Automated classification of articular cartilage surfaces based on surface texture.

PubMed

Stachowiak, G P; Stachowiak, G W; Podsiadlo, P

2006-11-01

In this study the automated classification system previously developed by the authors was used to classify articular cartilage surfaces with different degrees of wear. This automated system classifies surfaces based on their texture. Plug samples of sheep cartilage (pins) were run on stainless steel discs under various conditions using a pin-on-disc tribometer. Testing conditions were specifically designed to produce different severities of cartilage damage due to wear. Environmental scanning electron microscope (SEM) (ESEM) images of cartilage surfaces, that formed a database for pattern recognition analysis, were acquired. The ESEM images of cartilage were divided into five groups (classes), each class representing different wear conditions or wear severity. Each class was first examined and assessed visually. Next, the automated classification system (pattern recognition) was applied to all classes. The results of the automated surface texture classification were compared to those based on visual assessment of surface morphology. It was shown that the texture-based automated classification system was an efficient and accurate method of distinguishing between various cartilage surfaces generated under different wear conditions. It appears that the texture-based classification method has potential to become a useful tool in medical diagnostics.

Classifying Radio Galaxies with the Convolutional Neural Network

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

Aniyan, A. K.; Thorat, K.

We present the application of a deep machine learning technique to classify radio images of extended sources on a morphological basis using convolutional neural networks (CNN). In this study, we have taken the case of the Fanaroff–Riley (FR) class of radio galaxies as well as radio galaxies with bent-tailed morphology. We have used archival data from the Very Large Array (VLA)—Faint Images of the Radio Sky at Twenty Centimeters survey and existing visually classified samples available in the literature to train a neural network for morphological classification of these categories of radio sources. Our training sample size for each of these categoriesmore » is ∼200 sources, which has been augmented by rotated versions of the same. Our study shows that CNNs can classify images of the FRI and FRII and bent-tailed radio galaxies with high accuracy (maximum precision at 95%) using well-defined samples and a “fusion classifier,” which combines the results of binary classifications, while allowing for a mechanism to find sources with unusual morphologies. The individual precision is highest for bent-tailed radio galaxies at 95% and is 91% and 75% for the FRI and FRII classes, respectively, whereas the recall is highest for FRI and FRIIs at 91% each, while the bent-tailed class has a recall of 79%. These results show that our results are comparable to that of manual classification, while being much faster. Finally, we discuss the computational and data-related challenges associated with the morphological classification of radio galaxies with CNNs.« less

Method for evaluation of human induced pluripotent stem cell quality using image analysis based on the biological morphology of cells.

PubMed

Wakui, Takashi; Matsumoto, Tsuyoshi; Matsubara, Kenta; Kawasaki, Tomoyuki; Yamaguchi, Hiroshi; Akutsu, Hidenori

2017-10-01

We propose an image analysis method for quality evaluation of human pluripotent stem cells based on biologically interpretable features. It is important to maintain the undifferentiated state of induced pluripotent stem cells (iPSCs) while culturing the cells during propagation. Cell culture experts visually select good quality cells exhibiting the morphological features characteristic of undifferentiated cells. Experts have empirically determined that these features comprise prominent and abundant nucleoli, less intercellular spacing, and fewer differentiating cellular nuclei. We quantified these features based on experts' visual inspection of phase contrast images of iPSCs and found that these features are effective for evaluating iPSC quality. We then developed an iPSC quality evaluation method using an image analysis technique. The method allowed accurate classification, equivalent to visual inspection by experts, of three iPSC cell lines.

Classification of inflammatory bowel diseases by means of Raman spectroscopic imaging of epithelium cells

NASA Astrophysics Data System (ADS)

Bielecki, Christiane; Bocklitz, Thomas W.; Schmitt, Michael; Krafft, Christoph; Marquardt, Claudio; Gharbi, Akram; Knösel, Thomas; Stallmach, Andreas; Popp, Juergen

2012-07-01

We report on a Raman microspectroscopic characterization of the inflammatory bowel diseases (IBD) Crohn's disease (CD) and ulcerative colitis (UC). Therefore, Raman maps of human colon tissue sections were analyzed by utilizing innovative chemometric approaches. First, support vector machines were applied to highlight the tissue morphology (=Raman spectroscopic histopathology). In a second step, the biochemical tissue composition has been studied by analyzing the epithelium Raman spectra of sections of healthy control subjects (n=11), subjects with CD (n=14), and subjects with UC (n=13). These three groups exhibit significantly different molecular specific Raman signatures, allowing establishment of a classifier (support-vector-machine). By utilizing this classifier it was possible to separate between healthy control patients, patients with CD, and patients with UC with an accuracy of 98.90%. The automatic design of both classification steps (visualization of the tissue morphology and molecular classification of IBD) paves the way for an objective clinical diagnosis of IBD by means of Raman spectroscopy in combination with chemometric approaches.

VizieR Online Data Catalog: Double-component model fitting of elliptical gal. (Oh+, 2017)

NASA Astrophysics Data System (ADS)

Oh, S.; Greene, J. E.; Lackner, C. N.

2017-09-01

We use the detailed visual morphology classification catalog of Nair & Abraham (2010, J/ApJS/186/427) to construct a sample of nearby elliptical galaxies in a range of mass and environment. The catalog is based on the SDSS DR4 main spectroscopic sample, and extends to z=0.1. (1 data file).

Interspecific visual signalling in animals and plants: a functional classification.

PubMed

Caro, Tim; Allen, William L

2017-07-05

Organisms frequently gain advantages when they engage in signalling with individuals of other species. Here, we provide a functionally structured framework of the great variety of interspecific visual signals seen in nature, and then describe the different signalling mechanisms that have evolved in response to each of these functional requirements. We propose that interspecific visual signalling can be divided into six major functional categories: anti-predator, food acquisition, anti-parasite, host acquisition, reproductive and agonistic signalling, with each function enabled by several distinct mechanisms. We support our classification by reviewing the ecological and behavioural drivers of interspecific signalling in animals and plants, principally focusing on comparative studies that address large-scale patterns of diversity. Collating diverse examples of interspecific signalling into an organized set of functional and mechanistic categories places anachronistic behavioural and morphological labels in fresh context, clarifies terminology and redirects research effort towards understanding environmental influences driving interspecific signalling in nature.This article is part of the themed issue 'Animal coloration: production, perception, function and application'. © 2017 The Author(s).

Detailed Quantitative Classifications of Galaxy Morphology

NASA Astrophysics Data System (ADS)

Nair, Preethi

2018-01-01

Understanding the physical processes responsible for the growth of galaxies is one of the key challenges in extragalactic astronomy. The assembly history of a galaxy is imprinted in a galaxy’s detailed morphology. The bulge-to-total ratio of galaxies, the presence or absence of bars, rings, spiral arms, tidal tails etc, all have implications for the past merger, star formation, and feedback history of a galaxy. However, current quantitative galaxy classification schemes are only useful for broad binning. They cannot classify or exploit the wide variety of galaxy structures seen in nature. Therefore, comparisons of observations with theoretical predictions of secular structure formation have only been conducted on small samples of visually classified galaxies. However large samples are needed to disentangle the complex physical processes of galaxy formation. With the advent of large surveys, like the Sloan Digital Sky Survey (SDSS) and the upcoming Large Synoptic Survey Telescope (LSST) and WFIRST, the problem of statistics will be resolved. However, the need for a robust quantitative classification scheme will still remain. Here I will present early results on promising machine learning algorithms that are providing detailed classifications, identifying bars, rings, multi-armed spiral galaxies, and Hubble type.

An automatic taxonomy of galaxy morphology using unsupervised machine learning

NASA Astrophysics Data System (ADS)

Hocking, Alex; Geach, James E.; Sun, Yi; Davey, Neil

2018-01-01

We present an unsupervised machine learning technique that automatically segments and labels galaxies in astronomical imaging surveys using only pixel data. Distinct from previous unsupervised machine learning approaches used in astronomy we use no pre-selection or pre-filtering of target galaxy type to identify galaxies that are similar. We demonstrate the technique on the Hubble Space Telescope (HST) Frontier Fields. By training the algorithm using galaxies from one field (Abell 2744) and applying the result to another (MACS 0416.1-2403), we show how the algorithm can cleanly separate early and late type galaxies without any form of pre-directed training for what an 'early' or 'late' type galaxy is. We then apply the technique to the HST Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey (CANDELS) fields, creating a catalogue of approximately 60 000 classifications. We show how the automatic classification groups galaxies of similar morphological (and photometric) type and make the classifications public via a catalogue, a visual catalogue and galaxy similarity search. We compare the CANDELS machine-based classifications to human-classifications from the Galaxy Zoo: CANDELS project. Although there is not a direct mapping between Galaxy Zoo and our hierarchical labelling, we demonstrate a good level of concordance between human and machine classifications. Finally, we show how the technique can be used to identify rarer objects and present lensed galaxy candidates from the CANDELS imaging.

Human red blood cell recognition enhancement with three-dimensional morphological features obtained by digital holographic imaging

NASA Astrophysics Data System (ADS)

Jaferzadeh, Keyvan; Moon, Inkyu

2016-12-01

The classification of erythrocytes plays an important role in the field of hematological diagnosis, specifically blood disorders. Since the biconcave shape of red blood cell (RBC) is altered during the different stages of hematological disorders, we believe that the three-dimensional (3-D) morphological features of erythrocyte provide better classification results than conventional two-dimensional (2-D) features. Therefore, we introduce a set of 3-D features related to the morphological and chemical properties of RBC profile and try to evaluate the discrimination power of these features against 2-D features with a neural network classifier. The 3-D features include erythrocyte surface area, volume, average cell thickness, sphericity index, sphericity coefficient and functionality factor, MCH and MCHSD, and two newly introduced features extracted from the ring section of RBC at the single-cell level. In contrast, the 2-D features are RBC projected surface area, perimeter, radius, elongation, and projected surface area to perimeter ratio. All features are obtained from images visualized by off-axis digital holographic microscopy with a numerical reconstruction algorithm, and four categories of biconcave (doughnut shape), flat-disc, stomatocyte, and echinospherocyte RBCs are interested. Our experimental results demonstrate that the 3-D features can be more useful in RBC classification than the 2-D features. Finally, we choose the best feature set of the 2-D and 3-D features by sequential forward feature selection technique, which yields better discrimination results. We believe that the final feature set evaluated with a neural network classification strategy can improve the RBC classification accuracy.

Neuromuscular disease classification system

NASA Astrophysics Data System (ADS)

Sáez, Aurora; Acha, Begoña; Montero-Sánchez, Adoración; Rivas, Eloy; Escudero, Luis M.; Serrano, Carmen

2013-06-01

Diagnosis of neuromuscular diseases is based on subjective visual assessment of biopsies from patients by the pathologist specialist. A system for objective analysis and classification of muscular dystrophies and neurogenic atrophies through muscle biopsy images of fluorescence microscopy is presented. The procedure starts with an accurate segmentation of the muscle fibers using mathematical morphology and a watershed transform. A feature extraction step is carried out in two parts: 24 features that pathologists take into account to diagnose the diseases and 58 structural features that the human eye cannot see, based on the assumption that the biopsy is considered as a graph, where the nodes are represented by each fiber, and two nodes are connected if two fibers are adjacent. A feature selection using sequential forward selection and sequential backward selection methods, a classification using a Fuzzy ARTMAP neural network, and a study of grading the severity are performed on these two sets of features. A database consisting of 91 images was used: 71 images for the training step and 20 as the test. A classification error of 0% was obtained. It is concluded that the addition of features undetectable by the human visual inspection improves the categorization of atrophic patterns.

Fossil Signatures Using Elemental Abundance Distributions and Bayesian Probabilistic Classification

NASA Technical Reports Server (NTRS)

Hoover, Richard B.; Storrie-Lombardi, Michael C.

2004-01-01

Elemental abundances (C6, N7, O8, Na11, Mg12, Al3, P15, S16, Cl17, K19, Ca20, Ti22, Mn25, Fe26, and Ni28) were obtained for a set of terrestrial fossils and the rock matrix surrounding them. Principal Component Analysis extracted five factors accounting for the 92.5% of the data variance, i.e. information content, of the elemental abundance data. Hierarchical Cluster Analysis provided unsupervised sample classification distinguishing fossil from matrix samples on the basis of either raw abundances or PCA input that agreed strongly with visual classification. A stochastic, non-linear Artificial Neural Network produced a Bayesian probability of correct sample classification. The results provide a quantitative probabilistic methodology for discriminating terrestrial fossils from the surrounding rock matrix using chemical information. To demonstrate the applicability of these techniques to the assessment of meteoritic samples or in situ extraterrestrial exploration, we present preliminary data on samples of the Orgueil meteorite. In both systems an elemental signature produces target classification decisions remarkably consistent with morphological classification by a human expert using only structural (visual) information. We discuss the possibility of implementing a complexity analysis metric capable of automating certain image analysis and pattern recognition abilities of the human eye using low magnification optical microscopy images and discuss the extension of this technique across multiple scales.

Rotation-invariant convolutional neural networks for galaxy morphology prediction

NASA Astrophysics Data System (ADS)

Dieleman, Sander; Willett, Kyle W.; Dambre, Joni

2015-06-01

Measuring the morphological parameters of galaxies is a key requirement for studying their formation and evolution. Surveys such as the Sloan Digital Sky Survey have resulted in the availability of very large collections of images, which have permitted population-wide analyses of galaxy morphology. Morphological analysis has traditionally been carried out mostly via visual inspection by trained experts, which is time consuming and does not scale to large (≳104) numbers of images. Although attempts have been made to build automated classification systems, these have not been able to achieve the desired level of accuracy. The Galaxy Zoo project successfully applied a crowdsourcing strategy, inviting online users to classify images by answering a series of questions. Unfortunately, even this approach does not scale well enough to keep up with the increasing availability of galaxy images. We present a deep neural network model for galaxy morphology classification which exploits translational and rotational symmetry. It was developed in the context of the Galaxy Challenge, an international competition to build the best model for morphology classification based on annotated images from the Galaxy Zoo project. For images with high agreement among the Galaxy Zoo participants, our model is able to reproduce their consensus with near-perfect accuracy (>99 per cent) for most questions. Confident model predictions are highly accurate, which makes the model suitable for filtering large collections of images and forwarding challenging images to experts for manual annotation. This approach greatly reduces the experts' workload without affecting accuracy. The application of these algorithms to larger sets of training data will be critical for analysing results from future surveys such as the Large Synoptic Survey Telescope.

Accurate diagnosis of thyroid follicular lesions from nuclear morphology using supervised learning.

PubMed

Ozolek, John A; Tosun, Akif Burak; Wang, Wei; Chen, Cheng; Kolouri, Soheil; Basu, Saurav; Huang, Hu; Rohde, Gustavo K

2014-07-01

Follicular lesions of the thyroid remain significant diagnostic challenges in surgical pathology and cytology. The diagnosis often requires considerable resources and ancillary tests including immunohistochemistry, molecular studies, and expert consultation. Visual analyses of nuclear morphological features, generally speaking, have not been helpful in distinguishing this group of lesions. Here we describe a method for distinguishing between follicular lesions of the thyroid based on nuclear morphology. The method utilizes an optimal transport-based linear embedding for segmented nuclei, together with an adaptation of existing classification methods. We show the method outputs assignments (classification results) which are near perfectly correlated with the clinical diagnosis of several lesion types' lesions utilizing a database of 94 patients in total. Experimental comparisons also show the new method can significantly outperform standard numerical feature-type methods in terms of agreement with the clinical diagnosis gold standard. In addition, the new method could potentially be used to derive insights into biologically meaningful nuclear morphology differences in these lesions. Our methods could be incorporated into a tool for pathologists to aid in distinguishing between follicular lesions of the thyroid. In addition, these results could potentially provide nuclear morphological correlates of biological behavior and reduce health care costs by decreasing histotechnician and pathologist time and obviating the need for ancillary testing. Copyright © 2014 Elsevier B.V. All rights reserved.

Galaxy Zoo: morphological classifications for 120 000 galaxies in HST legacy imaging

NASA Astrophysics Data System (ADS)

Willett, Kyle W.; Galloway, Melanie A.; Bamford, Steven P.; Lintott, Chris J.; Masters, Karen L.; Scarlata, Claudia; Simmons, B. D.; Beck, Melanie; Cardamone, Carolin N.; Cheung, Edmond; Edmondson, Edward M.; Fortson, Lucy F.; Griffith, Roger L.; Häußler, Boris; Han, Anna; Hart, Ross; Melvin, Thomas; Parrish, Michael; Schawinski, Kevin; Smethurst, R. J.; Smith, Arfon M.

2017-02-01

We present the data release paper for the Galaxy Zoo: Hubble (GZH) project. This is the third phase in a large effort to measure reliable, detailed morphologies of galaxies by using crowdsourced visual classifications of colour-composite images. Images in GZH were selected from various publicly released Hubble Space Telescope legacy programmes conducted with the Advanced Camera for Surveys, with filters that probe the rest-frame optical emission from galaxies out to z ˜ 1. The bulk of the sample is selected to have mI814W < 23.5, but goes as faint as mI814W < 26.8 for deep images combined over five epochs. The median redshift of the combined samples is = 0.9 ± 0.6, with a tail extending out to z ≃ 4. The GZH morphological data include measurements of both bulge- and disc-dominated galaxies, details on spiral disc structure that relate to the Hubble type, bar identification, and numerous measurements of clump identification and geometry. This paper also describes a new method for calibrating morphologies for galaxies of different luminosities and at different redshifts by using artificially redshifted galaxy images as a baseline. The GZH catalogue contains both raw and calibrated morphological vote fractions for 119 849 galaxies, providing the largest data set to date suitable for large-scale studies of galaxy evolution out to z ˜ 1.

Listening to galaxies tuning at z ~ 2.5-3.0: The first strikes of the Hubble fork

NASA Astrophysics Data System (ADS)

Talia, M.; Cimatti, A.; Mignoli, M.; Pozzetti, L.; Renzini, A.; Kurk, J.; Halliday, C.

2014-02-01

Aims: We investigate the morphological properties of 494 galaxies selected from the Galaxy Mass Assembly ultra-deep Spectroscopic Survey (GMASS) at z > 1, primarily in their optical rest frame, using Hubble Space Telescope (HST) infrared images, from the Cosmic Assembly Near-IR Deep Extragalactic Legacy Survey (CANDELS). Methods: The morphological analysis of Wield Field Camera (WFC3) H160 band images was performed using two different methods: a visual classification identifying traditional Hubble types, and a quantitative analysis using parameters that describe structural properties, such as the concentration of light and the rotational asymmetry. The two classifications are compared. We then analysed how apparent morphologies correlate with the physical properties of galaxies. Results: The fractions of both elliptical and disk galaxies decrease between redshifts z ~ 1 to z ~ 3, while at z > 3 the galaxy population is dominated by irregular galaxies. The quantitative morphological analysis shows that, at 1 < z < 3, morphological parameters are not as effective in distinguishing the different morphological Hubble types as they are at low redshift. No significant morphological k-correction was found to be required for the Hubble type classification, with some exceptions. In general, different morphological types occupy the two peaks of the (U - B)rest colour bimodality of galaxies: most irregulars occupy the blue peak, while ellipticals are mainly found in the red peak, though with some level of contamination. Disks are more evenly distributed than either irregulars and ellipticals. We find that the position of a galaxy in a UVJ diagram is related to its morphological type: the "quiescent" region of the plot is mainly occupied by ellipticals and, to a lesser extent, by disks. We find that only ~33% of all morphological ellipticals in our sample are red and passively evolving galaxies, a percentage that is consistent with previous results obtained at z < 1. Blue galaxies morphologically classified as ellipticals show a remarkable structural similarity to red ones. We search for correlations between our morphological and spectroscopic galaxy classifications. Almost all irregulars have a star-forming galaxy spectrum. In addition, the majority of disks show some sign of star-formation activity in their spectra, though in some cases their red continuum is indicative of old stellar populations. Finally, an elliptical morphology may be associated with either passively evolving or strongly star-forming galaxies. Conclusions: We propose that the Hubble sequence of galaxy morphologies takes shape at redshift 2.5 < z < 3. The fractions of both ellipticals and disks decrease with increasing lookback time at z > 1, such that at redshifts z = 2.5-2.7 and above, the Hubble types cannot be identified, and most galaxies are classified as irregular. Appendix A is available in electronic form at http://www.aanda.org

[Automatic Sleep Stage Classification Based on an Improved K-means Clustering Algorithm].

PubMed

Xiao, Shuyuan; Wang, Bei; Zhang, Jian; Zhang, Qunfeng; Zou, Junzhong

2016-10-01

Sleep stage scoring is a hotspot in the field of medicine and neuroscience.Visual inspection of sleep is laborious and the results may be subjective to different clinicians.Automatic sleep stage classification algorithm can be used to reduce the manual workload.However,there are still limitations when it encounters complicated and changeable clinical cases.The purpose of this paper is to develop an automatic sleep staging algorithm based on the characteristics of actual sleep data.In the proposed improved K-means clustering algorithm,points were selected as the initial centers by using a concept of density to avoid the randomness of the original K-means algorithm.Meanwhile,the cluster centers were updated according to the‘Three-Sigma Rule’during the iteration to abate the influence of the outliers.The proposed method was tested and analyzed on the overnight sleep data of the healthy persons and patients with sleep disorders after continuous positive airway pressure(CPAP)treatment.The automatic sleep stage classification results were compared with the visual inspection by qualified clinicians and the averaged accuracy reached 76%.With the analysis of morphological diversity of sleep data,it was proved that the proposed improved K-means algorithm was feasible and valid for clinical practice.

An Automated Classification Technique for Detecting Defects in Battery Cells

NASA Technical Reports Server (NTRS)

McDowell, Mark; Gray, Elizabeth

2006-01-01

Battery cell defect classification is primarily done manually by a human conducting a visual inspection to determine if the battery cell is acceptable for a particular use or device. Human visual inspection is a time consuming task when compared to an inspection process conducted by a machine vision system. Human inspection is also subject to human error and fatigue over time. We present a machine vision technique that can be used to automatically identify defective sections of battery cells via a morphological feature-based classifier using an adaptive two-dimensional fast Fourier transformation technique. The initial area of interest is automatically classified as either an anode or cathode cell view as well as classified as an acceptable or a defective battery cell. Each battery cell is labeled and cataloged for comparison and analysis. The result is the implementation of an automated machine vision technique that provides a highly repeatable and reproducible method of identifying and quantifying defects in battery cells.

Quantitative Outline-based Shape Analysis and Classification of Planetary Craterforms using Supervised Learning Models

NASA Astrophysics Data System (ADS)

Slezak, Thomas Joseph; Radebaugh, Jani; Christiansen, Eric

2017-10-01

The shapes of craterform morphology on planetary surfaces provides rich information about their origins and evolution. While morphologic information provides rich visual clues to geologic processes and properties, the ability to quantitatively communicate this information is less easily accomplished. This study examines the morphology of craterforms using the quantitative outline-based shape methods of geometric morphometrics, commonly used in biology and paleontology. We examine and compare landforms on planetary surfaces using shape, a property of morphology that is invariant to translation, rotation, and size. We quantify the shapes of paterae on Io, martian calderas, terrestrial basaltic shield calderas, terrestrial ash-flow calderas, and lunar impact craters using elliptic Fourier analysis (EFA) and the Zahn and Roskies (Z-R) shape function, or tangent angle approach to produce multivariate shape descriptors. These shape descriptors are subjected to multivariate statistical analysis including canonical variate analysis (CVA), a multiple-comparison variant of discriminant analysis, to investigate the link between craterform shape and classification. Paterae on Io are most similar in shape to terrestrial ash-flow calderas and the shapes of terrestrial basaltic shield volcanoes are most similar to martian calderas. The shapes of lunar impact craters, including simple, transitional, and complex morphology, are classified with a 100% rate of success in all models. Multiple CVA models effectively predict and classify different craterforms using shape-based identification and demonstrate significant potential for use in the analysis of planetary surfaces.

Optimized color decomposition of localized whole slide images and convolutional neural network for intermediate prostate cancer classification

NASA Astrophysics Data System (ADS)

Zhou, Naiyun; Gao, Yi

2017-03-01

This paper presents a fully automatic approach to grade intermediate prostate malignancy with hematoxylin and eosin-stained whole slide images. Deep learning architectures such as convolutional neural networks have been utilized in the domain of histopathology for automated carcinoma detection and classification. However, few work show its power in discriminating intermediate Gleason patterns, due to sporadic distribution of prostate glands on stained surgical section samples. We propose optimized hematoxylin decomposition on localized images, followed by convolutional neural network to classify Gleason patterns 3+4 and 4+3 without handcrafted features or gland segmentation. Crucial glands morphology and structural relationship of nuclei are extracted twice in different color space by the multi-scale strategy to mimic pathologists' visual examination. Our novel classification scheme evaluated on 169 whole slide images yielded a 70.41% accuracy and corresponding area under the receiver operating characteristic curve of 0.7247.

Identification of immune cell infiltration in hematoxylin-eosin stained breast cancer samples: texture-based classification of tissue morphologies

NASA Astrophysics Data System (ADS)

Turkki, Riku; Linder, Nina; Kovanen, Panu E.; Pellinen, Teijo; Lundin, Johan

2016-03-01

The characteristics of immune cells in the tumor microenvironment of breast cancer capture clinically important information. Despite the heterogeneity of tumor-infiltrating immune cells, it has been shown that the degree of infiltration assessed by visual evaluation of hematoxylin-eosin (H and E) stained samples has prognostic and possibly predictive value. However, quantification of the infiltration in H and E-stained tissue samples is currently dependent on visual scoring by an expert. Computer vision enables automated characterization of the components of the tumor microenvironment, and texture-based methods have successfully been used to discriminate between different tissue morphologies and cell phenotypes. In this study, we evaluate whether local binary pattern texture features with superpixel segmentation and classification with support vector machine can be utilized to identify immune cell infiltration in H and E-stained breast cancer samples. Guided with the pan-leukocyte CD45 marker, we annotated training and test sets from 20 primary breast cancer samples. In the training set of arbitrary sized image regions (n=1,116) a 3-fold cross-validation resulted in 98% accuracy and an area under the receiver-operating characteristic curve (AUC) of 0.98 to discriminate between immune cell -rich and - poor areas. In the test set (n=204), we achieved an accuracy of 96% and AUC of 0.99 to label cropped tissue regions correctly into immune cell -rich and -poor categories. The obtained results demonstrate strong discrimination between immune cell -rich and -poor tissue morphologies. The proposed method can provide a quantitative measurement of the degree of immune cell infiltration and applied to digitally scanned H and E-stained breast cancer samples for diagnostic purposes.

The Evolution of Massive Morphological Spheroid and Disk Galaxies in CANDELS from 11 to 6 Billion Years Ago

NASA Astrophysics Data System (ADS)

McIntosh, Daniel H.; CANDELS Collaboration

2017-01-01

The premiere HST/WFC3 Treasury program CANDELS (Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey) has produced detailed visual classifications for statistically useful samples of bright (H>24.5mag) galaxies during and after z~2, the epoch of peak galaxy development. By averaging multiple classifications per galaxy that encompass spheroid-only, bulge-dominated, disk-dominated, disk-only, and irregular/peculiar appearances at visible rest-frame wavelengths, we find that 90% of massive (>1e10 Msun) galaxies at 0.62 to mostly Q at later times. Combining morphologies, structural properties, and SF nature, we find clear differences in the histories of spheroid and disk populations that are robust to selections based on visual or Sersic selection, and to either Q/SF divisor. Massive spheroids experience strong number density growth, substantial size growth, and rapid changes in SF fraction suggesting quenching processes that act on <0.5 Gyr timescales. In contrast, the massive disk population undergoes a steady addition of similar-size disks and a mild decline in average sSFR. Our results indicate that active SF in disks appears to slowly build up their inner mass (or bulge), which subsequently quenches these galaxies. Data-theory comparison is needed to better constrain which physical processes drive the transformation and quenching of massive galaxies.

Analysis of EEG-fMRI data in focal epilepsy based on automated spike classification and Signal Space Projection.

PubMed

Liston, Adam D; De Munck, Jan C; Hamandi, Khalid; Laufs, Helmut; Ossenblok, Pauly; Duncan, John S; Lemieux, Louis

2006-07-01

Simultaneous acquisition of EEG and fMRI data enables the investigation of the hemodynamic correlates of interictal epileptiform discharges (IEDs) during the resting state in patients with epilepsy. This paper addresses two issues: (1) the semi-automation of IED classification in statistical modelling for fMRI analysis and (2) the improvement of IED detection to increase experimental fMRI efficiency. For patients with multiple IED generators, sensitivity to IED-correlated BOLD signal changes can be improved when the fMRI analysis model distinguishes between IEDs of differing morphology and field. In an attempt to reduce the subjectivity of visual IED classification, we implemented a semi-automated system, based on the spatio-temporal clustering of EEG events. We illustrate the technique's usefulness using EEG-fMRI data from a subject with focal epilepsy in whom 202 IEDs were visually identified and then clustered semi-automatically into four clusters. Each cluster of IEDs was modelled separately for the purpose of fMRI analysis. This revealed IED-correlated BOLD activations in distinct regions corresponding to three different IED categories. In a second step, Signal Space Projection (SSP) was used to project the scalp EEG onto the dipoles corresponding to each IED cluster. This resulted in 123 previously unrecognised IEDs, the inclusion of which, in the General Linear Model (GLM), increased the experimental efficiency as reflected by significant BOLD activations. We have also shown that the detection of extra IEDs is robust in the face of fluctuations in the set of visually detected IEDs. We conclude that automated IED classification can result in more objective fMRI models of IEDs and significantly increased sensitivity.

Appraisal of Bleb Using Trio of Intraocular Pressure, Morphology on Slit Lamp, and Gonioscopy.

PubMed

Thatte, Shreya; Rana, Rimpi; Gaur, Neeraj

2016-01-01

The aim of this study was to assess bleb function using Wuerzburg bleb classification score (WBCS) for bleb morphology on slit lamp, intraocular pressure (IOP), and gonioscopy. A total of randomly selected 30 eyes posttrabeculectomy were assessed for bleb function with the trio of bleb morphology, IOP, and gonioscopy. Bleb was assessed using the WBCS of 0-12 on slit lamp, IOP was assessed using applanation tonometry, and visualization of inner ostium and iridectomy were assessed using gonioscopy. Postoperative patients of less than six weeks were excluded from the study. The correlation between WBCS and the duration of trabeculectomy was found to be highly significant ( P value = 0.029). The correlation of IOP with WBCS was also found to be strongly positive ( P = 0.000). IOP was found to be highly associated with peripheral iridectomy ( P = 0.000), internal window ( P = 0.001), and bleb characteristics.

Improving Geologic Mapping of Mid-ocean Ridges by Integrating sonar and Visual Observations through Seafloor Classification by Machine-learning Systems

NASA Astrophysics Data System (ADS)

White, S. M.; McClinton, J. T.

2011-12-01

Beyond the ability of modern near-bottom sonar systems to deliver air-photo-like images of the seafloor to help guide fieldwork, there is a tremendous amount of information hidden within sonar data that is rarely exploited for geologic mapping. Seafloor texture, backscatter amplitude, seafloor slope and roughness data can provide clues about seafloor geology but not straightforward to interpret. We present techniques for seafloor classification in volcanic terrains that integrate the capability of high-resolution, near-bottom sonar instruments to cover extensive areas of seafloor with the ability of visual mapping to discriminate differences in volcanic terrain. These techniques are adapted from the standard practices of terrestrial remote-sensing for use in the deep seafloor volcanic environment. A combination of sonar backscatter and bathymetry is used to supplement the direct seafloor visual observations by geologists to make quasi-geologic thematic maps that are consistent, objective, and most importantly spatially complete. We have taken two approaches to producing thematic maps of the seafloor for the accurate mapping of fine-scale lava morphology (e.g. pillow, lobate and sheet lava) and for the differentiation of distinct seafloor terrain types on a larger scale (e.g. hummocky or smooth). Mapping lava morphology is most accurate using fuzzy logic capable of making inferences between similar morphotypes (e.g. pillow and lobate) and where high-resolution side-scan and bathymetry data coexist. We present examples of lava morphology maps from the Galápagos Spreading Center that show the results from several analyses using different types of input data. Lava morphology is an important source of information on volcanic emplacement and eruptive dynamics. Terrain modeling can be accomplished at any resolution level, depending on the desired use of the model. For volcanic processes, input data needs to be at the appropriate scale to resolve individual volcanic features on the seafloor (e.g. small haystacks and lava channels). We present examples from the East Pacific Rise, which shows that the number of volcanic terrains differs from the tectonic provinces defined by following the spreading axis. Our terrain modeling suggests that differences in ocean crust construction and evolution can be meaningfully identified and explored without a priori assumptions about the geologic processes in a given region.

Impacts of soil moisture content on visual soil evaluation

NASA Astrophysics Data System (ADS)

Emmet-Booth, Jeremy; Forristal, Dermot; Fenton, Owen; Bondi, Giulia; Creamer, Rachel; Holden, Nick

2017-04-01

Visual Soil Examination and Evaluation (VSE) techniques offer tools for soil quality assessment. They involve the visual and tactile assessment of soil properties such as aggregate size and shape, porosity, redox morphology, soil colour and smell. An increasing body of research has demonstrated the reliability and utility of VSE techniques. However a number of limitations have been identified, including the potential impact of soil moisture variation during sampling. As part of a national survey of grassland soil quality in Ireland, an evaluation of the impact of soil moisture on two widely used VSE techniques was conducted. The techniques were Visual Evaluation of Soil Structure (VESS) (Guimarães et al., 2011) and Visual Soil Assessment (VSA) (Shepherd, 2009). Both generate summarising numeric scores that indicate soil structural quality, though employ different scoring mechanisms. The former requires the assessment of properties concurrently and the latter separately. Both methods were deployed on 20 sites across Ireland representing a range of soils. Additional samples were taken for soil volumetric water (θ) determination at 5-10 and 10-20 cm depth. No significant correlation was observed between θ 5-10 cm and either VSE technique. However, VESS scores were significantly related to θ 10-20 cm (rs = 0.40, sig = 0.02) while VSA scores were not (rs = -0.33, sig = 0.06). VESS and VSA scores can be grouped into quality classifications (good, moderate and poor). No significant mean difference was observed between θ 5-10 cm or θ 10-20 cm according to quality classification by either method. It was concluded that VESS scores may be affected by soil moisture variation while VSA appear unaffected. The different scoring mechanisms, where the separate assessment and scoring of individual properties employed by VSA, may limit soil moisture effects. However, moisture content appears not to affect overall structural quality classification by either method. References Guimarães, R.M.C., Ball, B.C. & Tormena, C.A. 2011. Improvements in the visual evaluation of soil structure, Soil Use and Management, 27, 3: 395-403 Shepherd, G.T. 2009. Visual Soil Assessment. Field guide for pastoral grazing and cropping on flat to rolling country. 2nd edn. Horizons regional council, New Zealand.

Cloud cover typing from environmental satellite imagery. Discriminating cloud structure with Fast Fourier Transforms (FFT)

NASA Technical Reports Server (NTRS)

Logan, T. L.; Huning, J. R.; Glackin, D. L.

1983-01-01

The use of two dimensional Fast Fourier Transforms (FFTs) subjected to pattern recognition technology for the identification and classification of low altitude stratus cloud structure from Geostationary Operational Environmental Satellite (GOES) imagery was examined. The development of a scene independent pattern recognition methodology, unconstrained by conventional cloud morphological classifications was emphasized. A technique for extracting cloud shape, direction, and size attributes from GOES visual imagery was developed. These attributes were combined with two statistical attributes (cloud mean brightness, cloud standard deviation), and interrogated using unsupervised clustering amd maximum likelihood classification techniques. Results indicate that: (1) the key cloud discrimination attributes are mean brightness, direction, shape, and minimum size; (2) cloud structure can be differentiated at given pixel scales; (3) cloud type may be identifiable at coarser scales; (4) there are positive indications of scene independence which would permit development of a cloud signature bank; (5) edge enhancement of GOES imagery does not appreciably improve cloud classification over the use of raw data; and (6) the GOES imagery must be apodized before generation of FFTs.

Galaxy Zoo 1: data release of morphological classifications for nearly 900 000 galaxies

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

Linott, C.; Slosar, A.; Lintott, C.

Morphology is a powerful indicator of a galaxy's dynamical and merger history. It is strongly correlated with many physical parameters, including mass, star formation history and the distribution of mass. The Galaxy Zoo project collected simple morphological classifications of nearly 900,000 galaxies drawn from the Sloan Digital Sky Survey, contributed by hundreds of thousands of volunteers. This large number of classifications allows us to exclude classifier error, and measure the influence of subtle biases inherent in morphological classification. This paper presents the data collected by the project, alongside measures of classification accuracy and bias. The data are now publicly availablemore » and full catalogues can be downloaded in electronic format from http://data.galaxyzoo.org.« less

Cell dynamic morphology classification using deep convolutional neural networks.

PubMed

Li, Heng; Pang, Fengqian; Shi, Yonggang; Liu, Zhiwen

2018-05-15

Cell morphology is often used as a proxy measurement of cell status to understand cell physiology. Hence, interpretation of cell dynamic morphology is a meaningful task in biomedical research. Inspired by the recent success of deep learning, we here explore the application of convolutional neural networks (CNNs) to cell dynamic morphology classification. An innovative strategy for the implementation of CNNs is introduced in this study. Mouse lymphocytes were collected to observe the dynamic morphology, and two datasets were thus set up to investigate the performances of CNNs. Considering the installation of deep learning, the classification problem was simplified from video data to image data, and was then solved by CNNs in a self-taught manner with the generated image data. CNNs were separately performed in three installation scenarios and compared with existing methods. Experimental results demonstrated the potential of CNNs in cell dynamic morphology classification, and validated the effectiveness of the proposed strategy. CNNs were successfully applied to the classification problem, and outperformed the existing methods in the classification accuracy. For the installation of CNNs, transfer learning was proved to be a promising scheme. © 2018 International Society for Advancement of Cytometry. © 2018 International Society for Advancement of Cytometry.

High resolution esophageal manometry--the switch from "intuitive" visual interpretation to Chicago classification.

PubMed

Srinivas, M; Balakumaran, T A; Palaniappan, S; Srinivasan, Vijaya; Batcha, M; Venkataraman, Jayanthi

2014-03-01

High resolution esophageal manometry (HREM) has been interpreted all along by visual interpretation of color plots until the recent introduction of Chicago classification which categorises HREM using objective measurements. It compares HREM diagnosis of esophageal motor disorders by visual interpretation and Chicago classification. Using software Trace 1.2v, 77 consecutive tracings diagnosed by visual interpretation were re-analyzed by Chicago classification and findings compared for concordance between the two systems of interpretation. Kappa agreement rate between the two observations was determined. There were 57 males (74 %) and cohort median age was 41 years (range: 14-83 years). Majority of the referrals were for gastroesophageal reflux disease, dysphagia and achalasia. By "intuitive" visual interpretation, the tracing were reported as normal in 45 (58.4 %), achalasia 14 (18.2 %), ineffective esophageal motility 3 (3.9 %), nutcracker esophagus 11 (14.3 %) and nonspecific motility changes 4 (5.2 %). By Chicago classification, there was 100 % agreement (Kappa 1) for achalasia (type 1: 9; type 2: 5) and ineffective esophageal motility ("failed peristalsis" on visual interpretation). Normal esophageal motility, nutcracker esophagus and nonspecific motility disorder on visual interpretation were reclassified as rapid contraction and esophagogastric junction (EGJ) outflow obstruction by Chicago classification. Chicago classification identified distinct clinical phenotypes including EGJ outflow obstruction not identified by visual interpretation. A significant number of unclassified HREM by visual interpretation were also classified by it.

Fuzzy Classification of High Resolution Remote Sensing Scenes Using Visual Attention Features.

PubMed

Li, Linyi; Xu, Tingbao; Chen, Yun

2017-01-01

In recent years the spatial resolutions of remote sensing images have been improved greatly. However, a higher spatial resolution image does not always lead to a better result of automatic scene classification. Visual attention is an important characteristic of the human visual system, which can effectively help to classify remote sensing scenes. In this study, a novel visual attention feature extraction algorithm was proposed, which extracted visual attention features through a multiscale process. And a fuzzy classification method using visual attention features (FC-VAF) was developed to perform high resolution remote sensing scene classification. FC-VAF was evaluated by using remote sensing scenes from widely used high resolution remote sensing images, including IKONOS, QuickBird, and ZY-3 images. FC-VAF achieved more accurate classification results than the others according to the quantitative accuracy evaluation indices. We also discussed the role and impacts of different decomposition levels and different wavelets on the classification accuracy. FC-VAF improves the accuracy of high resolution scene classification and therefore advances the research of digital image analysis and the applications of high resolution remote sensing images.

Fuzzy Classification of High Resolution Remote Sensing Scenes Using Visual Attention Features

PubMed Central

Xu, Tingbao; Chen, Yun

2017-01-01

In recent years the spatial resolutions of remote sensing images have been improved greatly. However, a higher spatial resolution image does not always lead to a better result of automatic scene classification. Visual attention is an important characteristic of the human visual system, which can effectively help to classify remote sensing scenes. In this study, a novel visual attention feature extraction algorithm was proposed, which extracted visual attention features through a multiscale process. And a fuzzy classification method using visual attention features (FC-VAF) was developed to perform high resolution remote sensing scene classification. FC-VAF was evaluated by using remote sensing scenes from widely used high resolution remote sensing images, including IKONOS, QuickBird, and ZY-3 images. FC-VAF achieved more accurate classification results than the others according to the quantitative accuracy evaluation indices. We also discussed the role and impacts of different decomposition levels and different wavelets on the classification accuracy. FC-VAF improves the accuracy of high resolution scene classification and therefore advances the research of digital image analysis and the applications of high resolution remote sensing images. PMID:28761440

The Evolution of the Observed Hubble Sequence over the past 6Gyr

NASA Astrophysics Data System (ADS)

Delgado-Serrano, R.; Hammer, F.; Yang, Y. B.; Puech, M.; Flores, H.; Rodrigues, M.

2011-10-01

During the past years we have confronted serious problems of methodology concerning the morphological and kinematic classification of distant galaxies. This has forced us to create a new simple and effective morphological classification methodology, in order to guarantee a morpho-kinematic correlation, make the reproducibility easier and restrict the classification subjectivity. Giving the characteristic of our morphological classification, we have thus been able to apply the same methodology, using equivalent observations, to representative samples of local and distant galaxies. It has allowed us to derive, for the first time, the distant Hubble sequence (~6 Gyr ago), and determine a morphological evolution of galaxies over the past 6 Gyr. Our results strongly suggest that more than half of the present-day spirals had peculiar morphologies, 6 Gyr ago.

A Saliency Guided Semi-Supervised Building Change Detection Method for High Resolution Remote Sensing Images

PubMed Central

Hou, Bin; Wang, Yunhong; Liu, Qingjie

2016-01-01

Characterizations of up to date information of the Earth’s surface are an important application providing insights to urban planning, resources monitoring and environmental studies. A large number of change detection (CD) methods have been developed to solve them by utilizing remote sensing (RS) images. The advent of high resolution (HR) remote sensing images further provides challenges to traditional CD methods and opportunities to object-based CD methods. While several kinds of geospatial objects are recognized, this manuscript mainly focuses on buildings. Specifically, we propose a novel automatic approach combining pixel-based strategies with object-based ones for detecting building changes with HR remote sensing images. A multiresolution contextual morphological transformation called extended morphological attribute profiles (EMAPs) allows the extraction of geometrical features related to the structures within the scene at different scales. Pixel-based post-classification is executed on EMAPs using hierarchical fuzzy clustering. Subsequently, the hierarchical fuzzy frequency vector histograms are formed based on the image-objects acquired by simple linear iterative clustering (SLIC) segmentation. Then, saliency and morphological building index (MBI) extracted on difference images are used to generate a pseudo training set. Ultimately, object-based semi-supervised classification is implemented on this training set by applying random forest (RF). Most of the important changes are detected by the proposed method in our experiments. This study was checked for effectiveness using visual evaluation and numerical evaluation. PMID:27618903

Phylogenetic versus functional signals in the evolution of form-function relationships in terrestrial vision.

PubMed

Motani, Ryosuke; Schmitz, Lars

2011-08-01

Phylogeny is deeply pertinent to evolutionary studies. Traits that perform a body function are expected to be strongly influenced by physical "requirements" of the function. We investigated if such traits exhibit phylogenetic signals, and, if so, how phylogenetic noises bias quantification of form-function relationships. A form-function system that is strongly influenced by physics, namely the relationship between eye morphology and visual optics in amniotes, was used. We quantified the correlation between form (i.e., eye morphology) and function (i.e., ocular optics) while varying the level of phylogenetic bias removal through adjusting Pagel's λ. Ocular soft-tissue dimensions exhibited the highest correlation with ocular optics when 1% of phylogenetic bias expected from Brownian motion was removed (i.e., λ= 0.01); the value for hard-tissue data were 8%. A small degree of phylogenetic bias therefore exists in morphology despite of the stringent functional constraints. We also devised a phylogenetically informed discriminant analysis and recorded the effects of phylogenetic bias on this method using the same data. Use of proper λ values during phylogenetic bias removal improved misidentification rates in resulting classifications when prior probabilities were assumed to be equal. Even a small degree of phylogenetic bias affected the classification resulting from phylogenetically informed discriminant analysis. © 2011 The Author(s). Evolution© 2011 The Society for the Study of Evolution.

A Saliency Guided Semi-Supervised Building Change Detection Method for High Resolution Remote Sensing Images.

PubMed

Hou, Bin; Wang, Yunhong; Liu, Qingjie

2016-08-27

Characterizations of up to date information of the Earth's surface are an important application providing insights to urban planning, resources monitoring and environmental studies. A large number of change detection (CD) methods have been developed to solve them by utilizing remote sensing (RS) images. The advent of high resolution (HR) remote sensing images further provides challenges to traditional CD methods and opportunities to object-based CD methods. While several kinds of geospatial objects are recognized, this manuscript mainly focuses on buildings. Specifically, we propose a novel automatic approach combining pixel-based strategies with object-based ones for detecting building changes with HR remote sensing images. A multiresolution contextual morphological transformation called extended morphological attribute profiles (EMAPs) allows the extraction of geometrical features related to the structures within the scene at different scales. Pixel-based post-classification is executed on EMAPs using hierarchical fuzzy clustering. Subsequently, the hierarchical fuzzy frequency vector histograms are formed based on the image-objects acquired by simple linear iterative clustering (SLIC) segmentation. Then, saliency and morphological building index (MBI) extracted on difference images are used to generate a pseudo training set. Ultimately, object-based semi-supervised classification is implemented on this training set by applying random forest (RF). Most of the important changes are detected by the proposed method in our experiments. This study was checked for effectiveness using visual evaluation and numerical evaluation.

A probabilistic approach to segmentation and classification of neoplasia in uterine cervix images using color and geometric features

NASA Astrophysics Data System (ADS)

Srinivasan, Yeshwanth; Hernes, Dana; Tulpule, Bhakti; Yang, Shuyu; Guo, Jiangling; Mitra, Sunanda; Yagneswaran, Sriraja; Nutter, Brian; Jeronimo, Jose; Phillips, Benny; Long, Rodney; Ferris, Daron

2005-04-01

Automated segmentation and classification of diagnostic markers in medical imagery are challenging tasks. Numerous algorithms for segmentation and classification based on statistical approaches of varying complexity are found in the literature. However, the design of an efficient and automated algorithm for precise classification of desired diagnostic markers is extremely image-specific. The National Library of Medicine (NLM), in collaboration with the National Cancer Institute (NCI), is creating an archive of 60,000 digitized color images of the uterine cervix. NLM is developing tools for the analysis and dissemination of these images over the Web for the study of visual features correlated with precancerous neoplasia and cancer. To enable indexing of images of the cervix, it is essential to develop algorithms for the segmentation of regions of interest, such as acetowhitened regions, and automatic identification and classification of regions exhibiting mosaicism and punctation. Success of such algorithms depends, primarily, on the selection of relevant features representing the region of interest. We present color and geometric features based statistical classification and segmentation algorithms yielding excellent identification of the regions of interest. The distinct classification of the mosaic regions from the non-mosaic ones has been obtained by clustering multiple geometric and color features of the segmented sections using various morphological and statistical approaches. Such automated classification methodologies will facilitate content-based image retrieval from the digital archive of uterine cervix and have the potential of developing an image based screening tool for cervical cancer.

The Reliability of Galaxy Classifications by Citizen Scientists

NASA Astrophysics Data System (ADS)

Francis, Lennox; Kautsch, Stefan J.; Bizyaev, Dmitry

2017-01-01

Citizen scientists are becoming more and more important in helping professionals working through big data. An example in astronomy is crowdsourced galaxy classification. But how reliable are these classifications for studies of galaxy evolution? We present a tool in order to investigate those morphological classifications and test it on a diverse population on our campus. We observe a slight offset towards earlier Hubble types in the crowdsourced morphologies, when compared to professional classifications.

Acoustic Seafloor Classification near the Duanqiao hydrothermal field at the Southwest Indian Ridge from Multibeam Backscatter Data

NASA Astrophysics Data System (ADS)

Wang, A.; Tao, C.; Xu, Y.; Zhang, G.; Liao, S.

2016-12-01

The inactive Duanqiao hydrothermal field is located on the 50.5°E SWIR axial high with a shallow depth of about 1700 meters. Seafloor morphology of the area surrounding the field is relatively flat, which exerts less influence on multibeam backscatter data than rugged terrains do. Therefore, it is an ideal experimental area to conduct seafloor classification utilizing multibeam sonar. This paper dealt with a backscatter analysis of Simrad EM120 multibeam sonar data, acquired during the Chinese DY115-34 cruise near the Duanqiao hydrothermal field, and comprehensively studied types and distribution characteristics of seafloor substrate by combining with visual interpretations and TV-Grab Samples. Firstly, a mosaic was built to analyze backscatter distribution after multibeam backscatter data were fully processed using Geocoder engine on CARIS HIPS&SIPS software. Prior information was gained by analyzing the link between the processed backscatter data and the visual interpretations of two deep-tow video survey lines. Among the two survey lines, one corresponds to sediment-dominated seafloor and the other corresponds to pillow basalt-dominated seafloor. Then, backscatter data of the mosaic were classified statistically to identify three types of seafloor: soft substrate, medium-hard substrate and hard substrate. Compared with visual interpretations and TV-Grab Samples, these three seafloor types were interpreted as sediment, breccia and pillow basalt, respectively. Finally, a seafloor classification map was generated. According to the results, we discovered two distinguished distribution characteristics of seafloor substrate: 1. there is a transition from pillow basalt-dominated seafloor to sediment-dominated seafloor away from the SWIR axis; 2. the Duanqiao hydrothermal field is mostly outcropped by pillow basalts and locally covered by breccias and sediments, the reason of which is probably that this field is a relatively recent volcanic area.

Evolution of galaxy structure using visual morphologies in CANDELS and Hydro-ART simulations

NASA Astrophysics Data System (ADS)

Mozena, Mark W.

2013-08-01

The general properties, morphologies, and classes of galaxies in the local Universe are well studied. Most local galaxies are morphologically members of the Hubble sequence and can be crudely separated into elliptical red quiescent galaxies or disky blue star-forming galaxies. This Hubble sequence of relaxed structures has been shown to dominate galaxy populations out to a redshift of z~1. The description of galaxies at earlier times is not well known nor is it understood how and at what epoch the Hubble sequence formed. Of particular interest is the structure of galaxies at z~2. This epoch was an active time for galaxy growth and was the peak epoch for star formation rate, active galactic nuclei activity, and mergers between galaxies. With the installation of the near-infrared Wide Field Camera 3 (WFC3) on the Hubble Space Telescope in 2009, large area photometric surveys of galaxies were able to be performed for the first time at moderate redshifts (z~2) in wavebands that effectively trace the older stellar populations and stellar mass of the galaxies rather than the clumpy star-forming regions. Using WFC3 HST images, an in-depth morphology classification system was developed to probe the galaxy populations at higher redshifts (focusing on z~2). These visual classifications were used with other galaxy parameters (stellar mass, color, star formation rate, radius, Sersic profiles, etc) to identify and quantify the moderate redshift galaxy populations and study how these populations changed with time to form the relaxed Hubble sequence Universe we observe today. Additionally, these same tools that were used to probe galaxy populations at z~2 in the observed Universe were also used on simulated galaxy images produced from state-of-the-art cosmological simulations. These Hydro-ART simulations build artificial galaxies that are compared to observations so as to shed light on the relevant mechanisms in galaxy evolution. By classifying and comparing the populations present in the simulations with our observations, we are able to probe the model's ability to create realistic galaxy populations. The first chapter of this thesis focuses on visually classifying and studying galaxy populations at z~2 and how they change with redshift for a given mass. The second chapter focuses on applying our techniques to Hydro-ART simulations at z~2 and comparing these mock 'observed' simulations with our real WFC3 HST observations. Both of these chapters closely resemble manuscripts in the process of being submitted for independent publication.

Channel morphology [Chapter 5

Treesearch

Jonathan W. Long; Alvin L. Medina; Daniel G. Neary

2012-01-01

Channel morphology has become an increasingly important subject for analyzing the health of rivers and associated fish populations, particularly since the popularization of channel classification and assessment methods. Morphological data can help to evaluate the flows of sediment and water that influence aquatic and riparian habitat. Channel classification systems,...

Genetic algorithm based feature selection combined with dual classification for the automated detection of proliferative diabetic retinopathy.

PubMed

Welikala, R A; Fraz, M M; Dehmeshki, J; Hoppe, A; Tah, V; Mann, S; Williamson, T H; Barman, S A

2015-07-01

Proliferative diabetic retinopathy (PDR) is a condition that carries a high risk of severe visual impairment. The hallmark of PDR is the growth of abnormal new vessels. In this paper, an automated method for the detection of new vessels from retinal images is presented. This method is based on a dual classification approach. Two vessel segmentation approaches are applied to create two separate binary vessel map which each hold vital information. Local morphology features are measured from each binary vessel map to produce two separate 4-D feature vectors. Independent classification is performed for each feature vector using a support vector machine (SVM) classifier. The system then combines these individual outcomes to produce a final decision. This is followed by the creation of additional features to generate 21-D feature vectors, which feed into a genetic algorithm based feature selection approach with the objective of finding feature subsets that improve the performance of the classification. Sensitivity and specificity results using a dataset of 60 images are 0.9138 and 0.9600, respectively, on a per patch basis and 1.000 and 0.975, respectively, on a per image basis. Copyright © 2015 Elsevier Ltd. All rights reserved.

Effects of pressure ulcer classification system education programme on knowledge and visual differential diagnostic ability of pressure ulcer classification and incontinence-associated dermatitis for clinical nurses in Korea.

PubMed

Lee, Yun Jin; Kim, Jung Yoon

2016-03-01

The objective of this study was to evaluate the effect of pressure ulcer classification system education on clinical nurses' knowledge and visual differential diagnostic ability of pressure ulcer (PU) classification and incontinence-associated dermatitis (IAD). One group pre and post-test was used. A convenience sample of 407 nurses, participating in PU classification education programme of continuing education, were enrolled. The education programme was composed of a 50-minute lecture on PU classification and case-studies. The PU Classification system and IAD knowledge test (PUCS-KT) and visual differential diagnostic ability tool (VDDAT), consisting of 21 photographs including clinical information were used. Paired t-test was performed using SPSS/WIN 20.0. The overall mean difference of PUCS-KT (t = -11·437, P<0·001) and VDDAT (t = -21·113, P<0·001) was significantly increased after PU classification education. Overall understanding of six PU classification and IAD after education programme was increased, but lacked visual differential diagnostic ability regarding Stage III PU, suspected deep tissue injury (SDTI), and Unstageable. Continuous differentiated education based on clinical practice is needed to improve knowledge and visual differential diagnostic ability for PU classification, and comparison experiment study is required to examine effects of education programmes. © 2016 Medicalhelplines.com Inc and John Wiley & Sons Ltd.

Objective automated quantification of fluorescence signal in histological sections of rat lens.

