ± 0.7 m/s vs. 2.2 ± 0.4 m/s, p < 0.01). A more heterogeneous PWV-distribution in the ApoE (-/-) -animals could be observed compared to the C57BL/6J-mice, representing the local character of lesion development in atherosclerosis. In the present work, we showed that k-t BLAST PC-MRI enables the measurement of the local PWV distribution in the mouse aorta. The semi-automatic segmentation method based on PC-CMR data allowed rapid determination of local PWV. The findings of this study demonstrate the ability of the proposed methods to non-invasively quantify the spatial variations in local PWV along the aorta of ApoE (-/-) -mice as a relevant model of atherosclerosis.

Graphical user interface (GUIDE) and semi-automatic system for the acquisition of anaglyphs

NASA Astrophysics Data System (ADS)

Canchola, Marco A.; Arízaga, Juan A.; Cortés, Obed; Tecpanecatl, Eduardo; Cantero, Jose M.

2013-09-01

Diverse educational experiences have shown greater acceptance of children to ideas related to science, compared with adults. That fact and showing great curiosity are factors to consider to undertake scientific outreach efforts for children, with prospects of success. Moreover now 3D digital images have become a topic that has gained importance in various areas, entertainment, film and video games mainly, but also in areas such as medical practice transcendental in disease detection This article presents a system model for 3D images for educational purposes that allows students of various grade levels, school and college, have an approach to image processing, explaining the use of filters for stereoscopic images that give brain impression of depth. The system is based on one of two hardware elements, centered on an Arduino board, and a software based on Matlab. The paper presents the design and construction of each of the elements, also presents information on the images obtained and finally how users can interact with the device.

Automated microscopy system for detection and genetic characterization of fetal nucleated red blood cells on slides

NASA Astrophysics Data System (ADS)

Ravkin, Ilya; Temov, Vladimir

1998-04-01

The detection and genetic analysis of fetal cells in maternal blood will permit noninvasive prenatal screening for genetic defects. Applied Imaging has developed and is currently evaluating a system for semiautomatic detection of fetal nucleated red blood cells on slides and acquisition of their DNA probe FISH images. The specimens are blood smears from pregnant women (9 - 16 weeks gestation) enriched for nucleated red blood cells (NRBC). The cells are identified by using labeled monoclonal antibodies directed to different types of hemoglobin chains (gamma, epsilon); the nuclei are stained with DAPI. The Applied Imaging system has been implemented with both Olympus BX and Nikon Eclipse series microscopes which were equipped with transmission and fluorescence optics. The system includes the following motorized components: stage, focus, transmission, and fluorescence filter wheels. A video camera with light integration (COHU 4910) permits low light imaging. The software capabilities include scanning, relocation, autofocusing, feature extraction, facilities for operator review, and data analysis. Detection of fetal NRBCs is achieved by employing a combination of brightfield and fluorescence images of nuclear and cytoplasmic markers. The brightfield and fluorescence images are all obtained with a single multi-bandpass dichroic mirror. A Z-stack of DNA probe FISH images is acquired by moving focus and switching excitation filters. This stack is combined to produce an enhanced image for presentation and spot counting.

Construction, implementation and testing of an image identification system using computer vision methods for fruit flies with economic importance (Diptera: Tephritidae).

PubMed

Wang, Jiang-Ning; Chen, Xiao-Lin; Hou, Xin-Wen; Zhou, Li-Bing; Zhu, Chao-Dong; Ji, Li-Qiang

2017-07-01

Many species of Tephritidae are damaging to fruit, which might negatively impact international fruit trade. Automatic or semi-automatic identification of fruit flies are greatly needed for diagnosing causes of damage and quarantine protocols for economically relevant insects. A fruit fly image identification system named AFIS1.0 has been developed using 74 species belonging to six genera, which include the majority of pests in the Tephritidae. The system combines automated image identification and manual verification, balancing operability and accuracy. AFIS1.0 integrates image analysis and expert system into a content-based image retrieval framework. In the the automatic identification module, AFIS1.0 gives candidate identification results. Afterwards users can do manual selection based on comparing unidentified images with a subset of images corresponding to the automatic identification result. The system uses Gabor surface features in automated identification and yielded an overall classification success rate of 87% to the species level by Independent Multi-part Image Automatic Identification Test. The system is useful for users with or without specific expertise on Tephritidae in the task of rapid and effective identification of fruit flies. It makes the application of computer vision technology to fruit fly recognition much closer to production level. © 2016 Society of Chemical Industry. © 2016 Society of Chemical Industry.

Left ventricular endocardial surface detection based on real-time 3D echocardiographic data

NASA Technical Reports Server (NTRS)

Corsi, C.; Borsari, M.; Consegnati, F.; Sarti, A.; Lamberti, C.; Travaglini, A.; Shiota, T.; Thomas, J. D.

2001-01-01

OBJECTIVE: A new computerized semi-automatic method for left ventricular (LV) chamber segmentation is presented. METHODS: The LV is imaged by real-time three-dimensional echocardiography (RT3DE). The surface detection model, based on level set techniques, is applied to RT3DE data for image analysis. The modified level set partial differential equation we use is solved by applying numerical methods for conservation laws. The initial conditions are manually established on some slices of the entire volume. The solution obtained for each slice is a contour line corresponding with the boundary between LV cavity and LV endocardium. RESULTS: The mathematical model has been applied to sequences of frames of human hearts (volume range: 34-109 ml) imaged by 2D and reconstructed off-line and RT3DE data. Volume estimation obtained by this new semi-automatic method shows an excellent correlation with those obtained by manual tracing (r = 0.992). Dynamic change of LV volume during the cardiac cycle is also obtained. CONCLUSION: The volume estimation method is accurate; edge based segmentation, image completion and volume reconstruction can be accomplished. The visualization technique also allows to navigate into the reconstructed volume and to display any section of the volume.

Color Image Processing and Object Tracking System

NASA Technical Reports Server (NTRS)

Klimek, Robert B.; Wright, Ted W.; Sielken, Robert S.

1996-01-01

This report describes a personal computer based system for automatic and semiautomatic tracking of objects on film or video tape, developed to meet the needs of the Microgravity Combustion and Fluids Science Research Programs at the NASA Lewis Research Center. The system consists of individual hardware components working under computer control to achieve a high degree of automation. The most important hardware components include 16-mm and 35-mm film transports, a high resolution digital camera mounted on a x-y-z micro-positioning stage, an S-VHS tapedeck, an Hi8 tapedeck, video laserdisk, and a framegrabber. All of the image input devices are remotely controlled by a computer. Software was developed to integrate the overall operation of the system including device frame incrementation, grabbing of image frames, image processing of the object's neighborhood, locating the position of the object being tracked, and storing the coordinates in a file. This process is performed repeatedly until the last frame is reached. Several different tracking methods are supported. To illustrate the process, two representative applications of the system are described. These applications represent typical uses of the system and include tracking the propagation of a flame front and tracking the movement of a liquid-gas interface with extremely poor visibility.

A web-based computer aided system for liver surgery planning: initial implementation on RayPlus

NASA Astrophysics Data System (ADS)

Luo, Ming; Yuan, Rong; Sun, Zhi; Li, Tianhong; Xie, Qingguo

2016-03-01

At present, computer aided systems for liver surgery design and risk evaluation are widely used in clinical all over the world. However, most systems are local applications that run on high-performance workstations, and the images have to processed offline. Compared with local applications, a web-based system is accessible anywhere and for a range of regardless of relative processing power or operating system. RayPlus (http://rayplus.life.hust.edu.cn), a B/S platform for medical image processing, was developed to give a jump start on web-based medical image processing. In this paper, we implement a computer aided system for liver surgery planning on the architecture of RayPlus. The system consists of a series of processing to CT images including filtering, segmentation, visualization and analyzing. Each processing is packaged into an executable program and runs on the server side. CT images in DICOM format are processed step by to interactive modeling on browser with zero-installation and server-side computing. The system supports users to semi-automatically segment the liver, intrahepatic vessel and tumor from the pre-processed images. Then, surface and volume models are built to analyze the vessel structure and the relative position between adjacent organs. The results show that the initial implementation meets satisfactorily its first-order objectives and provide an accurate 3D delineation of the liver anatomy. Vessel labeling and resection simulation are planned to add in the future. The system is available on Internet at the link mentioned above and an open username for testing is offered.

A Modular Hierarchical Approach to 3D Electron Microscopy Image Segmentation

PubMed Central

Liu, Ting; Jones, Cory; Seyedhosseini, Mojtaba; Tasdizen, Tolga

2014-01-01

The study of neural circuit reconstruction, i.e., connectomics, is a challenging problem in neuroscience. Automated and semi-automated electron microscopy (EM) image analysis can be tremendously helpful for connectomics research. In this paper, we propose a fully automatic approach for intra-section segmentation and inter-section reconstruction of neurons using EM images. A hierarchical merge tree structure is built to represent multiple region hypotheses and supervised classification techniques are used to evaluate their potentials, based on which we resolve the merge tree with consistency constraints to acquire final intra-section segmentation. Then, we use a supervised learning based linking procedure for the inter-section neuron reconstruction. Also, we develop a semi-automatic method that utilizes the intermediate outputs of our automatic algorithm and achieves intra-segmentation with minimal user intervention. The experimental results show that our automatic method can achieve close-to-human intra-segmentation accuracy and state-of-the-art inter-section reconstruction accuracy. We also show that our semi-automatic method can further improve the intra-segmentation accuracy. PMID:24491638

DeepNeuron: an open deep learning toolbox for neuron tracing.

PubMed

Zhou, Zhi; Kuo, Hsien-Chi; Peng, Hanchuan; Long, Fuhui

2018-06-06

Reconstructing three-dimensional (3D) morphology of neurons is essential for understanding brain structures and functions. Over the past decades, a number of neuron tracing tools including manual, semiautomatic, and fully automatic approaches have been developed to extract and analyze 3D neuronal structures. Nevertheless, most of them were developed based on coding certain rules to extract and connect structural components of a neuron, showing limited performance on complicated neuron morphology. Recently, deep learning outperforms many other machine learning methods in a wide range of image analysis and computer vision tasks. Here we developed a new Open Source toolbox, DeepNeuron, which uses deep learning networks to learn features and rules from data and trace neuron morphology in light microscopy images. DeepNeuron provides a family of modules to solve basic yet challenging problems in neuron tracing. These problems include but not limited to: (1) detecting neuron signal under different image conditions, (2) connecting neuronal signals into tree(s), (3) pruning and refining tree morphology, (4) quantifying the quality of morphology, and (5) classifying dendrites and axons in real time. We have tested DeepNeuron using light microscopy images including bright-field and confocal images of human and mouse brain, on which DeepNeuron demonstrates robustness and accuracy in neuron tracing.

Semiautomatic Segmentation of Glioma on Mobile Devices.

PubMed

Wu, Ya-Ping; Lin, Yu-Song; Wu, Wei-Guo; Yang, Cong; Gu, Jian-Qin; Bai, Yan; Wang, Mei-Yun

2017-01-01

Brain tumor segmentation is the first and the most critical step in clinical applications of radiomics. However, segmenting brain images by radiologists is labor intense and prone to inter- and intraobserver variability. Stable and reproducible brain image segmentation algorithms are thus important for successful tumor detection in radiomics. In this paper, we propose a supervised brain image segmentation method, especially for magnetic resonance (MR) brain images with glioma. This paper uses hard edge multiplicative intrinsic component optimization to preprocess glioma medical image on the server side, and then, the doctors could supervise the segmentation process on mobile devices in their convenient time. Since the preprocessed images have the same brightness for the same tissue voxels, they have small data size (typically 1/10 of the original image size) and simple structure of 4 types of intensity value. This observation thus allows follow-up steps to be processed on mobile devices with low bandwidth and limited computing performance. Experiments conducted on 1935 brain slices from 129 patients show that more than 30% of the sample can reach 90% similarity; over 60% of the samples can reach 85% similarity, and more than 80% of the sample could reach 75% similarity. The comparisons with other segmentation methods also demonstrate both efficiency and stability of the proposed approach.

A new user-assisted segmentation and tracking technique for an object-based video editing system

NASA Astrophysics Data System (ADS)

Yu, Hong Y.; Hong, Sung-Hoon; Lee, Mike M.; Choi, Jae-Gark

2004-03-01

This paper presents a semi-automatic segmentation method which can be used to generate video object plane (VOP) for object based coding scheme and multimedia authoring environment. Semi-automatic segmentation can be considered as a user-assisted segmentation technique. A user can initially mark objects of interest around the object boundaries and then the user-guided and selected objects are continuously separated from the unselected areas through time evolution in the image sequences. The proposed segmentation method consists of two processing steps: partially manual intra-frame segmentation and fully automatic inter-frame segmentation. The intra-frame segmentation incorporates user-assistance to define the meaningful complete visual object of interest to be segmentation and decides precise object boundary. The inter-frame segmentation involves boundary and region tracking to obtain temporal coherence of moving object based on the object boundary information of previous frame. The proposed method shows stable efficient results that could be suitable for many digital video applications such as multimedia contents authoring, content based coding and indexing. Based on these results, we have developed objects based video editing system with several convenient editing functions.

Antibiogramj: A tool for analysing images from disk diffusion tests.

PubMed

Alonso, C A; Domínguez, C; Heras, J; Mata, E; Pascual, V; Torres, C; Zarazaga, M

2017-05-01

Disk diffusion testing, known as antibiogram, is widely applied in microbiology to determine the antimicrobial susceptibility of microorganisms. The measurement of the diameter of the zone of growth inhibition of microorganisms around the antimicrobial disks in the antibiogram is frequently performed manually by specialists using a ruler. This is a time-consuming and error-prone task that might be simplified using automated or semi-automated inhibition zone readers. However, most readers are usually expensive instruments with embedded software that require significant changes in laboratory design and workflow. Based on the workflow employed by specialists to determine the antimicrobial susceptibility of microorganisms, we have designed a software tool that, from images of disk diffusion tests, semi-automatises the process. Standard computer vision techniques are employed to achieve such an automatisation. We present AntibiogramJ, a user-friendly and open-source software tool to semi-automatically determine, measure and categorise inhibition zones of images from disk diffusion tests. AntibiogramJ is implemented in Java and deals with images captured with any device that incorporates a camera, including digital cameras and mobile phones. The fully automatic procedure of AntibiogramJ for measuring inhibition zones achieves an overall agreement of 87% with an expert microbiologist; moreover, AntibiogramJ includes features to easily detect when the automatic reading is not correct and fix it manually to obtain the correct result. AntibiogramJ is a user-friendly, platform-independent, open-source, and free tool that, up to the best of our knowledge, is the most complete software tool for antibiogram analysis without requiring any investment in new equipment or changes in the laboratory. Copyright © 2017 Elsevier B.V. All rights reserved.

On the use of wavelet for extracting feature patterns from Multitemporal google earth satellite data sets

NASA Astrophysics Data System (ADS)

Lasaponara, R.

2012-04-01

The great amount of multispectral VHR satellite images, even available free of charge in Google earth has opened new strategic challenges in the field of remote sensing for archaeological studies. These challenges substantially deal with: (i) the strategic exploitation of satellite data as much as possible, (ii) the setting up of effective and reliable automatic and/or semiautomatic data processing strategies and (iii) the integration with other data sources from documentary resources to the traditional ground survey, historical documentation, geophysical prospection, etc. VHR satellites provide high resolution data which can improve knowledge on past human activities providing precious qualitative and quantitative information developed to such an extent that currently they share many of the physical characteristics of aerial imagery. This makes them ideal for investigations ranging from a local to a regional scale (see. for example, Lasaponara and Masini 2006a,b, 2007a, 2011; Masini and Lasaponara 2006, 2007, Sparavigna, 2010). Moreover, satellite data are still the only data source for research performed in areas where aerial photography is restricted because of military or political reasons. Among the main advantages of using satellite remote sensing compared to traditional field archaeology herein we briefly focalize on the use of wavelet data processing for enhancing google earth satellite data with particular reference to multitemporal datasets. Study areas selected from Southern Italy, Middle East and South America are presented and discussed. Results obtained point out the use of automatic image enhancement can successfully applied as first step of supervised classification and intelligent data analysis for semiautomatic identification of features of archaeological interest. Reference Lasaponara R, Masini N (2006a) On the potential of panchromatic and multispectral Quickbird data for archaeological prospection. Int J Remote Sens 27: 3607-3614. Lasaponara R, Masini N (2006b) Identification of archaeological buried remains based on Normalized Difference Vegetation Index (NDVI) from Quickbird satellite data. IEEE Geosci Remote S 3(3): 325-328. Lasaponara R, Masini N (2007a) Detection of archaeological crop marks by using satellite QuickBird multispectral imagery. J Archaeol Sci 34: 214-21. Lasaponara R, Masini N (2007b) Improving satellite Quickbird - based identification of landscape archaeological features trough tasselled cup transformation and PCA. 21st CIPA Symposium, Atene, 1-6 giugno 2007. Lasaponara R, Masini N (2010) Facing the archaeological looting in Peru by local spatial autocorrelation statistics of Very high resolution satellite imagery. In: Taniar D et al (Eds), Proceedings of ICSSA, The 2010 International Conference on Computational Science and its Application (Fukuoka-Japan, March 23 - 26, 2010), Springer, Berlin, 261-269. Lasaponara R, Masini N (2011) Satellite Remote Sensing in Archaeology : past, present and future. J Archaeol Sc 38: 1995-2002. Lasaponara R, Masini N, Rizzo E, Orefici G (2011) New discoveries in the Piramide Naranjada in Cahuachi (Peru) using satellite, Ground Probing Radar and magnetic investigations. J Archaeol Sci 38: 2031-2039. Lasaponara R, Masini N, Scardozzi G (2008) Satellite based archaeological research in ancient territory of Hierapolis. 1st International EARSeL Workshop. Advances in Remote Sensing for Archaeology and Cultural Heritage Management", CNR, Rome, September 30-October 4, Aracne, Rome, pp.11-16. Lillesand T M, Kiefer R W (2000) Remote Sensing and Image interpretation. John Wiley and Sons, New York. Masini N, Lasaponara R (2006) Satellite-based recognition of landscape archaeological features related to ancient human transformation. J Geophys Eng 3: 230-235, doi:10.1088/1742-2132/3/3/004. Masini N, Lasaponara R (2007) Investigating the spectral capability of QuickBird data to detect archaeological remains buried under vegetated and not vegetated areas. J Cult Heri 8 (1): 53-60. Sparavigna Enhancing the Google imagery using a wavelet filter, A.C. Sparavigna,http://arxiv.org/abs/1009.1590

Comparison of SAM and OBIA as Tools for Lava Morphology Classification - A Case Study in Krafla, NE Iceland

NASA Astrophysics Data System (ADS)

Aufaristama, Muhammad; Hölbling, Daniel; Höskuldsson, Ármann; Jónsdóttir, Ingibjörg

2017-04-01

The Krafla volcanic system is part of the Icelandic North Volcanic Zone (NVZ). During Holocene, two eruptive events occurred in Krafla, 1724-1729 and 1975-1984. The last eruptive episode (1975-1984), known as the "Krafla Fires", resulted in nine volcanic eruption episodes. The total area covered by the lavas from this eruptive episode is 36 km2 and the volume is about 0.25-0.3 km3. Lava morphology is related to the characteristics of the surface morphology of a lava flow after solidification. The typical morphology of lava can be used as primary basis for the classification of lava flows when rheological properties cannot be directly observed during emplacement, and also for better understanding the behavior of lava flow models. Although mapping of lava flows in the field is relatively accurate such traditional methods are time consuming, especially when the lava covers large areas such as it is the case in Krafla. Semi-automatic mapping methods that make use of satellite remote sensing data allow for an efficient and fast mapping of lava morphology. In this study, two semi-automatic methods for lava morphology classification are presented and compared using Landsat 8 (30 m spatial resolution) and SPOT-5 (10 m spatial resolution) satellite images. For assessing the classification accuracy, the results from semi-automatic mapping were compared to the respective results from visual interpretation. On the one hand, the Spectral Angle Mapper (SAM) classification method was used. With this method an image is classified according to the spectral similarity between the image reflectance spectrums and the reference reflectance spectra. SAM successfully produced detailed lava surface morphology maps. However, the pixel-based approach partly leads to a salt-and-pepper effect. On the other hand, we applied the Random Forest (RF) classification method within an object-based image analysis (OBIA) framework. This statistical classifier uses a randomly selected subset of training samples to produce multiple decision trees. For final classification of pixels or - in the present case - image objects, the average of the class assignments probability predicted by the different decision trees is used. While the resulting OBIA classification of lava morphology types shows a high coincidence with the reference data, the approach is sensitive to the segmentation-derived image objects that constitute the base units for classification. Both semi-automatic methods produce reasonable results in the Krafla lava field, even if the identification of different pahoehoe and aa types of lava appeared to be difficult. The use of satellite remote sensing data shows a high potential for fast and efficient classification of lava morphology, particularly over large and inaccessible areas.

Achieving Accurate Automatic Sleep Staging on Manually Pre-processed EEG Data Through Synchronization Feature Extraction and Graph Metrics.

PubMed

Chriskos, Panteleimon; Frantzidis, Christos A; Gkivogkli, Polyxeni T; Bamidis, Panagiotis D; Kourtidou-Papadeli, Chrysoula

2018-01-01

Sleep staging, the process of assigning labels to epochs of sleep, depending on the stage of sleep they belong, is an arduous, time consuming and error prone process as the initial recordings are quite often polluted by noise from different sources. To properly analyze such data and extract clinical knowledge, noise components must be removed or alleviated. In this paper a pre-processing and subsequent sleep staging pipeline for the sleep analysis of electroencephalographic signals is described. Two novel methods of functional connectivity estimation (Synchronization Likelihood/SL and Relative Wavelet Entropy/RWE) are comparatively investigated for automatic sleep staging through manually pre-processed electroencephalographic recordings. A multi-step process that renders signals suitable for further analysis is initially described. Then, two methods that rely on extracting synchronization features from electroencephalographic recordings to achieve computerized sleep staging are proposed, based on bivariate features which provide a functional overview of the brain network, contrary to most proposed methods that rely on extracting univariate time and frequency features. Annotation of sleep epochs is achieved through the presented feature extraction methods by training classifiers, which are in turn able to accurately classify new epochs. Analysis of data from sleep experiments on a randomized, controlled bed-rest study, which was organized by the European Space Agency and was conducted in the "ENVIHAB" facility of the Institute of Aerospace Medicine at the German Aerospace Center (DLR) in Cologne, Germany attains high accuracy rates, over 90% based on ground truth that resulted from manual sleep staging by two experienced sleep experts. Therefore, it can be concluded that the above feature extraction methods are suitable for semi-automatic sleep staging.

Achieving Accurate Automatic Sleep Staging on Manually Pre-processed EEG Data Through Synchronization Feature Extraction and Graph Metrics

PubMed Central

Chriskos, Panteleimon; Frantzidis, Christos A.; Gkivogkli, Polyxeni T.; Bamidis, Panagiotis D.; Kourtidou-Papadeli, Chrysoula

2018-01-01

Sleep staging, the process of assigning labels to epochs of sleep, depending on the stage of sleep they belong, is an arduous, time consuming and error prone process as the initial recordings are quite often polluted by noise from different sources. To properly analyze such data and extract clinical knowledge, noise components must be removed or alleviated. In this paper a pre-processing and subsequent sleep staging pipeline for the sleep analysis of electroencephalographic signals is described. Two novel methods of functional connectivity estimation (Synchronization Likelihood/SL and Relative Wavelet Entropy/RWE) are comparatively investigated for automatic sleep staging through manually pre-processed electroencephalographic recordings. A multi-step process that renders signals suitable for further analysis is initially described. Then, two methods that rely on extracting synchronization features from electroencephalographic recordings to achieve computerized sleep staging are proposed, based on bivariate features which provide a functional overview of the brain network, contrary to most proposed methods that rely on extracting univariate time and frequency features. Annotation of sleep epochs is achieved through the presented feature extraction methods by training classifiers, which are in turn able to accurately classify new epochs. Analysis of data from sleep experiments on a randomized, controlled bed-rest study, which was organized by the European Space Agency and was conducted in the “ENVIHAB” facility of the Institute of Aerospace Medicine at the German Aerospace Center (DLR) in Cologne, Germany attains high accuracy rates, over 90% based on ground truth that resulted from manual sleep staging by two experienced sleep experts. Therefore, it can be concluded that the above feature extraction methods are suitable for semi-automatic sleep staging. PMID:29628883

A simple and unsupervised semi-automatic workflow to detect shallow landslides in Alpine areas based on VHR remote sensing data

NASA Astrophysics Data System (ADS)

Amato, Gabriele; Eisank, Clemens; Albrecht, Florian

2017-04-01

Landslide detection from Earth observation imagery is an important preliminary work for landslide mapping, landslide inventories and landslide hazard assessment. In this context, the object-based image analysis (OBIA) concept has been increasingly used over the last decade. Within the framework of the Land@Slide project (Earth observation based landslide mapping: from methodological developments to automated web-based information delivery) a simple, unsupervised, semi-automatic and object-based approach for the detection of shallow landslides has been developed and implemented in the InterIMAGE open-source software. The method was applied to an Alpine case study in western Austria, exploiting spectral information from pansharpened 4-bands WorldView-2 satellite imagery (0.5 m spatial resolution) in combination with digital elevation models. First, we divided the image into sub-images, i.e. tiles, and then we applied the workflow to each of them without changing the parameters. The workflow was implemented as top-down approach: at the image tile level, an over-classification of the potential landslide area was produced; the over-estimated area was re-segmented and re-classified by several processing cycles until most false positive objects have been eliminated. In every step a Baatz algorithm based segmentation generates polygons "candidates" to be landslides. At the same time, the average values of normalized difference vegetation index (NDVI) and brightness are calculated for these polygons; after that, these values are used as thresholds to perform an objects selection in order to improve the quality of the classification results. In combination, also empirically determined values of slope and roughness are used in the selection process. Results for each tile were merged to obtain the landslide map for the test area. For final validation, the landslide map was compared to a geological map and a supervised landslide classification in order to estimate its accuracy. Results for the test area showed that the proposed method is capable of accurately distinguishing landslides from roofs and trees. Implementation of the workflow into InterIMAGE was straightforward. We conclude that the method is able to extract landslides in forested areas, but that there is still room for improvements concerning the extraction in non-forested high-alpine regions.

Using SAR Interferograms and Coherence Images for Object-Based Delineation of Unstable Slopes

NASA Astrophysics Data System (ADS)

Friedl, Barbara; Holbling, Daniel

2015-05-01

This study uses synthetic aperture radar (SAR) interferometric products for the semi-automated identification and delineation of unstable slopes and active landslides. Single-pair interferograms and coherence images are therefore segmented and classified in an object-based image analysis (OBIA) framework. The rule-based classification approach has been applied to landslide-prone areas located in Taiwan and Southern Germany. The semi-automatically obtained results were validated against landslide polygons derived from manual interpretation.

Comparison Of Semi-Automatic And Automatic Slick Detection Algorithms For Jiyeh Power Station Oil Spill, Lebanon

NASA Astrophysics Data System (ADS)

Osmanoglu, B.; Ozkan, C.; Sunar, F.

2013-10-01

After air strikes on July 14 and 15, 2006 the Jiyeh Power Station started leaking oil into the eastern Mediterranean Sea. The power station is located about 30 km south of Beirut and the slick covered about 170 km of coastline threatening the neighboring countries Turkey and Cyprus. Due to the ongoing conflict between Israel and Lebanon, cleaning efforts could not start immediately resulting in 12 000 to 15 000 tons of fuel oil leaking into the sea. In this paper we compare results from automatic and semi-automatic slick detection algorithms. The automatic detection method combines the probabilities calculated for each pixel from each image to obtain a joint probability, minimizing the adverse effects of atmosphere on oil spill detection. The method can readily utilize X-, C- and L-band data where available. Furthermore wind and wave speed observations can be used for a more accurate analysis. For this study, we utilize Envisat ASAR ScanSAR data. A probability map is generated based on the radar backscatter, effect of wind and dampening value. The semi-automatic algorithm is based on supervised classification. As a classifier, Artificial Neural Network Multilayer Perceptron (ANN MLP) classifier is used since it is more flexible and efficient than conventional maximum likelihood classifier for multisource and multi-temporal data. The learning algorithm for ANN MLP is chosen as the Levenberg-Marquardt (LM). Training and test data for supervised classification are composed from the textural information created from SAR images. This approach is semiautomatic because tuning the parameters of classifier and composing training data need a human interaction. We point out the similarities and differences between the two methods and their results as well as underlining their advantages and disadvantages. Due to the lack of ground truth data, we compare obtained results to each other, as well as other published oil slick area assessments.

State of the "art": a taxonomy of artistic stylization techniques for images and video.

PubMed

Kyprianidis, Jan Eric; Collomosse, John; Wang, Tinghuai; Isenberg, Tobias

2013-05-01

This paper surveys the field of nonphotorealistic rendering (NPR), focusing on techniques for transforming 2D input (images and video) into artistically stylized renderings. We first present a taxonomy of the 2D NPR algorithms developed over the past two decades, structured according to the design characteristics and behavior of each technique. We then describe a chronology of development from the semiautomatic paint systems of the early nineties, through to the automated painterly rendering systems of the late nineties driven by image gradient analysis. Two complementary trends in the NPR literature are then addressed, with reference to our taxonomy. First, the fusion of higher level computer vision and NPR, illustrating the trends toward scene analysis to drive artistic abstraction and diversity of style. Second, the evolution of local processing approaches toward edge-aware filtering for real-time stylization of images and video. The survey then concludes with a discussion of open challenges for 2D NPR identified in recent NPR symposia, including topics such as user and aesthetic evaluation.

User Interaction in Semi-Automatic Segmentation of Organs at Risk: a Case Study in Radiotherapy.

PubMed

Ramkumar, Anjana; Dolz, Jose; Kirisli, Hortense A; Adebahr, Sonja; Schimek-Jasch, Tanja; Nestle, Ursula; Massoptier, Laurent; Varga, Edit; Stappers, Pieter Jan; Niessen, Wiro J; Song, Yu

2016-04-01

Accurate segmentation of organs at risk is an important step in radiotherapy planning. Manual segmentation being a tedious procedure and prone to inter- and intra-observer variability, there is a growing interest in automated segmentation methods. However, automatic methods frequently fail to provide satisfactory result, and post-processing corrections are often needed. Semi-automatic segmentation methods are designed to overcome these problems by combining physicians' expertise and computers' potential. This study evaluates two semi-automatic segmentation methods with different types of user interactions, named the "strokes" and the "contour", to provide insights into the role and impact of human-computer interaction. Two physicians participated in the experiment. In total, 42 case studies were carried out on five different types of organs at risk. For each case study, both the human-computer interaction process and quality of the segmentation results were measured subjectively and objectively. Furthermore, different measures of the process and the results were correlated. A total of 36 quantifiable and ten non-quantifiable correlations were identified for each type of interaction. Among those pairs of measures, 20 of the contour method and 22 of the strokes method were strongly or moderately correlated, either directly or inversely. Based on those correlated measures, it is concluded that: (1) in the design of semi-automatic segmentation methods, user interactions need to be less cognitively challenging; (2) based on the observed workflows and preferences of physicians, there is a need for flexibility in the interface design; (3) the correlated measures provide insights that can be used in improving user interaction design.

Volumetric glioma quantification: comparison of manual and semi-automatic tumor segmentation for the quantification of tumor growth.

PubMed

Odland, Audun; Server, Andres; Saxhaug, Cathrine; Breivik, Birger; Groote, Rasmus; Vardal, Jonas; Larsson, Christopher; Bjørnerud, Atle

2015-11-01

Volumetric magnetic resonance imaging (MRI) is now widely available and routinely used in the evaluation of high-grade gliomas (HGGs). Ideally, volumetric measurements should be included in this evaluation. However, manual tumor segmentation is time-consuming and suffers from inter-observer variability. Thus, tools for semi-automatic tumor segmentation are needed. To present a semi-automatic method (SAM) for segmentation of HGGs and to compare this method with manual segmentation performed by experts. The inter-observer variability among experts manually segmenting HGGs using volumetric MRIs was also examined. Twenty patients with HGGs were included. All patients underwent surgical resection prior to inclusion. Each patient underwent several MRI examinations during and after adjuvant chemoradiation therapy. Three experts performed manual segmentation. The results of tumor segmentation by the experts and by the SAM were compared using Dice coefficients and kappa statistics. A relatively close agreement was seen among two of the experts and the SAM, while the third expert disagreed considerably with the other experts and the SAM. An important reason for this disagreement was a different interpretation of contrast enhancement as either surgically-induced or glioma-induced. The time required for manual tumor segmentation was an average of 16 min per scan. Editing of the tumor masks produced by the SAM required an average of less than 2 min per sample. Manual segmentation of HGG is very time-consuming and using the SAM could increase the efficiency of this process. However, the accuracy of the SAM ultimately depends on the expert doing the editing. Our study confirmed a considerable inter-observer variability among experts defining tumor volume from volumetric MRIs. © The Foundation Acta Radiologica 2014.

Model-based conifer crown surface reconstruction from multi-ocular high-resolution aerial imagery

NASA Astrophysics Data System (ADS)

Sheng, Yongwei

2000-12-01

Tree crown parameters such as width, height, shape and crown closure are desirable in forestry and ecological studies, but they are time-consuming and labor intensive to measure in the field. The stereoscopic capability of high-resolution aerial imagery provides a way to crown surface reconstruction. Existing photogrammetric algorithms designed to map terrain surfaces, however, cannot adequately extract crown surfaces, especially for steep conifer crowns. Considering crown surface reconstruction in a broader context of tree characterization from aerial images, we develop a rigorous perspective tree image formation model to bridge image-based tree extraction and crown surface reconstruction, and an integrated model-based approach to conifer crown surface reconstruction. Based on the fact that most conifer crowns are in a solid geometric form, conifer crowns are modeled as a generalized hemi-ellipsoid. Both the automatic and semi-automatic approaches are investigated to optimal tree model development from multi-ocular images. The semi-automatic 3D tree interpreter developed in this thesis is able to efficiently extract reliable tree parameters and tree models in complicated tree stands. This thesis starts with a sophisticated stereo matching algorithm, and incorporates tree models to guide stereo matching. The following critical problems are addressed in the model-based surface reconstruction process: (1) the problem of surface model composition from tree models, (2) the occlusion problem in disparity prediction from tree models, (3) the problem of integrating the predicted disparities into image matching, (4) the tree model edge effect reduction on the disparity map, (5) the occlusion problem in orthophoto production, and (6) the foreshortening problem in image matching, which is very serious for conifer crown surfaces. Solutions to the above problems are necessary for successful crown surface reconstruction. The model-based approach was applied to recover the canopy surface of a dense redwood stand using tri-ocular high-resolution images scanned from 1:2,400 aerial photographs. The results demonstrate the approach's ability to reconstruct complicated stands. The model-based approach proposed in this thesis is potentially applicable to other surfaces recovering problems with a priori knowledge about objects.

Three-dimensional rendering of segmented object using matlab - biomed 2010.

PubMed

Anderson, Jeffrey R; Barrett, Steven F

2010-01-01

The three-dimensional rendering of microscopic objects is a difficult and challenging task that often requires specialized image processing techniques. Previous work has been described of a semi-automatic segmentation process of fluorescently stained neurons collected as a sequence of slice images with a confocal laser scanning microscope. Once properly segmented, each individual object can be rendered and studied as a three-dimensional virtual object. This paper describes the work associated with the design and development of Matlab files to create three-dimensional images from the segmented object data previously mentioned. Part of the motivation for this work is to integrate both the segmentation and rendering processes into one software application, providing a seamless transition from the segmentation tasks to the rendering and visualization tasks. Previously these tasks were accomplished on two different computer systems, windows and Linux. This transition basically limits the usefulness of the segmentation and rendering applications to those who have both computer systems readily available. The focus of this work is to create custom Matlab image processing algorithms for object rendering and visualization, and merge these capabilities to the Matlab files that were developed especially for the image segmentation task. The completed Matlab application will contain both the segmentation and rendering processes in a single graphical user interface, or GUI. This process for rendering three-dimensional images in Matlab requires that a sequence of two-dimensional binary images, representing a cross-sectional slice of the object, be reassembled in a 3D space, and covered with a surface. Additional segmented objects can be rendered in the same 3D space. The surface properties of each object can be varied by the user to aid in the study and analysis of the objects. This inter-active process becomes a powerful visual tool to study and understand microscopic objects.

Detection and characterization of exercise induced muscle damage (EIMD) via thermography and image processing

NASA Astrophysics Data System (ADS)

Avdelidis, N. P.; Kappatos, V.; Georgoulas, G.; Karvelis, P.; Deli, C. K.; Theodorakeas, P.; Giakas, G.; Tsiokanos, A.; Koui, M.; Jamurtas, A. Z.

2017-04-01

Exercise induced muscle damage (EIMD), is usually experienced in i) humans who have been physically inactive for prolonged periods of time and then begin with sudden training trials and ii) athletes who train over their normal limits. EIMD is not so easy to be detected and quantified, by means of commonly measurement tools and methods. Thermography has been used successfully as a research detection tool in medicine for the last 6 decades but very limited work has been reported on EIMD area. The main purpose of this research is to assess and characterize EIMD, using thermography and image processing techniques. The first step towards that goal is to develop a reliable segmentation technique to isolate the region of interest (ROI). A semi-automatic image processing software was designed and regions of the left and right leg based on superpixels were segmented. The image is segmented into a number of regions and the user is able to intervene providing the regions which belong to each of the two legs. In order to validate the image processing software, an extensive experimental investigation was carried out, acquiring thermographic images of the rectus femoris muscle before, immediately post and 24, 48 and 72 hours after an acute bout of eccentric exercise (5 sets of 15 maximum repetitions), on males and females (20-30 year-old). Results indicate that the semi-automated approach provides an excellent bench-mark that can be used as a clinical reliable tool.

Quantitative morphometrical characterization of human pronuclear zygotes.

PubMed

Beuchat, A; Thévenaz, P; Unser, M; Ebner, T; Senn, A; Urner, F; Germond, M; Sorzano, C O S

2008-09-01

Identification of embryos with high implantation potential remains a challenge in in vitro fertilization (IVF). Subjective pronuclear (PN) zygote scoring systems have been developed for that purpose. The aim of this work was to provide a software tool that enables objective measuring of morphological characteristics of the human PN zygote. A computer program was created to analyse zygote images semi-automatically, providing precise morphological measurements. The accuracy of this approach was first validated by comparing zygotes from two different IVF centres with computer-assisted measurements or subjective scoring. Computer-assisted measurement and subjective scoring were then compared for their ability to classify zygotes with high and low implantation probability by using a linear discriminant analysis. Zygote images coming from the two IVF centres were analysed with the software, resulting in a series of precise measurements of 24 variables. Using subjective scoring, the cytoplasmic halo was the only feature which was significantly different between the two IVF centres. Computer-assisted measurements revealed significant differences between centres in PN centring, PN proximity, cytoplasmic halo and features related to nucleolar precursor bodies distribution. The zygote classification error achieved with the computer-assisted measurements (0.363) was slightly inferior to that of the subjective ones (0.393). A precise and objective characterization of the morphology of human PN zygotes can be achieved by the use of an advanced image analysis tool. This computer-assisted analysis allows for a better morphological characterization of human zygotes and can be used for classification.

Space -based monitoring of archaeological looting using multitemporal satellite data

NASA Astrophysics Data System (ADS)

Lasaponara, R.; Masini, N.

2012-04-01

Illegal excavations represent one of the main risk factors which affect the archaeological heritage all over the world, in particular in those countries, from Southern America to Middle East, where the surveillance on site is little effective and time consuming and the aerial surveillance is non practicable due to military or political restrictions. In such contexts satellite remote sensing offers a suitable chance to monitor this phenomenon.. Looting phenomenon is much more dramatic during wars or armed conflicts, as occurred in Iraq during the two Gulf Wars, where "total area looted was many times greater than all the archaeological investigations ever conducted in southern Iraq" (Stone E. 2008). Media reports described the massive looting in broad daylight and destruction of the Iraqi museums and other cultural institutions. Between 2003 and 2004, several buried ancient cities have been completely eaten away by crater-like holes (http://www.savingantiquities.org/feature_page.php?featureID=7), and many other archaeological sites would be pillaged without the valuable activity of the Italian Carabinieri, responsible for guarding archaeological sites in the region of Nassyriah. To contrast and limit this phenomenon a systematic monitoring is required. Up to now, the protection of archaeological heritage from illegal diggings is generally based on a direct or aerial surveillance, which are time consuming, expensive and not suitable for extensive areas. VHR satellite images offer a suitable chance thanks to their global coverage and frequent re-visitation times. In this paper, automatic data processing approaches, based on filtering, geospatial analysis and wavelet, have been applied to enhance spatial and spectral anomaly linked to illegal excavations to make their semiautomatic identification easier. Study areas from Middle east and Southern America have been processed and discussed.

Method for stitching microbial images using a neural network

NASA Astrophysics Data System (ADS)

Semenishchev, E. A.; Voronin, V. V.; Marchuk, V. I.; Tolstova, I. V.

2017-05-01

Currently an analog microscope has a wide distribution in the following fields: medicine, animal husbandry, monitoring technological objects, oceanography, agriculture and others. Automatic method is preferred because it will greatly reduce the work involved. Stepper motors are used to move the microscope slide and allow to adjust the focus in semi-automatic or automatic mode view with transfer images of microbiological objects from the eyepiece of the microscope to the computer screen. Scene analysis allows to locate regions with pronounced abnormalities for focusing specialist attention. This paper considers the method for stitching microbial images, obtained of semi-automatic microscope. The method allows to keep the boundaries of objects located in the area of capturing optical systems. Objects searching are based on the analysis of the data located in the area of the camera view. We propose to use a neural network for the boundaries searching. The stitching image boundary is held of the analysis borders of the objects. To auto focus, we use the criterion of the minimum thickness of the line boundaries of object. Analysis produced the object located in the focal axis of the camera. We use method of recovery of objects borders and projective transform for the boundary of objects which are based on shifted relative to the focal axis. Several examples considered in this paper show the effectiveness of the proposed approach on several test images.

Digital retrospective motion-mode display and processing of electron beam cine-computed tomography and other cross-sectional cardiac imaging techniques

NASA Astrophysics Data System (ADS)

Reed, Judd E.; Rumberger, John A.; Buithieu, Jean; Behrenbeck, Thomas; Breen, Jerome F.; Sheedy, Patrick F., II

1995-05-01

Electron beam computed tomography is unparalleled in its ability to consistently produce high quality dynamic images of the human heart. Its use in quantification of left ventricular dynamics is well established in both clinical and research applications. However, the image analysis tools supplied with the scanners offer a limited number of analysis options. They are based on embedded computer systems which have not been significantly upgraded since the scanner was introduced over a decade ago in spite of the explosive improvements in available computer power which have occured during this period. To address these shortcomings, a workstation-based ventricular analysis system has been developed at our institution. This system, which has been in use for over five years, is based on current workstation technology and therefore has benefited from the periodic upgrades in processor performance available to these systems. The dynamic image segmentation component of this system is an interactively supervised, semi-automatic surface identification and tracking system. It characterizes the endocardial and epicardial surfaces of the left ventricle as two concentric 4D hyper-space polyhedrons. Each of these polyhedrons have nearly ten thousand vertices which are deposited into a relational database. The right ventricle is also processed in a similar manner. This database is queried by other custom components which extract ventricular function parameters such as regional ejection fraction and wall stress. The interactive tool which supervises dynamic image segmentation has been enhanced with a temporal domain display. The operator interactively chooses the spatial location of the endpoints of a line segment while the corresponding space/time image is displayed. These images, with content resembling M-Mode echocardiography, benefit form electron beam computed tomography's high spatial and contrast resolution. The segmented surfaces are displayed along with the imagery. These displays give the operator valuable feedback pertaining to the contiguity of the extracted surfaces. As with M-Mode echocardiography, the velocity of moving structures can be easily visualized and measured. However, many views inaccessible to standard transthoracic echocardiography are easily generated. These features have augmented the interpretability of cine electron beam computed tomography and have prompted the recent cloning of this system into an 'omni-directional M-Mode display' system for use in digital post-processing of echocardiographic parasternal short axis tomograms. This enhances the functional assessment in orthogonal views of the left ventricle, accounting for shape changes particularly in the asymmetric post-infarction ventricle. Conclusions: A new tool has been developed for analysis and visualization of cine electron beam computed tomography. It has been found to be very useful in verifying the consistency of myocardial surface definition with a semi-automated segmentation tool. By drawing on M-Mode echocardiography experience, electron beam tomography's interpretability has been enhanced. Use of this feature, in conjunction with the existing image processing tools, will enhance the presentations of data on regional systolic and diastolic functions to clinicians in a format that is familiar to most cardiologists. Additionally, this tool reinforces the advantages of electron beam tomography as a single imaging modality for the assessment of left and right ventricular size, shape, and regional functions.

Semi-Automatic Building Models and FAÇADE Texture Mapping from Mobile Phone Images

NASA Astrophysics Data System (ADS)

Jeong, J.; Kim, T.

2016-06-01

Research on 3D urban modelling has been actively carried out for a long time. Recently the need of 3D urban modelling research is increased rapidly due to improved geo-web services and popularized smart devices. Nowadays 3D urban models provided by, for example, Google Earth use aerial photos for 3D urban modelling but there are some limitations: immediate update for the change of building models is difficult, many buildings are without 3D model and texture, and large resources for maintaining and updating are inevitable. To resolve the limitations mentioned above, we propose a method for semi-automatic building modelling and façade texture mapping from mobile phone images and analyze the result of modelling with actual measurements. Our method consists of camera geometry estimation step, image matching step, and façade mapping step. Models generated from this method were compared with actual measurement value of real buildings. Ratios of edge length of models and measurements were compared. Result showed 5.8% average error of length ratio. Through this method, we could generate a simple building model with fine façade textures without expensive dedicated tools and dataset.

Response Evaluation of Malignant Liver Lesions After TACE/SIRT: Comparison of Manual and Semi-Automatic Measurement of Different Response Criteria in Multislice CT.

PubMed

Höink, Anna Janina; Schülke, Christoph; Koch, Raphael; Löhnert, Annika; Kammerer, Sara; Fortkamp, Rasmus; Heindel, Walter; Buerke, Boris

2017-11-01

Purpose  To compare measurement precision and interobserver variability in the evaluation of hepatocellular carcinoma (HCC) and liver metastases in MSCT before and after transarterial local ablative therapies. Materials and Methods  Retrospective study of 72 patients with malignant liver lesions (42 metastases; 30 HCCs) before and after therapy (43 SIRT procedures; 29 TACE procedures). Established (LAD; SAD; WHO) and vitality-based parameters (mRECIST; mLAD; mSAD; EASL) were assessed manually and semi-automatically by two readers. The relative interobserver difference (RID) and intraclass correlation coefficient (ICC) were calculated. Results  The median RID for vitality-based parameters was lower from semi-automatic than from manual measurement of mLAD (manual 12.5 %; semi-automatic 3.4 %), mSAD (manual 12.7 %; semi-automatic 5.7 %) and EASL (manual 10.4 %; semi-automatic 1.8 %). The difference in established parameters was not statistically noticeable (p > 0.05). The ICCs of LAD (manual 0.984; semi-automatic 0.982), SAD (manual 0.975; semi-automatic 0.958) and WHO (manual 0.984; semi-automatic 0.978) are high, both in manual and semi-automatic measurements. The ICCs of manual measurements of mLAD (0.897), mSAD (0.844) and EASL (0.875) are lower. This decrease cannot be found in semi-automatic measurements of mLAD (0.997), mSAD (0.992) and EASL (0.998). Conclusion  Vitality-based tumor measurements of HCC and metastases after transarterial local therapies should be performed semi-automatically due to greater measurement precision, thus increasing the reproducibility and in turn the reliability of therapeutic decisions. Key points   · Liver lesion measurements according to EASL and mRECIST are more precise when performed semi-automatically.. · The higher reproducibility may facilitate a more reliable classification of therapy response.. · Measurements according to RECIST and WHO offer equivalent precision semi-automatically and manually.. Citation Format · Höink AJ, Schülke C, Koch R et al. Response Evaluation of Malignant Liver Lesions After TACE/SIRT: Comparison of Manual and Semi-Automatic Measurement of Different Response Criteria in Multislice CT. Fortschr Röntgenstr 2017; 189: 1067 - 1075. © Georg Thieme Verlag KG Stuttgart · New York.

Multimodal system for the planning and guidance of bronchoscopy

NASA Astrophysics Data System (ADS)

Higgins, William E.; Cheirsilp, Ronnarit; Zang, Xiaonan; Byrnes, Patrick

2015-03-01

Many technical innovations in multimodal radiologic imaging and bronchoscopy have emerged recently in the effort against lung cancer. Modern X-ray computed-tomography (CT) scanners provide three-dimensional (3D) high-resolution chest images, positron emission tomography (PET) scanners give complementary molecular imaging data, and new integrated PET/CT scanners combine the strengths of both modalities. State-of-the-art bronchoscopes permit minimally invasive tissue sampling, with vivid endobronchial video enabling navigation deep into the airway-tree periphery, while complementary endobronchial ultrasound (EBUS) reveals local views of anatomical structures outside the airways. In addition, image-guided intervention (IGI) systems have proven their utility for CT-based planning and guidance of bronchoscopy. Unfortunately, no IGI system exists that integrates all sources effectively through the complete lung-cancer staging work flow. This paper presents a prototype of a computer-based multimodal IGI system that strives to fill this need. The system combines a wide range of automatic and semi-automatic image-processing tools for multimodal data fusion and procedure planning. It also provides a flexible graphical user interface for follow-on guidance of bronchoscopy/EBUS. Human-study results demonstrate the system's potential.