PubMed

Talebizadeh, Nooshin; Hagström, Nanna Zhou; Yu, Zhaohua; Kronschläger, Martin; Söderberg, Per; Wählby, Carolina

2017-08-01

Visual quantification and classification of fluorescent signals is the gold standard in microscopy. The purpose of this study was to develop an automated method to delineate cells and to quantify expression of fluorescent signal of biomarkers in each nucleus and cytoplasm of lens epithelial cells in a histological section. A region of interest representing the lens epithelium was manually demarcated in each input image. Thereafter, individual cell nuclei within the region of interest were automatically delineated based on watershed segmentation and thresholding with an algorithm developed in Matlab™. Fluorescence signal was quantified within nuclei, cytoplasms and juxtaposed backgrounds. The classification of cells as labelled or not labelled was based on comparison of the fluorescence signal within cells with local background. The classification rule was thereafter optimized as compared with visual classification of a limited dataset. The performance of the automated classification was evaluated by asking 11 independent blinded observers to classify all cells (n = 395) in one lens image. Time consumed by the automatic algorithm and visual classification of cells was recorded. On an average, 77% of the cells were correctly classified as compared with the majority vote of the visual observers. The average agreement among visual observers was 83%. However, variation among visual observers was high, and agreement between two visual observers was as low as 71% in the worst case. Automated classification was on average 10 times faster than visual scoring. The presented method enables objective and fast detection of lens epithelial cells and quantification of expression of fluorescent signal with an accuracy comparable with the variability among visual observers. © 2017 International Society for Advancement of Cytometry. © 2017 International Society for Advancement of Cytometry.

Textural kinetics: a novel dynamic contrast-enhanced (DCE)-MRI feature for breast lesion classification.

PubMed

Agner, Shannon C; Soman, Salil; Libfeld, Edward; McDonald, Margie; Thomas, Kathleen; Englander, Sarah; Rosen, Mark A; Chin, Deanna; Nosher, John; Madabhushi, Anant

2011-06-01

Dynamic contrast-enhanced (DCE)-magnetic resonance imaging (MRI) of the breast has emerged as an adjunct imaging tool to conventional X-ray mammography due to its high detection sensitivity. Despite the increasing use of breast DCE-MRI, specificity in distinguishing malignant from benign breast lesions is low, and interobserver variability in lesion classification is high. The novel contribution of this paper is in the definition of a new DCE-MRI descriptor that we call textural kinetics, which attempts to capture spatiotemporal changes in breast lesion texture in order to distinguish malignant from benign lesions. We qualitatively and quantitatively demonstrated on 41 breast DCE-MRI studies that textural kinetic features outperform signal intensity kinetics and lesion morphology features in distinguishing benign from malignant lesions. A probabilistic boosting tree (PBT) classifier in conjunction with textural kinetic descriptors yielded an accuracy of 90%, sensitivity of 95%, specificity of 82%, and an area under the curve (AUC) of 0.92. Graph embedding, used for qualitative visualization of a low-dimensional representation of the data, showed the best separation between benign and malignant lesions when using textural kinetic features. The PBT classifier results and trends were also corroborated via a support vector machine classifier which showed that textural kinetic features outperformed the morphological, static texture, and signal intensity kinetics descriptors. When textural kinetic attributes were combined with morphologic descriptors, the resulting PBT classifier yielded 89% accuracy, 99% sensitivity, 76% specificity, and an AUC of 0.91.

Objects Classification by Learning-Based Visual Saliency Model and Convolutional Neural Network.

PubMed

Li, Na; Zhao, Xinbo; Yang, Yongjia; Zou, Xiaochun

2016-01-01

Humans can easily classify different kinds of objects whereas it is quite difficult for computers. As a hot and difficult problem, objects classification has been receiving extensive interests with broad prospects. Inspired by neuroscience, deep learning concept is proposed. Convolutional neural network (CNN) as one of the methods of deep learning can be used to solve classification problem. But most of deep learning methods, including CNN, all ignore the human visual information processing mechanism when a person is classifying objects. Therefore, in this paper, inspiring the completed processing that humans classify different kinds of objects, we bring forth a new classification method which combines visual attention model and CNN. Firstly, we use the visual attention model to simulate the processing of human visual selection mechanism. Secondly, we use CNN to simulate the processing of how humans select features and extract the local features of those selected areas. Finally, not only does our classification method depend on those local features, but also it adds the human semantic features to classify objects. Our classification method has apparently advantages in biology. Experimental results demonstrated that our method made the efficiency of classification improve significantly.

Audio-guided audiovisual data segmentation, indexing, and retrieval

NASA Astrophysics Data System (ADS)

Zhang, Tong; Kuo, C.-C. Jay

1998-12-01

While current approaches for video segmentation and indexing are mostly focused on visual information, audio signals may actually play a primary role in video content parsing. In this paper, we present an approach for automatic segmentation, indexing, and retrieval of audiovisual data, based on audio content analysis. The accompanying audio signal of audiovisual data is first segmented and classified into basic types, i.e., speech, music, environmental sound, and silence. This coarse-level segmentation and indexing step is based upon morphological and statistical analysis of several short-term features of the audio signals. Then, environmental sounds are classified into finer classes, such as applause, explosions, bird sounds, etc. This fine-level classification and indexing step is based upon time- frequency analysis of audio signals and the use of the hidden Markov model as the classifier. On top of this archiving scheme, an audiovisual data retrieval system is proposed. Experimental results show that the proposed approach has an accuracy rate higher than 90 percent for the coarse-level classification, and higher than 85 percent for the fine-level classification. Examples of audiovisual data segmentation and retrieval are also provided.

Visual attention based bag-of-words model for image classification

NASA Astrophysics Data System (ADS)

Wang, Qiwei; Wan, Shouhong; Yue, Lihua; Wang, Che

2014-04-01

Bag-of-words is a classical method for image classification. The core problem is how to count the frequency of the visual words and what visual words to select. In this paper, we propose a visual attention based bag-of-words model (VABOW model) for image classification task. The VABOW model utilizes visual attention method to generate a saliency map, and uses the saliency map as a weighted matrix to instruct the statistic process for the frequency of the visual words. On the other hand, the VABOW model combines shape, color and texture cues and uses L1 regularization logistic regression method to select the most relevant and most efficient features. We compare our approach with traditional bag-of-words based method on two datasets, and the result shows that our VABOW model outperforms the state-of-the-art method for image classification.

Probabilisitc Geobiological Classification Using Elemental Abundance Distributions and Lossless Image Compression in Recent and Modern Organisms

NASA Technical Reports Server (NTRS)

Storrie-Lombardi, Michael C.; Hoover, Richard B.

2005-01-01

Last year we presented techniques for the detection of fossils during robotic missions to Mars using both structural and chemical signatures[Storrie-Lombardi and Hoover, 2004]. Analyses included lossless compression of photographic images to estimate the relative complexity of a putative fossil compared to the rock matrix [Corsetti and Storrie-Lombardi, 2003] and elemental abundance distributions to provide mineralogical classification of the rock matrix [Storrie-Lombardi and Fisk, 2004]. We presented a classification strategy employing two exploratory classification algorithms (Principal Component Analysis and Hierarchical Cluster Analysis) and non-linear stochastic neural network to produce a Bayesian estimate of classification accuracy. We now present an extension of our previous experiments exploring putative fossil forms morphologically resembling cyanobacteria discovered in the Orgueil meteorite. Elemental abundances (C6, N7, O8, Na11, Mg12, Ai13, Si14, P15, S16, Cl17, K19, Ca20, Fe26) obtained for both extant cyanobacteria and fossil trilobites produce signatures readily distinguishing them from meteorite targets. When compared to elemental abundance signatures for extant cyanobacteria Orgueil structures exhibit decreased abundances for C6, N7, Na11, All3, P15, Cl17, K19, Ca20 and increases in Mg12, S16, Fe26. Diatoms and silicified portions of cyanobacterial sheaths exhibiting high levels of silicon and correspondingly low levels of carbon cluster more closely with terrestrial fossils than with extant cyanobacteria. Compression indices verify that variations in random and redundant textural patterns between perceived forms and the background matrix contribute significantly to morphological visual identification. The results provide a quantitative probabilistic methodology for discriminating putatitive fossils from the surrounding rock matrix and &om extant organisms using both structural and chemical information. The techniques described appear applicable to the geobiological analysis of meteoritic samples or in situ exploration of the Mars regolith. Keywords: cyanobacteria, microfossils, Mars, elemental abundances, complexity analysis, multifactor analysis, principal component analysis, hierarchical cluster analysis, artificial neural networks, paleo-biosignatures

Hyperspectral feature mapping classification based on mathematical morphology

NASA Astrophysics Data System (ADS)

Liu, Chang; Li, Junwei; Wang, Guangping; Wu, Jingli

2016-03-01

This paper proposed a hyperspectral feature mapping classification algorithm based on mathematical morphology. Without the priori information such as spectral library etc., the spectral and spatial information can be used to realize the hyperspectral feature mapping classification. The mathematical morphological erosion and dilation operations are performed respectively to extract endmembers. The spectral feature mapping algorithm is used to carry on hyperspectral image classification. The hyperspectral image collected by AVIRIS is applied to evaluate the proposed algorithm. The proposed algorithm is compared with minimum Euclidean distance mapping algorithm, minimum Mahalanobis distance mapping algorithm, SAM algorithm and binary encoding mapping algorithm. From the results of the experiments, it is illuminated that the proposed algorithm's performance is better than that of the other algorithms under the same condition and has higher classification accuracy.

An enhanced data visualization method for diesel engine malfunction classification using multi-sensor signals.

PubMed

Li, Yiqing; Wang, Yu; Zi, Yanyang; Zhang, Mingquan

2015-10-21

The various multi-sensor signal features from a diesel engine constitute a complex high-dimensional dataset. The non-linear dimensionality reduction method, t-distributed stochastic neighbor embedding (t-SNE), provides an effective way to implement data visualization for complex high-dimensional data. However, irrelevant features can deteriorate the performance of data visualization, and thus, should be eliminated a priori. This paper proposes a feature subset score based t-SNE (FSS-t-SNE) data visualization method to deal with the high-dimensional data that are collected from multi-sensor signals. In this method, the optimal feature subset is constructed by a feature subset score criterion. Then the high-dimensional data are visualized in 2-dimension space. According to the UCI dataset test, FSS-t-SNE can effectively improve the classification accuracy. An experiment was performed with a large power marine diesel engine to validate the proposed method for diesel engine malfunction classification. Multi-sensor signals were collected by a cylinder vibration sensor and a cylinder pressure sensor. Compared with other conventional data visualization methods, the proposed method shows good visualization performance and high classification accuracy in multi-malfunction classification of a diesel engine.

An Enhanced Data Visualization Method for Diesel Engine Malfunction Classification Using Multi-Sensor Signals

PubMed Central

Li, Yiqing; Wang, Yu; Zi, Yanyang; Zhang, Mingquan

2015-01-01

The various multi-sensor signal features from a diesel engine constitute a complex high-dimensional dataset. The non-linear dimensionality reduction method, t-distributed stochastic neighbor embedding (t-SNE), provides an effective way to implement data visualization for complex high-dimensional data. However, irrelevant features can deteriorate the performance of data visualization, and thus, should be eliminated a priori. This paper proposes a feature subset score based t-SNE (FSS-t-SNE) data visualization method to deal with the high-dimensional data that are collected from multi-sensor signals. In this method, the optimal feature subset is constructed by a feature subset score criterion. Then the high-dimensional data are visualized in 2-dimension space. According to the UCI dataset test, FSS-t-SNE can effectively improve the classification accuracy. An experiment was performed with a large power marine diesel engine to validate the proposed method for diesel engine malfunction classification. Multi-sensor signals were collected by a cylinder vibration sensor and a cylinder pressure sensor. Compared with other conventional data visualization methods, the proposed method shows good visualization performance and high classification accuracy in multi-malfunction classification of a diesel engine. PMID:26506347

Visualizing red blood cell sickling and the effects of inhibition of sphingosine kinase 1 using soft X-ray tomography

DOE PAGES

Darrow, Michele C.; Zhang, Yujin; Cinquin, Bertrand P.; ...

2016-08-09

Sickle cell disease is a destructive genetic disorder characterized by the formation of fibrils of deoxygenated hemoglobin, leading to the red blood cell (RBC) morphology changes that underlie the clinical manifestations of this disease. Here, using cryogenic soft X-ray tomography (SXT), we characterized the morphology of sickled RBCs in terms of volume and the number of protrusions per cell. We were able to identify statistically a relationship between the number of protrusions and the volume of the cell, which is known to correlate to the severity of sickling. This structural polymorphism allows for the classification of the stages of themore » sickling process. Recent studies have shown that elevated sphingosine kinase 1 (Sphk1)-mediated sphingosine 1-phosphate production contributes to sickling. Here, we further demonstrate that compound 5C, an inhibitor of Sphk1, has anti-sickling properties. Additionally, the variation in cellular morphology upon treatment suggests that this drug acts to delay the sickling process. SXT is an effective tool that can be used to identify the morphology of the sickling process and assess the effectiveness of potential therapeutics.« less

Visualizing red blood cell sickling and the effects of inhibition of sphingosine kinase 1 using soft X-ray tomography

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

Darrow, Michele C.; Zhang, Yujin; Cinquin, Bertrand P.

Sickle cell disease is a destructive genetic disorder characterized by the formation of fibrils of deoxygenated hemoglobin, leading to the red blood cell (RBC) morphology changes that underlie the clinical manifestations of this disease. Here, using cryogenic soft X-ray tomography (SXT), we characterized the morphology of sickled RBCs in terms of volume and the number of protrusions per cell. We were able to identify statistically a relationship between the number of protrusions and the volume of the cell, which is known to correlate to the severity of sickling. This structural polymorphism allows for the classification of the stages of themore » sickling process. Recent studies have shown that elevated sphingosine kinase 1 (Sphk1)-mediated sphingosine 1-phosphate production contributes to sickling. Here, we further demonstrate that compound 5C, an inhibitor of Sphk1, has anti-sickling properties. Additionally, the variation in cellular morphology upon treatment suggests that this drug acts to delay the sickling process. SXT is an effective tool that can be used to identify the morphology of the sickling process and assess the effectiveness of potential therapeutics.« less

Polyp morphology: an interobserver evaluation for the Paris classification among international experts.

PubMed

van Doorn, Sascha C; Hazewinkel, Y; East, James E; van Leerdam, Monique E; Rastogi, Amit; Pellisé, Maria; Sanduleanu-Dascalescu, Silvia; Bastiaansen, Barbara A J; Fockens, Paul; Dekker, Evelien

2015-01-01

The Paris classification is an international classification system for describing polyp morphology. Thus far, the validity and reproducibility of this classification have not been assessed. We aimed to determine the interobserver agreement for the Paris classification among seven Western expert endoscopists. A total of 85 short endoscopic video clips depicting polyps were created and assessed by seven expert endoscopists according to the Paris classification. After a digital training module, the same 85 polyps were assessed again. We calculated the interobserver agreement with a Fleiss kappa and as the proportion of pairwise agreement. The interobserver agreement of the Paris classification among seven experts was moderate with a Fleiss kappa of 0.42 and a mean pairwise agreement of 67%. The proportion of lesions assessed as "flat" by the experts ranged between 13 and 40% (P<0.001). After the digital training, the interobserver agreement did not change (kappa 0.38, pairwise agreement 60%). Our study is the first to validate the Paris classification for polyp morphology. We demonstrated only a moderate interobserver agreement among international Western experts for this classification system. Our data suggest that, in its current version, the use of this classification system in daily practice is questionable and it is unsuitable for comparative endoscopic research. We therefore suggest introduction of a simplification of the classification system.

A subgeneric classification of Selaginella (Selaginellaceae).

PubMed

Weststrand, Stina; Korall, Petra

2016-12-01

The lycophyte family Selaginellaceae includes approximately 750 herbaceous species worldwide, with the main species richness in the tropics and subtropics. We recently presented a phylogenetic analysis of Selaginellaceae based on DNA sequence data and, with the phylogeny as a framework, the study discussed the character evolution of the group focusing on gross morphology. Here we translate these findings into a new classification. To present a robust and useful classification, we identified well-supported monophyletic groups from our previous phylogenetic analysis of 223 species, which together represent the diversity of the family with respect to morphology, taxonomy, and geographical distribution. Care was taken to choose groups with supporting morphology. In this classification, we recognize a single genus Selaginella and seven subgenera: Selaginella, Rupestrae, Lepidophyllae, Gymnogynum, Exaltatae, Ericetorum, and Stachygynandrum. The subgenera are all well supported based on analysis of DNA sequence data and morphology. A key to the subgenera is presented. Our new classification is based on a well-founded hypothesis of the evolutionary relationships of Selaginella, and each subgenus can be identified by a suite of morphological features, most of them possible to study in the field. Our intention is that the classification will be useful not only to experts in the field, but also to a broader audience. © 2016 Weststrand and Korall. Published by the Botanical Society of America. This work is licensed under a Creative Commons Attribution License (CC-BY 4.0).

Morphological phylogeny of Tradescantia L. (Commelinaceae) sheds light on a new infrageneric classification for the genus and novelties on the systematics of subtribe Tradescantiinae

PubMed Central

Pellegrini, Marco O. O.

2017-01-01

Abstract Throughout the years, three infrageneric classifications were proposed for Tradescantia along with several informal groups and species complexes. The current infrageneric classification accepts 12 sections – with T. sect. Tradescantia being further divided into four series – and assimilates many concepts adopted by previous authors. Recent molecular-based phylogenetic studies indicate that the currently accepted sections might not represent monophyletic groups within Tradescantia. Based on newly gathered morphological data on the group, complemented with available micromorphological, cytological and phytochemical data, I present the first morphology-based evolutionary hypothesis for Tradescantia. Furthermore, I reduce subtribe Thyrsantheminae to a synonym of subtribe Tradescantiinae, and propose a new infrageneric classification for Tradescantia, based on the total evidence of the present morphological phylogeny, in accordance to the previously published molecular data. PMID:29118649

[A research on real-time ventricular QRS classification methods for single-chip-microcomputers].

PubMed

Peng, L; Yang, Z; Li, L; Chen, H; Chen, E; Lin, J

1997-05-01

Ventricular QRS classification is key technique of ventricular arrhythmias detection in single-chip-microcomputer based dynamic electrocardiogram real-time analyser. This paper adopts morphological feature vector including QRS amplitude, interval information to reveal QRS morphology. After studying the distribution of QRS morphology feature vector of MIT/BIH DB ventricular arrhythmia files, we use morphological feature vector cluster to classify multi-morphology QRS. Based on the method, morphological feature parameters changing method which is suitable to catch occasional ventricular arrhythmias is presented. Clinical experiments verify missed ventricular arrhythmia is less than 1% by this method.

Gaia eclipsing binary and multiple systems. Supervised classification and self-organizing maps

NASA Astrophysics Data System (ADS)

Süveges, M.; Barblan, F.; Lecoeur-Taïbi, I.; Prša, A.; Holl, B.; Eyer, L.; Kochoska, A.; Mowlavi, N.; Rimoldini, L.

2017-07-01

Context. Large surveys producing tera- and petabyte-scale databases require machine-learning and knowledge discovery methods to deal with the overwhelming quantity of data and the difficulties of extracting concise, meaningful information with reliable assessment of its uncertainty. This study investigates the potential of a few machine-learning methods for the automated analysis of eclipsing binaries in the data of such surveys. Aims: We aim to aid the extraction of samples of eclipsing binaries from such databases and to provide basic information about the objects. We intend to estimate class labels according to two different, well-known classification systems, one based on the light curve morphology (EA/EB/EW classes) and the other based on the physical characteristics of the binary system (system morphology classes; detached through overcontact systems). Furthermore, we explore low-dimensional surfaces along which the light curves of eclipsing binaries are concentrated, and consider their use in the characterization of the binary systems and in the exploration of biases of the full unknown Gaia data with respect to the training sets. Methods: We have explored the performance of principal component analysis (PCA), linear discriminant analysis (LDA), Random Forest classification and self-organizing maps (SOM) for the above aims. We pre-processed the photometric time series by combining a double Gaussian profile fit and a constrained smoothing spline, in order to de-noise and interpolate the observed light curves. We achieved further denoising, and selected the most important variability elements from the light curves using PCA. Supervised classification was performed using Random Forest and LDA based on the PC decomposition, while SOM gives a continuous 2-dimensional manifold of the light curves arranged by a few important features. We estimated the uncertainty of the supervised methods due to the specific finite training set using ensembles of models constructed on randomized training sets. Results: We obtain excellent results (about 5% global error rate) with classification into light curve morphology classes on the Hipparcos data. The classification into system morphology classes using the Catalog and Atlas of Eclipsing binaries (CALEB) has a higher error rate (about 10.5%), most importantly due to the (sometimes strong) similarity of the photometric light curves originating from physically different systems. When trained on CALEB and then applied to Kepler-detected eclipsing binaries subsampled according to Gaia observing times, LDA and SOM provide tractable, easy-to-visualize subspaces of the full (functional) space of light curves that summarize the most important phenomenological elements of the individual light curves. The sequence of light curves ordered by their first linear discriminant coefficient is compared to results obtained using local linear embedding. The SOM method proves able to find a 2-dimensional embedded surface in the space of the light curves which separates the system morphology classes in its different regions, and also identifies a few other phenomena, such as the asymmetry of the light curves due to spots, eccentric systems, and systems with a single eclipse. Furthermore, when data from other surveys are projected to the same SOM surface, the resulting map yields a good overview of the general biases and distortions due to differences in time sampling or population.

Geologic mapping on the deep seafloor: Reconstructing lava flow emplacement and eruptive history at the Galápagos Spreading Center

NASA Astrophysics Data System (ADS)

McClinton, J. T.; White, S.; Colman, A.; Sinton, J. M.; Bowles, J. A.

2012-12-01

The deep seafloor imposes significant difficulties on data collection that require the integration of multiple data sets and the implementation of unconventional geologic mapping techniques. We combine visual mapping of geological contacts by submersible with lava flow morphology maps and relative and absolute age constraints to create a spatiotemporal framework for examining submarine lava flow emplacement at the intermediate-spreading, hotspot-affected Galápagos Spreading Center (GSC). We mapped 18 lava flow fields, interpreted to be separate eruptive episodes, within two study areas at the GSC using visual observations of superposition, surface preservation and sediment cover from submersible and towed camera surveys, augmented by high-resolution sonar surveys and sample petrology [Colman et al., Effects of variable magma supply on mid-ocean ridge eruptions: Constraints from mapped lava flow fields along the Galápagos Spreading Center; 2012 G3]. We also mapped the lava flow morphology within the majority of these eruptive units using an automated, machine-learning classification method [McClinton et al., Neuro-fuzzy classification of submarine lava flow morphology; 2012 PE&RS]. The method combines detailed geometric, acoustic, and textural attributes derived from high-resolution sonar data with visual observations and a machine-learning algorithm to classify submarine lava flow morphology as pillows, lobates, or sheets. The resulting lava morphology maps are a valuable tool for interpreting patterns in the emplacement of submarine lava flows at a mid-ocean ridge (MOR). Within our study area at 92°W, where the GSC has a relatively high magma supply, high effusion rate sheet and lobate lavas are more abundant in the oldest mapped eruptive units, while the most recent eruptions mostly consist of low effusion rate pillow lavas. The older eruptions (roughly 400yrs BP by paleomagnetic intensity) extend up to 1km off axis via prominent channels and tubes, while the most recent eruptions (<100yrs BP by paleomagnetic intensity) are mainly on-axis pillow ridges and domes. These spatial and temporal trends suggest a gradual transition from low-relief, "paving" eruptions to relief-building, "constructional" eruptions. In our second study area at 95°W, where magma supply is lower, eruptions mostly consist of axial seamounts and irregularly shaped clusters of pillow mounds. Many have summit plateaus with inflated, partially collapsed lobate lavas suggesting variable effusion rates and topographic influence on lava flows. In addition, a relatively extensive (~9.5km2) flow field of inflated lobate and sheet lavas erupted from vents ~1km north of the ridge axis and flowed ~1km into the inner axial graben through channels and tubes, ponding against older structures and leaving prominent "bathtub rings" and collapse features. This eruption provides direct evidence that large, high effusion rate eruptions can occur in low magma supply settings at MORs.

Classification and phylogenetic analysis of Chinese hawthorn assessed by plant and pollen morphology.

PubMed

Ma, S L Y; Lu, Y M

2016-09-19

The Chinese hawthorn (Crataegus pinnatifida Bge. var. major N.E.Br.) is uniquely originated in northern China. The ecological and horticultural importance of Chinese hawthorn is considerable and some varieties are valued for their fruit or medicine extracts. Its taxonomy and phylogeny remain poorly understood. Apart from general plant morphological traits, pollen is an important trait for the classification of plants and their evolutionary origin. However, few studies have investigated the pollen of Chinese hawthorn. Here, an analysis of plant and pollen morphological characteristics was conducted in 57 cultivars from the Shenyang region. Thirty plant morphological characters and nine pollen grain characters were investigated. The plant morphological analysis revealed that the coefficient of variation for 13 traits was >20%, which indicates a high degree of variability. We also found that the pollen grains varied greatly in size, shape (from prolate to perprolate), and exine pattern (striate-perforate predominantly). The number of apertures was typically three. Based on these findings, we suggest that pollen morphology associated with plant morphological traits can be used for classification and phylogenetic analysis of Chinese hawthorn cultivars. In sum, our results provide new insights and constitute a scientific basis for future studies on the classification and evolution of Chinese hawthorn.

J-Plus: Morphological Classification Of Compact And Extended Sources By Pdf Analysis

NASA Astrophysics Data System (ADS)

López-Sanjuan, C.; Vázquez-Ramió, H.; Varela, J.; Spinoso, D.; Cristóbal-Hornillos, D.; Viironen, K.; Muniesa, D.; J-PLUS Collaboration

2017-10-01

We present a morphological classification of J-PLUS EDR sources into compact (i.e. stars) and extended (i.e. galaxies). Such classification is based on the Bayesian modelling of the concentration distribution, including observational errors and magnitude + sky position priors. We provide the star / galaxy probability of each source computed from the gri images. The comparison with the SDSS number counts support our classification up to r 21. The 31.7 deg² analised comprises 150k stars and 101k galaxies.

How much a galaxy knows about its large-scale environment?: An information theoretic perspective

NASA Astrophysics Data System (ADS)

Pandey, Biswajit; Sarkar, Suman

2017-05-01

The small-scale environment characterized by the local density is known to play a crucial role in deciding the galaxy properties but the role of large-scale environment on galaxy formation and evolution still remain a less clear issue. We propose an information theoretic framework to investigate the influence of large-scale environment on galaxy properties and apply it to the data from the Galaxy Zoo project that provides the visual morphological classifications of ˜1 million galaxies from the Sloan Digital Sky Survey. We find a non-zero mutual information between morphology and environment that decreases with increasing length-scales but persists throughout the entire length-scales probed. We estimate the conditional mutual information and the interaction information between morphology and environment by conditioning the environment on different length-scales and find a synergic interaction between them that operates up to at least a length-scales of ˜30 h-1 Mpc. Our analysis indicates that these interactions largely arise due to the mutual information shared between the environments on different length-scales.

Automatic Approach to Morphological Classification of Galaxies With Analysis of Galaxy Populations in Clusters

NASA Astrophysics Data System (ADS)

Sultanova, Madina; Barkhouse, Wayne; Rude, Cody

2018-01-01

The classification of galaxies based on their morphology is a field in astrophysics that aims to understand galaxy formation and evolution based on their physical differences. Whether structural differences are due to internal factors or a result of local environment, the dominate mechanism that determines galaxy type needs to be robustly quantified in order to have a thorough grasp of the origin of the different types of galaxies. The main subject of my Ph.D. dissertation is to explore the use of computers to automatically classify and analyze large numbers of galaxies according to their morphology, and to analyze sub-samples of galaxies selected by type to understand galaxy formation in various environments. I have developed a computer code to classify galaxies by measuring five parameters from their images in FITS format. The code was trained and tested using visually classified SDSS galaxies from Galaxy Zoo and the EFIGI data set. I apply my morphology software to numerous galaxies from diverse data sets. Among the data analyzed are the 15 Abell galaxy clusters (0.03 < z < 0.184) from Rude et al. 2017 (in preparation), which were observed by the Canada-France-Hawaii Telescope. Additionally, I studied 57 galaxy clusters from Barkhouse et al. (2007), 77 clusters from the WINGS survey (Fasano et al. 2006), and the six Hubble Space Telescope (HST) Frontier Field galaxy clusters. The high resolution of HST allows me to compare distant clusters with those nearby to look for evolutionary changes in the galaxy cluster population. I use the results from the software to examine the properties (e.g. luminosity functions, radial dependencies, star formation rates) of selected galaxies. Due to the large amount of data that will be available from wide-area surveys in the future, the use of computer software to classify and analyze the morphology of galaxies will be extremely important in terms of efficiency. This research aims to contribute to the solution of this problem.

A statistical approach to root system classification

PubMed Central

Bodner, Gernot; Leitner, Daniel; Nakhforoosh, Alireza; Sobotik, Monika; Moder, Karl; Kaul, Hans-Peter

2013-01-01

Plant root systems have a key role in ecology and agronomy. In spite of fast increase in root studies, still there is no classification that allows distinguishing among distinctive characteristics within the diversity of rooting strategies. Our hypothesis is that a multivariate approach for “plant functional type” identification in ecology can be applied to the classification of root systems. The classification method presented is based on a data-defined statistical procedure without a priori decision on the classifiers. The study demonstrates that principal component based rooting types provide efficient and meaningful multi-trait classifiers. The classification method is exemplified with simulated root architectures and morphological field data. Simulated root architectures showed that morphological attributes with spatial distribution parameters capture most distinctive features within root system diversity. While developmental type (tap vs. shoot-borne systems) is a strong, but coarse classifier, topological traits provide the most detailed differentiation among distinctive groups. Adequacy of commonly available morphologic traits for classification is supported by field data. Rooting types emerging from measured data, mainly distinguished by diameter/weight and density dominated types. Similarity of root systems within distinctive groups was the joint result of phylogenetic relation and environmental as well as human selection pressure. We concluded that the data-define classification is appropriate for integration of knowledge obtained with different root measurement methods and at various scales. Currently root morphology is the most promising basis for classification due to widely used common measurement protocols. To capture details of root diversity efforts in architectural measurement techniques are essential. PMID:23914200

A statistical approach to root system classification.

PubMed

Bodner, Gernot; Leitner, Daniel; Nakhforoosh, Alireza; Sobotik, Monika; Moder, Karl; Kaul, Hans-Peter

2013-01-01

Plant root systems have a key role in ecology and agronomy. In spite of fast increase in root studies, still there is no classification that allows distinguishing among distinctive characteristics within the diversity of rooting strategies. Our hypothesis is that a multivariate approach for "plant functional type" identification in ecology can be applied to the classification of root systems. The classification method presented is based on a data-defined statistical procedure without a priori decision on the classifiers. The study demonstrates that principal component based rooting types provide efficient and meaningful multi-trait classifiers. The classification method is exemplified with simulated root architectures and morphological field data. Simulated root architectures showed that morphological attributes with spatial distribution parameters capture most distinctive features within root system diversity. While developmental type (tap vs. shoot-borne systems) is a strong, but coarse classifier, topological traits provide the most detailed differentiation among distinctive groups. Adequacy of commonly available morphologic traits for classification is supported by field data. Rooting types emerging from measured data, mainly distinguished by diameter/weight and density dominated types. Similarity of root systems within distinctive groups was the joint result of phylogenetic relation and environmental as well as human selection pressure. We concluded that the data-define classification is appropriate for integration of knowledge obtained with different root measurement methods and at various scales. Currently root morphology is the most promising basis for classification due to widely used common measurement protocols. To capture details of root diversity efforts in architectural measurement techniques are essential.

Bag of Visual Words Model with Deep Spatial Features for Geographical Scene Classification

PubMed Central

Wu, Lin

2017-01-01

With the popular use of geotagging images, more and more research efforts have been placed on geographical scene classification. In geographical scene classification, valid spatial feature selection can significantly boost the final performance. Bag of visual words (BoVW) can do well in selecting feature in geographical scene classification; nevertheless, it works effectively only if the provided feature extractor is well-matched. In this paper, we use convolutional neural networks (CNNs) for optimizing proposed feature extractor, so that it can learn more suitable visual vocabularies from the geotagging images. Our approach achieves better performance than BoVW as a tool for geographical scene classification, respectively, in three datasets which contain a variety of scene categories. PMID:28706534

Texture characterization for joint compression and classification based on human perception in the wavelet domain.

PubMed

Fahmy, Gamal; Black, John; Panchanathan, Sethuraman

2006-06-01

Today's multimedia applications demand sophisticated compression and classification techniques in order to store, transmit, and retrieve audio-visual information efficiently. Over the last decade, perceptually based image compression methods have been gaining importance. These methods take into account the abilities (and the limitations) of human visual perception (HVP) when performing compression. The upcoming MPEG 7 standard also addresses the need for succinct classification and indexing of visual content for efficient retrieval. However, there has been no research that has attempted to exploit the characteristics of the human visual system to perform both compression and classification jointly. One area of HVP that has unexplored potential for joint compression and classification is spatial frequency perception. Spatial frequency content that is perceived by humans can be characterized in terms of three parameters, which are: 1) magnitude; 2) phase; and 3) orientation. While the magnitude of spatial frequency content has been exploited in several existing image compression techniques, the novel contribution of this paper is its focus on the use of phase coherence for joint compression and classification in the wavelet domain. Specifically, this paper describes a human visual system-based method for measuring the degree to which an image contains coherent (perceptible) phase information, and then exploits that information to provide joint compression and classification. Simulation results that demonstrate the efficiency of this method are presented.

Inter- and intraobserver reliability of the Rockwood classification in acute acromioclavicular joint dislocations.

PubMed

Schneider, M M; Balke, M; Koenen, P; Fröhlich, M; Wafaisade, A; Bouillon, B; Banerjee, M

2016-07-01

The reliability of the Rockwood classification, the gold standard for acute acromioclavicular (AC) joint separations, has not yet been tested. The purpose of this study was to investigate the reliability of visual and measured AC joint lesion grades according to the Rockwood classification. Four investigators (two shoulder specialists and two second-year residents) examined radiographs (bilateral panoramic stress and axial views) in 58 patients and graded the injury according to the Rockwood classification using the following sequence: (1) visual classification of the AC joint lesion, (2) digital measurement of the coracoclavicular distance (CCD) and the horizontal dislocation (HD) with Osirix Dicom Viewer (Pixmeo, Switzerland), (3) classification of the AC joint lesion according to the measurements and (4) repetition of (1) and (2) after repeated anonymization by an independent physician. Visual and measured Rockwood grades as well as the CCD and HD of every patient were documented, and a CC index was calculated (CCD injured/CCD healthy). All records were then used to evaluate intra- and interobserver reliability. The disagreement between visual and measured diagnosis ranged from 6.9 to 27.6 %. Interobserver reliability for visual diagnosis was good (0.72-0.74) and excellent (0.85-0.93) for measured Rockwood grades. Intraobserver reliability was good to excellent (0.67-0.93) for visual diagnosis and excellent for measured diagnosis (0.90-0.97). The correlations between measurements of the axial view varied from 0.68 to 0.98 (good to excellent) for interobserver reliability and from 0.90 to 0.97 (excellent) for intraobserver reliability. Bilateral panoramic stress and axial radiographs are reliable examinations for grading AC joint injuries according to Rockwood's classification. Clinicians of all experience levels can precisely classify AC joint lesions according to the Rockwood classification. We recommend to grade acute ACG lesions by performing a digital measurement instead of a sole visual diagnosis because of the higher intra- and interobserver reliability. Case series, Level IV.

[Three-dimensional morphological modeling and visualization of wheat root system].

PubMed

Tan, Feng; Tang, Liang; Hu, Jun-Cheng; Jiang, Hai-Yan; Cao, Wei-Xing; Zhu, Yan

2011-01-01

Crop three-dimensional (3D) morphological modeling and visualization is an important part of digital plant study. This paper aimed to develop a 3D morphological model of wheat root system based on the parameters of wheat root morphological features, and to realize the visualization of wheat root growth. According to the framework of visualization technology for wheat root growth, a 3D visualization model of wheat root axis, including root axis growth model, branch geometric model, and root axis curve model, was developed firstly. Then, by integrating root topology, the corresponding pixel was determined, and the whole wheat root system was three-dimensionally re-constructed by using the morphological feature parameters in the root morphological model. Finally, based on the platform of OpenGL, and by integrating the technologies of texture mapping, lighting rendering, and collision detection, the 3D visualization of wheat root growth was realized. The 3D output of wheat root system from the model was vivid, which could realize the 3D root system visualization of different wheat cultivars under different water regimes and nitrogen application rates. This study could lay a technical foundation for further development of an integral visualization system of wheat plant.

Lung texture classification using bag of visual words

NASA Astrophysics Data System (ADS)

Asherov, Marina; Diamant, Idit; Greenspan, Hayit

2014-03-01

Interstitial lung diseases (ILD) refer to a group of more than 150 parenchymal lung disorders. High-Resolution Computed Tomography (HRCT) is the most essential imaging modality of ILD diagnosis. Nonetheless, classification of various lung tissue patterns caused by ILD is still regarded as a challenging task. The current study focuses on the classification of five most common categories of lung tissues of ILD in HRCT images: normal, emphysema, ground glass, fibrosis and micronodules. The objective of the research is to classify an expert-given annotated region of interest (AROI) using a bag of visual words (BoVW) framework. The images are divided into small patches and a collection of representative patches are defined as visual words. This procedure, termed dictionary construction, is performed for each individual lung texture category. The assumption is that different lung textures are represented by a different visual word distribution. The classification is performed using an SVM classifier with histogram intersection kernel. In the experiments, we use a dataset of 1018 AROIs from 95 patients. Classification using a leave-one-patient-out cross validation (LOPO CV) is used. Current classification accuracy obtained is close to 80%.

New Features for Neuron Classification.

PubMed

Hernández-Pérez, Leonardo A; Delgado-Castillo, Duniel; Martín-Pérez, Rainer; Orozco-Morales, Rubén; Lorenzo-Ginori, Juan V

2018-04-28

This paper addresses the problem of obtaining new neuron features capable of improving results of neuron classification. Most studies on neuron classification using morphological features have been based on Euclidean geometry. Here three one-dimensional (1D) time series are derived from the three-dimensional (3D) structure of neuron instead, and afterwards a spatial time series is finally constructed from which the features are calculated. Digitally reconstructed neurons were separated into control and pathological sets, which are related to three categories of alterations caused by epilepsy, Alzheimer's disease (long and local projections), and ischemia. These neuron sets were then subjected to supervised classification and the results were compared considering three sets of features: morphological, features obtained from the time series and a combination of both. The best results were obtained using features from the time series, which outperformed the classification using only morphological features, showing higher correct classification rates with differences of 5.15, 3.75, 5.33% for epilepsy and Alzheimer's disease (long and local projections) respectively. The morphological features were better for the ischemia set with a difference of 3.05%. Features like variance, Spearman auto-correlation, partial auto-correlation, mutual information, local minima and maxima, all related to the time series, exhibited the best performance. Also we compared different evaluators, among which ReliefF was the best ranked.

A comprehensive statistical classifier of foci in the cell transformation assay for carcinogenicity testing.

PubMed

Callegaro, Giulia; Malkoc, Kasja; Corvi, Raffaella; Urani, Chiara; Stefanini, Federico M

2017-12-01

The identification of the carcinogenic risk of chemicals is currently mainly based on animal studies. The in vitro Cell Transformation Assays (CTAs) are a promising alternative to be considered in an integrated approach. CTAs measure the induction of foci of transformed cells. CTAs model key stages of the in vivo neoplastic process and are able to detect both genotoxic and some non-genotoxic compounds, being the only in vitro method able to deal with the latter. Despite their favorable features, CTAs can be further improved, especially reducing the possible subjectivity arising from the last phase of the protocol, namely visual scoring of foci using coded morphological features. By taking advantage of digital image analysis, the aim of our work is to translate morphological features into statistical descriptors of foci images, and to use them to mimic the classification performances of the visual scorer to discriminate between transformed and non-transformed foci. Here we present a classifier based on five descriptors trained on a dataset of 1364 foci, obtained with different compounds and concentrations. Our classifier showed accuracy, sensitivity and specificity equal to 0.77 and an area under the curve (AUC) of 0.84. The presented classifier outperforms a previously published model. Copyright © 2017 Elsevier Ltd. All rights reserved.

Structure-function analysis of genetically defined neuronal populations.

PubMed

Groh, Alexander; Krieger, Patrik

2013-10-01

Morphological and functional classification of individual neurons is a crucial aspect of the characterization of neuronal networks. Systematic structural and functional analysis of individual neurons is now possible using transgenic mice with genetically defined neurons that can be visualized in vivo or in brain slice preparations. Genetically defined neurons are useful for studying a particular class of neurons and also for more comprehensive studies of the neuronal content of a network. Specific subsets of neurons can be identified by fluorescence imaging of enhanced green fluorescent protein (eGFP) or another fluorophore expressed under the control of a cell-type-specific promoter. The advantages of such genetically defined neurons are not only their homogeneity and suitability for systematic descriptions of networks, but also their tremendous potential for cell-type-specific manipulation of neuronal networks in vivo. This article describes a selection of procedures for visualizing and studying the anatomy and physiology of genetically defined neurons in transgenic mice. We provide information about basic equipment, reagents, procedures, and analytical approaches for obtaining three-dimensional (3D) cell morphologies and determining the axonal input and output of genetically defined neurons. We exemplify with genetically labeled cortical neurons, but the procedures are applicable to other brain regions with little or no alterations.

Morphology of retinal ganglion cells in the ferret (Mustela putorius furo).

PubMed

Isayama, Tomoki; O'Brien, Brendan J; Ugalde, Irma; Muller, Jay F; Frenz, Aaron; Aurora, Vikas; Tsiaras, William; Berson, David M

2009-12-01

The ferret is the premiere mammalian model of retinal and visual system development, but the spectrum and properties of its retinal ganglion cells are less well understood than in another member of the Carnivora, the domestic cat. Here, we have extensively surveyed the dendritic architecture of ferret ganglion cells and report that the classification scheme previously developed for cat ganglion cells can be applied with few modifications to the ferret retina. We confirm the presence of alpha and beta cells in ferret retina, which are very similar to those in cat retina. Both cell types exhibited an increase in dendritic field size with distance from the area centralis (eccentricity) and with distance from the visual streak. Both alpha and beta cell populations existed as two subtypes whose dendrites stratified mainly in sublamina a or b of the inner plexiform layer. Six additional morphological types of ganglion cells were identified: four monostratified cell types (delta, epsilon, zeta, and eta) and two bistratified types (theta and iota). These types closely resembled their counterparts in the cat in terms of form, relative field size, and stratification. Our data indicate that, among carnivore species, the retinal ganglion cells resemble one another closely and that the ferret is a useful model for studies of the ontogenetic differentiation of ganglion cell types.

Computer-aided Classification of Mammographic Masses Using Visually Sensitive Image Features

PubMed Central

Wang, Yunzhi; Aghaei, Faranak; Zarafshani, Ali; Qiu, Yuchen; Qian, Wei; Zheng, Bin

2017-01-01

Purpose To develop a new computer-aided diagnosis (CAD) scheme that computes visually sensitive image features routinely used by radiologists to develop a machine learning classifier and distinguish between the malignant and benign breast masses detected from digital mammograms. Methods An image dataset including 301 breast masses was retrospectively selected. From each segmented mass region, we computed image features that mimic five categories of visually sensitive features routinely used by radiologists in reading mammograms. We then selected five optimal features in the five feature categories and applied logistic regression models for classification. A new CAD interface was also designed to show lesion segmentation, computed feature values and classification score. Results Areas under ROC curves (AUC) were 0.786±0.026 and 0.758±0.027 when to classify mass regions depicting on two view images, respectively. By fusing classification scores computed from two regions, AUC increased to 0.806±0.025. Conclusion This study demonstrated a new approach to develop CAD scheme based on 5 visually sensitive image features. Combining with a “visual aid” interface, CAD results may be much more easily explainable to the observers and increase their confidence to consider CAD generated classification results than using other conventional CAD approaches, which involve many complicated and visually insensitive texture features. PMID:27911353

Morphology classification of galaxies in CL 0939+4713 using a ground-based telescope image

NASA Technical Reports Server (NTRS)

Fukugita, M.; Doi, M.; Dressler, A.; Gunn, J. E.

1995-01-01

Morphological classification is studied for galaxies in cluster CL 0939+4712 at z = 0.407 using simple photometric parameters obtained from a ground-based telescope image with seeing of 1-2 arcseconds full width at half maximim (FWHM). By ploting the galaxies in a plane of the concentration parameter versus mean surface brightness, we find a good correlation between the location on the plane and galaxy colors, which are known to correlate with morphological types from a recent Hubble Space Telescope (HST) study. Using the present method, we expect a success rate of classification into early and late types of about 70% or possibly more.

Classification of the visual landscape for transmission planning

Treesearch

Curtis Miller; Nargis Jetha; Rod MacDonald

1979-01-01

The Visual Landscape Type Classification method of the Route and Site Selection Division, Ontario Hydro, defines and delineates the landscape into discrete visual units using parametric and judgmental data. This qualitative and quantitative information is documented in a prescribed format to give each of the approximately 1100 Landscape Types a unique description....

VizieR Online Data Catalog: Massive early-type galaxies (Buitrago+, 2013)

NASA Astrophysics Data System (ADS)

Buitrago, F.; Trujillo, I.; Conselice, C. J.; Haussler, B.

2013-08-01

Present-day massive galaxies are composed mostly of early-type objects. It is unknown whether this was also the case at higher redshifts. In a hierarchical assembling scenario the morphological content of the massive population is expected to change with time from disc-like objects in the early Universe to spheroid-like galaxies at present. In this paper we have probed this theoretical expectation by compiling a large sample of massive (Mstellar>=1011h-270M⊙) galaxies in the redshift interval 0

Visualizing histopathologic deep learning classification and anomaly detection using nonlinear feature space dimensionality reduction.

PubMed

Faust, Kevin; Xie, Quin; Han, Dominick; Goyle, Kartikay; Volynskaya, Zoya; Djuric, Ugljesa; Diamandis, Phedias

2018-05-16

There is growing interest in utilizing artificial intelligence, and particularly deep learning, for computer vision in histopathology. While accumulating studies highlight expert-level performance of convolutional neural networks (CNNs) on focused classification tasks, most studies rely on probability distribution scores with empirically defined cutoff values based on post-hoc analysis. More generalizable tools that allow humans to visualize histology-based deep learning inferences and decision making are scarce. Here, we leverage t-distributed Stochastic Neighbor Embedding (t-SNE) to reduce dimensionality and depict how CNNs organize histomorphologic information. Unique to our workflow, we develop a quantitative and transparent approach to visualizing classification decisions prior to softmax compression. By discretizing the relationships between classes on the t-SNE plot, we show we can super-impose randomly sampled regions of test images and use their distribution to render statistically-driven classifications. Therefore, in addition to providing intuitive outputs for human review, this visual approach can carry out automated and objective multi-class classifications similar to more traditional and less-transparent categorical probability distribution scores. Importantly, this novel classification approach is driven by a priori statistically defined cutoffs. It therefore serves as a generalizable classification and anomaly detection tool less reliant on post-hoc tuning. Routine incorporation of this convenient approach for quantitative visualization and error reduction in histopathology aims to accelerate early adoption of CNNs into generalized real-world applications where unanticipated and previously untrained classes are often encountered.

Early-type galaxies have been the predominant morphological class for massive galaxies since only z ˜ 1

NASA Astrophysics Data System (ADS)

Buitrago, Fernando; Trujillo, Ignacio; Conselice, Christopher J.; Häußler, Boris

2013-01-01

Present-day massive galaxies are composed mostly of early-type objects. It is unknown whether this was also the case at higher redshifts. In a hierarchical assembling scenario the morphological content of the massive population is expected to change with time from disc-like objects in the early Universe to spheroid-like galaxies at present. In this paper we have probed this theoretical expectation by compiling a large sample of massive (Mstellar ≥ 1011 h- 270 M⊙) galaxies in the redshift interval 0 < z < 3. Our sample of 1082 objects comprises 207 local galaxies selected from Sloan Digital Sky Survey plus 875 objects observed with the Hubble Space Telescope belonging to the Palomar Observatory Wide-field InfraRed/DEEP2 and GOODS NICMOS Survey surveys. 639 of our objects have spectroscopic redshifts. Our morphological classification is performed as close as possible to the optical rest frame according to the photometric bands available in our observations both quantitatively (using the Sérsic index as a morphological proxy) and qualitatively (by visual inspection). Using both techniques we find an enormous change on the dominant morphological class with cosmic time. The fraction of early-type galaxies among the massive galaxy population has changed from ˜20-30 per cent at z ˜ 3 to ˜70 per cent at z = 0. Early-type galaxies have been the predominant morphological class for massive galaxies since only z ˜ 1.

Creating a classification of image types in the medical literature for visual categorization

NASA Astrophysics Data System (ADS)

Müller, Henning; Kalpathy-Cramer, Jayashree; Demner-Fushman, Dina; Antani, Sameer

2012-02-01

Content-based image retrieval (CBIR) from specialized collections has often been proposed for use in such areas as diagnostic aid, clinical decision support, and teaching. The visual retrieval from broad image collections such as teaching files, the medical literature or web images, by contrast, has not yet reached a high maturity level compared to textual information retrieval. Visual image classification into a relatively small number of classes (20-100) on the other hand, has shown to deliver good results in several benchmarks. It is, however, currently underused as a basic technology for retrieval tasks, for example, to limit the search space. Most classification schemes for medical images are focused on specific areas and consider mainly the medical image types (modalities), imaged anatomy, and view, and merge them into a single descriptor or classification hierarchy. Furthermore, they often ignore other important image types such as biological images, statistical figures, flowcharts, and diagrams that frequently occur in the biomedical literature. Most of the current classifications have also been created for radiology images, which are not the only types to be taken into account. With Open Access becoming increasingly widespread particularly in medicine, images from the biomedical literature are more easily available for use. Visual information from these images and knowledge that an image is of a specific type or medical modality could enrich retrieval. This enrichment is hampered by the lack of a commonly agreed image classification scheme. This paper presents a hierarchy for classification of biomedical illustrations with the goal of using it for visual classification and thus as a basis for retrieval. The proposed hierarchy is based on relevant parts of existing terminologies, such as the IRMA-code (Image Retrieval in Medical Applications), ad hoc classifications and hierarchies used in imageCLEF (Image retrieval task at the Cross-Language Evaluation Forum) and NLM's (National Library of Medicine) OpenI. Furtheron, mappings to NLM's MeSH (Medical Subject Headings), RSNA's RadLex (Radiological Society of North America, Radiology Lexicon), and the IRMA code are also attempted for relevant image types. Advantages derived from such hierarchical classification for medical image retrieval are being evaluated through benchmarks such as imageCLEF, and R&D systems such as NLM's OpenI. The goal is to extend this hierarchy progressively and (through adding image types occurring in the biomedical literature) to have a terminology for visual image classification based on image types distinguishable by visual means and occurring in the medical open access literature.

Classification deficits in Alzheimer's disease with special reference to living and nonliving things.

PubMed

Montanes, P; Goldblum, M C; Boller, F

1996-08-01

The present study was conducted to assess the hypothesis that visual similarity between exemplars within a semantic category may affect differentially the recognition process of living and nonliving things, according to task demands, in patients with semantic memory disorders. Thirty-nine Alzheimer's patients and 39 normal elderly subjects were presented with a task in which they had to classify pictures and words, depicting either living or nonliving things, at two levels of classification: subordinate (e.g., mammals versus birds or tools versus vehicles) and attribute (e.g., wild versus domestic animals or fast versus slow vehicles). Contrary to previous results (Montañes, Goldblum, & Boller, 1995) in a naming task, but as expected, living things were better classified than nonliving ones by both controls and patients. As expected, classifications at the subordinate level also gave rise to better performance than classifications at the attribute level. Although (and somewhat unexpectedly) no advantage of picture over word classification emerged, some effects consistent with the hypothesis that visual similarity affects picture classification emerged, in particular within a subgroup of patients with predominant verbal deficits and the most severe semantic memory disorders. This subgroup obtained a better score on classification of pictures than of words depicting living items (that share many visual features) when classification is at the subordinate level (for which visual similarity is a reliable clue to classification), but met with major difficulties when classifying those pictures at the attribute level (for which shared visual features are not reliable clues to classification). These results emphasize the fact that some "normal" effects specific to items in living and nonliving categories have to be considered among the factors causing selective category-specific deficits in patients, as well as their relevance in achieving tasks which require either differentiation between competing exemplars in the same semantic category (naming) or detection of resemblance between those exemplars (categorization).

Mapping lava morphology of the Galapagos Spreading Center at 92°W: fuzzy logic provides a classification of high-resolution bathymetry and backscatter

NASA Astrophysics Data System (ADS)

McClinton, J. T.; White, S. M.; Sinton, J. M.; Rubin, K. H.; Bowles, J. A.

2010-12-01

Differences in axial lava morphology along the Galapagos Spreading Center (GSC) can indicate variations in magma supply and emplacement dynamics due to the influence of the adjacent Galapagos hot spot. Unfortunately, the ability to discriminate fine-scale lava morphology has historically been limited to observations of the small coverage areas of towed camera surveys and submersible operations. This research presents a neuro-fuzzy approach to automated seafloor classification using spatially coincident, high-resolution bathymetry and backscatter data. The classification method implements a Sugeno-type fuzzy inference system trained by a multi-layered adaptive neural network and is capable of rapidly classifying seafloor morphology based on attributes of surface geometry and texture. The system has been applied to the 92°W segment of the western GSC in order to quantify coverage areas and distributions of pillow, lobate, and sheet lava morphology. An accuracy assessment has been performed on the classification results. The resulting classified maps provide a high-resolution view of GSC axial morphology and indicate the study area terrain is approximately 40% pillow flows, 40% lobate and sheet flows, and 10% fissured or faulted area, with about 10% of the study area unclassifiable. Fine-scale features such as eruptive fissures, tumuli, and individual pillowed lava flow fronts are also visible. Although this system has been applied to lava morphology, its design and implementation are applicable to other undersea mapping applications.

Orientation selectivity based structure for texture classification

NASA Astrophysics Data System (ADS)

Wu, Jinjian; Lin, Weisi; Shi, Guangming; Zhang, Yazhong; Lu, Liu

2014-10-01

Local structure, e.g., local binary pattern (LBP), is widely used in texture classification. However, LBP is too sensitive to disturbance. In this paper, we introduce a novel structure for texture classification. Researches on cognitive neuroscience indicate that the primary visual cortex presents remarkable orientation selectivity for visual information extraction. Inspired by this, we investigate the orientation similarities among neighbor pixels, and propose an orientation selectivity based pattern for local structure description. Experimental results on texture classification demonstrate that the proposed structure descriptor is quite robust to disturbance.

Neural attractor network for application in visual field data classification.

PubMed

Fink, Wolfgang

2004-07-07

The purpose was to introduce a novel method for computer-based classification of visual field data derived from perimetric examination, that may act as a 'counsellor', providing an independent 'second opinion' to the diagnosing physician. The classification system consists of a Hopfield-type neural attractor network that obtains its input data from perimetric examination results. An iterative relaxation process determines the states of the neurons dynamically. Therefore, even 'noisy' perimetric output, e.g., early stages of a disease, may eventually be classified correctly according to the predefined idealized visual field defect (scotoma) patterns, stored as attractors of the network, that are found with diseases of the eye, optic nerve and the central nervous system. Preliminary tests of the classification system on real visual field data derived from perimetric examinations have shown a classification success of over 80%. Some of the main advantages of the Hopfield-attractor-network-based approach over feed-forward type neural networks are: (1) network architecture is defined by the classification problem; (2) no training is required to determine the neural coupling strengths; (3) assignment of an auto-diagnosis confidence level is possible by means of an overlap parameter and the Hamming distance. In conclusion, the novel method for computer-based classification of visual field data, presented here, furnishes a valuable first overview and an independent 'second opinion' in judging perimetric examination results, pointing towards a final diagnosis by a physician. It should not be considered a substitute for the diagnosing physician. Thanks to the worldwide accessibility of the Internet, the classification system offers a promising perspective towards modern computer-assisted diagnosis in both medicine and tele-medicine, for example and in particular, with respect to non-ophthalmic clinics or in communities where perimetric expertise is not readily available.

Whole Organism High-Content Screening by Label-Free, Image-Based Bayesian Classification for Parasitic Diseases

PubMed Central

Paveley, Ross A.; Mansour, Nuha R.; Hallyburton, Irene; Bleicher, Leo S.; Benn, Alex E.; Mikic, Ivana; Guidi, Alessandra; Gilbert, Ian H.; Hopkins, Andrew L.; Bickle, Quentin D.

2012-01-01

Sole reliance on one drug, Praziquantel, for treatment and control of schistosomiasis raises concerns about development of widespread resistance, prompting renewed interest in the discovery of new anthelmintics. To discover new leads we designed an automated label-free, high content-based, high throughput screen (HTS) to assess drug-induced effects on in vitro cultured larvae (schistosomula) using bright-field imaging. Automatic image analysis and Bayesian prediction models define morphological damage, hit/non-hit prediction and larval phenotype characterization. Motility was also assessed from time-lapse images. In screening a 10,041 compound library the HTS correctly detected 99.8% of the hits scored visually. A proportion of these larval hits were also active in an adult worm ex-vivo screen and are the subject of ongoing studies. The method allows, for the first time, screening of large compound collections against schistosomes and the methods are adaptable to other whole organism and cell-based screening by morphology and motility phenotyping. PMID:22860151

The redshift evolution of major merger triggering of luminous AGNs: a slight enhancement at z ˜ 2

NASA Astrophysics Data System (ADS)

Hewlett, Timothy; Villforth, Carolin; Wild, Vivienne; Mendez-Abreu, Jairo; Pawlik, Milena; Rowlands, Kate

2017-09-01

Active galactic nuclei (AGNs), particularly the most luminous AGNs, are commonly assumed to be triggered through major mergers; however, observational evidence for this scenario is mixed. To investigate any influence of galaxy mergers on AGN triggering and luminosities through cosmic time, we present a sample of 106 luminous X-ray-selected type 1 AGNs from the COSMOS survey. These AGNs occupy a large redshift range (0.5 < z < 2.2) and two orders of magnitude in X-ray luminosity (˜1043-1045 erg s-1). AGN hosts are carefully mass and redshift matched to 486 control galaxies. A novel technique for identifying and quantifying merger features in galaxies is developed, subtracting galfit galaxy models and quantifying the residuals. Comparison to visual classification confirms this measure reliably picks out disturbance features in galaxies. No enhancement of merger features with increasing AGN luminosity is found with this metric, or by visual inspection. We analyse the redshift evolution of AGNs associated with galaxy mergers and find no merger enhancement in lower redshift bins. Contrarily, in the highest redshift bin (z ˜ 2) AGNs are ˜4 times more likely to be in galaxies exhibiting evidence of morphological disturbance compared to control galaxies, at 99 per cent confidence level (˜2.4σ) from visual inspection. Since only ˜15 per cent of these AGNs are found to be in morphologically disturbed galaxies, it is implied that major mergers at high redshift make a noticeable but subdominant contribution to AGN fuelling. At low redshifts, other processes dominate and mergers become a less significant triggering mechanism.

Craniopharyngioma arising in a Rathke's cleft cyst: case report.

PubMed

Alomari, Ahmed K; Kelley, Brian J; Damisah, Eyiyemisi; Marks, Asher; Hui, Pei; DiLuna, Michael; Vortmeyer, Alexander

2015-03-01

Craniopharyngioma is one of the most common non-glial intracranial tumors of childhood. Its relation to Rathke's cleft cyst (RCC) is controversial, and both lesions have been hypothesized to lie on a continuum of cystic ectodermal lesions of the sellar region. The authors report on a 7-year-old boy who presented with decreased visual acuity, presumably of at least 2 years' duration, and was found to have a 5.2-cm sellar lesion with rim enhancement. Histological examination of the resected lesion showed a mixture of areas with simple RCC morphology with focal squamous metaplasia and areas with typical craniopharyngioma morphology. Immunohistochemical staining with CK20 and Ki 67 differentially highlighted the 2 morphological components. Testing for beta-catenin and BRAF mutations was negative in the craniopharyngioma component, precluding definitive molecular classification. Follow-up imaging showed minimal residual enhancement and the patient will be closely followed up with serial MRI. Given the clinical and histological findings in the case, a progressive transformation of the RCC to craniopharyngioma seems to be the most plausible explanation for the co-occurrence of the 2 lesion types in this patient. An extensive review of previously proposed theories of the relationship between craniopharyngioma and RCC is also presented.

Comparison of alternative image reformatting techniques in micro-computed tomography and tooth clearing for detailed canal morphology.

PubMed

Lee, Ki-Wook; Kim, Yeun; Perinpanayagam, Hiran; Lee, Jong-Ki; Yoo, Yeon-Jee; Lim, Sang-Min; Chang, Seok Woo; Ha, Byung-Hyun; Zhu, Qiang; Kum, Kee-Yeon

2014-03-01

Micro-computed tomography (MCT) shows detailed root canal morphology that is not seen with traditional tooth clearing. However, alternative image reformatting techniques in MCT involving 2-dimensional (2D) minimum intensity projection (MinIP) and 3-dimensional (3D) volume-rendering reconstruction have not been directly compared with clearing. The aim was to compare alternative image reformatting techniques in MCT with tooth clearing on the mesiobuccal (MB) root of maxillary first molars. Eighteen maxillary first molar MB roots were scanned, and 2D MinIP and 3D volume-rendered images were reconstructed. Subsequently, the same MB roots were processed by traditional tooth clearing. Images from 2D, 3D, 2D + 3D, and clearing techniques were assessed by 4 endodontists to classify canal configuration and to identify fine anatomic structures such as accessory canals, intercanal communications, and loops. All image reformatting techniques in MCT showed detailed configurations and numerous fine structures, such that none were classified as simple type I or II canals; several were classified as types III and IV according to Weine classification or types IV, V, and VI according to Vertucci; and most were nonclassifiable because of their complexity. The clearing images showed less detail, few fine structures, and numerous type I canals. Classification of canal configuration was in 100% intraobserver agreement for all 18 roots visualized by any of the image reformatting techniques in MCT but for only 4 roots (22.2%) classified according to Weine and 6 (33.3%) classified according to Vertucci, when using the clearing technique. The combination of 2D MinIP and 3D volume-rendered images showed the most detailed canal morphology and fine anatomic structures. Copyright © 2014 American Association of Endodontists. Published by Elsevier Inc. All rights reserved.

Validation of the Lung Subtyping Panel in Multiple Fresh-Frozen and Formalin-Fixed, Paraffin-Embedded Lung Tumor Gene Expression Data Sets.

PubMed

Faruki, Hawazin; Mayhew, Gregory M; Fan, Cheng; Wilkerson, Matthew D; Parker, Scott; Kam-Morgan, Lauren; Eisenberg, Marcia; Horten, Bruce; Hayes, D Neil; Perou, Charles M; Lai-Goldman, Myla

2016-06-01

Context .- A histologic classification of lung cancer subtypes is essential in guiding therapeutic management. Objective .- To complement morphology-based classification of lung tumors, a previously developed lung subtyping panel (LSP) of 57 genes was tested using multiple public fresh-frozen gene-expression data sets and a prospectively collected set of formalin-fixed, paraffin-embedded lung tumor samples. Design .- The LSP gene-expression signature was evaluated in multiple lung cancer gene-expression data sets totaling 2177 patients collected from 4 platforms: Illumina RNAseq (San Diego, California), Agilent (Santa Clara, California) and Affymetrix (Santa Clara) microarrays, and quantitative reverse transcription-polymerase chain reaction. Gene centroids were calculated for each of 3 genomic-defined subtypes: adenocarcinoma, squamous cell carcinoma, and neuroendocrine, the latter of which encompassed both small cell carcinoma and carcinoid. Classification by LSP into 3 subtypes was evaluated in both fresh-frozen and formalin-fixed, paraffin-embedded tumor samples, and agreement with the original morphology-based diagnosis was determined. Results .- The LSP-based classifications demonstrated overall agreement with the original clinical diagnosis ranging from 78% (251 of 322) to 91% (492 of 538 and 869 of 951) in the fresh-frozen public data sets and 84% (65 of 77) in the formalin-fixed, paraffin-embedded data set. The LSP performance was independent of tissue-preservation method and gene-expression platform. Secondary, blinded pathology review of formalin-fixed, paraffin-embedded samples demonstrated concordance of 82% (63 of 77) with the original morphology diagnosis. Conclusions .- The LSP gene-expression signature is a reproducible and objective method for classifying lung tumors and demonstrates good concordance with morphology-based classification across multiple data sets. The LSP panel can supplement morphologic assessment of lung cancers, particularly when classification by standard methods is challenging.

Gold-standard for computer-assisted morphological sperm analysis.

PubMed

Chang, Violeta; Garcia, Alejandra; Hitschfeld, Nancy; Härtel, Steffen

2017-04-01

Published algorithms for classification of human sperm heads are based on relatively small image databases that are not open to the public, and thus no direct comparison is available for competing methods. We describe a gold-standard for morphological sperm analysis (SCIAN-MorphoSpermGS), a dataset of sperm head images with expert-classification labels in one of the following classes: normal, tapered, pyriform, small or amorphous. This gold-standard is for evaluating and comparing known techniques and future improvements to present approaches for classification of human sperm heads for semen analysis. Although this paper does not provide a computational tool for morphological sperm analysis, we present a set of experiments for comparing sperm head description and classification common techniques. This classification base-line is aimed to be used as a reference for future improvements to present approaches for human sperm head classification. The gold-standard provides a label for each sperm head, which is achieved by majority voting among experts. The classification base-line compares four supervised learning methods (1- Nearest Neighbor, naive Bayes, decision trees and Support Vector Machine (SVM)) and three shape-based descriptors (Hu moments, Zernike moments and Fourier descriptors), reporting the accuracy and the true positive rate for each experiment. We used Fleiss' Kappa Coefficient to evaluate the inter-expert agreement and Fisher's exact test for inter-expert variability and statistical significant differences between descriptors and learning techniques. Our results confirm the high degree of inter-expert variability in the morphological sperm analysis. Regarding the classification base line, we show that none of the standard descriptors or classification approaches is best suitable for tackling the problem of sperm head classification. We discovered that the correct classification rate was highly variable when trying to discriminate among non-normal sperm heads. By using the Fourier descriptor and SVM, we achieved the best mean correct classification: only 49%. We conclude that the SCIAN-MorphoSpermGS will provide a standard tool for evaluation of characterization and classification approaches for human sperm heads. Indeed, there is a clear need for a specific shape-based descriptor for human sperm heads and a specific classification approach to tackle the problem of high variability within subcategories of abnormal sperm cells. Copyright © 2017 Elsevier Ltd. All rights reserved.

Early Decomposition in Visual Word Recognition: Dissociating Morphology, Form, and Meaning

ERIC Educational Resources Information Center

Marslen-Wilson, William D.; Bozic, Mirjana; Randall, Billi

2008-01-01

The role of morphological, semantic, and form-based factors in the early stages of visual word recognition was investigated across different SOAs in a masked priming paradigm, focusing on English derivational morphology. In a first set of experiments, stimulus pairs co-varying in morphological decomposability and in semantic and orthographic…

Hierarchical classification method and its application in shape representation

NASA Astrophysics Data System (ADS)

Ireton, M. A.; Oakley, John P.; Xydeas, Costas S.

1992-04-01

In this paper we describe a technique for performing shaped-based content retrieval of images from a large database. In order to be able to formulate such user-generated queries about visual objects, we have developed an hierarchical classification technique. This hierarchical classification technique enables similarity matching between objects, with the position in the hierarchy signifying the level of generality to be used in the query. The classification technique is unsupervised, robust, and general; it can be applied to any suitable parameter set. To establish the potential of this classifier for aiding visual querying, we have applied it to the classification of the 2-D outlines of leaves.

Distinct Visual Evoked Potential Morphological Patterns for Apparent Motion Processing in School-Aged Children.

PubMed

Campbell, Julia; Sharma, Anu

2016-01-01

Measures of visual cortical development in children demonstrate high variability and inconsistency throughout the literature. This is partly due to the specificity of the visual system in processing certain features. It may then be advantageous to activate multiple cortical pathways in order to observe maturation of coinciding networks. Visual stimuli eliciting the percept of apparent motion and shape change is designed to simultaneously activate both dorsal and ventral visual streams. However, research has shown that such stimuli also elicit variable visual evoked potential (VEP) morphology in children. The aim of this study was to describe developmental changes in VEPs, including morphological patterns, and underlying visual cortical generators, elicited by apparent motion and shape change in school-aged children. Forty-one typically developing children underwent high-density EEG recordings in response to a continuously morphing, radially modulated, circle-star grating. VEPs were then compared across the age groups of 5-7, 8-10, and 11-15 years according to latency and amplitude. Current density reconstructions (CDR) were performed on VEP data in order to observe activated cortical regions. It was found that two distinct VEP morphological patterns occurred in each age group. However, there were no major developmental differences between the age groups according to each pattern. CDR further demonstrated consistent visual generators across age and pattern. These results describe two novel VEP morphological patterns in typically developing children, but with similar underlying cortical sources. The importance of these morphological patterns is discussed in terms of future studies and the investigation of a relationship to visual cognitive performance.

Distinct Visual Evoked Potential Morphological Patterns for Apparent Motion Processing in School-Aged Children

PubMed Central

Campbell, Julia; Sharma, Anu

2016-01-01

Measures of visual cortical development in children demonstrate high variability and inconsistency throughout the literature. This is partly due to the specificity of the visual system in processing certain features. It may then be advantageous to activate multiple cortical pathways in order to observe maturation of coinciding networks. Visual stimuli eliciting the percept of apparent motion and shape change is designed to simultaneously activate both dorsal and ventral visual streams. However, research has shown that such stimuli also elicit variable visual evoked potential (VEP) morphology in children. The aim of this study was to describe developmental changes in VEPs, including morphological patterns, and underlying visual cortical generators, elicited by apparent motion and shape change in school-aged children. Forty-one typically developing children underwent high-density EEG recordings in response to a continuously morphing, radially modulated, circle-star grating. VEPs were then compared across the age groups of 5–7, 8–10, and 11–15 years according to latency and amplitude. Current density reconstructions (CDR) were performed on VEP data in order to observe activated cortical regions. It was found that two distinct VEP morphological patterns occurred in each age group. However, there were no major developmental differences between the age groups according to each pattern. CDR further demonstrated consistent visual generators across age and pattern. These results describe two novel VEP morphological patterns in typically developing children, but with similar underlying cortical sources. The importance of these morphological patterns is discussed in terms of future studies and the investigation of a relationship to visual cognitive performance. PMID:27445738

Classifying Physical Morphology of Cocoa Beans Digital Images using Multiclass Ensemble Least-Squares Support Vector Machine

NASA Astrophysics Data System (ADS)

Lawi, Armin; Adhitya, Yudhi

2018-03-01

The objective of this research is to determine the quality of cocoa beans through morphology of their digital images. Samples of cocoa beans were scattered on a bright white paper under a controlled lighting condition. A compact digital camera was used to capture the images. The images were then processed to extract their morphological parameters. Classification process begins with an analysis of cocoa beans image based on morphological feature extraction. Parameters for extraction of morphological or physical feature parameters, i.e., Area, Perimeter, Major Axis Length, Minor Axis Length, Aspect Ratio, Circularity, Roundness, Ferret Diameter. The cocoa beans are classified into 4 groups, i.e.: Normal Beans, Broken Beans, Fractured Beans, and Skin Damaged Beans. The model of classification used in this paper is the Multiclass Ensemble Least-Squares Support Vector Machine (MELS-SVM), a proposed improvement model of SVM using ensemble method in which the separate hyperplanes are obtained by least square approach and the multiclass procedure uses One-Against- All method. The result of our proposed model showed that the classification with morphological feature input parameters were accurately as 99.705% for the four classes, respectively.

Multivariate Classification of Structural MRI Data Detects Chronic Low Back Pain

PubMed Central

Ung, Hoameng; Brown, Justin E.; Johnson, Kevin A.; Younger, Jarred; Hush, Julia; Mackey, Sean

2014-01-01

Chronic low back pain (cLBP) has a tremendous personal and socioeconomic impact, yet the underlying pathology remains a mystery in the majority of cases. An objective measure of this condition, that augments self-report of pain, could have profound implications for diagnostic characterization and therapeutic development. Contemporary research indicates that cLBP is associated with abnormal brain structure and function. Multivariate analyses have shown potential to detect a number of neurological diseases based on structural neuroimaging. Therefore, we aimed to empirically evaluate such an approach in the detection of cLBP, with a goal to also explore the relevant neuroanatomy. We extracted brain gray matter (GM) density from magnetic resonance imaging scans of 47 patients with cLBP and 47 healthy controls. cLBP was classified with an accuracy of 76% by support vector machine analysis. Primary drivers of the classification included areas of the somatosensory, motor, and prefrontal cortices—all areas implicated in the pain experience. Differences in areas of the temporal lobe, including bordering the amygdala, medial orbital gyrus, cerebellum, and visual cortex, were also useful for the classification. Our findings suggest that cLBP is characterized by a pattern of GM changes that can have discriminative power and reflect relevant pathological brain morphology. PMID:23246778

Automated Classification of Pathology Reports.

PubMed

Oleynik, Michel; Finger, Marcelo; Patrão, Diogo F C

2015-01-01

This work develops an automated classifier of pathology reports which infers the topography and the morphology classes of a tumor using codes from the International Classification of Diseases for Oncology (ICD-O). Data from 94,980 patients of the A.C. Camargo Cancer Center was used for training and validation of Naive Bayes classifiers, evaluated by the F1-score. Measures greater than 74% in the topographic group and 61% in the morphologic group are reported. Our work provides a successful baseline for future research for the classification of medical documents written in Portuguese and in other domains.

Morphological Processing during Visual Word Recognition in Hebrew as a First and a Second Language

ERIC Educational Resources Information Center

Norman, Tal; Degani, Tamar; Peleg, Orna

2017-01-01

The present study examined whether sublexical morphological processing takes place during visual word-recognition in Hebrew, and whether morphological decomposition of written words depends on lexical activation of the complete word. Furthermore, it examined whether morphological processing is similar when reading Hebrew as a first language (L1)…

Antibody-supervised deep learning for quantification of tumor-infiltrating immune cells in hematoxylin and eosin stained breast cancer samples.

PubMed

Turkki, Riku; Linder, Nina; Kovanen, Panu E; Pellinen, Teijo; Lundin, Johan

2016-01-01

Immune cell infiltration in tumor is an emerging prognostic biomarker in breast cancer. The gold standard for quantification of immune cells in tissue sections is visual assessment through a microscope, which is subjective and semi-quantitative. In this study, we propose and evaluate an approach based on antibody-guided annotation and deep learning to quantify immune cell-rich areas in hematoxylin and eosin (H&E) stained samples. Consecutive sections of formalin-fixed parafin-embedded samples obtained from the primary tumor of twenty breast cancer patients were cut and stained with H&E and the pan-leukocyte CD45 antibody. The stained slides were digitally scanned, and a training set of immune cell-rich and cell-poor tissue regions was annotated in H&E whole-slide images using the CD45-expression as a guide. In analysis, the images were divided into small homogenous regions, superpixels, from which features were extracted using a pretrained convolutional neural network (CNN) and classified with a support of vector machine. The CNN approach was compared to texture-based classification and to visual assessments performed by two pathologists. In a set of 123,442 labeled superpixels, the CNN approach achieved an F-score of 0.94 (range: 0.92-0.94) in discrimination of immune cell-rich and cell-poor regions, as compared to an F-score of 0.88 (range: 0.87-0.89) obtained with the texture-based classification. When compared to visual assessment of 200 images, an agreement of 90% (κ = 0.79) to quantify immune infiltration with the CNN approach was achieved while the inter-observer agreement between pathologists was 90% (κ = 0.78). Our findings indicate that deep learning can be applied to quantify immune cell infiltration in breast cancer samples using a basic morphology staining only. A good discrimination of immune cell-rich areas was achieved, well in concordance with both leukocyte antigen expression and pathologists' visual assessment.

Visual modifications on the P300 speller BCI paradigm

NASA Astrophysics Data System (ADS)

Salvaris, M.; Sepulveda, F.

2009-08-01

The best known P300 speller brain-computer interface (BCI) paradigm is the Farwell and Donchin paradigm. In this paper, various changes to the visual aspects of this protocol are explored as well as their effects on classification. Changes to the dimensions of the symbols, the distance between the symbols and the colours used were tested. The purpose of the present work was not to achieve the highest possible accuracy results, but to ascertain whether these simple modifications to the visual protocol will provide classification differences between them and what these differences will be. Eight subjects were used, with each subject carrying out a total of six different experiments. In each experiment, the user spelt a total of 39 characters. Two types of classifiers were trained and tested to determine whether the results were classifier dependant. These were a support vector machine (SVM) with a radial basis function (RBF) kernel and Fisher's linear discriminant (FLD). The single-trial classification results and multiple-trial classification results were recorded and compared. Although no visual protocol was the best for all subjects, the best performances, across both classifiers, were obtained with the white background (WB) visual protocol. The worst performance was obtained with the small symbol size (SSS) visual protocol.

Voice classification and vocal tract of singers: a study of x-ray images and morphology.

PubMed

Roers, Friederike; Mürbe, Dirk; Sundberg, Johan

2009-01-01

This investigation compares vocal tract dimensions and the classification of singer voices by examining an x-ray material assembled between 1959 and 1991 of students admitted to the solo singing education at the University of Music, Dresden, Germany. A total of 132 images were available to analysis. Different classifications' values of the lengths of the total vocal tract, the pharynx, and mouth cavities as well as of the relative position of the larynx, the height of the palatal arch, and the estimated vocal fold length were analyzed statistically, and some significant differences were found. The length of the pharynx cavity seemed particularly influential on the total vocal tract length, which varied systematically with classification. Also studied were the relationships between voice classification and the body height and weight and the body mass index. The data support the hypothesis that there are consistent morphological vocal tract differences between singers of different voice classifications.

Con-Text: Text Detection for Fine-grained Object Classification.

PubMed

Karaoglu, Sezer; Tao, Ran; van Gemert, Jan C; Gevers, Theo

2017-05-24

This work focuses on fine-grained object classification using recognized scene text in natural images. While the state-of-the-art relies on visual cues only, this paper is the first work which proposes to combine textual and visual cues. Another novelty is the textual cue extraction. Unlike the state-of-the-art text detection methods, we focus more on the background instead of text regions. Once text regions are detected, they are further processed by two methods to perform text recognition i.e. ABBYY commercial OCR engine and a state-of-the-art character recognition algorithm. Then, to perform textual cue encoding, bi- and trigrams are formed between the recognized characters by considering the proposed spatial pairwise constraints. Finally, extracted visual and textual cues are combined for fine-grained classification. The proposed method is validated on four publicly available datasets: ICDAR03, ICDAR13, Con-Text and Flickr-logo. We improve the state-of-the-art end-to-end character recognition by a large margin of 15% on ICDAR03. We show that textual cues are useful in addition to visual cues for fine-grained classification. We show that textual cues are also useful for logo retrieval. Adding textual cues outperforms visual- and textual-only in fine-grained classification (70.7% to 60.3%) and logo retrieval (57.4% to 54.8%).

Iris Image Classification Based on Hierarchical Visual Codebook.

PubMed

Zhenan Sun; Hui Zhang; Tieniu Tan; Jianyu Wang

2014-06-01

Iris recognition as a reliable method for personal identification has been well-studied with the objective to assign the class label of each iris image to a unique subject. In contrast, iris image classification aims to classify an iris image to an application specific category, e.g., iris liveness detection (classification of genuine and fake iris images), race classification (e.g., classification of iris images of Asian and non-Asian subjects), coarse-to-fine iris identification (classification of all iris images in the central database into multiple categories). This paper proposes a general framework for iris image classification based on texture analysis. A novel texture pattern representation method called Hierarchical Visual Codebook (HVC) is proposed to encode the texture primitives of iris images. The proposed HVC method is an integration of two existing Bag-of-Words models, namely Vocabulary Tree (VT), and Locality-constrained Linear Coding (LLC). The HVC adopts a coarse-to-fine visual coding strategy and takes advantages of both VT and LLC for accurate and sparse representation of iris texture. Extensive experimental results demonstrate that the proposed iris image classification method achieves state-of-the-art performance for iris liveness detection, race classification, and coarse-to-fine iris identification. A comprehensive fake iris image database simulating four types of iris spoof attacks is developed as the benchmark for research of iris liveness detection.

7 CFR 51.1860 - Color classification.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 7 Agriculture 2 2012-01-01 2012-01-01 false Color classification. 51.1860 Section 51.1860... STANDARDS) United States Standards for Fresh Tomatoes 1 Color Classification § 51.1860 Color classification... illustrating the color classification requirements, as set forth in this section. This visual aid may be...

Multiclass fMRI data decoding and visualization using supervised self-organizing maps.

PubMed

Hausfeld, Lars; Valente, Giancarlo; Formisano, Elia

2014-08-01

When multivariate pattern decoding is applied to fMRI studies entailing more than two experimental conditions, a most common approach is to transform the multiclass classification problem into a series of binary problems. Furthermore, for decoding analyses, classification accuracy is often the only outcome reported although the topology of activation patterns in the high-dimensional features space may provide additional insights into underlying brain representations. Here we propose to decode and visualize voxel patterns of fMRI datasets consisting of multiple conditions with a supervised variant of self-organizing maps (SSOMs). Using simulations and real fMRI data, we evaluated the performance of our SSOM-based approach. Specifically, the analysis of simulated fMRI data with varying signal-to-noise and contrast-to-noise ratio suggested that SSOMs perform better than a k-nearest-neighbor classifier for medium and large numbers of features (i.e. 250 to 1000 or more voxels) and similar to support vector machines (SVMs) for small and medium numbers of features (i.e. 100 to 600voxels). However, for a larger number of features (>800voxels), SSOMs performed worse than SVMs. When applied to a challenging 3-class fMRI classification problem with datasets collected to examine the neural representation of three human voices at individual speaker level, the SSOM-based algorithm was able to decode speaker identity from auditory cortical activation patterns. Classification performances were similar between SSOMs and other decoding algorithms; however, the ability to visualize decoding models and underlying data topology of SSOMs promotes a more comprehensive understanding of classification outcomes. We further illustrated this visualization ability of SSOMs with a re-analysis of a dataset examining the representation of visual categories in the ventral visual cortex (Haxby et al., 2001). This analysis showed that SSOMs could retrieve and visualize topography and neighborhood relations of the brain representation of eight visual categories. We conclude that SSOMs are particularly suited for decoding datasets consisting of more than two classes and are optimally combined with approaches that reduce the number of voxels used for classification (e.g. region-of-interest or searchlight approaches). Copyright © 2014. Published by Elsevier Inc.

The morphology and classification of α ganglion cells in the rat retinae: a fractal analysis study.

PubMed

Jelinek, Herbert F; Ristanović, Dušan; Milošević, Nebojša T

2011-09-30

Rat retinal ganglion cells have been proposed to consist of a varying number of subtypes. Dendritic morphology is an essential aspect of classification and a necessary step toward understanding structure-function relationships of retinal ganglion cells. This study aimed at using a heuristic classification procedure in combination with the box-counting analysis to classify the alpha ganglion cells in the rat retinae based on the dendritic branching pattern and to investigate morphological changes with retinal eccentricity. The cells could be divided into two groups: cells with simple dendritic pattern (box dimension lower than 1.390) and cells with complex dendritic pattern (box dimension higher than 1.390) according to their dendritic branching pattern complexity. Both were further divided into two subtypes due to the stratification within the inner plexiform layer. In the present study we have shown that the alpha rat RCGs can be classified further by their dendritic branching complexity and thus extend those of previous reports that fractal analysis can be successfully used in neuronal classification, particularly that the fractal dimension represents a robust and sensitive tool for the classification of retinal ganglion cells. A hypothesis of possible functional significance of our classification scheme is also discussed. Copyright © 2011 Elsevier B.V. All rights reserved.

Classification of Regional Radiographic Emphysematous Patterns Using Low-Attenuation Gap Length Matrix

NASA Astrophysics Data System (ADS)

Tan, Kok Liang; Tanaka, Toshiyuki; Nakamura, Hidetoshi; Shirahata, Toru; Sugiura, Hiroaki

The standard computer-tomography-based method for measuring emphysema uses percentage of area of low attenuation which is called the pixel index (PI). However, the PI method is susceptible to the problem of averaging effect and this causes the discrepancy between what the PI method describes and what radiologists observe. Knowing that visual recognition of the different types of regional radiographic emphysematous tissues in a CT image can be fuzzy, this paper proposes a low-attenuation gap length matrix (LAGLM) based algorithm for classifying the regional radiographic lung tissues into four emphysema types distinguishing, in particular, radiographic patterns that imply obvious or subtle bullous emphysema from those that imply diffuse emphysema or minor destruction of airway walls. Neural network is used for discrimination. The proposed LAGLM method is inspired by, but different from, former texture-based methods like gray level run length matrix (GLRLM) and gray level gap length matrix (GLGLM). The proposed algorithm is successfully validated by classifying 105 lung regions that are randomly selected from 270 images. The lung regions are hand-annotated by radiologists beforehand. The average four-class classification accuracies in the form of the proposed algorithm/PI/GLRLM/GLGLM methods are: 89.00%/82.97%/52.90%/51.36%, respectively. The p-values from the correlation analyses between the classification results of 270 images and pulmonary function test results are generally less than 0.01. The classification results are useful for a followup study especially for monitoring morphological changes with progression of pulmonary disease.

Morphologic observation and classification criteria of atretic follicles in guinea pigs.

PubMed

Wang, Wei; Liu, Hong-Lin; Tian, Wei; Zhang, Fen-Fen; Gong, Yan; Chen, Jin-Wei; Mao, Da-Gan; Shi, Fang-Xiong

2010-05-01

There is a lack of appropriate classification criteria for the determination of atretic follicles in guinea pigs. In the present study, new criteria were established based on the latest morphologic criteria for cell death proposed by the Nomenclature Committee on Cell Death (NCCD) in 2009. Ovaries of guinea pigs were sampled on different stages of estrous cycle, and the morphologic observations of atretic follicles were investigated in serial sections. The results showed that the process of follicular atresia could be classified into four continuous stages: (1) the granulosa layer became loose, and some apoptotic bodies began to appear; (2) the granulosa cells were massively eliminated; (3) the theca interna cells differentiated; and (4) the residual follicular cells degenerated. In addition, the examination revealed that these morphologic criteria were accurate and feasible. In conclusion, this study provides new criteria for the classification of atretic follicles in guinea pigs, and this knowledge can inform future research in the area.

Classification of visual and linguistic tasks using eye-movement features.

PubMed

Coco, Moreno I; Keller, Frank

2014-03-07

The role of the task has received special attention in visual-cognition research because it can provide causal explanations of goal-directed eye-movement responses. The dependency between visual attention and task suggests that eye movements can be used to classify the task being performed. A recent study by Greene, Liu, and Wolfe (2012), however, fails to achieve accurate classification of visual tasks based on eye-movement features. In the present study, we hypothesize that tasks can be successfully classified when they differ with respect to the involvement of other cognitive domains, such as language processing. We extract the eye-movement features used by Greene et al. as well as additional features from the data of three different tasks: visual search, object naming, and scene description. First, we demonstrated that eye-movement responses make it possible to characterize the goals of these tasks. Then, we trained three different types of classifiers and predicted the task participants performed with an accuracy well above chance (a maximum of 88% for visual search). An analysis of the relative importance of features for classification accuracy reveals that just one feature, i.e., initiation time, is sufficient for above-chance performance (a maximum of 79% accuracy in object naming). Crucially, this feature is independent of task duration, which differs systematically across the three tasks we investigated. Overall, the best task classification performance was obtained with a set of seven features that included both spatial information (e.g., entropy of attention allocation) and temporal components (e.g., total fixation on objects) of the eye-movement record. This result confirms the task-dependent allocation of visual attention and extends previous work by showing that task classification is possible when tasks differ in the cognitive processes involved (purely visual tasks such as search vs. communicative tasks such as scene description).

Advances in Spectral-Spatial Classification of Hyperspectral Images

NASA Technical Reports Server (NTRS)

Fauvel, Mathieu; Tarabalka, Yuliya; Benediktsson, Jon Atli; Chanussot, Jocelyn; Tilton, James C.

2012-01-01

Recent advances in spectral-spatial classification of hyperspectral images are presented in this paper. Several techniques are investigated for combining both spatial and spectral information. Spatial information is extracted at the object (set of pixels) level rather than at the conventional pixel level. Mathematical morphology is first used to derive the morphological profile of the image, which includes characteristics about the size, orientation and contrast of the spatial structures present in the image. Then the morphological neighborhood is defined and used to derive additional features for classification. Classification is performed with support vector machines using the available spectral information and the extracted spatial information. Spatial post-processing is next investigated to build more homogeneous and spatially consistent thematic maps. To that end, three presegmentation techniques are applied to define regions that are used to regularize the preliminary pixel-wise thematic map. Finally, a multiple classifier system is defined to produce relevant markers that are exploited to segment the hyperspectral image with the minimum spanning forest algorithm. Experimental results conducted on three real hyperspectral images with different spatial and spectral resolutions and corresponding to various contexts are presented. They highlight the importance of spectral-spatial strategies for the accurate classification of hyperspectral images and validate the proposed methods.

Toward semantic-based retrieval of visual information: a model-based approach

NASA Astrophysics Data System (ADS)

Park, Youngchoon; Golshani, Forouzan; Panchanathan, Sethuraman

2002-07-01

This paper center around the problem of automated visual content classification. To enable classification based image or visual object retrieval, we propose a new image representation scheme called visual context descriptor (VCD) that is a multidimensional vector in which each element represents the frequency of a unique visual property of an image or a region. VCD utilizes the predetermined quality dimensions (i.e., types of features and quantization level) and semantic model templates mined in priori. Not only observed visual cues, but also contextually relevant visual features are proportionally incorporated in VCD. Contextual relevance of a visual cue to a semantic class is determined by using correlation analysis of ground truth samples. Such co-occurrence analysis of visual cues requires transformation of a real-valued visual feature vector (e.g., color histogram, Gabor texture, etc.,) into a discrete event (e.g., terms in text). Good-feature to track, rule of thirds, iterative k-means clustering and TSVQ are involved in transformation of feature vectors into unified symbolic representations called visual terms. Similarity-based visual cue frequency estimation is also proposed and used for ensuring the correctness of model learning and matching since sparseness of sample data causes the unstable results of frequency estimation of visual cues. The proposed method naturally allows integration of heterogeneous visual or temporal or spatial cues in a single classification or matching framework, and can be easily integrated into a semantic knowledge base such as thesaurus, and ontology. Robust semantic visual model template creation and object based image retrieval are demonstrated based on the proposed content description scheme.

[Systematization of diseases and lesions of the endometrium by etiological and pathogenetical mechanisms of development to choose an optimal treatment].

PubMed

Boroda, A M

2004-03-01

Current clinical gynecology considers pathological states of endometrium (PSE) as one of the most challenging issue of the day. Many questions of etiology, pathogenesis, diagnostics, and treatment of PSE are still under discussion. Nowadays there isn't a whole agreed classification of PSE. Morphological classification remains the most widely used one, but morphological changes occurring in the endometrium don't show a wide variety of disorders related to these pathological states. A new clinicopathogenetic classification of PSE was proposed, which is based on choosing the optimal treatment with functional state of the disease taken into account. This classification helps us to perceive the problem as a whole with choosing functionally based treatment for each patient.

Visual word ambiguity.

PubMed

van Gemert, Jan C; Veenman, Cor J; Smeulders, Arnold W M; Geusebroek, Jan-Mark

2010-07-01

This paper studies automatic image classification by modeling soft assignment in the popular codebook model. The codebook model describes an image as a bag of discrete visual words selected from a vocabulary, where the frequency distributions of visual words in an image allow classification. One inherent component of the codebook model is the assignment of discrete visual words to continuous image features. Despite the clear mismatch of this hard assignment with the nature of continuous features, the approach has been successfully applied for some years. In this paper, we investigate four types of soft assignment of visual words to image features. We demonstrate that explicitly modeling visual word assignment ambiguity improves classification performance compared to the hard assignment of the traditional codebook model. The traditional codebook model is compared against our method for five well-known data sets: 15 natural scenes, Caltech-101, Caltech-256, and Pascal VOC 2007/2008. We demonstrate that large codebook vocabulary sizes completely deteriorate the performance of the traditional model, whereas the proposed model performs consistently. Moreover, we show that our method profits in high-dimensional feature spaces and reaps higher benefits when increasing the number of image categories.

Multidimensional Shape Similarity in the Development of Visual Object Classification

ERIC Educational Resources Information Center

Mash, Clay

2006-01-01

The current work examined age differences in the classification of novel object images that vary in continuous dimensions of structural shape. The structural dimensions employed are two that share a privileged status in the visual analysis and representation of objects: the shape of discrete prominent parts and the attachment positions of those…

Interactive Classification of Construction Materials: Feedback Driven Framework for Annotation and Analysis of 3d Point Clouds

NASA Astrophysics Data System (ADS)

Hess, M. R.; Petrovic, V.; Kuester, F.

2017-08-01

Digital documentation of cultural heritage structures is increasingly more common through the application of different imaging techniques. Many works have focused on the application of laser scanning and photogrammetry techniques for the acquisition of threedimensional (3D) geometry detailing cultural heritage sites and structures. With an abundance of these 3D data assets, there must be a digital environment where these data can be visualized and analyzed. Presented here is a feedback driven visualization framework that seamlessly enables interactive exploration and manipulation of massive point cloud data. The focus of this work is on the classification of different building materials with the goal of building more accurate as-built information models of historical structures. User defined functions have been tested within the interactive point cloud visualization framework to evaluate automated and semi-automated classification of 3D point data. These functions include decisions based on observed color, laser intensity, normal vector or local surface geometry. Multiple case studies are presented here to demonstrate the flexibility and utility of the presented point cloud visualization framework to achieve classification objectives.

Automatic cell identification and visualization using digital holographic microscopy with head mounted augmented reality devices.

PubMed

O'Connor, Timothy; Rawat, Siddharth; Markman, Adam; Javidi, Bahram

2018-03-01

We propose a compact imaging system that integrates an augmented reality head mounted device with digital holographic microscopy for automated cell identification and visualization. A shearing interferometer is used to produce holograms of biological cells, which are recorded using customized smart glasses containing an external camera. After image acquisition, segmentation is performed to isolate regions of interest containing biological cells in the field-of-view, followed by digital reconstruction of the cells, which is used to generate a three-dimensional (3D) pseudocolor optical path length profile. Morphological features are extracted from the cell's optical path length map, including mean optical path length, coefficient of variation, optical volume, projected area, projected area to optical volume ratio, cell skewness, and cell kurtosis. Classification is performed using the random forest classifier, support vector machines, and K-nearest neighbor, and the results are compared. Finally, the augmented reality device displays the cell's pseudocolor 3D rendering of its optical path length profile, extracted features, and the identified cell's type or class. The proposed system could allow a healthcare worker to quickly visualize cells using augmented reality smart glasses and extract the relevant information for rapid diagnosis. To the best of our knowledge, this is the first report on the integration of digital holographic microscopy with augmented reality devices for automated cell identification and visualization.

Visual brain activity patterns classification with simultaneous EEG-fMRI: A multimodal approach.

PubMed

Ahmad, Rana Fayyaz; Malik, Aamir Saeed; Kamel, Nidal; Reza, Faruque; Amin, Hafeez Ullah; Hussain, Muhammad

2017-01-01

Classification of the visual information from the brain activity data is a challenging task. Many studies reported in the literature are based on the brain activity patterns using either fMRI or EEG/MEG only. EEG and fMRI considered as two complementary neuroimaging modalities in terms of their temporal and spatial resolution to map the brain activity. For getting a high spatial and temporal resolution of the brain at the same time, simultaneous EEG-fMRI seems to be fruitful. In this article, we propose a new method based on simultaneous EEG-fMRI data and machine learning approach to classify the visual brain activity patterns. We acquired EEG-fMRI data simultaneously on the ten healthy human participants by showing them visual stimuli. Data fusion approach is used to merge EEG and fMRI data. Machine learning classifier is used for the classification purposes. Results showed that superior classification performance has been achieved with simultaneous EEG-fMRI data as compared to the EEG and fMRI data standalone. This shows that multimodal approach improved the classification accuracy results as compared with other approaches reported in the literature. The proposed simultaneous EEG-fMRI approach for classifying the brain activity patterns can be helpful to predict or fully decode the brain activity patterns.

An optimal transportation approach for nuclear structure-based pathology.

PubMed

Wang, Wei; Ozolek, John A; Slepčev, Dejan; Lee, Ann B; Chen, Cheng; Rohde, Gustavo K

2011-03-01

Nuclear morphology and structure as visualized from histopathology microscopy images can yield important diagnostic clues in some benign and malignant tissue lesions. Precise quantitative information about nuclear structure and morphology, however, is currently not available for many diagnostic challenges. This is due, in part, to the lack of methods to quantify these differences from image data. We describe a method to characterize and contrast the distribution of nuclear structure in different tissue classes (normal, benign, cancer, etc.). The approach is based on quantifying chromatin morphology in different groups of cells using the optimal transportation (Kantorovich-Wasserstein) metric in combination with the Fisher discriminant analysis and multidimensional scaling techniques. We show that the optimal transportation metric is able to measure relevant biological information as it enables automatic determination of the class (e.g., normal versus cancer) of a set of nuclei. We show that the classification accuracies obtained using this metric are, on average, as good or better than those obtained utilizing a set of previously described numerical features. We apply our methods to two diagnostic challenges for surgical pathology: one in the liver and one in the thyroid. Results automatically computed using this technique show potentially biologically relevant differences in nuclear structure in liver and thyroid cancers.

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

NASA Astrophysics Data System (ADS)

Mohammad Alipour, Shirin Hajeb; Rabbani, Hossein

2013-09-01

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

An optimal transportation approach for nuclear structure-based pathology

PubMed Central

Wang, Wei; Ozolek, John A.; Slepčev, Dejan; Lee, Ann B.; Chen, Cheng; Rohde, Gustavo K.

2012-01-01

Nuclear morphology and structure as visualized from histopathology microscopy images can yield important diagnostic clues in some benign and malignant tissue lesions. Precise quantitative information about nuclear structure and morphology, however, is currently not available for many diagnostic challenges. This is due, in part, to the lack of methods to quantify these differences from image data. We describe a method to characterize and contrast the distribution of nuclear structure in different tissue classes (normal, benign, cancer, etc.). The approach is based on quantifying chromatin morphology in different groups of cells using the optimal transportation (Kantorovich-Wasserstein) metric in combination with the Fisher discriminant analysis and multidimensional scaling techniques. We show that the optimal transportation metric is able to measure relevant biological information as it enables automatic determination of the class (e.g. normal vs. cancer) of a set of nuclei. We show that the classification accuracies obtained using this metric are, on average, as good or better than those obtained utilizing a set of previously described numerical features. We apply our methods to two diagnostic challenges for surgical pathology: one in the liver and one in the thyroid. Results automatically computed using this technique show potentially biologically relevant differences in nuclear structure in liver and thyroid cancers. PMID:20977984

Comprehensive decision tree models in bioinformatics.

PubMed

Stiglic, Gregor; Kocbek, Simon; Pernek, Igor; Kokol, Peter

2012-01-01

Classification is an important and widely used machine learning technique in bioinformatics. Researchers and other end-users of machine learning software often prefer to work with comprehensible models where knowledge extraction and explanation of reasoning behind the classification model are possible. This paper presents an extension to an existing machine learning environment and a study on visual tuning of decision tree classifiers. The motivation for this research comes from the need to build effective and easily interpretable decision tree models by so called one-button data mining approach where no parameter tuning is needed. To avoid bias in classification, no classification performance measure is used during the tuning of the model that is constrained exclusively by the dimensions of the produced decision tree. The proposed visual tuning of decision trees was evaluated on 40 datasets containing classical machine learning problems and 31 datasets from the field of bioinformatics. Although we did not expected significant differences in classification performance, the results demonstrate a significant increase of accuracy in less complex visually tuned decision trees. In contrast to classical machine learning benchmarking datasets, we observe higher accuracy gains in bioinformatics datasets. Additionally, a user study was carried out to confirm the assumption that the tree tuning times are significantly lower for the proposed method in comparison to manual tuning of the decision tree. The empirical results demonstrate that by building simple models constrained by predefined visual boundaries, one not only achieves good comprehensibility, but also very good classification performance that does not differ from usually more complex models built using default settings of the classical decision tree algorithm. In addition, our study demonstrates the suitability of visually tuned decision trees for datasets with binary class attributes and a high number of possibly redundant attributes that are very common in bioinformatics.

Comprehensive Decision Tree Models in Bioinformatics

PubMed Central

Stiglic, Gregor; Kocbek, Simon; Pernek, Igor; Kokol, Peter

2012-01-01

Purpose Classification is an important and widely used machine learning technique in bioinformatics. Researchers and other end-users of machine learning software often prefer to work with comprehensible models where knowledge extraction and explanation of reasoning behind the classification model are possible. Methods This paper presents an extension to an existing machine learning environment and a study on visual tuning of decision tree classifiers. The motivation for this research comes from the need to build effective and easily interpretable decision tree models by so called one-button data mining approach where no parameter tuning is needed. To avoid bias in classification, no classification performance measure is used during the tuning of the model that is constrained exclusively by the dimensions of the produced decision tree. Results The proposed visual tuning of decision trees was evaluated on 40 datasets containing classical machine learning problems and 31 datasets from the field of bioinformatics. Although we did not expected significant differences in classification performance, the results demonstrate a significant increase of accuracy in less complex visually tuned decision trees. In contrast to classical machine learning benchmarking datasets, we observe higher accuracy gains in bioinformatics datasets. Additionally, a user study was carried out to confirm the assumption that the tree tuning times are significantly lower for the proposed method in comparison to manual tuning of the decision tree. Conclusions The empirical results demonstrate that by building simple models constrained by predefined visual boundaries, one not only achieves good comprehensibility, but also very good classification performance that does not differ from usually more complex models built using default settings of the classical decision tree algorithm. In addition, our study demonstrates the suitability of visually tuned decision trees for datasets with binary class attributes and a high number of possibly redundant attributes that are very common in bioinformatics. PMID:22479449

Algorithms for Hyperspectral Endmember Extraction and Signature Classification with Morphological Dendritic Networks

NASA Astrophysics Data System (ADS)

Schmalz, M.; Ritter, G.

Accurate multispectral or hyperspectral signature classification is key to the nonimaging detection and recognition of space objects. Additionally, signature classification accuracy depends on accurate spectral endmember determination [1]. Previous approaches to endmember computation and signature classification were based on linear operators or neural networks (NNs) expressed in terms of the algebra (R, +, x) [1,2]. Unfortunately, class separation in these methods tends to be suboptimal, and the number of signatures that can be accurately classified often depends linearly on the number of NN inputs. This can lead to poor endmember distinction, as well as potentially significant classification errors in the presence of noise or densely interleaved signatures. In contrast to traditional CNNs, autoassociative morphological memories (AMM) are a construct similar to Hopfield autoassociatived memories defined on the (R, +, ?,?) lattice algebra [3]. Unlimited storage and perfect recall of noiseless real valued patterns has been proven for AMMs [4]. However, AMMs suffer from sensitivity to specific noise models, that can be characterized as erosive and dilative noise. On the other hand, the prior definition of a set of endmembers corresponds to material spectra lying on vertices of the minimum convex region covering the image data. These vertices can be characterized as morphologically independent patterns. It has further been shown that AMMs can be based on dendritic computation [3,6]. These techniques yield improved accuracy and class segmentation/separation ability in the presence of highly interleaved signature data. In this paper, we present a procedure for endmember determination based on AMM noise sensitivity, which employs morphological dendritic computation. We show that detected endmembers can be exploited by AMM based classification techniques, to achieve accurate signature classification in the presence of noise, closely spaced or interleaved signatures, and simulated camera optical distortions. In particular, we examine two critical cases: (1) classification of multiple closely spaced signatures that are difficult to separate using distance measures, and (2) classification of materials in simulated hyperspectral images of spaceborne satellites. In each case, test data are derived from a NASA database of space material signatures. Additional analysis pertains to computational complexity and noise sensitivity, which are superior to classical NN based techniques.

Comprehensive 4-stage categorization of bicuspid aortic valve leaflet morphology by cardiac MRI in 386 patients.

PubMed

Murphy, I G; Collins, J; Powell, A; Markl, M; McCarthy, P; Malaisrie, S C; Carr, J C; Barker, A J

2017-08-01

Bicuspid aortic valve (BAV) disease is heterogeneous and related to valve dysfunction and aortopathy. Appropriate follow up and surveillance of patients with BAV may depend on correct phenotypic categorization. There are multiple classification schemes, however a need exists to comprehensively capture commissure fusion, leaflet asymmetry, and valve orifice orientation. Our aim was to develop a BAV classification scheme for use at MRI to ascertain the frequency of different phenotypes and the consistency of BAV classification. The BAV classification scheme builds on the Sievers surgical BAV classification, adding valve orifice orientation, partial leaflet fusion and leaflet asymmetry. A single observer successfully applied this classification to 386 of 398 Cardiac MRI studies. Repeatability of categorization was ascertained with intraobserver and interobserver kappa scores. Sensitivity and specificity of MRI findings was determined from operative reports, where available. Fusion of the right and left leaflets accounted for over half of all cases. Partial leaflet fusion was seen in 46% of patients. Good interobserver agreement was seen for orientation of the valve opening (κ = 0.90), type (κ = 0.72) and presence of partial fusion (κ = 0.83, p < 0.0001). Retrospective review of operative notes showed sensitivity and specificity for orientation (90, 93%) and for Sievers type (73, 87%). The proposed BAV classification schema was assessed by MRI for its reliability to classify valve morphology in addition to illustrating the wide heterogeneity of leaflet size, orifice orientation, and commissural fusion. The classification may be helpful in further understanding the relationship between valve morphology, flow derangement and aortopathy.

Expert identification of visual primitives used by CNNs during mammogram classification

NASA Astrophysics Data System (ADS)

Wu, Jimmy; Peck, Diondra; Hsieh, Scott; Dialani, Vandana; Lehman, Constance D.; Zhou, Bolei; Syrgkanis, Vasilis; Mackey, Lester; Patterson, Genevieve

2018-02-01

This work interprets the internal representations of deep neural networks trained for classification of diseased tissue in 2D mammograms. We propose an expert-in-the-loop inter- pretation method to label the behavior of internal units in convolutional neural networks (CNNs). Expert radiologists identify that the visual patterns detected by the units are correlated with meaningful medical phenomena such as mass tissue and calcificated vessels. We demonstrate that several trained CNN models are able to produce explanatory descriptions to support the final classification decisions. We view this as an important first step toward interpreting the internal representations of medical classification CNNs and explaining their predictions.

[Results of the use of antioxidant and angioprotective agents in type 2 diabetes patients with diabetic retinopathy and age-related macular degeneration].

PubMed

Moshetova, L K; Vorob'eva, I V; Alekseev, I B; Mikhaleva, L G

2015-01-01

to investigate changes in clinical, functional, and morphological parameters of the retina in type 2 diabetes patients with diabetic retinopathy (DR) and those with combined fundus pathology (DR plus age-related macular degeneration (AMD)) before and after a course of antioxidants and angioprotectors in the form of mono- or combination therapy. The study included 180 patients (180 eyes) with type 2 diabetes divided into 6 groups of 30 each. DR was graded according to E. Kohner and M. Porta classification, AMD--AREDS classification. Thus, group 1 consisted of patients with DRO,; group 2--DR1 without DM, group 3--DR1 with DM, group 4--DRO and "dry" AMD (AREDS 1-3), group 5--DR1 with no DM but with AMD (AREDS 1-3), and group 6--DR1 with DM and AMD (AREDS 1-3). A drug containing lutein 6 mg, zeaxanthin 0.5 mg, vitamin C 60 mg, vitamin E 7 mg, vitamin A 1.5 mg, vitamin B2 1.2 mg, rutin 25 mg, zinc 5 mg, selenium 25 mcg, and bilberry extract 60 mg was used for antioxidative therapy. Ginkgo biloba leaf extract 60 mg was chosen as the angioprotector. In all patients visual acuity, macular thickness and morphology (OCT) as well as light sensitivity (microperimetry) were assessed before and after the treatment course. Analysis of baseline measurements demonstrated a significant decrease in best corrected visual acuity (p < 0.05) in study groups 2-6 as compared with group 1. Macular thickness was increased in all groups, however, the changes were statistically significant only in groups 3 and 6 (p<0.05). Light sensitivity of the macula showed a reduction, which was statistically significant in groups 4-6 (p < 0.05). After the course of antioxidant and angioprotective therapy, these parameters improved in all groups. The greatest effect was achieved with simultaneous antioxidant and double-dose angioprotective therapy (240 mg per day): visual acuity increased significantly (p < 0.05) in all groups except group 1; macular thickness decreased in all groups, however, the changes were statistically significant (p < 0.05) only in groups 1-3 and 5; light sensitivity also improved in all groups, significantly (p < 0.05) in groups 1-3 and 4. Extended analysis of clinical, functional and morphological changes in the retina at the onset of DR in type 2 diabetes patients with concomitant "dry" AMD enables timely diagnosis, prognosis, prevention, and early treatment. Conservative treatment with antioxidant and angioprotective agents has been proved effective in type 2 diabetes patients with preclinical (DRO) and early (DR1) diabetic retinopathy and those with DR and "dry" AMD (AREDS 1-3) in terms of functional and morphological parameters of the retina. From all the regimens, a combined antioxidant and double-dose angioprotective (240 mg) therapy appeared to be the most effective and can be considered not only a preventive, but also a therapeutic measure in type 2 diabetes patients with initial stages of DR (DRO, DR1) or those with DR and DM or combined DR and AMD (AREDS 1-3).

Spectroscopic classification of icy satellites of Saturn II: Identification of terrain units on Rhea

NASA Astrophysics Data System (ADS)

Scipioni, F.; Tosi, F.; Stephan, K.; Filacchione, G.; Ciarniello, M.; Capaccioni, F.; Cerroni, P.

2014-05-01

Rhea is the second largest icy satellites of Saturn and it is mainly composed of water ice. Its surface is characterized by a leading hemisphere slightly brighter than the trailing side. The main goal of this work is to identify homogeneous compositional units on Rhea by applying the Spectral Angle Mapper (SAM) classification technique to Rhea’s hyperspectral images acquired by the Visual and Infrared Mapping Spectrometer (VIMS) onboard the Cassini Orbiter in the infrared range (0.88-5.12 μm). The first step of the classification is dedicated to the identification of Rhea’s spectral endmembers by applying the k-means unsupervised clustering technique to four hyperspectral images representative of a limited portion of the surface, imaged at relatively high spatial resolution. We then identified eight spectral endmembers, corresponding to as many terrain units, which mostly distinguish for water ice abundance and ice grain size. In the second step, endmembers are used as reference spectra in SAM classification method to achieve a comprehensive classification of the entire surface. From our analysis of the infrared spectra returned by VIMS, it clearly emerges that Rhea’ surface units shows differences in terms of water ice bands depths, average ice grain size, and concentration of contaminants, particularly CO2 and hydrocarbons. The spectral units that classify optically dark terrains are those showing suppressed water ice bands, a finer ice grain size and a higher concentration of carbon dioxide. Conversely, spectral units labeling brighter regions have deeper water ice absorption bands, higher albedo and a smaller concentration of contaminants. All these variations reflect surface’s morphological and geological structures. Finally, we performed a comparison between Rhea and Dione, to highlight different magnitudes of space weathering effects in the icy satellites as a function of the distance from Saturn.

Automated morphological analysis of bone marrow cells in microscopic images for diagnosis of leukemia: nucleus-plasma separation and cell classification using a hierarchical tree model of hematopoesis

NASA Astrophysics Data System (ADS)

Krappe, Sebastian; Wittenberg, Thomas; Haferlach, Torsten; Münzenmayer, Christian

2016-03-01

The morphological differentiation of bone marrow is fundamental for the diagnosis of leukemia. Currently, the counting and classification of the different types of bone marrow cells is done manually under the use of bright field microscopy. This is a time-consuming, subjective, tedious and error-prone process. Furthermore, repeated examinations of a slide may yield intra- and inter-observer variances. For that reason a computer assisted diagnosis system for bone marrow differentiation is pursued. In this work we focus (a) on a new method for the separation of nucleus and plasma parts and (b) on a knowledge-based hierarchical tree classifier for the differentiation of bone marrow cells in 16 different classes. Classification trees are easily interpretable and understandable and provide a classification together with an explanation. Using classification trees, expert knowledge (i.e. knowledge about similar classes and cell lines in the tree model of hematopoiesis) is integrated in the structure of the tree. The proposed segmentation method is evaluated with more than 10,000 manually segmented cells. For the evaluation of the proposed hierarchical classifier more than 140,000 automatically segmented bone marrow cells are used. Future automated solutions for the morphological analysis of bone marrow smears could potentially apply such an approach for the pre-classification of bone marrow cells and thereby shortening the examination time.

"Relative CIR": an image enhancement and visualization technique

USGS Publications Warehouse

Fleming, Michael D.