ProFound: Source Extraction and Application to Modern Survey Data

NASA Astrophysics Data System (ADS)

Robotham, A. S. G.

2018-04-01

ProFound detects sources in noisy images, generates segmentation maps identifying the pixels belonging to each source, and measures statistics like flux, size, and ellipticity. These inputs are key requirements of ProFit (ascl:1612.004), our galaxy profiling package; these two packages used in unison semi-automatically profile large samples of galaxies. The key novel feature introduced in ProFound is that all photometry is executed on dilated segmentation maps that fully contain the identifiable flux, rather than using more traditional circular or ellipse-based photometry. Also, to be less sensitive to pathological segmentation issues, the de-blending is made across saddle points in flux. ProFound offers good initial parameter estimation for ProFit, and also segmentation maps that follow the sometimes complex geometry of resolved sources, whilst capturing nearly all of the flux. A number of bulge-disc decomposition projects are already making use of the ProFound and ProFit pipeline.

Semi-automated identification of leopard frogs

USGS Publications Warehouse

Petrovska-Delacrétaz, Dijana; Edwards, Aaron; Chiasson, John; Chollet, Gérard; Pilliod, David S.

2014-01-01

Principal component analysis is used to implement a semi-automatic recognition system to identify recaptured northern leopard frogs (Lithobates pipiens). Results of both open set and closed set experiments are given. The presented algorithm is shown to provide accurate identification of 209 individual leopard frogs from a total set of 1386 images.

27 CFR 478.133 - Records of transactions in semiautomatic assault weapons.

Code of Federal Regulations, 2012 CFR

2012-04-01

... semiautomatic assault weapons. 478.133 Section 478.133 Alcohol, Tobacco Products, and Firearms BUREAU OF ALCOHOL... AMMUNITION Records § 478.133 Records of transactions in semiautomatic assault weapons. The evidence specified in § 478.40(c), relating to transactions in semiautomatic assault weapons, shall be retained in the...

27 CFR 478.133 - Records of transactions in semiautomatic assault weapons.

Code of Federal Regulations, 2014 CFR

2014-04-01

... semiautomatic assault weapons. 478.133 Section 478.133 Alcohol, Tobacco Products, and Firearms BUREAU OF ALCOHOL... AMMUNITION Records § 478.133 Records of transactions in semiautomatic assault weapons. The evidence specified in § 478.40(c), relating to transactions in semiautomatic assault weapons, shall be retained in the...

27 CFR 478.133 - Records of transactions in semiautomatic assault weapons.

Code of Federal Regulations, 2010 CFR

2010-04-01

... semiautomatic assault weapons. 478.133 Section 478.133 Alcohol, Tobacco Products, and Firearms BUREAU OF ALCOHOL... AMMUNITION Records § 478.133 Records of transactions in semiautomatic assault weapons. The evidence specified in § 478.40(c), relating to transactions in semiautomatic assault weapons, shall be retained in the...

27 CFR 478.133 - Records of transactions in semiautomatic assault weapons.

Code of Federal Regulations, 2013 CFR

2013-04-01

... semiautomatic assault weapons. 478.133 Section 478.133 Alcohol, Tobacco Products, and Firearms BUREAU OF ALCOHOL... AMMUNITION Records § 478.133 Records of transactions in semiautomatic assault weapons. The evidence specified in § 478.40(c), relating to transactions in semiautomatic assault weapons, shall be retained in the...

27 CFR 478.133 - Records of transactions in semiautomatic assault weapons.

Code of Federal Regulations, 2011 CFR

2011-04-01

... semiautomatic assault weapons. 478.133 Section 478.133 Alcohol, Tobacco Products, and Firearms BUREAU OF ALCOHOL... AMMUNITION Records § 478.133 Records of transactions in semiautomatic assault weapons. The evidence specified in § 478.40(c), relating to transactions in semiautomatic assault weapons, shall be retained in the...

WE-E-17A-05: Complementary Prognostic Value of CT and 18F-FDG PET Non-Small Cell Lung Cancer Tumor Heterogeneity Features Quantified Through Texture Analysis

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

Desseroit, M; Cheze Le Rest, C; Tixier, F

2014-06-15

Purpose: Previous studies have shown that CT or 18F-FDG PET intratumor heterogeneity features computed using texture analysis may have prognostic value in Non-Small Cell Lung Cancer (NSCLC), but have been mostly investigated separately. The purpose of this study was to evaluate the potential added value with respect to prognosis regarding the combination of non-enhanced CT and 18F-FDG PET heterogeneity textural features on primary NSCLC tumors. Methods: One hundred patients with non-metastatic NSCLC (stage I–III), treated with surgery and/or (chemo)radiotherapy, that underwent staging 18F-FDG PET/CT images, were retrospectively included. Morphological tumor volumes were semi-automatically delineated on non-enhanced CT using 3D SlicerTM.more » Metabolically active tumor volumes (MATV) were automatically delineated on PET using the Fuzzy Locally Adaptive Bayesian (FLAB) method. Intratumoral tissue density and FDG uptake heterogeneities were quantified using texture parameters calculated from co-occurrence, difference, and run-length matrices. In addition to these textural features, first order histogram-derived metrics were computed on the whole morphological CT tumor volume, as well as on sub-volumes corresponding to fine, medium or coarse textures determined through various levels of LoG-filtering. Association with survival regarding all extracted features was assessed using Cox regression for both univariate and multivariate analysis. Results: Several PET and CT heterogeneity features were prognostic factors of overall survival in the univariate analysis. CT histogram-derived kurtosis and uniformity, as well as Low Grey-level High Run Emphasis (LGHRE), and PET local entropy were independent prognostic factors. Combined with stage and MATV, they led to a powerful prognostic model (p<0.0001), with median survival of 49 vs. 12.6 months and a hazard ratio of 3.5. Conclusion: Intratumoral heterogeneity quantified through textural features extracted from both CT and FDG PET images have complementary and independent prognostic value in NSCLC.« less

Prospective MR image alignment between breath-holds: Application to renal BOLD MRI.

PubMed

Kalis, Inge M; Pilutti, David; Krafft, Axel J; Hennig, Jürgen; Bock, Michael

2017-04-01

To present an image registration method for renal blood oxygen level-dependent (BOLD) measurements that enables semiautomatic assessment of parenchymal and medullary R2* changes under a functional challenge. In a series of breath-hold acquisitions, three-dimensional data were acquired initially for prospective image registration of subsequent BOLD measurements. An algorithm for kidney alignment for BOLD renal imaging (KALIBRI) was implemented to detect the positions of the left and right kidney so that the kidneys were acquired in the subsequent BOLD measurement at consistent anatomical locations. Residual in-plane distortions were corrected retrospectively so that semiautomatic dynamic R2* measurements of the renal cortex and medulla become feasible. KALIBRI was tested in six healthy volunteers during a series of BOLD experiments, which included a 600- to 1000-mL water challenge. Prospective image registration and BOLD imaging of each kidney was achieved within a total measurement time of about 17 s, enabling its execution within a single breath-hold. KALIBRI improved the registration by up to 35% as found with mutual information measures. In four volunteers, a medullary R2* decrease of up to 40% was observed after water ingestion. KALIBRI improves the quality of two-dimensional time-resolved renal BOLD MRI by aligning local renal anatomy, which allows for consistent R2* measurements over many breath-holds. Magn Reson Med 77:1573-1582, 2017. © 2016 International Society for Magnetic Resonance in Medicine. © 2016 International Society for Magnetic Resonance in Medicine.

Semi-automatic volume measurement for orbital fat and total extraocular muscles based on Cube FSE-flex sequence in patients with thyroid-associated ophthalmopathy.

PubMed

Tang, X; Liu, H; Chen, L; Wang, Q; Luo, B; Xiang, N; He, Y; Zhu, W; Zhang, J

2018-05-24

To investigate the accuracy of two semi-automatic segmentation measurements based on magnetic resonance imaging (MRI) three-dimensional (3D) Cube fast spin echo (FSE)-flex sequence in phantoms, and to evaluate the feasibility of determining the volumetric alterations of orbital fat (OF) and total extraocular muscles (TEM) in patients with thyroid-associated ophthalmopathy (TAO) by semi-automatic segmentation. Forty-four fatty (n=22) and lean (n=22) phantoms were scanned by using Cube FSE-flex sequence with a 3 T MRI system. Their volumes were measured by manual segmentation (MS) and two semi-automatic segmentation algorithms (regional growing [RG], multi-dimensional threshold [MDT]). Pearson correlation and Bland-Altman analysis were used to evaluate the measuring accuracy of MS, RG, and MDT in phantoms as compared with the true volume. Then, OF and TEM volumes of 15 TAO patients and 15 normal controls were measured using MDT. Paired-sample t-tests were used to compare the volumes and volume ratios of different orbital tissues between TAO patients and controls. Each segmentation (MS RG, MDT) has a significant correlation (p<0.01) with true volume. There was a minimal bias for MS, and a stronger agreement between MDT and the true volume than RG and the true volume both in fatty and lean phantoms. The reproducibility of Cube FSE-flex determined MDT was adequate. The volumetric ratios of OF/globe (p<0.01), TEM/globe (p<0.01), whole orbit/globe (p<0.01) and bone orbit/globe (p<0.01) were significantly greater in TAO patients than those in healthy controls. MRI Cube FSE-flex determined MDT is a relatively accurate semi-automatic segmentation that can be used to evaluate OF and TEM volumes in clinic. Copyright © 2018 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

Three Dimensional Imaging of Paraffin Embedded Human Lung Tissue Samples by Micro-Computed Tomography

PubMed Central

Scott, Anna E.; Vasilescu, Dragos M.; Seal, Katherine A. D.; Keyes, Samuel D.; Mavrogordato, Mark N.; Hogg, James C.; Sinclair, Ian; Warner, Jane A.; Hackett, Tillie-Louise; Lackie, Peter M.

2015-01-01

Background Understanding the three-dimensional (3-D) micro-architecture of lung tissue can provide insights into the pathology of lung disease. Micro computed tomography (µCT) has previously been used to elucidate lung 3D histology and morphometry in fixed samples that have been stained with contrast agents or air inflated and dried. However, non-destructive microstructural 3D imaging of formalin-fixed paraffin embedded (FFPE) tissues would facilitate retrospective analysis of extensive tissue archives of lung FFPE lung samples with linked clinical data. Methods FFPE human lung tissue samples (n = 4) were scanned using a Nikon metrology µCT scanner. Semi-automatic techniques were used to segment the 3D structure of airways and blood vessels. Airspace size (mean linear intercept, Lm) was measured on µCT images and on matched histological sections from the same FFPE samples imaged by light microscopy to validate µCT imaging. Results The µCT imaging protocol provided contrast between tissue and paraffin in FFPE samples (15mm x 7mm). Resolution (voxel size 6.7 µm) in the reconstructed images was sufficient for semi-automatic image segmentation of airways and blood vessels as well as quantitative airspace analysis. The scans were also used to scout for regions of interest, enabling time-efficient preparation of conventional histological sections. The Lm measurements from µCT images were not significantly different to those from matched histological sections. Conclusion We demonstrated how non-destructive imaging of routinely prepared FFPE samples by laboratory µCT can be used to visualize and assess the 3D morphology of the lung including by morphometric analysis. PMID:26030902

Web-based system for surgical planning and simulation

NASA Astrophysics Data System (ADS)

Eldeib, Ayman M.; Ahmed, Mohamed N.; Farag, Aly A.; Sites, C. B.

1998-10-01

The growing scientific knowledge and rapid progress in medical imaging techniques has led to an increasing demand for better and more efficient methods of remote access to high-performance computer facilities. This paper introduces a web-based telemedicine project that provides interactive tools for surgical simulation and planning. The presented approach makes use of client-server architecture based on new internet technology where clients use an ordinary web browser to view, send, receive and manipulate patients' medical records while the server uses the supercomputer facility to generate online semi-automatic segmentation, 3D visualization, surgical simulation/planning and neuroendoscopic procedures navigation. The supercomputer (SGI ONYX 1000) is located at the Computer Vision and Image Processing Lab, University of Louisville, Kentucky. This system is under development in cooperation with the Department of Neurological Surgery, Alliant Health Systems, Louisville, Kentucky. The server is connected via a network to the Picture Archiving and Communication System at Alliant Health Systems through a DICOM standard interface that enables authorized clients to access patients' images from different medical modalities.

SoilJ - An ImageJ plugin for semi-automatized image-processing of 3-D X-ray images of soil columns

NASA Astrophysics Data System (ADS)

Koestel, John

2016-04-01

3-D X-ray imaging is a formidable tool for quantifying soil structural properties which are known to be extremely diverse. This diversity necessitates the collection of large sample sizes for adequately representing the spatial variability of soil structure at a specific sampling site. One important bottleneck of using X-ray imaging is however the large amount of time required by a trained specialist to process the image data which makes it difficult to process larger amounts of samples. The software SoilJ aims at removing this bottleneck by automatizing most of the required image processing steps needed to analyze image data of cylindrical soil columns. SoilJ is a plugin of the free Java-based image-processing software ImageJ. The plugin is designed to automatically process all images located with a designated folder. In a first step, SoilJ recognizes the outlines of the soil column upon which the column is rotated to an upright position and placed in the center of the canvas. Excess canvas is removed from the images. Then, SoilJ samples the grey values of the column material as well as the surrounding air in Z-direction. Assuming that the column material (mostly PVC of aluminium) exhibits a spatially constant density, these grey values serve as a proxy for the image illumination at a specific Z-coordinate. Together with the grey values of the air they are used to correct image illumination fluctuations which often occur along the axis of rotation during image acquisition. SoilJ includes also an algorithm for beam-hardening artefact removal and extended image segmentation options. Finally, SoilJ integrates the morphology analyses plugins of BoneJ (Doube et al., 2006, BoneJ Free and extensible bone image analysis in ImageJ. Bone 47: 1076-1079) and provides an ASCII file summarizing these measures for each investigated soil column, respectively. In the future it is planned to integrate SoilJ into FIJI, the maintained and updated edition of ImageJ with selected plugins.

2dx_automator: implementation of a semiautomatic high-throughput high-resolution cryo-electron crystallography pipeline.

PubMed

Scherer, Sebastian; Kowal, Julia; Chami, Mohamed; Dandey, Venkata; Arheit, Marcel; Ringler, Philippe; Stahlberg, Henning

2014-05-01

The introduction of direct electron detectors (DED) to cryo-electron microscopy has tremendously increased the signal-to-noise ratio (SNR) and quality of the recorded images. We discuss the optimal use of DEDs for cryo-electron crystallography, introduce a new automatic image processing pipeline, and demonstrate the vast improvement in the resolution achieved by the use of both together, especially for highly tilted samples. The new processing pipeline (now included in the software package 2dx) exploits the high SNR and frame readout frequency of DEDs to automatically correct for beam-induced sample movement, and reliably processes individual crystal images without human interaction as data are being acquired. A new graphical user interface (GUI) condenses all information required for quality assessment in one window, allowing the imaging conditions to be verified and adjusted during the data collection session. With this new pipeline an automatically generated unit cell projection map of each recorded 2D crystal is available less than 5 min after the image was recorded. The entire processing procedure yielded a three-dimensional reconstruction of the 2D-crystallized ion-channel membrane protein MloK1 with a much-improved resolution of 5Å in-plane and 7Å in the z-direction, within 2 days of data acquisition and simultaneous processing. The results obtained are superior to those delivered by conventional photographic film-based methodology of the same sample, and demonstrate the importance of drift-correction. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

A Hybrid Hierarchical Approach for Brain Tissue Segmentation by Combining Brain Atlas and Least Square Support Vector Machine

PubMed Central

Kasiri, Keyvan; Kazemi, Kamran; Dehghani, Mohammad Javad; Helfroush, Mohammad Sadegh

2013-01-01

In this paper, we present a new semi-automatic brain tissue segmentation method based on a hybrid hierarchical approach that combines a brain atlas as a priori information and a least-square support vector machine (LS-SVM). The method consists of three steps. In the first two steps, the skull is removed and the cerebrospinal fluid (CSF) is extracted. These two steps are performed using the toolbox FMRIB's automated segmentation tool integrated in the FSL software (FSL-FAST) developed in Oxford Centre for functional MRI of the brain (FMRIB). Then, in the third step, the LS-SVM is used to segment grey matter (GM) and white matter (WM). The training samples for LS-SVM are selected from the registered brain atlas. The voxel intensities and spatial positions are selected as the two feature groups for training and test. SVM as a powerful discriminator is able to handle nonlinear classification problems; however, it cannot provide posterior probability. Thus, we use a sigmoid function to map the SVM output into probabilities. The proposed method is used to segment CSF, GM and WM from the simulated magnetic resonance imaging (MRI) using Brainweb MRI simulator and real data provided by Internet Brain Segmentation Repository. The semi-automatically segmented brain tissues were evaluated by comparing to the corresponding ground truth. The Dice and Jaccard similarity coefficients, sensitivity and specificity were calculated for the quantitative validation of the results. The quantitative results show that the proposed method segments brain tissues accurately with respect to corresponding ground truth. PMID:24696800

A geometric level set model for ultrasounds analysis

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

Sarti, A.; Malladi, R.

We propose a partial differential equation (PDE) for filtering and segmentation of echocardiographic images based on a geometric-driven scheme. The method allows edge-preserving image smoothing and a semi-automatic segmentation of the heart chambers, that regularizes the shapes and improves edge fidelity especially in presence of distinct gaps in the edge map as is common in ultrasound imagery. A numerical scheme for solving the proposed PDE is borrowed from level set methods. Results on human in vivo acquired 2D, 2D+time,3D, 3D+time echocardiographic images are shown.

Improved workflow modelling using role activity diagram-based modelling with application to a radiology service case study.

PubMed

Shukla, Nagesh; Keast, John E; Ceglarek, Darek

2014-10-01

The modelling of complex workflows is an important problem-solving technique within healthcare settings. However, currently most of the workflow models use a simplified flow chart of patient flow obtained using on-site observations, group-based debates and brainstorming sessions, together with historic patient data. This paper presents a systematic and semi-automatic methodology for knowledge acquisition with detailed process representation using sequential interviews of people in the key roles involved in the service delivery process. The proposed methodology allows the modelling of roles, interactions, actions, and decisions involved in the service delivery process. This approach is based on protocol generation and analysis techniques such as: (i) initial protocol generation based on qualitative interviews of radiology staff, (ii) extraction of key features of the service delivery process, (iii) discovering the relationships among the key features extracted, and, (iv) a graphical representation of the final structured model of the service delivery process. The methodology is demonstrated through a case study of a magnetic resonance (MR) scanning service-delivery process in the radiology department of a large hospital. A set of guidelines is also presented in this paper to visually analyze the resulting process model for identifying process vulnerabilities. A comparative analysis of different workflow models is also conducted. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

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

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

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

1989-07-01

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

Fuzzy C-means classification for corrosion evolution of steel images

NASA Astrophysics Data System (ADS)

Trujillo, Maite; Sadki, Mustapha

2004-05-01

An unavoidable problem of metal structures is their exposure to rust degradation during their operational life. Thus, the surfaces need to be assessed in order to avoid potential catastrophes. There is considerable interest in the use of patch repair strategies which minimize the project costs. However, to operate such strategies with confidence in the long useful life of the repair, it is essential that the condition of the existing coatings and the steel substrate can be accurately quantified and classified. This paper describes the application of fuzzy set theory for steel surfaces classification according to the steel rust time. We propose a semi-automatic technique to obtain image clustering using the Fuzzy C-means (FCM) algorithm and we analyze two kinds of data to study the classification performance. Firstly, we investigate the use of raw images" pixels without any pre-processing methods and neighborhood pixels. Secondly, we apply Gaussian noise to the images with different standard deviation to study the FCM method tolerance to Gaussian noise. The noisy images simulate the possible perturbations of the images due to the weather or rust deposits in the steel surfaces during typical on-site acquisition procedures

Generation of High Resolution Global DSM from ALOS PRISM

NASA Astrophysics Data System (ADS)

Takaku, J.; Tadono, T.; Tsutsui, K.

2014-04-01

Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM), one of onboard sensors carried on the Advanced Land Observing Satellite (ALOS), was designed to generate worldwide topographic data with its optical stereoscopic observation. The sensor consists of three independent panchromatic radiometers for viewing forward, nadir, and backward in 2.5 m ground resolution producing a triplet stereoscopic image along its track. The sensor had observed huge amount of stereo images all over the world during the mission life of the satellite from 2006 through 2011. We have semi-automatically processed Digital Surface Model (DSM) data with the image archives in some limited areas. The height accuracy of the dataset was estimated at less than 5 m (rms) from the evaluation with ground control points (GCPs) or reference DSMs derived from the Light Detection and Ranging (LiDAR). Then, we decided to process the global DSM datasets from all available archives of PRISM stereo images by the end of March 2016. This paper briefly reports on the latest processing algorithms for the global DSM datasets as well as their preliminary results on some test sites. The accuracies and error characteristics of datasets are analyzed and discussed on various fields by the comparison with existing global datasets such as Ice, Cloud, and land Elevation Satellite (ICESat) data and Shuttle Radar Topography Mission (SRTM) data, as well as the GCPs and the reference airborne LiDAR/DSM.

Gaussian mixtures on tensor fields for segmentation: applications to medical imaging.

PubMed

de Luis-García, Rodrigo; Westin, Carl-Fredrik; Alberola-López, Carlos

2011-01-01

In this paper, we introduce a new approach for tensor field segmentation based on the definition of mixtures of Gaussians on tensors as a statistical model. Working over the well-known Geodesic Active Regions segmentation framework, this scheme presents several interesting advantages. First, it yields a more flexible model than the use of a single Gaussian distribution, which enables the method to better adapt to the complexity of the data. Second, it can work directly on tensor-valued images or, through a parallel scheme that processes independently the intensity and the local structure tensor, on scalar textured images. Two different applications have been considered to show the suitability of the proposed method for medical imaging segmentation. First, we address DT-MRI segmentation on a dataset of 32 volumes, showing a successful segmentation of the corpus callosum and favourable comparisons with related approaches in the literature. Second, the segmentation of bones from hand radiographs is studied, and a complete automatic-semiautomatic approach has been developed that makes use of anatomical prior knowledge to produce accurate segmentation results. Copyright © 2010 Elsevier Ltd. All rights reserved.

Semi-automatic segmentation of the placenta into fetal and maternal compartments using intravoxel incoherent motion MRI

NASA Astrophysics Data System (ADS)

You, Wonsang; Andescavage, Nickie; Zun, Zungho; Limperopoulos, Catherine

2017-03-01

Intravoxel incoherent motion (IVIM) magnetic resonance imaging is an emerging non-invasive technique that has been recently applied to quantify in vivo global placental perfusion. We propose a robust semi-automated method for segmenting the placenta into fetal and maternal compartments from IVIM data, using a multi-label image segmentation algorithm called `GrowCut'. Placental IVIM data were acquired on a 1.5T scanner from 16 healthy pregnant women between 21-37 gestational weeks. The voxel-wise perfusion fraction was then estimated after non-rigid image registration. The seed regions of the fetal and maternal compartments were determined using structural T2-weighted reference images, and improved progressively through an iterative process of the GrowCut algorithm to accurately encompass fetal and maternal compartments. We demonstrated that the placental perfusion fraction decreased in both fetal (-0.010/week) and maternal compartments (-0.013/week) while their relative difference (ffetal-fmaternal) gradually increased with advancing gestational age (+0.003/week, p=0.065). Our preliminary results show that the proposed method was effective in distinguishing placental compartments using IVIM.

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

PubMed

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

2013-11-01

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

Group-wise feature-based registration of CT and ultrasound images of spine

NASA Astrophysics Data System (ADS)

Rasoulian, Abtin; Mousavi, Parvin; Hedjazi Moghari, Mehdi; Foroughi, Pezhman; Abolmaesumi, Purang

2010-02-01

Registration of pre-operative CT and freehand intra-operative ultrasound of lumbar spine could aid surgeons in the spinal needle injection which is a common procedure for pain management. Patients are always in a supine position during the CT scan, and in the prone or sitting position during the intervention. This leads to a difference in the spinal curvature between the two imaging modalities, which means a single rigid registration cannot be used for all of the lumbar vertebrae. In this work, a method for group-wise registration of pre-operative CT and intra-operative freehand 2-D ultrasound images of the lumbar spine is presented. The approach utilizes a pointbased registration technique based on the unscented Kalman filter, taking as input segmented vertebrae surfaces in both CT and ultrasound data. Ultrasound images are automatically segmented using a dynamic programming approach, while the CT images are semi-automatically segmented using thresholding. Since the curvature of the spine is different between the pre-operative and the intra-operative data, the registration approach is designed to simultaneously align individual groups of points segmented from each vertebra in the two imaging modalities. A biomechanical model is used to constrain the vertebrae transformation parameters during the registration and to ensure convergence. The mean target registration error achieved for individual vertebrae on five spine phantoms generated from CT data of patients, is 2.47 mm with standard deviation of 1.14 mm.

Geometric modeling of the temporal bone for cochlea implant simulation

NASA Astrophysics Data System (ADS)

Todd, Catherine A.; Naghdy, Fazel; O'Leary, Stephen

2004-05-01

The first stage in the development of a clinically valid surgical simulator for training otologic surgeons in performing cochlea implantation is presented. For this purpose, a geometric model of the temporal bone has been derived from a cadaver specimen using the biomedical image processing software package Analyze (AnalyzeDirect, Inc) and its three-dimensional reconstruction is examined. Simulator construction begins with registration and processing of a Computer Tomography (CT) medical image sequence. Important anatomical structures of the middle and inner ear are identified and segmented from each scan in a semi-automated threshold-based approach. Linear interpolation between image slices produces a three-dimensional volume dataset: the geometrical model. Artefacts are effectively eliminated using a semi-automatic seeded region-growing algorithm and unnecessary bony structures are removed. Once validated by an Ear, Nose and Throat (ENT) specialist, the model may be imported into the Reachin Application Programming Interface (API) (Reachin Technologies AB) for visual and haptic rendering associated with a virtual mastoidectomy. Interaction with the model is realized with haptics interfacing, providing the user with accurate torque and force feedback. Electrode array insertion into the cochlea will be introduced in the final stage of design.

Comparative UAV and Field Phenotyping to Assess Yield and Nitrogen Use Efficiency in Hybrid and Conventional Barley.

PubMed

Kefauver, Shawn C; Vicente, Rubén; Vergara-Díaz, Omar; Fernandez-Gallego, Jose A; Kerfal, Samir; Lopez, Antonio; Melichar, James P E; Serret Molins, María D; Araus, José L

2017-01-01

With the commercialization and increasing availability of Unmanned Aerial Vehicles (UAVs) multiple rotor copters have expanded rapidly in plant phenotyping studies with their ability to provide clear, high resolution images. As such, the traditional bottleneck of plant phenotyping has shifted from data collection to data processing. Fortunately, the necessarily controlled and repetitive design of plant phenotyping allows for the development of semi-automatic computer processing tools that may sufficiently reduce the time spent in data extraction. Here we present a comparison of UAV and field based high throughput plant phenotyping (HTPP) using the free, open-source image analysis software FIJI (Fiji is just ImageJ) using RGB (conventional digital cameras), multispectral and thermal aerial imagery in combination with a matching suite of ground sensors in a study of two hybrids and one conventional barely variety with ten different nitrogen treatments, combining different fertilization levels and application schedules. A detailed correlation network for physiological traits and exploration of the data comparing between treatments and varieties provided insights into crop performance under different management scenarios. Multivariate regression models explained 77.8, 71.6, and 82.7% of the variance in yield from aerial, ground, and combined data sets, respectively.

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

PubMed

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

1997-01-01

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

Derivation of groundwater flow-paths based on semi-automatic extraction of lineaments from remote sensing data

NASA Astrophysics Data System (ADS)

Mallast, U.; Gloaguen, R.; Geyer, S.; Rödiger, T.; Siebert, C.

2011-08-01

In this paper we present a semi-automatic method to infer groundwater flow-paths based on the extraction of lineaments from digital elevation models. This method is especially adequate in remote and inaccessible areas where in-situ data are scarce. The combined method of linear filtering and object-based classification provides a lineament map with a high degree of accuracy. Subsequently, lineaments are differentiated into geological and morphological lineaments using auxiliary information and finally evaluated in terms of hydro-geological significance. Using the example of the western catchment of the Dead Sea (Israel/Palestine), the orientation and location of the differentiated lineaments are compared to characteristics of known structural features. We demonstrate that a strong correlation between lineaments and structural features exists. Using Euclidean distances between lineaments and wells provides an assessment criterion to evaluate the hydraulic significance of detected lineaments. Based on this analysis, we suggest that the statistical analysis of lineaments allows a delineation of flow-paths and thus significant information on groundwater movements. To validate the flow-paths we compare them to existing results of groundwater models that are based on well data.

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

NASA Astrophysics Data System (ADS)

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

2017-03-01

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

Statistical physics approach to quantifying differences in myelinated nerve fibers

PubMed Central

Comin, César H.; Santos, João R.; Corradini, Dario; Morrison, Will; Curme, Chester; Rosene, Douglas L.; Gabrielli, Andrea; da F. Costa, Luciano; Stanley, H. Eugene

2014-01-01

We present a new method to quantify differences in myelinated nerve fibers. These differences range from morphologic characteristics of individual fibers to differences in macroscopic properties of collections of fibers. Our method uses statistical physics tools to improve on traditional measures, such as fiber size and packing density. As a case study, we analyze cross–sectional electron micrographs from the fornix of young and old rhesus monkeys using a semi-automatic detection algorithm to identify and characterize myelinated axons. We then apply a feature selection approach to identify the features that best distinguish between the young and old age groups, achieving a maximum accuracy of 94% when assigning samples to their age groups. This analysis shows that the best discrimination is obtained using the combination of two features: the fraction of occupied axon area and the effective local density. The latter is a modified calculation of axon density, which reflects how closely axons are packed. Our feature analysis approach can be applied to characterize differences that result from biological processes such as aging, damage from trauma or disease or developmental differences, as well as differences between anatomical regions such as the fornix and the cingulum bundle or corpus callosum. PMID:24676146

Statistical physics approach to quantifying differences in myelinated nerve fibers

NASA Astrophysics Data System (ADS)

Comin, César H.; Santos, João R.; Corradini, Dario; Morrison, Will; Curme, Chester; Rosene, Douglas L.; Gabrielli, Andrea; da F. Costa, Luciano; Stanley, H. Eugene

2014-03-01

We present a new method to quantify differences in myelinated nerve fibers. These differences range from morphologic characteristics of individual fibers to differences in macroscopic properties of collections of fibers. Our method uses statistical physics tools to improve on traditional measures, such as fiber size and packing density. As a case study, we analyze cross-sectional electron micrographs from the fornix of young and old rhesus monkeys using a semi-automatic detection algorithm to identify and characterize myelinated axons. We then apply a feature selection approach to identify the features that best distinguish between the young and old age groups, achieving a maximum accuracy of 94% when assigning samples to their age groups. This analysis shows that the best discrimination is obtained using the combination of two features: the fraction of occupied axon area and the effective local density. The latter is a modified calculation of axon density, which reflects how closely axons are packed. Our feature analysis approach can be applied to characterize differences that result from biological processes such as aging, damage from trauma or disease or developmental differences, as well as differences between anatomical regions such as the fornix and the cingulum bundle or corpus callosum.

Rapid, semi-automatic fracture and contact mapping for point clouds, images and geophysical data

NASA Astrophysics Data System (ADS)

Thiele, Samuel T.; Grose, Lachlan; Samsu, Anindita; Micklethwaite, Steven; Vollgger, Stefan A.; Cruden, Alexander R.

2017-12-01

The advent of large digital datasets from unmanned aerial vehicle (UAV) and satellite platforms now challenges our ability to extract information across multiple scales in a timely manner, often meaning that the full value of the data is not realised. Here we adapt a least-cost-path solver and specially tailored cost functions to rapidly interpolate structural features between manually defined control points in point cloud and raster datasets. We implement the method in the geographic information system QGIS and the point cloud and mesh processing software CloudCompare. Using these implementations, the method can be applied to a variety of three-dimensional (3-D) and two-dimensional (2-D) datasets, including high-resolution aerial imagery, digital outcrop models, digital elevation models (DEMs) and geophysical grids. We demonstrate the algorithm with four diverse applications in which we extract (1) joint and contact patterns in high-resolution orthophotographs, (2) fracture patterns in a dense 3-D point cloud, (3) earthquake surface ruptures of the Greendale Fault associated with the Mw7.1 Darfield earthquake (New Zealand) from high-resolution light detection and ranging (lidar) data, and (4) oceanic fracture zones from bathymetric data of the North Atlantic. The approach improves the consistency of the interpretation process while retaining expert guidance and achieves significant improvements (35-65 %) in digitisation time compared to traditional methods. Furthermore, it opens up new possibilities for data synthesis and can quantify the agreement between datasets and an interpretation.

Three-dimensional reconstruction of teeth and jaws based on segmentation of CT images using watershed transformation.

PubMed

Naumovich, S S; Naumovich, S A; Goncharenko, V G

2015-01-01

The objective of the present study was the development and clinical testing of a three-dimensional (3D) reconstruction method of teeth and a bone tissue of the jaw on the basis of CT images of the maxillofacial region. 3D reconstruction was performed using the specially designed original software based on watershed transformation. Computed tomograms in digital imaging and communications in medicine format obtained on multispiral CT and CBCT scanners were used for creation of 3D models of teeth and the jaws. The processing algorithm is realized in the stepwise threshold image segmentation with the placement of markers in the mode of a multiplanar projection in areas relating to the teeth and a bone tissue. The developed software initially creates coarse 3D models of the entire dentition and the jaw. Then, certain procedures specify the model of the jaw and cut the dentition into separate teeth. The proper selection of the segmentation threshold is very important for CBCT images having a low contrast and high noise level. The developed semi-automatic algorithm of multispiral and cone beam computed tomogram processing allows 3D models of teeth to be created separating them from a bone tissue of the jaws. The software is easy to install in a dentist's workplace, has an intuitive interface and takes little time in processing. The obtained 3D models can be used for solving a wide range of scientific and clinical tasks.

Three-dimensional analysis of implanted magnetic-resonance-visible meshes.

PubMed

Sindhwani, Nikhil; Feola, Andrew; De Keyzer, Frederik; Claus, Filip; Callewaert, Geertje; Urbankova, Iva; Ourselin, Sebastien; D'hooge, Jan; Deprest, Jan

2015-10-01

Our primary objective was to develop relevant algorithms for quantification of mesh position and 3D shape in magnetic resonance (MR) images. In this proof-of-principle study, one patient with severe anterior vaginal wall prolapse was implanted with an MR-visible mesh. High-resolution MR images of the pelvis were acquired 6 weeks and 8 months postsurgery. 3D models were created using semiautomatic segmentation techniques. Conformational changes were recorded quantitatively using part-comparison analysis. An ellipticity measure is proposed to record longitudinal conformational changes in the mesh arms. The surface that is the effective reinforcement provided by the mesh is calculated using a novel methodology. The area of this surface is the effective support area (ESA). MR-visible mesh was clearly outlined in the images, which allowed us to longitudinally quantify mesh configuration between 6 weeks and 8 months after implantation. No significant changes were found in mesh position, effective support area, conformation of the mesh's main body, and arm length during the period of observation. Ellipticity profiles show longitudinal conformational changes in posterior arms. This paper proposes novel methodologies for a systematic 3D assessment of the position and morphology of MR-visible meshes. A novel semiautomatic tool was developed to calculate the effective area of support provided by the mesh, a potentially clinically important parameter.

A segmentation and point-matching enhanced efficient deformable image registration method for dose accumulation between HDR CT images

NASA Astrophysics Data System (ADS)

Zhen, Xin; Chen, Haibin; Yan, Hao; Zhou, Linghong; Mell, Loren K.; Yashar, Catheryn M.; Jiang, Steve; Jia, Xun; Gu, Xuejun; Cervino, Laura

2015-04-01

Deformable image registration (DIR) of fractional high-dose-rate (HDR) CT images is challenging due to the presence of applicators in the brachytherapy image. Point-to-point correspondence fails because of the undesired deformation vector fields (DVF) propagated from the applicator region (AR) to the surrounding tissues, which can potentially introduce significant DIR errors in dose mapping. This paper proposes a novel segmentation and point-matching enhanced efficient DIR (named SPEED) scheme to facilitate dose accumulation among HDR treatment fractions. In SPEED, a semi-automatic seed point generation approach is developed to obtain the incremented fore/background point sets to feed the random walks algorithm, which is used to segment and remove the AR, leaving empty AR cavities in the HDR CT images. A feature-based ‘thin-plate-spline robust point matching’ algorithm is then employed for AR cavity surface points matching. With the resulting mapping, a DVF defining on each voxel is estimated by B-spline approximation, which serves as the initial DVF for the subsequent Demons-based DIR between the AR-free HDR CT images. The calculated DVF via Demons combined with the initial one serve as the final DVF to map doses between HDR fractions. The segmentation and registration accuracy are quantitatively assessed by nine clinical HDR cases from three gynecological cancer patients. The quantitative analysis and visual inspection of the DIR results indicate that SPEED can suppress the impact of applicator on DIR, and accurately register HDR CT images as well as deform and add interfractional HDR doses.

A segmentation and point-matching enhanced efficient deformable image registration method for dose accumulation between HDR CT images.

PubMed

Zhen, Xin; Chen, Haibin; Yan, Hao; Zhou, Linghong; Mell, Loren K; Yashar, Catheryn M; Jiang, Steve; Jia, Xun; Gu, Xuejun; Cervino, Laura

2015-04-07

Deformable image registration (DIR) of fractional high-dose-rate (HDR) CT images is challenging due to the presence of applicators in the brachytherapy image. Point-to-point correspondence fails because of the undesired deformation vector fields (DVF) propagated from the applicator region (AR) to the surrounding tissues, which can potentially introduce significant DIR errors in dose mapping. This paper proposes a novel segmentation and point-matching enhanced efficient DIR (named SPEED) scheme to facilitate dose accumulation among HDR treatment fractions. In SPEED, a semi-automatic seed point generation approach is developed to obtain the incremented fore/background point sets to feed the random walks algorithm, which is used to segment and remove the AR, leaving empty AR cavities in the HDR CT images. A feature-based 'thin-plate-spline robust point matching' algorithm is then employed for AR cavity surface points matching. With the resulting mapping, a DVF defining on each voxel is estimated by B-spline approximation, which serves as the initial DVF for the subsequent Demons-based DIR between the AR-free HDR CT images. The calculated DVF via Demons combined with the initial one serve as the final DVF to map doses between HDR fractions. The segmentation and registration accuracy are quantitatively assessed by nine clinical HDR cases from three gynecological cancer patients. The quantitative analysis and visual inspection of the DIR results indicate that SPEED can suppress the impact of applicator on DIR, and accurately register HDR CT images as well as deform and add interfractional HDR doses.

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

NASA Astrophysics Data System (ADS)

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

A multiresolution prostate representation for automatic segmentation in magnetic resonance images.

PubMed

Alvarez, Charlens; Martínez, Fabio; Romero, Eduardo

2017-04-01

Accurate prostate delineation is necessary in radiotherapy processes for concentrating the dose onto the prostate and reducing side effects in neighboring organs. Currently, manual delineation is performed over magnetic resonance imaging (MRI) taking advantage of its high soft tissue contrast property. Nevertheless, as human intervention is a consuming task with high intra- and interobserver variability rates, (semi)-automatic organ delineation tools have emerged to cope with these challenges, reducing the time spent for these tasks. This work presents a multiresolution representation that defines a novel metric and allows to segment a new prostate by combining a set of most similar prostates in a dataset. The proposed method starts by selecting the set of most similar prostates with respect to a new one using the proposed multiresolution representation. This representation characterizes the prostate through a set of salient points, extracted from a region of interest (ROI) that encloses the organ and refined using structural information, allowing to capture main relevant features of the organ boundary. Afterward, the new prostate is automatically segmented by combining the nonrigidly registered expert delineations associated to the previous selected similar prostates using a weighted patch-based strategy. Finally, the prostate contour is smoothed based on morphological operations. The proposed approach was evaluated with respect to the expert manual segmentation under a leave-one-out scheme using two public datasets, obtaining averaged Dice coefficients of 82% ± 0.07 and 83% ± 0.06, and demonstrating a competitive performance with respect to atlas-based state-of-the-art methods. The proposed multiresolution representation provides a feature space that follows a local salient point criteria and a global rule of the spatial configuration among these points to find out the most similar prostates. This strategy suggests an easy adaptation in the clinical routine, as supporting tool for annotation. © 2017 American Association of Physicists in Medicine.

Image search engine with selective filtering and feature-element-based classification

NASA Astrophysics Data System (ADS)

Li, Qing; Zhang, Yujin; Dai, Shengyang

2001-12-01

With the growth of Internet and storage capability in recent years, image has become a widespread information format in World Wide Web. However, it has become increasingly harder to search for images of interest, and effective image search engine for the WWW needs to be developed. We propose in this paper a selective filtering process and a novel approach for image classification based on feature element in the image search engine we developed for the WWW. First a selective filtering process is embedded in a general web crawler to filter out the meaningless images with GIF format. Two parameters that can be obtained easily are used in the filtering process. Our classification approach first extract feature elements from images instead of feature vectors. Compared with feature vectors, feature elements can better capture visual meanings of the image according to subjective perception of human beings. Different from traditional image classification method, our classification approach based on feature element doesn't calculate the distance between two vectors in the feature space, while trying to find associations between feature element and class attribute of the image. Experiments are presented to show the efficiency of the proposed approach.

SU-E-J-262: Variability in Texture Analysis of Gynecological Tumors in the Context of An 18F-FDG PET Adaptive Protocol

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

Nawrocki, J; Chino, J; Das, S

Purpose: This study examines the effect on texture analysis due to variable reconstruction of PET images in the context of an adaptive FDG PET protocol for node positive gynecologic cancer patients. By measuring variability in texture features from baseline and intra-treatment PET-CT, we can isolate unreliable texture features due to large variation. Methods: A subset of seven patients with node positive gynecological cancers visible on PET was selected for this study. Prescribed dose varied between 45–50.4Gy, with a 55–70Gy boost to the PET positive nodes. A baseline and intratreatment (between 30–36Gy) PET-CT were obtained on a Siemens Biograph mCT. Eachmore » clinical PET image set was reconstructed 6 times using a TrueX+TOF algorithm with varying iterations and Gaussian filter. Baseline and intra-treatment primary GTVs were segmented using PET Edge (MIM Software Inc., Cleveland, OH), a semi-automatic gradient-based algorithm, on the clinical PET and transferred to the other reconstructed sets. Using an in-house MATLAB program, four 3D texture matrices describing relationships between voxel intensities in the GTV were generated: co-occurrence, run length, size zone, and neighborhood difference. From these, 39 textural features characterizing texture were calculated in addition to SUV histogram features. The percent variability among parameters was first calculated. Each reconstructed texture feature from baseline and intra-treatment per patient was normalized to the clinical baseline scan and compared using the Wilcoxon signed-rank test in order to isolate variations due to reconstruction parameters. Results: For the baseline scans, 13 texture features showed a mean range greater than 10%. For the intra scans, 28 texture features showed a mean range greater than 10%. Comparing baseline to intra scans, 25 texture features showed p <0.05. Conclusion: Variability due to different reconstruction parameters increased with treatment, however, the majority of texture features showed significant changes during treatment independent of reconstruction effects.« less

Semi-automated intra-operative fluoroscopy guidance for osteotomy and external-fixator.

PubMed

Lin, Hong; Samchukov, Mikhail L; Birch, John G; Cherkashin, Alexander

2006-01-01

This paper outlines a semi-automated intra-operative fluoroscopy guidance and monitoring approach for osteotomy and external-fixator application in orthopedic surgery. Intra-operative Guidance module is one component of the "LegPerfect Suite" developed for assisting the surgical correction of lower extremity angular deformity. The Intra-operative Guidance module utilizes information from the preoperative surgical planning module as a guideline to overlay (register) its bone outline semi-automatically with the bone edge from the real-time fluoroscopic C-Arm X-Ray image in the operating room. In the registration process, scaling factor is obtained automatically through matching a fiducial template in the fluoroscopic image and a marker in the module. A triangle metal plate, placed on the operating table is used as fiducial template. The area of template image within the viewing area of the fluoroscopy machine is obtained by the image processing techniques such as edge detection and Hough transformation to extract the template from other objects in the fluoroscopy image. The area of fiducial template from fluoroscopic image is then compared with the area of the marker from the planning so as to obtain the scaling factor. After the scaling factor is obtained, the user can use simple operations by mouse to shift and rotate the preoperative planning to overlay the bone outline from planning with the bone edge from fluoroscopy image. In this way osteotomy levels and external fixator positioning on the limb can guided by the computerized preoperative plan.

An evaluation of automatic coronary artery calcium scoring methods with cardiac CT using the orCaScore framework.

PubMed

Wolterink, Jelmer M; Leiner, Tim; de Vos, Bob D; Coatrieux, Jean-Louis; Kelm, B Michael; Kondo, Satoshi; Salgado, Rodrigo A; Shahzad, Rahil; Shu, Huazhong; Snoeren, Miranda; Takx, Richard A P; van Vliet, Lucas J; van Walsum, Theo; Willems, Tineke P; Yang, Guanyu; Zheng, Yefeng; Viergever, Max A; Išgum, Ivana

2016-05-01

The amount of coronary artery calcification (CAC) is a strong and independent predictor of cardiovascular disease (CVD) events. In clinical practice, CAC is manually identified and automatically quantified in cardiac CT using commercially available software. This is a tedious and time-consuming process in large-scale studies. Therefore, a number of automatic methods that require no interaction and semiautomatic methods that require very limited interaction for the identification of CAC in cardiac CT have been proposed. Thus far, a comparison of their performance has been lacking. The objective of this study was to perform an independent evaluation of (semi)automatic methods for CAC scoring in cardiac CT using a publicly available standardized framework. Cardiac CT exams of 72 patients distributed over four CVD risk categories were provided for (semi)automatic CAC scoring. Each exam consisted of a noncontrast-enhanced calcium scoring CT (CSCT) and a corresponding coronary CT angiography (CCTA) scan. The exams were acquired in four different hospitals using state-of-the-art equipment from four major CT scanner vendors. The data were divided into 32 training exams and 40 test exams. A reference standard for CAC in CSCT was defined by consensus of two experts following a clinical protocol. The framework organizers evaluated the performance of (semi)automatic methods on test CSCT scans, per lesion, artery, and patient. Five (semi)automatic methods were evaluated. Four methods used both CSCT and CCTA to identify CAC, and one method used only CSCT. The evaluated methods correctly detected between 52% and 94% of CAC lesions with positive predictive values between 65% and 96%. Lesions in distal coronary arteries were most commonly missed and aortic calcifications close to the coronary ostia were the most common false positive errors. The majority (between 88% and 98%) of correctly identified CAC lesions were assigned to the correct artery. Linearly weighted Cohen's kappa for patient CVD risk categorization by the evaluated methods ranged from 0.80 to 1.00. A publicly available standardized framework for the evaluation of (semi)automatic methods for CAC identification in cardiac CT is described. An evaluation of five (semi)automatic methods within this framework shows that automatic per patient CVD risk categorization is feasible. CAC lesions at ambiguous locations such as the coronary ostia remain challenging, but their detection had limited impact on CVD risk determination.

Construction of a Phonotactic Dialect Corpus using Semiautomatic Annotation

DTIC Science & Technology

2007-01-01

phonological or phonetic feature is speaker specific or commonly found in that speaker’s dialect. Historically, the sociolinguistic study of dialect...variation in speech has been limited to small-scale population studies , usually conducted through sociolinguistic interviews [1]. Dialect analysis of... study , we created a superset of rules 943 derived from phenomena described in the sociolinguistic literature [8][9][10][11]. Rule Example ROI [r]->0

Volumetric neuroimage analysis extensions for the MIPAV software package.

PubMed

Bazin, Pierre-Louis; Cuzzocreo, Jennifer L; Yassa, Michael A; Gandler, William; McAuliffe, Matthew J; Bassett, Susan S; Pham, Dzung L

2007-09-15

We describe a new collection of publicly available software tools for performing quantitative neuroimage analysis. The tools perform semi-automatic brain extraction, tissue classification, Talairach alignment, and atlas-based measurements within a user-friendly graphical environment. They are implemented as plug-ins for MIPAV, a freely available medical image processing software package from the National Institutes of Health. Because the plug-ins and MIPAV are implemented in Java, both can be utilized on nearly any operating system platform. In addition to the software plug-ins, we have also released a digital version of the Talairach atlas that can be used to perform regional volumetric analyses. Several studies are conducted applying the new tools to simulated and real neuroimaging data sets.