1993-01-01

Many techniques exist to spectrally and spatially enhance digital multispectral scanner data. One technique enhances an image while keeping the colors as they would appear in a color-infrared (CIR) image. This "relative CIR" technique generates an image that is both spectrally and spatially enhanced, while displaying a maximum range of colors. The technique enables an interpreter to visualize either spectral or land cover classes by their relative CIR characteristics. A relative CIR image is generated by developed spectral statistics for each class in the classifications and then, using a nonparametric approach for spectral enhancement, the means of the classes for each band are ranked. A 3 by 3 pixel smoothing filter is applied to the classification for spatial enhancement and the classes are mapped to the representative rank for each band. Practical applications of the technique include displaying an image classification product as a CIR image that was not derived directly from a spectral image, visualizing how a land cover classification would look as a CIR image, and displaying a spectral classification or intermediate product that will be used to label spectral classes.

Graphtables

DTIC Science & Technology

2015-06-01

Visualization, Graph Navigation, Visual Literacy 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT UU 18. NUMBER OF PAGES 19a. NAME OF...3 2.3. Visual Literacy ...obscured and individual edges that could be traversed before bundled are now completely lost among the bundled edges. 2.3. Visual Literacy Visual

Multilocus resolution of Mugilidae phylogeny (Teleostei: Mugiliformes): Implications for the family's taxonomy.

PubMed

Xia, Rong; Durand, Jean-Dominique; Fu, Cuizhang

2016-03-01

The interrelationships among mugilids (Mugiliformes: Mugilidae) remain highly debated. Using a mitochondrial gene-based phylogeny as criterion, a revised classification with 25 genera in the Mugilidae has recently been proposed. However, phylogenetic relationships of major mitochondrial lineages remain unresolved and to gain a general acceptance the classification requires confirmation based on multilocus evidence and diagnostic morphological characters. Here, we construct a species-tree using twelve nuclear and three mitochondrial loci and infer the evolution of 71 morphological characters. Our multilocus phylogeny does not agree with previous morphology-based hypotheses for the relationships within Mugilidae, confirms the revised classification with 25 genera and further resolves their phylogenetic relationships. Using the well-resolved multilocus phylogeny as the criterion, we reclassify Mugilidae genera into three new subfamilies (Myxinae, Rhinomugilinae, and Cheloninae) and one new, recombined, subfamily (Mugilinae). The Rhinomugilinae subfamily is further divided into four tribes. The revised classification of Mugilidae is supported by morpho-anatomical synapomorphies or a combination of characters. These characters are used to erect a key to the subfamilies and genera. Copyright © 2015 Elsevier Inc. All rights reserved.

Visual Recognition Software for Binary Classification and its Application to Pollen Identification

NASA Astrophysics Data System (ADS)

Punyasena, S. W.; Tcheng, D. K.; Nayak, A.

2014-12-01

An underappreciated source of uncertainty in paleoecology is the uncertainty of palynological identifications. The confidence of any given identification is not regularly reported in published results, so cannot be incorporated into subsequent meta-analyses. Automated identifications systems potentially provide a means of objectively measuring the confidence of a given count or single identification, as well as a mechanism for increasing sample sizes and throughput. We developed the software ARLO (Automated Recognition with Layered Optimization) to tackle difficult visual classification problems such as pollen identification. ARLO applies pattern recognition and machine learning to the analysis of pollen images. The features that the system discovers are not the traditional features of pollen morphology. Instead, general purpose image features, such as pixel lines and grids of different dimensions, size, spacing, and resolution, are used. ARLO adapts to a given problem by searching for the most effective combination of feature representation and learning strategy. We present a two phase approach which uses our machine learning process to first segment pollen grains from the background and then classify pollen pixels and report species ratios. We conducted two separate experiments that utilized two distinct sets of algorithms and optimization procedures. The first analysis focused on reconstructing black and white spruce pollen ratios, training and testing our classification model at the slide level. This allowed us to directly compare our automated counts and expert counts to slides of known spruce ratios. Our second analysis focused on maximizing classification accuracy at the individual pollen grain level. Instead of predicting ratios of given slides, we predicted the species represented in a given image window. The resulting analysis was more scalable, as we were able to adapt the most efficient parts of the methodology from our first analysis. ARLO was able to distinguish between the pollen of black and white spruce with an accuracy of ~83.61%. This compared favorably to human expert performance. At the writing of this abstract, we are also experimenting with experimenting with the analysis of higher diversity samples, including modern tropical pollen material collected from ground pollen traps.

Intraoperative neuropathology of glioma recurrence: cell detection and classification

NASA Astrophysics Data System (ADS)

Abas, Fazly S.; Gokozan, Hamza N.; Goksel, Behiye; Otero, Jose J.; Gurcan, Metin N.

2016-03-01

Intraoperative neuropathology of glioma recurrence represents significant visual challenges to pathologists as they carry significant clinical implications. For example, rendering a diagnosis of recurrent glioma can help the surgeon decide to perform more aggressive resection if surgically appropriate. In addition, the success of recent clinical trials for intraoperative administration of therapies, such as inoculation with oncolytic viruses, may suggest that refinement of the intraoperative diagnosis during neurosurgery is an emerging need for pathologists. Typically, these diagnoses require rapid/STAT processing lasting only 20-30 minutes after receipt from neurosurgery. In this relatively short time frame, only dyes, such as hematoxylin and eosin (H and E), can be implemented. The visual challenge lies in the fact that these patients have undergone chemotherapy and radiation, both of which induce cytological atypia in astrocytes, and pathologists are unable to implement helpful biomarkers in their diagnoses. Therefore, there is a need to help pathologists differentiate between astrocytes that are cytologically atypical due to treatment versus infiltrating, recurrent, neoplastic astrocytes. This study focuses on classification of neoplastic versus non-neoplastic astrocytes with the long term goal of providing a better neuropathological computer-aided consultation via classification of cells into reactive gliosis versus recurrent glioma. We present a method to detect cells in H and E stained digitized slides of intraoperative cytologic preparations. The method uses a combination of the `value' component of the HSV color space and `b*' component of the CIE L*a*b* color space to create an enhanced image that suppresses the background while revealing cells on an image. A composite image is formed based on the morphological closing of the hue-luminance combined image. Geometrical and textural features extracted from Discrete Wavelet Frames and combined to classify cells into neoplastic and non-neoplastic categories. Experimental results show that there is a strong consensus between the proposed method's cell detection markings with those of the pathologist's. Experiments on 48 images from six patients resulted in F1-score as high as 87.48%, 88.08% and 86.12% for Reader 1, Reader 2 and the reader consensus, respectively. Classification results showed that for both readers, binary classification tree and support vector machine performed the best with F1-scores ranging 0.92 to 0.94.

The new WHO 2016 classification of brain tumors-what neurosurgeons need to know.

PubMed

Banan, Rouzbeh; Hartmann, Christian

2017-03-01

The understanding of molecular alterations of tumors has severely changed the concept of classification in all fields of pathology. The availability of high-throughput technologies such as next-generation sequencing allows for a much more precise definition of tumor entities. Also in the field of brain tumors a dramatic increase of knowledge has occurred over the last years partially calling into question the purely morphologically based concepts that were used as exclusive defining criteria in the WHO 2007 classification. Review of the WHO 2016 classification of brain tumors as well as a search and review of publications in the literature relevant for brain tumor classification from 2007 up to now. The idea of incorporating the molecular features in classifying tumors of the central nervous system led the authors of the new WHO 2016 classification to encounter inevitable conceptual problems, particularly with respect to linking morphology to molecular alterations. As a solution they introduced the concept of a "layered diagnosis" to the classification of brain tumors that still allows at a lower level a purely morphologically based diagnosis while partially forcing the incorporation of molecular characteristics for an "integrated diagnosis" at the highest diagnostic level. In this context the broad availability of molecular assays was debated. On the one hand molecular antibodies specifically targeting mutated proteins should be available in nearly all neuropathological laboratories. On the other hand, different high-throughput assays are accessible only in few first-world neuropathological institutions. As examples oligodendrogliomas are now primarily defined by molecular characteristics since the required assays are generally established, whereas molecular grouping of ependymomas, found to clearly outperform morphologically based tumor interpretation, was rejected from inclusion in the WHO 2016 classification because the required assays are currently only established in a small number of institutions. In summary, while neuropathologists have now encountered various challenges in the transitional phase from the previous WHO 2007 version to the new WHO 2016 classification of brain tumors, clinical neurooncologists now face many new diagnoses allowing a clearly improved understanding that could offer them more effective therapeutic opportunities in neurooncological treatment. The new WHO 2016 classification presumably presents the highest number of modifications since the initial WHO classification of 1979 and thereby forces all professionals in the field of neurooncology to intensively understand the new concepts. This review article aims to present the basic concepts of the new WHO 2016 brain tumor classification for neurosurgeons with a focus on neurooncology.

A New Classification System for Unilateral Cleft Lip and Palate Infants to assist Presurgical Infant Orthopedics.

PubMed

Daigavane, P S; Hazarey, P V; Niranjane, P; Vasudevan, S D; Thombare, B R; Daigavane, S

2015-01-01

The proposed advantages of pre-surgical naso-alveolar moulding (PNAM) are easy primary lip repair which heals under minimum tension reducing the scar formation and improving the aesthetic results in addition to reshaping of alar cartilage and improvement of nasal symmetry.However, the anatomy and alveolar morphology varies for each cleft child; the procedure for PNAM differs accordingly. In an attempt to categorize unilateral cleft lip and palate cases as per anatomical variations, a new classification system has been proposed. This classification aims to give an insight in unilateral cleft morphology based on which modification in PNAM procedure could be done.

Automatic classification of thermal patterns in diabetic foot based on morphological pattern spectrum

NASA Astrophysics Data System (ADS)

Hernandez-Contreras, D.; Peregrina-Barreto, H.; Rangel-Magdaleno, J.; Ramirez-Cortes, J.; Renero-Carrillo, F.

2015-11-01

This paper presents a novel approach to characterize and identify patterns of temperature in thermographic images of the human foot plant in support of early diagnosis and follow-up of diabetic patients. Composed feature vectors based on 3D morphological pattern spectrum (pecstrum) and relative position, allow the system to quantitatively characterize and discriminate non-diabetic (control) and diabetic (DM) groups. Non-linear classification using neural networks is used for that purpose. A classification rate of 94.33% in average was obtained with the composed feature extraction process proposed in this paper. Performance evaluation and obtained results are presented.

Neural Correlates of Morphological Decomposition in a Morphologically Rich Language: An fMRI Study

ERIC Educational Resources Information Center

Lehtonen, Minna; Vorobyev, Victor A.; Hugdahl, Kenneth; Tuokkola, Terhi; Laine, Matti

2006-01-01

By employing visual lexical decision and functional MRI, we studied the neural correlates of morphological decomposition in a highly inflected language (Finnish) where most inflected noun forms elicit a consistent processing cost during word recognition. This behavioral effect could reflect suffix stripping at the visual word form level and/or…

Mining CANDELS for Tidal Features to Constrain Major Merging During Cosmic Noon

NASA Astrophysics Data System (ADS)

McIntosh, Daniel H.; Mantha, Kameswara; Ciaschi, Cody; Evan, Rubyet A.; Fries, Logan B.; Landry, Luther; Thompson, Scott E.; Snyder, Gregory; Guo, Yicheng; Ceverino, Daniel; Häuβler, Boris; Primack, Joel; Simons, Raymond C.; Zheng, Xianzhong; Cosmic Assembly Near-Infrared Deep Extragalactic Legacy Survey (CANDELS) Team

2018-01-01

The role of major merging in the rapid buildup and development of massive galaxies at z>1 remains an open question. New theories and observations suggest that non-merging processes like violent disk instabilities may be more vital than previously thought at assembling bulges, producing clumps, and inducing morphological disturbances that may be misinterpreted as the product of major merging. We will present initial results on a systematic search for hallmark tidal indicators of major merging in a complete sample of nearly 6000 massive z>1 galaxies from CANDELS (Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey), the premiere HST/WFC3 Treasury program. We have visually inspected published GALFIT F160W residual (image-model) maps and produced a comprehensive new catalog of Sersic residual characteristics based on a variety of natural features and poor-fit artifacts. Using this catalog, we find the frequency of galaxies with tidal signatures is very small in CANDELS data. Accounting for the brief time scale associated with faint transient tidal features, our preliminary finding indicates that merger fractions derived from the CANDELS morphological classification efforts are substantially overestimated. We are using the database of residual classifications as a baseline to (1) produce improved multi-component residual maps using GALFIT_M, (2) automatically extract and quantify plausible tidal indicators and substructures (clumps vs. multiple nuclei), (3) develop a new deep-learning classification pipeline to robustly identify merger indicators in imaging data, and (4) inform the systematic analyses of synthetic mock (CANDELized) images from zoom-in hydrodynamic simulations to thoroughly quantify the impacts of cosmological dimming, and calibrate the observability timescale of tidal feature detections. Our study will ultimately yield novel constraints on merger rates at z>1 and a definitive census of massive high-noon galaxies with tidal and double-nuclei merging signatures in rest-frame optical HST imaging.

Automated classification of bone marrow cells in microscopic images for diagnosis of leukemia: a comparison of two classification schemes with respect to the segmentation quality

NASA Astrophysics Data System (ADS)

Krappe, Sebastian; Benz, Michaela; Wittenberg, Thomas; Haferlach, Torsten; Münzenmayer, Christian

2015-03-01

The morphological analysis of bone marrow smears is fundamental for the diagnosis of leukemia. Currently, the counting and classification of the different types of bone marrow cells is done manually with the use of bright field microscope. This is a time consuming, partly subjective and tedious process. Furthermore, repeated examinations of a slide yield intra- and inter-observer variances. For this reason an automation of morphological bone marrow analysis is pursued. This analysis comprises several steps: image acquisition and smear detection, cell localization and segmentation, feature extraction and cell classification. The automated classification of bone marrow cells is depending on the automated cell segmentation and the choice of adequate features extracted from different parts of the cell. In this work we focus on the evaluation of support vector machines (SVMs) and random forests (RFs) for the differentiation of bone marrow cells in 16 different classes, including immature and abnormal cell classes. Data sets of different segmentation quality are used to test the two approaches. Automated solutions for the morphological analysis for bone marrow smears could use such a classifier to pre-classify bone marrow cells and thereby shortening the examination duration.

Advances in Spectral-Spatial Classification of Hyperspectral Images

NASA Technical Reports Server (NTRS)

Fauvel, Mathieu; Tarabalka, Yuliya; Benediktsson, Jon Atli; Chanussot, Jocelyn; Tilton, James C.

2012-01-01

Recent advances in spectral-spatial classification of hyperspectral images are presented in this paper. Several techniques are investigated for combining both spatial and spectral information. Spatial information is extracted at the object (set of pixels) level rather than at the conventional pixel level. Mathematical morphology is first used to derive the morphological profile of the image, which includes characteristics about the size, orientation, and contrast of the spatial structures present in the image. Then, the morphological neighborhood is defined and used to derive additional features for classification. Classification is performed with support vector machines (SVMs) using the available spectral information and the extracted spatial information. Spatial postprocessing is next investigated to build more homogeneous and spatially consistent thematic maps. To that end, three presegmentation techniques are applied to define regions that are used to regularize the preliminary pixel-wise thematic map. Finally, a multiple-classifier (MC) system is defined to produce relevant markers that are exploited to segment the hyperspectral image with the minimum spanning forest algorithm. Experimental results conducted on three real hyperspectral images with different spatial and spectral resolutions and corresponding to various contexts are presented. They highlight the importance of spectral–spatial strategies for the accurate classification of hyperspectral images and validate the proposed methods.

Comparison Between Supervised and Unsupervised Classifications of Neuronal Cell Types: A Case Study

PubMed Central

Guerra, Luis; McGarry, Laura M; Robles, Víctor; Bielza, Concha; Larrañaga, Pedro; Yuste, Rafael

2011-01-01

In the study of neural circuits, it becomes essential to discern the different neuronal cell types that build the circuit. Traditionally, neuronal cell types have been classified using qualitative descriptors. More recently, several attempts have been made to classify neurons quantitatively, using unsupervised clustering methods. While useful, these algorithms do not take advantage of previous information known to the investigator, which could improve the classification task. For neocortical GABAergic interneurons, the problem to discern among different cell types is particularly difficult and better methods are needed to perform objective classifications. Here we explore the use of supervised classification algorithms to classify neurons based on their morphological features, using a database of 128 pyramidal cells and 199 interneurons from mouse neocortex. To evaluate the performance of different algorithms we used, as a “benchmark,” the test to automatically distinguish between pyramidal cells and interneurons, defining “ground truth” by the presence or absence of an apical dendrite. We compared hierarchical clustering with a battery of different supervised classification algorithms, finding that supervised classifications outperformed hierarchical clustering. In addition, the selection of subsets of distinguishing features enhanced the classification accuracy for both sets of algorithms. The analysis of selected variables indicates that dendritic features were most useful to distinguish pyramidal cells from interneurons when compared with somatic and axonal morphological variables. We conclude that supervised classification algorithms are better matched to the general problem of distinguishing neuronal cell types when some information on these cell groups, in our case being pyramidal or interneuron, is known a priori. As a spin-off of this methodological study, we provide several methods to automatically distinguish neocortical pyramidal cells from interneurons, based on their morphologies. © 2010 Wiley Periodicals, Inc. Develop Neurobiol 71: 71–82, 2011 PMID:21154911

Very Deep Convolutional Neural Networks for Morphologic Classification of Erythrocytes.

PubMed

Durant, Thomas J S; Olson, Eben M; Schulz, Wade L; Torres, Richard

2017-12-01

Morphologic profiling of the erythrocyte population is a widely used and clinically valuable diagnostic modality, but one that relies on a slow manual process associated with significant labor cost and limited reproducibility. Automated profiling of erythrocytes from digital images by capable machine learning approaches would augment the throughput and value of morphologic analysis. To this end, we sought to evaluate the performance of leading implementation strategies for convolutional neural networks (CNNs) when applied to classification of erythrocytes based on morphology. Erythrocytes were manually classified into 1 of 10 classes using a custom-developed Web application. Using recent literature to guide architectural considerations for neural network design, we implemented a "very deep" CNN, consisting of >150 layers, with dense shortcut connections. The final database comprised 3737 labeled cells. Ensemble model predictions on unseen data demonstrated a harmonic mean of recall and precision metrics of 92.70% and 89.39%, respectively. Of the 748 cells in the test set, 23 misclassification errors were made, with a correct classification frequency of 90.60%, represented as a harmonic mean across the 10 morphologic classes. These findings indicate that erythrocyte morphology profiles could be measured with a high degree of accuracy with "very deep" CNNs. Further, these data support future efforts to expand classes and optimize practical performance in a clinical environment as a prelude to full implementation as a clinical tool. © 2017 American Association for Clinical Chemistry.

Classification of threespine stickleback along the benthic-limnetic axis.

PubMed

Willacker, James J; von Hippel, Frank A; Wilton, Peter R; Walton, Kelly M

2010-11-01

Many species of fish display morphological divergence between individuals feeding on macroinvertebrates associated with littoral habitats (benthic morphotypes) and individuals feeding on zooplankton in the limnetic zone (limnetic morphotypes). Threespine stickleback (Gasterosteus aculeatus L.) have diverged along the benthic-limnetic axis into allopatric morphotypes in thousands of populations and into sympatric species pairs in several lakes. However, only a few well known populations have been studied because identifying additional populations as either benthic or limnetic requires detailed dietary or observational studies. Here we develop a Fisher's linear discriminant function based on the skull morphology of known benthic and limnetic stickleback populations from the Cook Inlet Basin of Alaska and test the feasibility of using this function to identify other morphologically divergent populations. Benthic and limnetic morphotypes were separable using this technique and of 45 populations classified, three were identified as morphologically extreme (two benthic and one limnetic), nine as moderately divergent (three benthic and six limnetic) and the remaining 33 populations as morphologically intermediate. Classification scores were found to correlate with eye size, the depth profile of lakes, and the presence of invasive northern pike (Esox lucius). This type of classification function provides a means of integrating the complex morphological differences between morphotypes into a single score that reflects the position of a population along the benthic-limnetic axis and can be used to relate that position to other aspects of stickleback biology.

Classification of threespine stickleback along the benthic-limnetic axis

PubMed Central

Willacker, James J.; von Hippel, Frank A.; Wilton, Peter R.; Walton, Kelly M.

2010-01-01

Many species of fish display morphological divergence between individuals feeding on macroinvertebrates associated with littoral habitats (benthic morphotypes) and individuals feeding on zooplankton in the limnetic zone (limnetic morphotypes). Threespine stickleback (Gasterosteus aculeatus L.) have diverged along the benthic-limnetic axis into allopatric morphotypes in thousands of populations and into sympatric species pairs in several lakes. However, only a few well known populations have been studied because identifying additional populations as either benthic or limnetic requires detailed dietary or observational studies. Here we develop a Fisher’s linear discriminant function based on the skull morphology of known benthic and limnetic stickleback populations from the Cook Inlet Basin of Alaska and test the feasibility of using this function to identify other morphologically divergent populations. Benthic and limnetic morphotypes were separable using this technique and of 45 populations classified, three were identified as morphologically extreme (two benthic and one limnetic), nine as moderately divergent (three benthic and six limnetic) and the remaining 33 populations as morphologically intermediate. Classification scores were found to correlate with eye size, the depth profile of lakes, and the presence of invasive northern pike (Esox lucius). This type of classification function provides a means of integrating the complex morphological differences between morphotypes into a single score that reflects the position of a population along the benthic-limnetic axis and can be used to relate that position to other aspects of stickleback biology. PMID:21221422

Detection of Cardiac Abnormalities from Multilead ECG using Multiscale Phase Alternation Features.

PubMed

Tripathy, R K; Dandapat, S

2016-06-01

The cardiac activities such as the depolarization and the relaxation of atria and ventricles are observed in electrocardiogram (ECG). The changes in the morphological features of ECG are the symptoms of particular heart pathology. It is a cumbersome task for medical experts to visually identify any subtle changes in the morphological features during 24 hours of ECG recording. Therefore, the automated analysis of ECG signal is a need for accurate detection of cardiac abnormalities. In this paper, a novel method for automated detection of cardiac abnormalities from multilead ECG is proposed. The method uses multiscale phase alternation (PA) features of multilead ECG and two classifiers, k-nearest neighbor (KNN) and fuzzy KNN for classification of bundle branch block (BBB), myocardial infarction (MI), heart muscle defect (HMD) and healthy control (HC). The dual tree complex wavelet transform (DTCWT) is used to decompose the ECG signal of each lead into complex wavelet coefficients at different scales. The phase of the complex wavelet coefficients is computed and the PA values at each wavelet scale are used as features for detection and classification of cardiac abnormalities. A publicly available multilead ECG database (PTB database) is used for testing of the proposed method. The experimental results show that, the proposed multiscale PA features and the fuzzy KNN classifier have better performance for detection of cardiac abnormalities with sensitivity values of 78.12 %, 80.90 % and 94.31 % for BBB, HMD and MI classes. The sensitivity value of proposed method for MI class is compared with the state-of-art techniques from multilead ECG.

Considering the Spatial Layout Information of Bag of Features (BoF) Framework for Image Classification.

PubMed

Mu, Guangyu; Liu, Ying; Wang, Limin

2015-01-01

The spatial pooling method such as spatial pyramid matching (SPM) is very crucial in the bag of features model used in image classification. SPM partitions the image into a set of regular grids and assumes that the spatial layout of all visual words obey the uniform distribution over these regular grids. However, in practice, we consider that different visual words should obey different spatial layout distributions. To improve SPM, we develop a novel spatial pooling method, namely spatial distribution pooling (SDP). The proposed SDP method uses an extension model of Gauss mixture model to estimate the spatial layout distributions of the visual vocabulary. For each visual word type, SDP can generate a set of flexible grids rather than the regular grids from the traditional SPM. Furthermore, we can compute the grid weights for visual word tokens according to their spatial coordinates. The experimental results demonstrate that SDP outperforms the traditional spatial pooling methods, and is competitive with the state-of-the-art classification accuracy on several challenging image datasets.

Changes in Intraocular Straylight and Visual Acuity with Age in Cataracts of Different Morphologies

PubMed Central

Reus, Nicolaas J.; van den Berg, Thomas J. T. P.

2017-01-01

Purpose To investigate the significance of difference in straylight of cataract eyes with different morphologies, as a function of age and visual acuity. Methods A literature review to collect relevant papers on straylight, age, and visual acuity of three common cataract morphologies leads to including five eligible papers for the analysis. The effect of morphology was incorporated to categorize straylight dependency on the two variables. We also determined the amount of progression in a cataract group using a control group. Results The mean straylight was 1.22 log units ± 0.20 (SD) in nuclear (592 eyes), 1.26 log units ± 0.23 in cortical (776 eyes), and 1.48 log units ± 0.34 in posterior subcapsular (75 eyes) groups. The slope of straylight-age relationship was 0.009 (R 2 = 0.20) in nuclear, 0.012 (R 2 = 0.22) in cortical, and 0.014 (R 2 = 0.11) in posterior subcapsular groups. The slope of straylight-visual acuity relationship was 0.62 (R 2 = 0.25) in nuclear, 0.33 (R 2 = 0.13) in cortical, and 1.03 (R 2 = 0.34) in posterior subcapsular groups. Conclusion Considering morphology of cataract provides a better insight in assessing visual functions of cataract eyes, in posterior subcapsular cataract, particularly, in spite of notable elevated straylight, visual acuity might not manifest severe loss. PMID:28831307

Visual affective classification by combining visual and text features.

PubMed

Liu, Ningning; Wang, Kai; Jin, Xin; Gao, Boyang; Dellandréa, Emmanuel; Chen, Liming

2017-01-01

Affective analysis of images in social networks has drawn much attention, and the texts surrounding images are proven to provide valuable semantic meanings about image content, which can hardly be represented by low-level visual features. In this paper, we propose a novel approach for visual affective classification (VAC) task. This approach combines visual representations along with novel text features through a fusion scheme based on Dempster-Shafer (D-S) Evidence Theory. Specifically, we not only investigate different types of visual features and fusion methods for VAC, but also propose textual features to effectively capture emotional semantics from the short text associated to images based on word similarity. Experiments are conducted on three public available databases: the International Affective Picture System (IAPS), the Artistic Photos and the MirFlickr Affect set. The results demonstrate that the proposed approach combining visual and textual features provides promising results for VAC task.

Visual affective classification by combining visual and text features

PubMed Central

Liu, Ningning; Wang, Kai; Jin, Xin; Gao, Boyang; Dellandréa, Emmanuel; Chen, Liming

2017-01-01

Affective analysis of images in social networks has drawn much attention, and the texts surrounding images are proven to provide valuable semantic meanings about image content, which can hardly be represented by low-level visual features. In this paper, we propose a novel approach for visual affective classification (VAC) task. This approach combines visual representations along with novel text features through a fusion scheme based on Dempster-Shafer (D-S) Evidence Theory. Specifically, we not only investigate different types of visual features and fusion methods for VAC, but also propose textual features to effectively capture emotional semantics from the short text associated to images based on word similarity. Experiments are conducted on three public available databases: the International Affective Picture System (IAPS), the Artistic Photos and the MirFlickr Affect set. The results demonstrate that the proposed approach combining visual and textual features provides promising results for VAC task. PMID:28850566

Galaxy And Mass Assembly: automatic morphological classification of galaxies using statistical learning

NASA Astrophysics Data System (ADS)

Sreejith, Sreevarsha; Pereverzyev, Sergiy, Jr.; Kelvin, Lee S.; Marleau, Francine R.; Haltmeier, Markus; Ebner, Judith; Bland-Hawthorn, Joss; Driver, Simon P.; Graham, Alister W.; Holwerda, Benne W.; Hopkins, Andrew M.; Liske, Jochen; Loveday, Jon; Moffett, Amanda J.; Pimbblet, Kevin A.; Taylor, Edward N.; Wang, Lingyu; Wright, Angus H.

2018-03-01

We apply four statistical learning methods to a sample of 7941 galaxies (z < 0.06) from the Galaxy And Mass Assembly survey to test the feasibility of using automated algorithms to classify galaxies. Using 10 features measured for each galaxy (sizes, colours, shape parameters, and stellar mass), we apply the techniques of Support Vector Machines, Classification Trees, Classification Trees with Random Forest (CTRF) and Neural Networks, and returning True Prediction Ratios (TPRs) of 75.8 per cent, 69.0 per cent, 76.2 per cent, and 76.0 per cent, respectively. Those occasions whereby all four algorithms agree with each other yet disagree with the visual classification (`unanimous disagreement') serves as a potential indicator of human error in classification, occurring in ˜ 9 per cent of ellipticals, ˜ 9 per cent of little blue spheroids, ˜ 14 per cent of early-type spirals, ˜ 21 per cent of intermediate-type spirals, and ˜ 4 per cent of late-type spirals and irregulars. We observe that the choice of parameters rather than that of algorithms is more crucial in determining classification accuracy. Due to its simplicity in formulation and implementation, we recommend the CTRF algorithm for classifying future galaxy data sets. Adopting the CTRF algorithm, the TPRs of the five galaxy types are : E, 70.1 per cent; LBS, 75.6 per cent; S0-Sa, 63.6 per cent; Sab-Scd, 56.4 per cent, and Sd-Irr, 88.9 per cent. Further, we train a binary classifier using this CTRF algorithm that divides galaxies into spheroid-dominated (E, LBS, and S0-Sa) and disc-dominated (Sab-Scd and Sd-Irr), achieving an overall accuracy of 89.8 per cent. This translates into an accuracy of 84.9 per cent for spheroid-dominated systems and 92.5 per cent for disc-dominated systems.

Description of congenital hand anomalies: a personal view.

PubMed

Tonkin, M A

2006-10-01

A series of four congenital hand cases exhibiting central clefting are presented. The cases are morphologically similar and exhibit characteristics of both symbrachydactyly and central longitudinal deficiency. The cases demonstrate difficulties in classification by either the IFSSH classification system or the JSSH modification of it. An alternative descriptive approach to classification is suggested.

Clast Size, Void Space, and Degree of Contortion in Spatter Piles at Craters of the Moon, ID. Implications for Eruptions Conditions of Lunar Basalts.

NASA Astrophysics Data System (ADS)

Rader, E. L.; Heldmann, J. L.

2016-12-01

Spatter is an explosive volcanic product consisting of partially-molten clasts found predominantly in mafic eruptions. Classification of spatter deposits is currently based on qualitative visual identification, and its presence signifies little more than a near-vent environment. However, the variables that effect spatter morphology (density of clasts, aspect ratio of clasts, rind thickness, etc.) are related to heat transfer from the vent via convection and radiation to the atmosphere and conduction through the spatter pile. Subsequently, the heat flux is proportional to the volume and rate of eruption, as faster and more voluminous eruptions result in a higher degree of welding between clasts. With a quantitative classification scheme, spatter deposits may reveal important eruption conditions such as eruption duration, eruption vigor, and fountain height. These factors are particularly important for non-terrestrial volcanoes whose eruptions have never been observed and whose products will likely be sampled on too small of a scale for more detailed chemical and thermal analysis. This study describes physical aspects of multiple spatter deposits at Craters of the Moon National Monument in Idaho, and suggests different eruptions conditions will produce quantitatively unique spatter deposits.

Deep learning based tissue analysis predicts outcome in colorectal cancer.

PubMed

Bychkov, Dmitrii; Linder, Nina; Turkki, Riku; Nordling, Stig; Kovanen, Panu E; Verrill, Clare; Walliander, Margarita; Lundin, Mikael; Haglund, Caj; Lundin, Johan

2018-02-21

Image-based machine learning and deep learning in particular has recently shown expert-level accuracy in medical image classification. In this study, we combine convolutional and recurrent architectures to train a deep network to predict colorectal cancer outcome based on images of tumour tissue samples. The novelty of our approach is that we directly predict patient outcome, without any intermediate tissue classification. We evaluate a set of digitized haematoxylin-eosin-stained tumour tissue microarray (TMA) samples from 420 colorectal cancer patients with clinicopathological and outcome data available. The results show that deep learning-based outcome prediction with only small tissue areas as input outperforms (hazard ratio 2.3; CI 95% 1.79-3.03; AUC 0.69) visual histological assessment performed by human experts on both TMA spot (HR 1.67; CI 95% 1.28-2.19; AUC 0.58) and whole-slide level (HR 1.65; CI 95% 1.30-2.15; AUC 0.57) in the stratification into low- and high-risk patients. Our results suggest that state-of-the-art deep learning techniques can extract more prognostic information from the tissue morphology of colorectal cancer than an experienced human observer.

Rapid assessment of urban wetlands: Do hydrogeomorpic classification and reference criteria work?

EPA Science Inventory

The Hydrogeomorphic (HGM) functional assessment method is predicated on the ability of a wetland classification method based on hydrology (HGM classification) and a visual assessment of disturbance and alteration to provide reference standards against which functions in individua...

Discrimination of Mediterranean mussel (Mytilus galloprovincialis) feces in deposited materials by fecal morphology.

PubMed

Akiyama, Yoshihiro B; Iseri, Erina; Kataoka, Tomoya; Tanaka, Makiko; Katsukoshi, Kiyonori; Moki, Hirotada; Naito, Ryoji; Hem, Ramrav; Okada, Tomonari

2017-02-15

In the present study, we determined the common morphological characteristics of the feces of Mytilus galloprovincialis to develop a method for visually discriminating the feces of this mussel in deposited materials. This method can be used to assess the effect of mussel feces on benthic environments. The accuracy of visual morphology-based discrimination of mussel feces in deposited materials was confirmed by DNA analysis. Eighty-nine percent of mussel feces shared five common morphological characteristics. Of the 372 animal species investigated, only four species shared all five of these characteristics. More than 96% of the samples were visually identified as M. galloprovincialis feces on the basis of morphology of the particles containing the appropriate mitochondrial DNA. These results suggest that mussel feces can be discriminated with high accuracy on the basis of their morphological characteristics. Thus, our method can be used to quantitatively assess the effect of mussel feces on local benthic environments. Copyright © 2016 Elsevier Ltd. All rights reserved.

Comparison of Threshold Saccadic Vector Optokinetic Perimetry (SVOP) and Standard Automated Perimetry (SAP) in Glaucoma. Part II: Patterns of Visual Field Loss and Acceptability.

PubMed

McTrusty, Alice D; Cameron, Lorraine A; Perperidis, Antonios; Brash, Harry M; Tatham, Andrew J; Agarwal, Pankaj K; Murray, Ian C; Fleck, Brian W; Minns, Robert A

2017-09-01

We compared patterns of visual field loss detected by standard automated perimetry (SAP) to saccadic vector optokinetic perimetry (SVOP) and examined patient perceptions of each test. A cross-sectional study was done of 58 healthy subjects and 103 with glaucoma who were tested using SAP and two versions of SVOP (v1 and v2). Visual fields from both devices were categorized by masked graders as: 0, normal; 1, paracentral defect; 2, nasal step; 3, arcuate defect; 4, altitudinal; 5, biarcuate; and 6, end-stage field loss. SVOP and SAP classifications were cross-tabulated. Subjects completed a questionnaire on their opinions of each test. We analyzed 142 (v1) and 111 (v2) SVOP and SAP test pairs. SVOP v2 had a sensitivity of 97.7% and specificity of 77.9% for identifying normal versus abnormal visual fields. SAP and SVOP v2 classifications showed complete agreement in 54% of glaucoma patients, with a further 23% disagreeing by one category. On repeat testing, 86% of SVOP v2 classifications agreed with the previous test, compared to 91% of SAP classifications; 71% of subjects preferred SVOP compared to 20% who preferred SAP. Eye-tracking perimetry can be used to obtain threshold visual field sensitivity values in patients with glaucoma and produce maps of visual field defects, with patterns exhibiting close agreement to SAP. Patients preferred eye-tracking perimetry compared to SAP. This first report of threshold eye tracking perimetry shows good agreement with conventional automated perimetry and provides a benchmark for future iterations.

Analyzing Sub-Classifications of Glaucoma via SOM Based Clustering of Optic Nerve Images.

PubMed

Yan, Sanjun; Abidi, Syed Sibte Raza; Artes, Paul Habib

2005-01-01

We present a data mining framework to cluster optic nerve images obtained by Confocal Scanning Laser Tomography (CSLT) in normal subjects and patients with glaucoma. We use self-organizing maps and expectation maximization methods to partition the data into clusters that provide insights into potential sub-classification of glaucoma based on morphological features. We conclude that our approach provides a first step towards a better understanding of morphological features in optic nerve images obtained from glaucoma patients and healthy controls.

Antibody-supervised deep learning for quantification of tumor-infiltrating immune cells in hematoxylin and eosin stained breast cancer samples

PubMed Central

Turkki, Riku; Linder, Nina; Kovanen, Panu E.; Pellinen, Teijo; Lundin, Johan

2016-01-01

Background: Immune cell infiltration in tumor is an emerging prognostic biomarker in breast cancer. The gold standard for quantification of immune cells in tissue sections is visual assessment through a microscope, which is subjective and semi-quantitative. In this study, we propose and evaluate an approach based on antibody-guided annotation and deep learning to quantify immune cell-rich areas in hematoxylin and eosin (H&E) stained samples. Methods: Consecutive sections of formalin-fixed parafin-embedded samples obtained from the primary tumor of twenty breast cancer patients were cut and stained with H&E and the pan-leukocyte CD45 antibody. The stained slides were digitally scanned, and a training set of immune cell-rich and cell-poor tissue regions was annotated in H&E whole-slide images using the CD45-expression as a guide. In analysis, the images were divided into small homogenous regions, superpixels, from which features were extracted using a pretrained convolutional neural network (CNN) and classified with a support of vector machine. The CNN approach was compared to texture-based classification and to visual assessments performed by two pathologists. Results: In a set of 123,442 labeled superpixels, the CNN approach achieved an F-score of 0.94 (range: 0.92–0.94) in discrimination of immune cell-rich and cell-poor regions, as compared to an F-score of 0.88 (range: 0.87–0.89) obtained with the texture-based classification. When compared to visual assessment of 200 images, an agreement of 90% (κ = 0.79) to quantify immune infiltration with the CNN approach was achieved while the inter-observer agreement between pathologists was 90% (κ = 0.78). Conclusions: Our findings indicate that deep learning can be applied to quantify immune cell infiltration in breast cancer samples using a basic morphology staining only. A good discrimination of immune cell-rich areas was achieved, well in concordance with both leukocyte antigen expression and pathologists’ visual assessment. PMID:27688929

Classification of Pelteobagrus fish in Poyang Lake based on mitochondrial COI gene sequence.

PubMed

Zhong, Bin; Chen, Ting-Ting; Gong, Rui-Yue; Zhao, Zhe-Xia; Wang, Binhua; Fang, Chunlin; Mao, Hui-Ling

2016-11-01

We use DNA molecular marker technology to correct the deficiency of traditional morphological taxonomy. Totality 770 Pelteobagrus fish from Poyang Lake were collected. After preliminary morphological classification, random selected eight samples in each species for DNA extraction. Mitochondrial COI gene sequence was cloned with universal primers and sequenced. The results showed that there are four species of Pelteobagrus living in Poyang Lake. The average of intraspecific genetic distance value was 0.003, while the average interspecific genetic distance was 0.128. The interspecific genetic distance is far more than intraspecific genetic distance. Besides, phylogenetic tree analysis revealed that molecular systematics was in accord with morphological classification. It indicated that COI gene is an effective DNA molecular marker in Pelteobagrus classification. Surprisingly, the intraspecific difference of some individuals (P. e6, P. n6, P. e5, and P. v4) from their original named exceeded species threshold (2%), which should be renewedly classified into Pelteobagrus fulvidraco. However, another individual P. v3 was very different, because its genetic distance was over 8.4% difference from original named Pelteobagrus vachelli. Its taxonomic status remained to be further studied.

Visualizing and Clustering Protein Similarity Networks: Sequences, Structures, and Functions.

PubMed

Mai, Te-Lun; Hu, Geng-Ming; Chen, Chi-Ming

2016-07-01

Research in the recent decade has demonstrated the usefulness of protein network knowledge in furthering the study of molecular evolution of proteins, understanding the robustness of cells to perturbation, and annotating new protein functions. In this study, we aimed to provide a general clustering approach to visualize the sequence-structure-function relationship of protein networks, and investigate possible causes for inconsistency in the protein classifications based on sequences, structures, and functions. Such visualization of protein networks could facilitate our understanding of the overall relationship among proteins and help researchers comprehend various protein databases. As a demonstration, we clustered 1437 enzymes by their sequences and structures using the minimum span clustering (MSC) method. The general structure of this protein network was delineated at two clustering resolutions, and the second level MSC clustering was found to be highly similar to existing enzyme classifications. The clustering of these enzymes based on sequence, structure, and function information is consistent with each other. For proteases, the Jaccard's similarity coefficient is 0.86 between sequence and function classifications, 0.82 between sequence and structure classifications, and 0.78 between structure and function classifications. From our clustering results, we discussed possible examples of divergent evolution and convergent evolution of enzymes. Our clustering approach provides a panoramic view of the sequence-structure-function network of proteins, helps visualize the relation between related proteins intuitively, and is useful in predicting the structure and function of newly determined protein sequences.

Classification of high-resolution multispectral satellite remote sensing images using extended morphological attribute profiles and independent component analysis

NASA Astrophysics Data System (ADS)

Wu, Yu; Zheng, Lijuan; Xie, Donghai; Zhong, Ruofei

2017-07-01

In this study, the extended morphological attribute profiles (EAPs) and independent component analysis (ICA) were combined for feature extraction of high-resolution multispectral satellite remote sensing images and the regularized least squares (RLS) approach with the radial basis function (RBF) kernel was further applied for the classification. Based on the major two independent components, the geometrical features were extracted using the EAPs method. In this study, three morphological attributes were calculated and extracted for each independent component, including area, standard deviation, and moment of inertia. The extracted geometrical features classified results using RLS approach and the commonly used LIB-SVM library of support vector machines method. The Worldview-3 and Chinese GF-2 multispectral images were tested, and the results showed that the features extracted by EAPs and ICA can effectively improve the accuracy of the high-resolution multispectral image classification, 2% larger than EAPs and principal component analysis (PCA) method, and 6% larger than APs and original high-resolution multispectral data. Moreover, it is also suggested that both the GURLS and LIB-SVM libraries are well suited for the multispectral remote sensing image classification. The GURLS library is easy to be used with automatic parameter selection but its computation time may be larger than the LIB-SVM library. This study would be helpful for the classification application of high-resolution multispectral satellite remote sensing images.

Morphological classification of plant cell deaths.

PubMed

van Doorn, W G; Beers, E P; Dangl, J L; Franklin-Tong, V E; Gallois, P; Hara-Nishimura, I; Jones, A M; Kawai-Yamada, M; Lam, E; Mundy, J; Mur, L A J; Petersen, M; Smertenko, A; Taliansky, M; Van Breusegem, F; Wolpert, T; Woltering, E; Zhivotovsky, B; Bozhkov, P V

2011-08-01

Programmed cell death (PCD) is an integral part of plant development and of responses to abiotic stress or pathogens. Although the morphology of plant PCD is, in some cases, well characterised and molecular mechanisms controlling plant PCD are beginning to emerge, there is still confusion about the classification of PCD in plants. Here we suggest a classification based on morphological criteria. According to this classification, the use of the term 'apoptosis' is not justified in plants, but at least two classes of PCD can be distinguished: vacuolar cell death and necrosis. During vacuolar cell death, the cell contents are removed by a combination of autophagy-like process and release of hydrolases from collapsed lytic vacuoles. Necrosis is characterised by early rupture of the plasma membrane, shrinkage of the protoplast and absence of vacuolar cell death features. Vacuolar cell death is common during tissue and organ formation and elimination, whereas necrosis is typically found under abiotic stress. Some examples of plant PCD cannot be ascribed to either major class and are therefore classified as separate modalities. These are PCD associated with the hypersensitive response to biotrophic pathogens, which can express features of both necrosis and vacuolar cell death, PCD in starchy cereal endosperm and during self-incompatibility. The present classification is not static, but will be subject to further revision, especially when specific biochemical pathways are better defined.

The use of morphological characteristics and texture analysis in the identification of tissue composition in prostatic neoplasia.

PubMed

Diamond, James; Anderson, Neil H; Bartels, Peter H; Montironi, Rodolfo; Hamilton, Peter W

2004-09-01

Quantitative examination of prostate histology offers clues in the diagnostic classification of lesions and in the prediction of response to treatment and prognosis. To facilitate the collection of quantitative data, the development of machine vision systems is necessary. This study explored the use of imaging for identifying tissue abnormalities in prostate histology. Medium-power histological scenes were recorded from whole-mount radical prostatectomy sections at x 40 objective magnification and assessed by a pathologist as exhibiting stroma, normal tissue (nonneoplastic epithelial component), or prostatic carcinoma (PCa). A machine vision system was developed that divided the scenes into subregions of 100 x 100 pixels and subjected each to image-processing techniques. Analysis of morphological characteristics allowed the identification of normal tissue. Analysis of image texture demonstrated that Haralick feature 4 was the most suitable for discriminating stroma from PCa. Using these morphological and texture measurements, it was possible to define a classification scheme for each subregion. The machine vision system is designed to integrate these classification rules and generate digital maps of tissue composition from the classification of subregions; 79.3% of subregions were correctly classified. Established classification rates have demonstrated the validity of the methodology on small scenes; a logical extension was to apply the methodology to whole slide images via scanning technology. The machine vision system is capable of classifying these images. The machine vision system developed in this project facilitates the exploration of morphological and texture characteristics in quantifying tissue composition. It also illustrates the potential of quantitative methods to provide highly discriminatory information in the automated identification of prostatic lesions using computer vision.

Shape asymmetry: a morphological indicator for automatic detection of galaxies in the post-coalescence merger stages

NASA Astrophysics Data System (ADS)

Pawlik, M. M.; Wild, V.; Walcher, C. J.; Johansson, P. H.; Villforth, C.; Rowlands, K.; Mendez-Abreu, J.; Hewlett, T.

2016-03-01

We present a new morphological indicator designed for automated recognition of galaxies with faint asymmetric tidal features suggestive of an ongoing or past merger. We use the new indicator, together with pre-existing diagnostics of galaxy structure to study the role of galaxy mergers in inducing (post-) starburst spectral signatures in local galaxies, and investigate whether (post-) starburst galaxies play a role in the build-up of the `red sequence'. Our morphological and structural analysis of an evolutionary sample of 335 (post-) starburst galaxies in the Sloan Digital Sky Survey DR7 with starburst ages 0 < tSB < 0.6 Gyr, shows that 45 per cent of galaxies with young starbursts (tSB < 0.1 Gyr) show signatures of an ongoing or past merger. This fraction declines with starburst age, and we find a good agreement between automated and visual classifications. The majority of the oldest (post-) starburst galaxies in our sample (tSB ˜ 0.6 Gyr) have structural properties characteristic of early-type discs and are not as highly concentrated as the fully quenched galaxies commonly found on the `red sequence' in the present day Universe. This suggests that, if (post-) starburst galaxies are a transition phase between active star-formation and quiescence, they do not attain the structure of presently quenched galaxies within the first 0.6 Gyr after the starburst.

SEM/EDS and optical microscopy analyses of microplastics in ocean trawl and fish guts.

PubMed

Wang, Zhong-Min; Wagner, Jeff; Ghosal, Sutapa; Bedi, Gagandeep; Wall, Stephen

2017-12-15

Microplastic particles from Atlantic and Pacific Ocean trawls, lab-fed fish guts and ocean fish guts have been characterized using optical microscopy and SEM/EDS in terms of size, morphology, and chemistry. We assessed whether these measurements could serve as a rapid screening process for subsequent identification of the likely microplastic candidates by micro-spectroscopy. Optical microscopy enabled morphological classification of the types of particles or fibers present in the sample, as well as the quantification of particle size ranges and fiber lengths. SEM/EDS analysis was used to rule out non-plastic particles and screen the prepared samples for potential microplastic, based on their element signatures and surface characteristics. Chlorinated plastics such as polyvinyl chloride (PVC) could be easily identified with SEM/EDS due to their unique elemental signatures including chlorine, as could mineral species that are falsely identified as plastics by optical microscopy. Particle morphology determined by optical microscopy and SEM suggests the fish ingested particles contained both degradation fragments from larger plastic pieces and also manufactured microplastics. SEM images of microplastic particle surfaces revealed characteristic cracks consistent with environmental exposure, as well as pigment particles consistent with manufactured materials. Most of the microplastic surfaces in the fish guts and ocean trawls were covered with biofilms, radiolarians, and crustaceans. Many of the fish stomachs contained micro-shell pieces which visually resembled microplastics. Copyright © 2017 Elsevier B.V. All rights reserved.

The Morphology of Passively Evolving Galaxies at Z-2 from HST/WFC3 in the Hubble Ultra Deep Field

NASA Technical Reports Server (NTRS)

Cassata, P.; Giavalisco, M.; Guo, Yicheng; Ferguson, H.; Koekemoer, A.; Renzini, A.; Fontana, A.; Salimbeni, S.; Dickinson, M.; Casertano, S.;

Stimulus specificity of a steady-state visual-evoked potential-based brain-computer interface.

PubMed

Ng, Kian B; Bradley, Andrew P; Cunnington, Ross

2012-06-01

The mechanisms of neural excitation and inhibition when given a visual stimulus are well studied. It has been established that changing stimulus specificity such as luminance contrast or spatial frequency can alter the neuronal activity and thus modulate the visual-evoked response. In this paper, we study the effect that stimulus specificity has on the classification performance of a steady-state visual-evoked potential-based brain-computer interface (SSVEP-BCI). For example, we investigate how closely two visual stimuli can be placed before they compete for neural representation in the cortex and thus influence BCI classification accuracy. We characterize stimulus specificity using the four stimulus parameters commonly encountered in SSVEP-BCI design: temporal frequency, spatial size, number of simultaneously displayed stimuli and their spatial proximity. By varying these quantities and measuring the SSVEP-BCI classification accuracy, we are able to determine the parameters that provide optimal performance. Our results show that superior SSVEP-BCI accuracy is attained when stimuli are placed spatially more than 5° apart, with size that subtends at least 2° of visual angle, when using a tagging frequency of between high alpha and beta band. These findings may assist in deciding the stimulus parameters for optimal SSVEP-BCI design.

Stimulus specificity of a steady-state visual-evoked potential-based brain-computer interface

NASA Astrophysics Data System (ADS)

Ng, Kian B.; Bradley, Andrew P.; Cunnington, Ross

2012-06-01

The mechanisms of neural excitation and inhibition when given a visual stimulus are well studied. It has been established that changing stimulus specificity such as luminance contrast or spatial frequency can alter the neuronal activity and thus modulate the visual-evoked response. In this paper, we study the effect that stimulus specificity has on the classification performance of a steady-state visual-evoked potential-based brain-computer interface (SSVEP-BCI). For example, we investigate how closely two visual stimuli can be placed before they compete for neural representation in the cortex and thus influence BCI classification accuracy. We characterize stimulus specificity using the four stimulus parameters commonly encountered in SSVEP-BCI design: temporal frequency, spatial size, number of simultaneously displayed stimuli and their spatial proximity. By varying these quantities and measuring the SSVEP-BCI classification accuracy, we are able to determine the parameters that provide optimal performance. Our results show that superior SSVEP-BCI accuracy is attained when stimuli are placed spatially more than 5° apart, with size that subtends at least 2° of visual angle, when using a tagging frequency of between high alpha and beta band. These findings may assist in deciding the stimulus parameters for optimal SSVEP-BCI design.

Classification-Based Spatial Error Concealment for Visual Communications

NASA Astrophysics Data System (ADS)

Chen, Meng; Zheng, Yefeng; Wu, Min

2006-12-01

In an error-prone transmission environment, error concealment is an effective technique to reconstruct the damaged visual content. Due to large variations of image characteristics, different concealment approaches are necessary to accommodate the different nature of the lost image content. In this paper, we address this issue and propose using classification to integrate the state-of-the-art error concealment techniques. The proposed approach takes advantage of multiple concealment algorithms and adaptively selects the suitable algorithm for each damaged image area. With growing awareness that the design of sender and receiver systems should be jointly considered for efficient and reliable multimedia communications, we proposed a set of classification-based block concealment schemes, including receiver-side classification, sender-side attachment, and sender-side embedding. Our experimental results provide extensive performance comparisons and demonstrate that the proposed classification-based error concealment approaches outperform the conventional approaches.

Concept relationship editor: a visual interface to support the assertion of synonymy relationships between taxonomic classifications

NASA Astrophysics Data System (ADS)

Craig, Paul; Kennedy, Jessie

2008-01-01

An increasingly common approach being taken by taxonomists to define the relationships between taxa in alternative hierarchical classifications is to use a set-based notation which states relationship between two taxa from alternative classifications. Textual recording of these relationships is cumbersome and difficult for taxonomists to manage. While text based GUI tools are beginning to appear which ease the process, these have several limitations. Interactive visual tools offer greater potential to allow taxonomists to explore the taxa in these hierarchies and specify such relationships. This paper describes the Concept Relationship Editor, an interactive visualisation tool designed to support the assertion of relationships between taxonomic classifications. The tool operates using an interactive space-filling adjacency layout which allows users to expand multiple lists of taxa with common parents so they can explore and assert relationships between two classifications.

Intraclass Evolution and Classification of the Colpodea (Ciliophora)

PubMed Central

FOISSNER, WILHELM; STOECK, THORSTEN; AGATHA, SABINE; DUNTHORN, MICAH

2012-01-01

Using nine new taxa and statistical inferences based on morphological and molecular data, we analyze the evolution within the class Colpodea. The molecular and cladistic analyses show four well-supported clades: platyophryids, bursariomorphids, cyrtolophosidids, and colpodids. There is a widespread occurrence of homoplasies, affecting even conspicuous morphological characteristics, e.g. the inclusion of the micronucleus in the perinuclear space of the macronucleus. The most distinct changes in the morphological classification are the lack of a basal divergence into two subclasses and the split of the cyrtolophosidids into two main clades, differing mainly by the presence vs. absence of an oral cavity. The most complex clade is that of the colpodids. We partially reconcile the morphological and molecular data using evolutionary systematics, providing a scenario in which the colpodids evolved from a Bardeliella-like ancestor and the genus Colpoda performed an intense adaptive radiation, giving rise to three main clades: Colpodina n. subord., Grossglockneriina, and Bryophryina. Three new taxa are established: Colpodina n. subord., Tillinidae n. fam., and Ottowphryidae n. fam. Colpodean evolution and classification are far from being understood because sequences are lacking for most species and half of their diversity is possibly undescribed. PMID:21762424

Classification of CT examinations for COPD visual severity analysis

NASA Astrophysics Data System (ADS)

Tan, Jun; Zheng, Bin; Wang, Xingwei; Pu, Jiantao; Gur, David; Sciurba, Frank C.; Leader, J. Ken

2012-03-01

In this study we present a computational method of CT examination classification into visual assessed emphysema severity. The visual severity categories ranged from 0 to 5 and were rated by an experienced radiologist. The six categories were none, trace, mild, moderate, severe and very severe. Lung segmentation was performed for every input image and all image features are extracted from the segmented lung only. We adopted a two-level feature representation method for the classification. Five gray level distribution statistics, six gray level co-occurrence matrix (GLCM), and eleven gray level run-length (GLRL) features were computed for each CT image depicted segment lung. Then we used wavelets decomposition to obtain the low- and high-frequency components of the input image, and again extract from the lung region six GLCM features and eleven GLRL features. Therefore our feature vector length is 56. The CT examinations were classified using the support vector machine (SVM) and k-nearest neighbors (KNN) and the traditional threshold (density mask) approach. The SVM classifier had the highest classification performance of all the methods with an overall sensitivity of 54.4% and a 69.6% sensitivity to discriminate "no" and "trace visually assessed emphysema. We believe this work may lead to an automated, objective method to categorically classify emphysema severity on CT exam.

The chemotaxonomic classification of Rhodiola plants and its correlation with morphological characteristics and genetic taxonomy.

PubMed

Liu, Zhenli; Liu, Yuanyan; Liu, Chunsheng; Song, Zhiqian; Li, Qing; Zha, Qinglin; Lu, Cheng; Wang, Chun; Ning, Zhangchi; Zhang, Yuxin; Tian, Cheng; Lu, Aiping

2013-07-12

Rhodiola plants are used as a natural remedy in the western world and as a traditional herbal medicine in China, and are valued for their ability to enhance human resistance to stress or fatigue and to promote longevity. Due to the morphological similarities among different species, the identification of the genus remains somewhat controversial, which may affect their safety and effectiveness in clinical use. In this paper, 47 Rhodiola samples of seven species were collected from thirteen local provinces of China. They were identified by their morphological characteristics and genetic and phytochemical taxonomies. Eight bioactive chemotaxonomic markers from four chemical classes (phenylpropanoids, phenylethanol derivatives, flavonoids and phenolic acids) were determined to evaluate and distinguish the chemotaxonomy of Rhodiola samples using an HPLC-DAD/UV method. Hierarchical cluster analysis (HCA) and principal component analysis (PCA) were applied to compare the two classification methods between genetic and phytochemical taxonomy. The established chemotaxonomic classification could be effectively used for Rhodiola species identification.