StandFood: Standardization of Foods Using a Semi-Automatic System for Classifying and Describing Foods According to FoodEx2

PubMed Central

Eftimov, Tome; Korošec, Peter; Koroušić Seljak, Barbara

2017-01-01

The European Food Safety Authority has developed a standardized food classification and description system called FoodEx2. It uses facets to describe food properties and aspects from various perspectives, making it easier to compare food consumption data from different sources and perform more detailed data analyses. However, both food composition data and food consumption data, which need to be linked, are lacking in FoodEx2 because the process of classification and description has to be manually performed—a process that is laborious and requires good knowledge of the system and also good knowledge of food (composition, processing, marketing, etc.). In this paper, we introduce a semi-automatic system for classifying and describing foods according to FoodEx2, which consists of three parts. The first involves a machine learning approach and classifies foods into four FoodEx2 categories, with two for single foods: raw (r) and derivatives (d), and two for composite foods: simple (s) and aggregated (c). The second uses a natural language processing approach and probability theory to describe foods. The third combines the result from the first and the second part by defining post-processing rules in order to improve the result for the classification part. We tested the system using a set of food items (from Slovenia) manually-coded according to FoodEx2. The new semi-automatic system obtained an accuracy of 89% for the classification part and 79% for the description part, or an overall result of 79% for the whole system. PMID:28587103

StandFood: Standardization of Foods Using a Semi-Automatic System for Classifying and Describing Foods According to FoodEx2.

PubMed

Eftimov, Tome; Korošec, Peter; Koroušić Seljak, Barbara

2017-05-26

The European Food Safety Authority has developed a standardized food classification and description system called FoodEx2. It uses facets to describe food properties and aspects from various perspectives, making it easier to compare food consumption data from different sources and perform more detailed data analyses. However, both food composition data and food consumption data, which need to be linked, are lacking in FoodEx2 because the process of classification and description has to be manually performed-a process that is laborious and requires good knowledge of the system and also good knowledge of food (composition, processing, marketing, etc.). In this paper, we introduce a semi-automatic system for classifying and describing foods according to FoodEx2, which consists of three parts. The first involves a machine learning approach and classifies foods into four FoodEx2 categories, with two for single foods: raw (r) and derivatives (d), and two for composite foods: simple (s) and aggregated (c). The second uses a natural language processing approach and probability theory to describe foods. The third combines the result from the first and the second part by defining post-processing rules in order to improve the result for the classification part. We tested the system using a set of food items (from Slovenia) manually-coded according to FoodEx2. The new semi-automatic system obtained an accuracy of 89% for the classification part and 79% for the description part, or an overall result of 79% for the whole system.

Automated synthesis, insertion and detection of polyps for CT colonography

NASA Astrophysics Data System (ADS)

Sezille, Nicolas; Sadleir, Robert J. T.; Whelan, Paul F.

2003-03-01

CT Colonography (CTC) is a new non-invasive colon imaging technique which has the potential to replace conventional colonoscopy for colorectal cancer screening. A novel system which facilitates automated detection of colorectal polyps at CTC is introduced. As exhaustive testing of such a system using real patient data is not feasible, more complete testing is achieved through synthesis of artificial polyps and insertion into real datasets. The polyp insertion is semi-automatic: candidate points are manually selected using a custom GUI, suitable points are determined automatically from an analysis of the local neighborhood surrounding each of the candidate points. Local density and orientation information are used to generate polyps based on an elliptical model. Anomalies are identified from the modified dataset by analyzing the axial images. Detected anomalies are classified as potential polyps or natural features using 3D morphological techniques. The final results are flagged for review. The system was evaluated using 15 scenarios. The sensitivity of the system was found to be 65% with 34% false positive detections. Automated diagnosis at CTC is possible and thorough testing is facilitated by augmenting real patient data with computer generated polyps. Ultimately, automated diagnosis will enhance standard CTC and increase performance.

MEASURING PROJECTOR

DOEpatents

Franck, J.V.; Broadhead, P.S.; Skiff, E.W.

1959-07-14

A semiautomatic measuring projector particularly adapted for measurement of the coordinates of photographic images of particle tracks as prcduced in a bubble or cloud chamber is presented. A viewing screen aids the operator in selecting a particle track for measurement. After approximate manual alignment, an image scanning system coupled to a servo control provides automatic exact alignment of a track image with a reference point. The apparatus can follow along a track with a continuous motion while recording coordinate data at various selected points along the track. The coordinate data is recorded on punched cards for subsequent computer calculation of particle trajectory, momentum, etc.

Image processing for hazard recognition in on-board weather radar

NASA Technical Reports Server (NTRS)

Kelly, Wallace E. (Inventor); Rand, Timothy W. (Inventor); Uckun, Serdar (Inventor); Ruokangas, Corinne C. (Inventor)

2003-01-01

A method of providing weather radar images to a user includes obtaining radar image data corresponding to a weather radar image to be displayed. The radar image data is image processed to identify a feature of the weather radar image which is potentially indicative of a hazardous weather condition. The weather radar image is displayed to the user along with a notification of the existence of the feature which is potentially indicative of the hazardous weather condition. Notification can take the form of textual information regarding the feature, including feature type and proximity information. Notification can also take the form of visually highlighting the feature, for example by forming a visual border around the feature. Other forms of notification can also be used.

Glioma survival prediction with the combined analysis of in vivo 11C-MET-PET, ex vivo and patient features by supervised machine learning.

PubMed

Papp, Laszlo; Poetsch, Nina; Grahovac, Marko; Schmidbauer, Victor; Woehrer, Adelheid; Preusser, Matthias; Mitterhauser, Markus; Kiesel, Barbara; Wadsak, Wolfgang; Beyer, Thomas; Hacker, Marcus; Traub-Weidinger, Tatjana

2017-11-24

Gliomas are the most common types of tumors in the brain. While the definite diagnosis is routinely made ex vivo by histopathologic and molecular examination, diagnostic work-up of patients with suspected glioma is mainly done by using magnetic resonance imaging (MRI). Nevertheless, L-S-methyl- 11 C-methionine ( 11 C-MET) Positron Emission Tomography (PET) holds a great potential in characterization of gliomas. The aim of this study was to establish machine learning (ML) driven survival models for glioma built on 11 C-MET-PET, ex vivo and patient characteristics. Methods: 70 patients with a treatment naïve glioma, who had a positive 11 C-MET-PET and histopathology-derived ex vivo feature extraction, such as World Health Organization (WHO) 2007 tumor grade, histology and isocitrate dehydrogenase (IDH1-R132H) mutation status were included. The 11 C-MET-positive primary tumors were delineated semi-automatically on PET images followed by the feature extraction of tumor-to-background ratio based general and higher-order textural features by applying five different binning approaches. In vivo and ex vivo features, as well as patient characteristics (age, weight, height, body-mass-index, Karnofsky-score) were merged to characterize the tumors. Machine learning approaches were utilized to identify relevant in vivo, ex vivo and patient features and their relative weights for 36 months survival prediction. The resulting feature weights were used to establish three predictive models per binning configuration based on a combination of: in vivo/ex vivo and clinical patient information (M36IEP), in vivo and patient-only information (M36IP), and in vivo only (M36I). In addition a binning-independent ex vivo and patient-only (M36EP) model was created. The established models were validated in a Monte Carlo (MC) cross-validation scheme. Results: Most prominent ML-selected and -weighted features were patient and ex vivo based followed by in vivo features. The highest area under the curve (AUC) values of our models as revealed by the MC cross-validation were: 0.9 (M36IEP), 0.87 (M36EP), 0.77 (M36IP) and 0.72 (M36I). Conclusion: Survival prediction of glioma patients based on amino acid PET using computer-supported predictive models based on in vivo, ex vivo and patient features is highly accurate. Copyright © 2017 by the Society of Nuclear Medicine and Molecular Imaging, Inc.

Neural Network for Nanoscience Scanning Electron Microscope Image Recognition.

PubMed

Modarres, Mohammad Hadi; Aversa, Rossella; Cozzini, Stefano; Ciancio, Regina; Leto, Angelo; Brandino, Giuseppe Piero

2017-10-16

In this paper we applied transfer learning techniques for image recognition, automatic categorization, and labeling of nanoscience images obtained by scanning electron microscope (SEM). Roughly 20,000 SEM images were manually classified into 10 categories to form a labeled training set, which can be used as a reference set for future applications of deep learning enhanced algorithms in the nanoscience domain. The categories chosen spanned the range of 0-Dimensional (0D) objects such as particles, 1D nanowires and fibres, 2D films and coated surfaces, and 3D patterned surfaces such as pillars. The training set was used to retrain on the SEM dataset and to compare many convolutional neural network models (Inception-v3, Inception-v4, ResNet). We obtained compatible results by performing a feature extraction of the different models on the same dataset. We performed additional analysis of the classifier on a second test set to further investigate the results both on particular cases and from a statistical point of view. Our algorithm was able to successfully classify around 90% of a test dataset consisting of SEM images, while reduced accuracy was found in the case of images at the boundary between two categories or containing elements of multiple categories. In these cases, the image classification did not identify a predominant category with a high score. We used the statistical outcomes from testing to deploy a semi-automatic workflow able to classify and label images generated by the SEM. Finally, a separate training was performed to determine the volume fraction of coherently aligned nanowires in SEM images. The results were compared with what was obtained using the Local Gradient Orientation method. This example demonstrates the versatility and the potential of transfer learning to address specific tasks of interest in nanoscience applications.

Automatic rocks detection and classification on high resolution images of planetary surfaces

NASA Astrophysics Data System (ADS)

Aboudan, A.; Pacifici, A.; Murana, A.; Cannarsa, F.; Ori, G. G.; Dell'Arciprete, I.; Allemand, P.; Grandjean, P.; Portigliotti, S.; Marcer, A.; Lorenzoni, L.

2013-12-01

High-resolution images can be used to obtain rocks location and size on planetary surfaces. In particular rock size-frequency distribution is a key parameter to evaluate the surface roughness, to investigate the geologic processes that formed the surface and to assess the hazards related with spacecraft landing. The manual search for rocks on high-resolution images (even for small areas) can be a very intensive work. An automatic or semi-automatic algorithm to identify rocks is mandatory to enable further processing as determining the rocks presence, size, height (by means of shadows) and spatial distribution over an area of interest. Accurate rocks and shadows contours localization are the key steps for rock detection. An approach to contour detection based on morphological operators and statistical thresholding is presented in this work. The identified contours are then fitted using a proper geometric model of the rocks or shadows and used to estimate salient rocks parameters (position, size, area, height). The performances of this approach have been evaluated both on images of Martian analogue area of Morocco desert and on HiRISE images. Results have been compared with ground truth obtained by means of manual rock mapping and proved the effectiveness of the algorithm. The rock abundance and rocks size-frequency distribution derived on selected HiRISE images have been compared with the results of similar analyses performed for the landing site certification of Mars landers (Viking, Pathfinder, MER, MSL) and with the available thermal data from IRTM and TES.

System for definition of the central-chest vasculature

NASA Astrophysics Data System (ADS)

Taeprasartsit, Pinyo; Higgins, William E.

2009-02-01

Accurate definition of the central-chest vasculature from three-dimensional (3D) multi-detector CT (MDCT) images is important for pulmonary applications. For instance, the aorta and pulmonary artery help in automatic definition of the Mountain lymph-node stations for lung-cancer staging. This work presents a system for defining major vascular structures in the central chest. The system provides automatic methods for extracting the aorta and pulmonary artery and semi-automatic methods for extracting the other major central chest arteries/veins, such as the superior vena cava and azygos vein. Automatic aorta and pulmonary artery extraction are performed by model fitting and selection. The system also extracts certain vascular structure information to validate outputs. A semi-automatic method extracts vasculature by finding the medial axes between provided important sites. Results of the system are applied to lymph-node station definition and guidance of bronchoscopic biopsy.

Development of a quantitative intracranial vascular features extraction tool on 3D MRA using semiautomated open-curve active contour vessel tracing.

PubMed

Chen, Li; Mossa-Basha, Mahmud; Balu, Niranjan; Canton, Gador; Sun, Jie; Pimentel, Kristi; Hatsukami, Thomas S; Hwang, Jenq-Neng; Yuan, Chun

2018-06-01

To develop a quantitative intracranial artery measurement technique to extract comprehensive artery features from time-of-flight MR angiography (MRA). By semiautomatically tracing arteries based on an open-curve active contour model in a graphical user interface, 12 basic morphometric features and 16 basic intensity features for each artery were identified. Arteries were then classified as one of 24 types using prediction from a probability model. Based on the anatomical structures, features were integrated within 34 vascular groups for regional features of vascular trees. Eight 3D MRA acquisitions with intracranial atherosclerosis were assessed to validate this technique. Arterial tracings were validated by an experienced neuroradiologist who checked agreement at bifurcation and stenosis locations. This technique achieved 94% sensitivity and 85% positive predictive values (PPV) for bifurcations, and 85% sensitivity and PPV for stenosis. Up to 1,456 features, such as length, volume, and averaged signal intensity for each artery, as well as vascular group in each of the MRA images, could be extracted to comprehensively reflect characteristics, distribution, and connectivity of arteries. Length for the M1 segment of the middle cerebral artery extracted by this technique was compared with reviewer-measured results, and the intraclass correlation coefficient was 0.97. A semiautomated quantitative method to trace, label, and measure intracranial arteries from 3D-MRA was developed and validated. This technique can be used to facilitate quantitative intracranial vascular research, such as studying cerebrovascular adaptation to aging and disease conditions. Magn Reson Med 79:3229-3238, 2018. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

Diagnostic and prognostic histopathology system using morphometric indices

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

Parvin, Bahram; Chang, Hang; Han, Ju

Determining at least one of a prognosis or a therapy for a patient based on a stained tissue section of the patient. An image of a stained tissue section of a patient is processed by a processing device. A set of features values for a set of cell-based features is extracted from the processed image, and the processed image is associated with a particular cluster of a plurality of clusters based on the set of feature values, where the plurality of clusters is defined with respect to a feature space corresponding to the set of features.

Quantitative comparison of clustered microcalcifications in for-presentation and for-processing mammograms in full-field digital mammography.

PubMed

Wang, Juan; Nishikawa, Robert M; Yang, Yongyi

2017-07-01

Mammograms acquired with full-field digital mammography (FFDM) systems are provided in both "for-processing'' and "for-presentation'' image formats. For-presentation images are traditionally intended for visual assessment by the radiologists. In this study, we investigate the feasibility of using for-presentation images in computerized analysis and diagnosis of microcalcification (MC) lesions. We make use of a set of 188 matched mammogram image pairs of MC lesions from 95 cases (biopsy proven), in which both for-presentation and for-processing images are provided for each lesion. We then analyze and characterize the MC lesions from for-presentation images and compare them with their counterparts in for-processing images. Specifically, we consider three important aspects in computer-aided diagnosis (CAD) of MC lesions. First, we quantify each MC lesion with a set of 10 image features of clustered MCs and 12 textural features of the lesion area. Second, we assess the detectability of individual MCs in each lesion from the for-presentation images by a commonly used difference-of-Gaussians (DoG) detector. Finally, we study the diagnostic accuracy in discriminating between benign and malignant MC lesions from the for-presentation images by a pretrained support vector machine (SVM) classifier. To accommodate the underlying background suppression and image enhancement in for-presentation images, a normalization procedure is applied. The quantitative image features of MC lesions from for-presentation images are highly consistent with that from for-processing images. The values of Pearson's correlation coefficient between features from the two formats range from 0.824 to 0.961 for the 10 MC image features, and from 0.871 to 0.963 for the 12 textural features. In detection of individual MCs, the FROC curve from for-presentation is similar to that from for-processing. In particular, at sensitivity level of 80%, the average number of false-positives (FPs) per image region is 9.55 for both for-presentation and for-processing images. Finally, for classifying MC lesions as malignant or benign, the area under the ROC curve is 0.769 in for-presentation, compared to 0.761 in for-processing (P = 0.436). The quantitative results demonstrate that MC lesions in for-presentation images are highly consistent with that in for-processing images in terms of image features, detectability of individual MCs, and classification accuracy between malignant and benign lesions. These results indicate that for-presentation images can be compatible with for-processing images for use in CAD algorithms for MC lesions. © 2017 American Association of Physicists in Medicine.

Extracting Fluorescent Reporter Time Courses of Cell Lineages from High-Throughput Microscopy at Low Temporal Resolution

PubMed Central

Downey, Mike J.; Jeziorska, Danuta M.; Ott, Sascha; Tamai, T. Katherine; Koentges, Georgy; Vance, Keith W.; Bretschneider, Till

2011-01-01

The extraction of fluorescence time course data is a major bottleneck in high-throughput live-cell microscopy. Here we present an extendible framework based on the open-source image analysis software ImageJ, which aims in particular at analyzing the expression of fluorescent reporters through cell divisions. The ability to track individual cell lineages is essential for the analysis of gene regulatory factors involved in the control of cell fate and identity decisions. In our approach, cell nuclei are identified using Hoechst, and a characteristic drop in Hoechst fluorescence helps to detect dividing cells. We first compare the efficiency and accuracy of different segmentation methods and then present a statistical scoring algorithm for cell tracking, which draws on the combination of various features, such as nuclear intensity, area or shape, and importantly, dynamic changes thereof. Principal component analysis is used to determine the most significant features, and a global parameter search is performed to determine the weighting of individual features. Our algorithm has been optimized to cope with large cell movements, and we were able to semi-automatically extract cell trajectories across three cell generations. Based on the MTrackJ plugin for ImageJ, we have developed tools to efficiently validate tracks and manually correct them by connecting broken trajectories and reassigning falsely connected cell positions. A gold standard consisting of two time-series with 15,000 validated positions will be released as a valuable resource for benchmarking. We demonstrate how our method can be applied to analyze fluorescence distributions generated from mouse stem cells transfected with reporter constructs containing transcriptional control elements of the Msx1 gene, a regulator of pluripotency, in mother and daughter cells. Furthermore, we show by tracking zebrafish PAC2 cells expressing FUCCI cell cycle markers, our framework can be easily adapted to different cell types and fluorescent markers. PMID:22194797

A medical software system for volumetric analysis of cerebral pathologies in magnetic resonance imaging (MRI) data.

PubMed

Egger, Jan; Kappus, Christoph; Freisleben, Bernd; Nimsky, Christopher

2012-08-01

In this contribution, a medical software system for volumetric analysis of different cerebral pathologies in magnetic resonance imaging (MRI) data is presented. The software system is based on a semi-automatic segmentation algorithm and helps to overcome the time-consuming process of volume determination during monitoring of a patient. After imaging, the parameter settings-including a seed point-are set up in the system and an automatic segmentation is performed by a novel graph-based approach. Manually reviewing the result leads to reseeding, adding seed points or an automatic surface mesh generation. The mesh is saved for monitoring the patient and for comparisons with follow-up scans. Based on the mesh, the system performs a voxelization and volume calculation, which leads to diagnosis and therefore further treatment decisions. The overall system has been tested with different cerebral pathologies-glioblastoma multiforme, pituitary adenomas and cerebral aneurysms- and evaluated against manual expert segmentations using the Dice Similarity Coefficient (DSC). Additionally, intra-physician segmentations have been performed to provide a quality measure for the presented system.

Brain tumor segmentation in MR slices using improved GrowCut algorithm

NASA Astrophysics Data System (ADS)

Ji, Chunhong; Yu, Jinhua; Wang, Yuanyuan; Chen, Liang; Shi, Zhifeng; Mao, Ying

2015-12-01

The detection of brain tumor from MR images is very significant for medical diagnosis and treatment. However, the existing methods are mostly based on manual or semiautomatic segmentation which are awkward when dealing with a large amount of MR slices. In this paper, a new fully automatic method for the segmentation of brain tumors in MR slices is presented. Based on the hypothesis of the symmetric brain structure, the method improves the interactive GrowCut algorithm by further using the bounding box algorithm in the pre-processing step. More importantly, local reflectional symmetry is used to make up the deficiency of the bounding box method. After segmentation, 3D tumor image is reconstructed. We evaluate the accuracy of the proposed method on MR slices with synthetic tumors and actual clinical MR images. Result of the proposed method is compared with the actual position of simulated 3D tumor qualitatively and quantitatively. In addition, our automatic method produces equivalent performance as manual segmentation and the interactive GrowCut with manual interference while providing fully automatic segmentation.

DoctorEye: A clinically driven multifunctional platform, for accurate processing of tumors in medical images.

PubMed

Skounakis, Emmanouil; Farmaki, Christina; Sakkalis, Vangelis; Roniotis, Alexandros; Banitsas, Konstantinos; Graf, Norbert; Marias, Konstantinos

2010-01-01

This paper presents a novel, open access interactive platform for 3D medical image analysis, simulation and visualization, focusing in oncology images. The platform was developed through constant interaction and feedback from expert clinicians integrating a thorough analysis of their requirements while having an ultimate goal of assisting in accurately delineating tumors. It allows clinicians not only to work with a large number of 3D tomographic datasets but also to efficiently annotate multiple regions of interest in the same session. Manual and semi-automatic segmentation techniques combined with integrated correction tools assist in the quick and refined delineation of tumors while different users can add different components related to oncology such as tumor growth and simulation algorithms for improving therapy planning. The platform has been tested by different users and over large number of heterogeneous tomographic datasets to ensure stability, usability, extensibility and robustness with promising results. the platform, a manual and tutorial videos are available at: http://biomodeling.ics.forth.gr. it is free to use under the GNU General Public License.

Automatic, semi-automatic and manual validation of urban drainage data.

PubMed

Branisavljević, N; Prodanović, D; Pavlović, D

2010-01-01

Advances in sensor technology and the possibility of automated long distance data transmission have made continuous measurements the preferable way of monitoring urban drainage processes. Usually, the collected data have to be processed by an expert in order to detect and mark the wrong data, remove them and replace them with interpolated data. In general, the first step in detecting the wrong, anomaly data is called the data quality assessment or data validation. Data validation consists of three parts: data preparation, validation scores generation and scores interpretation. This paper will present the overall framework for the data quality improvement system, suitable for automatic, semi-automatic or manual operation. The first two steps of the validation process are explained in more detail, using several validation methods on the same set of real-case data from the Belgrade sewer system. The final part of the validation process, which is the scores interpretation, needs to be further investigated on the developed system.

Research on oral test modeling based on multi-feature fusion

NASA Astrophysics Data System (ADS)

Shi, Yuliang; Tao, Yiyue; Lei, Jun

2018-04-01

In this paper, the spectrum of speech signal is taken as an input of feature extraction. The advantage of PCNN in image segmentation and other processing is used to process the speech spectrum and extract features. And a new method combining speech signal processing and image processing is explored. At the same time of using the features of the speech map, adding the MFCC to establish the spectral features and integrating them with the features of the spectrogram to further improve the accuracy of the spoken language recognition. Considering that the input features are more complicated and distinguishable, we use Support Vector Machine (SVM) to construct the classifier, and then compare the extracted test voice features with the standard voice features to achieve the spoken standard detection. Experiments show that the method of extracting features from spectrograms using PCNN is feasible, and the fusion of image features and spectral features can improve the detection accuracy.

Simultaneous extraction of centerlines, stenosis, and thrombus detection in renal CT angiography

NASA Astrophysics Data System (ADS)

Subramanyan, Krishna; Durgan, Jacob; Hodgkiss, Thomas D.; Chandra, Shalabh

2004-05-01

The Renal Artery Stenosis (RAS) is the major cause of renovascular hypertension and CT angiography has shown tremendous promise as a noninvasive method for reliably detecting renal artery stenosis. The purpose of this study was to validate the semi-automated methods to assist in extraction of renal branches and characterizing the associated renal artery stenosis. Automatically computed diagnostic images such as straight MIP, curved MPR, cross-sections, and diameters from multi-slice CT are presented and evaluated for its acceptance. We used vessel-tracking image processing methods to extract the aortic-renal vessel tree in a CT data in axial slice images. Next, from the topology and anatomy of the aortic vessel tree, the stenosis, and thrombus section and branching of the renal arteries are extracted. The results are presented in curved MPR and continuously variable MIP images. In this study, 15 patients were scanned with contrast on Mx8000 CT scanner (Philips Medical Systems), with 1.0 mm thickness, 0.5mm slice spacing, and 120kVp and a stack of 512x512x150 volume sets were reconstructed. The automated image processing took less than 50 seconds to compute the centerline and borders of the aortic/renal vessel tree. The overall assessment of manual and automatically generated stenosis yielded a weighted kappa statistic of 0.97 at right renal arteries, 0.94 at the left renal branches. The thrombus region contoured manually and semi-automatically agreed upon at 0.93. The manual time to process each case is approximately 25 to 30 minutes.

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

PubMed

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

2018-01-25

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

Automated Recognition of 3D Features in GPIR Images

NASA Technical Reports Server (NTRS)

Park, Han; Stough, Timothy; Fijany, Amir

2007-01-01

A method of automated recognition of three-dimensional (3D) features in images generated by ground-penetrating imaging radar (GPIR) is undergoing development. GPIR 3D images can be analyzed to detect and identify such subsurface features as pipes and other utility conduits. Until now, much of the analysis of GPIR images has been performed manually by expert operators who must visually identify and track each feature. The present method is intended to satisfy a need for more efficient and accurate analysis by means of algorithms that can automatically identify and track subsurface features, with minimal supervision by human operators. In this method, data from multiple sources (for example, data on different features extracted by different algorithms) are fused together for identifying subsurface objects. The algorithms of this method can be classified in several different ways. In one classification, the algorithms fall into three classes: (1) image-processing algorithms, (2) feature- extraction algorithms, and (3) a multiaxis data-fusion/pattern-recognition algorithm that includes a combination of machine-learning, pattern-recognition, and object-linking algorithms. The image-processing class includes preprocessing algorithms for reducing noise and enhancing target features for pattern recognition. The feature-extraction algorithms operate on preprocessed data to extract such specific features in images as two-dimensional (2D) slices of a pipe. Then the multiaxis data-fusion/ pattern-recognition algorithm identifies, classifies, and reconstructs 3D objects from the extracted features. In this process, multiple 2D features extracted by use of different algorithms and representing views along different directions are used to identify and reconstruct 3D objects. In object linking, which is an essential part of this process, features identified in successive 2D slices and located within a threshold radius of identical features in adjacent slices are linked in a directed-graph data structure. Relative to past approaches, this multiaxis approach offers the advantages of more reliable detections, better discrimination of objects, and provision of redundant information, which can be helpful in filling gaps in feature recognition by one of the component algorithms. The image-processing class also includes postprocessing algorithms that enhance identified features to prepare them for further scrutiny by human analysts (see figure). Enhancement of images as a postprocessing step is a significant departure from traditional practice, in which enhancement of images is a preprocessing step.

Interactive segmentation of tongue contours in ultrasound video sequences using quality maps

NASA Astrophysics Data System (ADS)

Ghrenassia, Sarah; Ménard, Lucie; Laporte, Catherine

2014-03-01

Ultrasound (US) imaging is an effective and non invasive way of studying the tongue motions involved in normal and pathological speech, and the results of US studies are of interest for the development of new strategies in speech therapy. State-of-the-art tongue shape analysis techniques based on US images depend on semi-automated tongue segmentation and tracking techniques. Recent work has mostly focused on improving the accuracy of the tracking techniques themselves. However, occasional errors remain inevitable, regardless of the technique used, and the tongue tracking process must thus be supervised by a speech scientist who will correct these errors manually or semi-automatically. This paper proposes an interactive framework to facilitate this process. In this framework, the user is guided towards potentially problematic portions of the US image sequence by a segmentation quality map that is based on the normalized energy of an active contour model and automatically produced during tracking. When a problematic segmentation is identified, corrections to the segmented contour can be made on one image and propagated both forward and backward in the problematic subsequence, thereby improving the user experience. The interactive tools were tested in combination with two different tracking algorithms. Preliminary results illustrate the potential of the proposed framework, suggesting that the proposed framework generally improves user interaction time, with little change in segmentation repeatability.

Image processing and analysis of Saturn's rings

NASA Technical Reports Server (NTRS)

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

1981-01-01

Processing of Voyager image data of Saturn's rings at JPL's Image Processing Laboratory is described. A software system to navigate the flight images, facilitate feature tracking, and to project the rings has been developed. This system has been used to make measurements of ring radii and to measure the velocities of the spoke features in the B-Ring. A projected ring movie to study the development of these spoke features has been generated. Finally, processing to facilitate comparison of the photometric properties of Saturn's rings at various phase angles is described.

Variability in CT lung-nodule quantification: Effects of dose reduction and reconstruction methods on density and texture based features.

PubMed

Lo, P; Young, S; Kim, H J; Brown, M S; McNitt-Gray, M F

2016-08-01

To investigate the effects of dose level and reconstruction method on density and texture based features computed from CT lung nodules. This study had two major components. In the first component, a uniform water phantom was scanned at three dose levels and images were reconstructed using four conventional filtered backprojection (FBP) and four iterative reconstruction (IR) methods for a total of 24 different combinations of acquisition and reconstruction conditions. In the second component, raw projection (sinogram) data were obtained for 33 lung nodules from patients scanned as a part of their clinical practice, where low dose acquisitions were simulated by adding noise to sinograms acquired at clinical dose levels (a total of four dose levels) and reconstructed using one FBP kernel and two IR kernels for a total of 12 conditions. For the water phantom, spherical regions of interest (ROIs) were created at multiple locations within the water phantom on one reference image obtained at a reference condition. For the lung nodule cases, the ROI of each nodule was contoured semiautomatically (with manual editing) from images obtained at a reference condition. All ROIs were applied to their corresponding images reconstructed at different conditions. For 17 of the nodule cases, repeat contours were performed to assess repeatability. Histogram (eight features) and gray level co-occurrence matrix (GLCM) based texture features (34 features) were computed for all ROIs. For the lung nodule cases, the reference condition was selected to be 100% of clinical dose with FBP reconstruction using the B45f kernel; feature values calculated from other conditions were compared to this reference condition. A measure was introduced, which the authors refer to as Q, to assess the stability of features across different conditions, which is defined as the ratio of reproducibility (across conditions) to repeatability (across repeat contours) of each feature. The water phantom results demonstrated substantial variability among feature values calculated across conditions, with the exception of histogram mean. Features calculated from lung nodules demonstrated similar results with histogram mean as the most robust feature (Q ≤ 1), having a mean and standard deviation Q of 0.37 and 0.22, respectively. Surprisingly, histogram standard deviation and variance features were also quite robust. Some GLCM features were also quite robust across conditions, namely, diff. variance, sum variance, sum average, variance, and mean. Except for histogram mean, all features have a Q of larger than one in at least one of the 3% dose level conditions. As expected, the histogram mean is the most robust feature in their study. The effects of acquisition and reconstruction conditions on GLCM features vary widely, though trending toward features involving summation of product between intensities and probabilities being more robust, barring a few exceptions. Overall, care should be taken into account for variation in density and texture features if a variety of dose and reconstruction conditions are used for the quantification of lung nodules in CT, otherwise changes in quantification results may be more reflective of changes due to acquisition and reconstruction conditions than in the nodule itself.

Documentation of procedures for textural/spatial pattern recognition techniques

NASA Technical Reports Server (NTRS)

Haralick, R. M.; Bryant, W. F.

1976-01-01

A C-130 aircraft was flown over the Sam Houston National Forest on March 21, 1973 at 10,000 feet altitude to collect multispectral scanner (MSS) data. Existing textural and spatial automatic processing techniques were used to classify the MSS imagery into specified timber categories. Several classification experiments were performed on this data using features selected from the spectral bands and a textural transform band. The results indicate that (1) spatial post-processing a classified image can cut the classification error to 1/2 or 1/3 of its initial value, (2) spatial post-processing the classified image using combined spectral and textural features produces a resulting image with less error than post-processing a classified image using only spectral features and (3) classification without spatial post processing using the combined spectral textural features tends to produce about the same error rate as a classification without spatial post processing using only spectral features.

Semiautomatic mapping of permafrost in the Yukon Flats, Alaska

NASA Astrophysics Data System (ADS)

Gulbrandsen, Mats Lundh; Minsley, Burke J.; Ball, Lyndsay B.; Hansen, Thomas Mejer

2016-12-01

Thawing of permafrost due to global warming can have major impacts on hydrogeological processes, climate feedback, arctic ecology, and local environments. To understand these effects and processes, it is crucial to know the distribution of permafrost. In this study we exploit the fact that airborne electromagnetic (AEM) data are sensitive to the distribution of permafrost and demonstrate how the distribution of permafrost in the Yukon Flats, Alaska, is mapped in an efficient (semiautomatic) way, using a combination of supervised and unsupervised (machine) learning algorithms, i.e., Smart Interpretation and K-means clustering. Clustering is used to sort unfrozen and frozen regions, and Smart Interpretation is used to predict the depth of permafrost based on expert interpretations. This workflow allows, for the first time, a quantitative and objective approach to efficiently map permafrost based on large amounts of AEM data.

Semiautomatic mapping of permafrost in the Yukon Flats, Alaska

USGS Publications Warehouse

Gulbrandsen, Mats Lundh; Minsley, Burke J.; Ball, Lyndsay B.; Hansen, Thomas Mejer

2016-01-01

Thawing of permafrost due to global warming can have major impacts on hydrogeological processes, climate feedback, arctic ecology, and local environments. To understand these effects and processes, it is crucial to know the distribution of permafrost. In this study we exploit the fact that airborne electromagnetic (AEM) data are sensitive to the distribution of permafrost and demonstrate how the distribution of permafrost in the Yukon Flats, Alaska, is mapped in an efficient (semiautomatic) way, using a combination of supervised and unsupervised (machine) learning algorithms, i.e., Smart Interpretation and K-means clustering. Clustering is used to sort unfrozen and frozen regions, and Smart Interpretation is used to predict the depth of permafrost based on expert interpretations. This workflow allows, for the first time, a quantitative and objective approach to efficiently map permafrost based on large amounts of AEM data.

An automatic multi-atlas prostate segmentation in MRI using a multiscale representation and a label fusion strategy

NASA Astrophysics Data System (ADS)

Álvarez, Charlens; Martínez, Fabio; Romero, Eduardo

2015-01-01

The pelvic magnetic Resonance images (MRI) are used in Prostate cancer radiotherapy (RT), a process which is part of the radiation planning. Modern protocols require a manual delineation, a tedious and variable activity that may take about 20 minutes per patient, even for trained experts. That considerable time is an important work ow burden in most radiological services. Automatic or semi-automatic methods might improve the efficiency by decreasing the measure times while conserving the required accuracy. This work presents a fully automatic atlas- based segmentation strategy that selects the more similar templates for a new MRI using a robust multi-scale SURF analysis. Then a new segmentation is achieved by a linear combination of the selected templates, which are previously non-rigidly registered towards the new image. The proposed method shows reliable segmentations, obtaining an average DICE Coefficient of 79%, when comparing with the expert manual segmentation, under a leave-one-out scheme with the training database.

"Proximal Sensing" capabilities for snow cover monitoring

NASA Astrophysics Data System (ADS)

Valt, Mauro; Salvatori, Rosamaria; Plini, Paolo; Salzano, Roberto; Giusti, Marco; Montagnoli, Mauro; Sigismondi, Daniele; Cagnati, Anselmo

2013-04-01

The seasonal snow cover represents one of the most important land cover class in relation to environmental studies in mountain areas, especially considering its variation during time. Snow cover and its extension play a relevant role for the studies on the atmospheric dynamics and the evolution of climate. It is also important for the analysis and management of water resources and for the management of touristic activities in mountain areas. Recently, webcam images collected at daily or even hourly intervals are being used as tools to observe the snow covered areas; those images, properly processed, can be considered a very important environmental data source. Images captured by digital cameras become a useful tool at local scale providing images even when the cloud coverage makes impossible the observation by satellite sensors. When suitably processed these images can be used for scientific purposes, having a good resolution (at least 800x600x16 million colours) and a very good sampling frequency (hourly images taken through the whole year). Once stored in databases, those images represent therefore an important source of information for the study of recent climatic changes, to evaluate the available water resources and to analyse the daily surface evolution of the snow cover. The Snow-noSnow software has been specifically designed to automatically detect the extension of snow cover collected from webcam images with a very limited human intervention. The software was tested on images collected on Alps (ARPAV webcam network) and on Apennine in a pilot station properly equipped for this project by CNR-IIA. The results obtained through the use of Snow-noSnow are comparable to the one achieved by photo-interpretation and could be considered as better as the ones obtained using the image segmentation routine implemented into image processing commercial softwares. Additionally, Snow-noSnow operates in a semi-automatic way and has a reduced processing time. The analysis of this kind of images could represent an useful element to support the interpretation of remote sensing images, especially those provided by high spatial resolution sensors. Keywords: snow cover monitoring, digital images, software, Alps, Apennines.

Semiautomatic estimation of breast density with DM-Scan software.

PubMed

Martínez Gómez, I; Casals El Busto, M; Antón Guirao, J; Ruiz Perales, F; Llobet Azpitarte, R

2014-01-01

To evaluate the reproducibility of the calculation of breast density with DM-Scan software, which is based on the semiautomatic segmentation of fibroglandular tissue, and to compare it with the reproducibility of estimation by visual inspection. The study included 655 direct digital mammograms acquired using craniocaudal projections. Three experienced radiologists analyzed the density of the mammograms using DM-Scan, and the inter- and intra-observer agreement between pairs of radiologists for the Boyd and BI-RADS® scales were calculated using the intraclass correlation coefficient. The Kappa index was used to compare the inter- and intra-observer agreements with those obtained previously for visual inspection in the same set of images. For visual inspection, the mean interobserver agreement was 0,876 (95% CI: 0,873-0,879) on the Boyd scale and 0,823 (95% CI: 0,818-0,829) on the BI-RADS® scale. The mean intraobserver agreement was 0,813 (95% CI: 0,796-0,829) on the Boyd scale and 0,770 (95% CI: 0,742-0,797) on the BI-RADS® scale. For DM-Scan, the mean inter- and intra-observer agreement was 0,92, considerably higher than the agreement for visual inspection. The semiautomatic calculation of breast density using DM-Scan software is more reliable and reproducible than visual estimation and reduces the subjectivity and variability in determining breast density. Copyright © 2012 SERAM. Published by Elsevier Espana. All rights reserved.

Large-scale classification of traffic signs under real-world conditions

NASA Astrophysics Data System (ADS)

Hazelhoff, Lykele; Creusen, Ivo; van de Wouw, Dennis; de With, Peter H. N.

2012-02-01

Traffic sign inventories are important to governmental agencies as they facilitate evaluation of traffic sign locations and are beneficial for road and sign maintenance. These inventories can be created (semi-)automatically based on street-level panoramic images. In these images, object detection is employed to detect the signs in each image, followed by a classification stage to retrieve the specific sign type. Classification of traffic signs is a complicated matter, since sign types are very similar with only minor differences within the sign, a high number of different signs is involved and multiple distortions occur, including variations in capturing conditions, occlusions, viewpoints and sign deformations. Therefore, we propose a method for robust classification of traffic signs, based on the Bag of Words approach for generic object classification. We extend the approach with a flexible, modular codebook to model the specific features of each sign type independently, in order to emphasize at the inter-sign differences instead of the parts common for all sign types. Additionally, this allows us to model and label the present false detections. Furthermore, analysis of the classification output provides the unreliable results. This classification system has been extensively tested for three different sign classes, covering 60 different sign types in total. These three data sets contain the sign detection results on street-level panoramic images, extracted from a country-wide database. The introduction of the modular codebook shows a significant improvement for all three sets, where the system is able to classify about 98% of the reliable results correctly.

Technical note: A simple approach for efficient collection of field reference data for calibrating remote sensing mapping of northern wetlands

NASA Astrophysics Data System (ADS)

Gålfalk, Magnus; Karlson, Martin; Crill, Patrick; Bousquet, Philippe; Bastviken, David

2018-03-01

The calibration and validation of remote sensing land cover products are highly dependent on accurate field reference data, which are costly and practically challenging to collect. We describe an optical method for collection of field reference data that is a fast, cost-efficient, and robust alternative to field surveys and UAV imaging. A lightweight, waterproof, remote-controlled RGB camera (GoPro HERO4 Silver, GoPro Inc.) was used to take wide-angle images from 3.1 to 4.5 m in altitude using an extendable monopod, as well as representative near-ground (< 1 m) images to identify spectral and structural features that correspond to various land covers in present lighting conditions. A semi-automatic classification was made based on six surface types (graminoids, water, shrubs, dry moss, wet moss, and rock). The method enables collection of detailed field reference data, which is critical in many remote sensing applications, such as satellite-based wetland mapping. The method uses common non-expensive equipment, does not require special skills or training, and is facilitated by a step-by-step manual that is included in the Supplement. Over time a global ground cover database can be built that can be used as reference data for studies of non-forested wetlands from satellites such as Sentinel 1 and 2 (10 m pixel size).

Conceptual design of semi-automatic wheelbarrow to overcome ergonomics problems among palm oil plantation workers

NASA Astrophysics Data System (ADS)

Nawik, N. S. M.; Deros, B. M.; Rahman, M. N. A.; Sukadarin, E. H.; Nordin, N.; Tamrin, S. B. M.; Bakar, S. A.; Norzan, M. L.

2015-12-01

An ergonomics problem is one of the main issues faced by palm oil plantation workers especially during harvesting and collecting of fresh fruit bunches (FFB). Intensive manual handling and labor activities involved have been associated with high prevalence of musculoskeletal disorders (MSDs) among palm oil plantation workers. New and safe technology on machines and equipment in palm oil plantation are very important in order to help workers reduce risks and injuries while working. The aim of this research is to improve the design of a wheelbarrow, which is suitable for workers and a small size oil palm plantation. The wheelbarrow design was drawn using CATIA ergonomic features. The characteristic of ergonomics assessment is performed by comparing the existing design of wheelbarrow. Conceptual design was developed based on the problems that have been reported by workers. From the analysis of the problem, finally have resulting concept design the ergonomic quality of semi-automatic wheelbarrow with safe and suitable used for palm oil plantation workers.

Processing Dynamic Image Sequences from a Moving Sensor.

DTIC Science & Technology

1984-02-01

65 Roadsign Image Sequence ..... ................ ... 70 Roadsign Sequence with Redundant Features .. ........ . 79 Roadsign Subimage...Selected Feature Error Values .. ........ 66 2c. Industrial Image Selected Feature Local Search Values. .. .... 67 3ab. Roadsign Image Error Values...72 3c. Roadsign Image Local Search Values ............. 73 4ab. Roadsign Redundant Feature Error Values. ............ 8 4c. Roadsign

Methodological development of topographic correction in 2D/3D ToF-SIMS images using AFM images

NASA Astrophysics Data System (ADS)

Jung, Seokwon; Lee, Nodo; Choi, Myungshin; Lee, Jungmin; Cho, Eunkyunng; Joo, Minho

2018-02-01

Time-of-flight secondary-ion mass spectrometry (ToF-SIMS) is an emerging technique that provides chemical information directly from the surface of electronic materials, e.g. OLED and solar cell. It is very versatile and highly sensitive mass spectrometric technique that provides surface molecular information with their lateral distribution as a two-dimensional (2D) molecular image. Extending the usefulness of ToF-SIMS, a 3D molecular image can be generated by acquiring multiple 2D images in a stack. These imaging techniques by ToF-SIMS provide an insight into understanding the complex structures of unknown composition in electronic material. However, one drawback in ToF-SIMS is not able to represent topographical information in 2D and 3D mapping images. To overcome this technical limitation, topographic information by ex-situ technique such as atomic force microscopy (AFM) has been combined with chemical information from SIMS that provides both chemical and physical information in one image. The key to combine two different images obtained from ToF-SIMS and AFM techniques is to develop the image processing algorithm, which performs resize and alignment by comparing the specific pixel information of each image. In this work, we present methodological development of the semiautomatic alignment and the 3D structure interpolation system for the combination of 2D/3D images obtained by ToF-SIMS and AFM measurements, which allows providing useful analytical information in a single representation.

27 CFR 478.132 - Dispositions of semiautomatic assault weapons and large capacity ammunition feeding devices to...

Code of Federal Regulations, 2012 CFR

2012-04-01

... semiautomatic assault weapons and large capacity ammunition feeding devices to law enforcement officers for... assault weapons and large capacity ammunition feeding devices to law enforcement officers for official use... licensed dealers in semiautomatic assault weapons, as well as persons who manufacture, import, or deal in...

27 CFR 478.132 - Dispositions of semiautomatic assault weapons and large capacity ammunition feeding devices to...

Code of Federal Regulations, 2013 CFR

2013-04-01

... semiautomatic assault weapons and large capacity ammunition feeding devices to law enforcement officers for... assault weapons and large capacity ammunition feeding devices to law enforcement officers for official use... licensed dealers in semiautomatic assault weapons, as well as persons who manufacture, import, or deal in...

27 CFR 478.132 - Dispositions of semiautomatic assault weapons and large capacity ammunition feeding devices to...

Code of Federal Regulations, 2014 CFR

2014-04-01

... semiautomatic assault weapons and large capacity ammunition feeding devices to law enforcement officers for... assault weapons and large capacity ammunition feeding devices to law enforcement officers for official use... licensed dealers in semiautomatic assault weapons, as well as persons who manufacture, import, or deal in...

27 CFR 478.132 - Dispositions of semiautomatic assault weapons and large capacity ammunition feeding devices to...

Code of Federal Regulations, 2011 CFR

2011-04-01

... semiautomatic assault weapons and large capacity ammunition feeding devices to law enforcement officers for... assault weapons and large capacity ammunition feeding devices to law enforcement officers for official use... licensed dealers in semiautomatic assault weapons, as well as persons who manufacture, import, or deal in...

27 CFR 478.39 - Assembly of semiautomatic rifles or shotguns.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 27 Alcohol, Tobacco Products and Firearms 3 2011-04-01 2010-04-01 true Assembly of semiautomatic... AMMUNITION Administrative and Miscellaneous Provisions § 478.39 Assembly of semiautomatic rifles or shotguns.... (b) The provisions of this section shall not apply to: (1) The assembly of such rifle or shotgun for...

27 CFR 478.39 - Assembly of semiautomatic rifles or shotguns.

Code of Federal Regulations, 2014 CFR

2014-04-01

... 27 Alcohol, Tobacco Products and Firearms 3 2014-04-01 2014-04-01 false Assembly of semiautomatic... AMMUNITION Administrative and Miscellaneous Provisions § 478.39 Assembly of semiautomatic rifles or shotguns.... (b) The provisions of this section shall not apply to: (1) The assembly of such rifle or shotgun for...

27 CFR 478.39 - Assembly of semiautomatic rifles or shotguns.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 27 Alcohol, Tobacco Products and Firearms 3 2010-04-01 2010-04-01 false Assembly of semiautomatic... AMMUNITION Administrative and Miscellaneous Provisions § 478.39 Assembly of semiautomatic rifles or shotguns.... (b) The provisions of this section shall not apply to: (1) The assembly of such rifle or shotgun for...

27 CFR 478.39 - Assembly of semiautomatic rifles or shotguns.

Code of Federal Regulations, 2013 CFR

2013-04-01

... 27 Alcohol, Tobacco Products and Firearms 3 2013-04-01 2013-04-01 false Assembly of semiautomatic... AMMUNITION Administrative and Miscellaneous Provisions § 478.39 Assembly of semiautomatic rifles or shotguns.... (b) The provisions of this section shall not apply to: (1) The assembly of such rifle or shotgun for...

27 CFR 478.39 - Assembly of semiautomatic rifles or shotguns.

Code of Federal Regulations, 2012 CFR

2012-04-01

... 27 Alcohol, Tobacco Products and Firearms 3 2012-04-01 2010-04-01 true Assembly of semiautomatic... AMMUNITION Administrative and Miscellaneous Provisions § 478.39 Assembly of semiautomatic rifles or shotguns.... (b) The provisions of this section shall not apply to: (1) The assembly of such rifle or shotgun for...

Quantitative 3-D Imaging, Segmentation and Feature Extraction of the Respiratory System in Small Mammals for Computational Biophysics Simulations

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

Trease, Lynn L.; Trease, Harold E.; Fowler, John

2007-03-15

One of the critical steps toward performing computational biology simulations, using mesh based integration methods, is in using topologically faithful geometry derived from experimental digital image data as the basis for generating the computational meshes. Digital image data representations contain both the topology of the geometric features and experimental field data distributions. The geometric features that need to be captured from the digital image data are three-dimensional, therefore the process and tools we have developed work with volumetric image data represented as data-cubes. This allows us to take advantage of 2D curvature information during the segmentation and feature extraction process.more » The process is basically: 1) segmenting to isolate and enhance the contrast of the features that we wish to extract and reconstruct, 2) extracting the geometry of the features in an isosurfacing technique, and 3) building the computational mesh using the extracted feature geometry. “Quantitative” image reconstruction and feature extraction is done for the purpose of generating computational meshes, not just for producing graphics "screen" quality images. For example, the surface geometry that we extract must represent a closed water-tight surface.« less

New Common Proper-Motion Pairs with R.A. Between 00h and 01h

NASA Astrophysics Data System (ADS)

Caballero, Rafael

2015-07-01

This paper presents 37 new common proper-motion pairs. The new pairs have been obtained employing a semi-automatic procedure based on the inspection of images using the tool Aladin, completed with information obtained from the catalogs available at VizieR. All the pairs fulfill the Halbwachs criteria, employed to increase the probability of a physical bond between the two components.