The chemotaxonomic classification of Rhodiola plants and its correlation with morphological characteristics and genetic taxonomy

PubMed Central

2013-01-01

Background Rhodiola plants are used as a natural remedy in the western world and as a traditional herbal medicine in China, and are valued for their ability to enhance human resistance to stress or fatigue and to promote longevity. Due to the morphological similarities among different species, the identification of the genus remains somewhat controversial, which may affect their safety and effectiveness in clinical use. Results In this paper, 47 Rhodiola samples of seven species were collected from thirteen local provinces of China. They were identified by their morphological characteristics and genetic and phytochemical taxonomies. Eight bioactive chemotaxonomic markers from four chemical classes (phenylpropanoids, phenylethanol derivatives, flavonoids and phenolic acids) were determined to evaluate and distinguish the chemotaxonomy of Rhodiola samples using an HPLC-DAD/UV method. Hierarchical cluster analysis (HCA) and principal component analysis (PCA) were applied to compare the two classification methods between genetic and phytochemical taxonomy. Conclusions The established chemotaxonomic classification could be effectively used for Rhodiola species identification. PMID:23844866

Revisiting flow maps: a classification and a 3D alternative to visual clutter

NASA Astrophysics Data System (ADS)

Gu, Yuhang; Kraak, Menno-Jan; Engelhardt, Yuri

2018-05-01

Flow maps have long been servicing people in exploring movement by representing origin-destination data (OD data). Due to recent developments in data collecting techniques the amount of movement data is increasing dramatically. With such huge amounts of data, visual clutter in flow maps is becoming a challenge. This paper revisits flow maps, provides an overview of the characteristics of OD data and proposes a classification system for flow maps. For dealing with problems of visual clutter, 3D flow maps are proposed as potential alternative to 2D flow maps.

A volumetric pulmonary CT segmentation method with applications in emphysema assessment

NASA Astrophysics Data System (ADS)

Silva, José Silvestre; Silva, Augusto; Santos, Beatriz S.

2006-03-01

A segmentation method is a mandatory pre-processing step in many automated or semi-automated analysis tasks such as region identification and densitometric analysis, or even for 3D visualization purposes. In this work we present a fully automated volumetric pulmonary segmentation algorithm based on intensity discrimination and morphologic procedures. Our method first identifies the trachea as well as primary bronchi and then the pulmonary region is identified by applying a threshold and morphologic operations. When both lungs are in contact, additional procedures are performed to obtain two separated lung volumes. To evaluate the performance of the method, we compared contours extracted from 3D lung surfaces with reference contours, using several figures of merit. Results show that the worst case generally occurs at the middle sections of high resolution CT exams, due the presence of aerial and vascular structures. Nevertheless, the average error is inferior to the average error associated with radiologist inter-observer variability, which suggests that our method produces lung contours similar to those drawn by radiologists. The information created by our segmentation algorithm is used by an identification and representation method in pulmonary emphysema that also classifies emphysema according to its severity degree. Two clinically proved thresholds are applied which identify regions with severe emphysema, and with highly severe emphysema. Based on this thresholding strategy, an application for volumetric emphysema assessment was developed offering new display paradigms concerning the visualization of classification results. This framework is easily extendable to accommodate other classifiers namely those related with texture based segmentation as it is often the case with interstitial diseases.

Automated artery-venous classification of retinal blood vessels based on structural mapping method

NASA Astrophysics Data System (ADS)

Joshi, Vinayak S.; Garvin, Mona K.; Reinhardt, Joseph M.; Abramoff, Michael D.

2012-03-01

Retinal blood vessels show morphologic modifications in response to various retinopathies. However, the specific responses exhibited by arteries and veins may provide a precise diagnostic information, i.e., a diabetic retinopathy may be detected more accurately with the venous dilatation instead of average vessel dilatation. In order to analyze the vessel type specific morphologic modifications, the classification of a vessel network into arteries and veins is required. We previously described a method for identification and separation of retinal vessel trees; i.e. structural mapping. Therefore, we propose the artery-venous classification based on structural mapping and identification of color properties prominent to the vessel types. The mean and standard deviation of each of green channel intensity and hue channel intensity are analyzed in a region of interest around each centerline pixel of a vessel. Using the vector of color properties extracted from each centerline pixel, it is classified into one of the two clusters (artery and vein), obtained by the fuzzy-C-means clustering. According to the proportion of clustered centerline pixels in a particular vessel, and utilizing the artery-venous crossing property of retinal vessels, each vessel is assigned a label of an artery or a vein. The classification results are compared with the manually annotated ground truth (gold standard). We applied the proposed method to a dataset of 15 retinal color fundus images resulting in an accuracy of 88.28% correctly classified vessel pixels. The automated classification results match well with the gold standard suggesting its potential in artery-venous classification and the respective morphology analysis.

Analysis of landscape character for visual resource management

Treesearch

Paul F. Anderson

1979-01-01

Description, classification and delineation of visual landscape character are initial steps in developing visual resource management plans. Landscape characteristics identified as key factors in visual landscape analysis include land cover/land use and landform. Landscape types, which are combinations of landform and surface features, were delineated for management...

The Functional Classification of Brain Damage-Related Vision Loss

ERIC Educational Resources Information Center

Colenbrander, August

2009-01-01

This article provides a terminological framework to show the relationships among different types of visual deficits. It distinguishes between visual functions, which describe how the eye and the lower visual system function, and functional vision, which describes how a person functions. When visual functions are disturbed, the term "visual…

How Temporal and Spatial Aspects of Presenting Visualizations Affect Learning about Locomotion Patterns

ERIC Educational Resources Information Center

Imhof, Birgit; Scheiter, Katharina; Edelmann, Jorg; Gerjets, Peter

2012-01-01

Two studies investigated the effectiveness of dynamic and static visualizations for a perceptual learning task (locomotion pattern classification). In Study 1, seventy-five students viewed either dynamic, static-sequential, or static-simultaneous visualizations. For tasks of intermediate difficulty, dynamic visualizations led to better…

What nephrolopathologists need to know about antiphospholipid syndrome-associated nephropathy: Is it time for formulating a classification for renal morphologic lesions?

PubMed

Mubarak, Muhammed; Nasri, Hamid

2014-01-01

Antiphospholipid syndrome (APS) is a systemic autoimmune disorder which commonly affects kidneys. Directory of Open Access Journals (DOAJ), Google Scholar, PubMed (NLM), LISTA (EBSCO) and Web of Science have been searched. There is sufficient epidemiological, clinical and histopathological evidence to show that antiphospholipid syndrome is a distinctive lesion caused by antiphospholipid antibodies in patients with different forms of antiphospholipid syndrome. It is now time to devise a classification for an accurate diagnosis and prognostication of the disease. Now that the morphological lesions of APSN are sufficiently well characterized, it is prime time to devise a classification which is of diagnostic and prognostic utility in this disease.

What nephrolopathologists need to know about antiphospholipid syndrome-associated nephropathy: Is it time for formulating a classification for renal morphologic lesions?

PubMed Central

Mubarak, Muhammed; Nasri, Hamid

2014-01-01

Context: Antiphospholipid syndrome (APS) is a systemic autoimmune disorder which commonly affects kidneys. Evidence Acquisitions: Directory of Open Access Journals (DOAJ), Google Scholar, PubMed (NLM), LISTA (EBSCO) and Web of Science have been searched. Results: There is sufficient epidemiological, clinical and histopathological evidence to show that antiphospholipid syndrome is a distinctive lesion caused by antiphospholipid antibodies in patients with different forms of antiphospholipid syndrome. It is now time to devise a classification for an accurate diagnosis and prognostication of the disease. Conclusions: Now that the morphological lesions of APSN are sufficiently well characterized, it is prime time to devise a classification which is of diagnostic and prognostic utility in this disease. PMID:24644536

The problem of the origin of tektites from Zhamanshin astrobleme

NASA Astrophysics Data System (ADS)

Takhauov, Artur; Anoshin, Dmitriy; Plotnikova, Irina

2013-04-01

Tektites (from Greek. Tektós - molten, melted) - are natural glassy formations of yellow, green, but mostly black color, completely fused, that may have different shape and size. According to their composition tektites are high-silicon (acidic) glasses. The content of SiO may reach 88,5%, AlO - 20,5%, FeO - 11,5%, and CaO - 8,5%. The presence of Ni and relatively low content of water in comparison with other glasses (only 0.02%, which is 10 times less than in the volcanic glass) is of particular importance. The name to tektites was given by Austrian geologist E. Suess (1900). Tektites are often denominated according to their location: irgizites and zhamanshinites (river Irgiz and Zhamanshin hole in Kazakhstan), moldavites [river Moldava (modern Vltava, Czech Republic)] filippinites (Philippines), idoshinites (Indo-china), avstralites (Australia), etc. There is still no generally accepted hypothesis about the origin of tektites: some consider them to be a part of meteorites, others suggest that tektites are the result of the explosion and melting of terrestrial matter that happens when meteorites, asteroids or comets fall down on Earth. The aim of the present work is to conduct analytic studies of tektites from Zhamanshin crater. We examined 50 samples that are part of the collection from the Zhamanshin crater gathered in 1979 by I.N. Plotnikova during her student field expedition led by P.V. Florenskiy. For the convenience of the research of tektites during its early stages the authors compiled a morphological classification of the given samples. On the basis of the visual inspection by the help of a magnifying glass we distinguished the following groups of tektites, which are characterized by certain morphological features: 1. Porous (cavernous), black, isometric shape 2. Porous (cavernous), yellowish-brown, isometric shape 3. Elongated, torose 4. Elongated, with glassy luster 5. Elongated, twisted 6. worm-shaped 7. Vitreous, glassy 8. Deformated The vast majority of researchers believe that the surface of tektites reflects the dynamic resistance of the medium (air) that they were experiencing in their movement. Visual examination of the samples confirms that, and looking at the most tektites, it is difficult not to agree with this idea. Consequently, we can make an assumption that the morphology of tektites depends on the conditions of the genesis (in particular, on the distance and the expansion velocity of tektites). Aubrey Whymark, a famous contemporary specialist in tektites, also believes that the morphology is associated with the range of expansion of tektites from an impact crater, which, according to his theory, are formed as a result of impact metamorphism. This opinion is shared by many scientists involved in the research of tektites. In the next phase of the research the samples of all the morphological groups were examined using electron microscopy. During this analysis, we have found non-melted areas of the rock in 3 out of 50 samples (these were the rocks of the 2nd and the 7th groups from our classification). We also detected the features of the surface patterns that testify the impact formation of tektites.

Steady-state visual evoked potential (SSVEP)-based communication: impact of harmonic frequency components

NASA Astrophysics Data System (ADS)

Müller-Putz, Gernot R.; Scherer, Reinhold; Brauneis, Christian; Pfurtscheller, Gert

2005-12-01

Brain-computer interfaces (BCIs) can be realized on the basis of steady-state evoked potentials (SSEPs). These types of brain signals resulting from repetitive stimulation have the same fundamental frequency as the stimulation but also include higher harmonics. This study investigated how the classification accuracy of a 4-class BCI system can be improved by incorporating visually evoked harmonic oscillations. The current study revealed that the use of three SSVEP harmonics yielded a significantly higher classification accuracy than was the case for one or two harmonics. During feedback experiments, the five subjects investigated reached a classification accuracy between 42.5% and 94.4%.

Steady-state visual evoked potential (SSVEP)-based communication: impact of harmonic frequency components.

PubMed

Müller-Putz, Gernot R; Scherer, Reinhold; Brauneis, Christian; Pfurtscheller, Gert

2005-12-01

Brain-computer interfaces (BCIs) can be realized on the basis of steady-state evoked potentials (SSEPs). These types of brain signals resulting from repetitive stimulation have the same fundamental frequency as the stimulation but also include higher harmonics. This study investigated how the classification accuracy of a 4-class BCI system can be improved by incorporating visually evoked harmonic oscillations. The current study revealed that the use of three SSVEP harmonics yielded a significantly higher classification accuracy than was the case for one or two harmonics. During feedback experiments, the five subjects investigated reached a classification accuracy between 42.5% and 94.4%.

Task-Driven Dictionary Learning Based on Mutual Information for Medical Image Classification.

PubMed

Diamant, Idit; Klang, Eyal; Amitai, Michal; Konen, Eli; Goldberger, Jacob; Greenspan, Hayit

2017-06-01

We present a novel variant of the bag-of-visual-words (BoVW) method for automated medical image classification. Our approach improves the BoVW model by learning a task-driven dictionary of the most relevant visual words per task using a mutual information-based criterion. Additionally, we generate relevance maps to visualize and localize the decision of the automatic classification algorithm. These maps demonstrate how the algorithm works and show the spatial layout of the most relevant words. We applied our algorithm to three different tasks: chest x-ray pathology identification (of four pathologies: cardiomegaly, enlarged mediastinum, right consolidation, and left consolidation), liver lesion classification into four categories in computed tomography (CT) images and benign/malignant clusters of microcalcifications (MCs) classification in breast mammograms. Validation was conducted on three datasets: 443 chest x-rays, 118 portal phase CT images of liver lesions, and 260 mammography MCs. The proposed method improves the classical BoVW method for all tested applications. For chest x-ray, area under curve of 0.876 was obtained for enlarged mediastinum identification compared to 0.855 using classical BoVW (with p-value 0.01). For MC classification, a significant improvement of 4% was achieved using our new approach (with p-value = 0.03). For liver lesion classification, an improvement of 6% in sensitivity and 2% in specificity were obtained (with p-value 0.001). We demonstrated that classification based on informative selected set of words results in significant improvement. Our new BoVW approach shows promising results in clinically important domains. Additionally, it can discover relevant parts of images for the task at hand without explicit annotations for training data. This can provide computer-aided support for medical experts in challenging image analysis tasks.

Evidence from neglect dyslexia for morphological decomposition at the early stages of orthographic-visual analysis

PubMed Central

Reznick, Julia; Friedmann, Naama

2015-01-01

This study examined whether and how the morphological structure of written words affects reading in word-based neglect dyslexia (neglexia), and what can be learned about morphological decomposition in reading from the effect of morphology on neglexia. The oral reading of 7 Hebrew-speaking participants with acquired neglexia at the word level—6 with left neglexia and 1 with right neglexia—was evaluated. The main finding was that the morphological role of the letters on the neglected side of the word affected neglect errors: When an affix appeared on the neglected side, it was neglected significantly more often than when the neglected side was part of the root; root letters on the neglected side were never omitted, whereas affixes were. Perceptual effects of length and final letter form were found for words with an affix on the neglected side, but not for words in which a root letter appeared in the neglected side. Semantic and lexical factors did not affect the participants' reading and error pattern, and neglect errors did not preserve the morpho-lexical characteristics of the target words. These findings indicate that an early morphological decomposition of words to their root and affixes occurs before access to the lexicon and to semantics, at the orthographic-visual analysis stage, and that the effects did not result from lexical feedback. The same effects of morphological structure on reading were manifested by the participants with left- and right-sided neglexia. Since neglexia is a deficit at the orthographic-visual analysis level, the effect of morphology on reading patterns in neglexia further supports that morphological decomposition occurs in the orthographic-visual analysis stage, prelexically, and that the search for the three letters of the root in Hebrew is a trigger for attention shift in neglexia. PMID:26528159

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.

Advances of optical coherence tomography in myopia and pathologic myopia

PubMed Central

Ng, D S C; Cheung, C Y L; Luk, F O; Mohamed, S; Brelen, M E; Yam, J C S; Tsang, C W; Lai, T Y Y

2016-01-01

The natural course of high-axial myopia is variable and the development of pathologic myopia is not fully understood. Advancements in optical coherence tomography (OCT) technology have revealed peculiar intraocular structures in highly myopic eyes and unprecedented pathologies that cause visual impairment. New OCT findings include posterior precortical vitreous pocket and precursor stages of posterior vitreous detachment; peripapillary intrachoroidal cavitation; morphological patterns of scleral inner curvature and dome-shaped macula. Swept source OCT is capable of imaging deeper layers in the posterior pole for investigation of optic nerve pits, stretched and thinned lamina cribrosa, elongated dural attachment at posterior scleral canal, and enlargement of retrobulbar subarachnoid spaces. This has therefore enabled further evaluation of various visual field defects in high myopia and the pathogenesis of glaucomatous optic neuropathy. OCT has many potential clinical uses in managing visual impairing conditions in pathologic myopia. Understanding how retinal nerve fibers are redistributed in axial elongation will allow the development of auto-segmentation software for diagnosis and monitoring progression of glaucoma. OCT is indispensable in the diagnosis of various conditions associated with myopic traction maculopathy and monitoring of post-surgical outcomes. In addition, OCT is commonly used in the multimodal imaging assessment of myopic choroidal neovascularization. Biometry and topography of the retinal layers and choroid will soon be validated for the classification of myopic maculopathy for utilization in epidemiological studies as well as clinical trials. PMID:27055674

Classification of small lesions in dynamic breast MRI: Eliminating the need for precise lesion segmentation through spatio-temporal analysis of contrast enhancement over time.

PubMed

Nagarajan, Mahesh B; Huber, Markus B; Schlossbauer, Thomas; Leinsinger, Gerda; Krol, Andrzej; Wismüller, Axel

2013-10-01

Characterizing the dignity of breast lesions as benign or malignant is specifically difficult for small lesions; they don't exhibit typical characteristics of malignancy and are harder to segment since margins are harder to visualize. Previous attempts at using dynamic or morphologic criteria to classify small lesions (mean lesion diameter of about 1 cm) have not yielded satisfactory results. The goal of this work was to improve the classification performance in such small diagnostically challenging lesions while concurrently eliminating the need for precise lesion segmentation. To this end, we introduce a method for topological characterization of lesion enhancement patterns over time. Three Minkowski Functionals were extracted from all five post-contrast images of sixty annotated lesions on dynamic breast MRI exams. For each Minkowski Functional, topological features extracted from each post-contrast image of the lesions were combined into a high-dimensional texture feature vector. These feature vectors were classified in a machine learning task with support vector regression. For comparison, conventional Haralick texture features derived from gray-level co-occurrence matrices (GLCM) were also used. A new method for extracting thresholded GLCM features was also introduced and investigated here. The best classification performance was observed with Minkowski Functionals area and perimeter , thresholded GLCM features f8 and f9, and conventional GLCM features f4 and f6. However, both Minkowski Functionals and thresholded GLCM achieved such results without lesion segmentation while the performance of GLCM features significantly deteriorated when lesions were not segmented ( p < 0.05). This suggests that such advanced spatio-temporal characterization can improve the classification performance achieved in such small lesions, while simultaneously eliminating the need for precise segmentation.

Automated classification of cell morphology by coherence-controlled holographic microscopy

NASA Astrophysics Data System (ADS)

Strbkova, Lenka; Zicha, Daniel; Vesely, Pavel; Chmelik, Radim

2017-08-01

In the last few years, classification of cells by machine learning has become frequently used in biology. However, most of the approaches are based on morphometric (MO) features, which are not quantitative in terms of cell mass. This may result in poor classification accuracy. Here, we study the potential contribution of coherence-controlled holographic microscopy enabling quantitative phase imaging for the classification of cell morphologies. We compare our approach with the commonly used method based on MO features. We tested both classification approaches in an experiment with nutritionally deprived cancer tissue cells, while employing several supervised machine learning algorithms. Most of the classifiers provided higher performance when quantitative phase features were employed. Based on the results, it can be concluded that the quantitative phase features played an important role in improving the performance of the classification. The methodology could be valuable help in refining the monitoring of live cells in an automated fashion. We believe that coherence-controlled holographic microscopy, as a tool for quantitative phase imaging, offers all preconditions for the accurate automated analysis of live cell behavior while enabling noninvasive label-free imaging with sufficient contrast and high-spatiotemporal phase sensitivity.

Automated classification of cell morphology by coherence-controlled holographic microscopy.

PubMed

Strbkova, Lenka; Zicha, Daniel; Vesely, Pavel; Chmelik, Radim

2017-08-01

In the last few years, classification of cells by machine learning has become frequently used in biology. However, most of the approaches are based on morphometric (MO) features, which are not quantitative in terms of cell mass. This may result in poor classification accuracy. Here, we study the potential contribution of coherence-controlled holographic microscopy enabling quantitative phase imaging for the classification of cell morphologies. We compare our approach with the commonly used method based on MO features. We tested both classification approaches in an experiment with nutritionally deprived cancer tissue cells, while employing several supervised machine learning algorithms. Most of the classifiers provided higher performance when quantitative phase features were employed. Based on the results, it can be concluded that the quantitative phase features played an important role in improving the performance of the classification. The methodology could be valuable help in refining the monitoring of live cells in an automated fashion. We believe that coherence-controlled holographic microscopy, as a tool for quantitative phase imaging, offers all preconditions for the accurate automated analysis of live cell behavior while enabling noninvasive label-free imaging with sufficient contrast and high-spatiotemporal phase sensitivity. (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).

Please Don't Move-Evaluating Motion Artifact From Peripheral Quantitative Computed Tomography Scans Using Textural Features.

PubMed

Rantalainen, Timo; Chivers, Paola; Beck, Belinda R; Robertson, Sam; Hart, Nicolas H; Nimphius, Sophia; Weeks, Benjamin K; McIntyre, Fleur; Hands, Beth; Siafarikas, Aris

Most imaging methods, including peripheral quantitative computed tomography (pQCT), are susceptible to motion artifacts particularly in fidgety pediatric populations. Methods currently used to address motion artifact include manual screening (visual inspection) and objective assessments of the scans. However, previously reported objective methods either cannot be applied on the reconstructed image or have not been tested for distal bone sites. Therefore, the purpose of the present study was to develop and validate motion artifact classifiers to quantify motion artifact in pQCT scans. Whether textural features could provide adequate motion artifact classification performance in 2 adolescent datasets with pQCT scans from tibial and radial diaphyses and epiphyses was tested. The first dataset was split into training (66% of sample) and validation (33% of sample) datasets. Visual classification was used as the ground truth. Moderate to substantial classification performance (J48 classifier, kappa coefficients from 0.57 to 0.80) was observed in the validation dataset with the novel texture-based classifier. In applying the same classifier to the second cross-sectional dataset, a slight-to-fair (κ = 0.01-0.39) classification performance was observed. Overall, this novel textural analysis-based classifier provided a moderate-to-substantial classification of motion artifact when the classifier was specifically trained for the measurement device and population. Classification based on textural features may be used to prescreen obviously acceptable and unacceptable scans, with a subsequent human-operated visual classification of any remaining scans. Copyright © 2017 The International Society for Clinical Densitometry. Published by Elsevier Inc. All rights reserved.

FTO gene variant modulates the neural correlates of visual food perception.

PubMed

Kühn, Anne B; Feis, Delia-Lisa; Schilbach, Leonhard; Kracht, Lutz; Hess, Martin E; Mauer, Jan; Brüning, Jens C; Tittgemeyer, Marc

2016-03-01

Variations in the fat mass and obesity associated (FTO) gene are currently the strongest known genetic factor predisposing humans to non-monogenic obesity. Recent experiments have linked these variants to a broad spectrum of behavioural alterations, including food choice and substance abuse. Yet, the underlying neurobiological mechanisms by which these genetic variations influence body weight remain elusive. Here, we explore the brain structural substrate of the obesity-predisposing rs9939609 T/A variant of the FTO gene in non-obese subjects by means of multivariate classification and use fMRI to investigate genotype-specific differences in neural food-cue reactivity by analysing correlates of a visual food perception task. Our findings demonstrate that MRI-derived measures of morphology along middle and posterior fusiform gyrus (FFG) are highly predictive for FTO at-risk allele carriers, who also show enhanced neural responses elicited by food cues in the same posterior FFG area. In brief, these findings provide first-time evidence for FTO-specific differences in both brain structure and function already in non-obese individuals, thereby contributing to a mechanistic understanding of why FTO is a predisposing factor for obesity. Copyright © 2015 Elsevier Inc. All rights reserved.

Landmark Image Retrieval by Jointing Feature Refinement and Multimodal Classifier Learning.

PubMed

Zhang, Xiaoming; Wang, Senzhang; Li, Zhoujun; Ma, Shuai; Xiaoming Zhang; Senzhang Wang; Zhoujun Li; Shuai Ma; Ma, Shuai; Zhang, Xiaoming; Wang, Senzhang; Li, Zhoujun

2018-06-01

Landmark retrieval is to return a set of images with their landmarks similar to those of the query images. Existing studies on landmark retrieval focus on exploiting the geometries of landmarks for visual similarity matches. However, the visual content of social images is of large diversity in many landmarks, and also some images share common patterns over different landmarks. On the other side, it has been observed that social images usually contain multimodal contents, i.e., visual content and text tags, and each landmark has the unique characteristic of both visual content and text content. Therefore, the approaches based on similarity matching may not be effective in this environment. In this paper, we investigate whether the geographical correlation among the visual content and the text content could be exploited for landmark retrieval. In particular, we propose an effective multimodal landmark classification paradigm to leverage the multimodal contents of social image for landmark retrieval, which integrates feature refinement and landmark classifier with multimodal contents by a joint model. The geo-tagged images are automatically labeled for classifier learning. Visual features are refined based on low rank matrix recovery, and multimodal classification combined with group sparse is learned from the automatically labeled images. Finally, candidate images are ranked by combining classification result and semantic consistence measuring between the visual content and text content. Experiments on real-world datasets demonstrate the superiority of the proposed approach as compared to existing methods.

Barrier island morphodynamic classification based on lidar metrics for north Assateague Island, Maryland

USGS Publications Warehouse

Brock, John C.; Krabill, William; Sallenger, Asbury H.

2004-01-01

In order to reap the potential of airborne lidar surveys to provide geological information useful in understanding coastal sedimentary processes acting on various time scales, a new set of analysis methods are needed. This paper presents a multi-temporal lidar analysis of north Assateague Island, Maryland, and demonstrates the calculation of lidar metrics that condense barrier island morphology and morphological change into attributed linear features that may be used to analyze trends in coastal evolution. The new methods proposed in this paper are also of significant practical value, because lidar metric analysis reduces large volumes of point elevations into linear features attributed with essential morphological variables that are ideally suited for inclusion in Geographic Information Systems. A morphodynamic classification of north Assategue Island for a recent 10 month time period that is based on the recognition of simple patterns described by lidar change metrics is presented. Such morphodynamic classification reveals the relative magnitude and the fine scale alongshore variation in the importance of coastal changes over the study area during a defined time period. More generally, through the presentation of this morphodynamic classification of north Assateague Island, the value of lidar metrics in both examining large lidar data sets for coherent trends and in building hypotheses regarding processes driving barrier evolution is demonstrated

Classification of grass pollen through the quantitative analysis of surface ornamentation and texture.

PubMed

Mander, Luke; Li, Mao; Mio, Washington; Fowlkes, Charless C; Punyasena, Surangi W

2013-11-07

Taxonomic identification of pollen and spores uses inherently qualitative descriptions of morphology. Consequently, identifications are restricted to categories that can be reliably classified by multiple analysts, resulting in the coarse taxonomic resolution of the pollen and spore record. Grass pollen represents an archetypal example; it is not routinely identified below family level. To address this issue, we developed quantitative morphometric methods to characterize surface ornamentation and classify grass pollen grains. This produces a means of quantifying morphological features that are traditionally described qualitatively. We used scanning electron microscopy to image 240 specimens of pollen from 12 species within the grass family (Poaceae). We classified these species by developing algorithmic features that quantify the size and density of sculptural elements on the pollen surface, and measure the complexity of the ornamentation they form. These features yielded a classification accuracy of 77.5%. In comparison, a texture descriptor based on modelling the statistical distribution of brightness values in image patches yielded a classification accuracy of 85.8%, and seven human subjects achieved accuracies between 68.33 and 81.67%. The algorithmic features we developed directly relate to biologically meaningful features of grass pollen morphology, and could facilitate direct interpretation of unsupervised classification results from fossil material.

Methods for assessing the quality of mammalian embryos: How far we are from the gold standard?

PubMed

Rocha, José C; Passalia, Felipe; Matos, Felipe D; Maserati, Marc P; Alves, Mayra F; Almeida, Tamie G de; Cardoso, Bruna L; Basso, Andrea C; Nogueira, Marcelo F G

2016-08-01

Morphological embryo classification is of great importance for many laboratory techniques, from basic research to the ones applied to assisted reproductive technology. However, the standard classification method for both human and cattle embryos, is based on quality parameters that reflect the overall morphological quality of the embryo in cattle, or the quality of the individual embryonic structures, more relevant in human embryo classification. This assessment method is biased by the subjectivity of the evaluator and even though several guidelines exist to standardize the classification, it is not a method capable of giving reliable and trustworthy results. Latest approaches for the improvement of quality assessment include the use of data from cellular metabolism, a new morphological grading system, development kinetics and cleavage symmetry, embryo cell biopsy followed by pre-implantation genetic diagnosis, zona pellucida birefringence, ion release by the embryo cells and so forth. Nowadays there exists a great need for evaluation methods that are practical and non-invasive while being accurate and objective. A method along these lines would be of great importance to embryo evaluation by embryologists, clinicians and other professionals who work with assisted reproductive technology. Several techniques shows promising results in this sense, one being the use of digital images of the embryo as basis for features extraction and classification by means of artificial intelligence techniques (as genetic algorithms and artificial neural networks). This process has the potential to become an accurate and objective standard for embryo quality assessment.

Methods for assessing the quality of mammalian embryos: How far we are from the gold standard?

PubMed Central

Rocha, José C.; Passalia, Felipe; Matos, Felipe D.; Maserati Jr, Marc P.; Alves, Mayra F.; de Almeida, Tamie G.; Cardoso, Bruna L.; Basso, Andrea C.; Nogueira, Marcelo F. G.

2016-01-01

Morphological embryo classification is of great importance for many laboratory techniques, from basic research to the ones applied to assisted reproductive technology. However, the standard classification method for both human and cattle embryos, is based on quality parameters that reflect the overall morphological quality of the embryo in cattle, or the quality of the individual embryonic structures, more relevant in human embryo classification. This assessment method is biased by the subjectivity of the evaluator and even though several guidelines exist to standardize the classification, it is not a method capable of giving reliable and trustworthy results. Latest approaches for the improvement of quality assessment include the use of data from cellular metabolism, a new morphological grading system, development kinetics and cleavage symmetry, embryo cell biopsy followed by pre-implantation genetic diagnosis, zona pellucida birefringence, ion release by the embryo cells and so forth. Nowadays there exists a great need for evaluation methods that are practical and non-invasive while being accurate and objective. A method along these lines would be of great importance to embryo evaluation by embryologists, clinicians and other professionals who work with assisted reproductive technology. Several techniques shows promising results in this sense, one being the use of digital images of the embryo as basis for features extraction and classification by means of artificial intelligence techniques (as genetic algorithms and artificial neural networks). This process has the potential to become an accurate and objective standard for embryo quality assessment. PMID:27584609

Development of a preference-based index from the National Eye Institute Visual Function Questionnaire-25.

PubMed

Rentz, Anne M; Kowalski, Jonathan W; Walt, John G; Hays, Ron D; Brazier, John E; Yu, Ren; Lee, Paul; Bressler, Neil; Revicki, Dennis A

2014-03-01

Understanding how individuals value health states is central to patient-centered care and to health policy decision making. Generic preference-based measures of health may not effectively capture the impact of ocular diseases. Recently, 6 items from the National Eye Institute Visual Function Questionnaire-25 were used to develop the Visual Function Questionnaire-Utility Index health state classification, which defines visual function health states. To describe elicitation of preferences for health states generated from the Visual Function Questionnaire-Utility Index health state classification and development of an algorithm to estimate health preference scores for any health state. Nonintervention, cross-sectional study of the general community in 4 countries (Australia, Canada, United Kingdom, and United States). A total of 607 adult participants were recruited from local newspaper advertisements. In the United Kingdom, an existing database of participants from previous studies was used for recruitment. Eight of 15,625 possible health states from the Visual Function Questionnaire-Utility Index were valued using time trade-off technique. A θ severity score was calculated for Visual Function Questionnaire-Utility Index-defined health states using item response theory analysis. Regression models were then used to develop an algorithm to assign health state preference values for all potential health states defined by the Visual Function Questionnaire-Utility Index. Health state preference values for the 8 states ranged from a mean (SD) of 0.343 (0.395) to 0.956 (0.124). As expected, preference values declined with worsening visual function. Results indicate that the Visual Function Questionnaire-Utility Index describes states that participants view as spanning most of the continuum from full health to dead. Visual Function Questionnaire-Utility Index health state classification produces health preference scores that can be estimated in vision-related studies that include the National Eye Institute Visual Function Questionnaire-25. These preference scores may be of value for estimating utilities in economic and health policy analyses.

Lesion classification using clinical and visual data fusion by multiple kernel learning

NASA Astrophysics Data System (ADS)

Kisilev, Pavel; Hashoul, Sharbell; Walach, Eugene; Tzadok, Asaf

2014-03-01

To overcome operator dependency and to increase diagnosis accuracy in breast ultrasound (US), a lot of effort has been devoted to developing computer-aided diagnosis (CAD) systems for breast cancer detection and classification. Unfortunately, the efficacy of such CAD systems is limited since they rely on correct automatic lesions detection and localization, and on robustness of features computed based on the detected areas. In this paper we propose a new approach to boost the performance of a Machine Learning based CAD system, by combining visual and clinical data from patient files. We compute a set of visual features from breast ultrasound images, and construct the textual descriptor of patients by extracting relevant keywords from patients' clinical data files. We then use the Multiple Kernel Learning (MKL) framework to train SVM based classifier to discriminate between benign and malignant cases. We investigate different types of data fusion methods, namely, early, late, and intermediate (MKL-based) fusion. Our database consists of 408 patient cases, each containing US images, textual description of complaints and symptoms filled by physicians, and confirmed diagnoses. We show experimentally that the proposed MKL-based approach is superior to other classification methods. Even though the clinical data is very sparse and noisy, its MKL-based fusion with visual features yields significant improvement of the classification accuracy, as compared to the image features only based classifier.

Automatic classification of visual evoked potentials based on wavelet decomposition

NASA Astrophysics Data System (ADS)

Stasiakiewicz, Paweł; Dobrowolski, Andrzej P.; Tomczykiewicz, Kazimierz

2017-04-01

Diagnosis of part of the visual system, that is responsible for conducting compound action potential, is generally based on visual evoked potentials generated as a result of stimulation of the eye by external light source. The condition of patient's visual path is assessed by set of parameters that describe the time domain characteristic extremes called waves. The decision process is compound therefore diagnosis significantly depends on experience of a doctor. The authors developed a procedure - based on wavelet decomposition and linear discriminant analysis - that ensures automatic classification of visual evoked potentials. The algorithm enables to assign individual case to normal or pathological class. The proposed classifier has a 96,4% sensitivity at 10,4% probability of false alarm in a group of 220 cases and area under curve ROC equals to 0,96 which, from the medical point of view, is a very good result.

Classification model based on Raman spectra of selected morphological and biochemical tissue constituents for identification of atherosclerosis in human coronary arteries.

PubMed

Peres, Marines Bertolo; Silveira, Landulfo; Zângaro, Renato Amaro; Pacheco, Marcos Tadeu Tavares; Pasqualucci, Carlos Augusto

2011-09-01

This study presents the results of Raman spectroscopy applied to the classification of arterial tissue based on a simplified model using basal morphological and biochemical information extracted from the Raman spectra of arteries. The Raman spectrograph uses an 830-nm diode laser, imaging spectrograph, and a CCD camera. A total of 111 Raman spectra from arterial fragments were used to develop the model, and those spectra were compared to the spectra of collagen, fat cells, smooth muscle cells, calcification, and cholesterol in a linear fit model. Non-atherosclerotic (NA), fatty and fibrous-fatty atherosclerotic plaques (A) and calcified (C) arteries exhibited different spectral signatures related to different morphological structures presented in each tissue type. Discriminant analysis based on Mahalanobis distance was employed to classify the tissue type with respect to the relative intensity of each compound. This model was subsequently tested prospectively in a set of 55 spectra. The simplified diagnostic model showed that cholesterol, collagen, and adipocytes were the tissue constituents that gave the best classification capability and that those changes were correlated to histopathology. The simplified model, using spectra obtained from a few tissue morphological and biochemical constituents, showed feasibility by using a small amount of variables, easily extracted from gross samples.

Zooniverse: Combining Human and Machine Classifiers for the Big Survey Era

NASA Astrophysics Data System (ADS)

Fortson, Lucy; Wright, Darryl; Beck, Melanie; Lintott, Chris; Scarlata, Claudia; Dickinson, Hugh; Trouille, Laura; Willi, Marco; Laraia, Michael; Boyer, Amy; Veldhuis, Marten; Zooniverse

2018-01-01

Many analyses of astronomical data sets, ranging from morphological classification of galaxies to identification of supernova candidates, have relied on humans to classify data into distinct categories. Crowdsourced galaxy classifications via the Galaxy Zoo project provided a solution that scaled visual classification for extant surveys by harnessing the combined power of thousands of volunteers. However, the much larger data sets anticipated from upcoming surveys will require a different approach. Automated classifiers using supervised machine learning have improved considerably over the past decade but their increasing sophistication comes at the expense of needing ever more training data. Crowdsourced classification by human volunteers is a critical technique for obtaining these training data. But several improvements can be made on this zeroth order solution. Efficiency gains can be achieved by implementing a “cascade filtering” approach whereby the task structure is reduced to a set of binary questions that are more suited to simpler machines while demanding lower cognitive loads for humans.Intelligent subject retirement based on quantitative metrics of volunteer skill and subject label reliability also leads to dramatic improvements in efficiency. We note that human and machine classifiers may retire subjects differently leading to trade-offs in performance space. Drawing on work with several Zooniverse projects including Galaxy Zoo and Supernova Hunter, we will present recent findings from experiments that combine cohorts of human and machine classifiers. We show that the most efficient system results when appropriate subsets of the data are intelligently assigned to each group according to their particular capabilities.With sufficient online training, simple machines can quickly classify “easy” subjects, leaving more difficult (and discovery-oriented) tasks for volunteers. We also find humans achieve higher classification purity while samples produced by machines are typically more complete. These findings set the stage for further investigations, with the ultimate goal of efficiently and accurately labeling the wide range of data classes that will arise from the planned large astronomical surveys.

Classification of male lower torso for underwear design

NASA Astrophysics Data System (ADS)

Cheng, Z.; Kuzmichev, V. E.

2017-10-01

By means of scanning technology we have got new information about the morphology of male bodies and have redistricted the classification of men’s underwear by adopting one to consumer demands. To build the new classification in accordance with male body characteristic factors of lower torso, we make the method of underwear designing which allow to get the accurate and convenience for consumers products.

Cognitive approaches for patterns analysis and security applications

NASA Astrophysics Data System (ADS)

Ogiela, Marek R.; Ogiela, Lidia

2017-08-01

In this paper will be presented new opportunities for developing innovative solutions for semantic pattern classification and visual cryptography, which will base on cognitive and bio-inspired approaches. Such techniques can be used for evaluation of the meaning of analyzed patterns or encrypted information, and allow to involve such meaning into the classification task or encryption process. It also allows using some crypto-biometric solutions to extend personalized cryptography methodologies based on visual pattern analysis. In particular application of cognitive information systems for semantic analysis of different patterns will be presented, and also a novel application of such systems for visual secret sharing will be described. Visual shares for divided information can be created based on threshold procedure, which may be dependent on personal abilities to recognize some image details visible on divided images.

The dependence on morphology of the gas content in galactic disks

NASA Technical Reports Server (NTRS)

Hogg, D. E.; Roberts, M. S.

1993-01-01

The classification S0 was introduced by Hubble to serve as a description of galaxies whose morphological characteristics seemed to lie between the disk-dominated spirals and the spheroidal elliptical systems. Since then there has been extensive discussion as to whether this classification sequence is also an evolutionary sequence. Many studies have focussed on a particular feature such as the luminosity profile, the bulge-to-disk ratio, or the nature of the interstellar matter, but the question of the evolution remains contentious. Equally contentious is the question of the classification itself. For systems with well-developed disks there usually is no problem. Many spheroidal systems also are unambiguously classified as ellipticals in most catalogs. However, there are a number of early systems which have been reclassified following review using improved optical material. For example, Eder et al. (AJ, 102, 572, 1991) found that many of the S0 galaxies which are rich in neutral hydrogen have faint spiral features. The confusion about classification propagates into the discussion of the properties of early-type systems. Attempts to put the classification system on a quantitative basis have in general been unsuccessful. Recently Sandage (private communication) has reviewed the classification of early systems and has defined a set of sub-classes for these objects. The S0 galaxies are divided into three groups, depending on the prominence of the disk. There are six subdivisions of Sa galaxies, depending upon the relative prominence of knots and other arm-like characteristics. We have explored the total gas content in these objects to see if there is a dependence on the galaxy morphology, as denoted by these new subclasses.

Examining the effectiveness of discriminant function analysis and cluster analysis in species identification of male field crickets based on their calling songs.

PubMed

Jaiswara, Ranjana; Nandi, Diptarup; Balakrishnan, Rohini

2013-01-01

Traditional taxonomy based on morphology has often failed in accurate species identification owing to the occurrence of cryptic species, which are reproductively isolated but morphologically identical. Molecular data have thus been used to complement morphology in species identification. The sexual advertisement calls in several groups of acoustically communicating animals are species-specific and can thus complement molecular data as non-invasive tools for identification. Several statistical tools and automated identifier algorithms have been used to investigate the efficiency of acoustic signals in species identification. Despite a plethora of such methods, there is a general lack of knowledge regarding the appropriate usage of these methods in specific taxa. In this study, we investigated the performance of two commonly used statistical methods, discriminant function analysis (DFA) and cluster analysis, in identification and classification based on acoustic signals of field cricket species belonging to the subfamily Gryllinae. Using a comparative approach we evaluated the optimal number of species and calling song characteristics for both the methods that lead to most accurate classification and identification. The accuracy of classification using DFA was high and was not affected by the number of taxa used. However, a constraint in using discriminant function analysis is the need for a priori classification of songs. Accuracy of classification using cluster analysis, which does not require a priori knowledge, was maximum for 6-7 taxa and decreased significantly when more than ten taxa were analysed together. We also investigated the efficacy of two novel derived acoustic features in improving the accuracy of identification. Our results show that DFA is a reliable statistical tool for species identification using acoustic signals. Our results also show that cluster analysis of acoustic signals in crickets works effectively for species classification and identification.

Family Traits of Galaxies: From the Tuning Fork to a Physical Classification in a Multi-Wavelength Context

NASA Astrophysics Data System (ADS)

Rampazzo, Roberto; D'Onofrio, Mauro; Zaggia, Simone; Elmegreen, Debra M.; Laurikainen, Eija; Duc, Pierre-Alain; Gallart, Carme; Fraix-Burnet, Didier

At the time of the Great Debate nebulæ where recognized to have different morphologies and first classifications, sometimes only descriptive, have been attempted. A review of these early classification systems are well documented by the Allan Sandage's review in 2005 (Sandage 2005). This review emphasized the debt, in term of continuity of forms of spiral galaxies, due by the Hubble's classification scheme to the Reynold's systems proposed in 1920 (Reynolds, 1920).

Evidence for Early Morphological Decomposition in Visual Word Recognition

ERIC Educational Resources Information Center

Solomyak, Olla; Marantz, Alec

2010-01-01

We employ a single-trial correlational MEG analysis technique to investigate early processing in the visual recognition of morphologically complex words. Three classes of affixed words were presented in a lexical decision task: free stems (e.g., taxable), bound roots (e.g., tolerable), and unique root words (e.g., vulnerable, the root of which…

Evaluation of image deblurring methods via a classification metric

NASA Astrophysics Data System (ADS)

Perrone, Daniele; Humphreys, David; Lamb, Robert A.; Favaro, Paolo

2012-09-01

The performance of single image deblurring algorithms is typically evaluated via a certain discrepancy measure between the reconstructed image and the ideal sharp image. The choice of metric, however, has been a source of debate and has also led to alternative metrics based on human visual perception. While fixed metrics may fail to capture some small but visible artifacts, perception-based metrics may favor reconstructions with artifacts that are visually pleasant. To overcome these limitations, we propose to assess the quality of reconstructed images via a task-driven metric. In this paper we consider object classification as the task and therefore use the rate of classification as the metric to measure deblurring performance. In our evaluation we use data with different types of blur in two cases: Optical Character Recognition (OCR), where the goal is to recognise characters in a black and white image, and object classification with no restrictions on pose, illumination and orientation. Finally, we show how off-the-shelf classification algorithms benefit from working with deblurred images.

Ontology-based malaria parasite stage and species identification from peripheral blood smear images.

PubMed

Makkapati, Vishnu V; Rao, Raghuveer M

2011-01-01

The diagnosis and treatment of malaria infection requires detecting the presence of the malaria parasite in the patient as well as identification of the parasite species. We present an image processing-based approach to detect parasites in microscope images of a blood smear and an ontology-based classification of the stage of the parasite for identifying the species of infection. This approach is patterned after the diagnosis approach adopted by a pathologist for visual examination, and hence, is expected to deliver similar results. We formulate several rules based on the morphology of the basic components of a parasite, namely, chromatin dot(s) and cytoplasm, to identify the parasite stage and species. Numerical results are presented for data taken from various patients. A sensitivity of 88% and a specificity of 95% is reported by evaluation of the scheme on 55 images.

Personalized, relevance-based Multimodal Robotic Imaging and augmented reality for Computer Assisted Interventions.

PubMed

Navab, Nassir; Fellow, Miccai; Hennersperger, Christoph; Frisch, Benjamin; Fürst, Bernhard

2016-10-01

In the last decade, many researchers in medical image computing and computer assisted interventions across the world focused on the development of the Virtual Physiological Human (VPH), aiming at changing the practice of medicine from classification and treatment of diseases to that of modeling and treating patients. These projects resulted in major advancements in segmentation, registration, morphological, physiological and biomechanical modeling based on state of art medical imaging as well as other sensory data. However, a major issue which has not yet come into the focus is personalizing intra-operative imaging, allowing for optimal treatment. In this paper, we discuss the personalization of imaging and visualization process with particular focus on satisfying the challenging requirements of computer assisted interventions. We discuss such requirements and review a series of scientific contributions made by our research team to tackle some of these major challenges. Copyright © 2016. Published by Elsevier B.V.

Detection and classification of interstitial lung diseases and emphysema using a joint morphological-fuzzy approach

NASA Astrophysics Data System (ADS)

Chang Chien, Kuang-Che; Fetita, Catalin; Brillet, Pierre-Yves; Prêteux, Françoise; Chang, Ruey-Feng

2009-02-01

Multi-detector computed tomography (MDCT) has high accuracy and specificity on volumetrically capturing serial images of the lung. It increases the capability of computerized classification for lung tissue in medical research. This paper proposes a three-dimensional (3D) automated approach based on mathematical morphology and fuzzy logic for quantifying and classifying interstitial lung diseases (ILDs) and emphysema. The proposed methodology is composed of several stages: (1) an image multi-resolution decomposition scheme based on a 3D morphological filter is used to detect and analyze the different density patterns of the lung texture. Then, (2) for each pattern in the multi-resolution decomposition, six features are computed, for which fuzzy membership functions define a probability of association with a pathology class. Finally, (3) for each pathology class, the probabilities are combined up according to the weight assigned to each membership function and two threshold values are used to decide the final class of the pattern. The proposed approach was tested on 10 MDCT cases and the classification accuracy was: emphysema: 95%, fibrosis/honeycombing: 84% and ground glass: 97%.

Microglia Morphological Categorization in a Rat Model of Neuroinflammation by Hierarchical Cluster and Principal Components Analysis.

PubMed

Fernández-Arjona, María Del Mar; Grondona, Jesús M; Granados-Durán, Pablo; Fernández-Llebrez, Pedro; López-Ávalos, María D

2017-01-01

It is known that microglia morphology and function are closely related, but only few studies have objectively described different morphological subtypes. To address this issue, morphological parameters of microglial cells were analyzed in a rat model of aseptic neuroinflammation. After the injection of a single dose of the enzyme neuraminidase (NA) within the lateral ventricle (LV) an acute inflammatory process occurs. Sections from NA-injected animals and sham controls were immunolabeled with the microglial marker IBA1, which highlights ramifications and features of the cell shape. Using images obtained by section scanning, individual microglial cells were sampled from various regions (septofimbrial nucleus, hippocampus and hypothalamus) at different times post-injection (2, 4 and 12 h). Each cell yielded a set of 15 morphological parameters by means of image analysis software. Five initial parameters (including fractal measures) were statistically different in cells from NA-injected rats (most of them IL-1β positive, i.e., M1-state) compared to those from control animals (none of them IL-1β positive, i.e., surveillant state). However, additional multimodal parameters were revealed more suitable for hierarchical cluster analysis (HCA). This method pointed out the classification of microglia population in four clusters. Furthermore, a linear discriminant analysis (LDA) suggested three specific parameters to objectively classify any microglia by a decision tree. In addition, a principal components analysis (PCA) revealed two extra valuable variables that allowed to further classifying microglia in a total of eight sub-clusters or types. The spatio-temporal distribution of these different morphotypes in our rat inflammation model allowed to relate specific morphotypes with microglial activation status and brain location. An objective method for microglia classification based on morphological parameters is proposed. Main points Microglia undergo a quantifiable morphological change upon neuraminidase induced inflammation.Hierarchical cluster and principal components analysis allow morphological classification of microglia.Brain location of microglia is a relevant factor.

Microglia Morphological Categorization in a Rat Model of Neuroinflammation by Hierarchical Cluster and Principal Components Analysis

PubMed Central

Fernández-Arjona, María del Mar; Grondona, Jesús M.; Granados-Durán, Pablo; Fernández-Llebrez, Pedro; López-Ávalos, María D.

2017-01-01

It is known that microglia morphology and function are closely related, but only few studies have objectively described different morphological subtypes. To address this issue, morphological parameters of microglial cells were analyzed in a rat model of aseptic neuroinflammation. After the injection of a single dose of the enzyme neuraminidase (NA) within the lateral ventricle (LV) an acute inflammatory process occurs. Sections from NA-injected animals and sham controls were immunolabeled with the microglial marker IBA1, which highlights ramifications and features of the cell shape. Using images obtained by section scanning, individual microglial cells were sampled from various regions (septofimbrial nucleus, hippocampus and hypothalamus) at different times post-injection (2, 4 and 12 h). Each cell yielded a set of 15 morphological parameters by means of image analysis software. Five initial parameters (including fractal measures) were statistically different in cells from NA-injected rats (most of them IL-1β positive, i.e., M1-state) compared to those from control animals (none of them IL-1β positive, i.e., surveillant state). However, additional multimodal parameters were revealed more suitable for hierarchical cluster analysis (HCA). This method pointed out the classification of microglia population in four clusters. Furthermore, a linear discriminant analysis (LDA) suggested three specific parameters to objectively classify any microglia by a decision tree. In addition, a principal components analysis (PCA) revealed two extra valuable variables that allowed to further classifying microglia in a total of eight sub-clusters or types. The spatio-temporal distribution of these different morphotypes in our rat inflammation model allowed to relate specific morphotypes with microglial activation status and brain location. An objective method for microglia classification based on morphological parameters is proposed. Main points Microglia undergo a quantifiable morphological change upon neuraminidase induced inflammation.Hierarchical cluster and principal components analysis allow morphological classification of microglia.Brain location of microglia is a relevant factor. PMID:28848398

The neocortex of cetartiodactyls. II. Neuronal morphology of the visual and motor cortices in the giraffe (Giraffa camelopardalis).

PubMed

Jacobs, Bob; Harland, Tessa; Kennedy, Deborah; Schall, Matthew; Wicinski, Bridget; Butti, Camilla; Hof, Patrick R; Sherwood, Chet C; Manger, Paul R

2015-09-01

The present quantitative study extends our investigation of cetartiodactyls by exploring the neuronal morphology in the giraffe (Giraffa camelopardalis) neocortex. Here, we investigate giraffe primary visual and motor cortices from perfusion-fixed brains of three subadults stained with a modified rapid Golgi technique. Neurons (n = 244) were quantified on a computer-assisted microscopy system. Qualitatively, the giraffe neocortex contained an array of complex spiny neurons that included both "typical" pyramidal neuron morphology and "atypical" spiny neurons in terms of morphology and/or orientation. In general, the neocortex exhibited a vertical columnar organization of apical dendrites. Although there was no significant quantitative difference in dendritic complexity for pyramidal neurons between primary visual (n = 78) and motor cortices (n = 65), there was a significant difference in dendritic spine density (motor cortex > visual cortex). The morphology of aspiny neurons in giraffes appeared to be similar to that of other eutherian mammals. For cross-species comparison of neuron morphology, giraffe pyramidal neurons were compared to those quantified with the same methodology in African elephants and some cetaceans (e.g., bottlenose dolphin, minke whale, humpback whale). Across species, the giraffe (and cetaceans) exhibited less widely bifurcating apical dendrites compared to elephants. Quantitative dendritic measures revealed that the elephant and humpback whale had more extensive dendrites than giraffes, whereas the minke whale and bottlenose dolphin had less extensive dendritic arbors. Spine measures were highest in the giraffe, perhaps due to the high quality, perfusion fixation. The neuronal morphology in giraffe neocortex is thus generally consistent with what is known about other cetartiodactyls.

Classifying Structures in the ISM with Machine Learning Techniques

NASA Astrophysics Data System (ADS)

Beaumont, Christopher; Goodman, A. A.; Williams, J. P.

2011-01-01

The processes which govern molecular cloud evolution and star formation often sculpt structures in the ISM: filaments, pillars, shells, outflows, etc. Because of their morphological complexity, these objects are often identified manually. Manual classification has several disadvantages; the process is subjective, not easily reproducible, and does not scale well to handle increasingly large datasets. We have explored to what extent machine learning algorithms can be trained to autonomously identify specific morphological features in molecular cloud datasets. We show that the Support Vector Machine algorithm can successfully locate filaments and outflows blended with other emission structures. When the objects of interest are morphologically distinct from the surrounding emission, this autonomous classification achieves >90% accuracy. We have developed a set of IDL-based tools to apply this technique to other datasets.

Morphological effects in children word reading: a priming study in fourth graders.

PubMed

Casalis, Séverine; Dusautoir, Marion; Colé, Pascale; Ducrot, Stéphanie

2009-09-01

A growing corpus of evidence suggests that morphology could play a role in reading acquisition, and that young readers could be sensitive to the morphemic structure of written words. In the present experiment, we examined whether and when morphological information is activated in word recognition. French fourth graders made visual lexical decisions to derived words preceded by primes sharing either a morphological or an orthographic relationship with the target. Results showed significant and equivalent facilitation priming effects in cases of morphologically and orthographically related primes at the shortest prime duration, and a significant facilitation priming effect in the case of only morphologically related primes at the longer prime duration. Thus, these results strongly suggest that a morphological level is involved in children's visual word recognition, although it is not distinct from the formal one at an early stage of word processing.

Expert consensus statement to guide the evidence-based classification of Paralympic athletes with vision impairment: a Delphi study.

PubMed

Ravensbergen, H J C Rianne; Mann, D L; Kamper, S J

2016-04-01

Paralympic sports are required to develop evidence-based systems that allocate athletes into 'classes' on the basis of the impact of their impairment on sport performance. However, sports for athletes with vision impairment (VI) classify athletes solely based on the WHO criteria for low vision and blindness. One key barrier to evidence-based classification is the absence of guidance on how to address classification issues unique to VI sport. The aim of this study was to reach expert consensus on how issues specific to VI sport should be addressed in evidence-based classification. A four-round Delphi study was conducted with 25 participants who had expertise as a coach, athlete, classifier and/or administrator in Paralympic sport for VI athletes. The experts agreed that the current method of classification does not fulfil the requirements of Paralympic classification, and that the system should be different for each sport to account for the sports' unique visual demands. Instead of relying only on tests of visual acuity and visual field, the panel agreed that additional tests are required to better account for the impact of impairment on sport performance. There was strong agreement that all athletes should not be required to wear a blindfold as a means of equalising the impairment during competition. There is strong support within the Paralympic movement to change the way that VI athletes are classified. This consensus statement provides clear guidance on how the most important issues specific to VI should be addressed, removing key barriers to the development of evidence-based classification. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Pattern Recognition Approaches for Breast Cancer DCE-MRI Classification: A Systematic Review.

PubMed

Fusco, Roberta; Sansone, Mario; Filice, Salvatore; Carone, Guglielmo; Amato, Daniela Maria; Sansone, Carlo; Petrillo, Antonella

2016-01-01

We performed a systematic review of several pattern analysis approaches for classifying breast lesions using dynamic, morphological, and textural features in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Several machine learning approaches, namely artificial neural networks (ANN), support vector machines (SVM), linear discriminant analysis (LDA), tree-based classifiers (TC), and Bayesian classifiers (BC), and features used for classification are described. The findings of a systematic review of 26 studies are presented. The sensitivity and specificity are respectively 91 and 83 % for ANN, 85 and 82 % for SVM, 96 and 85 % for LDA, 92 and 87 % for TC, and 82 and 85 % for BC. The sensitivity and specificity are respectively 82 and 74 % for dynamic features, 93 and 60 % for morphological features, 88 and 81 % for textural features, 95 and 86 % for a combination of dynamic and morphological features, and 88 and 84 % for a combination of dynamic, morphological, and other features. LDA and TC have the best performance. A combination of dynamic and morphological features gives the best performance.

[Achene morphology cluster analysis of Taraxacum F. H. Wigg. from northeast China and molecule systematics evidence determined by SRAP].

PubMed

Li, Hai-juan; Zhao, Xin; Jia, Qing-fei; Li, Tian-lai; Ning, Wei

2012-08-01

The achenes morphological and micro-morphological characteristics of six species of genus Taraxacum from northeastern China as well as SRAP cluster analysis were observed for their classification evidences. The achenes were observed by microscope and EPMA. Cluster analysis was given on the basis of the size, shape, cone proportion, color and surface sculpture of achenes. The Taraxacum inter-species achene shape characteristic difference is obvious, particularly spinulose distribution and size, achene color and achene size; with the Taraxacum plant achene shape the cluster method T. antungense Kitag. and the T. urbanum Kitag. should combine for the identical kind; the achene morphology cluster analysis and the SRAP tagged molecule systematics's cluster result retrieves in the table with "the Chinese flora". The class group to divide the result is consistent. Taraxacum plant achene shape characteristic stable conservative, may carry on the inter-species division and the sibship analysis according to the achene shape characteristic combination difference; the achene morphology cluster analysis as well as the SRAP tagged molecule systematics confirmation support dandelion classification result of "the Chinese flora".

Use of mutation profiles to refine the classification of endometrial carcinomas

PubMed Central

Cheang, Maggie CU; Wiegand, Kimberly; Senz, Janine; Tone, Alicia; Yang, Winnie; Prentice, Leah; Tse, Kane; Zeng, Thomas; McDonald, Helen; Schmidt, Amy P.; Mutch, David G.; McAlpine, Jessica N; Hirst, Martin; Shah, Sohrab P; Lee, Cheng-Han; Goodfellow, Paul J; Gilks, C. Blake; Huntsman, David G

2014-01-01

The classification of endometrial carcinomas is based on pathological assessment of tumour cell type; the different cell types (endometrioid, serous, carcinosarcoma, mixed, and clear cell) are associated with distinct molecular alterations. This current classification system for high-grade subtypes, in particular the distinction between high-grade endometrioid (EEC-3) and serous carcinomas (ESC), is limited in its reproducibility and prognostic abilities. Therefore, a search for specific molecular classifiers to improve endometrial carcinoma subclassification is warranted. We performed target enrichment sequencing on 393 endometrial carcinomas from two large cohorts, sequencing exons from the following 9 genes; ARID1A, PPP2R1A, PTEN, PIK3CA, KRAS, CTNNB1, TP53, BRAF and PPP2R5C. Based on this gene panel each endometrial carcinoma subtype shows a distinct mutation profile. EEC-3s have significantly different frequencies of PTEN and TP53 mutations when compared to low-grade endometrioid carcinomas. ESCs and EEC-3s are distinct subtypes with significantly different frequencies of mutations in PTEN, ARID1A, PPP2R1A, TP53, and CTNNB1. From the mutation profiles we were able to identify subtype outliers, i.e. cases diagnosed morphologically as one subtype but with a mutation profile suggestive of a different subtype. Careful review of these diagnostically challenging cases suggested that the original morphological classification was incorrect in most instances. The molecular profile of carcinosarcomas suggests two distinct mutation profiles for these tumours; endometrioid-type (PTEN, PIK3CA, ARID1A, KRAS mutations), and serous-type (TP53 and PPP2R1A mutations). While this nine gene panel does not allow for a purely molecularly based classification of endometrial carcinoma, it may prove useful as an adjunct to morphological classification and serve as an aid in the classification of problematic cases. If used in practice, it may lead to improved diagnostic reproducibility and may also serve to stratify patients for targeted therapeutics. PMID:22653804

A Stream Morphology Classification for Eco-hydraulic Purposes Based on Geospatial Data: a Solute Transport Application Case

NASA Astrophysics Data System (ADS)

Jiménez Jaramillo, M. A.; Camacho Botero, L. A.; Vélez Upegui, J. I.

2010-12-01

Variation in stream morphology along a basin drainage network leads to different hydraulic patterns and sediment transport processes. Moreover, solute transport processes along streams, and stream habitats for fisheries and microorganisms, rely on stream corridor structure, including elements such as bed forms, channel patterns, riparian vegetation, and the floodplain. In this work solute transport processes simulation and stream habitat identification are carried out at the basin scale. A reach-scale morphological classification system based on channel slope and specific stream power was implemented by using digital elevation models and hydraulic geometry relationships. Although the morphological framework allows identification of cascade, step-pool, plane bed and pool-riffle morphologies along the drainage network, it still does not account for floodplain configuration and bed-forms identification of those channel types. Hence, as a first application case in order to obtain parsimonious three-dimensional characterizations of drainage channels, the morphological framework has been updated by including topographical floodplain delimitation through a Multi-resolution Valley Bottom Flatness Index assessing, and a stochastic bed form representation of the step-pool morphology. Model outcomes were tested in relation to in-stream water storage for different flow conditions and representative travel times according to the Aggregated Dead Zone -ADZ- model conceptualization of solute transport processes.

Hyperspectral imaging for presumptive identification of bacterial colonies on solid chromogenic culture media

NASA Astrophysics Data System (ADS)

Guillemot, Mathilde; Midahuen, Rony; Archeny, Delpine; Fulchiron, Corine; Montvernay, Regis; Perrin, Guillaume; Leroux, Denis F.

2016-04-01

BioMérieux is automating the microbiology laboratory in order to reduce cost (less manpower and consumables), to improve performance (increased sensitivity, machine algorithms) and to gain traceability through optimization of the clinical laboratory workflow. In this study, we evaluate the potential of Hyperspectral imaging (HSI) as a substitute to human visual observation when performing the task of microbiological culture interpretation. Microbial colonies from 19 strains subcategorized in 6 chromogenic classes were analyzed after a 24h-growth on a chromogenic culture medium (chromID® CPS Elite, bioMérieux, France). The HSI analysis was performed in the VNIR region (400-900 nm) using a linescan configuration. Using algorithms relying on Linear Spectral Unmixing, and using exclusively Diffuse Reflectance Spectra (DRS) as input data, we report interclass classification accuracies of 100% using a fully automatable approach and no use of morphological information. In order to eventually simplify the instrument, the performance of degraded DRS was also evaluated using only the most discriminant 14 spectral channels (a model for a multispectral approach) or 3 channels (model of a RGB image). The overall classification performance remains unchanged for our multispectral model but is degraded for the predicted RGB model, hints that a multispectral solution might bring the answer for an improved colony recognition.

Submorphotypes of the maxillary first molar and their effects on alignment and rotation.

PubMed

Kim, Hong-Kyun; Kwon, Ho Beom; Hyun, Hong-Keun; Jung, Min-Ho; Han, Seong Ho; Park, Young-Seok

2014-09-01

The aim of this study was to explore the shape differences in maxillary first molars with orthographic measurements using 3-dimensional virtual models to assess whether there is variability in morphology that could affect the alignment results when treated by straight-wire appliance systems. A total of 175 maxillary first molars with 4 cusps were selected for classification. With 3-dimensional laser scanning and reconstruction software, virtual casts were constructed. After performing several linear and angular measurements on the virtual occlusal plane, the teeth were clustered into 2 groups by the method of partitioning around medoids. To visualize the 2 groups, occlusal polygons were constructed using the average data of these groups. The resultant 2 clusters showed statistically significant differences in the measurements describing the cusp locations and the buccal and lingual outlines. The rotation along the centers made the 2 cluster polygons look similar, but there was a difference in the direction of the midsagittal lines. There was considerable variability in morphology according to 2 clusters in the population of this study. The occlusal polygons showed that the outlines of the 2 clusters were similar, but the midsagittal line directions and inner geometries were different. The difference between the morphologies of the 2 clusters could result in occlusal contact differences, which might be considered for better alignment of the maxillary posterior segment. Copyright © 2014 American Association of Orthodontists. Published by Elsevier Inc. All rights reserved.

Ocular linguatuliasis in Ecuador: case report and morphometric study of the larva of Linguatula serrata.

PubMed

Lazo, R F; Hidalgo, E; Lazo, J E; Bermeo, A; Llaguno, M; Murillo, J; Teixeira, V P

1999-03-01

Linguatula serrata is a pentastomid, a cosmopolitan parasite belonging to the Phylum Pentastomida. Humans may act as an intermediate or accidental definitive host of this parasite, manifesting the nasopharyngeal or visceral form, with the latter having been described more frequently. The occurrence of ocular linguatuliasis is extremely rare, but it has been reported in the United States and Israel. The objective of the present paper was to report the first case of ocular linguatuliasis in Ecuador and to extend the morphologic study of L. serrata by morphometric analysis. The patient studied was a 34-year old woman from Guayaquil, Ecuador who complained of ocular pain with conjunctivitis and visual difficulties of two-months duration. Biomicroscopic examination revealed a mobile body in the anterior chamber of the eye. The mobile body was surgically removed. The specimen was fixed in alcohol, cleared using the technique of Loos, stained with acetic carmine, and mounted on balsam between a slide and a coverslip. It was observed with stereoscopic and common light microscopes in combination with an automatic system for image analysis and processing. The morphologic and morphometric characteristics corresponded to the third-instar larval form of L. serrata. To our knowledge, ocular linguatuliasis has not been previously described in South America, with this being the first report for Ecuador and South America. The present study shows that computer morphometry can adequately contribute both to the morphologic study and to the systematic classification of Pentastomids, and L. serrata in particular.

Land-use Scene Classification in High-Resolution Remote Sensing Images by Multiscale Deeply Described Correlatons

NASA Astrophysics Data System (ADS)

Qi, K.; Qingfeng, G.

2017-12-01

With the popular use of High-Resolution Satellite (HRS) images, more and more research efforts have been placed on land-use scene classification. However, it makes the task difficult with HRS images for the complex background and multiple land-cover classes or objects. This article presents a multiscale deeply described correlaton model for land-use scene classification. Specifically, the convolutional neural network is introduced to learn and characterize the local features at different scales. Then, learnt multiscale deep features are explored to generate visual words. The spatial arrangement of visual words is achieved through the introduction of adaptive vector quantized correlograms at different scales. Experiments on two publicly available land-use scene datasets demonstrate that the proposed model is compact and yet discriminative for efficient representation of land-use scene images, and achieves competitive classification results with the state-of-art methods.

Describing Peripancreatic Collections According to the Revised Atlanta Classification of Acute Pancreatitis: An International Interobserver Agreement Study.

PubMed

Bouwense, Stefan A; van Brunschot, Sandra; van Santvoort, Hjalmar C; Besselink, Marc G; Bollen, Thomas L; Bakker, Olaf J; Banks, Peter A; Boermeester, Marja A; Cappendijk, Vincent C; Carter, Ross; Charnley, Richard; van Eijck, Casper H; Freeny, Patrick C; Hermans, John J; Hough, David M; Johnson, Colin D; Laméris, Johan S; Lerch, Markus M; Mayerle, Julia; Mortele, Koenraad J; Sarr, Michael G; Stedman, Brian; Vege, Santhi Swaroop; Werner, Jens; Dijkgraaf, Marcel G; Gooszen, Hein G; Horvath, Karen D

2017-08-01

Severe acute pancreatitis is associated with peripancreatic morphologic changes as seen on imaging. Uniform communication regarding these morphologic findings is crucial for accurate diagnosis and treatment. For the original 1992 Atlanta classification, interobserver agreement is poor. We hypothesized that for the revised Atlanta classification, interobserver agreement will be better. An international, interobserver agreement study was performed among expert and nonexpert radiologists (n = 14), surgeons (n = 15), and gastroenterologists (n = 8). Representative computed tomographies of all stages of acute pancreatitis were selected from 55 patients and were assessed according to the revised Atlanta classification. The interobserver agreement was calculated among all reviewers and subgroups, that is, expert and nonexpert reviewers; interobserver agreement was defined as poor (≤0.20), fair (0.21-0.40), moderate (0.41-0.60), good (0.61-0.80), or very good (0.81-1.00). Interobserver agreement among all reviewers was good (0.75 [standard deviation, 0.21]) for describing the type of acute pancreatitis and good (0.62 [standard deviation, 0.19]) for the type of peripancreatic collection. Expert radiologists showed the best and nonexpert clinicians the lowest interobserver agreement. Interobserver agreement was good for the revised Atlanta classification, supporting the importance for widespread adaption of this revised classification for clinical and research communications.

Visual classification of medical data using MLP mapping.

PubMed

Cağatay Güler, E; Sankur, B; Kahya, Y P; Raudys, S

1998-05-01

In this work we discuss the design of a novel non-linear mapping method for visual classification based on multilayer perceptrons (MLP) and assigned class target values. In training the perceptron, one or more target output values for each class in a 2-dimensional space are used. In other words, class membership information is interpreted visually as closeness to target values in a 2D feature space. This mapping is obtained by training the multilayer perceptron (MLP) using class membership information, input data and judiciously chosen target values. Weights are estimated in such a way that each training feature of the corresponding class is forced to be mapped onto the corresponding 2-dimensional target value.

Hybrid 2-D and 3-D Immersive and Interactive User Interface for Scientific Data Visualization

DTIC Science & Technology

2017-08-01

visualization, 3-D interactive visualization, scientific visualization, virtual reality, real -time ray tracing 16. SECURITY CLASSIFICATION OF: 17...scientists to employ in the real world. Other than user-friendly software and hardware setup, scientists also need to be able to perform their usual...and scientific visualization communities mostly have different research priorities. For the VR community, the ability to support real -time user

The Effects of Explicit Visual Cues in Reading Biological Diagrams

ERIC Educational Resources Information Center

Ge, Yun-Ping; Unsworth, Len; Wang, Kuo-Hua

2017-01-01

Drawing on cognitive theories, this study intends to investigate the effects of explicit visual cues which have been proposed as a critical factor in facilitating understanding of biological images. Three diagrams from Taiwanese textbooks with implicit visual cues, involving the concepts of biological classification systems, fish taxonomy, and…

Molecular phylogeny and biogeography of the fern genus Pteris (Pteridaceae).

PubMed

Chao, Yi-Shan; Rouhan, Germinal; Amoroso, Victor B; Chiou, Wen-Liang

2014-07-01

Pteris (Pteridaceae), comprising over 250 species, had been thought to be a monophyletic genus until the three monotypic genera Neurocallis, Ochropteris and Platyzoma were included. However, the relationships between the type species of the genus Pteris, P. longifolia, and other species are still unknown. Furthermore, several infrageneric morphological classifications have been proposed, but are debated. To date, no worldwide phylogenetic hypothesis has been proposed for the genus, and no comprehensive biogeographical history of Pteris, crucial to understanding its cosmopolitan distribution, has been presented. A molecular phylogeny of Pteris is presented for 135 species, based on cpDNA rbcL and matK and using maximum parsimony, maximum likelihood and Bayesian inference approaches. The inferred phylogeny was used to assess the biogeographical history of Pteris and to reconstruct the evolution of one ecological and four morphological characters commonly used for infrageneric classifications. The monophyly of Pteris remains uncertain, especially regarding the relationship of Pteris with Actiniopteris + Onychium and Platyzoma. Pteris comprises 11 clades supported by combinations of ecological and morphological character states, but none of the characters used in previous classifications were found to be exclusive synapomorphies. The results indicate that Pteris diversified around 47 million years ago, and when species colonized new geographical areas they generated new lineages, which are associated with morphological character transitions. This first phylogeny of Pteris on a global scale and including more than half of the diversity of the genus should contribute to a new, more reliable infrageneric classification of Pteris, based not only on a few morphological characters but also on ecological traits and geographical distribution. The inferred biogeographical history highlights long-distance dispersal as a major process shaping the worldwide distribution of the species. Colonization of different niches was followed by subsequent morphological diversification. Dispersal events followed by allopatric and parapatric speciation contribute to the species diversity of Pteris. © The Author 2014. Published by Oxford University Press on behalf of the Annals of Botany Company. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

The footprints of visual attention in the Posner cueing paradigm revealed by classification images

NASA Technical Reports Server (NTRS)

Eckstein, Miguel P.; Shimozaki, Steven S.; Abbey, Craig K.

2002-01-01

In the Posner cueing paradigm, observers' performance in detecting a target is typically better in trials in which the target is present at the cued location than in trials in which the target appears at the uncued location. This effect can be explained in terms of a Bayesian observer where visual attention simply weights the information differently at the cued (attended) and uncued (unattended) locations without a change in the quality of processing at each location. Alternatively, it could also be explained in terms of visual attention changing the shape of the perceptual filter at the cued location. In this study, we use the classification image technique to compare the human perceptual filters at the cued and uncued locations in a contrast discrimination task. We did not find statistically significant differences between the shapes of the inferred perceptual filters across the two locations, nor did the observed differences account for the measured cueing effects in human observers. Instead, we found a difference in the magnitude of the classification images, supporting the idea that visual attention changes the weighting of information at the cued and uncued location, but does not change the quality of processing at each individual location.

Collagen morphology and texture analysis: from statistics to classification

PubMed Central

Mostaço-Guidolin, Leila B.; Ko, Alex C.-T.; Wang, Fei; Xiang, Bo; Hewko, Mark; Tian, Ganghong; Major, Arkady; Shiomi, Masashi; Sowa, Michael G.

2013-01-01

In this study we present an image analysis methodology capable of quantifying morphological changes in tissue collagen fibril organization caused by pathological conditions. Texture analysis based on first-order statistics (FOS) and second-order statistics such as gray level co-occurrence matrix (GLCM) was explored to extract second-harmonic generation (SHG) image features that are associated with the structural and biochemical changes of tissue collagen networks. Based on these extracted quantitative parameters, multi-group classification of SHG images was performed. With combined FOS and GLCM texture values, we achieved reliable classification of SHG collagen images acquired from atherosclerosis arteries with >90% accuracy, sensitivity and specificity. The proposed methodology can be applied to a wide range of conditions involving collagen re-modeling, such as in skin disorders, different types of fibrosis and muscular-skeletal diseases affecting ligaments and cartilage. PMID:23846580

SDSS-IV MaNGA: stellar angular momentum of about 2300 galaxies: unveiling the bimodality of massive galaxy properties

NASA Astrophysics Data System (ADS)

Graham, Mark T.; Cappellari, Michele; Li, Hongyu; Mao, Shude; Bershady, Matthew A.; Bizyaev, Dmitry; Brinkmann, Jonathan; Brownstein, Joel R.; Bundy, Kevin; Drory, Niv; Law, David R.; Pan, Kaike; Thomas, Daniel; Wake, David A.; Weijmans, Anne-Marie; Westfall, Kyle B.; Yan, Renbin

2018-07-01

We measure λ _{R_e}, a proxy for galaxy specific stellar angular momentum within one effective radius, and the ellipticity, ɛ, for about 2300 galaxies of all morphological types observed with integral field spectroscopy as part of the Mapping Nearby Galaxies at Apache Point Observatory survey, the largest such sample to date. We use the (λ _{R_e}, ɛ ) diagram to separate early-type galaxies into fast and slow rotators. We also visually classify each galaxy according to its optical morphology and two-dimensional stellar velocity field. Comparing these classifications to quantitative λ _{R_e} measurements reveals tight relationships between angular momentum and galaxy structure. In order to account for atmospheric seeing, we use realistic models of galaxy kinematics to derive a general approximate analytic correction for λ _{R_e}. Thanks to the size of the sample and the large number of massive galaxies, we unambiguously detect a clear bimodality in the (λ _{R_e}, ɛ ) diagram which may result from fundamental differences in galaxy assembly history. There is a sharp secondary density peak inside the region of the diagram with low λ _{R_e} and ɛ < 0.4, previously suggested as the definition for slow rotators. Most of these galaxies are visually classified as non-regular rotators and have high velocity dispersion. The intrinsic bimodality must be stronger, as it tends to be smoothed by noise and inclination. The large sample of slow rotators allows us for the first time to unveil a secondary peak at ±90° in their distribution of the misalignments between the photometric and kinematic position angles. We confirm that genuine slow rotators start appearing above M ≥ 2 × 1011 M⊙ where a significant number of high-mass fast rotators also exist.

A Catalog of Visually Classified Galaxies in the Local (z ∼ 0.01) Universe

NASA Astrophysics Data System (ADS)

Ann, H. B.; Seo, Mira; Ha, D. K.

2015-04-01

The morphological types of 5836 galaxies were classified by a visual inspection of color images using the Sloan Digital Sky Survey Data Release 7 to produce a morphology catalog of a representative sample of local galaxies with z\\lt 0.01. The sample galaxies are almost complete for galaxies brighter than {{r}pet}=17.77. Our classification system is basically the same as that of the Third Reference Catalog of Bright Galaxies with some simplifications for giant galaxies. On the other hand, we distinguish the fine features of dwarf elliptical (dE)-like galaxies to classify five subtypes: dE, blue-cored dwarf ellipticals, dwarf spheroidals (dSph), blue dwarf ellipticals (dEblue), and dwarf lenticulars (dS0). In addition, we note the presence of nucleation in dE, dSph, and dS0. Elliptical galaxies and lenticular galaxies contribute only ∼ 1.5 and ∼ 4.9% of local galaxies, respectively, whereas spirals and irregulars contribute ∼ 32.1 and ∼ 42.8%, respectively. The dEblue galaxies, which are a recently discovered population of galaxies, contribute a significant fraction of dwarf galaxies. There seem to be structural differences between dSph and dE galaxies. The dSph galaxies are fainter and bluer with a shallower surface brightness gradient than dE galaxies. They also have a lower fraction of galaxies with small axis ratios (b/a≲ 0.4) than dE galaxies. The mean projected distance to the nearest neighbor galaxy is ∼260 kpc. About 1% of local galaxies have no neighbors with comparable luminosity within a projected distance of 2 Mpc.

SDSS-IV MaNGA: Stellar angular momentum of about 2300 galaxies: unveiling the bimodality of massive galaxy properties

NASA Astrophysics Data System (ADS)

Graham, Mark T.; Cappellari, Michele; Li, Hongyu; Mao, Shude; Bershady, Matthew; Bizyaev, Dmitry; Brinkmann, Jonathan; Brownstein, Joel R.; Bundy, Kevin; Drory, Niv; Law, David R.; Pan, Kaike; Thomas, Daniel; Wake, David A.; Weijmans, Anne-Marie; Westfall, Kyle B.; Yan, Renbin

2018-03-01

We measure λ _{R_e}, a proxy for galaxy specific stellar angular momentum within one effective radius, and the ellipticity, ɛ, for about 2300 galaxies of all morphological types observed with integral field spectroscopy as part of the MaNGA survey, the largest such sample to date. We use the (λ _{R_e}, ɛ ) diagram to separate early-type galaxies into fast and slow rotators. We also visually classify each galaxy according to its optical morphology and two-dimensional stellar velocity field. Comparing these classifications to quantitative λ _{R_e} measurements reveals tight relationships between angular momentum and galaxy structure. In order to account for atmospheric seeing, we use realistic models of galaxy kinematics to derive a general approximate analytic correction for λ _{R_e}. Thanks to the size of the sample and the large number of massive galaxies, we unambiguously detect a clear bimodality in the (λ _{R_e}, ɛ ) diagram which may result from fundamental differences in galaxy assembly history. There is a sharp secondary density peak inside the region of the diagram with low λ _{R_e} and ɛ < 0.4, previously suggested as the definition for slow rotators. Most of these galaxies are visually classified as non-regular rotators and have high velocity dispersion. The intrinsic bimodality must be stronger, as it tends to be smoothed by noise and inclination. The large sample of slow rotators allows us for the first time to unveil a secondary peak at ±90○ in their distribution of the misalignments between the photometric and kinematic position angles. We confirm that genuine slow rotators start appearing above M ≥ 2 × 1011M⊙ where a significant number of high-mass fast rotators also exist.

Classifying four-category visual objects using multiple ERP components in single-trial ERP.

PubMed

Qin, Yu; Zhan, Yu; Wang, Changming; Zhang, Jiacai; Yao, Li; Guo, Xiaojuan; Wu, Xia; Hu, Bin

2016-08-01

Object categorization using single-trial electroencephalography (EEG) data measured while participants view images has been studied intensively. In previous studies, multiple event-related potential (ERP) components (e.g., P1, N1, P2, and P3) were used to improve the performance of object categorization of visual stimuli. In this study, we introduce a novel method that uses multiple-kernel support vector machine to fuse multiple ERP component features. We investigate whether fusing the potential complementary information of different ERP components (e.g., P1, N1, P2a, and P2b) can improve the performance of four-category visual object classification in single-trial EEGs. We also compare the classification accuracy of different ERP component fusion methods. Our experimental results indicate that the classification accuracy increases through multiple ERP fusion. Additional comparative analyses indicate that the multiple-kernel fusion method can achieve a mean classification accuracy higher than 72 %, which is substantially better than that achieved with any single ERP component feature (55.07 % for the best single ERP component, N1). We compare the classification results with those of other fusion methods and determine that the accuracy of the multiple-kernel fusion method is 5.47, 4.06, and 16.90 % higher than those of feature concatenation, feature extraction, and decision fusion, respectively. Our study shows that our multiple-kernel fusion method outperforms other fusion methods and thus provides a means to improve the classification performance of single-trial ERPs in brain-computer interface research.

Planum Sphenoidale and Tuberculum Sellae Meningiomas: Operative Nuances of a Modern Surgical Technique with Outcome and Proposal of a New Classification System.

PubMed

Mortazavi, Martin M; Brito da Silva, Harley; Ferreira, Manuel; Barber, Jason K; Pridgeon, James S; Sekhar, Laligam N

2016-02-01

The resection of planum sphenoidale and tuberculum sellae meningiomas is challenging. A universally accepted classification system predicting surgical risk and outcome is still lacking. We report a modern surgical technique specific for planum sphenoidale and tuberculum sellae meningiomas with associated outcome. A new classification system that can guide the surgical approach and may predict surgical risk is proposed. We conducted a retrospective review of the patients who between 2005 and March 2015 underwent a craniotomy or endoscopic surgery for the resection of meningiomas involving the suprasellar region. Operative nuances of a modified frontotemporal craniotomy and orbital osteotomy technique for meningioma removal and reconstruction are described. Twenty-seven patients were found to have tumors arising mainly from the planum sphenoidale or the tuberculum sellae; 25 underwent frontotemporal craniotomy and tumor removal with orbital osteotomy and bilateral optic canal decompression, and 2 patients underwent endonasal transphenoidal resection. The most common presenting symptom was visual disturbance (77%). Vision improved in 90% of those who presented with visual decline, and there was no permanent visual deterioration. Cerebrospinal fluid leak occurred in one of the 25 cranial cases (4%) and in 1 of 2 transphenoidal cases (50%), and in both cases it resolved with treatment. There was no surgical mortality. An orbitotomy and early decompression of the involved optic canal are important for achieving gross total resection, maximizing visual improvement, and avoiding recurrence. The visual outcomes were excellent. A new classification system that can allow the comparison of different series and approaches and indicate cases that are more suitable for an endoscopic transsphenoidal approach is presented. Copyright © 2016 Elsevier Inc. All rights reserved.

Attribution of local climate zones using a multitemporal land use/land cover classification scheme

NASA Astrophysics Data System (ADS)

Wicki, Andreas; Parlow, Eberhard

2017-04-01

Worldwide, the number of people living in an urban environment exceeds the rural population with increasing tendency. Especially in relation to global climate change, cities play a major role considering the impacts of extreme heat waves on the population. For urban planners, it is important to know which types of urban structures are beneficial for a comfortable urban climate and which actions can be taken to improve urban climate conditions. Therefore, it is essential to differ between not only urban and rural environments, but also between different levels of urban densification. To compare these built-up types within different cities worldwide, Stewart and Oke developed the concept of local climate zones (LCZ) defined by morphological characteristics. The original LCZ scheme often has considerable problems when adapted to European cities with historical city centers, including narrow streets and irregular patterns. In this study, a method to bridge the gap between a classical land use/land cover (LULC) classification and the LCZ scheme is presented. Multitemporal Landsat 8 data are used to create a high accuracy LULC map, which is linked to the LCZ by morphological parameters derived from a high-resolution digital surface model and cadastral data. A bijective combination of the different classification schemes could not be achieved completely due to overlapping threshold values and the spatially homogeneous distribution of morphological parameters, but the attribution of LCZ to the LULC classification was successful.

Comparing Economic Systems.

ERIC Educational Resources Information Center

Wolken, Lawrence C.

1984-01-01

Defines the predominate classifications of economic systems: traditional, command, market, capitalism, socialism, and communism. Considers property rights, role of government, economic freedom, incentives, market structure, economic goals and means of achieving those goals for each classification. Identifies 26 print and audio-visual sources for…

Reduction in the retinotopic early visual cortex with normal aging and magnitude of perceptual learning.

PubMed

Chang, Li-Hung; Yotsumoto, Yuko; Salat, David H; Andersen, George J; Watanabe, Takeo; Sasaki, Yuka

2015-01-01

Although normal aging is known to reduce cortical structures globally, the effects of aging on local structures and functions of early visual cortex are less understood. Here, using standard retinotopic mapping and magnetic resonance imaging morphologic analyses, we investigated whether aging affects areal size of the early visual cortex, which were retinotopically localized, and whether those morphologic measures were associated with individual performance on visual perceptual learning. First, significant age-associated reduction was found in the areal size of V1, V2, and V3. Second, individual ability of visual perceptual learning was significantly correlated with areal size of V3 in older adults. These results demonstrate that aging changes local structures of the early visual cortex, and the degree of change may be associated with individual visual plasticity. Copyright © 2015 Elsevier Inc. All rights reserved.

Classification of EEG abnormalities in partial epilepsy with simultaneous EEG-fMRI recordings.

PubMed

Pedreira, C; Vaudano, A E; Thornton, R C; Chaudhary, U J; Vulliemoz, S; Laufs, H; Rodionov, R; Carmichael, D W; Lhatoo, S D; Guye, M; Quian Quiroga, R; Lemieux, L

2014-10-01

Scalp EEG recordings and the classification of interictal epileptiform discharges (IED) in patients with epilepsy provide valuable information about the epileptogenic network, particularly by defining the boundaries of the "irritative zone" (IZ), and hence are helpful during pre-surgical evaluation of patients with severe refractory epilepsies. The current detection and classification of epileptiform signals essentially rely on expert observers. This is a very time-consuming procedure, which also leads to inter-observer variability. Here, we propose a novel approach to automatically classify epileptic activity and show how this method provides critical and reliable information related to the IZ localization beyond the one provided by previous approaches. We applied Wave_clus, an automatic spike sorting algorithm, for the classification of IED visually identified from pre-surgical simultaneous Electroencephalogram-functional Magnetic Resonance Imagining (EEG-fMRI) recordings in 8 patients affected by refractory partial epilepsy candidate for surgery. For each patient, two fMRI analyses were performed: one based on the visual classification and one based on the algorithmic sorting. This novel approach successfully identified a total of 29 IED classes (compared to 26 for visual identification). The general concordance between methods was good, providing a full match of EEG patterns in 2 cases, additional EEG information in 2 other cases and, in general, covering EEG patterns of the same areas as expert classification in 7 of the 8 cases. Most notably, evaluation of the method with EEG-fMRI data analysis showed hemodynamic maps related to the majority of IED classes representing improved performance than the visual IED classification-based analysis (72% versus 50%). Furthermore, the IED-related BOLD changes revealed by using the algorithm were localized within the presumed IZ for a larger number of IED classes (9) in a greater number of patients than the expert classification (7 and 5, respectively). In contrast, in only one case presented the new algorithm resulted in fewer classes and activation areas. We propose that the use of automated spike sorting algorithms to classify IED provides an efficient tool for mapping IED-related fMRI changes and increases the EEG-fMRI clinical value for the pre-surgical assessment of patients with severe epilepsy. Copyright © 2014 Elsevier Inc. All rights reserved.

3D texture analysis for classification of second harmonic generation images of human ovarian cancer

NASA Astrophysics Data System (ADS)

Wen, Bruce; Campbell, Kirby R.; Tilbury, Karissa; Nadiarnykh, Oleg; Brewer, Molly A.; Patankar, Manish; Singh, Vikas; Eliceiri, Kevin. W.; Campagnola, Paul J.

2016-10-01

Remodeling of the collagen architecture in the extracellular matrix (ECM) has been implicated in ovarian cancer. To quantify these alterations we implemented a form of 3D texture analysis to delineate the fibrillar morphology observed in 3D Second Harmonic Generation (SHG) microscopy image data of normal (1) and high risk (2) ovarian stroma, benign ovarian tumors (3), low grade (4) and high grade (5) serous tumors, and endometrioid tumors (6). We developed a tailored set of 3D filters which extract textural features in the 3D image sets to build (or learn) statistical models of each tissue class. By applying k-nearest neighbor classification using these learned models, we achieved 83-91% accuracies for the six classes. The 3D method outperformed the analogous 2D classification on the same tissues, where we suggest this is due the increased information content. This classification based on ECM structural changes will complement conventional classification based on genetic profiles and can serve as an additional biomarker. Moreover, the texture analysis algorithm is quite general, as it does not rely on single morphological metrics such as fiber alignment, length, and width but their combined convolution with a customizable basis set.

Correlation-based pattern recognition for implantable defibrillators.

PubMed Central

Wilkins, J.

1996-01-01

An estimated 300,000 Americans die each year from cardiac arrhythmias. Historically, drug therapy or surgery were the only treatment options available for patients suffering from arrhythmias. Recently, implantable arrhythmia management devices have been developed. These devices allow abnormal cardiac rhythms to be sensed and corrected in vivo. Proper arrhythmia classification is critical to selecting the appropriate therapeutic intervention. The classification problem is made more challenging by the power/computation constraints imposed by the short battery life of implantable devices. Current devices utilize heart rate-based classification algorithms. Although easy to implement, rate-based approaches have unacceptably high error rates in distinguishing supraventricular tachycardia (SVT) from ventricular tachycardia (VT). Conventional morphology assessment techniques used in ECG analysis often require too much computation to be practical for implantable devices. In this paper, a computationally-efficient, arrhythmia classification architecture using correlation-based morphology assessment is presented. The architecture classifies individuals heart beats by assessing similarity between an incoming cardiac signal vector and a series of prestored class templates. A series of these beat classifications are used to make an overall rhythm assessment. The system makes use of several new results in the field of pattern recognition. The resulting system achieved excellent accuracy in discriminating SVT and VT. PMID:8947674

Classification of neocortical interneurons using affinity propagation.

PubMed

Santana, Roberto; McGarry, Laura M; Bielza, Concha; Larrañaga, Pedro; Yuste, Rafael

2013-01-01

In spite of over a century of research on cortical circuits, it is still unknown how many classes of cortical neurons exist. In fact, neuronal classification is a difficult problem because it is unclear how to designate a neuronal cell class and what are the best characteristics to define them. Recently, unsupervised classifications using cluster analysis based on morphological, physiological, or molecular characteristics, have provided quantitative and unbiased identification of distinct neuronal subtypes, when applied to selected datasets. However, better and more robust classification methods are needed for increasingly complex and larger datasets. Here, we explored the use of affinity propagation, a recently developed unsupervised classification algorithm imported from machine learning, which gives a representative example or exemplar for each cluster. As a case study, we applied affinity propagation to a test dataset of 337 interneurons belonging to four subtypes, previously identified based on morphological and physiological characteristics. We found that affinity propagation correctly classified most of the neurons in a blind, non-supervised manner. Affinity propagation outperformed Ward's method, a current standard clustering approach, in classifying the neurons into 4 subtypes. Affinity propagation could therefore be used in future studies to validly classify neurons, as a first step to help reverse engineer neural circuits.

USUING STREAM MORPHOLOGY CLASSIFICATION TO MANAGE ECOLOGICAL RISKS FROM LAND USE CHANGES IN THE LMR WATERSHED

EPA Science Inventory

Changes in the amount and types of land use in a watershed can destabilize stream channel structure, increase sediment loading and degrade in-stream habitat. Stream classification systems (e.g. Rosgen) may be useful for determining the susceptibility of stream channel segments t...

USING STREAM MORPHOLOGY CLASSIFICATION TO MANAGE ECOLOGICAL RISKS FROM LAND USE CHANGES IN THE LMR WATERSHED

EPA Science Inventory

Changes in the amount and types of land use in a watershed can destabilize stream channel structure, increase sediment loading and degrade in-stream habitat. Stream classification systems (e.g. Rosgen) may be useful for determining the susceptibility of stream channel segments t...

Le Predicat Adjectif en Esperanto (The Predicate Adjective in Esperanto)

ERIC Educational Resources Information Center

Lo Jocomo, Francois

1977-01-01

A study of the predicate adjective in Esperanto based on the opposition established between the complementary classifications, monemes and words. Because morphological complications do not exist in Esperanto, study of the two classifications could proceed to the benefit of the study of other languages. (Text is in French.) (AMH)

Is geography an accurate predictor of evolutionary history in the millipede family Xystodesmidae?

PubMed Central

Marek, Paul E.

2017-01-01

For the past several centuries, millipede taxonomists have used the morphology of male copulatory structures (modified legs called gonopods), which are strongly variable and suggestive of species-level differences, as a source to understand taxon relationships. Millipedes in the family Xystodesmidae are blind, dispersal-limited and have narrow habitat requirements. Therefore, geographical proximity may instead be a better predictor of evolutionary relationship than morphology, especially since gonopodal anatomy is extremely divergent and similarities may be masked by evolutionary convergence. Here we provide a phylogenetics-based test of the power of morphological versus geographical character sets for resolving phylogenetic relationships in xystodesmid millipedes. Molecular data from 90 species-group taxa in the family were included in a six-gene phylogenetic analysis to provide the basis for comparing trees generated from these alternative character sets. The molecular phylogeny was compared to topologies representing three hypotheses: (1) a prior classification formulated using morphological and geographical data, (2) hierarchical groupings derived from Euclidean geographical distance, and (3) one based solely on morphological data. Euclidean geographical distance was not found to be a better predictor of evolutionary relationship than the prior classification, the latter of which was the most similar to the molecular topology. However, all three of the alternative topologies were highly divergent (Bayes factor >10) from the molecular topology, with the tree inferred exclusively from morphology being the most divergent. The results of this analysis show that a high degree of morphological convergence from substantial gonopod shape divergence generated spurious phylogenetic relationships. These results indicate the impact that a high degree of morphological homoplasy may have had on prior treatments of the family. Using the results of our phylogenetic analysis, we make several changes to the classification of the family, including transferring the rare state-threatened species Sigmoria whiteheadi Shelley, 1986 to the genus Apheloria Chamberlin, 1921—a relationship not readily apparent based on morphology alone. We show that while gonopod differences are a premier source of taxonomic characters to diagnose species pairwise, the traits should be viewed critically as taxonomic features uniting higher levels. PMID:29038750

Research on Optimization of GLCM Parameter in Cell Classification

NASA Astrophysics Data System (ADS)

Zhang, Xi-Kun; Hou, Jie; Hu, Xin-Hua

2016-05-01

Real-time classification of biological cells according to their 3D morphology is highly desired in a flow cytometer setting. Gray level co-occurrence matrix (GLCM) algorithm has been developed to extract feature parameters from measured diffraction images ,which are too complicated to coordinate with the real-time system for a large amount of calculation. An optimization of GLCM algorithm is provided based on correlation analysis of GLCM parameters. The results of GLCM analysis and subsequent classification demonstrate optimized method can lower the time complexity significantly without loss of classification accuracy.

Real-time classification of vehicles by type within infrared imagery

NASA Astrophysics Data System (ADS)

Kundegorski, Mikolaj E.; Akçay, Samet; Payen de La Garanderie, Grégoire; Breckon, Toby P.

2016-10-01

Real-time classification of vehicles into sub-category types poses a significant challenge within infra-red imagery due to the high levels of intra-class variation in thermal vehicle signatures caused by aspects of design, current operating duration and ambient thermal conditions. Despite these challenges, infra-red sensing offers significant generalized target object detection advantages in terms of all-weather operation and invariance to visual camouflage techniques. This work investigates the accuracy of a number of real-time object classification approaches for this task within the wider context of an existing initial object detection and tracking framework. Specifically we evaluate the use of traditional feature-driven bag of visual words and histogram of oriented gradient classification approaches against modern convolutional neural network architectures. Furthermore, we use classical photogrammetry, within the context of current target detection and classification techniques, as a means of approximating 3D target position within the scene based on this vehicle type classification. Based on photogrammetric estimation of target position, we then illustrate the use of regular Kalman filter based tracking operating on actual 3D vehicle trajectories. Results are presented using a conventional thermal-band infra-red (IR) sensor arrangement where targets are tracked over a range of evaluation scenarios.

Wing Shape as an Indicator of Larval Rearing Conditions for Aedes albopictus and Ae. aegypti (Diptera: Culicidae)

PubMed Central

Stephens, C. R.; Juliano, S. A.

2012-01-01

Estimating a mosquito’s vector competence, or likelihood of transmitting disease, if it takes an infectious blood meal, is an important aspect of predicting when and where outbreaks of infectious diseases will occur. Vector competence can be affected by rearing temperature and inter- and intraspecific competition experienced by the individual mosquito during its larval development. This research investigates whether a new morphological indicator of larval rearing conditions, wing shape, can be used to distinguish reliably temperature and competitive conditions experienced during larval stages. Aedes albopictus and Aedes aegypti larvae were reared in low intra-specific, high intra-specific, or high inter-specific competition treatments at either 22°C or 32°C. The right wing of each dried female was removed and photographed. Nineteen landmarks and twenty semilandmarks were digitized on each wing. Shape variables were calculated using geometric morphometric software. Canonical variate analysis, randomization multivariate analysis of variance, and visualization of landmark movement using deformation grids provided evidence that although semilandmark position was significantly affected by larval competition and temperature for both species, the differences in position did not translate into differences in wing shape, as shown in deformation grids. Two classification procedures yielded success rates of 26–49%. Accounting for wing size produced no increase in classification success. There appeared to be a significant relationship between shape and size. These results, particularly the low success rate of classification based on wing shape, show that shape is unlikely to be a reliable indicator of larval rearing competition and temperature conditions for Aedes albopictus and Aedes aegypti. PMID:22897054

Automated detection of tuberculosis on sputum smeared slides using stepwise classification

NASA Astrophysics Data System (ADS)

Divekar, Ajay; Pangilinan, Corina; Coetzee, Gerrit; Sondh, Tarlochan; Lure, Fleming Y. M.; Kennedy, Sean

2012-03-01

Routine visual slide screening for identification of tuberculosis (TB) bacilli in stained sputum slides under microscope system is a tedious labor-intensive task and can miss up to 50% of TB. Based on the Shannon cofactor expansion on Boolean function for classification, a stepwise classification (SWC) algorithm is developed to remove different types of false positives, one type at a time, and to increase the detection of TB bacilli at different concentrations. Both bacilli and non-bacilli objects are first analyzed and classified into several different categories including scanty positive, high concentration positive, and several non-bacilli categories: small bright objects, beaded, dim elongated objects, etc. The morphological and contrast features are extracted based on aprior clinical knowledge. The SWC is composed of several individual classifiers. Individual classifier to increase the bacilli counts utilizes an adaptive algorithm based on a microbiologist's statistical heuristic decision process. Individual classifier to reduce false positive is developed through minimization from a binary decision tree to classify different types of true and false positive based on feature vectors. Finally, the detection algorithm is was tested on 102 independent confirmed negative and 74 positive cases. A multi-class task analysis shows high accordance rate for negative, scanty, and high-concentration as 88.24%, 56.00%, and 97.96%, respectively. A binary-class task analysis using a receiver operating characteristics method with the area under the curve (Az) is also utilized to analyze the performance of this detection algorithm, showing the superior detection performance on the high-concentration cases (Az=0.913) and cases mixed with high-concentration and scanty cases (Az=0.878).

Max-plus and min-plus projection autoassociative morphological memories and their compositions for pattern classification.

PubMed

Dos Santos, Alex Santana; Valle, Marcos Eduardo

2018-04-01

Autoassociative morphological memories (AMMs) are robust and computationally efficient memory models with unlimited storage capacity. In this paper, we present the max-plus and min-plus projection autoassociative morphological memories (PAMMs) as well as their compositions. Briefly, the max-plus PAMM yields the largest max-plus combination of the stored vectors which is less than or equal to the input. Dually, the vector recalled by the min-plus PAMM corresponds to the smallest min-plus combination which is larger than or equal to the input. Apart from unlimited absolute storage capacity and one step retrieval, PAMMs and their compositions exhibit an excellent noise tolerance. Furthermore, the new memories yielded quite promising results in classification problems with a large number of features and classes. Copyright © 2018 Elsevier Ltd. All rights reserved.

Modality-Driven Classification and Visualization of Ensemble Variance

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

Bensema, Kevin; Gosink, Luke; Obermaier, Harald

Paper for the IEEE Visualization Conference Advances in computational power now enable domain scientists to address conceptual and parametric uncertainty by running simulations multiple times in order to sufficiently sample the uncertain input space.

Assessment of the Activation State of RAS and Map Kinase in Human Breast Cancer Specimens (96Breast)

DTIC Science & Technology

1999-09-01

Cancer 16. PRICE CODE 17. SECURITY CLASSIFICATION 18 . SECURITY CLASSIFICATION 19. SECURITY CLASSIFICATION 20. LIMITATION OF ABSTRACT OF REPORT OF...THIS PAGE OF ABSTRACT Unclassified Unclassified Unclassified Unlimited NSN 7640-01-280-5500 Standard Form 298 (Rev. 2-89) Prescribed by ANSI Std. Z39- 18 ...transformation and regulate cell morphology, adhesion and motility through cytoskeletal dynamics and play an important role in carcinogenesis ( 18 ). Rho

Classification of orbital morphology for decompression surgery in Graves' orbitopathy: two-dimensional versus three-dimensional orbital parameters.

PubMed

Borumandi, Farzad; Hammer, Beat; Noser, Hansrudi; Kamer, Lukas

2013-05-01

Three-dimensional (3D) CT reconstruction of the bony orbit for accurate measurement and classification of the complex orbital morphology may not be suitable for daily practice. We present an easily measurable two-dimensional (2D) reference dataset of the bony orbit for study of individual orbital morphology prior to decompression surgery in Graves' orbitopathy. CT images of 70 European adults (140 orbits) with unaffected orbits were included. On axial views, the following orbital dimensions were assessed: orbital length (OL), globe length (GL), GL/OL ratio and cone angle. Postprocessed CT data were required to measure the corresponding 3D orbital parameters. The 2D and 3D orbital parameters were correlated. The 2D orbital parameters were significantly correlated to the corresponding 3D parameters (significant at the 0.01 level). The average GL was 25 mm (SD±1.0), the average OL was 42 mm (SD±2.0) and the average GL/OL ratio was 0.6 (SD±0.03). The posterior cone angle was, on average, 50.2° (SD±4.1). Three orbital sizes were classified: short (OL≤40 mm), medium (OL>40 to <45 mm) and large (OL≥45 mm). We present easily measurable reference data for the orbit that can be used for preoperative study and classification of individual orbital morphology. A short and shallow orbit may require a different decompression technique than a large and deep orbit. Prospective clinical trials are needed to demonstrate how individual orbital morphology affects the outcome of decompression surgery.

Does bimodal stimulus presentation increase ERP components usable in BCIs?

NASA Astrophysics Data System (ADS)

Thurlings, Marieke E.; Brouwer, Anne-Marie; Van Erp, Jan B. F.; Blankertz, Benjamin; Werkhoven, Peter J.

2012-08-01

Event-related potential (ERP)-based brain-computer interfaces (BCIs) employ differences in brain responses to attended and ignored stimuli. Typically, visual stimuli are used. Tactile stimuli have recently been suggested as a gaze-independent alternative. Bimodal stimuli could evoke additional brain activity due to multisensory integration which may be of use in BCIs. We investigated the effect of visual-tactile stimulus presentation on the chain of ERP components, BCI performance (classification accuracies and bitrates) and participants’ task performance (counting of targets). Ten participants were instructed to navigate a visual display by attending (spatially) to targets in sequences of either visual, tactile or visual-tactile stimuli. We observe that attending to visual-tactile (compared to either visual or tactile) stimuli results in an enhanced early ERP component (N1). This bimodal N1 may enhance BCI performance, as suggested by a nonsignificant positive trend in offline classification accuracies. A late ERP component (P300) is reduced when attending to visual-tactile compared to visual stimuli, which is consistent with the nonsignificant negative trend of participants’ task performance. We discuss these findings in the light of affected spatial attention at high-level compared to low-level stimulus processing. Furthermore, we evaluate bimodal BCIs from a practical perspective and for future applications.

An Analysis of Machine- and Human-Analytics in Classification.

PubMed

Tam, Gary K L; Kothari, Vivek; Chen, Min

2017-01-01

In this work, we present a study that traces the technical and cognitive processes in two visual analytics applications to a common theoretic model of soft knowledge that may be added into a visual analytics process for constructing a decision-tree model. Both case studies involved the development of classification models based on the "bag of features" approach. Both compared a visual analytics approach using parallel coordinates with a machine-learning approach using information theory. Both found that the visual analytics approach had some advantages over the machine learning approach, especially when sparse datasets were used as the ground truth. We examine various possible factors that may have contributed to such advantages, and collect empirical evidence for supporting the observation and reasoning of these factors. We propose an information-theoretic model as a common theoretic basis to explain the phenomena exhibited in these two case studies. Together we provide interconnected empirical and theoretical evidence to support the usefulness of visual analytics.

Evolution of middle-late Pleistocene human cranio-facial form: a 3-D approach.

PubMed

Harvati, Katerina; Hublin, Jean-Jacques; Gunz, Philipp

2010-11-01

The classification and phylogenetic relationships of the middle Pleistocene human fossil record remains one of the most intractable problems in paleoanthropology. Several authors have noted broad resemblances between European and African fossils from this period, suggesting a single taxon ancestral to both modern humans and Neanderthals. Others point out 'incipient' Neanderthal features in the morphology of the European sample and have argued for their inclusion in the Neanderthal lineage exclusively, following a model of accretionary evolution of Neanderthals. We approach these questions using geometric morphometric methods which allow the intuitive visualization and quantification of features previously described qualitatively. We apply these techniques to evaluate proposed cranio-facial 'incipient' facial, vault, and basicranial traits in a middle-late Pleistocene European hominin sample when compared to a sample of the same time depth from Africa. Some of the features examined followed the predictions of the accretion model and relate the middle Pleistocene European material to the later Neanderthals. However, although our analysis showed a clear separation between Neanderthals and early/recent modern humans and morphological proximity between European specimens from OIS 7 to 3, it also shows that the European hominins from the first half of the middle Pleistocene still shared most of their cranio-facial architecture with their African contemporaries. Copyright © 2010 Elsevier Ltd. All rights reserved.

Ex vivo nonlinear microscopy imaging of Ehlers-Danlos syndrome-affected skin.

PubMed

Kiss, Norbert; Haluszka, Dóra; Lőrincz, Kende; Kuroli, Enikő; Hársing, Judit; Mayer, Balázs; Kárpáti, Sarolta; Fekete, György; Szipőcs, Róbert; Wikonkál, Norbert; Medvecz, Márta

2018-07-01

Ehlers-Danlos syndrome (EDS) is the name for a heterogenous group of rare genetic connective tissue disorders with an overall incidence of 1 in 5000. The histological characteristics of EDS have been previously described in detail in the late 1970s and early 1980s. Since that time, the classification of EDS has undergone significant changes, yet the description of the histological features of collagen morphology in different EDS subtypes has endured the test of time. Nonlinear microscopy techniques can be utilized for non-invasive in vivo label-free imaging of the skin. Among these techniques, two-photon absorption fluorescence (TPF) microscopy can visualize endogenous fluorophores, such as elastin, while the morphology of collagen fibers can be assessed by second-harmonic generation (SHG) microscopy. In our present work, we performed TPF and SHG microscopy imaging on ex vivo skin samples of one patient with classical EDS and two patients with vascular EDS and two healthy controls. We detected irregular, loosely dispersed collagen fibers in a non-parallel arrangement in the dermis of the EDS patients, while as expected, there was no noticeable impairment in the elastin content. Based on further studies on a larger number of patients, in vivo nonlinear microscopic imaging could be utilized for the assessment of the skin status of EDS patients in the future.

A GPU-based computer-assisted microscopy system for assessing the importance of different families of histological characteristics in cancer diagnosis

NASA Astrophysics Data System (ADS)

Glotsos, Dimitris; Kostopoulos, Spiros; Sidiropoulos, Konstantinos; Ravazoula, Panagiota; Kalatzis, Ioannis; Asvestas, Pantelis; Cavouras, Dionisis

2014-01-01

In this study a Computer-Aided Microscopy (CAM) system is proposed for investigating the importance of the histological criteria involved in diagnosing of cancers in microscopy in order to suggest the more informative features for discriminating low from high-grade brain tumours. Four families of criteria have been examined, involving the greylevel variations (i.e. texture), the morphology (i.e. roundness), the architecture (i.e. cellularity) and the overall tumour qualities (expert's ordinal scale). The proposed CAM system was constructed using a modified Seeded Region Growing algorithm for image segmentation, and the Probabilistic Neural Network classifier for image classification. The implementation was designed on a commercial Graphics Processing Unit card using parallel programming. The system's performance using textural, morphological, architectural and ordinal information was 90.8%, 87.0%, 81.2% and 88.9% respectively. Results indicate that nuclei texture is the most important family of features regarding the degree of malignancy, and, thus, may guide more accurate predictions for discriminating low from high grade gliomas. Considering that nuclei texture is almost impractical to be encoded by visual observation, the need to incorporate computer-aided diagnostic tools as second opinion in daily clinical practice of diagnosing rare brain tumours may be justified.

Lightness computation by the human visual system

NASA Astrophysics Data System (ADS)

Rudd, Michael E.

2017-05-01

A model of achromatic color computation by the human visual system is presented, which is shown to account in an exact quantitative way for a large body of appearance matching data collected with simple visual displays. The model equations are closely related to those of the original Retinex model of Land and McCann. However, the present model differs in important ways from Land and McCann's theory in that it invokes additional biological and perceptual mechanisms, including contrast gain control, different inherent neural gains for incremental, and decremental luminance steps, and two types of top-down influence on the perceptual weights applied to local luminance steps in the display: edge classification and spatial integration attentional windowing. Arguments are presented to support the claim that these various visual processes must be instantiated by a particular underlying neural architecture. By pointing to correspondences between the architecture of the model and findings from visual neurophysiology, this paper suggests that edge classification involves a top-down gating of neural edge responses in early visual cortex (cortical areas V1 and/or V2) while spatial integration windowing occurs in cortical area V4 or beyond.

Skimming Digits: Neuromorphic Classification of Spike-Encoded Images

PubMed Central

Cohen, Gregory K.; Orchard, Garrick; Leng, Sio-Hoi; Tapson, Jonathan; Benosman, Ryad B.; van Schaik, André

2016-01-01

The growing demands placed upon the field of computer vision have renewed the focus on alternative visual scene representations and processing paradigms. Silicon retinea provide an alternative means of imaging the visual environment, and produce frame-free spatio-temporal data. This paper presents an investigation into event-based digit classification using N-MNIST, a neuromorphic dataset created with a silicon retina, and the Synaptic Kernel Inverse Method (SKIM), a learning method based on principles of dendritic computation. As this work represents the first large-scale and multi-class classification task performed using the SKIM network, it explores different training patterns and output determination methods necessary to extend the original SKIM method to support multi-class problems. Making use of SKIM networks applied to real-world datasets, implementing the largest hidden layer sizes and simultaneously training the largest number of output neurons, the classification system achieved a best-case accuracy of 92.87% for a network containing 10,000 hidden layer neurons. These results represent the highest accuracies achieved against the dataset to date and serve to validate the application of the SKIM method to event-based visual classification tasks. Additionally, the study found that using a square pulse as the supervisory training signal produced the highest accuracy for most output determination methods, but the results also demonstrate that an exponential pattern is better suited to hardware implementations as it makes use of the simplest output determination method based on the maximum value. PMID:27199646

Network Visualization Project (NVP)

DTIC Science & Technology

2016-07-01

network visualization, network traffic analysis, network forensics 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT UU 18. NUMBER OF...shell, is a command-line framework used for network forensic analysis. Dshell processes existing pcap files and filters output information based on

On the dynamical basis of the classification of normal galaxies

PubMed Central

Haass, J.; Bertin, G.; Lin, C. C.