[Image Feature Extraction and Discriminant Analysis of Xinjiang Uygur Medicine Based on Color Histogram].

PubMed

Hamit, Murat; Yun, Weikang; Yan, Chuanbo; Kutluk, Abdugheni; Fang, Yang; Alip, Elzat

2015-06-01

Image feature extraction is an important part of image processing and it is an important field of research and application of image processing technology. Uygur medicine is one of Chinese traditional medicine and researchers pay more attention to it. But large amounts of Uygur medicine data have not been fully utilized. In this study, we extracted the image color histogram feature of herbal and zooid medicine of Xinjiang Uygur. First, we did preprocessing, including image color enhancement, size normalizition and color space transformation. Then we extracted color histogram feature and analyzed them with statistical method. And finally, we evaluated the classification ability of features by Bayes discriminant analysis. Experimental results showed that high accuracy for Uygur medicine image classification was obtained by using color histogram feature. This study would have a certain help for the content-based medical image retrieval for Xinjiang Uygur medicine.

Online coupled camera pose estimation and dense reconstruction from video

DOEpatents

Medioni, Gerard; Kang, Zhuoliang

2016-11-01

A product may receive each image in a stream of video image of a scene, and before processing the next image, generate information indicative of the position and orientation of an image capture device that captured the image at the time of capturing the image. The product may do so by identifying distinguishable image feature points in the image; determining a coordinate for each identified image feature point; and for each identified image feature point, attempting to identify one or more distinguishable model feature points in a three dimensional (3D) model of at least a portion of the scene that appears likely to correspond to the identified image feature point. Thereafter, the product may find each of the following that, in combination, produce a consistent projection transformation of the 3D model onto the image: a subset of the identified image feature points for which one or more corresponding model feature points were identified; and, for each image feature point that has multiple likely corresponding model feature points, one of the corresponding model feature points. The product may update a 3D model of at least a portion of the scene following the receipt of each video image and before processing the next video image base on the generated information indicative of the position and orientation of the image capture device at the time of capturing the received image. The product may display the updated 3D model after each update to the model.

Street curb recognition in 3d point cloud data using morphological operations

NASA Astrophysics Data System (ADS)

Rodríguez-Cuenca, Borja; Concepción Alonso-Rodríguez, María; García-Cortés, Silverio; Ordóñez, Celestino

2015-04-01

Accurate and automatic detection of cartographic-entities saves a great deal of time and money when creating and updating cartographic databases. The current trend in remote sensing feature extraction is to develop methods that are as automatic as possible. The aim is to develop algorithms that can obtain accurate results with the least possible human intervention in the process. Non-manual curb detection is an important issue in road maintenance, 3D urban modeling, and autonomous navigation fields. This paper is focused on the semi-automatic recognition of curbs and street boundaries using a 3D point cloud registered by a mobile laser scanner (MLS) system. This work is divided into four steps. First, a coordinate system transformation is carried out, moving from a global coordinate system to a local one. After that and in order to simplify the calculations involved in the procedure, a rasterization based on the projection of the measured point cloud on the XY plane was carried out, passing from the 3D original data to a 2D image. To determine the location of curbs in the image, different image processing techniques such as thresholding and morphological operations were applied. Finally, the upper and lower edges of curbs are detected by an unsupervised classification algorithm on the curvature and roughness of the points that represent curbs. The proposed method is valid in both straight and curved road sections and applicable both to laser scanner and stereo vision 3D data due to the independence of its scanning geometry. This method has been successfully tested with two datasets measured by different sensors. The first dataset corresponds to a point cloud measured by a TOPCON sensor in the Spanish town of Cudillero. That point cloud comprises more than 6,000,000 points and covers a 400-meter street. The second dataset corresponds to a point cloud measured by a RIEGL sensor in the Austrian town of Horn. That point cloud comprises 8,000,000 points and represents a 160-meter street. The proposed method provides success rates in curb recognition of over 85% in both datasets.

Deceleration and dispersion of large-scale coronal bright fronts

NASA Astrophysics Data System (ADS)

Long, D. M.; Gallagher, P. T.; McAteer, R. T. J.; Bloomfield, D. S.

2011-07-01

Context. One of the most dramatic manifestations of solar activity are large-scale coronal bright fronts (CBFs) observed in extreme ultraviolet (EUV) images of the solar atmosphere. To date, the energetics and kinematics of CBFs remain poorly understood, due to the low image cadence and sensitivity of previous EUV imagers and the limited methods used to extract the features. Aims: In this paper, the trajectory and morphology of CBFs was determined in order to investigate the varying properties of a sample of CBFs, including their kinematics and pulse shape, dispersion, and dissipation. Methods: We have developed a semi-automatic intensity profiling technique to extract the morphology and accurate positions of CBFs in 2.5-10 min cadence images from STEREO/EUVI. The technique was applied to sequences of 171 Å and 195 Å images from STEREO/EUVI in order to measure the wave properties of four separate CBF events. Results: Following launch at velocities of ~240-450 km s-1 each of the four events studied showed significant negative acceleration ranging from ~-290 to -60 m s-2. The CBF spatial and temporal widths were found to increase from ~50 Mm to ~200 Mm and ~100 s to ~1500 s respectively, suggesting that they are dispersive in nature. The variation in position-angle averaged pulse-integrated intensity with propagation shows no clear trend across the four events studied. These results are most consistent with CBFs being dispersive magnetoacoustic waves. Figures 3-8, 10, 11, 13-15, 17, 18 and the movie are available in electronic form at http://www.aanda.org

Imaging Foveal Microvasculature: Optical Coherence Tomography Angiography Versus Adaptive Optics Scanning Light Ophthalmoscope Fluorescein Angiography.

PubMed

Mo, Shelley; Krawitz, Brian; Efstathiadis, Eleni; Geyman, Lawrence; Weitz, Rishard; Chui, Toco Y P; Carroll, Joseph; Dubra, Alfredo; Rosen, Richard B

2016-07-01

To compare the use of optical coherence tomography angiography (OCTA) and adaptive optics scanning light ophthalmoscope fluorescein angiography (AOSLO FA) for characterizing the foveal microvasculature in healthy and vasculopathic eyes. Four healthy controls and 11 vasculopathic patients (4 diabetic retinopathy, 4 retinal vein occlusion, and 3 sickle cell retinopathy) were imaged with OCTA and AOSLO FA. Foveal perfusion maps were semiautomatically skeletonized for quantitative analysis, which included foveal avascular zone (FAZ) metrics (area, perimeter, acircularity index) and vessel density in three concentric annular regions of interest. On each set of OCTA and AOSLO FA images, matching vessel segments were used for lumen diameter measurement. Qualitative image comparisons were performed by visual identification of microaneurysms, vessel loops, leakage, and vessel segments. Adaptive optics scanning light ophthalmoscope FA and OCTA showed no statistically significant differences in FAZ perimeter, acircularity index, and vessel densities. Foveal avascular zone area, however, showed a small but statistically significant difference of 1.8% (P = 0.004). Lumen diameter was significantly larger on OCTA (mean difference 5.7 μm, P < 0.001). Microaneurysms, fine structure of vessel loops, leakage, and some vessel segments were visible on AOSLO FA but not OCTA, while blood vessels obscured by leakage were visible only on OCTA. Optical coherence tomography angiography is comparable to AOSLO FA at imaging the foveal microvasculature except for differences in FAZ area, lumen diameter, and some qualitative features. These results, together with its ease of use, short acquisition time, and avoidance of potentially phototoxic blue light, support OCTA as a tool for monitoring ocular pathology and detecting early disease.

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

PubMed

Chang, Yu-Ning; Chang, Herng-Hua

2015-01-01

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

A new metric for detecting change in slowly evolving brain tumors: validation in meningioma patients.

PubMed

Pohl, Kilian M; Konukoglu, Ender; Novellas, Sebastian; Ayache, Nicholas; Fedorov, Andriy; Talos, Ion-Florin; Golby, Alexandra; Wells, William M; Kikinis, Ron; Black, Peter M

2011-03-01

Change detection is a critical component in the diagnosis and monitoring of many slowly evolving pathologies. This article describes a semiautomatic monitoring approach using longitudinal medical images. We test the method on brain scans of patients with meningioma, which experts have found difficult to monitor because the tumor evolution is very slow and may be obscured by artifacts related to image acquisition. We describe a semiautomatic procedure targeted toward identifying difficult-to-detect changes in brain tumor imaging. The tool combines input from a medical expert with state-of-the-art technology. The software is easy to calibrate and, in less than 5 minutes, returns the total volume of tumor change in mm. We test the method on postgadolinium, T1-weighted magnetic resonance images of 10 patients with meningioma and compare our results with experts' findings. We also perform benchmark testing with synthetic data. Our experiments indicated that experts' visual inspections are not sensitive enough to detect subtle growth. Measurements based on experts' manual segmentations were highly accurate but also labor intensive. The accuracy of our approach was comparable to the experts' results. However, our approach required far less user input and generated more consistent measurements. The sensitivity of experts' visual inspection is often too low to detect subtle growth of meningiomas from longitudinal scans. Measurements based on experts' segmentation are highly accurate but generally too labor intensive for standard clinical settings. We described an alternative metric that provides accurate and robust measurements of subtle tumor changes while requiring a minimal amount of user input.

Mixed deep learning and natural language processing method for fake-food image recognition and standardization to help automated dietary assessment.

PubMed

Mezgec, Simon; Eftimov, Tome; Bucher, Tamara; Koroušić Seljak, Barbara

2018-04-06

The present study tested the combination of an established and a validated food-choice research method (the 'fake food buffet') with a new food-matching technology to automate the data collection and analysis. The methodology combines fake-food image recognition using deep learning and food matching and standardization based on natural language processing. The former is specific because it uses a single deep learning network to perform both the segmentation and the classification at the pixel level of the image. To assess its performance, measures based on the standard pixel accuracy and Intersection over Union were applied. Food matching firstly describes each of the recognized food items in the image and then matches the food items with their compositional data, considering both their food names and their descriptors. The final accuracy of the deep learning model trained on fake-food images acquired by 124 study participants and providing fifty-five food classes was 92·18 %, while the food matching was performed with a classification accuracy of 93 %. The present findings are a step towards automating dietary assessment and food-choice research. The methodology outperforms other approaches in pixel accuracy, and since it is the first automatic solution for recognizing the images of fake foods, the results could be used as a baseline for possible future studies. As the approach enables a semi-automatic description of recognized food items (e.g. with respect to FoodEx2), these can be linked to any food composition database that applies the same classification and description system.

Object-oriented software design in semiautomatic building extraction

NASA Astrophysics Data System (ADS)

Guelch, Eberhard; Mueller, Hardo

1997-08-01

Developing a system for semiautomatic building acquisition is a complex process, that requires constant integration and updating of software modules and user interfaces. To facilitate these processes we apply an object-oriented design not only for the data but also for the software involved. We use the unified modeling language (UML) to describe the object-oriented modeling of the system in different levels of detail. We can distinguish between use cases from the users point of view, that represent a sequence of actions, yielding in an observable result and the use cases for the programmers, who can use the system as a class library to integrate the acquisition modules in their own software. The structure of the system is based on the model-view-controller (MVC) design pattern. An example from the integration of automated texture extraction for the visualization of results demonstrate the feasibility of this approach.

Data processing software suite SITENNO for coherent X-ray diffraction imaging using the X-ray free-electron laser SACLA.

PubMed

Sekiguchi, Yuki; Oroguchi, Tomotaka; Takayama, Yuki; Nakasako, Masayoshi

2014-05-01

Coherent X-ray diffraction imaging is a promising technique for visualizing the structures of non-crystalline particles with dimensions of micrometers to sub-micrometers. Recently, X-ray free-electron laser sources have enabled efficient experiments in the `diffraction before destruction' scheme. Diffraction experiments have been conducted at SPring-8 Angstrom Compact free-electron LAser (SACLA) using the custom-made diffraction apparatus KOTOBUKI-1 and two multiport CCD detectors. In the experiments, ten thousands of single-shot diffraction patterns can be collected within several hours. Then, diffraction patterns with significant levels of intensity suitable for structural analysis must be found, direct-beam positions in diffraction patterns determined, diffraction patterns from the two CCD detectors merged, and phase-retrieval calculations for structural analyses performed. A software suite named SITENNO has been developed to semi-automatically apply the four-step processing to a huge number of diffraction data. Here, details of the algorithm used in the suite are described and the performance for approximately 9000 diffraction patterns collected from cuboid-shaped copper oxide particles reported. Using the SITENNO suite, it is possible to conduct experiments with data processing immediately after the data collection, and to characterize the size distribution and internal structures of the non-crystalline particles.

Data processing software suite SITENNO for coherent X-ray diffraction imaging using the X-ray free-electron laser SACLA

PubMed Central

Sekiguchi, Yuki; Oroguchi, Tomotaka; Takayama, Yuki; Nakasako, Masayoshi

2014-01-01

Coherent X-ray diffraction imaging is a promising technique for visualizing the structures of non-crystalline particles with dimensions of micrometers to sub-micrometers. Recently, X-ray free-electron laser sources have enabled efficient experiments in the ‘diffraction before destruction’ scheme. Diffraction experiments have been conducted at SPring-8 Angstrom Compact free-electron LAser (SACLA) using the custom-made diffraction apparatus KOTOBUKI-1 and two multiport CCD detectors. In the experiments, ten thousands of single-shot diffraction patterns can be collected within several hours. Then, diffraction patterns with significant levels of intensity suitable for structural analysis must be found, direct-beam positions in diffraction patterns determined, diffraction patterns from the two CCD detectors merged, and phase-retrieval calculations for structural analyses performed. A software suite named SITENNO has been developed to semi-automatically apply the four-step processing to a huge number of diffraction data. Here, details of the algorithm used in the suite are described and the performance for approximately 9000 diffraction patterns collected from cuboid-shaped copper oxide particles reported. Using the SITENNO suite, it is possible to conduct experiments with data processing immediately after the data collection, and to characterize the size distribution and internal structures of the non-crystalline particles. PMID:24763651

Textural features for radar image analysis

NASA Technical Reports Server (NTRS)

Shanmugan, K. S.; Narayanan, V.; Frost, V. S.; Stiles, J. A.; Holtzman, J. C.

1981-01-01

Texture is seen as an important spatial feature useful for identifying objects or regions of interest in an image. While textural features have been widely used in analyzing a variety of photographic images, they have not been used in processing radar images. A procedure for extracting a set of textural features for characterizing small areas in radar images is presented, and it is shown that these features can be used in classifying segments of radar images corresponding to different geological formations.

Hybrid Discrete Wavelet Transform and Gabor Filter Banks Processing for Features Extraction from Biomedical Images

PubMed Central

Lahmiri, Salim; Boukadoum, Mounir

2013-01-01

A new methodology for automatic feature extraction from biomedical images and subsequent classification is presented. The approach exploits the spatial orientation of high-frequency textural features of the processed image as determined by a two-step process. First, the two-dimensional discrete wavelet transform (DWT) is applied to obtain the HH high-frequency subband image. Then, a Gabor filter bank is applied to the latter at different frequencies and spatial orientations to obtain new Gabor-filtered image whose entropy and uniformity are computed. Finally, the obtained statistics are fed to a support vector machine (SVM) binary classifier. The approach was validated on mammograms, retina, and brain magnetic resonance (MR) images. The obtained classification accuracies show better performance in comparison to common approaches that use only the DWT or Gabor filter banks for feature extraction. PMID:27006906

Tumor propagation model using generalized hidden Markov model

NASA Astrophysics Data System (ADS)

Park, Sun Young; Sargent, Dustin

2017-02-01

Tumor tracking and progression analysis using medical images is a crucial task for physicians to provide accurate and efficient treatment plans, and monitor treatment response. Tumor progression is tracked by manual measurement of tumor growth performed by radiologists. Several methods have been proposed to automate these measurements with segmentation, but many current algorithms are confounded by attached organs and vessels. To address this problem, we present a new generalized tumor propagation model considering time-series prior images and local anatomical features using a Hierarchical Hidden Markov model (HMM) for tumor tracking. First, we apply the multi-atlas segmentation technique to identify organs/sub-organs using pre-labeled atlases. Second, we apply a semi-automatic direct 3D segmentation method to label the initial boundary between the lesion and neighboring structures. Third, we detect vessels in the ROI surrounding the lesion. Finally, we apply the propagation model with the labeled organs and vessels to accurately segment and measure the target lesion. The algorithm has been designed in a general way to be applicable to various body parts and modalities. In this paper, we evaluate the proposed algorithm on lung and lung nodule segmentation and tracking. We report the algorithm's performance by comparing the longest diameter and nodule volumes using the FDA lung Phantom data and a clinical dataset.

An image-processing method to detect sub-optical features based on understanding noise in intensity measurements.

PubMed

Bhatia, Tripta

2018-07-01

Accurate quantitative analysis of image data requires that we distinguish between fluorescence intensity (true signal) and the noise inherent to its measurements to the extent possible. We image multilamellar membrane tubes and beads that grow from defects in the fluid lamellar phase of the lipid 1,2-dioleoyl-sn-glycero-3-phosphocholine dissolved in water and water-glycerol mixtures by using fluorescence confocal polarizing microscope. We quantify image noise and determine the noise statistics. Understanding the nature of image noise also helps in optimizing image processing to detect sub-optical features, which would otherwise remain hidden. We use an image-processing technique "optimum smoothening" to improve the signal-to-noise ratio of features of interest without smearing their structural details. A high SNR renders desired positional accuracy with which it is possible to resolve features of interest with width below optical resolution. Using optimum smoothening, the smallest and the largest core diameter detected is of width [Formula: see text] and [Formula: see text] nm, respectively, discussed in this paper. The image-processing and analysis techniques and the noise modeling discussed in this paper can be used for detailed morphological analysis of features down to sub-optical length scales that are obtained by any kind of fluorescence intensity imaging in the raster mode.

Miniaturization of the Clonogenic Assay Using Confluence Measurement

PubMed Central

Mayr, Christian; Beyreis, Marlena; Dobias, Heidemarie; Gaisberger, Martin; Pichler, Martin; Ritter, Markus; Jakab, Martin; Neureiter, Daniel; Kiesslich, Tobias

2018-01-01

The clonogenic assay is a widely used method to study the ability of cells to ‘infinitely’ produce progeny and is, therefore, used as a tool in tumor biology to measure tumor-initiating capacity and stem cell status. However, the standard protocol of using 6-well plates has several disadvantages. By miniaturizing the assay to a 96-well microplate format, as well as by utilizing the confluence detection function of a multimode reader, we here describe a new and modified protocol that allows comprehensive experimental setups and a non-endpoint, label-free semi-automatic analysis. Comparison of bright field images with confluence images demonstrated robust and reproducible detection of clones by the confluence detection function. Moreover, time-resolved non-endpoint confluence measurement of the same well showed that semi-automatic analysis was suitable for determining the mean size and colony number. By treating cells with an inhibitor of clonogenic growth (PTC-209), we show that our modified protocol is suitable for comprehensive (broad concentration range, addition of technical replicates) concentration- and time-resolved analysis of the effect of substances or treatments on clonogenic growth. In summary, this protocol represents a time- and cost-effective alternative to the commonly used 6-well protocol (with endpoint staining) and also provides additional information about the kinetics of clonogenic growth. PMID:29510509

Computer-aided diagnosis of lung cancer: definition and detection of ground-glass opacity type of nodules by high-resolution computed tomography.

PubMed

Okada, Tohru; Iwano, Shingo; Ishigaki, Takeo; Kitasaka, Takayuki; Hirano, Yasushi; Mori, Kensaku; Suenaga, Yasuhito; Naganawa, Shinji

2009-02-01

The ground-glass opacity (GGO) of lung cancer is identified only subjectively on computed tomography (CT) images as no quantitative characteristic has been defined for GGOs. We sought to define GGOs quantitatively and to differentiate between GGOs and solid-type lung cancers semiautomatically with a computer-aided diagnosis (CAD). High-resolution CT images of 100 pulmonary nodules (all peripheral lung cancers) were collected from our clinical records. Two radiologists traced the contours of nodules and distinguished GGOs from solid areas. The CT attenuation value of each area was measured. Differentiation between cancer types was assessed by a receiver-operating characteristic (ROC) analysis. The mean CT attenuation of the GGO areas was -618.4 +/- 212.2 HU, whereas that of solid areas was -68.1 +/- 230.3 HU. CAD differentiated between solidand GGO-type lung cancers with a sensitivity of 86.0% and specificity of 96.5% when the threshold value was -370 HU. Four nodules of mixed GGOs were incorrectly classified as the solid type. CAD detected 96.3% of GGO areas when the threshold between GGO and solid areas was 194 HU. Objective definition of GGO area by CT attenuation is feasible. This method is useful for semiautomatic differentiation between GGOs and solid types of lung cancer.

Classification of malignant and benign liver tumors using a radiomics approach

NASA Astrophysics Data System (ADS)

Starmans, Martijn P. A.; Miclea, Razvan L.; van der Voort, Sebastian R.; Niessen, Wiro J.; Thomeer, Maarten G.; Klein, Stefan

2018-03-01

Correct diagnosis of the liver tumor phenotype is crucial for treatment planning, especially the distinction between malignant and benign lesions. Clinical practice includes manual scoring of the tumors on Magnetic Resonance (MR) images by a radiologist. As this is challenging and subjective, it is often followed by a biopsy. In this study, we propose a radiomics approach as an objective and non-invasive alternative for distinguishing between malignant and benign phenotypes. T2-weighted (T2w) MR sequences of 119 patients from multiple centers were collected. We developed an efficient semi-automatic segmentation method, which was used by a radiologist to delineate the tumors. Within these regions, features quantifying tumor shape, intensity, texture, heterogeneity and orientation were extracted. Patient characteristics and semantic features were added for a total of 424 features. Classification was performed using Support Vector Machines (SVMs). The performance was evaluated using internal random-split cross-validation. On the training set within each iteration, feature selection and hyperparameter optimization were performed. To this end, another cross validation was performed by splitting the training sets in training and validation parts. The optimal settings were evaluated on the independent test sets. Manual scoring by a radiologist was also performed. The radiomics approach resulted in 95% confidence intervals of the AUC of [0.75, 0.92], specificity [0.76, 0.96] and sensitivity [0.52, 0.82]. These approach the performance of the radiologist, which were an AUC of 0.93, specificity 0.70 and sensitivity 0.93. Hence, radiomics has the potential to predict the liver tumor benignity in an objective and non-invasive manner.

Feature extraction from multiple data sources using genetic programming

NASA Astrophysics Data System (ADS)

Szymanski, John J.; Brumby, Steven P.; Pope, Paul A.; Eads, Damian R.; Esch-Mosher, Diana M.; Galassi, Mark C.; Harvey, Neal R.; McCulloch, Hersey D.; Perkins, Simon J.; Porter, Reid B.; Theiler, James P.; Young, Aaron C.; Bloch, Jeffrey J.; David, Nancy A.

2002-08-01

Feature extraction from imagery is an important and long-standing problem in remote sensing. In this paper, we report on work using genetic programming to perform feature extraction simultaneously from multispectral and digital elevation model (DEM) data. We use the GENetic Imagery Exploitation (GENIE) software for this purpose, which produces image-processing software that inherently combines spatial and spectral processing. GENIE is particularly useful in exploratory studies of imagery, such as one often does in combining data from multiple sources. The user trains the software by painting the feature of interest with a simple graphical user interface. GENIE then uses genetic programming techniques to produce an image-processing pipeline. Here, we demonstrate evolution of image processing algorithms that extract a range of land cover features including towns, wildfire burnscars, and forest. We use imagery from the DOE/NNSA Multispectral Thermal Imager (MTI) spacecraft, fused with USGS 1:24000 scale DEM data.

Microscopic validation of whole mouse micro-metastatic tumor imaging agents using cryo-imaging and sliding organ image registration.

PubMed

Liu, Yiqiao; Zhou, Bo; Qutaish, Mohammed; Wilson, David L

2016-01-01

We created a metastasis imaging, analysis platform consisting of software and multi-spectral cryo-imaging system suitable for evaluating emerging imaging agents targeting micro-metastatic tumor. We analyzed CREKA-Gd in MRI, followed by cryo-imaging which repeatedly sectioned and tiled microscope images of the tissue block face, providing anatomical bright field and molecular fluorescence, enabling 3D microscopic imaging of the entire mouse with single metastatic cell sensitivity. To register MRI volumes to the cryo bright field reference, we used our standard mutual information, non-rigid registration which proceeded: preprocess → affine → B-spline non-rigid 3D registration. In this report, we created two modified approaches: mask where we registered locally over a smaller rectangular solid, and sliding organ . Briefly, in sliding organ , we segmented the organ, registered the organ and body volumes separately and combined results. Though s liding organ required manual annotation, it provided the best result as a standard to measure other registration methods. Regularization parameters for standard and mask methods were optimized in a grid search. Evaluations consisted of DICE, and visual scoring of a checkerboard display. Standard had accuracy of 2 voxels in all regions except near the kidney, where there were 5 voxels sliding. After mask and sliding organ correction, kidneys sliding were within 2 voxels, and Dice overlap increased 4%-10% in mask compared to standard . Mask generated comparable results with sliding organ and allowed a semi-automatic process.

Microscopic validation of whole mouse micro-metastatic tumor imaging agents using cryo-imaging and sliding organ image registration

NASA Astrophysics Data System (ADS)

Liu, Yiqiao; Zhou, Bo; Qutaish, Mohammed; Wilson, David L.

2016-03-01

We created a metastasis imaging, analysis platform consisting of software and multi-spectral cryo-imaging system suitable for evaluating emerging imaging agents targeting micro-metastatic tumor. We analyzed CREKA-Gd in MRI, followed by cryo-imaging which repeatedly sectioned and tiled microscope images of the tissue block face, providing anatomical bright field and molecular fluorescence, enabling 3D microscopic imaging of the entire mouse with single metastatic cell sensitivity. To register MRI volumes to the cryo bright field reference, we used our standard mutual information, non-rigid registration which proceeded: preprocess --> affine --> B-spline non-rigid 3D registration. In this report, we created two modified approaches: mask where we registered locally over a smaller rectangular solid, and sliding organ. Briefly, in sliding organ, we segmented the organ, registered the organ and body volumes separately and combined results. Though sliding organ required manual annotation, it provided the best result as a standard to measure other registration methods. Regularization parameters for standard and mask methods were optimized in a grid search. Evaluations consisted of DICE, and visual scoring of a checkerboard display. Standard had accuracy of 2 voxels in all regions except near the kidney, where there were 5 voxels sliding. After mask and sliding organ correction, kidneys sliding were within 2 voxels, and Dice overlap increased 4%-10% in mask compared to standard. Mask generated comparable results with sliding organ and allowed a semi-automatic process.

SKL algorithm based fabric image matching and retrieval

NASA Astrophysics Data System (ADS)

Cao, Yichen; Zhang, Xueqin; Ma, Guojian; Sun, Rongqing; Dong, Deping

2017-07-01

Intelligent computer image processing technology provides convenience and possibility for designers to carry out designs. Shape analysis can be achieved by extracting SURF feature. However, high dimension of SURF feature causes to lower matching speed. To solve this problem, this paper proposed a fast fabric image matching algorithm based on SURF K-means and LSH algorithm. By constructing the bag of visual words on K-Means algorithm, and forming feature histogram of each image, the dimension of SURF feature is reduced at the first step. Then with the help of LSH algorithm, the features are encoded and the dimension is further reduced. In addition, the indexes of each image and each class of image are created, and the number of matching images is decreased by LSH hash bucket. Experiments on fabric image database show that this algorithm can speed up the matching and retrieval process, the result can satisfy the requirement of dress designers with accuracy and speed.

[Quality indicators in the storage and dispensing process in a Hospital Pharmacy].

PubMed

Rabuñal-Álvarez, M T; Calvin-Lamas, M; Feal-Cortizas, B; Martínez-López, L M; Pedreira-Vázquez, I; Martín-Herranz, M I

2014-01-01

To establish indicators for the evaluation of the quality of the storage and dispensing processes related to semiautomatic vertical (SAVCS) and horizontal (SAHCS) carousel systems. Descriptive observational study conducted between January-December 2012. Definition of quality indicators, a target value is established and an obtained value is calculated for 2012. Five quality indicators in the process of storage and dispensing of drugs were defined and calculated: indicator 1, error filling unidose trolleys: target (<1.67%), obtained (1.03%); indicator 2, filling accuracy unidose trolleys by using an SAVCS: target (<15%), obtained (11.5%); indicator 3, reliability of drug inventory in the process of drug entries using an SAHCS: target (<15%), obtained (6.53%); indicator 4, reliability of drug inventory in the picking process of orders replacement stock of clinical units using an SAHCS: target (<10%), obtained (1.97%); indicator 5, accuracy of the picking process of drug orders using an SAHCS: target (<10%), obtained (10.41%). Establishing indicators has allowed the quality in terms of safety, precision and reliability of semiautomatic systems for storage and dispensing drugs to be assessed. Copyright © 2014 SECA. Published by Elsevier Espana. All rights reserved.

New developments in FeynCalc 9.0

NASA Astrophysics Data System (ADS)

Shtabovenko, Vladyslav; Mertig, Rolf; Orellana, Frederik

2016-10-01

In this note we report on the new version of FEYNCALC, a MATHEMATICA package for symbolic semi-automatic evaluation of Feynman diagrams and algebraic expressions in quantum field theory. The main features of version 9.0 are: improved tensor reduction and partial fractioning of loop integrals, new functions for using FEYNCALC together with tools for reduction of scalar loop integrals using integration-by-parts (IBP) identities, better interface to FEYNARTS and support for SU(N) generators with explicit fundamental indices.

An Interactive Scheduling Method for Railway Rolling Stock Allocation

NASA Astrophysics Data System (ADS)

Otsuki, Tomoshi; Nakajima, Masayoshi; Fuse, Toru; Shimizu, Tadashi; Aisu, Hideyuki; Yasumoto, Takanori; Kaneko, Kenichi; Yokoyama, Nobuyuki

Experts working for railway schedule planners still have to devote considerable time and effort for creating rolling stock allocation plans. In this paper, we propose a semiautomatic planning method for creating these plans. Our scheduler is able to interactively deal with flexible constraint-expression inputs and to output easy-to-understand failure messages. Owing to these useful features, the scheduler can provide results that are comparable to those obtained by experts and are obtained faster than before.

3D Texture Features Mining for MRI Brain Tumor Identification

NASA Astrophysics Data System (ADS)

Rahim, Mohd Shafry Mohd; Saba, Tanzila; Nayer, Fatima; Syed, Afraz Zahra

2014-03-01

Medical image segmentation is a process to extract region of interest and to divide an image into its individual meaningful, homogeneous components. Actually, these components will have a strong relationship with the objects of interest in an image. For computer-aided diagnosis and therapy process, medical image segmentation is an initial mandatory step. Medical image segmentation is a sophisticated and challenging task because of the sophisticated nature of the medical images. Indeed, successful medical image analysis heavily dependent on the segmentation accuracy. Texture is one of the major features to identify region of interests in an image or to classify an object. 2D textures features yields poor classification results. Hence, this paper represents 3D features extraction using texture analysis and SVM as segmentation technique in the testing methodologies.

Real-Time Feature Tracking Using Homography

NASA Technical Reports Server (NTRS)

Clouse, Daniel S.; Cheng, Yang; Ansar, Adnan I.; Trotz, David C.; Padgett, Curtis W.

2010-01-01

This software finds feature point correspondences in sequences of images. It is designed for feature matching in aerial imagery. Feature matching is a fundamental step in a number of important image processing operations: calibrating the cameras in a camera array, stabilizing images in aerial movies, geo-registration of images, and generating high-fidelity surface maps from aerial movies. The method uses a Shi-Tomasi corner detector and normalized cross-correlation. This process is likely to result in the production of some mismatches. The feature set is cleaned up using the assumption that there is a large planar patch visible in both images. At high altitude, this assumption is often reasonable. A mathematical transformation, called an homography, is developed that allows us to predict the position in image 2 of any point on the plane in image 1. Any feature pair that is inconsistent with the homography is thrown out. The output of the process is a set of feature pairs, and the homography. The algorithms in this innovation are well known, but the new implementation improves the process in several ways. It runs in real-time at 2 Hz on 64-megapixel imagery. The new Shi-Tomasi corner detector tries to produce the requested number of features by automatically adjusting the minimum distance between found features. The homography-finding code now uses an implementation of the RANSAC algorithm that adjusts the number of iterations automatically to achieve a pre-set probability of missing a set of inliers. The new interface allows the caller to pass in a set of predetermined points in one of the images. This allows the ability to track the same set of points through multiple frames.

Decomposition and extraction: a new framework for visual classification.

PubMed

Fang, Yuqiang; Chen, Qiang; Sun, Lin; Dai, Bin; Yan, Shuicheng

2014-08-01

In this paper, we present a novel framework for visual classification based on hierarchical image decomposition and hybrid midlevel feature extraction. Unlike most midlevel feature learning methods, which focus on the process of coding or pooling, we emphasize that the mechanism of image composition also strongly influences the feature extraction. To effectively explore the image content for the feature extraction, we model a multiplicity feature representation mechanism through meaningful hierarchical image decomposition followed by a fusion step. In particularly, we first propose a new hierarchical image decomposition approach in which each image is decomposed into a series of hierarchical semantical components, i.e, the structure and texture images. Then, different feature extraction schemes can be adopted to match the decomposed structure and texture processes in a dissociative manner. Here, two schemes are explored to produce property related feature representations. One is based on a single-stage network over hand-crafted features and the other is based on a multistage network, which can learn features from raw pixels automatically. Finally, those multiple midlevel features are incorporated by solving a multiple kernel learning task. Extensive experiments are conducted on several challenging data sets for visual classification, and experimental results demonstrate the effectiveness of the proposed method.

A processing work-flow for measuring erythrocytes velocity in extended vascular networks from wide field high-resolution optical imaging data.

PubMed

Deneux, Thomas; Takerkart, Sylvain; Grinvald, Amiram; Masson, Guillaume S; Vanzetta, Ivo

2012-02-01

Comprehensive information on the spatio-temporal dynamics of the vascular response is needed to underpin the signals used in hemodynamics-based functional imaging. It has recently been shown that red blood cells (RBCs) velocity and its changes can be extracted from wide-field optical imaging recordings of intrinsic absorption changes in cortex. Here, we describe a complete processing work-flow for reliable RBC velocity estimation in cortical networks. Several pre-processing steps are implemented: image co-registration, necessary to correct for small movements of the vasculature, semi-automatic image segmentation for fast and reproducible vessel selection, reconstruction of RBC trajectories patterns for each micro-vessel, and spatio-temporal filtering to enhance the desired data characteristics. The main analysis step is composed of two robust algorithms for estimating the RBCs' velocity field. Vessel diameter and its changes are also estimated, as well as local changes in backscattered light intensity. This full processing chain is implemented with a software suite that is freely distributed. The software uses efficient data management for handling the very large data sets obtained with in vivo optical imaging. It offers a complete and user-friendly graphical user interface with visualization tools for displaying and exploring data and results. A full data simulation framework is also provided in order to optimize the performances of the algorithm with respect to several characteristics of the data. We illustrate the performance of our method in three different cases of in vivo data. We first document the massive RBC speed response evoked by a spreading depression in anesthetized rat somato-sensory cortex. Second, we show the velocity response elicited by a visual stimulation in anesthetized cat visual cortex. Finally, we report, for the first time, visually-evoked RBC speed responses in an extended vascular network in awake monkey extrastriate cortex. Copyright © 2011 Elsevier Inc. All rights reserved.

Investigation of computer-aided colonic crypt pattern analysis

NASA Astrophysics Data System (ADS)

Qi, Xin; Pan, Yinsheng; Sivak, Michael V., Jr.; Olowe, Kayode; Rollins, Andrew M.

2007-02-01

Colorectal cancer is the second leading cause of cancer-related death in the United States. Approximately 50% of these deaths could be prevented by earlier detection through screening. Magnification chromoendoscopy is a technique which utilizes tissue stains applied to the gastrointestinal mucosa and high-magnification endoscopy to better visualize and characterize lesions. Prior studies have shown that shapes of colonic crypts change with disease and show characteristic patterns. Current methods for assessing colonic crypt patterns are somewhat subjective and not standardized. Computerized algorithms could be used to standardize colonic crypt pattern assessment. We have imaged resected colonic mucosa in vitro (N = 70) using methylene blue dye and a surgical microscope to approximately simulate in vivo imaging with magnification chromoendoscopy. We have developed a method of computerized processing to analyze the crypt patterns in the images. The quantitative image analysis consists of three steps. First, the crypts within the region of interest of colonic tissue are semi-automatically segmented using watershed morphological processing. Second, crypt size and shape parameters are extracted from the segmented crypts. Third, each sample is assigned to a category according to the Kudo criteria. The computerized classification is validated by comparison with human classification using the Kudo classification criteria. The computerized colonic crypt pattern analysis algorithm will enable a study of in vivo magnification chromoendoscopy of colonic crypt pattern correlated with risk of colorectal cancer. This study will assess the feasibility of screening and surveillance of the colon using magnification chromoendoscopy.

Dynamic feature analysis for Voyager at the Image Processing Laboratory

NASA Technical Reports Server (NTRS)

Yagi, G. M.; Lorre, J. J.; Jepsen, P. L.

1978-01-01

Voyager 1 and 2 were launched from Cape Kennedy to Jupiter, Saturn, and beyond on September 5, 1977 and August 20, 1977. The role of the Image Processing Laboratory is to provide the Voyager Imaging Team with the necessary support to identify atmospheric features (tiepoints) for Jupiter and Saturn data, and to analyze and display them in a suitable form. This support includes the software needed to acquire and store tiepoints, the hardware needed to interactively display images and tiepoints, and the general image processing environment necessary for decalibration and enhancement of the input images. The objective is an understanding of global circulation in the atmospheres of Jupiter and Saturn. Attention is given to the Voyager imaging subsystem, the Voyager imaging science objectives, hardware, software, display monitors, a dynamic feature study, decalibration, navigation, and data base.

Low-power coprocessor for Haar-like feature extraction with pixel-based pipelined architecture

NASA Astrophysics Data System (ADS)

Luo, Aiwen; An, Fengwei; Fujita, Yuki; Zhang, Xiangyu; Chen, Lei; Jürgen Mattausch, Hans

2017-04-01

Intelligent analysis of image and video data requires image-feature extraction as an important processing capability for machine-vision realization. A coprocessor with pixel-based pipeline (CFEPP) architecture is developed for real-time Haar-like cell-based feature extraction. Synchronization with the image sensor’s pixel frequency and immediate usage of each input pixel for the feature-construction process avoids the dependence on memory-intensive conventional strategies like integral-image construction or frame buffers. One 180 nm CMOS prototype can extract the 1680-dimensional Haar-like feature vectors, applied in the speeded up robust features (SURF) scheme, using an on-chip memory of only 96 kb (kilobit). Additionally, a low power dissipation of only 43.45 mW at 1.8 V supply voltage is achieved during VGA video procession at 120 MHz frequency with more than 325 fps. The Haar-like feature-extraction coprocessor is further evaluated by the practical application of vehicle recognition, achieving the expected high accuracy which is comparable to previous work.

Image processing tool for automatic feature recognition and quantification

DOEpatents

Chen, Xing; Stoddard, Ryan J.

2017-05-02

A system for defining structures within an image is described. The system includes reading of an input file, preprocessing the input file while preserving metadata such as scale information and then detecting features of the input file. In one version the detection first uses an edge detector followed by identification of features using a Hough transform. The output of the process is identified elements within the image.

Fault Diagnosis for Rotating Machinery: A Method based on Image Processing

PubMed Central

Lu, Chen; Wang, Yang; Ragulskis, Minvydas; Cheng, Yujie

2016-01-01

Rotating machinery is one of the most typical types of mechanical equipment and plays a significant role in industrial applications. Condition monitoring and fault diagnosis of rotating machinery has gained wide attention for its significance in preventing catastrophic accident and guaranteeing sufficient maintenance. With the development of science and technology, fault diagnosis methods based on multi-disciplines are becoming the focus in the field of fault diagnosis of rotating machinery. This paper presents a multi-discipline method based on image-processing for fault diagnosis of rotating machinery. Different from traditional analysis method in one-dimensional space, this study employs computing method in the field of image processing to realize automatic feature extraction and fault diagnosis in a two-dimensional space. The proposed method mainly includes the following steps. First, the vibration signal is transformed into a bi-spectrum contour map utilizing bi-spectrum technology, which provides a basis for the following image-based feature extraction. Then, an emerging approach in the field of image processing for feature extraction, speeded-up robust features, is employed to automatically exact fault features from the transformed bi-spectrum contour map and finally form a high-dimensional feature vector. To reduce the dimensionality of the feature vector, thus highlighting main fault features and reducing subsequent computing resources, t-Distributed Stochastic Neighbor Embedding is adopt to reduce the dimensionality of the feature vector. At last, probabilistic neural network is introduced for fault identification. Two typical rotating machinery, axial piston hydraulic pump and self-priming centrifugal pumps, are selected to demonstrate the effectiveness of the proposed method. Results show that the proposed method based on image-processing achieves a high accuracy, thus providing a highly effective means to fault diagnosis for rotating machinery. PMID:27711246

Fault Diagnosis for Rotating Machinery: A Method based on Image Processing.

PubMed

Lu, Chen; Wang, Yang; Ragulskis, Minvydas; Cheng, Yujie

2016-01-01

Rotating machinery is one of the most typical types of mechanical equipment and plays a significant role in industrial applications. Condition monitoring and fault diagnosis of rotating machinery has gained wide attention for its significance in preventing catastrophic accident and guaranteeing sufficient maintenance. With the development of science and technology, fault diagnosis methods based on multi-disciplines are becoming the focus in the field of fault diagnosis of rotating machinery. This paper presents a multi-discipline method based on image-processing for fault diagnosis of rotating machinery. Different from traditional analysis method in one-dimensional space, this study employs computing method in the field of image processing to realize automatic feature extraction and fault diagnosis in a two-dimensional space. The proposed method mainly includes the following steps. First, the vibration signal is transformed into a bi-spectrum contour map utilizing bi-spectrum technology, which provides a basis for the following image-based feature extraction. Then, an emerging approach in the field of image processing for feature extraction, speeded-up robust features, is employed to automatically exact fault features from the transformed bi-spectrum contour map and finally form a high-dimensional feature vector. To reduce the dimensionality of the feature vector, thus highlighting main fault features and reducing subsequent computing resources, t-Distributed Stochastic Neighbor Embedding is adopt to reduce the dimensionality of the feature vector. At last, probabilistic neural network is introduced for fault identification. Two typical rotating machinery, axial piston hydraulic pump and self-priming centrifugal pumps, are selected to demonstrate the effectiveness of the proposed method. Results show that the proposed method based on image-processing achieves a high accuracy, thus providing a highly effective means to fault diagnosis for rotating machinery.

A tool for the estimation of the distribution of landslide area in R

NASA Astrophysics Data System (ADS)

Rossi, M.; Cardinali, M.; Fiorucci, F.; Marchesini, I.; Mondini, A. C.; Santangelo, M.; Ghosh, S.; Riguer, D. E. L.; Lahousse, T.; Chang, K. T.; Guzzetti, F.

2012-04-01

We have developed a tool in R (the free software environment for statistical computing, http://www.r-project.org/) to estimate the probability density and the frequency density of landslide area. The tool implements parametric and non-parametric approaches to the estimation of the probability density and the frequency density of landslide area, including: (i) Histogram Density Estimation (HDE), (ii) Kernel Density Estimation (KDE), and (iii) Maximum Likelihood Estimation (MLE). The tool is available as a standard Open Geospatial Consortium (OGC) Web Processing Service (WPS), and is accessible through the web using different GIS software clients. We tested the tool to compare Double Pareto and Inverse Gamma models for the probability density of landslide area in different geological, morphological and climatological settings, and to compare landslides shown in inventory maps prepared using different mapping techniques, including (i) field mapping, (ii) visual interpretation of monoscopic and stereoscopic aerial photographs, (iii) visual interpretation of monoscopic and stereoscopic VHR satellite images and (iv) semi-automatic detection and mapping from VHR satellite images. Results show that both models are applicable in different geomorphological settings. In most cases the two models provided very similar results. Non-parametric estimation methods (i.e., HDE and KDE) provided reasonable results for all the tested landslide datasets. For some of the datasets, MLE failed to provide a result, for convergence problems. The two tested models (Double Pareto and Inverse Gamma) resulted in very similar results for large and very large datasets (> 150 samples). Differences in the modeling results were observed for small datasets affected by systematic biases. A distinct rollover was observed in all analyzed landslide datasets, except for a few datasets obtained from landslide inventories prepared through field mapping or by semi-automatic mapping from VHR satellite imagery. The tool can also be used to evaluate the probability density and the frequency density of landslide volume.

Image processing based automatic diagnosis of glaucoma using wavelet features of segmented optic disc from fundus image.

PubMed

Singh, Anushikha; Dutta, Malay Kishore; ParthaSarathi, M; Uher, Vaclav; Burget, Radim

2016-02-01

Glaucoma is a disease of the retina which is one of the most common causes of permanent blindness worldwide. This paper presents an automatic image processing based method for glaucoma diagnosis from the digital fundus image. In this paper wavelet feature extraction has been followed by optimized genetic feature selection combined with several learning algorithms and various parameter settings. Unlike the existing research works where the features are considered from the complete fundus or a sub image of the fundus, this work is based on feature extraction from the segmented and blood vessel removed optic disc to improve the accuracy of identification. The experimental results presented in this paper indicate that the wavelet features of the segmented optic disc image are clinically more significant in comparison to features of the whole or sub fundus image in the detection of glaucoma from fundus image. Accuracy of glaucoma identification achieved in this work is 94.7% and a comparison with existing methods of glaucoma detection from fundus image indicates that the proposed approach has improved accuracy of classification. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Image counter-forensics based on feature injection

NASA Astrophysics Data System (ADS)

Iuliani, M.; Rossetto, S.; Bianchi, T.; De Rosa, Alessia; Piva, A.; Barni, M.

2014-02-01

Starting from the concept that many image forensic tools are based on the detection of some features revealing a particular aspect of the history of an image, in this work we model the counter-forensic attack as the injection of a specific fake feature pointing to the same history of an authentic reference image. We propose a general attack strategy that does not rely on a specific detector structure. Given a source image x and a target image y, the adversary processes x in the pixel domain producing an attacked image ~x, perceptually similar to x, whose feature f(~x) is as close as possible to f(y) computed on y. Our proposed counter-forensic attack consists in the constrained minimization of the feature distance Φ(z) =│ f(z) - f(y)│ through iterative methods based on gradient descent. To solve the intrinsic limit due to the numerical estimation of the gradient on large images, we propose the application of a feature decomposition process, that allows the problem to be reduced into many subproblems on the blocks the image is partitioned into. The proposed strategy has been tested by attacking three different features and its performance has been compared to state-of-the-art counter-forensic methods.

Total reduction of distorted echelle spectrograms - An automatic procedure. [for computer controlled microdensitometer

NASA Technical Reports Server (NTRS)

Peterson, R. C.; Title, A. M.

1975-01-01

A total reduction procedure, notable for its use of a computer-controlled microdensitometer for semi-automatically tracing curved spectra, is applied to distorted high-dispersion echelle spectra recorded by an image tube. Microdensitometer specifications are presented and the FORTRAN, TRACEN and SPOTS programs are outlined. The intensity spectrum of the photographic or electrographic plate is plotted on a graphic display. The time requirements are discussed in detail.

Semi-automatized segmentation method using image-based flow cytometry to study sperm physiology: the case of capacitation-induced tyrosine phosphorylation.

PubMed

Matamoros-Volante, Arturo; Moreno-Irusta, Ayelen; Torres-Rodriguez, Paulina; Giojalas, Laura; Gervasi, María G; Visconti, Pablo E; Treviño, Claudia L

2018-02-01

Is image-based flow cytometry a useful tool to study intracellular events in human sperm such as protein tyrosine phosphorylation or signaling processes? Image-based flow cytometry is a powerful tool to study intracellular events in a relevant number of sperm cells, which enables a robust statistical analysis providing spatial resolution in terms of the specific subcellular localization of the labeling. Sperm capacitation is required for fertilization. During this process, spermatozoa undergo numerous physiological changes, via activation of different signaling pathways, which are not completely understood. Classical approaches for studying sperm physiology include conventional microscopy, flow cytometry and Western blotting. These techniques present disadvantages for obtaining detailed subcellular information of signaling pathways in a relevant number of cells. This work describes a new semi-automatized analysis using image-based flow cytometry which enables the study, at the subcellular and population levels, of different sperm parameters associated with signaling. The increase in protein tyrosine phosphorylation during capacitation is presented as an example. Sperm cells were isolated from seminal plasma by the swim-up technique. We evaluated the intensity and distribution of protein tyrosine phosphorylation in sperm incubated in non-capacitation and capacitation-supporting media for 1 and 18 h under different experimental conditions. We used an antibody against FER kinase and pharmacological inhibitors in an attempt to identify the kinases involved in protein tyrosine phosphorylation during human sperm capacitation. Semen samples from normospermic donors were obtained by masturbation after 2-3 days of sexual abstinence. We used the innovative technique image-based flow cytometry and image analysis tools to segment individual images of spermatozoa. We evaluated and quantified the regions of sperm where protein tyrosine phosphorylation takes place at the subcellular level in a large number of cells. We also used immunocytochemistry and Western blot analysis. Independent experiments were performed with semen samples from seven different donors. Using image analysis tools, we developed a completely novel semi-automatic strategy useful for segmenting thousands of individual cell images obtained using image-based flow cytometry. Contrary to immunofluorescence which relies on the analysis of a limited sperm population and also on the observer, image-based flow cytometry allows for unbiased quantification and simultaneous localization of post-translational changes in an extended sperm population. Interestingly, important data can be independently analyzed by looking to the frame of interest. As an example, we evaluated the capacitation-associated increase in tyrosine phosphorylation in sperm incubated in non-capacitation and capacitation-supporting media for 1 and 18 h. As previously reported, protein tyrosine phosphorylation increases in a time-depending manner, but our method revealed that this increase occurs differentially among distinct sperm segments. FER kinase is reported to be the enzyme responsible for the increase in protein tyrosine phosphorylation in mouse sperm. Our Western blot analysis revealed for the first time the presence of this enzyme in human sperm. Using our segmentation strategy, we aimed to quantify the effect of pharmacological inhibition of FER kinase and found a marked reduction of protein tyrosine phosphorylation only in the flagellum, which corresponded to the physical localization of FER in human sperm. Our method provides an alternative strategy to study signaling markers associated with capacitation, such as protein tyrosine phosphorylation, in a fast and quantitative manner. None. This is an in vitro study performed under controlled conditions. Chemical inhibitors are not completely specific for the intended target; the possibility of side effects cannot be discarded. Our results demonstrate that the use of image-based flow cytometry is a very powerful tool to study sperm physiology. A large number of cells can be easily analyzed and information at the subcellular level can be obtained. As the segmentation process works with bright-field images, it can be extended to study expression of other proteins of interest using different antibodies or it can be used in living sperm to study intracellular parameters that can be followed using fluorescent dyes sensitive to the parameter of interest (e.g. pH, Ca2+). Therefore, this a versatile method that can be exploited to study several aspects of sperm physiology. This work was supported DGAPA (IN203116 to C. Treviño), Fronteras-CONACyT No. 71 and Eunice Kennedy Shriver National Institute of Child Health and Human Development NIH (RO1 HD38082) to P.E. Visconti and by a Lalor Foundation fellowship to M.G. Gervasi. A. Matamoros is a student of the Maestría en Ciencias Bioquímicas-UNAM program supported by CONACyT (416400) and DGAPA-UNAM. A. Moreno obtained a scholarship from Red MacroUniversidades and L. Giojalas obtained a schloarhip from CONICET and Universidad Nacional de Cordoba. The authors declare there are not conflicts of interest. © The Author 2017. Published by Oxford University Press on behalf of the European Society of Human Reproduction and Embryology. All rights reserved. For Permissions, please email:journals.permissions@oup.com

Identification and Mapping of Tree Species in Urban Areas Using WORLDVIEW-2 Imagery

NASA Astrophysics Data System (ADS)

Mustafa, Y. T.; Habeeb, H. N.; Stein, A.; Sulaiman, F. Y.

2015-10-01

Monitoring and mapping of urban trees are essential to provide urban forestry authorities with timely and consistent information. Modern techniques increasingly facilitate these tasks, but require the development of semi-automatic tree detection and classification methods. In this article, we propose an approach to delineate and map the crown of 15 tree species in the city of Duhok, Kurdistan Region of Iraq using WorldView-2 (WV-2) imagery. A tree crown object is identified first and is subsequently delineated as an image object (IO) using vegetation indices and texture measurements. Next, three classification methods: Maximum Likelihood, Neural Network, and Support Vector Machine were used to classify IOs using selected IO features. The best results are obtained with Support Vector Machine classification that gives the best map of urban tree species in Duhok. The overall accuracy was between 60.93% to 88.92% and κ-coefficient was between 0.57 to 0.75. We conclude that fifteen tree species were identified and mapped at a satisfactory accuracy in urban areas of this study.

Semi-automated contour recognition using DICOMautomaton

NASA Astrophysics Data System (ADS)

Clark, H.; Wu, J.; Moiseenko, V.; Lee, R.; Gill, B.; Duzenli, C.; Thomas, S.

2014-03-01

Purpose: A system has been developed which recognizes and classifies Digital Imaging and Communication in Medicine contour data with minimal human intervention. It allows researchers to overcome obstacles which tax analysis and mining systems, including inconsistent naming conventions and differences in data age or resolution. Methods: Lexicographic and geometric analysis is used for recognition. Well-known lexicographic methods implemented include Levenshtein-Damerau, bag-of-characters, Double Metaphone, Soundex, and (word and character)-N-grams. Geometrical implementations include 3D Fourier Descriptors, probability spheres, boolean overlap, simple feature comparison (e.g. eccentricity, volume) and rule-based techniques. Both analyses implement custom, domain-specific modules (e.g. emphasis differentiating left/right organ variants). Contour labels from 60 head and neck patients are used for cross-validation. Results: Mixed-lexicographical methods show an effective improvement in more than 10% of recognition attempts compared with a pure Levenshtein-Damerau approach when withholding 70% of the lexicon. Domain-specific and geometrical techniques further boost performance. Conclusions: DICOMautomaton allows users to recognize contours semi-automatically. As usage increases and the lexicon is filled with additional structures, performance improves, increasing the overall utility of the system.

Geospatial Image Mining For Nuclear Proliferation Detection: Challenges and New Opportunities

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

Vatsavai, Raju; Bhaduri, Budhendra L; Cheriyadat, Anil M

2010-01-01

With increasing understanding and availability of nuclear technologies, and increasing persuasion of nuclear technologies by several new countries, it is increasingly becoming important to monitor the nuclear proliferation activities. There is a great need for developing technologies to automatically or semi-automatically detect nuclear proliferation activities using remote sensing. Images acquired from earth observation satellites is an important source of information in detecting proliferation activities. High-resolution remote sensing images are highly useful in verifying the correctness, as well as completeness of any nuclear program. DOE national laboratories are interested in detecting nuclear proliferation by developing advanced geospatial image mining algorithms. Inmore » this paper we describe the current understanding of geospatial image mining techniques and enumerate key gaps and identify future research needs in the context of nuclear proliferation.« less

Research based on the SoPC platform of feature-based image registration

NASA Astrophysics Data System (ADS)

Shi, Yue-dong; Wang, Zhi-hui

2015-12-01

This paper focuses on the study of implementing feature-based image registration by System on a Programmable Chip (SoPC) hardware platform. We solidify the image registration algorithm on the FPGA chip, in which embedded soft core processor Nios II can speed up the image processing system. In this way, we can make image registration technology get rid of the PC. And, consequently, this kind of technology will be got an extensive use. The experiment result indicates that our system shows stable performance, particularly in terms of matching processing which noise immunity is good. And feature points of images show a reasonable distribution.

Enhancing facial features by using clear facial features

NASA Astrophysics Data System (ADS)

Rofoo, Fanar Fareed Hanna

2017-09-01

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

Color image processing and object tracking workstation

NASA Technical Reports Server (NTRS)

Klimek, Robert B.; Paulick, Michael J.

1992-01-01

A system is described for automatic and semiautomatic tracking of objects on film or video tape which was developed to meet the needs of the microgravity combustion and fluid science experiments at NASA Lewis. The system consists of individual hardware parts working under computer control to achieve a high degree of automation. The most important hardware parts include 16 mm film projector, a lens system, a video camera, an S-VHS tapedeck, a frame grabber, and some storage and output devices. Both the projector and tapedeck have a computer interface enabling remote control. Tracking software was developed to control the overall operation. In the automatic mode, the main tracking program controls the projector or the tapedeck frame incrementation, grabs a frame, processes it, locates the edge of the objects being tracked, and stores the coordinates in a file. This process is performed repeatedly until the last frame is reached. Three representative applications are described. These applications represent typical uses and include tracking the propagation of a flame front, tracking the movement of a liquid-gas interface with extremely poor visibility, and characterizing a diffusion flame according to color and shape.

Brain's tumor image processing using shearlet transform

NASA Astrophysics Data System (ADS)

Cadena, Luis; Espinosa, Nikolai; Cadena, Franklin; Korneeva, Anna; Kruglyakov, Alexey; Legalov, Alexander; Romanenko, Alexey; Zotin, Alexander

2017-09-01

Brain tumor detection is well known research area for medical and computer scientists. In last decades there has been much research done on tumor detection, segmentation, and classification. Medical imaging plays a central role in the diagnosis of brain tumors and nowadays uses methods non-invasive, high-resolution techniques, especially magnetic resonance imaging and computed tomography scans. Edge detection is a fundamental tool in image processing, particularly in the areas of feature detection and feature extraction, which aim at identifying points in a digital image at which the image has discontinuities. Shearlets is the most successful frameworks for the efficient representation of multidimensional data, capturing edges and other anisotropic features which frequently dominate multidimensional phenomena. The paper proposes an improved brain tumor detection method by automatically detecting tumor location in MR images, its features are extracted by new shearlet transform.

Validation of semi-automatic segmentation of the left atrium

NASA Astrophysics Data System (ADS)

Rettmann, M. E.; Holmes, D. R., III; Camp, J. J.; Packer, D. L.; Robb, R. A.

2008-03-01

Catheter ablation therapy has become increasingly popular for the treatment of left atrial fibrillation. The effect of this treatment on left atrial morphology, however, has not yet been completely quantified. Initial studies have indicated a decrease in left atrial size with a concomitant decrease in pulmonary vein diameter. In order to effectively study if catheter based therapies affect left atrial geometry, robust segmentations with minimal user interaction are required. In this work, we validate a method to semi-automatically segment the left atrium from computed-tomography scans. The first step of the technique utilizes seeded region growing to extract the entire blood pool including the four chambers of the heart, the pulmonary veins, aorta, superior vena cava, inferior vena cava, and other surrounding structures. Next, the left atrium and pulmonary veins are separated from the rest of the blood pool using an algorithm that searches for thin connections between user defined points in the volumetric data or on a surface rendering. Finally, pulmonary veins are separated from the left atrium using a three dimensional tracing tool. A single user segmented three datasets three times using both the semi-automatic technique as well as manual tracing. The user interaction time for the semi-automatic technique was approximately forty-five minutes per dataset and the manual tracing required between four and eight hours per dataset depending on the number of slices. A truth model was generated using a simple voting scheme on the repeated manual segmentations. A second user segmented each of the nine datasets using the semi-automatic technique only. Several metrics were computed to assess the agreement between the semi-automatic technique and the truth model including percent differences in left atrial volume, DICE overlap, and mean distance between the boundaries of the segmented left atria. Overall, the semi-automatic approach was demonstrated to be repeatable within and between raters, and accurate when compared to the truth model. Finally, we generated a visualization to assess the spatial variability in the segmentation errors between the semi-automatic approach and the truth model. The visualization demonstrates the highest errors occur at the boundaries between the left atium and pulmonary veins as well as the left atrium and left atrial appendage. In conclusion, we describe a semi-automatic approach for left atrial segmentation that demonstrates repeatability and accuracy, with the advantage of significant time reduction in user interaction time.

Quantitative evaluation method of the bubble structure of sponge cake by using morphology image processing

NASA Astrophysics Data System (ADS)

Tatebe, Hironobu; Kato, Kunihito; Yamamoto, Kazuhiko; Katsuta, Yukio; Nonaka, Masahiko

2005-12-01

Now a day, many evaluation methods for the food industry by using image processing are proposed. These methods are becoming new evaluation method besides the sensory test and the solid-state measurement that are using for the quality evaluation. An advantage of the image processing is to be able to evaluate objectively. The goal of our research is structure evaluation of sponge cake by using image processing. In this paper, we propose a feature extraction method of the bobble structure in the sponge cake. Analysis of the bubble structure is one of the important properties to understand characteristics of the cake from the image. In order to take the cake image, first we cut cakes and measured that's surface by using the CIS scanner. Because the depth of field of this type scanner is very shallow, the bubble region of the surface has low gray scale values, and it has a feature that is blur. We extracted bubble regions from the surface images based on these features. First, input image is binarized, and the feature of bubble is extracted by the morphology analysis. In order to evaluate the result of feature extraction, we compared correlation with "Size of the bubble" of the sensory test result. From a result, the bubble extraction by using morphology analysis gives good correlation. It is shown that our method is as well as the subjectivity evaluation.

An image-processing methodology for extracting bloodstain pattern features.

PubMed

Arthur, Ravishka M; Humburg, Philomena J; Hoogenboom, Jerry; Baiker, Martin; Taylor, Michael C; de Bruin, Karla G

2017-08-01

There is a growing trend in forensic science to develop methods to make forensic pattern comparison tasks more objective. This has generally involved the application of suitable image-processing methods to provide numerical data for identification or comparison. This paper outlines a unique image-processing methodology that can be utilised by analysts to generate reliable pattern data that will assist them in forming objective conclusions about a pattern. A range of features were defined and extracted from a laboratory-generated impact spatter pattern. These features were based in part on bloodstain properties commonly used in the analysis of spatter bloodstain patterns. The values of these features were consistent with properties reported qualitatively for such patterns. The image-processing method developed shows considerable promise as a way to establish measurable discriminating pattern criteria that are lacking in current bloodstain pattern taxonomies. Copyright © 2017 Elsevier B.V. All rights reserved.

Graph theory for feature extraction and classification: a migraine pathology case study.

PubMed

Jorge-Hernandez, Fernando; Garcia Chimeno, Yolanda; Garcia-Zapirain, Begonya; Cabrera Zubizarreta, Alberto; Gomez Beldarrain, Maria Angeles; Fernandez-Ruanova, Begonya

2014-01-01

Graph theory is also widely used as a representational form and characterization of brain connectivity network, as is machine learning for classifying groups depending on the features extracted from images. Many of these studies use different techniques, such as preprocessing, correlations, features or algorithms. This paper proposes an automatic tool to perform a standard process using images of the Magnetic Resonance Imaging (MRI) machine. The process includes pre-processing, building the graph per subject with different correlations, atlas, relevant feature extraction according to the literature, and finally providing a set of machine learning algorithms which can produce analyzable results for physicians or specialists. In order to verify the process, a set of images from prescription drug abusers and patients with migraine have been used. In this way, the proper functioning of the tool has been proved, providing results of 87% and 92% of success depending on the classifier used.

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

Szymanski, J. J.; Brumby, Steven P.; Pope, P. A.

Feature extration from imagery is an important and long-standing problem in remote sensing. In this paper, we report on work using genetic programming to perform feature extraction simultaneously from multispectral and digital elevation model (DEM) data. The tool used is the GENetic Imagery Exploitation (GENIE) software, which produces image-processing software that inherently combines spatial and spectral processing. GENIE is particularly useful in exploratory studies of imagery, such as one often does in combining data from multiple sources. The user trains the software by painting the feature of interest with a simple graphical user interface. GENIE then uses genetic programming techniquesmore » to produce an image-processing pipeline. Here, we demonstrate evolution of image processing algorithms that extract a range of land-cover features including towns, grasslands, wild fire burn scars, and several types of forest. We use imagery from the DOE/NNSA Multispectral Thermal Imager (MTI) spacecraft, fused with USGS 1:24000 scale DEM data.« less

Development of a piecewise linear omnidirectional 3D image registration method

NASA Astrophysics Data System (ADS)

Bae, Hyunsoo; Kang, Wonjin; Lee, SukGyu; Kim, Youngwoo

2016-12-01

This paper proposes a new piecewise linear omnidirectional image registration method. The proposed method segments an image captured by multiple cameras into 2D segments defined by feature points of the image and then stitches each segment geometrically by considering the inclination of the segment in the 3D space. Depending on the intended use of image registration, the proposed method can be used to improve image registration accuracy or reduce the computation time in image registration because the trade-off between the computation time and image registration accuracy can be controlled for. In general, nonlinear image registration methods have been used in 3D omnidirectional image registration processes to reduce image distortion by camera lenses. The proposed method depends on a linear transformation process for omnidirectional image registration, and therefore it can enhance the effectiveness of the geometry recognition process, increase image registration accuracy by increasing the number of cameras or feature points of each image, increase the image registration speed by reducing the number of cameras or feature points of each image, and provide simultaneous information on shapes and colors of captured objects.

Image processing based detection of lung cancer on CT scan images

NASA Astrophysics Data System (ADS)

Abdillah, Bariqi; Bustamam, Alhadi; Sarwinda, Devvi

2017-10-01

In this paper, we implement and analyze the image processing method for detection of lung cancer. Image processing techniques are widely used in several medical problems for picture enhancement in the detection phase to support the early medical treatment. In this research we proposed a detection method of lung cancer based on image segmentation. Image segmentation is one of intermediate level in image processing. Marker control watershed and region growing approach are used to segment of CT scan image. Detection phases are followed by image enhancement using Gabor filter, image segmentation, and features extraction. From the experimental results, we found the effectiveness of our approach. The results show that the best approach for main features detection is watershed with masking method which has high accuracy and robust.

Using automated texture features to determine the probability for masking of a tumor on mammography, but not ultrasound.

PubMed

Häberle, Lothar; Hack, Carolin C; Heusinger, Katharina; Wagner, Florian; Jud, Sebastian M; Uder, Michael; Beckmann, Matthias W; Schulz-Wendtland, Rüdiger; Wittenberg, Thomas; Fasching, Peter A

2017-08-30

Tumors in radiologically dense breast were overlooked on mammograms more often than tumors in low-density breasts. A fast reproducible and automated method of assessing percentage mammographic density (PMD) would be desirable to support decisions whether ultrasonography should be provided for women in addition to mammography in diagnostic mammography units. PMD assessment has still not been included in clinical routine work, as there are issues of interobserver variability and the procedure is quite time consuming. This study investigated whether fully automatically generated texture features of mammograms can replace time-consuming semi-automatic PMD assessment to predict a patient's risk of having an invasive breast tumor that is visible on ultrasound but masked on mammography (mammography failure). This observational study included 1334 women with invasive breast cancer treated at a hospital-based diagnostic mammography unit. Ultrasound was available for the entire cohort as part of routine diagnosis. Computer-based threshold PMD assessments ("observed PMD") were carried out and 363 texture features were obtained from each mammogram. Several variable selection and regression techniques (univariate selection, lasso, boosting, random forest) were applied to predict PMD from the texture features. The predicted PMD values were each used as new predictor for masking in logistic regression models together with clinical predictors. These four logistic regression models with predicted PMD were compared among themselves and with a logistic regression model with observed PMD. The most accurate masking prediction was determined by cross-validation. About 120 of the 363 texture features were selected for predicting PMD. Density predictions with boosting were the best substitute for observed PMD to predict masking. Overall, the corresponding logistic regression model performed better (cross-validated AUC, 0.747) than one without mammographic density (0.734), but less well than the one with the observed PMD (0.753). However, in patients with an assigned mammography failure risk >10%, covering about half of all masked tumors, the boosting-based model performed at least as accurately as the original PMD model. Automatically generated texture features can replace semi-automatically determined PMD in a prediction model for mammography failure, such that more than 50% of masked tumors could be discovered.

A semi-automatic method for quantification and classification of erythrocytes infected with malaria parasites in microscopic images.

PubMed

Díaz, Gloria; González, Fabio A; Romero, Eduardo

2009-04-01

Visual quantification of parasitemia in thin blood films is a very tedious, subjective and time-consuming task. This study presents an original method for quantification and classification of erythrocytes in stained thin blood films infected with Plasmodium falciparum. The proposed approach is composed of three main phases: a preprocessing step, which corrects luminance differences. A segmentation step that uses the normalized RGB color space for classifying pixels either as erythrocyte or background followed by an Inclusion-Tree representation that structures the pixel information into objects, from which erythrocytes are found. Finally, a two step classification process identifies infected erythrocytes and differentiates the infection stage, using a trained bank of classifiers. Additionally, user intervention is allowed when the approach cannot make a proper decision. Four hundred fifty malaria images were used for training and evaluating the method. Automatic identification of infected erythrocytes showed a specificity of 99.7% and a sensitivity of 94%. The infection stage was determined with an average sensitivity of 78.8% and average specificity of 91.2%.

3D image fusion of whole-heart dynamic cardiac MR perfusion and late gadolinium enhancement: Intuitive delineation of myocardial hypoperfusion and scar.

PubMed

von Spiczak, Jochen; Mannil, Manoj; Kozerke, Sebastian; Alkadhi, Hatem; Manka, Robert

2018-03-30

Since patients with myocardial hypoperfusion due to coronary artery disease (CAD) with preserved viability are known to benefit from revascularization, accurate differentiation of hypoperfusion from scar is desirable. To develop a framework for 3D fusion of whole-heart dynamic cardiac MR perfusion and late gadolinium enhancement (LGE) to delineate stress-induced myocardial hypoperfusion and scar. Prospective feasibility study. Sixteen patients (61 ± 14 years, two females) with known/suspected CAD. 1.5T (nine patients); 3.0T (seven patients); whole-heart dynamic 3D cardiac MR perfusion (3D-PERF, under adenosine stress); 3D LGE inversion recovery sequences (3D-SCAR). A software framework was developed for 3D fusion of 3D-PERF and 3D-SCAR. Computation steps included: 1) segmentation of the left ventricle in 3D-PERF and 3D-SCAR; 2) semiautomatic thresholding of perfusion/scar data; 3) automatic calculation of ischemic/scar burden (ie, pathologic relative to total myocardium); 4) projection of perfusion/scar values onto artificial template of the left ventricle; 5) semiautomatic coregistration to an exemplary heart contour easing 3D orientation; and 6) 3D rendering of the combined datasets using automatically defined color tables. All tasks were performed by two independent, blinded readers (J.S. and R.M.). Intraclass correlation coefficients (ICC) for determining interreader agreement. Image acquisition, postprocessing, and 3D fusion were feasible in all cases. In all, 10/16 patients showed stress-induced hypoperfusion in 3D-PERF; 8/16 patients showed LGE in 3D-SCAR. For 3D-PERF, semiautomatic thresholding was possible in all patients. For 3D-SCAR, automatic thresholding was feasible where applicable. Average ischemic burden was 11 ± 7% (J.S.) and 12 ± 7% (R.M.). Average scar burden was 8 ± 5% (J.S.) and 7 ± 4% (R.M.). Interreader agreement was excellent (ICC for 3D-PERF = 0.993, for 3D-SCAR = 0.99). 3D fusion of 3D-PERF and 3D-SCAR facilitates intuitive delineation of stress-induced myocardial hypoperfusion and scar. 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018. © 2018 International Society for Magnetic Resonance in Medicine.

Quantitative imaging features: extension of the oncology medical image database

NASA Astrophysics Data System (ADS)

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

2015-03-01

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

Autocorrelation techniques for soft photogrammetry

NASA Astrophysics Data System (ADS)

Yao, Wu

In this thesis research is carried out on image processing, image matching searching strategies, feature type and image matching, and optimal window size in image matching. To make comparisons, the soft photogrammetry package SoftPlotter is used. Two aerial photographs from the Iowa State University campus high flight 94 are scanned into digital format. In order to create a stereo model from them, interior orientation, single photograph rectification and stereo rectification are done. Two new image matching methods, multi-method image matching (MMIM) and unsquare window image matching are developed and compared. MMIM is used to determine the optimal window size in image matching. Twenty four check points from four different types of ground features are used for checking the results from image matching. Comparison between these four types of ground feature shows that the methods developed here improve the speed and the precision of image matching. A process called direct transformation is described and compared with the multiple steps in image processing. The results from image processing are consistent with those from SoftPlotter. A modified LAN image header is developed and used to store the information about the stereo model and image matching. A comparison is also made between cross correlation image matching (CCIM), least difference image matching (LDIM) and least square image matching (LSIM). The quality of image matching in relation to ground features are compared using two methods developed in this study, the coefficient surface for CCIM and the difference surface for LDIM. To reduce the amount of computation in image matching, the best-track searching algorithm, developed in this research, is used instead of the whole range searching algorithm.

Image-based red cell counting for wild animals blood.

PubMed

Mauricio, Claudio R M; Schneider, Fabio K; Dos Santos, Leonilda Correia

2010-01-01

An image-based red blood cell (RBC) automatic counting system is presented for wild animals blood analysis. Images with 2048×1536-pixel resolution acquired on an optical microscope using Neubauer chambers are used to evaluate RBC counting for three animal species (Leopardus pardalis, Cebus apella and Nasua nasua) and the error found using the proposed method is similar to that obtained for inter observer visual counting method, i.e., around 10%. Smaller errors (e.g., 3%) can be obtained in regions with less grid artifacts. These promising results allow the use of the proposed method either as a complete automatic counting tool in laboratories for wild animal's blood analysis or as a first counting stage in a semi-automatic counting tool.

TECHNOLOGY AND MANPOWER IN THE TELEPHONE INDUSTRY, 1965-75.

ERIC Educational Resources Information Center

LUSKIN, SHELDON H.; AND OTHERS

ELECTRONIC SOLID STATE SWITCHING SYSTEMS, COMMUNICATIONS SATELLITES, SEMIAUTOMATIC INFORMATION SERVICES, AUTOMATIC INTERCEPTING AND DATA PROCESSING, AND DEDICATED PLANT, THE PERMANENT ASSIGNMENT OF LINES FROM A CENTRAL OFFICE TO EACH ACTUAL AND POTENTIAL SUBSCRIBER, ARE SOME OF THE TECHNOLOGICAL INNOVATIONS WHICH WILL BRING SIGNIFICANT MANPOWER…

Static analysis techniques for semiautomatic synthesis of message passing software skeletons

DOE PAGES

Sottile, Matthew; Dagit, Jason; Zhang, Deli; ...

2015-06-29

The design of high-performance computing architectures demands performance analysis of large-scale parallel applications to derive various parameters concerning hardware design and software development. The process of performance analysis and benchmarking an application can be done in several ways with varying degrees of fidelity. One of the most cost-effective ways is to do a coarse-grained study of large-scale parallel applications through the use of program skeletons. The concept of a “program skeleton” that we discuss in this article is an abstracted program that is derived from a larger program where source code that is determined to be irrelevant is removed formore » the purposes of the skeleton. In this work, we develop a semiautomatic approach for extracting program skeletons based on compiler program analysis. Finally, we demonstrate correctness of our skeleton extraction process by comparing details from communication traces, as well as show the performance speedup of using skeletons by running simulations in the SST/macro simulator.« less

Novel semi-automated kidney volume measurements in autosomal dominant polycystic kidney disease.

PubMed

Muto, Satoru; Kawano, Haruna; Isotani, Shuji; Ide, Hisamitsu; Horie, Shigeo

2018-06-01

We assessed the effectiveness and convenience of a novel semi-automatic kidney volume (KV) measuring high-speed 3D-image analysis system SYNAPSE VINCENT ® (Fuji Medical Systems, Tokyo, Japan) for autosomal dominant polycystic kidney disease (ADPKD) patients. We developed a novel semi-automated KV measurement software for patients with ADPKD to be included in the imaging analysis software SYNAPSE VINCENT ® . The software extracts renal regions using image recognition software and measures KV (VINCENT KV). The algorithm was designed to work with the manual designation of a long axis of a kidney including cysts. After using the software to assess the predictive accuracy of the VINCENT method, we performed an external validation study and compared accurate KV and ellipsoid KV based on geometric modeling by linear regression analysis and Bland-Altman analysis. Median eGFR was 46.9 ml/min/1.73 m 2 . Median accurate KV, Vincent KV and ellipsoid KV were 627.7, 619.4 ml (IQR 431.5-947.0) and 694.0 ml (IQR 488.1-1107.4), respectively. Compared with ellipsoid KV (r = 0.9504), Vincent KV correlated strongly with accurate KV (r = 0.9968), without systematic underestimation or overestimation (ellipsoid KV; 14.2 ± 22.0%, Vincent KV; - 0.6 ± 6.0%). There were no significant slice thickness-specific differences (p = 0.2980). The VINCENT method is an accurate and convenient semi-automatic method to measure KV in patients with ADPKD compared with the conventional ellipsoid method.

[Several mechanisms of visual gnosis disorders in local brain lesions].

PubMed

Meerson, Ia A

1981-01-01

The object of the studies were peculiarities of recognizing visual images by patients with local cerebral lesions under conditions of incomplete sets of the image features, disjunction of the latter, distortion of their spatial arrangement, and unusual spatial orientation of the image as a whole. It was found that elimination of even one essential feature sharply hampered the recognition of the image both by healthy individuals (control), and patients with extraoccipital lesions, whereas elimination of several nonessential features only slowed down the process. In distinction from this the difficulties of the recognition of incomplete images by patients with occipital lesions were directly proportional to the number of the eliminated features irrespective of the latters' significance, i.e. these patients were unable to evaluate the hierarchy of the features. The recognition process in these patients were followed the way of scanning individual features. The reaccumulation and summation. The recognition of the fragmental, spatially distorted and unusually oriented images was found to be affected selectively in patients with parietal lobe affections. The patients with occipital lesions recognized such images practically as good as the ordinary ones.

Relative Recency Judgments in Learning Disabled Children: A Semi-Automatic Process.

ERIC Educational Resources Information Center

Stein, Debra K.; And Others

The ability of 20 learning disabled (LD) and 20 non-LD students (mean age of 9 years) to process temporal order information was assessed by employing a relative recency judgment task. Ss were administered lists composed of pictures of everyday objects and were then asked to indicate which item appeared latest on the list (that is, most recently).…

Development of the micro-scanning optical system of yellow laser applied to the ophthalmologic area

NASA Astrophysics Data System (ADS)

Ortega, Tiago A.; Mota, Alessandro D.; Costal, Glauco Z.; Fontes, Yuri C.; Rossi, Giuliano; Yasuoka, Fatima M. M.; Stefani, Mario A.; de Castro N., Jarbas C.

2012-10-01

In this work, the development of a laser scanning system for ophthalmology with micrometric positioning precision is presented. It is a semi-automatic scanning system for retina photocoagulation and laser trabeculoplasty. The equipment is a solid state laser fully integrated to the slit lamp. An optical system is responsible for producing different laser spot sizes on the image plane and a pair of galvanometer mirrors generates the scanning patterns.

Evaluation of security algorithms used for security processing on DICOM images

NASA Astrophysics Data System (ADS)

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

2005-04-01

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

Volume comparison of radiofrequency ablation at 3- and 5-cm target volumes for four different radiofrequency generators: MR volumetry in an open 1-T MRI system versus macroscopic measurement.

PubMed

Rathke, Hendrik; Hamm, Bernd; Guettler, Felix; Lohneis, Philipp; Stroux, Andrea; Suttmeyer, Britta; Jonczyk, Martin; Teichgräber, Ulf; de Bucourt, Maximilian

2015-12-01

In a patient, it is usually not macroscopically possible to estimate the non-viable volume induced by radiofrequency ablation (RFA) after the procedure. The purpose of this study was to use an ex vivo bovine liver model to perform magnetic resonance (MR) volumetry of the visible tissue signal change induced by RFA and to correlate the MR measurement with the actual macroscopic volume measured in the dissected specimens. Sixty-four liver specimens cut from 16 bovine livers were ablated under constant simulated, close physiological conditions with target volumes set to 14.14 ml (3-cm lesion) and 65.45 ml (5-cm lesion). Four commercially available radiofrequency (RF) systems were tested (n=16 for each system; n=8 for 3 cm and n=8 for 5 cm). A T1-weighted turbo spin echo (TSE) sequence with inversion recovery and a proton-density (PD)-weighted TSE sequence were acquired in a 1.0-T open magnetic resonance imaging (MRI) system. After manual dissection, actual macroscopic ablation diameters were measured and volumes calculated. MR volumetry was performed using a semiautomatic software tool. To validate the correctness and feasibility of the volume formula in macroscopic measurements, MR multiplanar reformation diameter measurements with subsequent volume calculation and semiautomatic MR volumes were correlated. Semiautomatic MR volumetry yielded smaller volumes than manual measurement after dissection, irrespective of RF system used, target lesion size, and MR sequence. For the 3-cm lesion, only 43.3% (T1) and 41.5% (PD) of the entire necrosis are detectable. For the 5-cm lesion, only 40.8% (T1) and 37.2% (PD) are visualized in MRI directly after intervention. The correlation between semiautomatic MR volumes and calculated MR volumes was 0.888 for the T1-weighted sequence and 0.875 for the PD sequence. After correlation of semiautomatic MR volumes and calculated MR volumes, it seems reasonable to use the respective volume formula for macroscopic volume calculation. Hyperacute MRI after ex vivo intervention may result in the underestimation of the real expansion of the produced necrosis zone. This must be kept in mind when using MRI for validating ablation success directly after RFA. One reason for the discrepancy between macroscopic and MRI appearance immediately after RFA may be that the transitional zone shows no or only partially visible MR signal change.

New method for identifying features of an image on a digital video display

NASA Astrophysics Data System (ADS)

Doyle, Michael D.

1991-04-01

The MetaMap process extends the concept of direct manipulation human-computer interfaces to new limits. Its specific capabilities include the correlation of discrete image elements to relevant text information and the correlation of these image features to other images as well as to program control mechanisms. The correlation is accomplished through reprogramming of both the color map and the image so that discrete image elements comprise unique sets of color indices. This process allows the correlation to be accomplished with very efficient data storage and program execution times. Image databases adapted to this process become object-oriented as a result. Very sophisticated interrelationships can be set up between images text and program control mechanisms using this process. An application of this interfacing process to the design of an interactive atlas of medical histology as well as other possible applications are described. The MetaMap process is protected by U. S. patent #4

MO-B-BRB-01: Optimize Treatment Planning Process in Clinical Environment

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

Feng, W.

The radiotherapy treatment planning process has evolved over the years with innovations in treatment planning, treatment delivery and imaging systems. Treatment modality and simulation technologies are also rapidly improving and affecting the planning process. For example, Image-guided-radiation-therapy has been widely adopted for patient setup, leading to margin reduction and isocenter repositioning after simulation. Stereotactic Body radiation therapy (SBRT) and Radiosurgery (SRS) have gradually become the standard of care for many treatment sites, which demand a higher throughput for the treatment plans even if the number of treatments per day remains the same. Finally, simulation, planning and treatment are traditionally sequentialmore » events. However, with emerging adaptive radiotherapy, they are becoming more tightly intertwined, leading to iterative processes. Enhanced efficiency of planning is therefore becoming more critical and poses serious challenge to the treatment planning process; Lean Six Sigma approaches are being utilized increasingly to balance the competing needs for speed and quality. In this symposium we will discuss the treatment planning process and illustrate effective techniques for managing workflow. Topics will include: Planning techniques: (a) beam placement, (b) dose optimization, (c) plan evaluation (d) export to RVS. Planning workflow: (a) import images, (b) Image fusion, (c) contouring, (d) plan approval (e) plan check (f) chart check, (g) sequential and iterative process Influence of upstream and downstream operations: (a) simulation, (b) immobilization, (c) motion management, (d) QA, (e) IGRT, (f) Treatment delivery, (g) SBRT/SRS (h) adaptive planning Reduction of delay between planning steps with Lean systems due to (a) communication, (b) limited resource, (b) contour, (c) plan approval, (d) treatment. Optimizing planning processes: (a) contour validation (b) consistent planning protocol, (c) protocol/template sharing, (d) semi-automatic plan evaluation, (e) quality checklist for error prevention, (f) iterative process, (g) balance of speed and quality Learning Objectives: Gain familiarity with the workflow of modern treatment planning process. Understand the scope and challenges of managing modern treatment planning processes. Gain familiarity with Lean Six Sigma approaches and their implementation in the treatment planning workflow.« less

MO-B-BRB-00: Optimizing the Treatment Planning Process

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

NONE

The radiotherapy treatment planning process has evolved over the years with innovations in treatment planning, treatment delivery and imaging systems. Treatment modality and simulation technologies are also rapidly improving and affecting the planning process. For example, Image-guided-radiation-therapy has been widely adopted for patient setup, leading to margin reduction and isocenter repositioning after simulation. Stereotactic Body radiation therapy (SBRT) and Radiosurgery (SRS) have gradually become the standard of care for many treatment sites, which demand a higher throughput for the treatment plans even if the number of treatments per day remains the same. Finally, simulation, planning and treatment are traditionally sequentialmore » events. However, with emerging adaptive radiotherapy, they are becoming more tightly intertwined, leading to iterative processes. Enhanced efficiency of planning is therefore becoming more critical and poses serious challenge to the treatment planning process; Lean Six Sigma approaches are being utilized increasingly to balance the competing needs for speed and quality. In this symposium we will discuss the treatment planning process and illustrate effective techniques for managing workflow. Topics will include: Planning techniques: (a) beam placement, (b) dose optimization, (c) plan evaluation (d) export to RVS. Planning workflow: (a) import images, (b) Image fusion, (c) contouring, (d) plan approval (e) plan check (f) chart check, (g) sequential and iterative process Influence of upstream and downstream operations: (a) simulation, (b) immobilization, (c) motion management, (d) QA, (e) IGRT, (f) Treatment delivery, (g) SBRT/SRS (h) adaptive planning Reduction of delay between planning steps with Lean systems due to (a) communication, (b) limited resource, (b) contour, (c) plan approval, (d) treatment. Optimizing planning processes: (a) contour validation (b) consistent planning protocol, (c) protocol/template sharing, (d) semi-automatic plan evaluation, (e) quality checklist for error prevention, (f) iterative process, (g) balance of speed and quality Learning Objectives: Gain familiarity with the workflow of modern treatment planning process. Understand the scope and challenges of managing modern treatment planning processes. Gain familiarity with Lean Six Sigma approaches and their implementation in the treatment planning workflow.« less

MO-B-BRB-03: Systems Engineering Tools for Treatment Planning Process Optimization in Radiation Medicine

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

Kapur, A.

The radiotherapy treatment planning process has evolved over the years with innovations in treatment planning, treatment delivery and imaging systems. Treatment modality and simulation technologies are also rapidly improving and affecting the planning process. For example, Image-guided-radiation-therapy has been widely adopted for patient setup, leading to margin reduction and isocenter repositioning after simulation. Stereotactic Body radiation therapy (SBRT) and Radiosurgery (SRS) have gradually become the standard of care for many treatment sites, which demand a higher throughput for the treatment plans even if the number of treatments per day remains the same. Finally, simulation, planning and treatment are traditionally sequentialmore » events. However, with emerging adaptive radiotherapy, they are becoming more tightly intertwined, leading to iterative processes. Enhanced efficiency of planning is therefore becoming more critical and poses serious challenge to the treatment planning process; Lean Six Sigma approaches are being utilized increasingly to balance the competing needs for speed and quality. In this symposium we will discuss the treatment planning process and illustrate effective techniques for managing workflow. Topics will include: Planning techniques: (a) beam placement, (b) dose optimization, (c) plan evaluation (d) export to RVS. Planning workflow: (a) import images, (b) Image fusion, (c) contouring, (d) plan approval (e) plan check (f) chart check, (g) sequential and iterative process Influence of upstream and downstream operations: (a) simulation, (b) immobilization, (c) motion management, (d) QA, (e) IGRT, (f) Treatment delivery, (g) SBRT/SRS (h) adaptive planning Reduction of delay between planning steps with Lean systems due to (a) communication, (b) limited resource, (b) contour, (c) plan approval, (d) treatment. Optimizing planning processes: (a) contour validation (b) consistent planning protocol, (c) protocol/template sharing, (d) semi-automatic plan evaluation, (e) quality checklist for error prevention, (f) iterative process, (g) balance of speed and quality Learning Objectives: Gain familiarity with the workflow of modern treatment planning process. Understand the scope and challenges of managing modern treatment planning processes. Gain familiarity with Lean Six Sigma approaches and their implementation in the treatment planning workflow.« less

High-performance camera module for fast quality inspection in industrial printing applications

NASA Astrophysics Data System (ADS)

Fürtler, Johannes; Bodenstorfer, Ernst; Mayer, Konrad J.; Brodersen, Jörg; Heiss, Dorothea; Penz, Harald; Eckel, Christian; Gravogl, Klaus; Nachtnebel, Herbert

2007-02-01

Today, printing products which must meet highest quality standards, e.g., banknotes, stamps, or vouchers, are automatically checked by optical inspection systems. Typically, the examination of fine details of the print or security features demands images taken from various perspectives, with different spectral sensitivity (visible, infrared, ultraviolet), and with high resolution. Consequently, the inspection system is equipped with several cameras and has to cope with an enormous data rate to be processed in real-time. Hence, it is desirable to move image processing tasks into the camera to reduce the amount of data which has to be transferred to the (central) image processing system. The idea is to transfer relevant information only, i.e., features of the image instead of the raw image data from the sensor. These features are then further processed. In this paper a color line-scan camera for line rates up to 100 kHz is presented. The camera is based on a commercial CMOS (complementary metal oxide semiconductor) area image sensor and a field programmable gate array (FPGA). It implements extraction of image features which are well suited to detect print flaws like blotches of ink, color smears, splashes, spots and scratches. The camera design and several image processing methods implemented on the FPGA are described, including flat field correction, compensation of geometric distortions, color transformation, as well as decimation and neighborhood operations.

Computer-aided classification of breast microcalcification clusters: merging of features from image processing and radiologists

NASA Astrophysics Data System (ADS)

Lo, Joseph Y.; Gavrielides, Marios A.; Markey, Mia K.; Jesneck, Jonathan L.

2003-05-01

We developed an ensemble classifier for the task of computer-aided diagnosis of breast microcalcification clusters,which are very challenging to characterize for radiologists and computer models alike. The purpose of this study is to help radiologists identify whether suspicious calcification clusters are benign vs. malignant, such that they may potentially recommend fewer unnecessary biopsies for actually benign lesions. The data consists of mammographic features extracted by automated image processing algorithms as well as manually interpreted by radiologists according to a standardized lexicon. We used 292 cases from a publicly available mammography database. From each cases, we extracted 22 image processing features pertaining to lesion morphology, 5 radiologist features also pertaining to morphology, and the patient age. Linear discriminant analysis (LDA) models were designed using each of the three data types. Each local model performed poorly; the best was one based upon image processing features which yielded ROC area index AZ of 0.59 +/- 0.03 and partial AZ above 90% sensitivity of 0.08 +/- 0.03. We then developed ensemble models using different combinations of those data types, and these models all improved performance compared to the local models. The final ensemble model was based upon 5 features selected by stepwise LDA from all 28 available features. This ensemble performed with AZ of 0.69 +/- 0.03 and partial AZ of 0.21 +/- 0.04, which was statistically significantly better than the model based on the image processing features alone (p<0.001 and p=0.01 for full and partial AZ respectively). This demonstrated the value of the radiologist-extracted features as a source of information for this task. It also suggested there is potential for improved performance using this ensemble classifier approach to combine different sources of currently available data.

Container Surface Evaluation by Function Estimation

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

Wendelberger, James G.

Container images are analyzed for specific surface features, such as, pits, cracks, and corrosion. The detection of these features is confounded with complicating features. These complication features include: shape/curvature, welds, edges, scratches, foreign objects among others. A method is provided to discriminate between the various features. The method consists of estimating the image background, determining a residual image and post processing to determine the features present. The methodology is not finalized but demonstrates the feasibility of a method to determine the kind and size of the features present.

a New Approach for the Semi-Automatic Texture Generation of the Buildings Facades, from Terrestrial Laser Scanner Data

NASA Astrophysics Data System (ADS)

Oniga, E.

2012-07-01

The result of the terrestrial laser scanning is an impressive number of spatial points, each of them being characterized as position by the X, Y and Z co-ordinates, by the value of the laser reflectance and their real color, expressed as RGB (Red, Green, Blue) values. The color code for each LIDAR point is taken from the georeferenced digital images, taken with a high resolution panoramic camera incorporated in the scanner system. In this article I propose a new algorithm for the semiautomatic texture generation, using the color information, the RGB values of every point that has been taken by terrestrial laser scanning technology and the 3D surfaces defining the buildings facades, generated with the Leica Cyclone software. The first step is when the operator defines the limiting value, i.e. the minimum distance between a point and the closest surface. The second step consists in calculating the distances, or the perpendiculars drawn from each point to the closest surface. In the third step we associate the points whose 3D coordinates are known, to every surface, depending on the limiting value. The fourth step consists in computing the Voronoi diagram for the points that belong to a surface. The final step brings automatic association between the RGB value of the color code and the corresponding polygon of the Voronoi diagram. The advantage of using this algorithm is that we can obtain, in a semi-automatic manner, a photorealistic 3D model of the building.

Highway 3D model from image and lidar data

NASA Astrophysics Data System (ADS)

Chen, Jinfeng; Chu, Henry; Sun, Xiaoduan

2014-05-01

We present a new method of highway 3-D model construction developed based on feature extraction in highway images and LIDAR data. We describe the processing road coordinate data that connect the image frames to the coordinates of the elevation data. Image processing methods are used to extract sky, road, and ground regions as well as significant objects (such as signs and building fronts) in the roadside for the 3D model. LIDAR data are interpolated and processed to extract the road lanes as well as other features such as trees, ditches, and elevated objects to form the 3D model. 3D geometry reasoning is used to match the image features to the 3D model. Results from successive frames are integrated to improve the final model.

High-resolution in vivo Wistar rodent brain atlas based on T1 weighted image

NASA Astrophysics Data System (ADS)

Huang, Su; Lu, Zhongkang; Huang, Weimin; Seramani, Sankar; Ramasamy, Boominathan; Sekar, Sakthivel; Guan, Cuntai; Bhakoo, Kishore

2016-03-01

Image based atlases for rats brain have a significant impact on pre-clinical research. In this project we acquired T1-weighted images from Wistar rodent brains with fine 59μm isotropical resolution for generation of the atlas template image. By applying post-process procedures using a semi-automatic brain extraction method, we delineated the brain tissues from source data. Furthermore, we applied a symmetric group-wise normalization method to generate an optimized template of T1 image of rodent brain, then aligned our template to the Waxholm Space. In addition, we defined several simple and explicit landmarks to corresponding our template with the well known Paxinos stereotaxic reference system. Anchoring at the origin of the Waxholm Space, we applied piece-wise linear transformation method to map the voxels of the template into the coordinates system in Paxinos' stereotoxic coordinates to facilitate the labelling task. We also cross-referenced our data with both published rodent brain atlas and image atlases available online, methodologically labelling the template to produce a Wistar brain atlas identifying more than 130 structures. Particular attention was paid to the cortex and cerebellum, as these areas encompass the most researched aspects of brain functions. Moreover, we adopted the structure hierarchy and naming nomenclature common to various atlases, so that the names and hierarchy structure presented in the atlas are readily recognised for easy use. It is believed the atlas will present a useful tool in rodent brain functional and pharmaceutical studies.

Workflow oriented software support for image guided radiofrequency ablation of focal liver malignancies

NASA Astrophysics Data System (ADS)

Weihusen, Andreas; Ritter, Felix; Kröger, Tim; Preusser, Tobias; Zidowitz, Stephan; Peitgen, Heinz-Otto

2007-03-01

Image guided radiofrequency (RF) ablation has taken a significant part in the clinical routine as a minimally invasive method for the treatment of focal liver malignancies. Medical imaging is used in all parts of the clinical workflow of an RF ablation, incorporating treatment planning, interventional targeting and result assessment. This paper describes a software application, which has been designed to support the RF ablation workflow under consideration of the requirements of clinical routine, such as easy user interaction and a high degree of robust and fast automatic procedures, in order to keep the physician from spending too much time at the computer. The application therefore provides a collection of specialized image processing and visualization methods for treatment planning and result assessment. The algorithms are adapted to CT as well as to MR imaging. The planning support contains semi-automatic methods for the segmentation of liver tumors and the surrounding vascular system as well as an interactive virtual positioning of RF applicators and a concluding numerical estimation of the achievable heat distribution. The assessment of the ablation result is supported by the segmentation of the coagulative necrosis and an interactive registration of pre- and post-interventional image data for the comparison of tumor and necrosis segmentation masks. An automatic quantification of surface distances is performed to verify the embedding of the tumor area into the thermal lesion area. The visualization methods support representations in the commonly used orthogonal 2D view as well as in 3D scenes.

Haralick texture features from apparent diffusion coefficient (ADC) MRI images depend on imaging and pre-processing parameters.

PubMed

Brynolfsson, Patrik; Nilsson, David; Torheim, Turid; Asklund, Thomas; Karlsson, Camilla Thellenberg; Trygg, Johan; Nyholm, Tufve; Garpebring, Anders

2017-06-22

In recent years, texture analysis of medical images has become increasingly popular in studies investigating diagnosis, classification and treatment response assessment of cancerous disease. Despite numerous applications in oncology and medical imaging in general, there is no consensus regarding texture analysis workflow, or reporting of parameter settings crucial for replication of results. The aim of this study was to assess how sensitive Haralick texture features of apparent diffusion coefficient (ADC) MR images are to changes in five parameters related to image acquisition and pre-processing: noise, resolution, how the ADC map is constructed, the choice of quantization method, and the number of gray levels in the quantized image. We found that noise, resolution, choice of quantization method and the number of gray levels in the quantized images had a significant influence on most texture features, and that the effect size varied between different features. Different methods for constructing the ADC maps did not have an impact on any texture feature. Based on our results, we recommend using images with similar resolutions and noise levels, using one quantization method, and the same number of gray levels in all quantized images, to make meaningful comparisons of texture feature results between different subjects.