1982-01-01

Some realistic galaxy models have been found to support discrete unstable spiral modes. Here, through the study of the relevant physical mechanisms and an extensive numerical investigation of the properties of the dominant modes in a wide class of galactic equilibria, we show how spiral structures are excited with different morphological features, depending on the properties of the equilibrium model. We identify the basic dynamical parameters and mechanisms and compare the resulting morphology of spiral modes with the actual classification of galaxies. The present study suggests a dynamical basis for the transition among various types and subclasses of normal and barred spiral galaxies. Images PMID:16593200

Surveying of morphological variations and Shoreline Classification of Bandar Abbas for providing Shoreline Management Plan

NASA Astrophysics Data System (ADS)

Ghobadi-Bistooni, Sadegh; Chegini, Vahid; Ershadi, Siroc; Tajziehchi, Mojtaba

2010-05-01

Since Bandar Abbas city is located in a strategic commercial, recreational, fishery, political and military region, its coastline has been employed for different application during last three decades, especially construction of Marian facilities. Therefore, conducting of research projects has become important for management decision making and development of coastal zone of the city. This fact becomes more realistic when the coast is classified based on different views and each part of the coast is investigated as a cell or sub cell due to its different behavior. In this, first, different methods for classification of coasts have been reviewed. Then, emphasizing on hydrodynamic and morphological classification, the slope, morphological aspects, kind of coastline and the gradation of its materials, as well as the slope of coastline have been determined. Moreover, the characteristics of wind waves in the region have been investigated using the SW module of MIKE21 software. On the other hands, the beach state has been determined using Masselink & Short, Masselink & Hegge, and Short & Hesp methods and employing RTR and Ω parameters. Finally, the regions of accretion and erosion in this coast line have been investigated using aerial and satellite images captured during last decades.

Automated 3D Phenotype Analysis Using Data Mining

PubMed Central

Plyusnin, Ilya; Evans, Alistair R.; Karme, Aleksis; Gionis, Aristides; Jernvall, Jukka

2008-01-01

The ability to analyze and classify three-dimensional (3D) biological morphology has lagged behind the analysis of other biological data types such as gene sequences. Here, we introduce the techniques of data mining to the study of 3D biological shapes to bring the analyses of phenomes closer to the efficiency of studying genomes. We compiled five training sets of highly variable morphologies of mammalian teeth from the MorphoBrowser database. Samples were labeled either by dietary class or by conventional dental types (e.g. carnassial, selenodont). We automatically extracted a multitude of topological attributes using Geographic Information Systems (GIS)-like procedures that were then used in several combinations of feature selection schemes and probabilistic classification models to build and optimize classifiers for predicting the labels of the training sets. In terms of classification accuracy, computational time and size of the feature sets used, non-repeated best-first search combined with 1-nearest neighbor classifier was the best approach. However, several other classification models combined with the same searching scheme proved practical. The current study represents a first step in the automatic analysis of 3D phenotypes, which will be increasingly valuable with the future increase in 3D morphology and phenomics databases. PMID:18320060

Development, characterization, and lethal effect of monoclonal antibodies against hemocytes in an adult female tick, Ornithodoros moubata (Acari: Argasidae).

PubMed

Matsuo, T; Tsukamoto, D; Inoue, N; Fujisaki, K

2003-12-01

In the present study, 19 monoclonal antibodies (mAbs) against adult Ornithodoros moubata hemocytes were established, and the reactivity of the hemocytes to these mAbs was examined by an indirect fluorescent antibody test (IFAT), Western blot and immunoprecipitation analyses. It was shown that the reactivities of the hemocytes to the mAbs varied among morphologically similar hemocyte types, and most mAbs produced in the present study showed the multiple band reactivity. However, the presence of shared epitopes among peptide subunits of the same protein or entirely different proteins are not common, so their reactivity could not be explained in detail. These results suggest that there are morphologically similar but functionally differentiated hemocytes. Therefore, in addition to morphological classification, the molecular-based classification of the hemocytes is also required. In order to assess the lethal effect of blood meal containing each mAb, artificial feeding was performed. The OmHC 31 showed the strongest lethal effect on adult female O. moubata. In conclusion, anti-hemocyte mAbs produced in this study are useful not only for the immunological classification of hemocytes but also for the immunological control of the tick.

Combining convolutional neural networks and Hough Transform for classification of images containing lines

NASA Astrophysics Data System (ADS)

Sheshkus, Alexander; Limonova, Elena; Nikolaev, Dmitry; Krivtsov, Valeriy

2017-03-01

In this paper, we propose an expansion of convolutional neural network (CNN) input features based on Hough Transform. We perform morphological contrasting of source image followed by Hough Transform, and then use it as input for some convolutional filters. Thus, CNNs computational complexity and the number of units are not affected. Morphological contrasting and Hough Transform are the only additional computational expenses of introduced CNN input features expansion. Proposed approach was demonstrated on the example of CNN with very simple structure. We considered two image recognition problems, that were object classification on CIFAR-10 and printed character recognition on private dataset with symbols taken from Russian passports. Our approach allowed to reach noticeable accuracy improvement without taking much computational effort, which can be extremely important in industrial recognition systems or difficult problems utilising CNNs, like pressure ridge analysis and classification.

Waking the dead: morphological and molecular characterization of extant †Posoniella tricarinelloides (Thoracosphaeraceae, Dinophyceae).

PubMed

Gu, Haifeng; Kirsch, Monika; Zinssmeister, Carmen; Soehner, Sylvia; Meier, K J Sebastian; Liu, Tingting; Gottschling, Marc

2013-09-01

The Thoracosphaeraceae are dinophytes that produce calcareous shells during their life history, whose optical crystallography has been the basis for the division into subfamilies. To evaluate the validity of the classification (mainly applied by palaeontologists), living material of phylogenetic key species is necessary albeit frequently difficult to access for contemporary morphological and molecular analyses. We isolated and established five living strains of the rare fossil-taxon †Posoniella tricarinelloides from different sediment samples collected in the South China Sea, Yellow Sea and in the Mediterranean Sea (west coast off Italy). Here, we provide detailed descriptions of its morphology and conducted phylogenetic analyses based on hundreds of accessions and thousands of informative sites on concatenated rRNA datasets. Within the monophyletic Peridiniales, †P. tricarinelloides was reliably nested in the Thoracosphaeraceae and exhibited two distinct morphological types of coccoid cells. The two morphologies of coccoid cells would have been assigned to different taxa at the subfamily level if found separately in fossil samples. Our results thus challenge previous classification concepts within the dinophytes and underline the importance of comparative morphological and molecular studies to better understand the complex biology of unicellular organisms such as †P. tricarinelloides. Copyright © 2013 Elsevier GmbH. All rights reserved.

Bag-of-visual-ngrams for histopathology image classification

NASA Astrophysics Data System (ADS)

López-Monroy, A. Pastor; Montes-y-Gómez, Manuel; Escalante, Hugo Jair; Cruz-Roa, Angel; González, Fabio A.

2013-11-01

This paper describes an extension of the Bag-of-Visual-Words (BoVW) representation for image categorization (IC) of histophatology images. This representation is one of the most used approaches in several high-level computer vision tasks. However, the BoVW representation has an important limitation: the disregarding of spatial information among visual words. This information may be useful to capture discriminative visual-patterns in specific computer vision tasks. In order to overcome this problem we propose the use of visual n-grams. N-grams based-representations are very popular in the field of natural language processing (NLP), in particular within text mining and information retrieval. We propose building a codebook of n-grams and then representing images by histograms of visual n-grams. We evaluate our proposal in the challenging task of classifying histopathology images. The novelty of our proposal lies in the fact that we use n-grams as attributes for a classification model (together with visual-words, i.e., 1-grams). This is common practice within NLP, although, to the best of our knowledge, this idea has not been explored yet within computer vision. We report experimental results in a database of histopathology images where our proposed method outperforms the traditional BoVWs formulation.

Taxonomic complexity of powdery mildew pathogens found on lentil and pea in the US Pacific Northwest

USDA-ARS?s Scientific Manuscript database

Classification of powdery mildews found on lentil and pea in greenhouse and field production conditions in the US Pacific Northwest was investigated using morphological and molecular characters. Isolates collected from lentil plants grown in the greenhouse or field displayed morphologies in substant...

The joint use of the tangential electric field and surface Laplacian in EEG classification.

PubMed

Carvalhaes, C G; de Barros, J Acacio; Perreau-Guimaraes, M; Suppes, P

2014-01-01

We investigate the joint use of the tangential electric field (EF) and the surface Laplacian (SL) derivation as a method to improve the classification of EEG signals. We considered five classification tasks to test the validity of such approach. In all five tasks, the joint use of the components of the EF and the SL outperformed the scalar potential. The smallest effect occurred in the classification of a mental task, wherein the average classification rate was improved by 0.5 standard deviations. The largest effect was obtained in the classification of visual stimuli and corresponded to an improvement of 2.1 standard deviations.

A new taxonomic classification for species in Gomphus sensu lato

Treesearch

Admir J. Giachini; Michael A. Castellano

2011-01-01

Taxonomy of the Gomphales has been revisited by combining morphology and molecular data (DNA sequences) to provide a natural classification for the species of Gomphus sensu lato. Results indicate Gomphus s.l. to be non-monophyletic, leading to new combinations and the placement of its species into four genera: Gomphus...

Molecular systematics of the barklouse family Psocidae (Insecta: Psocodea: 'Psocoptera') and implications for morphological and behavioral evolution.

PubMed

Yoshizawa, Kazunori; Johnson, Kevin P

2008-02-01

We evaluated the higher level classification within the family Psocidae (Insecta: Psocodea: 'Psocoptera') based on combined analyses of nuclear 18S, Histone 3, wingless and mitochondrial 12S, 16S and COI gene sequences. Various analyses (inclusion/exclusion of incomplete taxa and/or rapidly evolving genes, data partitioning, and analytical method selection) all provided similar results, which were generally concordant with relationships inferred using morphological observations. Based on the phylogenetic trees estimated for Psocidae, we propose a revised higher level classification of this family, although uncertainty still exists regarding some aspects of this classification. This classification includes a basal division into two subfamilies, 'Amphigerontiinae' (possibly paraphyletic) and Psocinae. The Amphigerontiinae is divided into the tribes Kaindipsocini (new tribe), Blastini, Amphigerontini, and Stylatopsocini. Psocinae is divided into the tribes 'Ptyctini' (probably paraphyletic), Psocini, Atrichadenotecnini (new tribe), Sigmatoneurini, Metylophorini, and Thyrsophorini (the latter includes the taxon previously recognized as Cerastipsocini). We examined the evolution of symmetric/asymmetric male genitalia over this tree and found this character to be quite homoplasious.

[Surgical treatment of chronic pancreatitis based on classification of M. Buchler and coworkers].

PubMed

Krivoruchko, I A; Boĭko, V V; Goncharova, N N; Andreeshchev, S A

2011-08-01

The results of surgical treatment of 452 patients, suffering chronic pancreatitis (CHP), were analyzed. The CHP classification, elaborated by M. Buchler and coworkers (2009), based on clinical signs, morphological peculiarities and pancreatic function analysis, contains scientifically substantiated recommendations for choice of diagnostic methods and complex treatment of the disease. The classification proposed is simple in application and constitutes an instrument for studying and comparison of the CHP course severity, the patients prognosis and treatment.

Analysis of urban area land cover using SEASAT Synthetic Aperture Radar data

NASA Technical Reports Server (NTRS)

Henderson, F. M. (Principal Investigator)

1980-01-01

Digitally processed SEASAT synthetic aperture raar (SAR) imagery of the Denver, Colorado urban area was examined to explore the potential of SAR data for mapping urban land cover and the compatability of SAR derived land cover classes with the United States Geological Survey classification system. The imagery is examined at three different scales to determine the effect of image enlargement on accuracy and level of detail extractable. At each scale the value of employing a simplistic preprocessing smoothing algorithm to improve image interpretation is addressed. A visual interpretation approach and an automated machine/visual approach are employed to evaluate the feasibility of producing a semiautomated land cover classification from SAR data. Confusion matrices of omission and commission errors are employed to define classification accuracies for each interpretation approach and image scale.

A software tool for automatic classification and segmentation of 2D/3D medical images

NASA Astrophysics Data System (ADS)

Strzelecki, Michal; Szczypinski, Piotr; Materka, Andrzej; Klepaczko, Artur

2013-02-01

Modern medical diagnosis utilizes techniques of visualization of human internal organs (CT, MRI) or of its metabolism (PET). However, evaluation of acquired images made by human experts is usually subjective and qualitative only. Quantitative analysis of MR data, including tissue classification and segmentation, is necessary to perform e.g. attenuation compensation, motion detection, and correction of partial volume effect in PET images, acquired with PET/MR scanners. This article presents briefly a MaZda software package, which supports 2D and 3D medical image analysis aiming at quantification of image texture. MaZda implements procedures for evaluation, selection and extraction of highly discriminative texture attributes combined with various classification, visualization and segmentation tools. Examples of MaZda application in medical studies are also provided.

Slope histogram distribution-based parametrisation of Martian geomorphic features

NASA Astrophysics Data System (ADS)

Balint, Zita; Székely, Balázs; Kovács, Gábor

2014-05-01

The application of geomorphometric methods on the large Martian digital topographic datasets paves the way to analyse the Martian areomorphic processes in more detail. One of the numerous methods is the analysis is to analyse local slope distributions. To this implementation a visualization program code was developed that allows to calculate the local slope histograms and to compare them based on Kolmogorov distance criterion. As input data we used the digital elevation models (DTMs) derived from HRSC high-resolution stereo camera image from various Martian regions. The Kolmogorov-criterion based discrimination produces classes of slope histograms that displayed using coloration obtaining an image map. In this image map the distribution can be visualized by their different colours representing the various classes. Our goal is to create a local slope histogram based classification for large Martian areas in order to obtain information about general morphological characteristics of the region. This is a contribution of the TMIS.ascrea project, financed by the Austrian Research Promotion Agency (FFG). The present research is partly realized in the frames of TÁMOP 4.2.4.A/2-11-1-2012-0001 high priority "National Excellence Program - Elaborating and Operating an Inland Student and Researcher Personal Support System convergence program" project's scholarship support, using Hungarian state and European Union funds and cofinances from the European Social Fund.

A comparative study of auroral morphology distribution between the Northern and Southern Hemisphere based on automatic classification

NASA Astrophysics Data System (ADS)

Yang, Qiuju; Hu, Ze-Jun

2018-03-01

Aurora is a very important geophysical phenomenon in the high latitudes of Arctic and Antarctic regions, and it is important to make a comparative study of the auroral morphology between the two hemispheres. Based on the morphological characteristics of the four labeled dayside discrete auroral types (auroral arc, drapery corona, radial corona and hot-spot aurora) on the 8001 dayside auroral images at the Chinese Arctic Yellow River Station in 2003, and by extracting the local binary pattern (LBP) features and using a k-nearest classifier, this paper performs an automatic classification of the 65 361 auroral images of the Chinese Arctic Yellow River Station during 2004-2009 and the 39 335 auroral images of the South Pole Station between 2003 and 2005. Finally, it obtains the occurrence distribution of the dayside auroral morphology in the Northern and Southern Hemisphere. The statistical results indicate that the four dayside discrete auroral types present a similar occurrence distribution between the two stations. To the best of our knowledge, we are the first to report statistical comparative results of dayside auroral morphology distribution between the Northern and Southern Hemisphere.

Fine-grained visual marine vessel classification for coastal surveillance and defense applications

NASA Astrophysics Data System (ADS)

Solmaz, Berkan; Gundogdu, Erhan; Karaman, Kaan; Yücesoy, Veysel; Koç, Aykut

2017-10-01

The need for capabilities of automated visual content analysis has substantially increased due to presence of large number of images captured by surveillance cameras. With a focus on development of practical methods for extracting effective visual data representations, deep neural network based representations have received great attention due to their success in visual categorization of generic images. For fine-grained image categorization, a closely related yet a more challenging research problem compared to generic image categorization due to high visual similarities within subgroups, diverse applications were developed such as classifying images of vehicles, birds, food and plants. Here, we propose the use of deep neural network based representations for categorizing and identifying marine vessels for defense and security applications. First, we gather a large number of marine vessel images via online sources grouping them into four coarse categories; naval, civil, commercial and service vessels. Next, we subgroup naval vessels into fine categories such as corvettes, frigates and submarines. For distinguishing images, we extract state-of-the-art deep visual representations and train support-vector-machines. Furthermore, we fine tune deep representations for marine vessel images. Experiments address two scenarios, classification and verification of naval marine vessels. Classification experiment aims coarse categorization, as well as learning models of fine categories. Verification experiment embroils identification of specific naval vessels by revealing if a pair of images belongs to identical marine vessels by the help of learnt deep representations. Obtaining promising performance, we believe these presented capabilities would be essential components of future coastal and on-board surveillance systems.

[Review of current classification and terminology of vulvar disorders].

PubMed

Sláma, J

2012-08-01

To summarize current terminology and classification of vulvar disorders. Review article. Gynecologic oncology center, Department of Gynecology and Obstetrics, General Faculty Hospital and 1st Medical School of Charles University, Prague. Vulvar disorders include wide spectrum of different diagnoses. Multidisciplinary collaboration is frequently needed in diagnostical and therapeutical process. It is essential to use unified terminology using standard dermatological terms, and unified classification for comprehensible communication between different medical professions. Current classification, which is based on Clinical-pathological criteria, was established by International Society for the Study of Vulvovaginal Disease. Recently, there was introduced Clinical classification, which groups disorders according to main morphological finding. Adequate and unified classification and terminology are necessary for effective communication during the diagnostical process.

Chronic stress impairs acoustic conditioning more than visual conditioning in rats: morphological and behavioural evidence.

PubMed

Dagnino-Subiabre, A; Terreros, G; Carmona-Fontaine, C; Zepeda, R; Orellana, J A; Díaz-Véliz, G; Mora, S; Aboitiz, F

2005-01-01

Chronic stress affects brain areas involved in learning and emotional responses. These alterations have been related with the development of cognitive deficits in major depression. The aim of this study was to determine the effect of chronic immobilization stress on the auditory and visual mesencephalic regions in the rat brain. We analyzed in Golgi preparations whether stress impairs the neuronal morphology of the inferior (auditory processing) and superior colliculi (visual processing). Afterward, we examined the effect of stress on acoustic and visual conditioning using an avoidance conditioning test. We found that stress induced dendritic atrophy in inferior colliculus neurons and did not affect neuronal morphology in the superior colliculus. Furthermore, stressed rats showed a stronger impairment in acoustic conditioning than in visual conditioning. Fifteen days post-stress the inferior colliculus neurons completely restored their dendritic structure, showing a high level of neural plasticity that is correlated with an improvement in acoustic learning. These results suggest that chronic stress has more deleterious effects in the subcortical auditory system than in the visual system and may affect the aversive system and fear-like behaviors. Our study opens a new approach to understand the pathophysiology of stress and stress-related disorders such as major depression.

Assessment of the patellofemoral cartilage: Correlation of knee pain score with magnetic resonance cartilage grading and magnetization transfer ratio asymmetry of glycosaminoglycan chemical exchange saturation transfer.

PubMed

Lee, Young Han; Yang, Jaemoon; Jeong, Ha-Kyu; Suh, Jin-Suck

2017-01-01

Biochemical imaging of glycosaminoglycan chemical exchange saturation transfer (gagCEST) could predict the depletion of glycosaminoglycans (GAG) in early osteoarthritis. The purpose of this study was to evaluate the relationship between the magnetization transfer ratio asymmetry (MTR asym ) of gagCEST images and visual analog scale (VAS) pain scores in the knee joint. This retrospective study was approved by the institutional review board. A phantom study was performed using hyaluronic acid to validate the MTR asym values of gagCEST images. Knee magnetic resonance (MR) images of 22 patients (male, 9; female, 13; mean age, 50.3years; age range; 25-79years) with knee pain were included in this study. The MR imaging (MRI) protocol involved standard knee MRI as well as gagCEST imaging, which allowed region-of-interest analyses of the patellar facet and femoral trochlea. The MTR asym at 1.0ppm was calculated at each region. The cartilages of the patellar facets and femoral trochlea were graded according to the Outerbridge classification system. Data regarding the VAS scores of knee pain were collected from the electronic medical records of the patients. Statistical analysis was performed using Spearman's correlation. The results of the phantom study revealed excellent correlation between the MTR asym values and the concentration of GAGs (r=0.961; p=0.003). The cartilage grades on the MR images showed significant negative correlation with the MTR asym values in the patellar facet and femoral trochlea (r=-0.460; p=0.031 and r=-0.543; p=0.009, respectively). The VAS pain scores showed significant negative correlation with the MTR asym values in the patellar facet and femoral trochlea (r=-0.435; p=0.043 and r=-0.671; p=0.001, respectively). The pain scores were associated with the morphological and biochemical changes in articular cartilages visualized on knee MR images. The biochemical changes, visualized in terms of the MTR asym values of the gagCEST images, exhibited greater correlation with the pain scores than the morphological changes visualized on conventional MR images; these results provide evidence supporting the theory regarding the association of patellofemoral osteoarthritis with knee pain scores. Copyright © 2016 Elsevier Inc. All rights reserved.

Integrated biophotonics in endoscopic oncology

NASA Astrophysics Data System (ADS)

Muguruma, Naoki; DaCosta, Ralph S.; Wilson, Brian C.; Marcon, Norman E.

2009-02-01

Gastrointestinal endoscopy has made great progress during last decade. Diagnostic accuracy can be enhanced by better training, improved dye-contrast techniques method, and the development of new image processing technologies. However, diagnosis using conventional endoscopy with white-light optical imaging is essentially limited by being based on morphological changes and/or visual attribution: hue, saturation and intensity, interpretation of which depends on the endoscopist's eye and brain. In microlesions in the gastrointestinal tract, we still rely ultimately on the histopathological diagnosis from biopsy specimens. Autofluorescence imaging system has been applied for lesions which have been difficult to morphologically recognize or are indistinct with conventional endoscope, and this approach has potential application for the diagnosis of dysplastic lesions and early cancers in the gastrointestinal tract, supplementing the information from white light endoscopy. This system has an advantage that it needs no administration of a photosensitive agent, making it suitable as a screening method for the early detection of neoplastic tissues. Narrow band imaging (NBI) is a novel endoscopic technique which can distinguish neoplastic and non-neoplastic lesions without chromoendoscopy. Magnifying endoscopy in combination with NBI has an obvious advantage, namely analysis of the epithelial pit pattern and the vascular network. This new technique allows a detailed visualization in early neoplastic lesions of esophagus, stomach and colon. However, problems remain; how to combine these technologies in an optimum diagnostic strategy, how to apply them into the algorithm for therapeutic decision-making, and how to standardize several classifications surrounding them. 'Molecular imaging' is a concept representing the most novel imaging methods in medicine, although the definition of the word is still controversial. In the field of gastrointestinal endoscopy, the future of endoscopic diagnosis is likely to be impacted by a combination of biomarkers and technology, and 'endoscopic molecular imaging' should be defined as "visualization of molecular characteristics with endoscopy". These innovations will allow us not only to locate a tumor or dysplastic lesion but also to visualize its molecular characteristics (e.g., DNA mutations and polymorphisms, gene and/or protein expression), and the activity of specific molecules and biological processes that affect tumor behavior and/or its response to therapy. In the near future, these methods should be promising technologies that will play a central role in gastrointestinal oncology.

Application Of Empirical Phase Diagrams For Multidimensional Data Visualization Of High Throughput Microbatch Crystallization Experiments.

PubMed

Klijn, Marieke E; Hubbuch, Jürgen

2018-04-27

Protein phase diagrams are a tool to investigate cause and consequence of solution conditions on protein phase behavior. The effects are scored according to aggregation morphologies such as crystals or amorphous precipitates. Solution conditions affect morphological features, such as crystal size, as well as kinetic features, such as crystal growth time. Common used data visualization techniques include individual line graphs or symbols-based phase diagrams. These techniques have limitations in terms of handling large datasets, comprehensiveness or completeness. To eliminate these limitations, morphological and kinetic features obtained from crystallization images generated with high throughput microbatch experiments have been visualized with radar charts in combination with the empirical phase diagram (EPD) method. Morphological features (crystal size, shape, and number, as well as precipitate size) and kinetic features (crystal and precipitate onset and growth time) are extracted for 768 solutions with varying chicken egg white lysozyme concentration, salt type, ionic strength and pH. Image-based aggregation morphology and kinetic features were compiled into a single and easily interpretable figure, thereby showing that the EPD method can support high throughput crystallization experiments in its data amount as well as its data complexity. Copyright © 2018. Published by Elsevier Inc.

Examining the Effectiveness of Discriminant Function Analysis and Cluster Analysis in Species Identification of Male Field Crickets Based on Their Calling Songs

PubMed Central

Jaiswara, Ranjana; Nandi, Diptarup; Balakrishnan, Rohini

2013-01-01

Traditional taxonomy based on morphology has often failed in accurate species identification owing to the occurrence of cryptic species, which are reproductively isolated but morphologically identical. Molecular data have thus been used to complement morphology in species identification. The sexual advertisement calls in several groups of acoustically communicating animals are species-specific and can thus complement molecular data as non-invasive tools for identification. Several statistical tools and automated identifier algorithms have been used to investigate the efficiency of acoustic signals in species identification. Despite a plethora of such methods, there is a general lack of knowledge regarding the appropriate usage of these methods in specific taxa. In this study, we investigated the performance of two commonly used statistical methods, discriminant function analysis (DFA) and cluster analysis, in identification and classification based on acoustic signals of field cricket species belonging to the subfamily Gryllinae. Using a comparative approach we evaluated the optimal number of species and calling song characteristics for both the methods that lead to most accurate classification and identification. The accuracy of classification using DFA was high and was not affected by the number of taxa used. However, a constraint in using discriminant function analysis is the need for a priori classification of songs. Accuracy of classification using cluster analysis, which does not require a priori knowledge, was maximum for 6–7 taxa and decreased significantly when more than ten taxa were analysed together. We also investigated the efficacy of two novel derived acoustic features in improving the accuracy of identification. Our results show that DFA is a reliable statistical tool for species identification using acoustic signals. Our results also show that cluster analysis of acoustic signals in crickets works effectively for species classification and identification. PMID:24086666

Extracting alpha band modulation during visual spatial attention without flickering stimuli using common spatial pattern.

PubMed

Fujisawa, Junya; Touyama, Hideaki; Hirose, Michitaka

2008-01-01

In this paper, alpha band modulation during visual spatial attention without visual stimuli was focused. Visual spatial attention has been expected to provide a new channel of non-invasive independent brain computer interface (BCI), but little work has been done on the new interfacing method. The flickering stimuli used in previous work cause a decline of independency and have difficulties in a practical use. Therefore we investigated whether visual spatial attention could be detected without such stimuli. Further, the common spatial patterns (CSP) were for the first time applied to the brain states during visual spatial attention. The performance evaluation was based on three brain states of left, right and center direction attention. The 30-channel scalp electroencephalographic (EEG) signals over occipital cortex were recorded for five subjects. Without CSP, the analyses made 66.44 (range 55.42 to 72.27) % of average classification performance in discriminating left and right attention classes. With CSP, the averaged classification accuracy was 75.39 (range 63.75 to 86.13) %. It is suggested that CSP is useful in the context of visual spatial attention, and the alpha band modulation during visual spatial attention without flickering stimuli has the possibility of a new channel for independent BCI as well as motor imagery.

Multi-classification of cell deformation based on object alignment and run length statistic.

PubMed

Li, Heng; Liu, Zhiwen; An, Xing; Shi, Yonggang

2014-01-01

Cellular morphology is widely applied in digital pathology and is essential for improving our understanding of the basic physiological processes of organisms. One of the main issues of application is to develop efficient methods for cell deformation measurement. We propose an innovative indirect approach to analyze dynamic cell morphology in image sequences. The proposed approach considers both the cellular shape change and cytoplasm variation, and takes each frame in the image sequence into account. The cell deformation is measured by the minimum energy function of object alignment, which is invariant to object pose. Then an indirect analysis strategy is employed to overcome the limitation of gradual deformation by run length statistic. We demonstrate the power of the proposed approach with one application: multi-classification of cell deformation. Experimental results show that the proposed method is sensitive to the morphology variation and performs better than standard shape representation methods.

Kids, Take a Look at This! Visual Literacy Skills in the School Curriculum

ERIC Educational Resources Information Center

Vermeersch, Lode; Vandenbroucke, Anneloes

2015-01-01

Although the paradigm of visual literacy (VL) is rapidly emerging, the construct itself still lacks operational specificity. Based on a semiotic understanding of visual culture as an ongoing process of "making meaning", we present in this study a skill-based classification of VL, differentiating four sets of VL skills: perception;…

Molecular phylogeny of tribe Rhipsalideae (Cactaceae) and taxonomic implications for Schlumbergera and Hatiora.

PubMed

Calvente, Alice; Zappi, Daniela C; Forest, Félix; Lohmann, Lúcia G

2011-03-01

Tribe Rhipsalideae is composed of unusual epiphytic or lithophytic cacti that inhabit humid tropical and subtropical forests. Members of this tribe present a reduced vegetative body, a specialized adventitious root system, usually spineless areoles and flowers and fruits reduced in size. Despite the debate surrounding the classification of Rhipsalideae, no studies have ever attempted to reconstruct phylogenetic relationships among its members or to test the monophyly of its genera using DNA sequence data; all classifications formerly proposed for this tribe have only employed morphological data. In this study, we reconstruct the phylogeny of Rhipsalideae using plastid (trnQ-rps16, rpl32-trnL, psbA-trnH) and nuclear (ITS) markers to evaluate the classifications previously proposed for the group. We also examine morphological features traditionally used to delimit genera within Rhipsalideae in light of the resulting phylogenetic trees. In total new sequences for 35 species of Rhipsalideae were produced (out of 55; 63%). The molecular phylogeny obtained comprises four main clades supporting the recognition of genera Lepismium, Rhipsalis, Hatiora and Schlumbergera. The evidence gathered indicate that a broader genus Schlumbergera, including Hatiora subg. Rhipsalidopsis, should be recognized. Consistent morphological characters rather than homoplastic features are used in order to establish a more coherent and practical classification for the group. Nomenclatural changes and a key for the identification of the genera currently included in Rhipsalideae are provided. Copyright © 2011 Elsevier Inc. All rights reserved.

Embedding, serial sectioning and staining of zebrafish embryos using JB-4 resin.

PubMed

Sullivan-Brown, Jessica; Bisher, Margaret E; Burdine, Rebecca D

2011-01-01

Histological techniques are critical for observing tissue and cellular morphology. In this paper, we outline our protocol for embedding, serial sectioning, staining and visualizing zebrafish embryos embedded in JB-4 plastic resin-a glycol methacrylate-based medium that results in excellent preservation of tissue morphology. In addition, we describe our procedures for staining plastic sections with toluidine blue or hematoxylin and eosin, and show how to couple these stains with whole-mount RNA in situ hybridization. We also describe how to maintain and visualize immunofluorescence and EGFP signals in JB-4 resin. The protocol we outline-from embryo preparation, embedding, sectioning and staining to visualization-can be accomplished in 3 d. Overall, we reinforce that plastic embedding can provide higher resolution of cellular details and is a valuable tool for cellular and morphological studies in zebrafish.

Use of mutation profiles to refine the classification of endometrial carcinomas.

PubMed

McConechy, Melissa K; Ding, Jiarui; Cheang, Maggie Cu; Wiegand, Kimberly; Senz, Janine; Tone, Alicia; Yang, Winnie; Prentice, Leah; Tse, Kane; Zeng, Thomas; McDonald, Helen; Schmidt, Amy P; Mutch, David G; McAlpine, Jessica N; Hirst, Martin; Shah, Sohrab P; Lee, Cheng-Han; Goodfellow, Paul J; Gilks, C Blake; Huntsman, David G

2012-09-01

The classification of endometrial carcinomas is based on pathological assessment of tumour cell type; the different cell types (endometrioid, serous, carcinosarcoma, mixed, undifferentiated, and clear cell) are associated with distinct molecular alterations. This current classification system for high-grade subtypes, in particular the distinction between high-grade endometrioid (EEC-3) and serous carcinomas (ESC), is limited in its reproducibility and prognostic abilities. Therefore, a search for specific molecular classifiers to improve endometrial carcinoma subclassification is warranted. We performed target enrichment sequencing on 393 endometrial carcinomas from two large cohorts, sequencing exons from the following nine genes: ARID1A, PPP2R1A, PTEN, PIK3CA, KRAS, CTNNB1, TP53, BRAF, and PPP2R5C. Based on this gene panel, each endometrial carcinoma subtype shows a distinct mutation profile. EEC-3s have significantly different frequencies of PTEN and TP53 mutations when compared to low-grade endometrioid carcinomas. ESCs and EEC-3s are distinct subtypes with significantly different frequencies of mutations in PTEN, ARID1A, PPP2R1A, TP53, and CTNNB1. From the mutation profiles, we were able to identify subtype outliers, ie cases diagnosed morphologically as one subtype but with a mutation profile suggestive of a different subtype. Careful review of these diagnostically challenging cases suggested that the original morphological classification was incorrect in most instances. The molecular profile of carcinosarcomas suggests two distinct mutation profiles for these tumours: endometrioid-type (PTEN, PIK3CA, ARID1A, KRAS mutations) and serous-type (TP53 and PPP2R1A mutations). While this nine-gene panel does not allow for a purely molecularly based classification of endometrial carcinoma, it may prove useful as an adjunct to morphological classification and serve as an aid in the classification of problematic cases. If used in practice, it may lead to improved diagnostic reproducibility and may also serve to stratify patients for targeted therapeutics. Copyright © 2012 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Monitoring and Morphologic Classification of Pediatric Cataract Using Slit-Lamp-Adapted Photography.

PubMed

Long, Erping; Lin, Zhuoling; Chen, Jingjing; Liu, Zhenzhen; Cao, Qianzhong; Lin, Haotian; Chen, Weirong; Liu, Yizhi

2017-11-01

To investigate the feasibility of pediatric cataract monitoring and morphologic classification using slit lamp-adapted anterior segmental photography in a large cohort that included uncooperative children. Patients registered in the Childhood Cataract Program of the Chinese Ministry of Health were prospectively selected. Eligible patients underwent slit-lamp adapted anterior segmental photography to record and monitor the morphology of their cataractous lenses. A set of assistance techniques for slit lamp-adapted photography was developed to instruct the parents of uncooperative children how to help maintain the child's head position and keep the eyes open after sleep aid administration. Briefly, slit lamp-adapted photography was completed for all 438 children, including 260 (59.4%) uncooperative children with our assistance techniques. All 746 images of 438 patients successfully confirmed the diagnoses and classifications. Considering the lesion location, pediatric cataract morphologies could be objectively classified into the seven following types: total; nuclear; polar, including two subtypes (anterior and posterior); lamellar; nuclear combined with cortical, including three subtypes (coral-like, dust-like, and blue-dot); cortical; and Y suture. The top three types of unilateral cataracts were polar (55, 42.3%), total (42, 32.3%), and nuclear (23, 17.7%); and the top three types of bilateral cataracts were nuclear (110, 35.8%), total (102, 33.2%), and lamellar (34, 11.1%). Slit lamp-adapted anterior segmental photography is applicable for monitoring and classifying the morphologies of pediatric cataracts and is even safe and feasible for uncooperative children with assistance techniques and sleep aid administration. This study proposes a novel strategy for the preoperative evaluation and evidence-based management of pediatric ophthalmology (Clinical Trials.gov, NCT02748031).

Using Different Standardized Methods for Species Identification: A Case Study Using Beaks from Three Ommastrephid Species

NASA Astrophysics Data System (ADS)

Hu, Guanyu; Fang, Zhou; Liu, Bilin; Chen, Xinjun; Staples, Kevin; Chen, Yong

2018-04-01

The cephalopod beak is a vital hard structure with a stable configuration and has been widely used for the identification of cephalopod species. This study was conducted to determine the best standardization method for identifying different species by measuring 12 morphological variables of the beaks of Illex argentinus, Ommastrephes bartramii, and Dosidicus gigas that were collected by Chinese jigging vessels. To remove the effects of size, these morphometric variables were standardized using three methods. The average ratios of the upper beak morphological variables and upper crest length of O. bartramii and D. gigas were found to be greater than those of I. argentinus. However, for lower beaks, only the average of LRL (lower rostrum length)/ LCL (lower crest length), LRW (lower rostrum width)/ LCL, and LLWL (lower lateral wall length)/ LCL of O. bartramii and D. gigas were greater than those of I. argentinus. The ratios of beak morphological variables and crest length were found to be all significantly different among the three species ( P < 0.001). Among the three standardization methods, the correct classification rate of stepwise discriminant analysis (SDA) was the highest using the ratios of beak morphological variables and crest length. Compared with hood length, the correct classification rate was slightly higher when using beak variables standardized by crest length using an allometric model. The correct classification rate of the lower beak was also found to be greater than that of the upper beak. This study indicates that the ratios of beak morphological variables to crest length could be used for interspecies and intraspecies identification. Meanwhile, the lower beak variables were found to be more effective than upper beak variables in classifying beaks found in the stomachs of predators.

Assessment of respiratory flow cycle morphology in patients with chronic heart failure.

PubMed

Garde, Ainara; Sörnmo, Leif; Laguna, Pablo; Jané, Raimon; Benito, Salvador; Bayés-Genís, Antoni; Giraldo, Beatriz F

2017-02-01

Breathing pattern as periodic breathing (PB) in chronic heart failure (CHF) is associated with poor prognosis and high mortality risk. This work investigates the significance of a number of time domain parameters for characterizing respiratory flow cycle morphology in patients with CHF. Thus, our primary goal is to detect PB pattern and identify patients at higher risk. In addition, differences in respiratory flow cycle morphology between CHF patients (with and without PB) and healthy subjects are studied. Differences between these parameters are assessed by investigating the following three classification issues: CHF patients with PB versus with non-periodic breathing (nPB), CHF patients (both PB and nPB) versus healthy subjects, and nPB patients versus healthy subjects. Twenty-six CHF patients (8/18 with PB/nPB) and 35 healthy subjects are studied. The results show that the maximal expiratory flow interval is shorter and with lower dispersion in CHF patients than in healthy subjects. The flow slopes are much steeper in CHF patients, especially for PB. Both inspiration and expiration durations are reduced in CHF patients, mostly for PB. Using the classification and regression tree technique, the most discriminant parameters are selected. For signals shorter than 1 min, the time domain parameters produce better results than the spectral parameters, with accuracies for each classification of 82/78, 89/85, and 91/89 %, respectively. It is concluded that morphologic analysis in the time domain is useful, especially when short signals are analyzed.

A Classification of Remote Sensing Image Based on Improved Compound Kernels of Svm

NASA Astrophysics Data System (ADS)

Zhao, Jianing; Gao, Wanlin; Liu, Zili; Mou, Guifen; Lu, Lin; Yu, Lina

The accuracy of RS classification based on SVM which is developed from statistical learning theory is high under small number of train samples, which results in satisfaction of classification on RS using SVM methods. The traditional RS classification method combines visual interpretation with computer classification. The accuracy of the RS classification, however, is improved a lot based on SVM method, because it saves much labor and time which is used to interpret images and collect training samples. Kernel functions play an important part in the SVM algorithm. It uses improved compound kernel function and therefore has a higher accuracy of classification on RS images. Moreover, compound kernel improves the generalization and learning ability of the kernel.

Quantitative Evaluation of Medial Temporal Lobe Morphology in Children with Febrile Status Epilepticus: Results of the FEBSTAT Study.

PubMed

McClelland, A C; Gomes, W A; Shinnar, S; Hesdorffer, D C; Bagiella, E; Lewis, D V; Bello, J A; Chan, S; MacFall, J; Chen, M; Pellock, J M; Nordli, D R; Frank, L M; Moshé, S L; Shinnar, R C; Sun, S

2016-12-01

The pathogenesis of febrile status epilepticus is poorly understood, but prior studies have suggested an association with temporal lobe abnormalities, including hippocampal malrotation. We used a quantitative morphometric method to assess the association between temporal lobe morphology and febrile status epilepticus. Brain MR imaging was performed in children presenting with febrile status epilepticus and control subjects as part of the Consequences of Prolonged Febrile Seizures in Childhood study. Medial temporal lobe morphologic parameters were measured manually, including the distance of the hippocampus from the midline, hippocampal height:width ratio, hippocampal angle, collateral sulcus angle, and width of the temporal horn. Temporal lobe morphologic parameters were correlated with the presence of visual hippocampal malrotation; the strongest association was with left temporal horn width (P < .001; adjusted OR, 10.59). Multiple morphologic parameters correlated with febrile status epilepticus, encompassing both the right and left sides. This association was statistically strongest in the right temporal lobe, whereas hippocampal malrotation was almost exclusively left-sided in this cohort. The association between temporal lobe measurements and febrile status epilepticus persisted when the analysis was restricted to cases with visually normal imaging findings without hippocampal malrotation or other visually apparent abnormalities. Several component morphologic features of hippocampal malrotation are independently associated with febrile status epilepticus, even when complete hippocampal malrotation is absent. Unexpectedly, this association predominantly involves the right temporal lobe. These findings suggest that a spectrum of bilateral temporal lobe anomalies are associated with febrile status epilepticus in children. Hippocampal malrotation may represent a visually apparent subset of this spectrum. © 2016 by American Journal of Neuroradiology.

New Myositis Classification Criteria-What We Have Learned Since Bohan and Peter.

PubMed

Leclair, Valérie; Lundberg, Ingrid E

2018-03-17

Idiopathic inflammatory myopathy (IIM) classification criteria have been a subject of debate for many decades. Despite several limitations, the Bohan and Peter criteria are still widely used. The aim of this review is to discuss the evolution of IIM classification criteria. New IIM classification criteria are periodically proposed. The discovery of myositis-specific and myositis-associated autoantibodies led to the development of clinico-serological criteria, while in-depth description of IIM morphological features improved histopathology-based criteria. The long-awaited European League Against Rheumatism and American College of Rheumatology (EULAR/ACR) IIM classification criteria were recently published. The Bohan and Peter criteria are outdated and validated classification criteria are necessary to improve research in IIM. The new EULAR/ACR IIM classification criteria are thus a definite improvement and an important step forward in the field.

Supervised pixel classification using a feature space derived from an artificial visual system

NASA Technical Reports Server (NTRS)

Baxter, Lisa C.; Coggins, James M.

1991-01-01

Image segmentation involves labelling pixels according to their membership in image regions. This requires the understanding of what a region is. Using supervised pixel classification, the paper investigates how groups of pixels labelled manually according to perceived image semantics map onto the feature space created by an Artificial Visual System. Multiscale structure of regions are investigated and it is shown that pixels form clusters based on their geometric roles in the image intensity function, not by image semantics. A tentative abstract definition of a 'region' is proposed based on this behavior.

Is There a "Fete" in "Fetish"? Effects of Orthographic Opacity on Morpho-Orthographic Segmentation in Visual Word Recognition

ERIC Educational Resources Information Center

McCormick, Samantha F.; Rastle, Kathleen; Davis, Matthew H.

2008-01-01

Recent research using masked priming has suggested that there is a form of morphological decomposition that is based solely on the appearance of morphological complexity and that operates independently of semantic information [Longtin, C.M., Segui, J., & Halle, P. A. (2003). Morphological priming without morphological relationship. "Language and…

Nearshore Hydroacoustic Seafloor Mapping In The German Bight (North Sea): Hydroacoustic Interpretation With And Without Classification

NASA Astrophysics Data System (ADS)

Hass, H. C.; Mielck, F.; Papenmeier, S.

2016-12-01

Nearshore habitats are in constant dynamic change. They need regular assessment and appropriate monitoring of areas of special interest. To accomplish this, hydroacoustic seabed characterization tools are applied to allow for cost-effective and efficient mapping of the seafloor. In this context single beam echosounders (SBES) systems provide a comprehensive view by analyzing the hardness and roughness characteristics of the seafloor. Interpolation between transect lines becomes necessary when gapless maps are needed. This study presents a simple method to process and visualize data recorded with RoxAnn (Sonavision, Edinburgh, UK) and similar SBES. Both, hardness and roughness data are merged to one combined parameter that receives a color code (RGB) according to the acoustic properties of the seafloor. This color information is then interpolated to obtain an area-wide map that provides unclassified and thus unbiased seabed information. The RGB color data can subsequently be used for classification and modeling purposes. Four surveys are shown from a morphologically complex nearshore area west of the island of Helgoland (SE North Sea). The area has complex textural and dynamic characteristics reaching from outcropping bedrock via sandy to muddy areas with mostly gradual transitions. RoxAnn data allow to discriminate all seafloor types that were suggested by ground-truth information (seafloor samples, video). The area appears to be fluctuating within certain limits. Sediment import (sand and fluid mud) paths can be reconstructed. Manually, six RoxAnn zones (RZ) were identified and left without hard boundaries to better match the seafloor types of the study site. The k-means fuzzy cluster analysis employed yields best results with 3 classes. We show that interpretations on the basis of largely non-classified, color-coded and interpolated data provide the best gain of information in the highest possible resolution. Classification with hard boundaries is necessary for stakeholders but may cause reduction of information important to science. It becomes apparent that the type of classification addressing stakeholder issues is not always compatible with scientific objectives.

Automatic differentiation of melanoma and clark nevus skin lesions

NASA Astrophysics Data System (ADS)

LeAnder, R. W.; Kasture, A.; Pandey, A.; Umbaugh, S. E.

2007-03-01

Skin cancer is the most common form of cancer in the United States. Although melanoma accounts for just 11% of all types of skin cancer, it is responsible for most of the deaths, claiming more than 7910 lives annually. Melanoma is visually difficult for clinicians to differentiate from Clark nevus lesions which are benign. The application of pattern recognition techniques to these lesions may be useful as an educational tool for teaching physicians to differentiate lesions, as well as for contributing information about the essential optical characteristics that identify them. Purpose: This study sought to find the most effective features to extract from melanoma, melanoma in situ and Clark nevus lesions, and to find the most effective pattern-classification criteria and algorithms for differentiating those lesions, using the Computer Vision and Image Processing Tools (CVIPtools) software package. Methods: Due to changes in ambient lighting during the photographic process, color differences between images can occur. These differences were minimized by capturing dermoscopic images instead of photographic images. Differences in skin color between patients were minimized via image color normalization, by converting original color images to relative-color images. Relative-color images also helped minimize changes in color that occur due to changes in the photographic and digitization processes. Tumors in the relative-color images were segmented and morphologically filtered. Filtered, relative-color, tumor features were then extracted and various pattern-classification schemes were applied. Results: Experimentation resulted in four useful pattern classification methods, the best of which was an overall classification rate of 100% for melanoma and melanoma in situ (grouped) and 60% for Clark nevus. Conclusion: Melanoma and melanoma in situ have feature parameters and feature values that are similar enough to be considered one class of tumor that significantly differs from Clark nevus. Consequently, grouping melanoma and melanoma in situ together achieves the best results in classifying and automatically differentiating melanoma from Clark nevus lesions.

CP-CHARM: segmentation-free image classification made accessible.

PubMed

Uhlmann, Virginie; Singh, Shantanu; Carpenter, Anne E

2016-01-27

Automated classification using machine learning often relies on features derived from segmenting individual objects, which can be difficult to automate. WND-CHARM is a previously developed classification algorithm in which features are computed on the whole image, thereby avoiding the need for segmentation. The algorithm obtained encouraging results but requires considerable computational expertise to execute. Furthermore, some benchmark sets have been shown to be subject to confounding artifacts that overestimate classification accuracy. We developed CP-CHARM, a user-friendly image-based classification algorithm inspired by WND-CHARM in (i) its ability to capture a wide variety of morphological aspects of the image, and (ii) the absence of requirement for segmentation. In order to make such an image-based classification method easily accessible to the biological research community, CP-CHARM relies on the widely-used open-source image analysis software CellProfiler for feature extraction. To validate our method, we reproduced WND-CHARM's results and ensured that CP-CHARM obtained comparable performance. We then successfully applied our approach on cell-based assay data and on tissue images. We designed these new training and test sets to reduce the effect of batch-related artifacts. The proposed method preserves the strengths of WND-CHARM - it extracts a wide variety of morphological features directly on whole images thereby avoiding the need for cell segmentation, but additionally, it makes the methods easily accessible for researchers without computational expertise by implementing them as a CellProfiler pipeline. It has been demonstrated to perform well on a wide range of bioimage classification problems, including on new datasets that have been carefully selected and annotated to minimize batch effects. This provides for the first time a realistic and reliable assessment of the whole image classification strategy.

An Improved Representation of Regional Boundaries on Parcellated Morphological Surfaces

PubMed Central

Hao, Xuejun; Xu, Dongrong; Bansal, Ravi; Liu, Jun; Peterson, Bradley S.

2010-01-01

Establishing the correspondences of brain anatomy with function is important for understanding neuroimaging data. Regional delineations on morphological surfaces define anatomical landmarks and help to visualize and interpret both functional data and morphological measures mapped onto the cortical surface. We present an efficient algorithm that accurately delineates the morphological surface of the cerebral cortex in real time during generation of the surface using information from parcellated 3D data. With this accurate region delineation, we then develop methods for boundary-preserved simplification and smoothing, as well as procedures for the automated correction of small, misclassified regions to improve the quality of the delineated surface. We demonstrate that our delineation algorithm, together with a new method for double-snapshot visualization of cortical regions, can be used to establish a clear correspondence between brain anatomy and mapped quantities, such as morphological measures, across groups of subjects. PMID:21144708

Chinese Children's Character Recognition: Visuo-Orthographic, Phonological Processing and Morphological Skills

ERIC Educational Resources Information Center

Li, Hong; Shu, Hua; McBride-Chang, Catherine; Liu, Hongyun; Peng, Hong

2012-01-01

Tasks tapping visual skills, orthographic knowledge, phonological awareness, speeded naming, morphological awareness and Chinese character recognition were administered to 184 kindergarteners and 273 primary school students from Beijing. Regression analyses indicated that only syllable deletion, morphological construction and speeded number naming…

Typicality effects in artificial categories: is there a hemisphere difference?

PubMed

Richards, L G; Chiarello, C

1990-07-01

In category classification tasks, typicality effects are usually found: accuracy and reaction time depend upon distance from a prototype. In this study, subjects learned either verbal or nonverbal dot pattern categories, followed by a lateralized classification task. Comparable typicality effects were found in both reaction time and accuracy across visual fields for both verbal and nonverbal categories. Both hemispheres appeared to use a similarity-to-prototype matching strategy in classification. This indicates that merely having a verbal label does not differentiate classification in the two hemispheres.

Sessile serrated adenomas with dysplasia: morphological patterns and correlations with MLH1 immunohistochemistry.

PubMed

Liu, Cheng; Walker, Neal I; Leggett, Barbara A; Whitehall, Vicki Lj; Bettington, Mark L; Rosty, Christophe

2017-12-01

Sessile serrated adenomas are the precursor polyp of approximately 20% of colorectal carcinomas. Sessile serrated adenomas with dysplasia are rarely encountered and represent an intermediate step to malignant progression, frequently associated with loss of MLH1 expression. Accurate diagnosis of these lesions is important to facilitate appropriate surveillance, particularly because progression from dysplasia to carcinoma can be rapid. The current World Health Organization classification describes two main patterns of dysplasia occurring in sessile serrated adenomas, namely, serrated and conventional. However, this may not adequately reflect the spectrum of changes seen by pathologists in routine practice. Furthermore, subtle patterns of dysplasia that are nevertheless associated with loss of MLH1 expression are not encompassed in this classification. We performed a morphological analysis of 266 sessile serrated adenomas with dysplasia with concurrent MLH1 immunohistochemistry with the aims of better defining the spectrum of dysplasia occurring in these lesions and correlating dysplasia patterns with MLH1 expression. We found that dysplasia can be divided morphologically into four major patterns, comprising minimal deviation (19%), serrated (12%), adenomatous (8%) and not otherwise specified (79%) groups. Minimal deviation dysplasia is defined by minor architectural and cytological changes that typically requires loss of MLH1 immunohistochemical expression to support the diagnosis. Serrated dysplasia and adenomatous dysplasia have distinctive histological features and are less frequently associated with loss of MLH1 expression (13 and 5%, respectively). Finally, dysplasia not otherwise specified encompasses most cases and shows a diverse range of morphological changes that do not fall into the other subgroups and are frequently associated with loss of MLH1 expression (83%). This morphological classification of sessile serrated adenomas with dysplasia may represent an improvement on the current description as it correlates with the underlying mismatch repair protein status of the polyps and better highlights the range of morphologies seen by pathologists.

Using different classification models in wheat grading utilizing visual features

NASA Astrophysics Data System (ADS)

Basati, Zahra; Rasekh, Mansour; Abbaspour-Gilandeh, Yousef

2018-04-01

Wheat is one of the most important strategic crops in Iran and in the world. The major component that distinguishes wheat from other grains is the gluten section. In Iran, sunn pest is one of the most important factors influencing the characteristics of wheat gluten and in removing it from a balanced state. The existence of bug-damaged grains in wheat will reduce the quality and price of the product. In addition, damaged grains reduce the enrichment of wheat and the quality of bread products. In this study, after preprocessing and segmentation of images, 25 features including 9 colour features, 10 morphological features, and 6 textual statistical features were extracted so as to classify healthy and bug-damaged wheat grains of Azar cultivar of four levels of moisture content (9, 11.5, 14 and 16.5% w.b.) and two lighting colours (yellow light, the composition of yellow and white lights). Using feature selection methods in the WEKA software and the CfsSubsetEval evaluator, 11 features were chosen as inputs of artificial neural network, decision tree and discriment analysis classifiers. The results showed that the decision tree with the J.48 algorithm had the highest classification accuracy of 90.20%. This was followed by artificial neural network classifier with the topology of 11-19-2 and discrimient analysis classifier at 87.46 and 81.81%, respectively

Hip health at skeletal maturity: a population-based study of young adults with cerebral palsy.

PubMed

Wawrzuta, Joanna; Willoughby, Kate L; Molesworth, Charlotte; Ang, Soon Ghee; Shore, Benjamin J; Thomason, Pam; Graham, H Kerr

2016-12-01

We studied 'hip health' in a population-based cohort of adolescents and young adults with cerebral palsy to investigate associations between hip morphology, pain, and gross motor function. Ninety-eight young adults (65 males, 33 females) from the birth cohort were identified as having developed hip displacement (migration percentage >30) and were reviewed at a mean age of 18 years 10 months (range 15-24y). Hip morphology was classified using the Melbourne Cerebral Palsy Hip Classification Scale (MCPHCS). Severity and frequency of pain were recorded using Likert scales. Gross motor function was classified by the Gross Motor Function Classification System (GMFCS). Hip pain was reported in 72% of participants. Associations were found between pain scores and both hip morphology and GMFCS. Median pain severity score for MCPHCS grades 1 to 4 was 2 (interquartile range [IQR] 1.0-3.0) compared to 7 (IQR 6.0-8.0) for grades 5 and 6 (severe subluxation or dislocation). Hip surveillance and access to surgery were associated with improved hip morphology and less pain. Poor hip morphology at skeletal maturity was associated with high levels of pain. Limited hip surveillance and access to surgery, rather than GMFCS, was associated with poor hip morphology. The majority of young adults who had access to hip surveillance, and preventive and reconstructive surgery, had satisfactory hip morphology at skeletal maturity and less pain. © 2016 Mac Keith Press.

Timing in audiovisual speech perception: A mini review and new psychophysical data.

PubMed

Venezia, Jonathan H; Thurman, Steven M; Matchin, William; George, Sahara E; Hickok, Gregory

2016-02-01

Recent influential models of audiovisual speech perception suggest that visual speech aids perception by generating predictions about the identity of upcoming speech sounds. These models place stock in the assumption that visual speech leads auditory speech in time. However, it is unclear whether and to what extent temporally-leading visual speech information contributes to perception. Previous studies exploring audiovisual-speech timing have relied upon psychophysical procedures that require artificial manipulation of cross-modal alignment or stimulus duration. We introduce a classification procedure that tracks perceptually relevant visual speech information in time without requiring such manipulations. Participants were shown videos of a McGurk syllable (auditory /apa/ + visual /aka/ = perceptual /ata/) and asked to perform phoneme identification (/apa/ yes-no). The mouth region of the visual stimulus was overlaid with a dynamic transparency mask that obscured visual speech in some frames but not others randomly across trials. Variability in participants' responses (~35 % identification of /apa/ compared to ~5 % in the absence of the masker) served as the basis for classification analysis. The outcome was a high resolution spatiotemporal map of perceptually relevant visual features. We produced these maps for McGurk stimuli at different audiovisual temporal offsets (natural timing, 50-ms visual lead, and 100-ms visual lead). Briefly, temporally-leading (~130 ms) visual information did influence auditory perception. Moreover, several visual features influenced perception of a single speech sound, with the relative influence of each feature depending on both its temporal relation to the auditory signal and its informational content.