Multiple sclerosis lesion segmentation using dictionary learning and sparse coding.

PubMed

Weiss, Nick; Rueckert, Daniel; Rao, Anil

2013-01-01

The segmentation of lesions in the brain during the development of Multiple Sclerosis is part of the diagnostic assessment for this disease and gives information on its current severity. This laborious process is still carried out in a manual or semiautomatic fashion by clinicians because published automatic approaches have not been universal enough to be widely employed in clinical practice. Thus Multiple Sclerosis lesion segmentation remains an open problem. In this paper we present a new unsupervised approach addressing this problem with dictionary learning and sparse coding methods. We show its general applicability to the problem of lesion segmentation by evaluating our approach on synthetic and clinical image data and comparing it to state-of-the-art methods. Furthermore the potential of using dictionary learning and sparse coding for such segmentation tasks is investigated and various possibilities for further experiments are discussed.

Neuroimaging Feature Terminology: A Controlled Terminology for the Annotation of Brain Imaging Features.

PubMed

Iyappan, Anandhi; Younesi, Erfan; Redolfi, Alberto; Vrooman, Henri; Khanna, Shashank; Frisoni, Giovanni B; Hofmann-Apitius, Martin

2017-01-01

Ontologies and terminologies are used for interoperability of knowledge and data in a standard manner among interdisciplinary research groups. Existing imaging ontologies capture general aspects of the imaging domain as a whole such as methodological concepts or calibrations of imaging instruments. However, none of the existing ontologies covers the diagnostic features measured by imaging technologies in the context of neurodegenerative diseases. Therefore, the Neuro-Imaging Feature Terminology (NIFT) was developed to organize the knowledge domain of measured brain features in association with neurodegenerative diseases by imaging technologies. The purpose is to identify quantitative imaging biomarkers that can be extracted from multi-modal brain imaging data. This terminology attempts to cover measured features and parameters in brain scans relevant to disease progression. In this paper, we demonstrate the systematic retrieval of measured indices from literature and how the extracted knowledge can be further used for disease modeling that integrates neuroimaging features with molecular processes.

Building a time-saving and adaptable tool to report adverse drug events.

PubMed

Parès, Yves; Declerck, Gunnar; Hussain, Sajjad; Ng, Romain; Jaulent, Marie-Christine

2013-01-01

The difficult task of detecting adverse drug events (ADEs) and the tedious process of building manual reports of ADE occurrences out of patient profiles result in a majority of adverse reactions not being reported to health regulatory authorities. The SALUS individual case safety report (ICSR) reporting tool, a component currently developed within the SALUS project, aims to support semi-automatic reporting of ADEs to regulatory authorities. In this paper, we present an initial design and current state of of our ICSR reporting tool that features: (i) automatic pre-population of reporting forms through extraction of the patient data contained in an Electronic Health Record (EHR); (ii) generation and electronic submission of the completed ICSRs by the physician to regulatory authorities; and (iii) integration of the reporting process into the physician's work-flow to limit the disturbance. The objective is to increase the rates of ADE reporting and the quality of the reported data. The SALUS interoperability platform supports patient data extraction independently of the EHR data model in use and allows generation of reports using the format expected by regulatory authorities.

Feature Visibility Limits in the Non-Linear Enhancement of Turbid Images

NASA Technical Reports Server (NTRS)

Jobson, Daniel J.; Rahman, Zia-ur; Woodell, Glenn A.

2003-01-01

The advancement of non-linear processing methods for generic automatic clarification of turbid imagery has led us from extensions of entirely passive multiscale Retinex processing to a new framework of active measurement and control of the enhancement process called the Visual Servo. In the process of testing this new non-linear computational scheme, we have identified that feature visibility limits in the post-enhancement image now simplify to a single signal-to-noise figure of merit: a feature is visible if the feature-background signal difference is greater than the RMS noise level. In other words, a signal-to-noise limit of approximately unity constitutes a lower limit on feature visibility.

MO-B-BRB-02: Maintain the Quality of Treatment Planning for Time-Constraint Cases

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

Chang, J.

The radiotherapy treatment planning process has evolved over the years with innovations in treatment planning, treatment delivery and imaging systems. Treatment modality and simulation technologies are also rapidly improving and affecting the planning process. For example, Image-guided-radiation-therapy has been widely adopted for patient setup, leading to margin reduction and isocenter repositioning after simulation. Stereotactic Body radiation therapy (SBRT) and Radiosurgery (SRS) have gradually become the standard of care for many treatment sites, which demand a higher throughput for the treatment plans even if the number of treatments per day remains the same. Finally, simulation, planning and treatment are traditionally sequentialmore » events. However, with emerging adaptive radiotherapy, they are becoming more tightly intertwined, leading to iterative processes. Enhanced efficiency of planning is therefore becoming more critical and poses serious challenge to the treatment planning process; Lean Six Sigma approaches are being utilized increasingly to balance the competing needs for speed and quality. In this symposium we will discuss the treatment planning process and illustrate effective techniques for managing workflow. Topics will include: Planning techniques: (a) beam placement, (b) dose optimization, (c) plan evaluation (d) export to RVS. Planning workflow: (a) import images, (b) Image fusion, (c) contouring, (d) plan approval (e) plan check (f) chart check, (g) sequential and iterative process Influence of upstream and downstream operations: (a) simulation, (b) immobilization, (c) motion management, (d) QA, (e) IGRT, (f) Treatment delivery, (g) SBRT/SRS (h) adaptive planning Reduction of delay between planning steps with Lean systems due to (a) communication, (b) limited resource, (b) contour, (c) plan approval, (d) treatment. Optimizing planning processes: (a) contour validation (b) consistent planning protocol, (c) protocol/template sharing, (d) semi-automatic plan evaluation, (e) quality checklist for error prevention, (f) iterative process, (g) balance of speed and quality Learning Objectives: Gain familiarity with the workflow of modern treatment planning process. Understand the scope and challenges of managing modern treatment planning processes. Gain familiarity with Lean Six Sigma approaches and their implementation in the treatment planning workflow.« less

A semiautomatic segmentation method for prostate in CT images using local texture classification and statistical shape modeling.

PubMed

Shahedi, Maysam; Halicek, Martin; Guo, Rongrong; Zhang, Guoyi; Schuster, David M; Fei, Baowei

2018-06-01

Prostate segmentation in computed tomography (CT) images is useful for treatment planning and procedure guidance such as external beam radiotherapy and brachytherapy. However, because of the low, soft tissue contrast of CT images, manual segmentation of the prostate is a time-consuming task with high interobserver variation. In this study, we proposed a semiautomated, three-dimensional (3D) segmentation for prostate CT images using shape and texture analysis and we evaluated the method against manual reference segmentations. The prostate gland usually has a globular shape with a smoothly curved surface, and its shape could be accurately modeled or reconstructed having a limited number of well-distributed surface points. In a training dataset, using the prostate gland centroid point as the origin of a coordination system, we defined an intersubject correspondence between the prostate surface points based on the spherical coordinates. We applied this correspondence to generate a point distribution model for prostate shape using principal component analysis and to study the local texture difference between prostate and nonprostate tissue close to the different prostate surface subregions. We used the learned shape and texture characteristics of the prostate in CT images and then combined them with user inputs to segment a new image. We trained our segmentation algorithm using 23 CT images and tested the algorithm on two sets of 10 nonbrachytherapy and 37 postlow dose rate brachytherapy CT images. We used a set of error metrics to evaluate the segmentation results using two experts' manual reference segmentations. For both nonbrachytherapy and post-brachytherapy image sets, the average measured Dice similarity coefficient (DSC) was 88% and the average mean absolute distance (MAD) was 1.9 mm. The average measured differences between the two experts on both datasets were 92% (DSC) and 1.1 mm (MAD). The proposed, semiautomatic segmentation algorithm showed a fast, robust, and accurate performance for 3D prostate segmentation of CT images, specifically when no previous, intrapatient information, that is, previously segmented images, was available. The accuracy of the algorithm is comparable to the best performance results reported in the literature and approaches the interexpert variability observed in manual segmentation. © 2018 American Association of Physicists in Medicine.

ProFound: Source Extraction and Application to Modern Survey Data

NASA Astrophysics Data System (ADS)

Robotham, A. S. G.; Davies, L. J. M.; Driver, S. P.; Koushan, S.; Taranu, D. S.; Casura, S.; Liske, J.

2018-05-01

We introduce PROFOUND, a source finding and image analysis package. PROFOUND provides methods to detect sources in noisy images, generate segmentation maps identifying the pixels belonging to each source, and measure statistics like flux, size, and ellipticity. These inputs are key requirements of PROFIT, our recently released galaxy profiling package, where the design aim is that these two software packages will be used in unison to semi-automatically profile large samples of galaxies. The key novel feature introduced in PROFOUND is that all photometry is executed on dilated segmentation maps that fully contain the identifiable flux, rather than using more traditional circular or ellipse-based photometry. Also, to be less sensitive to pathological segmentation issues, the de-blending is made across saddle points in flux. We apply PROFOUND in a number of simulated and real-world cases, and demonstrate that it behaves reasonably given its stated design goals. In particular, it offers good initial parameter estimation for PROFIT, and also segmentation maps that follow the sometimes complex geometry of resolved sources, whilst capturing nearly all of the flux. A number of bulge-disc decomposition projects are already making use of the PROFOUND and PROFIT pipeline, and adoption is being encouraged by publicly releasing the software for the open source R data analysis platform under an LGPL-3 license on GitHub (github.com/asgr/ProFound).

Resolving carbonate platform geometries on the Island of Bonaire, Caribbean Netherlands through semi-automatic GPR facies classification

NASA Astrophysics Data System (ADS)

Bowling, R. D.; Laya, J. C.; Everett, M. E.

2018-07-01

The study of exposed carbonate platforms provides observational constraints on regional tectonics and sea-level history. In this work Miocene-aged carbonate platform units of the Seroe Domi Formation are investigated on the island of Bonaire, located in the Southern Caribbean. Ground penetrating radar (GPR) was used to probe near-surface structural geometries associated with these lithologies. The single cross-island transect described herein allowed for continuous mapping of geologic structures on kilometre length scales. Numerical analysis was applied to the data in the form of k-means clustering of structure-parallel vectors derived from image structure tensors. This methodology enables radar facies along the survey transect to be semi-automatically mapped. The results provide subsurface evidence to support previous surficial and outcrop observations, and reveal complex stratigraphy within the platform. From the GPR data analysis, progradational clinoform geometries were observed on the northeast side of the island which support the tectonics and depositional trends of the region. Furthermore, several leeward-side radar facies are identified which correlate to environments of deposition conducive to dolomitization via reflux mechanisms.

Pancreas and cyst segmentation

NASA Astrophysics Data System (ADS)

Dmitriev, Konstantin; Gutenko, Ievgeniia; Nadeem, Saad; Kaufman, Arie

2016-03-01

Accurate segmentation of abdominal organs from medical images is an essential part of surgical planning and computer-aided disease diagnosis. Many existing algorithms are specialized for the segmentation of healthy organs. Cystic pancreas segmentation is especially challenging due to its low contrast boundaries, variability in shape, location and the stage of the pancreatic cancer. We present a semi-automatic segmentation algorithm for pancreata with cysts. In contrast to existing automatic segmentation approaches for healthy pancreas segmentation which are amenable to atlas/statistical shape approaches, a pancreas with cysts can have even higher variability with respect to the shape of the pancreas due to the size and shape of the cyst(s). Hence, fine results are better attained with semi-automatic steerable approaches. We use a novel combination of random walker and region growing approaches to delineate the boundaries of the pancreas and cysts with respective best Dice coefficients of 85.1% and 86.7%, and respective best volumetric overlap errors of 26.0% and 23.5%. Results show that the proposed algorithm for pancreas and pancreatic cyst segmentation is accurate and stable.

Resolving Carbonate Platform Geometries on the Island of Bonaire, Caribbean Netherlands through Semi-Automatic GPR Facies Classification

NASA Astrophysics Data System (ADS)

Bowling, R. D.; Laya, J. C.; Everett, M. E.

2018-05-01

The study of exposed carbonate platforms provides observational constraints on regional tectonics and sea-level history. In this work Miocene-aged carbonate platform units of the Seroe Domi Formation are investigated, on the island of Bonaire, located in the Southern Caribbean. Ground penetrating radar (GPR) was used to probe near-surface structural geometries associated with these lithologies. The single cross-island transect described herein allowed for continuous mapping of geologic structures on kilometer length scales. Numerical analysis was applied to the data in the form of k-means clustering of structure-parallel vectors derived from image structure tensors. This methodology enables radar facies along the survey transect to be semi-automatically mapped. The results provide subsurface evidence to support previous surficial and outcrop observations, and reveal complex stratigraphy within the platform. From the GPR data analysis, progradational clinoform geometries were observed on the northeast side of the island which supports the tectonics and depositional trends of the region. Furthermore, several leeward-side radar facies are identified which correlate to environments of deposition conducive to dolomitization via reflux mechanisms.

Statistical classification approach to discrimination between weak earthquakes and quarry blasts recorded by the Israel Seismic Network

NASA Astrophysics Data System (ADS)

Kushnir, A. F.; Troitsky, E. V.; Haikin, L. M.; Dainty, A.

1999-06-01

A semi-automatic procedure has been developed to achieve statistically optimum discrimination between earthquakes and explosions at local or regional distances based on a learning set specific to a given region. The method is used for step-by-step testing of candidate discrimination features to find the optimum (combination) subset of features, with the decision taken on a rigorous statistical basis. Linear (LDF) and Quadratic (QDF) Discriminant Functions based on Gaussian distributions of the discrimination features are implemented and statistically grounded; the features may be transformed by the Box-Cox transformation z=(1/ α)( yα-1) to make them more Gaussian. Tests of the method were successfully conducted on seismograms from the Israel Seismic Network using features consisting of spectral ratios between and within phases. Results showed that the QDF was more effective than the LDF and required five features out of 18 candidates for the optimum set. It was found that discrimination improved with increasing distance within the local range, and that eliminating transformation of the features and failing to correct for noise led to degradation of discrimination.

Comparison of a semi-automatic annotation tool and a natural language processing application for the generation of clinical statement entries.

PubMed

Lin, Ching-Heng; Wu, Nai-Yuan; Lai, Wei-Shao; Liou, Der-Ming

2015-01-01

Electronic medical records with encoded entries should enhance the semantic interoperability of document exchange. However, it remains a challenge to encode the narrative concept and to transform the coded concepts into a standard entry-level document. This study aimed to use a novel approach for the generation of entry-level interoperable clinical documents. Using HL7 clinical document architecture (CDA) as the example, we developed three pipelines to generate entry-level CDA documents. The first approach was a semi-automatic annotation pipeline (SAAP), the second was a natural language processing (NLP) pipeline, and the third merged the above two pipelines. We randomly selected 50 test documents from the i2b2 corpora to evaluate the performance of the three pipelines. The 50 randomly selected test documents contained 9365 words, including 588 Observation terms and 123 Procedure terms. For the Observation terms, the merged pipeline had a significantly higher F-measure than the NLP pipeline (0.89 vs 0.80, p<0.0001), but a similar F-measure to that of the SAAP (0.89 vs 0.87). For the Procedure terms, the F-measure was not significantly different among the three pipelines. The combination of a semi-automatic annotation approach and the NLP application seems to be a solution for generating entry-level interoperable clinical documents. © The Author 2014. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.comFor numbered affiliation see end of article.

Effective Moment Feature Vectors for Protein Domain Structures

PubMed Central

Shi, Jian-Yu; Yiu, Siu-Ming; Zhang, Yan-Ning; Chin, Francis Yuk-Lun

2013-01-01

Imaging processing techniques have been shown to be useful in studying protein domain structures. The idea is to represent the pairwise distances of any two residues of the structure in a 2D distance matrix (DM). Features and/or submatrices are extracted from this DM to represent a domain. Existing approaches, however, may involve a large number of features (100–400) or complicated mathematical operations. Finding fewer but more effective features is always desirable. In this paper, based on some key observations on DMs, we are able to decompose a DM image into four basic binary images, each representing the structural characteristics of a fundamental secondary structure element (SSE) or a motif in the domain. Using the concept of moments in image processing, we further derive 45 structural features based on the four binary images. Together with 4 features extracted from the basic images, we represent the structure of a domain using 49 features. We show that our feature vectors can represent domain structures effectively in terms of the following. (1) We show a higher accuracy for domain classification. (2) We show a clear and consistent distribution of domains using our proposed structural vector space. (3) We are able to cluster the domains according to our moment features and demonstrate a relationship between structural variation and functional diversity. PMID:24391828

High-quality and small-capacity e-learning video featuring lecturer-superimposing PC screen images

NASA Astrophysics Data System (ADS)

Nomura, Yoshihiko; Murakami, Michinobu; Sakamoto, Ryota; Sugiura, Tokuhiro; Matsui, Hirokazu; Kato, Norihiko

2006-10-01

Information processing and communication technology are progressing quickly, and are prevailing throughout various technological fields. Therefore, the development of such technology should respond to the needs for improvement of quality in the e-learning education system. The authors propose a new video-image compression processing system that ingeniously employs the features of the lecturing scene. While dynamic lecturing scene is shot by a digital video camera, screen images are electronically stored by a PC screen image capturing software in relatively long period at a practical class. Then, a lecturer and a lecture stick are extracted from the digital video images by pattern recognition techniques, and the extracted images are superimposed on the appropriate PC screen images by off-line processing. Thus, we have succeeded to create a high-quality and small-capacity (HQ/SC) video-on-demand educational content featuring the advantages: the high quality of image sharpness, the small electronic file capacity, and the realistic lecturer motion.

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

NASA Astrophysics Data System (ADS)

Wang, Yilun; Qian, Yunsheng

2018-01-01

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

Registering 2D and 3D imaging data of bone during healing.

PubMed

Hoerth, Rebecca M; Baum, Daniel; Knötel, David; Prohaska, Steffen; Willie, Bettina M; Duda, Georg N; Hege, Hans-Christian; Fratzl, Peter; Wagermaier, Wolfgang

2015-04-01

PURPOSE/AIMS OF THE STUDY: Bone's hierarchical structure can be visualized using a variety of methods. Many techniques, such as light and electron microscopy generate two-dimensional (2D) images, while micro-computed tomography (µCT) allows a direct representation of the three-dimensional (3D) structure. In addition, different methods provide complementary structural information, such as the arrangement of organic or inorganic compounds. The overall aim of the present study is to answer bone research questions by linking information of different 2D and 3D imaging techniques. A great challenge in combining different methods arises from the fact that they usually reflect different characteristics of the real structure. We investigated bone during healing by means of µCT and a couple of 2D methods. Backscattered electron images were used to qualitatively evaluate the tissue's calcium content and served as a position map for other experimental data. Nanoindentation and X-ray scattering experiments were performed to visualize mechanical and structural properties. We present an approach for the registration of 2D data in a 3D µCT reference frame, where scanning electron microscopies serve as a methodic link. Backscattered electron images are perfectly suited for registration into µCT reference frames, since both show structures based on the same physical principles. We introduce specific registration tools that have been developed to perform the registration process in a semi-automatic way. By applying this routine, we were able to exactly locate structural information (e.g. mineral particle properties) in the 3D bone volume. In bone healing studies this will help to better understand basic formation, remodeling and mineralization processes.

Automatic Feature Extraction from Planetary Images

NASA Technical Reports Server (NTRS)

Troglio, Giulia; Le Moigne, Jacqueline; Benediktsson, Jon A.; Moser, Gabriele; Serpico, Sebastiano B.

2010-01-01

With the launch of several planetary missions in the last decade, a large amount of planetary images has already been acquired and much more will be available for analysis in the coming years. The image data need to be analyzed, preferably by automatic processing techniques because of the huge amount of data. Although many automatic feature extraction methods have been proposed and utilized for Earth remote sensing images, these methods are not always applicable to planetary data that often present low contrast and uneven illumination characteristics. Different methods have already been presented for crater extraction from planetary images, but the detection of other types of planetary features has not been addressed yet. Here, we propose a new unsupervised method for the extraction of different features from the surface of the analyzed planet, based on the combination of several image processing techniques, including a watershed segmentation and the generalized Hough Transform. The method has many applications, among which image registration and can be applied to arbitrary planetary images.

MO-C-17A-11: A Segmentation and Point Matching Enhanced Deformable Image Registration Method for Dose Accumulation Between HDR CT Images

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

Zhen, X; Chen, H; Zhou, L

2014-06-15

Purpose: To propose and validate a novel and accurate deformable image registration (DIR) scheme to facilitate dose accumulation among treatment fractions of high-dose-rate (HDR) gynecological brachytherapy. Method: We have developed a method to adapt DIR algorithms to gynecologic anatomies with HDR applicators by incorporating a segmentation step and a point-matching step into an existing DIR framework. In the segmentation step, random walks algorithm is used to accurately segment and remove the applicator region (AR) in the HDR CT image. A semi-automatic seed point generation approach is developed to obtain the incremented foreground and background point sets to feed the randommore » walks algorithm. In the subsequent point-matching step, a feature-based thin-plate spline-robust point matching (TPS-RPM) algorithm is employed for AR surface point matching. With the resulting mapping, a DVF characteristic of the deformation between the two AR surfaces is generated by B-spline approximation, which serves as the initial DVF for the following Demons DIR between the two AR-free HDR CT images. Finally, the calculated DVF via Demons combined with the initial one serve as the final DVF to map doses between HDR fractions. Results: The segmentation and registration accuracy are quantitatively assessed by nine clinical HDR cases from three gynecological cancer patients. The quantitative results as well as the visual inspection of the DIR indicate that our proposed method can suppress the interference of the applicator with the DIR algorithm, and accurately register HDR CT images as well as deform and add interfractional HDR doses. Conclusions: We have developed a novel and robust DIR scheme that can perform registration between HDR gynecological CT images and yield accurate registration results. This new DIR scheme has potential for accurate interfractional HDR dose accumulation. This work is supported in part by the National Natural ScienceFoundation of China (no 30970866 and no 81301940)« less

Feature and contrast enhancement of mammographic image based on multiscale analysis and morphology.

PubMed

Wu, Shibin; Yu, Shaode; Yang, Yuhan; Xie, Yaoqin

2013-01-01

A new algorithm for feature and contrast enhancement of mammographic images is proposed in this paper. The approach bases on multiscale transform and mathematical morphology. First of all, the Laplacian Gaussian pyramid operator is applied to transform the mammography into different scale subband images. In addition, the detail or high frequency subimages are equalized by contrast limited adaptive histogram equalization (CLAHE) and low-pass subimages are processed by mathematical morphology. Finally, the enhanced image of feature and contrast is reconstructed from the Laplacian Gaussian pyramid coefficients modified at one or more levels by contrast limited adaptive histogram equalization and mathematical morphology, respectively. The enhanced image is processed by global nonlinear operator. The experimental results show that the presented algorithm is effective for feature and contrast enhancement of mammogram. The performance evaluation of the proposed algorithm is measured by contrast evaluation criterion for image, signal-noise-ratio (SNR), and contrast improvement index (CII).

Feature and Contrast Enhancement of Mammographic Image Based on Multiscale Analysis and Morphology

PubMed Central

Wu, Shibin; Xie, Yaoqin

2013-01-01

A new algorithm for feature and contrast enhancement of mammographic images is proposed in this paper. The approach bases on multiscale transform and mathematical morphology. First of all, the Laplacian Gaussian pyramid operator is applied to transform the mammography into different scale subband images. In addition, the detail or high frequency subimages are equalized by contrast limited adaptive histogram equalization (CLAHE) and low-pass subimages are processed by mathematical morphology. Finally, the enhanced image of feature and contrast is reconstructed from the Laplacian Gaussian pyramid coefficients modified at one or more levels by contrast limited adaptive histogram equalization and mathematical morphology, respectively. The enhanced image is processed by global nonlinear operator. The experimental results show that the presented algorithm is effective for feature and contrast enhancement of mammogram. The performance evaluation of the proposed algorithm is measured by contrast evaluation criterion for image, signal-noise-ratio (SNR), and contrast improvement index (CII). PMID:24416072

Stereo Image Ranging For An Autonomous Robot Vision System

NASA Astrophysics Data System (ADS)

Holten, James R.; Rogers, Steven K.; Kabrisky, Matthew; Cross, Steven

1985-12-01

The principles of stereo vision for three-dimensional data acquisition are well-known and can be applied to the problem of an autonomous robot vehicle. Coincidental points in the two images are located and then the location of that point in a three-dimensional space can be calculated using the offset of the points and knowledge of the camera positions and geometry. This research investigates the application of artificial intelligence knowledge representation techniques as a means to apply heuristics to relieve the computational intensity of the low level image processing tasks. Specifically a new technique for image feature extraction is presented. This technique, the Queen Victoria Algorithm, uses formal language productions to process the image and characterize its features. These characterized features are then used for stereo image feature registration to obtain the required ranging information. The results can be used by an autonomous robot vision system for environmental modeling and path finding.

Data mining and visualization of average images in a digital hand atlas

NASA Astrophysics Data System (ADS)

Zhang, Aifeng; Gertych, Arkadiusz; Liu, Brent J.; Huang, H. K.

2005-04-01

We have collected a digital hand atlas containing digitized left hand radiographs of normally developed children grouped accordingly by age, sex, and race. A set of features stored in a database reflecting patient's stage of skeletal development has been calculated by automatic image processing procedures. This paper addresses a new concept, "average" image in the digital hand atlas. The "average" reference image in the digital atlas is selected for each of the groups of normal developed children with the best representative skeletal maturity based on bony features. A data mining procedure was designed and applied to find the average image through average feature vector matching. It also provides a temporary solution for the missing feature problem through polynomial regression. As more cases are added to the digital hand atlas, it can grow to provide clinicians accurate reference images to aid the bone age assessment process.

iPad: Semantic annotation and markup of radiological images.

PubMed

Rubin, Daniel L; Rodriguez, Cesar; Shah, Priyanka; Beaulieu, Chris

2008-11-06

Radiological images contain a wealth of information,such as anatomy and pathology, which is often not explicit and computationally accessible. Information schemes are being developed to describe the semantic content of images, but such schemes can be unwieldy to operationalize because there are few tools to enable users to capture structured information easily as part of the routine research workflow. We have created iPad, an open source tool enabling researchers and clinicians to create semantic annotations on radiological images. iPad hides the complexity of the underlying image annotation information model from users, permitting them to describe images and image regions using a graphical interface that maps their descriptions to structured ontologies semi-automatically. Image annotations are saved in a variety of formats,enabling interoperability among medical records systems, image archives in hospitals, and the Semantic Web. Tools such as iPad can help reduce the burden of collecting structured information from images, and it could ultimately enable researchers and physicians to exploit images on a very large scale and glean the biological and physiological significance of image content.

Recognition of Roasted Coffee Bean Levels using Image Processing and Neural Network

NASA Astrophysics Data System (ADS)

Nasution, T. H.; Andayani, U.

2017-03-01

The coffee beans roast levels have some characteristics. However, some people cannot recognize the coffee beans roast level. In this research, we propose to design a method to recognize the coffee beans roast level of images digital by processing the image and classifying with backpropagation neural network. The steps consist of how to collect the images data with image acquisition, pre-processing, feature extraction using Gray Level Co-occurrence Matrix (GLCM) method and finally normalization of data extraction using decimal scaling features. The values of decimal scaling features become an input of classifying in backpropagation neural network. We use the method of backpropagation to recognize the coffee beans roast levels. The results showed that the proposed method is able to identify the coffee roasts beans level with an accuracy of 97.5%.

Numerical image manipulation and display in solar astronomy

NASA Technical Reports Server (NTRS)

Levine, R. H.; Flagg, J. C.

1977-01-01

The paper describes the system configuration and data manipulation capabilities of a solar image display system which allows interactive analysis of visual images and on-line manipulation of digital data. Image processing features include smoothing or filtering of images stored in the display, contrast enhancement, and blinking or flickering images. A computer with a core memory of 28,672 words provides the capacity to perform complex calculations based on stored images, including computing histograms, selecting subsets of images for further analysis, combining portions of images to produce images with physical meaning, and constructing mathematical models of features in an image. Some of the processing modes are illustrated by some image sequences from solar observations.

Clinical evaluation of semi-automatic open-source algorithmic software segmentation of the mandibular bone: Practical feasibility and assessment of a new course of action.

PubMed

Wallner, Jürgen; Hochegger, Kerstin; Chen, Xiaojun; Mischak, Irene; Reinbacher, Knut; Pau, Mauro; Zrnc, Tomislav; Schwenzer-Zimmerer, Katja; Zemann, Wolfgang; Schmalstieg, Dieter; Egger, Jan

2018-01-01

Computer assisted technologies based on algorithmic software segmentation are an increasing topic of interest in complex surgical cases. However-due to functional instability, time consuming software processes, personnel resources or licensed-based financial costs many segmentation processes are often outsourced from clinical centers to third parties and the industry. Therefore, the aim of this trial was to assess the practical feasibility of an easy available, functional stable and licensed-free segmentation approach to be used in the clinical practice. In this retrospective, randomized, controlled trail the accuracy and accordance of the open-source based segmentation algorithm GrowCut was assessed through the comparison to the manually generated ground truth of the same anatomy using 10 CT lower jaw data-sets from the clinical routine. Assessment parameters were the segmentation time, the volume, the voxel number, the Dice Score and the Hausdorff distance. Overall semi-automatic GrowCut segmentation times were about one minute. Mean Dice Score values of over 85% and Hausdorff Distances below 33.5 voxel could be achieved between the algorithmic GrowCut-based segmentations and the manual generated ground truth schemes. Statistical differences between the assessment parameters were not significant (p<0.05) and correlation coefficients were close to the value one (r > 0.94) for any of the comparison made between the two groups. Complete functional stable and time saving segmentations with high accuracy and high positive correlation could be performed by the presented interactive open-source based approach. In the cranio-maxillofacial complex the used method could represent an algorithmic alternative for image-based segmentation in the clinical practice for e.g. surgical treatment planning or visualization of postoperative results and offers several advantages. Due to an open-source basis the used method could be further developed by other groups or specialists. Systematic comparisons to other segmentation approaches or with a greater data amount are areas of future works.

A Featured-Based Strategy for Stereovision Matching in Sensors with Fish-Eye Lenses for Forest Environments

PubMed Central

Herrera, Pedro Javier; Pajares, Gonzalo; Guijarro, Maria; Ruz, José J.; Cruz, Jesús M.; Montes, Fernando

2009-01-01

This paper describes a novel feature-based stereovision matching process based on a pair of omnidirectional images in forest stands acquired with a stereovision sensor equipped with fish-eye lenses. The stereo analysis problem consists of the following steps: image acquisition, camera modelling, feature extraction, image matching and depth determination. Once the depths of significant points on the trees are obtained, the growing stock volume can be estimated by considering the geometrical camera modelling, which is the final goal. The key steps are feature extraction and image matching. This paper is devoted solely to these two steps. At a first stage a segmentation process extracts the trunks, which are the regions used as features, where each feature is identified through a set of attributes of properties useful for matching. In the second step the features are matched based on the application of the following four well known matching constraints, epipolar, similarity, ordering and uniqueness. The combination of the segmentation and matching processes for this specific kind of sensors make the main contribution of the paper. The method is tested with satisfactory results and compared against the human expert criterion. PMID:22303134

Model-based vision for space applications

NASA Technical Reports Server (NTRS)

Chaconas, Karen; Nashman, Marilyn; Lumia, Ronald

1992-01-01

This paper describes a method for tracking moving image features by combining spatial and temporal edge information with model based feature information. The algorithm updates the two-dimensional position of object features by correlating predicted model features with current image data. The results of the correlation process are used to compute an updated model. The algorithm makes use of a high temporal sampling rate with respect to spatial changes of the image features and operates in a real-time multiprocessing environment. Preliminary results demonstrate successful tracking for image feature velocities between 1.1 and 4.5 pixels every image frame. This work has applications for docking, assembly, retrieval of floating objects and a host of other space-related tasks.

Reproducible MRI Measurement of Adipose Tissue Volumes in Genetic and Dietary Rodent Obesity Models

PubMed Central

Johnson, David H.; Flask, Chris A.; Ernsberger, Paul R.; Wong, Wilbur C. K.; Wilson, David L.

2010-01-01

Purpose To develop ratio MRI [lipid/(lipid+water)] methods for assessing lipid depots and compare measurement variability to biological differences in lean controls (spontaneously hypertensive rats, SHRs), dietary obese (SHR-DO), and genetic/dietary obese (SHROBs) animals. Materials and Methods Images with and without CHESS water-suppression were processed using a semi-automatic method accounting for relaxometry, chemical shift, receive coil sensitivity, and partial volume. Results Partial volume correction improved results by 10–15%. Over six operators, volume variation was reduced to 1.9 ml from 30.6 ml for single-image-analysis with intensity inhomogeneity. For three acquisitions on the same animal, volume reproducibility was <1%. SHROBs had 6X visceral and 8X subcutaneous adipose tissue than SHRs. SHR-DOs had enlarged visceral depots (3X SHRs). SHROB had significantly more subcutaneous adipose tissue, indicating a strong genetic component to this fat depot. Liver ratios in SHR-DO and SHROB were higher than SHR, indicating elevated fat content. Among SHROBs, evidence suggested a phenotype SHROB* having elevated liver ratios and visceral adipose tissue volumes. Conclusion Effects of diet and genetics on obesity were significantly larger than variations due to image acquisition and analysis, indicating that these methods can be used to assess accumulation/depletion of lipid depots in animal models of obesity. PMID:18821617

Uniform competency-based local feature extraction for remote sensing images

NASA Astrophysics Data System (ADS)

Sedaghat, Amin; Mohammadi, Nazila

2018-01-01

Local feature detectors are widely used in many photogrammetry and remote sensing applications. The quantity and distribution of the local features play a critical role in the quality of the image matching process, particularly for multi-sensor high resolution remote sensing image registration. However, conventional local feature detectors cannot extract desirable matched features either in terms of the number of correct matches or the spatial and scale distribution in multi-sensor remote sensing images. To address this problem, this paper proposes a novel method for uniform and robust local feature extraction for remote sensing images, which is based on a novel competency criterion and scale and location distribution constraints. The proposed method, called uniform competency (UC) local feature extraction, can be easily applied to any local feature detector for various kinds of applications. The proposed competency criterion is based on a weighted ranking process using three quality measures, including robustness, spatial saliency and scale parameters, which is performed in a multi-layer gridding schema. For evaluation, five state-of-the-art local feature detector approaches, namely, scale-invariant feature transform (SIFT), speeded up robust features (SURF), scale-invariant feature operator (SFOP), maximally stable extremal region (MSER) and hessian-affine, are used. The proposed UC-based feature extraction algorithms were successfully applied to match various synthetic and real satellite image pairs, and the results demonstrate its capability to increase matching performance and to improve the spatial distribution. The code to carry out the UC feature extraction is available from href="https://www.researchgate.net/publication/317956777_UC-Feature_Extraction.

Computer vision applications for coronagraphic optical alignment and image processing.

PubMed

Savransky, Dmitry; Thomas, Sandrine J; Poyneer, Lisa A; Macintosh, Bruce A

2013-05-10

Modern coronagraphic systems require very precise alignment between optical components and can benefit greatly from automated image processing. We discuss three techniques commonly employed in the fields of computer vision and image analysis as applied to the Gemini Planet Imager, a new facility instrument for the Gemini South Observatory. We describe how feature extraction and clustering methods can be used to aid in automated system alignment tasks, and also present a search algorithm for finding regular features in science images used for calibration and data processing. Along with discussions of each technique, we present our specific implementation and show results of each one in operation.

Training set size, scale, and features in Geographic Object-Based Image Analysis of very high resolution unmanned aerial vehicle imagery

NASA Astrophysics Data System (ADS)

Ma, Lei; Cheng, Liang; Li, Manchun; Liu, Yongxue; Ma, Xiaoxue

2015-04-01

Unmanned Aerial Vehicle (UAV) has been used increasingly for natural resource applications in recent years due to their greater availability and the miniaturization of sensors. In addition, Geographic Object-Based Image Analysis (GEOBIA) has received more attention as a novel paradigm for remote sensing earth observation data. However, GEOBIA generates some new problems compared with pixel-based methods. In this study, we developed a strategy for the semi-automatic optimization of object-based classification, which involves an area-based accuracy assessment that analyzes the relationship between scale and the training set size. We found that the Overall Accuracy (OA) increased as the training set ratio (proportion of the segmented objects used for training) increased when the Segmentation Scale Parameter (SSP) was fixed. The OA increased more slowly as the training set ratio became larger and a similar rule was obtained according to the pixel-based image analysis. The OA decreased as the SSP increased when the training set ratio was fixed. Consequently, the SSP should not be too large during classification using a small training set ratio. By contrast, a large training set ratio is required if classification is performed using a high SSP. In addition, we suggest that the optimal SSP for each class has a high positive correlation with the mean area obtained by manual interpretation, which can be summarized by a linear correlation equation. We expect that these results will be applicable to UAV imagery classification to determine the optimal SSP for each class.

Neuroimaging Feature Terminology: A Controlled Terminology for the Annotation of Brain Imaging Features

PubMed Central

Iyappan, Anandhi; Younesi, Erfan; Redolfi, Alberto; Vrooman, Henri; Khanna, Shashank; Frisoni, Giovanni B.; Hofmann-Apitius, Martin

2017-01-01

Ontologies and terminologies are used for interoperability of knowledge and data in a standard manner among interdisciplinary research groups. Existing imaging ontologies capture general aspects of the imaging domain as a whole such as methodological concepts or calibrations of imaging instruments. However, none of the existing ontologies covers the diagnostic features measured by imaging technologies in the context of neurodegenerative diseases. Therefore, the Neuro-Imaging Feature Terminology (NIFT) was developed to organize the knowledge domain of measured brain features in association with neurodegenerative diseases by imaging technologies. The purpose is to identify quantitative imaging biomarkers that can be extracted from multi-modal brain imaging data. This terminology attempts to cover measured features and parameters in brain scans relevant to disease progression. In this paper, we demonstrate the systematic retrieval of measured indices from literature and how the extracted knowledge can be further used for disease modeling that integrates neuroimaging features with molecular processes. PMID:28731430

Geologic interpretation of Seasat SAR imagery near the Rio Lacantum, Mexico

NASA Technical Reports Server (NTRS)

Rebillard, PH.; Dixon, T.

1984-01-01

A mosaic of the Seasat Synthetic Aperture Radar (SAR) optically processed images over Central America is presented. A SAR image of the Rio Lacantum area (southeastern Mexico) has been digitally processed and its interpretation is presented. The region is characterized by low relief and a dense vegetation canopy. Surface is believed to be indicative of subsurface structural features. The Seasat-SAR system had a steep imaging geometry (incidence angle 23 + or - 3 deg off-nadir) which is favorable for detection of subtle topographic variations. Subtle textural features in the image corresponding to surface topography were enhanced by image processing techniques. A structural and lithologic interpretation of the processed images is presented. Lineaments oriented NE-SW dominate and intersect broad folds trending NW-SE. Distinctive karst topography characterizes one high relief area

Novel and powerful 3D adaptive crisp active contour method applied in the segmentation of CT lung images.

PubMed

Rebouças Filho, Pedro Pedrosa; Cortez, Paulo César; da Silva Barros, Antônio C; C Albuquerque, Victor Hugo; R S Tavares, João Manuel

2017-01-01

The World Health Organization estimates that 300 million people have asthma, 210 million people have Chronic Obstructive Pulmonary Disease (COPD), and, according to WHO, COPD will become the third major cause of death worldwide in 2030. Computational Vision systems are commonly used in pulmonology to address the task of image segmentation, which is essential for accurate medical diagnoses. Segmentation defines the regions of the lungs in CT images of the thorax that must be further analyzed by the system or by a specialist physician. This work proposes a novel and powerful technique named 3D Adaptive Crisp Active Contour Method (3D ACACM) for the segmentation of CT lung images. The method starts with a sphere within the lung to be segmented that is deformed by forces acting on it towards the lung borders. This process is performed iteratively in order to minimize an energy function associated with the 3D deformable model used. In the experimental assessment, the 3D ACACM is compared against three approaches commonly used in this field: the automatic 3D Region Growing, the level-set algorithm based on coherent propagation and the semi-automatic segmentation by an expert using the 3D OsiriX toolbox. When applied to 40 CT scans of the chest the 3D ACACM had an average F-measure of 99.22%, revealing its superiority and competency to segment lungs in CT images. Copyright © 2016 Elsevier B.V. All rights reserved.

Estimating ice albedo from fine debris cover quantified by a semi-automatic method: the case study of Forni Glacier, Italian Alps

NASA Astrophysics Data System (ADS)

Azzoni, Roberto Sergio; Senese, Antonella; Zerboni, Andrea; Maugeri, Maurizio; Smiraglia, Claudio; Diolaiuti, Guglielmina Adele

2016-03-01

In spite of the quite abundant literature focusing on fine debris deposition over glacier accumulation areas, less attention has been paid to the glacier melting surface. Accordingly, we proposed a novel method based on semi-automatic image analysis to estimate ice albedo from fine debris coverage (d). Our procedure was tested on the surface of a wide Alpine valley glacier (the Forni Glacier, Italy), in summer 2011, 2012 and 2013, acquiring parallel data sets of in situ measurements of ice albedo and high-resolution surface images. Analysis of 51 images yielded d values ranging from 0.01 to 0.63 and albedo was found to vary from 0.06 to 0.32. The estimated d values are in a linear relation with the natural logarithm of measured ice albedo (R = -0.84). The robustness of our approach in evaluating d was analyzed through five sensitivity tests, and we found that it is largely replicable. On the Forni Glacier, we also quantified a mean debris coverage rate (Cr) equal to 6 g m-2 per day during the ablation season of 2013, thus supporting previous studies that describe ongoing darkening phenomena at Alpine debris-free glaciers surface. In addition to debris coverage, we also considered the impact of water (both from melt and rainfall) as a factor that tunes albedo: meltwater occurs during the central hours of the day, decreasing the albedo due to its lower reflectivity; instead, rainfall causes a subsequent mean daily albedo increase slightly higher than 20 %, although it is short-lasting (from 1 to 4 days).

Methods in Astronomical Image Processing

NASA Astrophysics Data System (ADS)

Jörsäter, S.

A Brief Introductory Note History of Astronomical Imaging Astronomical Image Data Images in Various Formats Digitized Image Data Digital Image Data Philosophy of Astronomical Image Processing Properties of Digital Astronomical Images Human Image Processing Astronomical vs. Computer Science Image Processing Basic Tools of Astronomical Image Processing Display Applications Calibration of Intensity Scales Calibration of Length Scales Image Re-shaping Feature Enhancement Noise Suppression Noise and Error Analysis Image Processing Packages: Design of AIPS and MIDAS AIPS MIDAS Reduction of CCD Data Bias Subtraction Clipping Preflash Subtraction Dark Subtraction Flat Fielding Sky Subtraction Extinction Correction Deconvolution Methods Rebinning/Combining Summary and Prospects for the Future

Context-based automated defect classification system using multiple morphological masks

DOEpatents

Gleason, Shaun S.; Hunt, Martin A.; Sari-Sarraf, Hamed

2002-01-01

Automatic detection of defects during the fabrication of semiconductor wafers is largely automated, but the classification of those defects is still performed manually by technicians. This invention includes novel digital image analysis techniques that generate unique feature vector descriptions of semiconductor defects as well as classifiers that use these descriptions to automatically categorize the defects into one of a set of pre-defined classes. Feature extraction techniques based on multiple-focus images, multiple-defect mask images, and segmented semiconductor wafer images are used to create unique feature-based descriptions of the semiconductor defects. These feature-based defect descriptions are subsequently classified by a defect classifier into categories that depend on defect characteristics and defect contextual information, that is, the semiconductor process layer(s) with which the defect comes in contact. At the heart of the system is a knowledge database that stores and distributes historical semiconductor wafer and defect data to guide the feature extraction and classification processes. In summary, this invention takes as its input a set of images containing semiconductor defect information, and generates as its output a classification for the defect that describes not only the defect itself, but also the location of that defect with respect to the semiconductor process layers.

Classification of tumor based on magnetic resonance (MR) brain images using wavelet energy feature and neuro-fuzzy model

NASA Astrophysics Data System (ADS)

Damayanti, A.; Werdiningsih, I.

2018-03-01

The brain is the organ that coordinates all the activities that occur in our bodies. Small abnormalities in the brain will affect body activity. Tumor of the brain is a mass formed a result of cell growth not normal and unbridled in the brain. MRI is a non-invasive medical test that is useful for doctors in diagnosing and treating medical conditions. The process of classification of brain tumor can provide the right decision and correct treatment and right on the process of treatment of brain tumor. In this study, the classification process performed to determine the type of brain tumor disease, namely Alzheimer’s, Glioma, Carcinoma and normal, using energy coefficient and ANFIS. Process stages in the classification of images of MR brain are the extraction of a feature, reduction of a feature, and process of classification. The result of feature extraction is a vector approximation of each wavelet decomposition level. The feature reduction is a process of reducing the feature by using the energy coefficients of the vector approximation. The feature reduction result for energy coefficient of 100 per feature is 1 x 52 pixels. This vector will be the input on the classification using ANFIS with Fuzzy C-Means and FLVQ clustering process and LM back-propagation. Percentage of success rate of MR brain images recognition using ANFIS-FLVQ, ANFIS, and LM back-propagation was obtained at 100%.

Difet: Distributed Feature Extraction Tool for High Spatial Resolution Remote Sensing Images

NASA Astrophysics Data System (ADS)

Eken, S.; Aydın, E.; Sayar, A.

2017-11-01

In this paper, we propose distributed feature extraction tool from high spatial resolution remote sensing images. Tool is based on Apache Hadoop framework and Hadoop Image Processing Interface. Two corner detection (Harris and Shi-Tomasi) algorithms and five feature descriptors (SIFT, SURF, FAST, BRIEF, and ORB) are considered. Robustness of the tool in the task of feature extraction from LandSat-8 imageries are evaluated in terms of horizontal scalability.

Multifractal-based nuclei segmentation in fish images.

PubMed

Reljin, Nikola; Slavkovic-Ilic, Marijeta; Tapia, Coya; Cihoric, Nikola; Stankovic, Srdjan

2017-09-01

The method for nuclei segmentation in fluorescence in-situ hybridization (FISH) images, based on the inverse multifractal analysis (IMFA) is proposed. From the blue channel of the FISH image in RGB format, the matrix of Holder exponents, with one-by-one correspondence with the image pixels, is determined first. The following semi-automatic procedure is proposed: initial nuclei segmentation is performed automatically from the matrix of Holder exponents by applying predefined hard thresholding; then the user evaluates the result and is able to refine the segmentation by changing the threshold, if necessary. After successful nuclei segmentation, the HER2 (human epidermal growth factor receptor 2) scoring can be determined in usual way: by counting red and green dots within segmented nuclei, and finding their ratio. The IMFA segmentation method is tested over 100 clinical cases, evaluated by skilled pathologist. Testing results show that the new method has advantages compared to already reported methods.

A model for calculating the costs of in vivo dosimetry and portal imaging in radiotherapy departments.

PubMed

Kesteloot, K; Dutreix, A; van der Schueren, E

1993-08-01

The costs of in vivo dosimetry and portal imaging in radiotherapy are estimated, on the basis of a detailed overview of the activities involved in both quality assurance techniques. These activities require the availability of equipment, the use of material and workload. The cost calculations allow to conclude that for most departments in vivo dosimetry with diodes will be a cheaper alternative than in vivo dosimetry with TLD-meters. Whether TLD measurements can be performed cheaper with an automatic reader (with a higher equipment cost, but lower workload) or with a semi-automatic reader (lower equipment cost, but higher workload), depends on the number of checks in the department. LSP-systems (with a very high equipment cost) as well as on-line imaging systems will be cheaper portal imaging techniques than conventional port films (with high material costs) for large departments, or for smaller departments that perform frequent volume checks.

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

PubMed Central

Tan, Bingyao; Wong, Alexander; Bizheva, Kostadinka

2018-01-01

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

Feature-based Alignment of Volumetric Multi-modal Images

PubMed Central

Toews, Matthew; Zöllei, Lilla; Wells, William M.

2014-01-01

This paper proposes a method for aligning image volumes acquired from different imaging modalities (e.g. MR, CT) based on 3D scale-invariant image features. A novel method for encoding invariant feature geometry and appearance is developed, based on the assumption of locally linear intensity relationships, providing a solution to poor repeatability of feature detection in different image modalities. The encoding method is incorporated into a probabilistic feature-based model for multi-modal image alignment. The model parameters are estimated via a group-wise alignment algorithm, that iteratively alternates between estimating a feature-based model from feature data, then realigning feature data to the model, converging to a stable alignment solution with few pre-processing or pre-alignment requirements. The resulting model can be used to align multi-modal image data with the benefits of invariant feature correspondence: globally optimal solutions, high efficiency and low memory usage. The method is tested on the difficult RIRE data set of CT, T1, T2, PD and MP-RAGE brain images of subjects exhibiting significant inter-subject variability due to pathology. PMID:24683955

A fully automated cell segmentation and morphometric parameter system for quantifying corneal endothelial cell morphology.

PubMed

Al-Fahdawi, Shumoos; Qahwaji, Rami; Al-Waisy, Alaa S; Ipson, Stanley; Ferdousi, Maryam; Malik, Rayaz A; Brahma, Arun

2018-07-01

Corneal endothelial cell abnormalities may be associated with a number of corneal and systemic diseases. Damage to the endothelial cells can significantly affect corneal transparency by altering hydration of the corneal stroma, which can lead to irreversible endothelial cell pathology requiring corneal transplantation. To date, quantitative analysis of endothelial cell abnormalities has been manually performed by ophthalmologists using time consuming and highly subjective semi-automatic tools, which require an operator interaction. We developed and applied a fully-automated and real-time system, termed the Corneal Endothelium Analysis System (CEAS) for the segmentation and computation of endothelial cells in images of the human cornea obtained by in vivo corneal confocal microscopy. First, a Fast Fourier Transform (FFT) Band-pass filter is applied to reduce noise and enhance the image quality to make the cells more visible. Secondly, endothelial cell boundaries are detected using watershed transformations and Voronoi tessellations to accurately quantify the morphological parameters of the human corneal endothelial cells. The performance of the automated segmentation system was tested against manually traced ground-truth images based on a database consisting of 40 corneal confocal endothelial cell images in terms of segmentation accuracy and obtained clinical features. In addition, the robustness and efficiency of the proposed CEAS system were compared with manually obtained cell densities using a separate database of 40 images from controls (n = 11), obese subjects (n = 16) and patients with diabetes (n = 13). The Pearson correlation coefficient between automated and manual endothelial cell densities is 0.9 (p < 0.0001) and a Bland-Altman plot shows that 95% of the data are between the 2SD agreement lines. We demonstrate the effectiveness and robustness of the CEAS system, and the possibility of utilizing it in a real world clinical setting to enable rapid diagnosis and for patient follow-up, with an execution time of only 6 seconds per image. Copyright © 2018 Elsevier B.V. All rights reserved.

BlobContours: adapting Blobworld for supervised color- and texture-based image segmentation

NASA Astrophysics Data System (ADS)

Vogel, Thomas; Nguyen, Dinh Quyen; Dittmann, Jana

2006-01-01

Extracting features is the first and one of the most crucial steps in recent image retrieval process. While the color features and the texture features of digital images can be extracted rather easily, the shape features and the layout features depend on reliable image segmentation. Unsupervised image segmentation, often used in image analysis, works on merely syntactical basis. That is, what an unsupervised segmentation algorithm can segment is only regions, but not objects. To obtain high-level objects, which is desirable in image retrieval, human assistance is needed. Supervised image segmentations schemes can improve the reliability of segmentation and segmentation refinement. In this paper we propose a novel interactive image segmentation technique that combines the reliability of a human expert with the precision of automated image segmentation. The iterative procedure can be considered a variation on the Blobworld algorithm introduced by Carson et al. from EECS Department, University of California, Berkeley. Starting with an initial segmentation as provided by the Blobworld framework, our algorithm, namely BlobContours, gradually updates it by recalculating every blob, based on the original features and the updated number of Gaussians. Since the original algorithm has hardly been designed for interactive processing we had to consider additional requirements for realizing a supervised segmentation scheme on the basis of Blobworld. Increasing transparency of the algorithm by applying usercontrolled iterative segmentation, providing different types of visualization for displaying the segmented image and decreasing computational time of segmentation are three major requirements which are discussed in detail.

Flame analysis using image processing techniques

NASA Astrophysics Data System (ADS)

Her Jie, Albert Chang; Zamli, Ahmad Faizal Ahmad; Zulazlan Shah Zulkifli, Ahmad; Yee, Joanne Lim Mun; Lim, Mooktzeng

2018-04-01

This paper presents image processing techniques with the use of fuzzy logic and neural network approach to perform flame analysis. Flame diagnostic is important in the industry to extract relevant information from flame images. Experiment test is carried out in a model industrial burner with different flow rates. Flame features such as luminous and spectral parameters are extracted using image processing and Fast Fourier Transform (FFT). Flame images are acquired using FLIR infrared camera. Non-linearities such as thermal acoustic oscillations and background noise affect the stability of flame. Flame velocity is one of the important characteristics that determines stability of flame. In this paper, an image processing method is proposed to determine flame velocity. Power spectral density (PSD) graph is a good tool for vibration analysis where flame stability can be approximated. However, a more intelligent diagnostic system is needed to automatically determine flame stability. In this paper, flame features of different flow rates are compared and analyzed. The selected flame features are used as inputs to the proposed fuzzy inference system to determine flame stability. Neural network is used to test the performance of the fuzzy inference system.

Model-Based Learning of Local Image Features for Unsupervised Texture Segmentation

NASA Astrophysics Data System (ADS)

Kiechle, Martin; Storath, Martin; Weinmann, Andreas; Kleinsteuber, Martin

2018-04-01

Features that capture well the textural patterns of a certain class of images are crucial for the performance of texture segmentation methods. The manual selection of features or designing new ones can be a tedious task. Therefore, it is desirable to automatically adapt the features to a certain image or class of images. Typically, this requires a large set of training images with similar textures and ground truth segmentation. In this work, we propose a framework to learn features for texture segmentation when no such training data is available. The cost function for our learning process is constructed to match a commonly used segmentation model, the piecewise constant Mumford-Shah model. This means that the features are learned such that they provide an approximately piecewise constant feature image with a small jump set. Based on this idea, we develop a two-stage algorithm which first learns suitable convolutional features and then performs a segmentation. We note that the features can be learned from a small set of images, from a single image, or even from image patches. The proposed method achieves a competitive rank in the Prague texture segmentation benchmark, and it is effective for segmenting histological images.

Semi-Automatic Grading of Students' Answers Written in Free Text

ERIC Educational Resources Information Center

Escudeiro, Nuno; Escudeiro, Paula; Cruz, Augusto

2011-01-01

The correct grading of free text answers to exam questions during an assessment process is time consuming and subject to fluctuations in the application of evaluation criteria, particularly when the number of answers is high (in the hundreds). In consequence of these fluctuations, inherent to human nature, and largely determined by emotional…

Multispectral and geomorphic studies of processed Voyager 2 images of Europa

NASA Technical Reports Server (NTRS)

Meier, T. A.

1984-01-01

High resolution images of Europa taken by the Voyager 2 spacecraft were used to study a portion of Europa's dark lineations and the major white line feature Agenor Linea. Initial image processing of images 1195J2-001 (violet filter), 1198J2-001 (blue filter), 1201J2-001 (orange filter), and 1204J2-001 (ultraviolet filter) was performed at the U.S.G.S. Branch of Astrogeology in Flagstaff, Arizona. Processing was completed through the stages of image registration and color ratio image construction. Pixel printouts were used in a new technique of linear feature profiling to compensate for image misregistration through the mapping of features on the printouts. In all, 193 dark lineation segments were mapped and profiled. The more accurate multispectral data derived by this method was plotted using a new application of the ternary diagram, with orange, blue, and violet relative spectral reflectances serving as end members. Statistical techniques were then applied to the ternary diagram plots. The image products generated at LPI were used mainly to cross-check and verify the results of the ternary diagram analysis.

Kwf-Grid workflow management system for Earth science applications

NASA Astrophysics Data System (ADS)

Tran, V.; Hluchy, L.

2009-04-01

In this paper, we present workflow management tool for Earth science applications in EGEE. The workflow management tool was originally developed within K-wf Grid project for GT4 middleware and has many advanced features like semi-automatic workflow composition, user-friendly GUI for managing workflows, knowledge management. In EGEE, we are porting the workflow management tool to gLite middleware for Earth science applications K-wf Grid workflow management system was developed within "Knowledge-based Workflow System for Grid Applications" under the 6th Framework Programme. The workflow mangement system intended to - semi-automatically compose a workflow of Grid services, - execute the composed workflow application in a Grid computing environment, - monitor the performance of the Grid infrastructure and the Grid applications, - analyze the resulting monitoring information, - capture the knowledge that is contained in the information by means of intelligent agents, - and finally to reuse the joined knowledge gathered from all participating users in a collaborative way in order to efficiently construct workflows for new Grid applications. Kwf Grid workflow engines can support different types of jobs (e.g. GRAM job, web services) in a workflow. New class of gLite job has been added to the system, allows system to manage and execute gLite jobs in EGEE infrastructure. The GUI has been adapted to the requirements of EGEE users, new credential management servlet is added to portal. Porting K-wf Grid workflow management system to gLite would allow EGEE users to use the system and benefit from its avanced features. The system is primarly tested and evaluated with applications from ES clusters.

FEX: A Knowledge-Based System For Planimetric Feature Extraction

NASA Astrophysics Data System (ADS)

Zelek, John S.

1988-10-01

Topographical planimetric features include natural surfaces (rivers, lakes) and man-made surfaces (roads, railways, bridges). In conventional planimetric feature extraction, a photointerpreter manually interprets and extracts features from imagery on a stereoplotter. Visual planimetric feature extraction is a very labour intensive operation. The advantages of automating feature extraction include: time and labour savings; accuracy improvements; and planimetric data consistency. FEX (Feature EXtraction) combines techniques from image processing, remote sensing and artificial intelligence for automatic feature extraction. The feature extraction process co-ordinates the information and knowledge in a hierarchical data structure. The system simulates the reasoning of a photointerpreter in determining the planimetric features. Present efforts have concentrated on the extraction of road-like features in SPOT imagery. Keywords: Remote Sensing, Artificial Intelligence (AI), SPOT, image understanding, knowledge base, apars.

Automatic Synthetic Aperture Radar based oil spill detection and performance estimation via a semi-automatic operational service benchmark.

PubMed

Singha, Suman; Vespe, Michele; Trieschmann, Olaf

2013-08-15

Today the health of ocean is in danger as it was never before mainly due to man-made pollutions. Operational activities show regular occurrence of accidental and deliberate oil spill in European waters. Since the areas covered by oil spills are usually large, satellite remote sensing particularly Synthetic Aperture Radar represents an effective option for operational oil spill detection. This paper describes the development of a fully automated approach for oil spill detection from SAR. Total of 41 feature parameters extracted from each segmented dark spot for oil spill and 'look-alike' classification and ranked according to their importance. The classification algorithm is based on a two-stage processing that combines classification tree analysis and fuzzy logic. An initial evaluation of this methodology on a large dataset has been carried out and degree of agreement between results from proposed algorithm and human analyst was estimated between 85% and 93% respectively for ENVISAT and RADARSAT. Copyright © 2013 Elsevier Ltd. All rights reserved.

Rotation covariant image processing for biomedical applications.

PubMed

Skibbe, Henrik; Reisert, Marco

2013-01-01

With the advent of novel biomedical 3D image acquisition techniques, the efficient and reliable analysis of volumetric images has become more and more important. The amount of data is enormous and demands an automated processing. The applications are manifold, ranging from image enhancement, image reconstruction, and image description to object/feature detection and high-level contextual feature extraction. In most scenarios, it is expected that geometric transformations alter the output in a mathematically well-defined manner. In this paper we emphasis on 3D translations and rotations. Many algorithms rely on intensity or low-order tensorial-like descriptions to fulfill this demand. This paper proposes a general mathematical framework based on mathematical concepts and theories transferred from mathematical physics and harmonic analysis into the domain of image analysis and pattern recognition. Based on two basic operations, spherical tensor differentiation and spherical tensor multiplication, we show how to design a variety of 3D image processing methods in an efficient way. The framework has already been applied to several biomedical applications ranging from feature and object detection tasks to image enhancement and image restoration techniques. In this paper, the proposed methods are applied on a variety of different 3D data modalities stemming from medical and biological sciences.

Geologic analyses of LANDSAT-1 multispectral imagery of a possible power plant site employing digital and analog image processing. [in Pennsylvania

NASA Technical Reports Server (NTRS)

Lovegreen, J. R.; Prosser, W. J.; Millet, R. A.

1975-01-01

A site in the Great Valley subsection of the Valley and Ridge physiographic province in eastern Pennsylvania was studied to evaluate the use of digital and analog image processing for geologic investigations. Ground truth at the site was obtained by a field mapping program, a subsurface exploration investigation and a review of available published and unpublished literature. Remote sensing data were analyzed using standard manual techniques. LANDSAT-1 imagery was analyzed using digital image processing employing the multispectral Image 100 system and using analog color processing employing the VP-8 image analyzer. This study deals primarily with linears identified employing image processing and correlation of these linears with known structural features and with linears identified manual interpretation; and the identification of rock outcrops in areas of extensive vegetative cover employing image processing. The results of this study indicate that image processing can be a cost-effective tool for evaluating geologic and linear features for regional studies encompassing large areas such as for power plant siting. Digital image processing can be an effective tool for identifying rock outcrops in areas of heavy vegetative cover.

Texture Feature Extraction and Classification for Iris Diagnosis

NASA Astrophysics Data System (ADS)

Ma, Lin; Li, Naimin

Appling computer aided techniques in iris image processing, and combining occidental iridology with the traditional Chinese medicine is a challenging research area in digital image processing and artificial intelligence. This paper proposes an iridology model that consists the iris image pre-processing, texture feature analysis and disease classification. To the pre-processing, a 2-step iris localization approach is proposed; a 2-D Gabor filter based texture analysis and a texture fractal dimension estimation method are proposed for pathological feature extraction; and at last support vector machines are constructed to recognize 2 typical diseases such as the alimentary canal disease and the nerve system disease. Experimental results show that the proposed iridology diagnosis model is quite effective and promising for medical diagnosis and health surveillance for both hospital and public use.

Automatic Sea Bird Detection from High Resolution Aerial Imagery

NASA Astrophysics Data System (ADS)

Mader, S.; Grenzdörffer, G. J.

2016-06-01

Great efforts are presently taken in the scientific community to develop computerized and (fully) automated image processing methods allowing for an efficient and automatic monitoring of sea birds and marine mammals in ever-growing amounts of aerial imagery. Currently the major part of the processing, however, is still conducted by especially trained professionals, visually examining the images and detecting and classifying the requested subjects. This is a very tedious task, particularly when the rate of void images regularly exceeds the mark of 90%. In the content of this contribution we will present our work aiming to support the processing of aerial images by modern methods from the field of image processing. We will especially focus on the combination of local, region-based feature detection and piecewise global image segmentation for automatic detection of different sea bird species. Large image dimensions resulting from the use of medium and large-format digital cameras in aerial surveys inhibit the applicability of image processing methods based on global operations. In order to efficiently handle those image sizes and to nevertheless take advantage of globally operating segmentation algorithms, we will describe the combined usage of a simple performant feature detector based on local operations on the original image with a complex global segmentation algorithm operating on extracted sub-images. The resulting exact segmentation of possible candidates then serves as a basis for the determination of feature vectors for subsequent elimination of false candidates and for classification tasks.

Automatic and semi-automatic approaches for arteriolar-to-venular computation in retinal photographs

NASA Astrophysics Data System (ADS)

Mendonça, Ana Maria; Remeseiro, Beatriz; Dashtbozorg, Behdad; Campilho, Aurélio

2017-03-01

The Arteriolar-to-Venular Ratio (AVR) is a popular dimensionless measure which allows the assessment of patients' condition for the early diagnosis of different diseases, including hypertension and diabetic retinopathy. This paper presents two new approaches for AVR computation in retinal photographs which include a sequence of automated processing steps: vessel segmentation, caliber measurement, optic disc segmentation, artery/vein classification, region of interest delineation, and AVR calculation. Both approaches have been tested on the INSPIRE-AVR dataset, and compared with a ground-truth provided by two medical specialists. The obtained results demonstrate the reliability of the fully automatic approach which provides AVR ratios very similar to at least one of the observers. Furthermore, the semi-automatic approach, which includes the manual modification of the artery/vein classification if needed, allows to significantly reduce the error to a level below the human error.

Splitting parameter yield (SPY): A program for semiautomatic analysis of shear-wave splitting

NASA Astrophysics Data System (ADS)

Zaccarelli, Lucia; Bianco, Francesca; Zaccarelli, Riccardo

2012-03-01

SPY is a Matlab algorithm that analyzes seismic waveforms in a semiautomatic way, providing estimates of the two observables of the anisotropy: the shear-wave splitting parameters. We chose to exploit those computational processes that require less intervention by the user, gaining objectivity and reliability as a result. The algorithm joins the covariance matrix and the cross-correlation techniques, and all the computation steps are interspersed by several automatic checks intended to verify the reliability of the yields. The resulting semiautomation generates two new advantages in the field of anisotropy studies: handling a huge amount of data at the same time, and comparing different yields. From this perspective, SPY has been developed in the Matlab environment, which is widespread, versatile, and user-friendly. Our intention is to provide the scientific community with a new monitoring tool for tracking the temporal variations of the crustal stress field.

IMAGE 100: The interactive multispectral image processing system

NASA Technical Reports Server (NTRS)

Schaller, E. S.; Towles, R. W.

1975-01-01

The need for rapid, cost-effective extraction of useful information from vast quantities of multispectral imagery available from aircraft or spacecraft has resulted in the design, implementation and application of a state-of-the-art processing system known as IMAGE 100. Operating on the general principle that all objects or materials possess unique spectral characteristics or signatures, the system uses this signature uniqueness to identify similar features in an image by simultaneously analyzing signatures in multiple frequency bands. Pseudo-colors, or themes, are assigned to features having identical spectral characteristics. These themes are displayed on a color CRT, and may be recorded on tape, film, or other media. The system was designed to incorporate key features such as interactive operation, user-oriented displays and controls, and rapid-response machine processing. Owing to these features, the user can readily control and/or modify the analysis process based on his knowledge of the input imagery. Effective use can be made of conventional photographic interpretation skills and state-of-the-art machine analysis techniques in the extraction of useful information from multispectral imagery. This approach results in highly accurate multitheme classification of imagery in seconds or minutes rather than the hours often involved in processing using other means.

A low-cost drone based application for identifying and mapping of coastal fish nursery grounds

NASA Astrophysics Data System (ADS)

Ventura, Daniele; Bruno, Michele; Jona Lasinio, Giovanna; Belluscio, Andrea; Ardizzone, Giandomenico

2016-03-01

Acquiring seabed, landform or other topographic data in the field of marine ecology has a pivotal role in defining and mapping key marine habitats. However, accessibility for this kind of data with a high level of detail for very shallow and inaccessible marine habitats has been often challenging, time consuming. Spatial and temporal coverage often has to be compromised to make more cost effective the monitoring routine. Nowadays, emerging technologies, can overcome many of these constraints. Here we describe a recent development in remote sensing based on a small unmanned drone (UAVs) that produce very fine scale maps of fish nursery areas. This technology is simple to use, inexpensive, and timely in producing aerial photographs of marine areas. Both technical details regarding aerial photos acquisition (drone and camera settings) and post processing workflow (3D model generation with Structure From Motion algorithm and photo-stitching) are given. Finally by applying modern algorithm of semi-automatic image analysis and classification (Maximum Likelihood, ECHO and Object-based Image Analysis) we compared the results of three thematic maps of nursery area for juvenile sparid fishes, highlighting the potential of this method in mapping and monitoring coastal marine habitats.

Processing Translational Motion Sequences.

DTIC Science & Technology

1982-10-01

the initial ROADSIGN image using a (del)**2g mask with a width of 5 pixels The distinctiveness values were computed using features which were 5x5 pixel...the initial step size of the local search quite large. 34 4. EX P R g NTg The following experiments were performed using the roadsign and industrial...the initial image of the sequence. The third experiment involves processing the roadsign image sequence using the features extracted at the positions

Content based image retrieval using local binary pattern operator and data mining techniques.

PubMed

Vatamanu, Oana Astrid; Frandeş, Mirela; Lungeanu, Diana; Mihalaş, Gheorghe-Ioan

2015-01-01

Content based image retrieval (CBIR) concerns the retrieval of similar images from image databases, using feature vectors extracted from images. These feature vectors globally define the visual content present in an image, defined by e.g., texture, colour, shape, and spatial relations between vectors. Herein, we propose the definition of feature vectors using the Local Binary Pattern (LBP) operator. A study was performed in order to determine the optimum LBP variant for the general definition of image feature vectors. The chosen LBP variant is then subsequently used to build an ultrasound image database, and a database with images obtained from Wireless Capsule Endoscopy. The image indexing process is optimized using data clustering techniques for images belonging to the same class. Finally, the proposed indexing method is compared to the classical indexing technique, which is nowadays widely used.

Hierarchical content-based image retrieval by dynamic indexing and guided search

NASA Astrophysics Data System (ADS)

You, Jane; Cheung, King H.; Liu, James; Guo, Linong

2003-12-01

This paper presents a new approach to content-based image retrieval by using dynamic indexing and guided search in a hierarchical structure, and extending data mining and data warehousing techniques. The proposed algorithms include: a wavelet-based scheme for multiple image feature extraction, the extension of a conventional data warehouse and an image database to an image data warehouse for dynamic image indexing, an image data schema for hierarchical image representation and dynamic image indexing, a statistically based feature selection scheme to achieve flexible similarity measures, and a feature component code to facilitate query processing and guide the search for the best matching. A series of case studies are reported, which include a wavelet-based image color hierarchy, classification of satellite images, tropical cyclone pattern recognition, and personal identification using multi-level palmprint and face features.

Noise Gating Solar Images

NASA Astrophysics Data System (ADS)

DeForest, Craig; Seaton, Daniel B.; Darnell, John A.

2017-08-01

I present and demonstrate a new, general purpose post-processing technique, "3D noise gating", that can reduce image noise by an order of magnitude or more without effective loss of spatial or temporal resolution in typical solar applications.Nearly all scientific images are, ultimately, limited by noise. Noise can be direct Poisson "shot noise" from photon counting effects, or introduced by other means such as detector read noise. Noise is typically represented as a random variable (perhaps with location- or image-dependent characteristics) that is sampled once per pixel or once per resolution element of an image sequence. Noise limits many aspects of image analysis, including photometry, spatiotemporal resolution, feature identification, morphology extraction, and background modeling and separation.Identifying and separating noise from image signal is difficult. The common practice of blurring in space and/or time works because most image "signal" is concentrated in the low Fourier components of an image, while noise is evenly distributed. Blurring in space and/or time attenuates the high spatial and temporal frequencies, reducing noise at the expense of also attenuating image detail. Noise-gating exploits the same property -- "coherence" -- that we use to identify features in images, to separate image features from noise.Processing image sequences through 3-D noise gating results in spectacular (more than 10x) improvements in signal-to-noise ratio, while not blurring bright, resolved features in either space or time. This improves most types of image analysis, including feature identification, time sequence extraction, absolute and relative photometry (including differential emission measure analysis), feature tracking, computer vision, correlation tracking, background modeling, cross-scale analysis, visual display/presentation, and image compression.I will introduce noise gating, describe the method, and show examples from several instruments (including SDO/AIA , SDO/HMI, STEREO/SECCHI, and GOES-R/SUVI) that explore the benefits and limits of the technique.

A Scalable Distributed Approach to Mobile Robot Vision

NASA Technical Reports Server (NTRS)

Kuipers, Benjamin; Browning, Robert L.; Gribble, William S.

1997-01-01

This paper documents our progress during the first year of work on our original proposal entitled 'A Scalable Distributed Approach to Mobile Robot Vision'. We are pursuing a strategy for real-time visual identification and tracking of complex objects which does not rely on specialized image-processing hardware. In this system perceptual schemas represent objects as a graph of primitive features. Distributed software agents identify and track these features, using variable-geometry image subwindows of limited size. Active control of imaging parameters and selective processing makes simultaneous real-time tracking of many primitive features tractable. Perceptual schemas operate independently from the tracking of primitive features, so that real-time tracking of a set of image features is not hurt by latency in recognition of the object that those features make up. The architecture allows semantically significant features to be tracked with limited expenditure of computational resources, and allows the visual computation to be distributed across a network of processors. Early experiments are described which demonstrate the usefulness of this formulation, followed by a brief overview of our more recent progress (after the first year).

A Semi-Automatic Image-Based Close Range 3D Modeling Pipeline Using a Multi-Camera Configuration

PubMed Central

Rau, Jiann-Yeou; Yeh, Po-Chia

2012-01-01

The generation of photo-realistic 3D models is an important task for digital recording of cultural heritage objects. This study proposes an image-based 3D modeling pipeline which takes advantage of a multi-camera configuration and multi-image matching technique that does not require any markers on or around the object. Multiple digital single lens reflex (DSLR) cameras are adopted and fixed with invariant relative orientations. Instead of photo-triangulation after image acquisition, calibration is performed to estimate the exterior orientation parameters of the multi-camera configuration which can be processed fully automatically using coded targets. The calibrated orientation parameters of all cameras are applied to images taken using the same camera configuration. This means that when performing multi-image matching for surface point cloud generation, the orientation parameters will remain the same as the calibrated results, even when the target has changed. Base on this invariant character, the whole 3D modeling pipeline can be performed completely automatically, once the whole system has been calibrated and the software was seamlessly integrated. Several experiments were conducted to prove the feasibility of the proposed system. Images observed include that of a human being, eight Buddhist statues, and a stone sculpture. The results for the stone sculpture, obtained with several multi-camera configurations were compared with a reference model acquired by an ATOS-I 2M active scanner. The best result has an absolute accuracy of 0.26 mm and a relative accuracy of 1:17,333. It demonstrates the feasibility of the proposed low-cost image-based 3D modeling pipeline and its applicability to a large quantity of antiques stored in a museum. PMID:23112656

A semi-automatic image-based close range 3D modeling pipeline using a multi-camera configuration.

PubMed

Rau, Jiann-Yeou; Yeh, Po-Chia

2012-01-01

The generation of photo-realistic 3D models is an important task for digital recording of cultural heritage objects. This study proposes an image-based 3D modeling pipeline which takes advantage of a multi-camera configuration and multi-image matching technique that does not require any markers on or around the object. Multiple digital single lens reflex (DSLR) cameras are adopted and fixed with invariant relative orientations. Instead of photo-triangulation after image acquisition, calibration is performed to estimate the exterior orientation parameters of the multi-camera configuration which can be processed fully automatically using coded targets. The calibrated orientation parameters of all cameras are applied to images taken using the same camera configuration. This means that when performing multi-image matching for surface point cloud generation, the orientation parameters will remain the same as the calibrated results, even when the target has changed. Base on this invariant character, the whole 3D modeling pipeline can be performed completely automatically, once the whole system has been calibrated and the software was seamlessly integrated. Several experiments were conducted to prove the feasibility of the proposed system. Images observed include that of a human being, eight Buddhist statues, and a stone sculpture. The results for the stone sculpture, obtained with several multi-camera configurations were compared with a reference model acquired by an ATOS-I 2M active scanner. The best result has an absolute accuracy of 0.26 mm and a relative accuracy of 1:17,333. It demonstrates the feasibility of the proposed low-cost image-based 3D modeling pipeline and its applicability to a large quantity of antiques stored in a museum.

WE-A-17A-06: Evaluation of An Automatic Interstitial Catheter Digitization Algorithm That Reduces Treatment Planning Time and Provide Means for Adaptive Re-Planning in HDR Brachytherapy of Gynecologic Cancers

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

Dise, J; Liang, X; Lin, L

Purpose: To evaluate an automatic interstitial catheter digitization algorithm that reduces treatment planning time and provide means for adaptive re-planning in HDR Brachytherapy of Gynecologic Cancers. Methods: The semi-automatic catheter digitization tool utilizes a region growing algorithm in conjunction with a spline model of the catheters. The CT images were first pre-processed to enhance the contrast between the catheters and soft tissue. Several seed locations were selected in each catheter for the region growing algorithm. The spline model of the catheters assisted in the region growing by preventing inter-catheter cross-over caused by air or metal artifacts. Source dwell positions frommore » day one CT scans were applied to subsequent CTs and forward calculated using the automatically digitized catheter positions. This method was applied to 10 patients who had received HDR interstitial brachytherapy on an IRB approved image-guided radiation therapy protocol. The prescribed dose was 18.75 or 20 Gy delivered in 5 fractions, twice daily, over 3 consecutive days. Dosimetric comparisons were made between automatic and manual digitization on day two CTs. Results: The region growing algorithm, assisted by the spline model of the catheters, was able to digitize all catheters. The difference between automatic and manually digitized positions was 0.8±0.3 mm. The digitization time ranged from 34 minutes to 43 minutes with a mean digitization time of 37 minutes. The bulk of the time was spent on manual selection of initial seed positions and spline parameter adjustments. There was no significance difference in dosimetric parameters between the automatic and manually digitized plans. D90% to the CTV was 91.5±4.4% for the manual digitization versus 91.4±4.4% for the automatic digitization (p=0.56). Conclusion: A region growing algorithm was developed to semi-automatically digitize interstitial catheters in HDR brachytherapy using the Syed-Neblett template. This automatic digitization tool was shown to be accurate compared to manual digitization.« less

Image Mosaic Method Based on SIFT Features of Line Segment

PubMed Central

Zhu, Jun; Ren, Mingwu

2014-01-01

This paper proposes a novel image mosaic method based on SIFT (Scale Invariant Feature Transform) feature of line segment, aiming to resolve incident scaling, rotation, changes in lighting condition, and so on between two images in the panoramic image mosaic process. This method firstly uses Harris corner detection operator to detect key points. Secondly, it constructs directed line segments, describes them with SIFT feature, and matches those directed segments to acquire rough point matching. Finally, Ransac method is used to eliminate wrong pairs in order to accomplish image mosaic. The results from experiment based on four pairs of images show that our method has strong robustness for resolution, lighting, rotation, and scaling. PMID:24511326

Automatic Extraction of Planetary Image Features

NASA Technical Reports Server (NTRS)

Troglio, G.; LeMoigne, J.; Moser, G.; Serpico, S. B.; Benediktsson, J. A.

2009-01-01

With the launch of several Lunar missions such as the Lunar Reconnaissance Orbiter (LRO) and Chandrayaan-1, a large amount of Lunar images will be acquired and will need to be analyzed. Although many automatic feature extraction methods have been proposed and utilized for Earth remote sensing images, these methods are not always applicable to Lunar data that often present low contrast and uneven illumination characteristics. In this paper, we propose a new method for the extraction of Lunar features (that can be generalized to other planetary images), based on the combination of several image processing techniques, a watershed segmentation and the generalized Hough Transform. This feature extraction has many applications, among which image registration.

Semi-automatic assessment of pediatric hydronephrosis severity in 3D ultrasound

NASA Astrophysics Data System (ADS)

Cerrolaza, Juan J.; Otero, Hansel; Yao, Peter; Biggs, Elijah; Mansoor, Awais; Ardon, Roberto; Jago, James; Peters, Craig A.; Linguraru, Marius George

2016-03-01

Hydronephrosis is the most common abnormal finding in pediatric urology. Thanks to its non-ionizing nature, ultrasound (US) imaging is the preferred diagnostic modality for the evaluation of the kidney and the urinary track. However, due to the lack of correlation of US with renal function, further invasive and/or ionizing studies might be required (e.g., diuretic renograms). This paper presents a computer-aided diagnosis (CAD) tool for the accurate and objective assessment of pediatric hydronephrosis based on morphological analysis of kidney from 3DUS scans. The integration of specific segmentation tools in the system, allows to delineate the relevant renal structures from 3DUS scans of the patients with minimal user interaction, and the automatic computation of 90 anatomical features. Using the washout half time (T1/2) as indicative of renal obstruction, an optimal subset of predictive features is selected to differentiate, with maximum sensitivity, those severe cases where further attention is required (e.g., in the form of diuretic renograms), from the noncritical ones. The performance of this new 3DUS-based CAD system is studied for two clinically relevant T1/2 thresholds, 20 and 30 min. Using a dataset of 20 hydronephrotic cases, pilot experiments show how the system outperforms previous 2D implementations by successfully identifying all the critical cases (100% of sensitivity), and detecting up to 100% (T1/2 = 20 min) and 67% (T1/2 = 30 min) of non-critical ones for T1/2 thresholds of 20 and 30 min, respectively.

Intelligent keyframe extraction for video printing

NASA Astrophysics Data System (ADS)

Zhang, Tong

2004-10-01

Nowadays most digital cameras have the functionality of taking short video clips, with the length of video ranging from several seconds to a couple of minutes. The purpose of this research is to develop an algorithm which extracts an optimal set of keyframes from each short video clip so that the user could obtain proper video frames to print out. In current video printing systems, keyframes are normally obtained by evenly sampling the video clip over time. Such an approach, however, may not reflect highlights or regions of interest in the video. Keyframes derived in this way may also be improper for video printing in terms of either content or image quality. In this paper, we present an intelligent keyframe extraction approach to derive an improved keyframe set by performing semantic analysis of the video content. For a video clip, a number of video and audio features are analyzed to first generate a candidate keyframe set. These features include accumulative color histogram and color layout differences, camera motion estimation, moving object tracking, face detection and audio event detection. Then, the candidate keyframes are clustered and evaluated to obtain a final keyframe set. The objective is to automatically generate a limited number of keyframes to show different views of the scene; to show different people and their actions in the scene; and to tell the story in the video shot. Moreover, frame extraction for video printing, which is a rather subjective problem, is considered in this work for the first time, and a semi-automatic approach is proposed.

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

USDA-ARS?s Scientific Manuscript database

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

Image Registration Algorithm Based on Parallax Constraint and Clustering Analysis

NASA Astrophysics Data System (ADS)

Wang, Zhe; Dong, Min; Mu, Xiaomin; Wang, Song

2018-01-01

To resolve the problem of slow computation speed and low matching accuracy in image registration, a new image registration algorithm based on parallax constraint and clustering analysis is proposed. Firstly, Harris corner detection algorithm is used to extract the feature points of two images. Secondly, use Normalized Cross Correlation (NCC) function to perform the approximate matching of feature points, and the initial feature pair is obtained. Then, according to the parallax constraint condition, the initial feature pair is preprocessed by K-means clustering algorithm, which is used to remove the feature point pairs with obvious errors in the approximate matching process. Finally, adopt Random Sample Consensus (RANSAC) algorithm to optimize the feature points to obtain the final feature point matching result, and the fast and accurate image registration is realized. The experimental results show that the image registration algorithm proposed in this paper can improve the accuracy of the image matching while ensuring the real-time performance of the algorithm.

Image detection and compression for memory efficient system analysis

NASA Astrophysics Data System (ADS)

Bayraktar, Mustafa

2015-02-01

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

Evaluation of solar angle variation over digital processing of LANDSAT imagery. [Brazil

NASA Technical Reports Server (NTRS)

Parada, N. D. J. (Principal Investigator); Novo, E. M. L. M.

1984-01-01

The effects of the seasonal variation of illumination over digital processing of LANDSAT images are evaluated. Original images are transformed by means of digital filtering to enhance their spatial features. The resulting images are used to obtain an unsupervised classification of relief units. After defining relief classes, which are supposed to be spectrally different, topographic variables (declivity, altitude, relief range and slope length) are used to identify the true relief units existing on the ground. The samples are also clustered by means of an unsupervised classification option. The results obtained for each LANDSAT overpass are compared. Digital processing is highly affected by illumination geometry. There is no correspondence between relief units as defined by spectral features and those resulting from topographic features.

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

PubMed

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

2015-01-01

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

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

NASA Astrophysics Data System (ADS)

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

2015-12-01

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

Object segmentation controls image reconstruction from natural scenes

PubMed Central

2017-01-01

The structure of the physical world projects images onto our eyes. However, those images are often poorly representative of environmental structure: well-defined boundaries within the eye may correspond to irrelevant features of the physical world, while critical features of the physical world may be nearly invisible at the retinal projection. The challenge for the visual cortex is to sort these two types of features according to their utility in ultimately reconstructing percepts and interpreting the constituents of the scene. We describe a novel paradigm that enabled us to selectively evaluate the relative role played by these two feature classes in signal reconstruction from corrupted images. Our measurements demonstrate that this process is quickly dominated by the inferred structure of the environment, and only minimally controlled by variations of raw image content. The inferential mechanism is spatially global and its impact on early visual cortex is fast. Furthermore, it retunes local visual processing for more efficient feature extraction without altering the intrinsic transduction noise. The basic properties of this process can be partially captured by a combination of small-scale circuit models and large-scale network architectures. Taken together, our results challenge compartmentalized notions of bottom-up/top-down perception and suggest instead that these two modes are best viewed as an integrated perceptual mechanism. PMID:28827801

Usefulness of model-based iterative reconstruction in semi-automatic volumetry for ground-glass nodules at ultra-low-dose CT: a phantom study.

PubMed

Maruyama, Shuki; Fukushima, Yasuhiro; Miyamae, Yuta; Koizumi, Koji

2018-06-01

This study aimed to investigate the effects of parameter presets of the forward projected model-based iterative reconstruction solution (FIRST) on the accuracy of pulmonary nodule volume measurement. A torso phantom with simulated nodules [diameter: 5, 8, 10, and 12 mm; computed tomography (CT) density: - 630 HU] was scanned with a multi-detector CT at tube currents of 10 mA (ultra-low-dose: UL-dose) and 270 mA (standard-dose: Std-dose). Images were reconstructed with filtered back projection [FBP; standard (Std-FBP), ultra-low-dose (UL-FBP)], FIRST Lung (UL-Lung), and FIRST Body (UL-Body), and analyzed with a semi-automatic software. The error in the volume measurement was determined. The errors with UL-Lung and UL-Body were smaller than that with UL-FBP. The smallest error was 5.8% ± 0.3 for the 12-mm nodule with UL-Body (middle lung). Our results indicated that FIRST Body would be superior to FIRST Lung in terms of accuracy of nodule measurement with UL-dose CT.

SU-E-J-249: Characterization of Gynecological Tumor Heterogeneity Using Texture Analysis in the Context of An 18F-FDG PET Adaptive Protocol

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

Nawrocki, J; Chino, J; Craciunescu, O

Purpose: We propose a method to examine gynecological tumor heterogeneity using texture analysis in the context of an adaptive PET protocol in order to establish if texture metrics from baseline PET-CT predict tumor response better than SUV metrics alone as well as determine texture features correlating with tumor response during radiation therapy. Methods: This IRB approved protocol included 29 women with node positive gynecological cancers visible on FDG-PET treated with EBRT to the PET positive nodes. A baseline and intra-treatment PET-CT was obtained. Tumor outcome was determined based on RECIST on posttreatment PET-CT. Primary GTVs were segmented using 40% thresholdmore » and a semi-automatic gradient-based contouring tool, PET Edge (MIM Software Inc., Cleveland, OH). SUV histogram features, Metabolic Volume (MV), and Total Lesion Glycolysis (TLG) were calculated. Four 3D texture matrices describing local and regional relationships between voxel intensities in the GTV were generated: co-occurrence, run length, size zone, and neighborhood difference. From these, 39 texture features were calculated. Prognostic power of baseline features derived from gradientbased and threshold GTVs were determined using the Wilcoxon rank-sum test. Receiver Operating Characteristics and logistic regression was performed using JMP (SAS Institute Inc., Cary, NC) to find probabilities of predicting response. Changes in features during treatment were determined using the Wilcoxon signed-rank test. Results: Of the 29 patients, there were 16 complete responders, 7 partial responders, and 6 non-responders. Comparing CR/PR vs. NR for gradient-based GTVs, 7 texture values, TLG, and SUV kurtosis had a p < 0.05. Threshold GTVs yielded 4 texture features and TLG with p < 0.05. From baseline to intra-treatment, 14 texture features, SUVmean, SUVmax, MV, and TLG changed with p < 0.05. Conclusion: Texture analysis of PET imaged gynecological tumors is an effective method for early prognosis and should be used complimentary to SUV metrics, especially when using gradient based segmentation.« less

Robust image matching via ORB feature and VFC for mismatch removal

NASA Astrophysics Data System (ADS)

Ma, Tao; Fu, Wenxing; Fang, Bin; Hu, Fangyu; Quan, Siwen; Ma, Jie

2018-03-01

Image matching is at the base of many image processing and computer vision problems, such as object recognition or structure from motion. Current methods rely on good feature descriptors and mismatch removal strategies for detection and matching. In this paper, we proposed a robust image match approach based on ORB feature and VFC for mismatch removal. ORB (Oriented FAST and Rotated BRIEF) is an outstanding feature, it has the same performance as SIFT with lower cost. VFC (Vector Field Consensus) is a state-of-the-art mismatch removing method. The experiment results demonstrate that our method is efficient and robust.

Histological Image Processing Features Induce a Quantitative Characterization of Chronic Tumor Hypoxia

PubMed Central

Grabocka, Elda; Bar-Sagi, Dafna; Mishra, Bud

2016-01-01

Hypoxia in tumors signifies resistance to therapy. Despite a wealth of tumor histology data, including anti-pimonidazole staining, no current methods use these data to induce a quantitative characterization of chronic tumor hypoxia in time and space. We use image-processing algorithms to develop a set of candidate image features that can formulate just such a quantitative description of xenographed colorectal chronic tumor hypoxia. Two features in particular give low-variance measures of chronic hypoxia near a vessel: intensity sampling that extends radially away from approximated blood vessel centroids, and multithresholding to segment tumor tissue into normal, hypoxic, and necrotic regions. From these features we derive a spatiotemporal logical expression whose truth value depends on its predicate clauses that are grounded in this histological evidence. As an alternative to the spatiotemporal logical formulation, we also propose a way to formulate a linear regression function that uses all of the image features to learn what chronic hypoxia looks like, and then gives a quantitative similarity score once it is trained on a set of histology images. PMID:27093539

Quantifying the robustness of [18F]FDG-PET/CT radiomic features with respect to tumor delineation in head and neck and pancreatic cancer patients.

PubMed

Belli, Maria Luisa; Mori, Martina; Broggi, Sara; Cattaneo, Giovanni Mauro; Bettinardi, Valentino; Dell'Oca, Italo; Fallanca, Federico; Passoni, Paolo; Vanoli, Emilia Giovanna; Calandrino, Riccardo; Di Muzio, Nadia; Picchio, Maria; Fiorino, Claudio

2018-05-01

To investigate the robustness of PET radiomic features (RF) against tumour delineation uncertainty in two clinically relevant situations. Twenty-five head-and-neck (HN) and 25 pancreatic cancer patients previously treated with 18 F-Fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT)-based planning optimization were considered. Seven FDG-based contours were delineated for tumour (T) and positive lymph nodes (N, for HN patients only) following manual (2 observers), semi-automatic (based on SUV maximum gradient: PET_Edge) and automatic (40%, 50%, 60%, 70% SUV_max thresholds) methods. Seventy-three RF (14 of first order and 59 of higher order) were extracted using the CGITA software (v.1.4). The impact of delineation on volume agreement and RF was assessed by DICE and Intra-class Correlation Coefficients (ICC). A large disagreement between manual and SUV_max method was found for thresholds  ≥50%. Inter-observer variability showed median DICE values between 0.81 (HN-T) and 0.73 (pancreas). Volumes defined by PET_Edge were better consistent with the manual ones compared to SUV40%. Regarding RF, 19%/19%/47% of the features showed ICC < 0.80 between observers for HN-N/HN-T/pancreas, mostly in the Voxel-alignment matrix and in the intensity-size zone matrix families. RFs with ICC < 0.80 against manual delineation (taking the worst value) increased to 44%/36%/61% for PET_Edge and to 69%/53%/75% for SUV40%. About 80%/50% of 72 RF were consistent between observers for HN/pancreas patients. PET_edge was sufficiently robust against manual delineation while SUV40% showed a worse performance. This result suggests the possibility to replace manual with semi-automatic delineation of HN and pancreas tumours in studies including PET radiomic analyses. Copyright © 2018 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

Vision-sensing image analysis for GTAW process control

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

Long, D.D.

1994-11-01

Image analysis of a gas tungsten arc welding (GTAW) process was completed using video images from a charge coupled device (CCD) camera inside a specially designed coaxial (GTAW) electrode holder. Video data was obtained from filtered and unfiltered images, with and without the GTAW arc present, showing weld joint features and locations. Data Translation image processing boards, installed in an IBM PC AT 386 compatible computer, and Media Cybernetics image processing software were used to investigate edge flange weld joint geometry for image analysis.

The virtual craniofacial patient: 3D jaw modeling and animation.

PubMed

Enciso, Reyes; Memon, Ahmed; Fidaleo, Douglas A; Neumann, Ulrich; Mah, James

2003-01-01

In this paper, we present new developments in the area of 3D human jaw modeling and animation. CT (Computed Tomography) scans have traditionally been used to evaluate patients with dental implants, assess tumors, cysts, fractures and surgical procedures. More recently this data has been utilized to generate models. Researchers have reported semi-automatic techniques to segment and model the human jaw from CT images and manually segment the jaw from MRI images. Recently opto-electronic and ultrasonic-based systems (JMA from Zebris) have been developed to record mandibular position and movement. In this research project we introduce: (1) automatic patient-specific three-dimensional jaw modeling from CT data and (2) three-dimensional jaw motion simulation using jaw tracking data from the JMA system (Zebris).

Development of numerical phantoms by MRI for RF electromagnetic dosimetry: a female model.

PubMed

Mazzurana, M; Sandrini, L; Vaccari, A; Malacarne, C; Cristoforetti, L; Pontalti, R

2004-01-01

Numerical human models for electromagnetic dosimetry are commonly obtained by segmentation of CT or MRI images and complex permittivity values are ascribed to each issue according to literature values. The aim of this study is to provide an alternative semi-automatic method by which non-segmented images, obtained by a MRI tomographer, can be automatically related to the complex permittivity values through two frequency dependent transfer functions. In this way permittivity and conductivity vary with continuity--even in the same tissue--reflecting the intrinsic realistic spatial dispersion of such parameters. A female human model impinged by a plane wave is tested using finite-difference time-domain algorithm and the results of the total body and layer-averaged specific absorption rate are reported.

Experiences with digital processing of images at INPE

NASA Technical Reports Server (NTRS)

Mascarenhas, N. D. A. (Principal Investigator)

1984-01-01

Four different research experiments with digital image processing at INPE will be described: (1) edge detection by hypothesis testing; (2) image interpolation by finite impulse response filters; (3) spatial feature extraction methods in multispectral classification; and (4) translational image registration by sequential tests of hypotheses.

Roof Type Selection Based on Patch-Based Classification Using Deep Learning for High Resolution Satellite Imagery

NASA Astrophysics Data System (ADS)

Partovi, T.; Fraundorfer, F.; Azimi, S.; Marmanis, D.; Reinartz, P.

2017-05-01

3D building reconstruction from remote sensing image data from satellites is still an active research topic and very valuable for 3D city modelling. The roof model is the most important component to reconstruct the Level of Details 2 (LoD2) for a building in 3D modelling. While the general solution for roof modelling relies on the detailed cues (such as lines, corners and planes) extracted from a Digital Surface Model (DSM), the correct detection of the roof type and its modelling can fail due to low quality of the DSM generated by dense stereo matching. To reduce dependencies of roof modelling on DSMs, the pansharpened satellite images as a rich resource of information are used in addition. In this paper, two strategies are employed for roof type classification. In the first one, building roof types are classified in a state-of-the-art supervised pre-trained convolutional neural network (CNN) framework. In the second strategy, deep features from deep layers of different pre-trained CNN model are extracted and then an RBF kernel using SVM is employed to classify the building roof type. Based on roof complexity of the scene, a roof library including seven types of roofs is defined. A new semi-automatic method is proposed to generate training and test patches of each roof type in the library. Using the pre-trained CNN model does not only decrease the computation time for training significantly but also increases the classification accuracy.

Feature Matching of Historical Images Based on Geometry of Quadrilaterals

NASA Astrophysics Data System (ADS)

Maiwald, F.; Schneider, D.; Henze, F.; Münster, S.; Niebling, F.

2018-05-01

This contribution shows an approach to match historical images from the photo library of the Saxon State and University Library Dresden (SLUB) in the context of a historical three-dimensional city model of Dresden. In comparison to recent images, historical photography provides diverse factors which make an automatical image analysis (feature detection, feature matching and relative orientation of images) difficult. Due to e.g. film grain, dust particles or the digitalization process, historical images are often covered by noise interfering with the image signal needed for a robust feature matching. The presented approach uses quadrilaterals in image space as these are commonly available in man-made structures and façade images (windows, stones, claddings). It is explained how to generally detect quadrilaterals in images. Consequently, the properties of the quadrilaterals as well as the relationship to neighbouring quadrilaterals are used for the description and matching of feature points. The results show that most of the matches are robust and correct but still small in numbers.

Differential Shift Estimation in the Absence of Coherence: Performance Analysis and Benefits of Polarimetry

NASA Astrophysics Data System (ADS)

Villano, Michelangelo; Papathanassiou, Konstantinos P.

2011-03-01

The estimation of the local differential shift between synthetic aperture radar (SAR) images has proven to be an effective technique for monitoring glacier surface motion. As images acquired over glaciers by short wavelength SAR systems, such as TerraSAR-X, often suffer from a lack of coherence, image features have to be exploited for the shift estimation (feature-tracking).The present paper addresses feature-tracking with special attention to the feasibility requirements and the achievable accuracy of the shift estimation. In particular, the dependence of the performance on image characteristics, such as texture parameters, signal-to-noise ratio (SNR) and resolution, as well as on processing techniques (despeckling, normalised cross-correlation versus maximum likelihood estimation) is analysed by means of Monte-Carlo simulations. TerraSAR-X data acquired over the Helheim glacier, Greenland, and the Aletsch glacier, Switzerland, have been processed to validate the simulation results.Feature-tracking can benefit of the availability of fully-polarimetric data. As some image characteristics, in fact, are polarisation-dependent, the selection of an optimum polarisation leads to improved performance. Furthermore, fully-polarimetric SAR images can be despeckled without degrading the resolution, so that additional (smaller-scale) features can be exploited.

Within-brain classification for brain tumor segmentation.

PubMed

Havaei, Mohammad; Larochelle, Hugo; Poulin, Philippe; Jodoin, Pierre-Marc

2016-05-01

In this paper, we investigate a framework for interactive brain tumor segmentation which, at its core, treats the problem of interactive brain tumor segmentation as a machine learning problem. This method has an advantage over typical machine learning methods for this task where generalization is made across brains. The problem with these methods is that they need to deal with intensity bias correction and other MRI-specific noise. In this paper, we avoid these issues by approaching the problem as one of within brain generalization. Specifically, we propose a semi-automatic method that segments a brain tumor by training and generalizing within that brain only, based on some minimum user interaction. We investigate how adding spatial feature coordinates (i.e., i, j, k) to the intensity features can significantly improve the performance of different classification methods such as SVM, kNN and random forests. This would only be possible within an interactive framework. We also investigate the use of a more appropriate kernel and the adaptation of hyper-parameters specifically for each brain. As a result of these experiments, we obtain an interactive method whose results reported on the MICCAI-BRATS 2013 dataset are the second most accurate compared to published methods, while using significantly less memory and processing power than most state-of-the-art methods.

Resident Space Object Characterization and Behavior Understanding via Machine Learning and Ontology-based Bayesian Networks

NASA Astrophysics Data System (ADS)

Furfaro, R.; Linares, R.; Gaylor, D.; Jah, M.; Walls, R.

2016-09-01

In this paper, we present an end-to-end approach that employs machine learning techniques and Ontology-based Bayesian Networks (BN) to characterize the behavior of resident space objects. State-of-the-Art machine learning architectures (e.g. Extreme Learning Machines, Convolutional Deep Networks) are trained on physical models to learn the Resident Space Object (RSO) features in the vectorized energy and momentum states and parameters. The mapping from measurements to vectorized energy and momentum states and parameters enables behavior characterization via clustering in the features space and subsequent RSO classification. Additionally, Space Object Behavioral Ontologies (SOBO) are employed to define and capture the domain knowledge-base (KB) and BNs are constructed from the SOBO in a semi-automatic fashion to execute probabilistic reasoning over conclusions drawn from trained classifiers and/or directly from processed data. Such an approach enables integrating machine learning classifiers and probabilistic reasoning to support higher-level decision making for space domain awareness applications. The innovation here is to use these methods (which have enjoyed great success in other domains) in synergy so that it enables a "from data to discovery" paradigm by facilitating the linkage and fusion of large and disparate sources of information via a Big Data Science and Analytics framework.

Automated x-ray/light field congruence using the LINAC EPID panel.

PubMed

Polak, Wojciech; O'Doherty, Jim; Jones, Matt

2013-03-01

X-ray/light field alignment is a test described in many guidelines for the routine quality control of clinical linear accelerators (LINAC). Currently, the gold standard method for measuring alignment is through utilization of radiographic film. However, many modern LINACs are equipped with an electronic portal imaging device (EPID) that may be used to perform this test and thus subsequently reducing overall cost, processing, and analysis time, removing operator dependency and the requirement to sustain the departmental film processor. This work describes a novel method of utilizing the EPID together with a custom inhouse designed jig and automatic image processing software allowing measurement of the light field size, x-ray field size, and congruence between them. The authors present results of testing the method for aS1000 and aS500 Varian EPID detectors for six LINACs at a range of energies (6, 10, and 15 MV) in comparison with the results obtained from the use of radiographic film. Reproducibility of the software in fully automatic operation under a range of operating conditions for a single image showed a congruence of 0.01 cm with a coefficient of variation of 0. Slight variation in congruence repeatability was noted through semiautomatic processing by four independent operators due to manual marking of positions on the jig. Testing of the methodology using the automatic method shows a high precision of 0.02 mm compared to a maximum of 0.06 mm determined by film processing. Intraindividual examination of operator measurements of congruence was shown to vary as much as 0.75 mm. Similar congruence measurements of 0.02 mm were also determined for a lower resolution EPID (aS500 model), after rescaling of the image to the aS1000 image size. The designed methodology was proven to be time efficient, cost effective, and at least as accurate as using the gold standard radiographic film. Additionally, congruence testing can be easily performed for all four cardinal gantry angles which can be difficult when using radiographic film. Therefore, the authors propose it can be used as an alternative to the radiographic film method allowing decommissioning of the film processor.

A neural network ActiveX based integrated image processing environment.

PubMed

Ciuca, I; Jitaru, E; Alaicescu, M; Moisil, I

2000-01-01

The paper outlines an integrated image processing environment that uses neural networks ActiveX technology for object recognition and classification. The image processing environment which is Windows based, encapsulates a Multiple-Document Interface (MDI) and is menu driven. Object (shape) parameter extraction is focused on features that are invariant in terms of translation, rotation and scale transformations. The neural network models that can be incorporated as ActiveX components into the environment allow both clustering and classification of objects from the analysed image. Mapping neural networks perform an input sensitivity analysis on the extracted feature measurements and thus facilitate the removal of irrelevant features and improvements in the degree of generalisation. The program has been used to evaluate the dimensions of the hydrocephalus in a study for calculating the Evans index and the angle of the frontal horns of the ventricular system modifications.

Steganalysis based on reducing the differences of image statistical characteristics

NASA Astrophysics Data System (ADS)

Wang, Ran; Niu, Shaozhang; Ping, Xijian; Zhang, Tao

2018-04-01

Compared with the process of embedding, the image contents make a more significant impact on the differences of image statistical characteristics. This makes the image steganalysis to be a classification problem with bigger withinclass scatter distances and smaller between-class scatter distances. As a result, the steganalysis features will be inseparate caused by the differences of image statistical characteristics. In this paper, a new steganalysis framework which can reduce the differences of image statistical characteristics caused by various content and processing methods is proposed. The given images are segmented to several sub-images according to the texture complexity. Steganalysis features are separately extracted from each subset with the same or close texture complexity to build a classifier. The final steganalysis result is figured out through a weighted fusing process. The theoretical analysis and experimental results can demonstrate the validity of the framework.

Evaluation of the effects of the seasonal variation of solar elevation angle and azimuth on the processes of digital filtering and thematic classification of relief units

NASA Technical Reports Server (NTRS)

Parada, N. D. J. (Principal Investigator); Novo, E. M. L. M.

1983-01-01

The effects of the seasonal variation of illumination over digital processing of LANDSAT images are evaluated. Two sets of LANDSAT data referring to the orbit 150 and row 28 were selected with illumination parameters varying from 43 deg to 64 deg for azimuth and from 30 deg to 36 deg for solar elevation respectively. IMAGE-100 system permitted the digital processing of LANDSAT data. Original images were transformed by means of digital filtering so as to enhance their spatial features. The resulting images were used to obtain an unsupervised classification of relief units. Topographic variables (declivity, altitude, relief range and slope length) were used to identify the true relief units existing on the ground. The LANDSAT over pass data show that digital processing is highly affected by illumination geometry, and there is no correspondence between relief units as defined by spectral features and those resulting from topographic features.

Cell segmentation in phase contrast microscopy images via semi-supervised classification over optics-related features.

PubMed

Su, Hang; Yin, Zhaozheng; Huh, Seungil; Kanade, Takeo

2013-10-01

Phase-contrast microscopy is one of the most common and convenient imaging modalities to observe long-term multi-cellular processes, which generates images by the interference of lights passing through transparent specimens and background medium with different retarded phases. Despite many years of study, computer-aided phase contrast microscopy analysis on cell behavior is challenged by image qualities and artifacts caused by phase contrast optics. Addressing the unsolved challenges, the authors propose (1) a phase contrast microscopy image restoration method that produces phase retardation features, which are intrinsic features of phase contrast microscopy, and (2) a semi-supervised learning based algorithm for cell segmentation, which is a fundamental task for various cell behavior analysis. Specifically, the image formation process of phase contrast microscopy images is first computationally modeled with a dictionary of diffraction patterns; as a result, each pixel of a phase contrast microscopy image is represented by a linear combination of the bases, which we call phase retardation features. Images are then partitioned into phase-homogeneous atoms by clustering neighboring pixels with similar phase retardation features. Consequently, cell segmentation is performed via a semi-supervised classification technique over the phase-homogeneous atoms. Experiments demonstrate that the proposed approach produces quality segmentation of individual cells and outperforms previous approaches. Copyright © 2013 Elsevier B.V. All rights reserved.

Non-rigid ultrasound image registration using generalized relaxation labeling process

NASA Astrophysics Data System (ADS)

Lee, Jong-Ha; Seong, Yeong Kyeong; Park, MoonHo; Woo, Kyoung-Gu; Ku, Jeonghun; Park, Hee-Jun

2013-03-01

This research proposes a novel non-rigid registration method for ultrasound images. The most predominant anatomical features in medical images are tissue boundaries, which appear as edges. In ultrasound images, however, other features can be identified as well due to the specular reflections that appear as bright lines superimposed on the ideal edge location. In this work, an image's local phase information (via the frequency domain) is used to find the ideal edge location. The generalized relaxation labeling process is then formulated to align the feature points extracted from the ideal edge location. In this work, the original relaxation labeling method was generalized by taking n compatibility coefficient values to improve non-rigid registration performance. This contextual information combined with a relaxation labeling process is used to search for a correspondence. Then the transformation is calculated by the thin plate spline (TPS) model. These two processes are iterated until the optimal correspondence and transformation are found. We have tested our proposed method and the state-of-the-art algorithms with synthetic data and bladder ultrasound images of in vivo human subjects. Experiments show that the proposed method improves registration performance significantly, as compared to other state-of-the-art non-rigid registration algorithms.

Grid point extraction and coding for structured light system

NASA Astrophysics Data System (ADS)

Song, Zhan; Chung, Ronald

2011-09-01

A structured light system simplifies three-dimensional reconstruction by illuminating a specially designed pattern to the target object, thereby generating a distinct texture on it for imaging and further processing. Success of the system hinges upon what features are to be coded in the projected pattern, extracted in the captured image, and matched between the projector's display panel and the camera's image plane. The codes have to be such that they are largely preserved in the image data upon illumination from the projector, reflection from the target object, and projective distortion in the imaging process. The features also need to be reliably extracted in the image domain. In this article, a two-dimensional pseudorandom pattern consisting of rhombic color elements is proposed, and the grid points between the pattern elements are chosen as the feature points. We describe how a type classification of the grid points plus the pseudorandomness of the projected pattern can equip each grid point with a unique label that is preserved in the captured image. We also present a grid point detector that extracts the grid points without the need of segmenting the pattern elements, and that localizes the grid points in subpixel accuracy. Extensive experiments are presented to illustrate that, with the proposed pattern feature definition and feature detector, more features points in higher accuracy can be reconstructed in comparison with the existing pseudorandomly encoded structured light systems.

A comparative analysis of image features between weave embroidered Thangka and piles embroidered Thangka

NASA Astrophysics Data System (ADS)

Li, Zhenjiang; Wang, Weilan

2018-04-01

Thangka is a treasure of Tibetan culture. In its digital protection, most of the current research focuses on the content of Thangka images, not the fabrication process. For silk embroidered Thangka of "Guo Tang", there are two craft methods, namely, weave embroidered and piles embroidered. The local texture of weave embroidered Thangka is rough, and that of piles embroidered Thangka is more smooth. In order to distinguish these two kinds of fabrication processes from images, a effectively segmentation algorithm of color blocks is designed firstly, and the obtained color blocks contain the local texture patterns of Thangka image; Secondly, the local texture features of the color block are extracted and screened; Finally, the selected features are analyzed experimentally. The experimental analysis shows that the proposed features can well reflect the difference between methods of weave embroidered and piles embroidered.

Features Extraction of Flotation Froth Images and BP Neural Network Soft-Sensor Model of Concentrate Grade Optimized by Shuffled Cuckoo Searching Algorithm

PubMed Central

Wang, Jie-sheng; Han, Shuang; Shen, Na-na; Li, Shu-xia

2014-01-01

For meeting the forecasting target of key technology indicators in the flotation process, a BP neural network soft-sensor model based on features extraction of flotation froth images and optimized by shuffled cuckoo search algorithm is proposed. Based on the digital image processing technique, the color features in HSI color space, the visual features based on the gray level cooccurrence matrix, and the shape characteristics based on the geometric theory of flotation froth images are extracted, respectively, as the input variables of the proposed soft-sensor model. Then the isometric mapping method is used to reduce the input dimension, the network size, and learning time of BP neural network. Finally, a shuffled cuckoo search algorithm is adopted to optimize the BP neural network soft-sensor model. Simulation results show that the model has better generalization results and prediction accuracy. PMID:25133210

Image fusion algorithm based on energy of Laplacian and PCNN

NASA Astrophysics Data System (ADS)

Li, Meili; Wang, Hongmei; Li, Yanjun; Zhang, Ke

2009-12-01

Owing to the global coupling and pulse synchronization characteristic of pulse coupled neural networks (PCNN), it has been proved to be suitable for image processing and successfully employed in image fusion. However, in almost all the literatures of image processing about PCNN, linking strength of each neuron is assigned the same value which is chosen by experiments. This is not consistent with the human vision system in which the responses to the region with notable features are stronger than that to the region with nonnotable features. It is more reasonable that notable features, rather than the same value, are employed to linking strength of each neuron. As notable feature, energy of Laplacian (EOL) is used to obtain the value of linking strength in PCNN in this paper. Experimental results demonstrate that the proposed algorithm outperforms Laplacian-based, wavelet-based, PCNN -based fusion algorithms.

Dynamic gadolinium-enhanced magnetic resonance imaging allows accurate assessment of the synovial inflammatory activity in rheumatoid arthritis knee joints: a comparison with synovial histology.

PubMed

Axelsen, M B; Stoltenberg, M; Poggenborg, R P; Kubassova, O; Boesen, M; Bliddal, H; Hørslev-Petersen, K; Hanson, L G; Østergaard, M

2012-03-01

To determine whether dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) evaluated using semi-automatic image processing software can accurately assess synovial inflammation in rheumatoid arthritis (RA) knee joints. In 17 RA patients undergoing knee surgery, the average grade of histological synovial inflammation was determined from four biopsies obtained during surgery. A preoperative series of T(1)-weighted dynamic fast low-angle shot (FLASH) MR images was obtained. Parameters characterizing contrast uptake dynamics, including the initial rate of enhancement (IRE), were generated by the software in three different areas: (I) the entire slice (Whole slice); (II) a manually outlined region of interest (ROI) drawn quickly around the joint, omitting large artefacts such as blood vessels (Quick ROI); and (III) a manually outlined ROI following the synovial capsule of the knee joint (Precise ROI). Intra- and inter-reader agreement was assessed using the intra-class correlation coefficient (ICC). The IRE from the Quick ROI and the Precise ROI revealed high correlations to the grade of histological inflammation (Spearman's correlation coefficient (rho) = 0.70, p = 0.001 and rho = 0.74, p = 0.001, respectively). Intra- and inter-reader ICCs were very high (0.93-1.00). No Whole slice parameters were correlated to histology. DCE-MRI provides fast and accurate assessment of synovial inflammation in RA patients. Manual outlining of the joint to omit large artefacts is necessary.

Blockface histology with optical coherence tomography: a comparison with Nissl staining.

PubMed

Magnain, Caroline; Augustinack, Jean C; Reuter, Martin; Wachinger, Christian; Frosch, Matthew P; Ragan, Timothy; Akkin, Taner; Wedeen, Van J; Boas, David A; Fischl, Bruce

2014-01-01

Spectral domain optical coherence tomography (SD-OCT) is a high resolution imaging technique that generates excellent contrast based on intrinsic optical properties of the tissue, such as neurons and fibers. The SD-OCT data acquisition is performed directly on the tissue block, diminishing the need for cutting, mounting and staining. We utilized SD-OCT to visualize the laminar structure of the isocortex and compared cortical cytoarchitecture with the gold standard Nissl staining, both qualitatively and quantitatively. In histological processing, distortions routinely affect registration to the blockface image and prevent accurate 3D reconstruction of regions of tissue. We compared blockface registration to SD-OCT and Nissl, respectively, and found that SD-OCT-blockface registration was significantly more accurate than Nissl-blockface registration. Two independent observers manually labeled cortical laminae (e.g. III, IV and V) in SD-OCT images and Nissl stained sections. Our results show that OCT images exhibit sufficient contrast in the cortex to reliably differentiate the cortical layers. Furthermore, the modalities were compared with regard to cortical laminar organization and showed good agreement. Taken together, these SD-OCT results suggest that SD-OCT contains information comparable to standard histological stains such as Nissl in terms of distinguishing cortical layers and architectonic areas. Given these data, we propose that SD-OCT can be used to reliably generate 3D reconstructions of multiple cubic centimeters of cortex that can be used to accurately and semi-automatically perform standard histological analyses. © 2013.

Blockface Histology with Optical Coherence Tomography: A Comparison with Nissl Staining

PubMed Central

Magnain, Caroline; Augustinack, Jean C.; Reuter, Martin; Wachinger, Christian; Frosch, Matthew P.; Ragan, Timothy; Akkin, Taner; Wedeen, Van J.; Boas, David A.; Fischl, Bruce

2015-01-01

Spectral domain optical coherence tomography (SD-OCT) is a high resolution imaging technique that generates excellent contrast based on intrinsic optical properties of the tissue, such as neurons and fibers. The SD-OCT data acquisition is performed directly on the tissue block, diminishing the need for cutting, mounting and staining. We utilized SD-OCT to visualize the laminar structure of the isocortex and compared cortical cytoarchitecture with the gold standard Nissl staining, both qualitatively and quantitatively. In histological processing, distortions routinely affect registration to the blockface image and prevent accurate 3D reconstruction of regions of tissue. We compared blockface registration to SD-OCT and Nissl, respectively, and found that SD-OCT-blockface registration was significantly more accurate than Nissl-blockface registration. Two independent observers manually labeled cortical laminae (e.g. III, IV and V) in SD-OCT images and Nissl stained sections. Our results show that OCT images exhibit sufficient contrast in the cortex to reliably differentiate the cortical layers. Furthermore, the modalities were compared with regard to cortical laminar organization and showed good agreement. Taken together, these SD-OCT results suggest that SD-OCT contains information comparable to standard histological stains such as Nissl in terms of distinguishing cortical layers and architectonic areas. Given these data, we propose that SD-OCT can be used to reliably generate 3D reconstructions of multiple cubic centimeters of cortex that can be used to accurately and semi-automatically perform standard histological analyses. PMID:24041872

Rotation Covariant Image Processing for Biomedical Applications

PubMed Central

Reisert, Marco

2013-01-01

With the advent of novel biomedical 3D image acquisition techniques, the efficient and reliable analysis of volumetric images has become more and more important. The amount of data is enormous and demands an automated processing. The applications are manifold, ranging from image enhancement, image reconstruction, and image description to object/feature detection and high-level contextual feature extraction. In most scenarios, it is expected that geometric transformations alter the output in a mathematically well-defined manner. In this paper we emphasis on 3D translations and rotations. Many algorithms rely on intensity or low-order tensorial-like descriptions to fulfill this demand. This paper proposes a general mathematical framework based on mathematical concepts and theories transferred from mathematical physics and harmonic analysis into the domain of image analysis and pattern recognition. Based on two basic operations, spherical tensor differentiation and spherical tensor multiplication, we show how to design a variety of 3D image processing methods in an efficient way. The framework has already been applied to several biomedical applications ranging from feature and object detection tasks to image enhancement and image restoration techniques. In this paper, the proposed methods are applied on a variety of different 3D data modalities stemming from medical and biological sciences. PMID:23710255

Optimization method of superpixel analysis for multi-contrast Jones matrix tomography (Conference Presentation)

NASA Astrophysics Data System (ADS)

Miyazawa, Arata; Hong, Young-Joo; Makita, Shuichi; Kasaragod, Deepa K.; Miura, Masahiro; Yasuno, Yoshiaki

2017-02-01

Local statistics are widely utilized for quantification and image processing of OCT. For example, local mean is used to reduce speckle, local variation of polarization state (degree-of-polarization-uniformity (DOPU)) is used to visualize melanin. Conventionally, these statistics are calculated in a rectangle kernel whose size is uniform over the image. However, the fixed size and shape of the kernel result in a tradeoff between image sharpness and statistical accuracy. Superpixel is a cluster of pixels which is generated by grouping image pixels based on the spatial proximity and similarity of signal values. Superpixels have variant size and flexible shapes which preserve the tissue structure. Here we demonstrate a new superpixel method which is tailored for multifunctional Jones matrix OCT (JM-OCT). This new method forms the superpixels by clustering image pixels in a 6-dimensional (6-D) feature space (spatial two dimensions and four dimensions of optical features). All image pixels were clustered based on their spatial proximity and optical feature similarity. The optical features are scattering, OCT-A, birefringence and DOPU. The method is applied to retinal OCT. Generated superpixels preserve the tissue structures such as retinal layers, sclera, vessels, and retinal pigment epithelium. Hence, superpixel can be utilized as a local statistics kernel which would be more suitable than a uniform rectangle kernel. Superpixelized image also can be used for further image processing and analysis. Since it reduces the number of pixels to be analyzed, it reduce the computational cost of such image processing.

Bubble structure evaluation method of sponge cake by using image morphology

NASA Astrophysics Data System (ADS)

Kato, Kunihito; Yamamoto, Kazuhiko; Nonaka, Masahiko; Katsuta, Yukiyo; Kasamatsu, Chinatsu

2007-01-01

Nowadays, many evaluation methods for food industry by using image processing are proposed. These methods are becoming new evaluation method besides the sensory test and the solid-state measurement that have been used for the quality evaluation recently. The goal of our research is structure evaluation of sponge cake by using the image processing. In this paper, we propose a feature extraction method of the bobble structure in the sponge cake. Analysis of the bubble structure is one of the important properties to understand characteristics of the cake from the image. In order to take the cake image, first we cut cakes and measured that's surface by using the CIS scanner, because the depth of field of this type scanner is very shallow. Therefore the bubble region of the surface has low gray scale value, and it has a feature that is blur. We extracted bubble regions from the surface images based on these features. The input image is binarized, and the feature of bubble is extracted by the morphology analysis. In order to evaluate the result of feature extraction, we compared correlation with "Size of the bubble" of the sensory test result. From a result, the bubble extraction by using morphology analysis gives good correlation. It is shown that our method is as well as the subjectivity evaluation.

Research on Remote Sensing Image Classification Based on Feature Level Fusion

NASA Astrophysics Data System (ADS)

Yuan, L.; Zhu, G.

2018-04-01

Remote sensing image classification, as an important direction of remote sensing image processing and application, has been widely studied. However, in the process of existing classification algorithms, there still exists the phenomenon of misclassification and missing points, which leads to the final classification accuracy is not high. In this paper, we selected Sentinel-1A and Landsat8 OLI images as data sources, and propose a classification method based on feature level fusion. Compare three kind of feature level fusion algorithms (i.e., Gram-Schmidt spectral sharpening, Principal Component Analysis transform and Brovey transform), and then select the best fused image for the classification experimental. In the classification process, we choose four kinds of image classification algorithms (i.e. Minimum distance, Mahalanobis distance, Support Vector Machine and ISODATA) to do contrast experiment. We use overall classification precision and Kappa coefficient as the classification accuracy evaluation criteria, and the four classification results of fused image are analysed. The experimental results show that the fusion effect of Gram-Schmidt spectral sharpening is better than other methods. In four kinds of classification algorithms, the fused image has the best applicability to Support Vector Machine classification, the overall classification precision is 94.01 % and the Kappa coefficients is 0.91. The fused image with Sentinel-1A and Landsat8 OLI is not only have more spatial information and spectral texture characteristics, but also enhances the distinguishing features of the images. The proposed method is beneficial to improve the accuracy and stability of remote sensing image classification.

Linear- and Repetitive Feature Detection Within Remotely Sensed Imagery

DTIC Science & Technology

2017-04-01

applicable to Python or other pro- gramming languages with image- processing capabilities. 4.1 Classification machine learning The first methodology uses...remotely sensed images that are in panchromatic or true-color formats. Image- processing techniques, in- cluding Hough transforms, machine learning, and...data fusion .................................................................................................... 44 6.3 Context-based processing

Hyperspectral image classification based on local binary patterns and PCANet

NASA Astrophysics Data System (ADS)

Yang, Huizhen; Gao, Feng; Dong, Junyu; Yang, Yang

2018-04-01

Hyperspectral image classification has been well acknowledged as one of the challenging tasks of hyperspectral data processing. In this paper, we propose a novel hyperspectral image classification framework based on local binary pattern (LBP) features and PCANet. In the proposed method, linear prediction error (LPE) is first employed to select a subset of informative bands, and LBP is utilized to extract texture features. Then, spectral and texture features are stacked into a high dimensional vectors. Next, the extracted features of a specified position are transformed to a 2-D image. The obtained images of all pixels are fed into PCANet for classification. Experimental results on real hyperspectral dataset demonstrate the effectiveness of the proposed method.

Feature evaluation of complex hysteresis smoothing and its practical applications to noisy SEM images.

PubMed

Suzuki, Kazuhiko; Oho, Eisaku

2013-01-01

Quality of a scanning electron microscopy (SEM) image is strongly influenced by noise. This is a fundamental drawback of the SEM instrument. Complex hysteresis smoothing (CHS) has been previously developed for noise removal of SEM images. This noise removal is performed by monitoring and processing properly the amplitude of the SEM signal. As it stands now, CHS may not be so utilized, though it has several advantages for SEM. For example, the resolution of image processed by CHS is basically equal to that of the original image. In order to find wide application of the CHS method in microscopy, the feature of CHS, which has not been so clarified until now is evaluated correctly. As the application of the result obtained by the feature evaluation, cursor width (CW), which is the sole processing parameter of CHS, is determined more properly using standard deviation of noise Nσ. In addition, disadvantage that CHS cannot remove the noise with excessively large amplitude is improved by a certain postprocessing. CHS is successfully applicable to SEM images with various noise amplitudes. © Wiley Periodicals, Inc.

Generating description with multi-feature fusion and saliency maps of image

NASA Astrophysics Data System (ADS)

Liu, Lisha; Ding, Yuxuan; Tian, Chunna; Yuan, Bo

2018-04-01

Generating description for an image can be regard as visual understanding. It is across artificial intelligence, machine learning, natural language processing and many other areas. In this paper, we present a model that generates description for images based on RNN (recurrent neural network) with object attention and multi-feature of images. The deep recurrent neural networks have excellent performance in machine translation, so we use it to generate natural sentence description for images. The proposed method uses single CNN (convolution neural network) that is trained on ImageNet to extract image features. But we think it can not adequately contain the content in images, it may only focus on the object area of image. So we add scene information to image feature using CNN which is trained on Places205. Experiments show that model with multi-feature extracted by two CNNs perform better than which with a single feature. In addition, we make saliency weights on images to emphasize the salient objects in images. We evaluate our model on MSCOCO based on public metrics, and the results show that our model performs better than several state-of-the-art methods.

Range and Panoramic Image Fusion Into a Textured Range Image for Culture Heritage Documentation

NASA Astrophysics Data System (ADS)

Bila, Z.; Reznicek, J.; Pavelka, K.

2013-07-01

This paper deals with a fusion of range and panoramic images, where the range image is acquired by a 3D laser scanner and the panoramic image is acquired with a digital still camera mounted on a panoramic head and tripod. The fused resulting dataset, called "textured range image", provides more reliable information about the investigated object for conservators and historians, than using both datasets separately. A simple example of fusion of a range and panoramic images, both obtained in St. Francis Xavier Church in town Opařany, is given here. Firstly, we describe the process of data acquisition, then the processing of both datasets into a proper format for following fusion and the process of fusion. The process of fusion can be divided into a two main parts: transformation and remapping. In the first, transformation, part, both images are related by matching similar features detected on both images with a proper detector, which results in transformation matrix enabling transformation of the range image onto a panoramic image. Then, the range data are remapped from the range image space into a panoramic image space and stored as an additional "range" channel. The process of image fusion is validated by comparing similar features extracted on both datasets.

Content-based cell pathology image retrieval by combining different features

NASA Astrophysics Data System (ADS)

Zhou, Guangquan; Jiang, Lu; Luo, Limin; Bao, Xudong; Shu, Huazhong

2004-04-01

Content Based Color Cell Pathology Image Retrieval is one of the newest computer image processing applications in medicine. Recently, some algorithms have been developed to achieve this goal. Because of the particularity of cell pathology images, the result of the image retrieval based on single characteristic is not satisfactory. A new method for pathology image retrieval by combining color, texture and morphologic features to search cell images is proposed. Firstly, nucleus regions of leukocytes in images are automatically segmented by K-mean clustering method. Then single leukocyte region is detected by utilizing thresholding algorithm segmentation and mathematics morphology. The features that include color, texture and morphologic features are extracted from single leukocyte to represent main attribute in the search query. The features are then normalized because the numerical value range and physical meaning of extracted features are different. Finally, the relevance feedback system is introduced. So that the system can automatically adjust the weights of different features and improve the results of retrieval system according to the feedback information. Retrieval results using the proposed method fit closely with human perception and are better than those obtained with the methods based on single feature.

Brain Morphometry Using Anatomical Magnetic Resonance Imaging

ERIC Educational Resources Information Center

Bansal, Ravi; Gerber, Andrew J.; Peterson, Bradley S.

2008-01-01

The efficacy of anatomical magnetic resonance imaging (MRI) in studying the morphological features of various regions of the brain is described, also providing the steps used in the processing and studying of the images. The ability to correlate these features with several clinical and psychological measures can help in using anatomical MRI to…

A TV Camera System Which Extracts Feature Points For Non-Contact Eye Movement Detection

NASA Astrophysics Data System (ADS)

Tomono, Akira; Iida, Muneo; Kobayashi, Yukio

1990-04-01

This paper proposes a highly efficient camera system which extracts, irrespective of background, feature points such as the pupil, corneal reflection image and dot-marks pasted on a human face in order to detect human eye movement by image processing. Two eye movement detection methods are sugested: One utilizing face orientation as well as pupil position, The other utilizing pupil and corneal reflection images. A method of extracting these feature points using LEDs as illumination devices and a new TV camera system designed to record eye movement are proposed. Two kinds of infra-red LEDs are used. These LEDs are set up a short distance apart and emit polarized light of different wavelengths. One light source beams from near the optical axis of the lens and the other is some distance from the optical axis. The LEDs are operated in synchronization with the camera. The camera includes 3 CCD image pick-up sensors and a prism system with 2 boundary layers. Incident rays are separated into 2 wavelengths by the first boundary layer of the prism. One set of rays forms an image on CCD-3. The other set is split by the half-mirror layer of the prism and forms an image including the regularly reflected component by placing a polarizing filter in front of CCD-1 or another image not including the component by not placing a polarizing filter in front of CCD-2. Thus, three images with different reflection characteristics are obtained by three CCDs. Through the experiment, it is shown that two kinds of subtraction operations between the three images output from CCDs accentuate three kinds of feature points: the pupil and corneal reflection images and the dot-marks. Since the S/N ratio of the subtracted image is extremely high, the thresholding process is simple and allows reducting the intensity of the infra-red illumination. A high speed image processing apparatus using this camera system is decribed. Realtime processing of the subtraction, thresholding and gravity position calculation of the feature points is possible.

TU-C-17A-03: An Integrated Contour Evaluation Software Tool Using Supervised Pattern Recognition for Radiotherapy

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

Chen, H; Tan, J; Kavanaugh, J

Purpose: Radiotherapy (RT) contours delineated either manually or semiautomatically require verification before clinical usage. Manual evaluation is very time consuming. A new integrated software tool using supervised pattern contour recognition was thus developed to facilitate this process. Methods: The contouring tool was developed using an object-oriented programming language C# and application programming interfaces, e.g. visualization toolkit (VTK). The C# language served as the tool design basis. The Accord.Net scientific computing libraries were utilized for the required statistical data processing and pattern recognition, while the VTK was used to build and render 3-D mesh models from critical RT structures in real-timemore » and 360° visualization. Principal component analysis (PCA) was used for system self-updating geometry variations of normal structures based on physician-approved RT contours as a training dataset. The inhouse design of supervised PCA-based contour recognition method was used for automatically evaluating contour normality/abnormality. The function for reporting the contour evaluation results was implemented by using C# and Windows Form Designer. Results: The software input was RT simulation images and RT structures from commercial clinical treatment planning systems. Several abilities were demonstrated: automatic assessment of RT contours, file loading/saving of various modality medical images and RT contours, and generation/visualization of 3-D images and anatomical models. Moreover, it supported the 360° rendering of the RT structures in a multi-slice view, which allows physicians to visually check and edit abnormally contoured structures. Conclusion: This new software integrates the supervised learning framework with image processing and graphical visualization modules for RT contour verification. This tool has great potential for facilitating treatment planning with the assistance of an automatic contour evaluation module in avoiding unnecessary manual verification for physicians/dosimetrists. In addition, its nature as a compact and stand-alone tool allows for future extensibility to include additional functions for physicians’ clinical needs.« less

An Investigation of Automatic Change Detection for Topographic Map Updating

NASA Astrophysics Data System (ADS)

Duncan, P.; Smit, J.

2012-08-01

Changes to the landscape are constantly occurring and it is essential for geospatial and mapping organisations that these changes are regularly detected and captured, so that map databases can be updated to reflect the current status of the landscape. The Chief Directorate of National Geospatial Information (CD: NGI), South Africa's national mapping agency, currently relies on manual methods of detecting changes and capturing these changes. These manual methods are time consuming and labour intensive, and rely on the skills and interpretation of the operator. It is therefore necessary to move towards more automated methods in the production process at CD: NGI. The aim of this research is to do an investigation into a methodology for automatic or semi-automatic change detection for the purpose of updating topographic databases. The method investigated for detecting changes is through image classification as well as spatial analysis and is focussed on urban landscapes. The major data input into this study is high resolution aerial imagery and existing topographic vector data. Initial results indicate the traditional pixel-based image classification approaches are unsatisfactory for large scale land-use mapping and that object-orientated approaches hold more promise. Even in the instance of object-oriented image classification generalization of techniques on a broad-scale has provided inconsistent results. A solution may lie with a hybrid approach of pixel and object-oriented techniques.

Image segmentation and registration for the analysis of joint motion from 3D MRI

NASA Astrophysics Data System (ADS)

Hu, Yangqiu; Haynor, David R.; Fassbind, Michael; Rohr, Eric; Ledoux, William

2006-03-01

We report an image segmentation and registration method for studying joint morphology and kinematics from in vivo MRI scans and its application to the analysis of ankle joint motion. Using an MR-compatible loading device, a foot was scanned in a single neutral and seven dynamic positions including maximal flexion, rotation and inversion/eversion. A segmentation method combining graph cuts and level sets was developed which allows a user to interactively delineate 14 bones in the neutral position volume in less than 30 minutes total, including less than 10 minutes of user interaction. In the subsequent registration step, a separate rigid body transformation for each bone is obtained by registering the neutral position dataset to each of the dynamic ones, which produces an accurate description of the motion between them. We have processed six datasets, including 3 normal and 3 pathological feet. For validation our results were compared with those obtained from 3DViewnix, a semi-automatic segmentation program, and achieved good agreement in volume overlap ratios (mean: 91.57%, standard deviation: 3.58%) for all bones. Our tool requires only 1/50 and 1/150 of the user interaction time required by 3DViewnix and NIH Image Plus, respectively, an improvement that has the potential to make joint motion analysis from MRI practical in research and clinical applications.

An evaluation of semi-automated methods for collecting ecosystem-level data in temperate marine systems.

PubMed

Griffin, Kingsley J; Hedge, Luke H; González-Rivero, Manuel; Hoegh-Guldberg, Ove I; Johnston, Emma L

2017-07-01

Historically, marine ecologists have lacked efficient tools that are capable of capturing detailed species distribution data over large areas. Emerging technologies such as high-resolution imaging and associated machine-learning image-scoring software are providing new tools to map species over large areas in the ocean. Here, we combine a novel diver propulsion vehicle (DPV) imaging system with free-to-use machine-learning software to semi-automatically generate dense and widespread abundance records of a habitat-forming algae over ~5,000 m 2 of temperate reef. We employ replicable spatial techniques to test the effectiveness of traditional diver-based sampling, and better understand the distribution and spatial arrangement of one key algal species. We found that the effectiveness of a traditional survey depended on the level of spatial structuring, and generally 10-20 transects (50 × 1 m) were required to obtain reliable results. This represents 2-20 times greater replication than have been collected in previous studies. Furthermore, we demonstrate the usefulness of fine-resolution distribution modeling for understanding patterns in canopy algae cover at multiple spatial scales, and discuss applications to other marine habitats. Our analyses demonstrate that semi-automated methods of data gathering and processing provide more accurate results than traditional methods for describing habitat structure at seascape scales, and therefore represent vastly improved techniques for understanding and managing marine seascapes.

Tear fluid proteomics multimarkers for diabetic retinopathy screening

PubMed Central

2013-01-01

Background The aim of the project was to develop a novel method for diabetic retinopathy screening based on the examination of tear fluid biomarker changes. In order to evaluate the usability of protein biomarkers for pre-screening purposes several different approaches were used, including machine learning algorithms. Methods All persons involved in the study had diabetes. Diabetic retinopathy (DR) was diagnosed by capturing 7-field fundus images, evaluated by two independent ophthalmologists. 165 eyes were examined (from 119 patients), 55 were diagnosed healthy and 110 images showed signs of DR. Tear samples were taken from all eyes and state-of-the-art nano-HPLC coupled ESI-MS/MS mass spectrometry protein identification was performed on all samples. Applicability of protein biomarkers was evaluated by six different optimally parameterized machine learning algorithms: Support Vector Machine, Recursive Partitioning, Random Forest, Naive Bayes, Logistic Regression, K-Nearest Neighbor. Results Out of the six investigated machine learning algorithms the result of Recursive Partitioning proved to be the most accurate. The performance of the system realizing the above algorithm reached 74% sensitivity and 48% specificity. Conclusions Protein biomarkers selected and classified with machine learning algorithms alone are at present not recommended for screening purposes because of low specificity and sensitivity values. This tool can be potentially used to improve the results of image processing methods as a complementary tool in automatic or semiautomatic systems. PMID:23919537

An Evaluation of Feature Learning Methods for High Resolution Image Classification

NASA Astrophysics Data System (ADS)

Tokarczyk, P.; Montoya, J.; Schindler, K.

2012-07-01

Automatic image classification is one of the fundamental problems of remote sensing research. The classification problem is even more challenging in high-resolution images of urban areas, where the objects are small and heterogeneous. Two questions arise, namely which features to extract from the raw sensor data to capture the local radiometry and image structure at each pixel or segment, and which classification method to apply to the feature vectors. While classifiers are nowadays well understood, selecting the right features remains a largely empirical process. Here we concentrate on the features. Several methods are evaluated which allow one to learn suitable features from unlabelled image data by analysing the image statistics. In a comparative study, we evaluate unsupervised feature learning with different linear and non-linear learning methods, including principal component analysis (PCA) and deep belief networks (DBN). We also compare these automatically learned features with popular choices of ad-hoc features including raw intensity values, standard combinations like the NDVI, a few PCA channels, and texture filters. The comparison is done in a unified framework using the same images, the target classes, reference data and a Random Forest classifier.

Distinction of Green Sweet Peppers by Using Various Color Space Models and Computation of 3 Dimensional Location Coordinates of Recognized Green Sweet Peppers Based on Parallel Stereovision System

NASA Astrophysics Data System (ADS)

Bachche, Shivaji; Oka, Koichi

2013-06-01

This paper presents the comparative study of various color space models to determine the suitable color space model for detection of green sweet peppers. The images were captured by using CCD cameras and infrared cameras and processed by using Halcon image processing software. The LED ring around the camera neck was used as an artificial lighting to enhance the feature parameters. For color images, CieLab, YIQ, YUV, HSI and HSV whereas for infrared images, grayscale color space models were selected for image processing. In case of color images, HSV color space model was found more significant with high percentage of green sweet pepper detection followed by HSI color space model as both provides information in terms of hue/lightness/chroma or hue/lightness/saturation which are often more relevant to discriminate the fruit from image at specific threshold value. The overlapped fruits or fruits covered by leaves can be detected in better way by using HSV color space model as the reflection feature from fruits had higher histogram than reflection feature from leaves. The IR 80 optical filter failed to distinguish fruits from images as filter blocks useful information on features. Computation of 3D coordinates of recognized green sweet peppers was also conducted in which Halcon image processing software provides location and orientation of the fruits accurately. The depth accuracy of Z axis was examined in which 500 to 600 mm distance between cameras and fruits was found significant to compute the depth distance precisely when distance between two cameras maintained to 100 mm.

Plexiform neurofibroma tissue classification

NASA Astrophysics Data System (ADS)

Weizman, L.; Hoch, L.; Ben Sira, L.; Joskowicz, L.; Pratt, L.; Constantini, S.; Ben Bashat, D.

2011-03-01

Plexiform Neurofibroma (PN) is a major complication of NeuroFibromatosis-1 (NF1), a common genetic disease that involving the nervous system. PNs are peripheral nerve sheath tumors extending along the length of the nerve in various parts of the body. Treatment decision is based on tumor volume assessment using MRI, which is currently time consuming and error prone, with limited semi-automatic segmentation support. We present in this paper a new method for the segmentation and tumor mass quantification of PN from STIR MRI scans. The method starts with a user-based delineation of the tumor area in a single slice and automatically detects the PN lesions in the entire image based on the tumor connectivity. Experimental results on seven datasets yield a mean volume overlap difference of 25% as compared to manual segmentation by expert radiologist with a mean computation and interaction time of 12 minutes vs. over an hour for manual annotation. Since the user interaction in the segmentation process is minimal, our method has the potential to successfully become part of the clinical workflow.

An ERTS-1 investigation for Lake Ontario and its basin

NASA Technical Reports Server (NTRS)

Polcyn, F. C.; Falconer, A. (Principal Investigator); Wagner, T. W.; Rebel, D. L.

1975-01-01

The author has identified the following significant results. Methods of manual, semi-automatic, and automatic (computer) data processing were evaluated, as were the requirements for spatial physiographic and limnological information. The coupling of specially processed ERTS data with simulation models of the watershed precipitation/runoff process provides potential for water resources management. Optimal and full use of the data requires a mix of data processing and analysis techniques, including single band editing, two band ratios, and multiband combinations. A combination of maximum likelihood ratio and near-IR/red band ratio processing was found to be particularly useful.

ImageParser: a tool for finite element generation from three-dimensional medical images

PubMed Central

Yin, HM; Sun, LZ; Wang, G; Yamada, T; Wang, J; Vannier, MW

2004-01-01

Background The finite element method (FEM) is a powerful mathematical tool to simulate and visualize the mechanical deformation of tissues and organs during medical examinations or interventions. It is yet a challenge to build up an FEM mesh directly from a volumetric image partially because the regions (or structures) of interest (ROIs) may be irregular and fuzzy. Methods A software package, ImageParser, is developed to generate an FEM mesh from 3-D tomographic medical images. This software uses a semi-automatic method to detect ROIs from the context of image including neighboring tissues and organs, completes segmentation of different tissues, and meshes the organ into elements. Results The ImageParser is shown to build up an FEM model for simulating the mechanical responses of the breast based on 3-D CT images. The breast is compressed by two plate paddles under an overall displacement as large as 20% of the initial distance between the paddles. The strain and tangential Young's modulus distributions are specified for the biomechanical analysis of breast tissues. Conclusion The ImageParser can successfully exact the geometry of ROIs from a complex medical image and generate the FEM mesh with customer-defined segmentation information. PMID:15461787

Towards improved characterization of northern wetlands (or other landscapes) by remote sensing - a rapid approach to collect ground truth data

NASA Astrophysics Data System (ADS)

Gålfalk, Magnus; Karlson, Martin; Crill, Patrick; Bastviken, David

2017-04-01

The calibration and validation of remote sensing land cover products is highly dependent on accurate ground truth data, which are costly and practically challenging to collect. This study evaluates a novel and efficient alternative to field surveys and UAV imaging commonly applied for this task. The method consists of i) a light weight, water proof, remote controlled RGB-camera mounted on an extendable monopod used for acquiring wide-field images of the ground from a height of 4.5 meters, and ii) a script for semi-automatic image classification. In the post-processing, the wide-field images are corrected for optical distortion and geometrically rectified so that the spatial resolution is the same over the surface area used for classification. The script distinguishes land surface components by color, brightness and spatial variability. The method was evaluated in wetland areas located around Abisko, northern Sweden. Proportional estimates of the six main surface components in the wetlands (wet and dry Sphagnum, shrub, grass, water, rock) were derived for 200 images, equivalent to 10 × 10 m field plots. These photo plots were then used as calibration data for a regional scale satellite based classification which separates the six wetland surface components using a Sentinel-1 time series. The method presented in this study is accurate, rapid, robust and cost efficient in comparison to field surveys (time consuming) and drone mapping (which require low wind speeds and no rain, suffer from battery limited flight times, have potential GPS/compass errors far north, and in some areas are prohibited by law).

Accuracy Considerations in Image-guided Cardiac Interventions: Experience and Lessons Learned

PubMed Central

Linte, Cristian A.; Lang, Pencilla; Rettmann, Maryam E.; Cho, Daniel S.; Holmes, David R.; Robb, Richard A.; Peters, Terry M.

2014-01-01

Motivation Medical imaging and its application in interventional guidance has revolutionized the development of minimally invasive surgical procedures leading to reduced patient trauma, fewer risks, and shorter recovery times. However, a frequently posed question with regards to an image guidance system is “how accurate is it?” On one hand, the accuracy challenge can be posed in terms of the tolerable clinical error associated with the procedure; on the other hand, accuracy is bound by the limitations of the system’s components, including modeling, patient registration, and surgical instrument tracking, all of which ultimately impact the overall targeting capabilities of the system. Methods While these processes are not unique to any interventional specialty, this paper discusses them in the context of two different cardiac image-guidance platforms: a model-enhanced ultrasound platform for intracardiac interventions and a prototype system for advanced visualization in image-guided cardiac ablation therapy. Results Pre-operative modeling techniques involving manual, semi-automatic and registration-based segmentation are discussed. The performance and limitations of clinically feasible approaches for patient registration evaluated both in the laboratory and operating room are presented. Our experience with two different magnetic tracking systems for instrument and ultrasound transducer localization is reported. Ultimately, the overall accuracy of the systems is discussed based on both in vitro and preliminary in vivo experience. Conclusion While clinical accuracy is specific to a particular patient and procedure and vastly dependent on the surgeon’s experience, the system’s engineering limitations are critical to determine whether the clinical requirements can be met. PMID:21671097

Semi-automatic Data Integration using Karma

NASA Astrophysics Data System (ADS)

Garijo, D.; Kejriwal, M.; Pierce, S. A.; Houser, P. I. Q.; Peckham, S. D.; Stanko, Z.; Hardesty Lewis, D.; Gil, Y.; Pennington, D. D.; Knoblock, C.

2017-12-01

Data integration applications are ubiquitous in scientific disciplines. A state-of-the-art data integration system accepts both a set of data sources and a target ontology as input, and semi-automatically maps the data sources in terms of concepts and relationships in the target ontology. Mappings can be both complex and highly domain-specific. Once such a semantic model, expressing the mapping using community-wide standard, is acquired, the source data can be stored in a single repository or database using the semantics of the target ontology. However, acquiring the mapping is a labor-prone process, and state-of-the-art artificial intelligence systems are unable to fully automate the process using heuristics and algorithms alone. Instead, a more realistic goal is to develop adaptive tools that minimize user feedback (e.g., by offering good mapping recommendations), while at the same time making it intuitive and easy for the user to both correct errors and to define complex mappings. We present Karma, a data integration system that has been developed over multiple years in the information integration group at the Information Sciences Institute, a research institute at the University of Southern California's Viterbi School of Engineering. Karma is a state-of-the-art data integration tool that supports an interactive graphical user interface, and has been featured in multiple domains over the last five years, including geospatial, biological, humanities and bibliographic applications. Karma allows a user to import their own ontology and datasets using widely used formats such as RDF, XML, CSV and JSON, can be set up either locally or on a server, supports a native backend database for prototyping queries, and can even be seamlessly integrated into external computational pipelines, including those ingesting data via streaming data sources, Web APIs and SQL databases. We illustrate a Karma workflow at a conceptual level, along with a live demo, and show use cases of Karma specifically for the geosciences. In particular, we show how Karma can be used intuitively to obtain the mapping model between case study data sources and a publicly available and expressive target ontology that has been designed to capture a broad set of concepts in geoscience with standardized, easily searchable names.