Timing in Audiovisual Speech Perception: A Mini Review and New Psychophysical Data

PubMed Central

Venezia, Jonathan H.; Thurman, Steven M.; Matchin, William; George, Sahara E.; Hickok, Gregory

2015-01-01

Recent influential models of audiovisual speech perception suggest that visual speech aids perception by generating predictions about the identity of upcoming speech sounds. These models place stock in the assumption that visual speech leads auditory speech in time. However, it is unclear whether and to what extent temporally-leading visual speech information contributes to perception. Previous studies exploring audiovisual-speech timing have relied upon psychophysical procedures that require artificial manipulation of cross-modal alignment or stimulus duration. We introduce a classification procedure that tracks perceptually-relevant visual speech information in time without requiring such manipulations. Participants were shown videos of a McGurk syllable (auditory /apa/ + visual /aka/ = perceptual /ata/) and asked to perform phoneme identification (/apa/ yes-no). The mouth region of the visual stimulus was overlaid with a dynamic transparency mask that obscured visual speech in some frames but not others randomly across trials. Variability in participants' responses (∼35% identification of /apa/ compared to ∼5% in the absence of the masker) served as the basis for classification analysis. The outcome was a high resolution spatiotemporal map of perceptually-relevant visual features. We produced these maps for McGurk stimuli at different audiovisual temporal offsets (natural timing, 50-ms visual lead, and 100-ms visual lead). Briefly, temporally-leading (∼130 ms) visual information did influence auditory perception. Moreover, several visual features influenced perception of a single speech sound, with the relative influence of each feature depending on both its temporal relation to the auditory signal and its informational content. PMID:26669309

A Graph-Embedding Approach to Hierarchical Visual Word Mergence.

PubMed

Wang, Lei; Liu, Lingqiao; Zhou, Luping

2017-02-01

Appropriately merging visual words are an effective dimension reduction method for the bag-of-visual-words model in image classification. The approach of hierarchically merging visual words has been extensively employed, because it gives a fully determined merging hierarchy. Existing supervised hierarchical merging methods take different approaches and realize the merging process with various formulations. In this paper, we propose a unified hierarchical merging approach built upon the graph-embedding framework. Our approach is able to merge visual words for any scenario, where a preferred structure and an undesired structure are defined, and, therefore, can effectively attend to all kinds of requirements for the word-merging process. In terms of computational efficiency, we show that our algorithm can seamlessly integrate a fast search strategy developed in our previous work and, thus, well maintain the state-of-the-art merging speed. To the best of our survey, the proposed approach is the first one that addresses the hierarchical visual word mergence in such a flexible and unified manner. As demonstrated, it can maintain excellent image classification performance even after a significant dimension reduction, and outperform all the existing comparable visual word-merging methods. In a broad sense, our work provides an open platform for applying, evaluating, and developing new criteria for hierarchical word-merging tasks.

Tissue classification for laparoscopic image understanding based on multispectral texture analysis

NASA Astrophysics Data System (ADS)

Zhang, Yan; Wirkert, Sebastian J.; Iszatt, Justin; Kenngott, Hannes; Wagner, Martin; Mayer, Benjamin; Stock, Christian; Clancy, Neil T.; Elson, Daniel S.; Maier-Hein, Lena

2016-03-01

Intra-operative tissue classification is one of the prerequisites for providing context-aware visualization in computer-assisted minimally invasive surgeries. As many anatomical structures are difficult to differentiate in conventional RGB medical images, we propose a classification method based on multispectral image patches. In a comprehensive ex vivo study we show (1) that multispectral imaging data is superior to RGB data for organ tissue classification when used in conjunction with widely applied feature descriptors and (2) that combining the tissue texture with the reflectance spectrum improves the classification performance. Multispectral tissue analysis could thus evolve as a key enabling technique in computer-assisted laparoscopy.

Spectroscopic classification of icy satellites of Saturn I: Identification of terrain units on Dione

NASA Astrophysics Data System (ADS)

Scipioni, F.; Tosi, F.; Stephan, K.; Filacchione, G.; Ciarniello, M.; Capaccioni, F.; Cerroni, P.

2013-11-01

Dione is one of the largest and densest icy satellites of Saturn. Its surface shows a marked asymmetry between its leading and trailing hemispheres, the leading side being brighter than the trailing side, which shows regions mantled by a dark veneer whose origin is likely exogenic. In order to identify different terrain units we applied the Spectral Angle Mapper (SAM) classification technique to Dione’s hyperspectral images acquired by the Visual and Infrared Mapping Spectrometer (VIMS) onboard the Cassini Orbiter in the infrared range (0.88-5.12 μm). On a relatively limited portion of the surface of Dione we first identified nine spectral endmembers, corresponding to as many terrain units, which mostly distinguish for water ice abundance and ice grain size. We then used these endmembers in SAM to achieve a comprehensive classification of the entire surface. The analysis of the infrared spectra returned by VIMS shows that different regions of Dione have variations in water ice bands depths, in average ice grain size, and in the concentration of contaminants, such as CO2 and hydrocarbons, which are clearly connected to morphological and geological structures. Generally, the spectral units that classify optically dark terrains are those showing suppressed water ice bands, a finer ice grain size and a higher concentration of carbon dioxide. Conversely, spectral units labeling brighter regions have deeper water ice absorption bands, higher albedo and a smaller concentration of contaminants. We also considered VIMS cubes of the small satellite Helene (one of the two Dione’s trojan moons) and we compared its infrared spectra to those of the spectral units found on Dione. We observe that the closest match between the spectra of the two satellites occurs for one of the youngest and freshest terrain units on Dione: the Creusa crater region.

Machine learning techniques for diabetic macular edema (DME) classification on SD-OCT images.

PubMed

Alsaih, Khaled; Lemaitre, Guillaume; Rastgoo, Mojdeh; Massich, Joan; Sidibé, Désiré; Meriaudeau, Fabrice

2017-06-07

Spectral domain optical coherence tomography (OCT) (SD-OCT) is most widely imaging equipment used in ophthalmology to detect diabetic macular edema (DME). Indeed, it offers an accurate visualization of the morphology of the retina as well as the retina layers. The dataset used in this study has been acquired by the Singapore Eye Research Institute (SERI), using CIRRUS TM (Carl Zeiss Meditec, Inc., Dublin, CA, USA) SD-OCT device. The dataset consists of 32 OCT volumes (16 DME and 16 normal cases). Each volume contains 128 B-scans with resolution of 1024 px × 512 px, resulting in more than 3800 images being processed. All SD-OCT volumes are read and assessed by trained graders and identified as normal or DME cases based on evaluation of retinal thickening, hard exudates, intraretinal cystoid space formation, and subretinal fluid. Within the DME sub-set, a large number of lesions has been selected to create a rather complete and diverse DME dataset. This paper presents an automatic classification framework for SD-OCT volumes in order to identify DME versus normal volumes. In this regard, a generic pipeline including pre-processing, feature detection, feature representation, and classification was investigated. More precisely, extraction of histogram of oriented gradients and local binary pattern (LBP) features within a multiresolution approach is used as well as principal component analysis (PCA) and bag of words (BoW) representations. Besides comparing individual and combined features, different representation approaches and different classifiers are evaluated. The best results are obtained for LBP[Formula: see text] vectors while represented and classified using PCA and a linear-support vector machine (SVM), leading to a sensitivity(SE) and specificity (SP) of 87.5 and 87.5%, respectively.

An arrhythmia classification algorithm using a dedicated wavelet adapted to different subjects.

PubMed

Kim, Jinkwon; Min, Se Dong; Lee, Myoungho

2011-06-27

Numerous studies have been conducted regarding a heartbeat classification algorithm over the past several decades. However, many algorithms have also been studied to acquire robust performance, as biosignals have a large amount of variation among individuals. Various methods have been proposed to reduce the differences coming from personal characteristics, but these expand the differences caused by arrhythmia. In this paper, an arrhythmia classification algorithm using a dedicated wavelet adapted to individual subjects is proposed. We reduced the performance variation using dedicated wavelets, as in the ECG morphologies of the subjects. The proposed algorithm utilizes morphological filtering and a continuous wavelet transform with a dedicated wavelet. A principal component analysis and linear discriminant analysis were utilized to compress the morphological data transformed by the dedicated wavelets. An extreme learning machine was used as a classifier in the proposed algorithm. A performance evaluation was conducted with the MIT-BIH arrhythmia database. The results showed a high sensitivity of 97.51%, specificity of 85.07%, accuracy of 97.94%, and a positive predictive value of 97.26%. The proposed algorithm achieves better accuracy than other state-of-the-art algorithms with no intrasubject between the training and evaluation datasets. And it significantly reduces the amount of intervention needed by physicians.

An arrhythmia classification algorithm using a dedicated wavelet adapted to different subjects

PubMed Central

2011-01-01

Background Numerous studies have been conducted regarding a heartbeat classification algorithm over the past several decades. However, many algorithms have also been studied to acquire robust performance, as biosignals have a large amount of variation among individuals. Various methods have been proposed to reduce the differences coming from personal characteristics, but these expand the differences caused by arrhythmia. Methods In this paper, an arrhythmia classification algorithm using a dedicated wavelet adapted to individual subjects is proposed. We reduced the performance variation using dedicated wavelets, as in the ECG morphologies of the subjects. The proposed algorithm utilizes morphological filtering and a continuous wavelet transform with a dedicated wavelet. A principal component analysis and linear discriminant analysis were utilized to compress the morphological data transformed by the dedicated wavelets. An extreme learning machine was used as a classifier in the proposed algorithm. Results A performance evaluation was conducted with the MIT-BIH arrhythmia database. The results showed a high sensitivity of 97.51%, specificity of 85.07%, accuracy of 97.94%, and a positive predictive value of 97.26%. Conclusions The proposed algorithm achieves better accuracy than other state-of-the-art algorithms with no intrasubject between the training and evaluation datasets. And it significantly reduces the amount of intervention needed by physicians. PMID:21707989

Molecular Pathology: Predictive, Prognostic, and Diagnostic Markers in Uterine Tumors.

PubMed

Ritterhouse, Lauren L; Howitt, Brooke E

2016-09-01

This article focuses on the diagnostic, prognostic, and predictive molecular biomarkers in uterine malignancies, in the context of morphologic diagnoses. The histologic classification of endometrial carcinomas is reviewed first, followed by the description and molecular classification of endometrial epithelial malignancies in the context of histologic classification. Taken together, the molecular and histologic classifications help clinicians to approach troublesome areas encountered in clinical practice and evaluate the utility of molecular alterations in the diagnosis and subclassification of endometrial carcinomas. Putative prognostic markers are reviewed. The use of molecular alterations and surrogate immunohistochemistry as prognostic and predictive markers is also discussed. Copyright © 2016 Elsevier Inc. All rights reserved.

Repetitive element signature-based visualization, distance computation, and classification of 1766 microbial genomes.

PubMed

Lee, Kang-Hoon; Shin, Kyung-Seop; Lim, Debora; Kim, Woo-Chan; Chung, Byung Chang; Han, Gyu-Bum; Roh, Jeongkyu; Cho, Dong-Ho; Cho, Kiho

2015-07-01

The genomes of living organisms are populated with pleomorphic repetitive elements (REs) of varying densities. Our hypothesis that genomic RE landscapes are species/strain/individual-specific was implemented into the Genome Signature Imaging system to visualize and compute the RE-based signatures of any genome. Following the occurrence profiling of 5-nucleotide REs/words, the information from top-50 frequency words was transformed into a genome-specific signature and visualized as Genome Signature Images (GSIs), using a CMYK scheme. An algorithm for computing distances among GSIs was formulated using the GSIs' variables (word identity, frequency, and frequency order). The utility of the GSI-distance computation system was demonstrated with control genomes. GSI-based computation of genome-relatedness among 1766 microbes (117 archaea and 1649 bacteria) identified their clustering patterns; although the majority paralleled the established classification, some did not. The Genome Signature Imaging system, with its visualization and distance computation functions, enables genome-scale evolutionary studies involving numerous genomes with varying sizes. Copyright © 2015 Elsevier Inc. All rights reserved.

The target plant concept-a history and brief overview

Treesearch

Thomas D. Landis

2011-01-01

The target plant concept originated with morphological classification of conifer nursery stock in the 1930s, and the concept was enhanced through physiological research and seedling testing towards the end of the century. Morphological grading standards such as shoot height, stem diameter, and root mass are the most common use of the target plant concept, and some...

Soil geomorphic classification, soil taxonomy, and effects on soil richness assessments

Treesearch

Jonathan D. Phillips; Daniel A. Marion

2007-01-01

The study of pedodiversity and soil richness depends on the notion of soils as discrete entities. Soil classifications are often criticized in this regard because they depend in part on arbitrary or subjective criteria. In this study soils were categorized on the basis of the presence or absence of six lithological and morphological characteristics. Richness vs. area...

Application of a hierarchical habitat unit classification system: stream habitat and salmonid distribution in Ward Creek, southeast Alaska.

Treesearch

M.D. Bryant; B.E. Wright; B.J. Davies

1992-01-01

A hierarchical classification system separating stream habitat into habitat units defined by stream morphology and hydrology was used in a pre-enhancement stream survey. The system separates habitat units into macrounits, mesounits, and micro- units and includes a separate evaluation of instream cover that also uses the hierarchical scheme. This paper presents an...

Single-trial EEG RSVP classification using convolutional neural networks

NASA Astrophysics Data System (ADS)

Shamwell, Jared; Lee, Hyungtae; Kwon, Heesung; Marathe, Amar R.; Lawhern, Vernon; Nothwang, William

2016-05-01

Traditionally, Brain-Computer Interfaces (BCI) have been explored as a means to return function to paralyzed or otherwise debilitated individuals. An emerging use for BCIs is in human-autonomy sensor fusion where physiological data from healthy subjects is combined with machine-generated information to enhance the capabilities of artificial systems. While human-autonomy fusion of physiological data and computer vision have been shown to improve classification during visual search tasks, to date these approaches have relied on separately trained classification models for each modality. We aim to improve human-autonomy classification performance by developing a single framework that builds codependent models of human electroencephalograph (EEG) and image data to generate fused target estimates. As a first step, we developed a novel convolutional neural network (CNN) architecture and applied it to EEG recordings of subjects classifying target and non-target image presentations during a rapid serial visual presentation (RSVP) image triage task. The low signal-to-noise ratio (SNR) of EEG inherently limits the accuracy of single-trial classification and when combined with the high dimensionality of EEG recordings, extremely large training sets are needed to prevent overfitting and achieve accurate classification from raw EEG data. This paper explores a new deep CNN architecture for generalized multi-class, single-trial EEG classification across subjects. We compare classification performance from the generalized CNN architecture trained across all subjects to the individualized XDAWN, HDCA, and CSP neural classifiers which are trained and tested on single subjects. Preliminary results show that our CNN meets and slightly exceeds the performance of the other classifiers despite being trained across subjects.

Non-target adjacent stimuli classification improves performance of classical ERP-based brain computer interface

NASA Astrophysics Data System (ADS)

Ceballos, G. A.; Hernández, L. F.

2015-04-01

Objective. The classical ERP-based speller, or P300 Speller, is one of the most commonly used paradigms in the field of Brain Computer Interfaces (BCI). Several alterations to the visual stimuli presentation system have been developed to avoid unfavorable effects elicited by adjacent stimuli. However, there has been little, if any, regard to useful information contained in responses to adjacent stimuli about spatial location of target symbols. This paper aims to demonstrate that combining the classification of non-target adjacent stimuli with standard classification (target versus non-target) significantly improves classical ERP-based speller efficiency. Approach. Four SWLDA classifiers were trained and combined with the standard classifier: the lower row, upper row, right column and left column classifiers. This new feature extraction procedure and the classification method were carried out on three open databases: the UAM P300 database (Universidad Autonoma Metropolitana, Mexico), BCI competition II (dataset IIb) and BCI competition III (dataset II). Main results. The inclusion of the classification of non-target adjacent stimuli improves target classification in the classical row/column paradigm. A gain in mean single trial classification of 9.6% and an overall improvement of 25% in simulated spelling speed was achieved. Significance. We have provided further evidence that the ERPs produced by adjacent stimuli present discriminable features, which could provide additional information about the spatial location of intended symbols. This work promotes the searching of information on the peripheral stimulation responses to improve the performance of emerging visual ERP-based spellers.

Bayesian classification for the selection of in vitro human embryos using morphological and clinical data.

PubMed

Morales, Dinora Araceli; Bengoetxea, Endika; Larrañaga, Pedro; García, Miguel; Franco, Yosu; Fresnada, Mónica; Merino, Marisa

2008-05-01

In vitro fertilization (IVF) is a medically assisted reproduction technique that enables infertile couples to achieve successful pregnancy. Given the uncertainty of the treatment, we propose an intelligent decision support system based on supervised classification by Bayesian classifiers to aid to the selection of the most promising embryos that will form the batch to be transferred to the woman's uterus. The aim of the supervised classification system is to improve overall success rate of each IVF treatment in which a batch of embryos is transferred each time, where the success is achieved when implantation (i.e. pregnancy) is obtained. Due to ethical reasons, different legislative restrictions apply in every country on this technique. In Spain, legislation allows a maximum of three embryos to form each transfer batch. As a result, clinicians prefer to select the embryos by non-invasive embryo examination based on simple methods and observation focused on morphology and dynamics of embryo development after fertilization. This paper proposes the application of Bayesian classifiers to this embryo selection problem in order to provide a decision support system that allows a more accurate selection than with the actual procedures which fully rely on the expertise and experience of embryologists. For this, we propose to take into consideration a reduced subset of feature variables related to embryo morphology and clinical data of patients, and from this data to induce Bayesian classification models. Results obtained applying a filter technique to choose the subset of variables, and the performance of Bayesian classifiers using them, are presented.

Machine learning on brain MRI data for differential diagnosis of Parkinson's disease and Progressive Supranuclear Palsy.

PubMed

Salvatore, C; Cerasa, A; Castiglioni, I; Gallivanone, F; Augimeri, A; Lopez, M; Arabia, G; Morelli, M; Gilardi, M C; Quattrone, A

2014-01-30

Supervised machine learning has been proposed as a revolutionary approach for identifying sensitive medical image biomarkers (or combination of them) allowing for automatic diagnosis of individual subjects. The aim of this work was to assess the feasibility of a supervised machine learning algorithm for the assisted diagnosis of patients with clinically diagnosed Parkinson's disease (PD) and Progressive Supranuclear Palsy (PSP). Morphological T1-weighted Magnetic Resonance Images (MRIs) of PD patients (28), PSP patients (28) and healthy control subjects (28) were used by a supervised machine learning algorithm based on the combination of Principal Components Analysis as feature extraction technique and on Support Vector Machines as classification algorithm. The algorithm was able to obtain voxel-based morphological biomarkers of PD and PSP. The algorithm allowed individual diagnosis of PD versus controls, PSP versus controls and PSP versus PD with an Accuracy, Specificity and Sensitivity>90%. Voxels influencing classification between PD and PSP patients involved midbrain, pons, corpus callosum and thalamus, four critical regions known to be strongly involved in the pathophysiological mechanisms of PSP. Classification accuracy of individual PSP patients was consistent with previous manual morphological metrics and with other supervised machine learning application to MRI data, whereas accuracy in the detection of individual PD patients was significantly higher with our classification method. The algorithm provides excellent discrimination of PD patients from PSP patients at an individual level, thus encouraging the application of computer-based diagnosis in clinical practice. Copyright © 2013 Elsevier B.V. All rights reserved.

Commercial Training Device Requirement (CTDR) for Initial Entry Rotary Wing (IERW) Aviation Training Helicopter

DTIC Science & Technology

1989-12-24

training; 16 . PRICE CODE 17. SECURITY CLASSIFICATION 18. SECURITY CLASSIFICATION 19. SECURITY CLASSIFICATION 20. LIMITATION OF ABSTRACT OF REPORT OF...to be leased is 205. One hundred aircraft will be VFR aircraft only. One hundred and five aircraft will be instrument flight rules ( IFR ) certified, 55...of which will be IFR equipped. The other IFR -certified aircraft will be visual flight rules equipped. c. The total lease cost is based on an assumed

Comparison of Pixel-Based and Object-Based Classification Using Parameters and Non-Parameters Approach for the Pattern Consistency of Multi Scale Landcover

NASA Astrophysics Data System (ADS)

Juniati, E.; Arrofiqoh, E. N.

2017-09-01

Information extraction from remote sensing data especially land cover can be obtained by digital classification. In practical some people are more comfortable using visual interpretation to retrieve land cover information. However, it is highly influenced by subjectivity and knowledge of interpreter, also takes time in the process. Digital classification can be done in several ways, depend on the defined mapping approach and assumptions on data distribution. The study compared several classifiers method for some data type at the same location. The data used Landsat 8 satellite imagery, SPOT 6 and Orthophotos. In practical, the data used to produce land cover map in 1:50,000 map scale for Landsat, 1:25,000 map scale for SPOT and 1:5,000 map scale for Orthophotos, but using visual interpretation to retrieve information. Maximum likelihood Classifiers (MLC) which use pixel-based and parameters approach applied to such data, and also Artificial Neural Network classifiers which use pixel-based and non-parameters approach applied too. Moreover, this study applied object-based classifiers to the data. The classification system implemented is land cover classification on Indonesia topographic map. The classification applied to data source, which is expected to recognize the pattern and to assess consistency of the land cover map produced by each data. Furthermore, the study analyse benefits and limitations the use of methods.

Analysis of the English morphology by semantic networks

NASA Astrophysics Data System (ADS)

Žáček, Martin; Homola, Dan

2017-11-01

The article is devoted to study the morphology of natural language, in this case English language. The research is of the language is from the perspective of knowledge representation, when we look at the word as a concept in the Concept languages. The research is in the relationship of the individual words and their classification in the sentence. For the analysis there are used several methods (syntax, lexical categories, morphology). This article focuses mainly on the word, as the foundation of every natural language (English).

Event-Related fMRI of Category Learning: Differences in Classification and Feedback Networks

ERIC Educational Resources Information Center

Little, Deborah M.; Shin, Silvia S.; Sisco, Shannon M.; Thulborn, Keith R.

2006-01-01

Eighteen healthy young adults underwent event-related (ER) functional magnetic resonance imaging (fMRI) of the brain while performing a visual category learning task. The specific category learning task required subjects to extract the rules that guide classification of quasi-random patterns of dots into categories. Following each classification…

The problem of regime summaries of the data from radar observations. [for cloud system identification

NASA Technical Reports Server (NTRS)

Divinskaya, B. S.; Salman, Y. M.

1975-01-01

Peculiarities of the radar information about clouds are examined in comparison with visual data. An objective radar classification is presented and the relation of it to the meteorological classification is shown. The advisability of storage and summarization of the primary radar data for regime purposes is substantiated.

Automatic Screening and Grading of Age-Related Macular Degeneration from Texture Analysis of Fundus Images

PubMed Central

Phan, Thanh Vân; Seoud, Lama; Chakor, Hadi; Cheriet, Farida

2016-01-01

Age-related macular degeneration (AMD) is a disease which causes visual deficiency and irreversible blindness to the elderly. In this paper, an automatic classification method for AMD is proposed to perform robust and reproducible assessments in a telemedicine context. First, a study was carried out to highlight the most relevant features for AMD characterization based on texture, color, and visual context in fundus images. A support vector machine and a random forest were used to classify images according to the different AMD stages following the AREDS protocol and to evaluate the features' relevance. Experiments were conducted on a database of 279 fundus images coming from a telemedicine platform. The results demonstrate that local binary patterns in multiresolution are the most relevant for AMD classification, regardless of the classifier used. Depending on the classification task, our method achieves promising performances with areas under the ROC curve between 0.739 and 0.874 for screening and between 0.469 and 0.685 for grading. Moreover, the proposed automatic AMD classification system is robust with respect to image quality. PMID:27190636

A Descriptive Genetic Classification for Glaciovolcanoes

NASA Astrophysics Data System (ADS)

Edwards, B. R.; Russell, K.; Porritt, L. A.

2014-12-01

We review the recently published descriptive genetic classification for glaciovolcanoes (Russell et al., Quat Sci Rv, 2014). The new classification uses 'tuya' as a root word for all glaciovolcanic edifices, and with modifiers that make the classification descriptive (e.g., andesitic, lava-dominated, flat topped tuya). Although tuyas can range in composition from basaltic to rhyolitic, many of the characteristics diagnostic of glaciovolcanic environments are largely independent of lava composition (e.g., edifice morphology, columnar jointing patterns, glass distributions, pyroclast shapes). Tuya subtypes are first classified on the basis of variations in edifice-scale morphologies (e.g., conical tuya) then, on the proportions of the essential lithofacies (e.g., tephra-dominated conical tuya), and lastly on magma composition (e.g., basaltic, tephra-dominated, conical tuya). The lithofacies associations within tuyas broadly record the interplay between magmatic and glaciohydraulic conditions extent during the active phases of the eruption, including the dominant style of eruption (e.g., explosive vs. effusive). We present nine distinct, endmember models for glaciovolcanic edifices that simultaneously record changes in eruption conditions (explosive, transitional, effusive) for different general glaciohydraulic conditions (closed/sealed, leaky/partly sealed, open/well-drained). To date we have identified potential examples for 7 of the 9 models. Use of a simplified, descriptive classification scheme for glaciovolcanoes will facilitate communications amongst volcanologists and planetary scientists and the use of tuyas for recovering critical paleo-environmental information, particularly the local glaciohydraulics extent during eruptions.

Imaging evaluation of traumatic thoracolumbar spine injuries: Radiological review

PubMed Central

Gamanagatti, Shivanand; Rathinam, Deepak; Rangarajan, Krithika; Kumar, Atin; Farooque, Kamran; Sharma, Vijay

2015-01-01

Spine fractures account for a large portion of musculoskeletal injuries worldwide. A classification of spine fractures is necessary in order to develop a common language for treatment indications and outcomes. Several classification systems have been developed based on injury anatomy or mechanisms of action, but they have demonstrated poor reliability, have yielded little prognostic information, and have not been widely used. For this reason, the Arbeitsgemeinschaftfür Osteosynthesefragen (AO) committee has classified thorocolumbar spine injuries based on the pathomorphological criteria into3 types (A: Compression; B: Distraction; C: Axial torque and rotational deformity). Each of these types is further divided into 3 groups and 3 subgroups reflecting progressive scale of morphological damage and the degree of instability. Because of its highly detailed sub classifications, the AO system has shown limited interobserver variability. It is similar to its predecessors in that it does not incorporate the patient’s neurologic status.The need for a reliable, reproducible, clinically relevant, prognostic classification system with an optimal balance of ease of use and detail of injury description contributed to the development of a new classification system, the thoracolumbar injury classification and severity score (TLICS). The TLICS defines injury based on three clinical characteristics: injury morphology, integrity of the posterior ligamentous complex, and neurologic status of the patient. The severity score offers prognostic information and is helpful in decision making about surgical vs nonsurgical management. PMID:26435776

Methods in hair research: how to objectively distinguish between anagen and catagen in human hair follicle organ culture.

PubMed

Kloepper, Jennifer Elisabeth; Sugawara, Koji; Al-Nuaimi, Yusur; Gáspár, Erzsébet; van Beek, Nina; Paus, Ralf

2010-03-01

The organ culture of human scalp hair follicles (HFs) is the best currently available assay for hair research in the human system. In order to determine the hair growth-modulatory effects of agents in this assay, one critical read-out parameter is the assessment of whether the test agent has prolonged anagen duration or induced catagen in vitro. However, objective criteria to distinguish between anagen VI HFs and early catagen in human HF organ culture, two hair cycle stages with a deceptively similar morphology, remain to be established. Here, we develop, document and test an objective classification system that allows to distinguish between anagen VI and early catagen in organ-cultured human HFs, using both qualitative and quantitative parameters that can be generated by light microscopy or immunofluorescence. Seven qualitative classification criteria are defined that are based on assessing the morphology of the hair matrix, the dermal papilla and the distribution of pigmentary markers (melanin, gp100). These are complemented by ten quantitative parameters. We have tested this classification system by employing the clinically used topical hair growth inhibitor, eflornithine, and show that eflornithine indeed produces the expected premature catagen induction, as identified by the novel classification criteria reported here. Therefore, this classification system offers a standardized, objective and reproducible new experimental method to reliably distinguish between human anagen VI and early catagen HFs in organ culture.

Reflecting on the structure of soil classification systems: insights from a proposal for integrating subsoil data into soil information systems

NASA Astrophysics Data System (ADS)

Dondeyne, Stefaan; Juilleret, Jérôme; Vancampenhout, Karen; Deckers, Jozef; Hissler, Christophe

2017-04-01

Classification of soils in both World Reference Base for soil resources (WRB) and Soil Taxonomy hinges on the identification of diagnostic horizons and characteristics. However as these features often occur within the first 100 cm, these classification systems convey little information on subsoil characteristics. An integrated knowledge of the soil, soil-to-substratum and deeper substratum continuum is required when dealing with environmental issues such as vegetation ecology, water quality or the Critical Zone in general. Therefore, we recently proposed a classification system of the subsolum complementing current soil classification systems. By reflecting on the structure of the subsoil classification system which is inspired by WRB, we aim at fostering a discussion on some potential future developments of WRB. For classifying the subsolum we define Regolite, Saprolite, Saprock and Bedrock as four Subsolum Reference Groups each corresponding to different weathering stages of the subsoil. Principal qualifiers can be used to categorize intergrades of these Subsoil Reference Groups while morphologic and lithologic characteristics can be presented with supplementary qualifiers. We argue that adopting a low hierarchical structure - akin to WRB and in contrast to a strong hierarchical structure as in Soil Taxonomy - offers the advantage of having an open classification system avoiding the need for a priori knowledge of all possible combinations which may be encountered in the field. Just as in WRB we also propose to use principal and supplementary qualifiers as a second level of classification. However, in contrast to WRB we propose to reserve the principal qualifiers for intergrades and to regroup the supplementary qualifiers into thematic categories (morphologic or lithologic). Structuring the qualifiers in this manner should facilitate the integration and handling of both soil and subsoil classification units into soil information systems and calls for paying attention to these structural issues in future developments of WRB.

A molecular phylogeny of the nightjars (Aves: Caprimulgidae) suggests extensive conservation of primitive morphological traits across multiple lineages.

PubMed

Larsen, Carl; Speed, Michael; Harvey, Nicholas; Noyes, Harry A

2007-03-01

We report a molecular re-assessment of the classification of the nightjars which draws conclusions that are strongly at odds with the traditional, morphology-based classifications. We used maximum likelihood and Bayesian methods to compare the cytochrome b gene for 14 species from seven of the 15 genera of the Caprimulgidae and partial cytochrome b sequence data was available for a further seven species including three further genera. We found that within the Caprimulgidae there were four geographically isolated clades with bootstrap support greater than 70%. One of these clades contained just Chordeiles species, the remaining three clades each contained a mixture of genera including Caprimulgus sp. A clade of exclusively South American nightjars included the genera Caprimulgus, Uropsalis, Eleopthreptus and Hydropsalis. A clade of African and Eurasian birds included Caprimulgus and Macrodipteryx. Phalaenoptilus nuttallii and Caprimulgus vociferous formed a clade of North American birds. Two ecological factors appear to make morphological classification potentially misleading: first, the apparent retention of primitive anti-predator and foraging-related traits across genetically divergent groups; second, rapid divergence in other traits, especially those related to mating, which generate high levels of morphological divergence between species that are genetically very similar. The cytochrome b data suggests that the genus Caprimulgus is not monophyletic and is restricted to Africa and Eurasia and that Caprimulgus species from outside this area have been misclassified as a consequence of retention of primitive adaptations for crepuscular/nocturnal living. Some other genera also appear to have little support from the cytochrome b data.

Molecular phylogenetics and character evolution of morphologically diverse groups, Dendrobium section Dendrobium and allies

PubMed Central

Takamiya, Tomoko; Wongsawad, Pheravut; Sathapattayanon, Apirada; Tajima, Natsuko; Suzuki, Shunichiro; Kitamura, Saki; Shioda, Nao; Handa, Takashi; Kitanaka, Susumu; Iijima, Hiroshi; Yukawa, Tomohisa

2014-01-01

It is always difficult to construct coherent classification systems for plant lineages having diverse morphological characters. The genus Dendrobium, one of the largest genera in the Orchidaceae, includes ∼1100 species, and enormous morphological diversification has hindered the establishment of consistent classification systems covering all major groups of this genus. Given the particular importance of species in Dendrobium section Dendrobium and allied groups as floriculture and crude drug genetic resources, there is an urgent need to establish a stable classification system. To clarify phylogenetic relationships in Dendrobium section Dendrobium and allied groups, we analysed the macromolecular characters of the group. Phylogenetic analyses of 210 taxa of Dendrobium were conducted on DNA sequences of internal transcribed spacer (ITS) regions of 18S–26S nuclear ribosomal DNA and the maturase-coding gene (matK) located in an intron of the plastid gene trnK using maximum parsimony and Bayesian methods. The parsimony and Bayesian analyses revealed 13 distinct clades in the group comprising section Dendrobium and its allied groups. Results also showed paraphyly or polyphyly of sections Amblyanthus, Aporum, Breviflores, Calcarifera, Crumenata, Dendrobium, Densiflora, Distichophyllae, Dolichocentrum, Holochrysa, Oxyglossum and Pedilonum. On the other hand, the monophyly of section Stachyobium was well supported. It was found that many of the morphological characters that have been believed to reflect phylogenetic relationships are, in fact, the result of convergence. As such, many of the sections that have been recognized up to this point were found to not be monophyletic, so recircumscription of sections is required. PMID:25107672

Image processing and applications based on visualizing navigation service

NASA Astrophysics Data System (ADS)

Hwang, Chyi-Wen

2015-07-01

When facing the "overabundant" of semantic web information, in this paper, the researcher proposes the hierarchical classification and visualizing RIA (Rich Internet Application) navigation system: Concept Map (CM) + Semantic Structure (SS) + the Knowledge on Demand (KOD) service. The aim of the Multimedia processing and empirical applications testing, was to investigating the utility and usability of this visualizing navigation strategy in web communication design, into whether it enables the user to retrieve and construct their personal knowledge or not. Furthermore, based on the segment markets theory in the Marketing model, to propose a User Interface (UI) classification strategy and formulate a set of hypermedia design principles for further UI strategy and e-learning resources in semantic web communication. These research findings: (1) Irrespective of whether the simple declarative knowledge or the complex declarative knowledge model is used, the "CM + SS + KOD navigation system" has a better cognition effect than the "Non CM + SS + KOD navigation system". However, for the" No web design experience user", the navigation system does not have an obvious cognition effect. (2) The essential of classification in semantic web communication design: Different groups of user have a diversity of preference needs and different cognitive styles in the CM + SS + KOD navigation system.

Visualizing and enhancing a deep learning framework using patients age and gender for chest x-ray image retrieval

NASA Astrophysics Data System (ADS)

Anavi, Yaron; Kogan, Ilya; Gelbart, Elad; Geva, Ofer; Greenspan, Hayit

2016-03-01

We explore the combination of text metadata, such as patients' age and gender, with image-based features, for X-ray chest pathology image retrieval. We focus on a feature set extracted from a pre-trained deep convolutional network shown in earlier work to achieve state-of-the-art results. Two distance measures are explored: a descriptor-based measure, which computes the distance between image descriptors, and a classification-based measure, which performed by a comparison of the corresponding SVM classification probabilities. We show that retrieval results increase once the age and gender information combined with the features extracted from the last layers of the network, with best results using the classification-based scheme. Visualization of the X-ray data is presented by embedding the high dimensional deep learning features in a 2-D dimensional space while preserving the pairwise distances using the t-SNE algorithm. The 2-D visualization gives the unique ability to find groups of X-ray images that are similar to the query image and among themselves, which is a characteristic we do not see in a 1-D traditional ranking.

Modeling Image Patches with a Generic Dictionary of Mini-Epitomes

PubMed Central

Papandreou, George; Chen, Liang-Chieh; Yuille, Alan L.

2015-01-01

The goal of this paper is to question the necessity of features like SIFT in categorical visual recognition tasks. As an alternative, we develop a generative model for the raw intensity of image patches and show that it can support image classification performance on par with optimized SIFT-based techniques in a bag-of-visual-words setting. Key ingredient of the proposed model is a compact dictionary of mini-epitomes, learned in an unsupervised fashion on a large collection of images. The use of epitomes allows us to explicitly account for photometric and position variability in image appearance. We show that this flexibility considerably increases the capacity of the dictionary to accurately approximate the appearance of image patches and support recognition tasks. For image classification, we develop histogram-based image encoding methods tailored to the epitomic representation, as well as an “epitomic footprint” encoding which is easy to visualize and highlights the generative nature of our model. We discuss in detail computational aspects and develop efficient algorithms to make the model scalable to large tasks. The proposed techniques are evaluated with experiments on the challenging PASCAL VOC 2007 image classification benchmark. PMID:26321859

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.

Morphological feature extraction for the classification of digital images of cancerous tissues.

PubMed

Thiran, J P; Macq, B

1996-10-01

This paper presents a new method for automatic recognition of cancerous tissues from an image of a microscopic section. Based on the shape and the size analysis of the observed cells, this method provides the physician with nonsubjective numerical values for four criteria of malignancy. This automatic approach is based on mathematical morphology, and more specifically on the use of Geodesy. This technique is used first to remove the background noise from the image and then to operate a segmentation of the nuclei of the cells and an analysis of their shape, their size, and their texture. From the values of the extracted criteria, an automatic classification of the image (cancerous or not) is finally operated.

The Effects of Asynchronous Visual Delays on Simulator Flight Performance and the Development of Simulator Sickness Symptomatology

DTIC Science & Technology

1986-12-26

NAVAL TRAINING SYSTEMS CENTER ORLANDO. FLORIDA IT FILE COPY THE EFFECTS OF ASYNCHRONOUS VISUAL DELAYS ON SIMULATOR FLIGHT PERFORMANCE AND THE...ASYNCHRONOUS VISUAL. DELAYS ON SI.WLATOR FLIGHT PERF OMANCE AND THE DEVELOPMENT OF SIMLATOR SICKNESS SYMPTOMATOLOGY K. C. Uliano, E. Y. Lambert, R. S. Kennedy...ACCESSION NO. N63733N SP-01 0785-7P6 I. 4780 11. TITLE (Include Security Classification) The Effects of Asynchronous Visual Delays on Simulator Flight

Learning machines and sleeping brains: Automatic sleep stage classification using decision-tree multi-class support vector machines.

PubMed

Lajnef, Tarek; Chaibi, Sahbi; Ruby, Perrine; Aguera, Pierre-Emmanuel; Eichenlaub, Jean-Baptiste; Samet, Mounir; Kachouri, Abdennaceur; Jerbi, Karim

2015-07-30

Sleep staging is a critical step in a range of electrophysiological signal processing pipelines used in clinical routine as well as in sleep research. Although the results currently achievable with automatic sleep staging methods are promising, there is need for improvement, especially given the time-consuming and tedious nature of visual sleep scoring. Here we propose a sleep staging framework that consists of a multi-class support vector machine (SVM) classification based on a decision tree approach. The performance of the method was evaluated using polysomnographic data from 15 subjects (electroencephalogram (EEG), electrooculogram (EOG) and electromyogram (EMG) recordings). The decision tree, or dendrogram, was obtained using a hierarchical clustering technique and a wide range of time and frequency-domain features were extracted. Feature selection was carried out using forward sequential selection and classification was evaluated using k-fold cross-validation. The dendrogram-based SVM (DSVM) achieved mean specificity, sensitivity and overall accuracy of 0.92, 0.74 and 0.88 respectively, compared to expert visual scoring. Restricting DSVM classification to data where both experts' scoring was consistent (76.73% of the data) led to a mean specificity, sensitivity and overall accuracy of 0.94, 0.82 and 0.92 respectively. The DSVM framework outperforms classification with more standard multi-class "one-against-all" SVM and linear-discriminant analysis. The promising results of the proposed methodology suggest that it may be a valuable alternative to existing automatic methods and that it could accelerate visual scoring by providing a robust starting hypnogram that can be further fine-tuned by expert inspection. Copyright © 2015 Elsevier B.V. All rights reserved.

Multi-dimensional classification of GABAergic interneurons with Bayesian network-modeled label uncertainty.

PubMed

Mihaljević, Bojan; Bielza, Concha; Benavides-Piccione, Ruth; DeFelipe, Javier; Larrañaga, Pedro

2014-01-01

Interneuron classification is an important and long-debated topic in neuroscience. A recent study provided a data set of digitally reconstructed interneurons classified by 42 leading neuroscientists according to a pragmatic classification scheme composed of five categorical variables, namely, of the interneuron type and four features of axonal morphology. From this data set we now learned a model which can classify interneurons, on the basis of their axonal morphometric parameters, into these five descriptive variables simultaneously. Because of differences in opinion among the neuroscientists, especially regarding neuronal type, for many interneurons we lacked a unique, agreed-upon classification, which we could use to guide model learning. Instead, we guided model learning with a probability distribution over the neuronal type and the axonal features, obtained, for each interneuron, from the neuroscientists' classification choices. We conveniently encoded such probability distributions with Bayesian networks, calling them label Bayesian networks (LBNs), and developed a method to predict them. This method predicts an LBN by forming a probabilistic consensus among the LBNs of the interneurons most similar to the one being classified. We used 18 axonal morphometric parameters as predictor variables, 13 of which we introduce in this paper as quantitative counterparts to the categorical axonal features. We were able to accurately predict interneuronal LBNs. Furthermore, when extracting crisp (i.e., non-probabilistic) predictions from the predicted LBNs, our method outperformed related work on interneuron classification. Our results indicate that our method is adequate for multi-dimensional classification of interneurons with probabilistic labels. Moreover, the introduced morphometric parameters are good predictors of interneuron type and the four features of axonal morphology and thus may serve as objective counterparts to the subjective, categorical axonal features.

Investigating Methodological Differences in the Assessment of Dendritic Morphology of Basolateral Amygdala Principal Neurons-A Comparison of Golgi-Cox and Neurobiotin Electroporation Techniques.

PubMed

Klenowski, Paul M; Wright, Sophie E; Mu, Erica W H; Noakes, Peter G; Lavidis, Nickolas A; Bartlett, Selena E; Bellingham, Mark C; Fogarty, Matthew J

2017-12-19

Quantitative assessments of neuronal subtypes in numerous brain regions show large variations in dendritic arbor size. A critical experimental factor is the method used to visualize neurons. We chose to investigate quantitative differences in basolateral amygdala (BLA) principal neuron morphology using two of the most common visualization methods: Golgi-Cox staining and neurobiotin (NB) filling. We show in 8-week-old Wistar rats that NB-filling reveals significantly larger dendritic arbors and different spine densities, compared to Golgi-Cox-stained BLA neurons. Our results demonstrate important differences and provide methodological insights into quantitative disparities of BLA principal neuron morphology reported in the literature.

Observers' cognitive states modulate how visual inputs relate to gaze control.

PubMed

Kardan, Omid; Henderson, John M; Yourganov, Grigori; Berman, Marc G

2016-09-01

Previous research has shown that eye-movements change depending on both the visual features of our environment, and the viewer's top-down knowledge. One important question that is unclear is the degree to which the visual goals of the viewer modulate how visual features of scenes guide eye-movements. Here, we propose a systematic framework to investigate this question. In our study, participants performed 3 different visual tasks on 135 scenes: search, memorization, and aesthetic judgment, while their eye-movements were tracked. Canonical correlation analyses showed that eye-movements were reliably more related to low-level visual features at fixations during the visual search task compared to the aesthetic judgment and scene memorization tasks. Different visual features also had different relevance to eye-movements between tasks. This modulation of the relationship between visual features and eye-movements by task was also demonstrated with classification analyses, where classifiers were trained to predict the viewing task based on eye movements and visual features at fixations. Feature loadings showed that the visual features at fixations could signal task differences independent of temporal and spatial properties of eye-movements. When classifying across participants, edge density and saliency at fixations were as important as eye-movements in the successful prediction of task, with entropy and hue also being significant, but with smaller effect sizes. When classifying within participants, brightness and saturation were also significant contributors. Canonical correlation and classification results, together with a test of moderation versus mediation, suggest that the cognitive state of the observer moderates the relationship between stimulus-driven visual features and eye-movements. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Visualizing Without Vision at the Microscale: Students With Visual Impairments Explore Cells With Touch

NASA Astrophysics Data System (ADS)

Jones, M. Gail; Minogue, James; Oppewal, Tom; Cook, Michelle P.; Broadwell, Bethany

2006-12-01

Science instruction is typically highly dependent on visual representations of scientific concepts that are communicated through textbooks, teacher presentations, and computer-based multimedia materials. Little is known about how students with visual impairments access and interpret these types of visually-dependent instructional materials. This study explored the efficacy of new haptic (simulated tactile feedback and kinesthetics) instructional technology for teaching cell morphology and function to middle and high school students with visual impairments. The study examined students' prior experiences learning about the cell and cell functions in classroom instruction, as well as how haptic feedback technology impacted students' awareness of the 3-D nature of an animal cell, the morphology and function of cell organelles, and students' interest in the haptic technology as an instructional tool. Twenty-one students with visual impairment participated in the study. Students explored a tactile model of the cell with a haptic point probe that allowed them to feel the cell and its organelles. Results showed that students made significant gains in their ability to identify cell organelles and found the technology to be highly interesting as an instructional tool. The need for additional adaptive technology for students with visual impairments is discussed.

Morphological Effects in Children Word Reading: A Priming Study in Fourth Graders

ERIC Educational Resources Information Center

Casalis, Severine; Dusautoir, Marion; Cole, Pascale; Ducrot, Stephanie

2009-01-01

A growing corpus of evidence suggests that morphology could play a role in reading acquisition, and that young readers could be sensitive to the morphemic structure of written words. In the present experiment, we examined whether and when morphological information is activated in word recognition. French fourth graders made visual lexical…

A morphologic characterisation of the 1963 Vajont Slide, Italy, using long-range terrestrial photogrammetry

NASA Astrophysics Data System (ADS)

Wolter, Andrea; Stead, Doug; Clague, John J.

2014-02-01

The 1963 Vajont Slide in northeast Italy is an important engineering and geological event. Although the landslide has been extensively studied, new insights can be derived by applying modern techniques such as remote sensing and numerical modelling. This paper presents the first digital terrestrial photogrammetric analyses of the failure scar, landslide deposits, and the area surrounding the failure, with a focus on the scar. We processed photogrammetric models to produce discontinuity stereonets, residual maps and profiles, and slope and aspect maps, all of which provide information on the failure scar morphology. Our analyses enabled the creation of a preliminary semi-quantitative morphologic classification of the Vajont failure scar based on the large-scale tectonic folds and step-paths that define it. The analyses and morphologic classification have implications for the kinematics, dynamics, and mechanism of the slide. Metre- and decametre-scale features affected the initiation, direction, and displacement rate of sliding. The most complexly folded and stepped areas occur close to the intersection of orthogonal synclinal features related to the Dinaric and Neoalpine deformation events. Our analyses also highlight, for the first time, the evolution of the Vajont failure scar from 1963 to the present.

Making Mosquito Taxonomy Useful: A Stable Classification of Tribe Aedini that Balances Utility with Current Knowledge of Evolutionary Relationships.

PubMed

Wilkerson, Richard C; Linton, Yvonne-Marie; Fonseca, Dina M; Schultz, Ted R; Price, Dana C; Strickman, Daniel A

2015-01-01

The tribe Aedini (Family Culicidae) contains approximately one-quarter of the known species of mosquitoes, including vectors of deadly or debilitating disease agents. This tribe contains the genus Aedes, which is one of the three most familiar genera of mosquitoes. During the past decade, Aedini has been the focus of a series of extensive morphology-based phylogenetic studies published by Reinert, Harbach, and Kitching (RH&K). Those authors created 74 new, elevated or resurrected genera from what had been the single genus Aedes, almost tripling the number of genera in the entire family Culicidae. The proposed classification is based on subjective assessments of the "number and nature of the characters that support the branches" subtending particular monophyletic groups in the results of cladistic analyses of a large set of morphological characters of representative species. To gauge the stability of RH&K's generic groupings we reanalyzed their data with unweighted parsimony jackknife and maximum-parsimony analyses, with and without ordering 14 of the characters as in RH&K. We found that their phylogeny was largely weakly supported and their taxonomic rankings failed priority and other useful taxon-naming criteria. Consequently, we propose simplified aedine generic designations that 1) restore a classification system that is useful for the operational community; 2) enhance the ability of taxonomists to accurately place new species into genera; 3) maintain the progress toward a natural classification based on monophyletic groups of species; and 4) correct the current classification system that is subject to instability as new species are described and existing species more thoroughly defined. We do not challenge the phylogenetic hypotheses generated by the above-mentioned series of morphological studies. However, we reduce the ranks of the genera and subgenera of RH&K to subgenera or informal species groups, respectively, to preserve stability as new data become available.

Making Mosquito Taxonomy Useful: A Stable Classification of Tribe Aedini that Balances Utility with Current Knowledge of Evolutionary Relationships

PubMed Central

Wilkerson, Richard C.; Linton, Yvonne-Marie; Fonseca, Dina M.; Schultz, Ted R.; Price, Dana C.; Strickman, Daniel A.

2015-01-01

The tribe Aedini (Family Culicidae) contains approximately one-quarter of the known species of mosquitoes, including vectors of deadly or debilitating disease agents. This tribe contains the genus Aedes, which is one of the three most familiar genera of mosquitoes. During the past decade, Aedini has been the focus of a series of extensive morphology-based phylogenetic studies published by Reinert, Harbach, and Kitching (RH&K). Those authors created 74 new, elevated or resurrected genera from what had been the single genus Aedes, almost tripling the number of genera in the entire family Culicidae. The proposed classification is based on subjective assessments of the “number and nature of the characters that support the branches” subtending particular monophyletic groups in the results of cladistic analyses of a large set of morphological characters of representative species. To gauge the stability of RH&K’s generic groupings we reanalyzed their data with unweighted parsimony jackknife and maximum-parsimony analyses, with and without ordering 14 of the characters as in RH&K. We found that their phylogeny was largely weakly supported and their taxonomic rankings failed priority and other useful taxon-naming criteria. Consequently, we propose simplified aedine generic designations that 1) restore a classification system that is useful for the operational community; 2) enhance the ability of taxonomists to accurately place new species into genera; 3) maintain the progress toward a natural classification based on monophyletic groups of species; and 4) correct the current classification system that is subject to instability as new species are described and existing species more thoroughly defined. We do not challenge the phylogenetic hypotheses generated by the above-mentioned series of morphological studies. However, we reduce the ranks of the genera and subgenera of RH&K to subgenera or informal species groups, respectively, to preserve stability as new data become available. PMID:26226613

Genetic parameters for type classification of Nelore cattle on central performance tests at pasture in Brazil.

PubMed

Lima, Paulo Ricardo Martins; Paiva, Samuel Rezende; Cobuci, Jaime Araujo; Braccini Neto, José; Machado, Carlos Henrique Cavallari; McManus, Concepta

2013-10-01

The objective of this study was to characterize Nelore cattle on central performance tests in pasture, ranked by the visual classification method EPMURAS (structure, precocity, muscle, navel, breed, posture, and sexual characteristics), and to estimate genetic and phenotypic correlations between these parameters, including visual as well as production traits (initial and final weight on test, weight gain, and weight corrected for 550 days). The information used in the study was obtained on 21,032 Nelore bulls which were participants in the central performance test at pasture of the Brazilian Association for Zebu Breeders (ABCZ). Heritabilities obtained were from 0.19 to 0.50. Phenotypic correlations were positive from 0.70 to 0.97 between the weight traits, from 0.65 to 0.74 between visual characteristics, and from 0.29 to 0.47 between visual characteristics and weight traits. The genetic correlations were positive ranging from 0.80 to 0.98 between the characteristics of structure, precocity and musculature, from 0.13 to 0.64 between the growth characteristics, and from 0.41 to 0.97 between visual scores and weight gains. Heritability and genetic correlations indicate that the use of visual scores, along with the selection for growth characteristics, can bring positive results in selection of beef cattle for rearing on pasture.

Characterization of Visual Scanning Patterns in Air Traffic Control

PubMed Central

McClung, Sarah N.; Kang, Ziho

2016-01-01

Characterization of air traffic controllers' (ATCs') visual scanning strategies is a challenging issue due to the dynamic movement of multiple aircraft and increasing complexity of scanpaths (order of eye fixations and saccades) over time. Additionally, terminologies and methods are lacking to accurately characterize the eye tracking data into simplified visual scanning strategies linguistically expressed by ATCs. As an intermediate step to automate the characterization classification process, we (1) defined and developed new concepts to systematically filter complex visual scanpaths into simpler and more manageable forms and (2) developed procedures to map visual scanpaths with linguistic inputs to reduce the human judgement bias during interrater agreement. The developed concepts and procedures were applied to investigating the visual scanpaths of expert ATCs using scenarios with different aircraft congestion levels. Furthermore, oculomotor trends were analyzed to identify the influence of aircraft congestion on scan time and number of comparisons among aircraft. The findings show that (1) the scanpaths filtered at the highest intensity led to more consistent mapping with the ATCs' linguistic inputs, (2) the pattern classification occurrences differed between scenarios, and (3) increasing aircraft congestion caused increased scan times and aircraft pairwise comparisons. The results provide a foundation for better characterizing complex scanpaths in a dynamic task and automating the analysis process. PMID:27239190

Integration of visual quality considerations in development of Israeli vegetation management policy.

PubMed

Misgav, A; Amir, S

2001-06-01

This article deals with the visual quality of Mediterranean vegetation groups in northern Israel, the public's preference of these groups as a visual resource, and the policy options for their management. The study is based on a sample of 44 Mediterranean vegetation groups and three population groups of local residents, who were interviewed using a questionnaire and photographs of the vegetation groups. The results of the research showed that plant classification methods based on flora composition, habitat, and external appearance were found to be suitable for visual plant classification and for the evaluation of visual preference of vegetation groups by the interviewed public. The vegetation groups of planted pine forests and olive groves, characterizing a cultured vegetation landscape, were preferred over typical Mediterranean landscapes such as scrub and grassed scrub. The researchers noted a marked difference between the two products of vegetation management policy, one that proposes the conservation and restoration of the variety of native Mediterranean vegetation landscape, and a second that advanced the development of the cultured landscape of planted olive groves and pines forests, which were highly preferred by the public. The authors suggested the development of an integrated vegetation management policy that would combine both needs and thus reduce the gap between the policy proposed by planners and the local population's visual preference.

Characterization of Visual Scanning Patterns in Air Traffic Control.

PubMed

McClung, Sarah N; Kang, Ziho

2016-01-01

Characterization of air traffic controllers' (ATCs') visual scanning strategies is a challenging issue due to the dynamic movement of multiple aircraft and increasing complexity of scanpaths (order of eye fixations and saccades) over time. Additionally, terminologies and methods are lacking to accurately characterize the eye tracking data into simplified visual scanning strategies linguistically expressed by ATCs. As an intermediate step to automate the characterization classification process, we (1) defined and developed new concepts to systematically filter complex visual scanpaths into simpler and more manageable forms and (2) developed procedures to map visual scanpaths with linguistic inputs to reduce the human judgement bias during interrater agreement. The developed concepts and procedures were applied to investigating the visual scanpaths of expert ATCs using scenarios with different aircraft congestion levels. Furthermore, oculomotor trends were analyzed to identify the influence of aircraft congestion on scan time and number of comparisons among aircraft. The findings show that (1) the scanpaths filtered at the highest intensity led to more consistent mapping with the ATCs' linguistic inputs, (2) the pattern classification occurrences differed between scenarios, and (3) increasing aircraft congestion caused increased scan times and aircraft pairwise comparisons. The results provide a foundation for better characterizing complex scanpaths in a dynamic task and automating the analysis process.

Pharmacologic attenuation of cross-modal sensory augmentation within the chronic pain insula

PubMed Central

Harte, Steven E.; Ichesco, Eric; Hampson, Johnson P.; Peltier, Scott J.; Schmidt-Wilcke, Tobias; Clauw, Daniel J.; Harris, Richard E.

2016-01-01

Abstract Pain can be elicited through all mammalian sensory pathways yet cross-modal sensory integration, and its relationship to clinical pain, is largely unexplored. Centralized chronic pain conditions such as fibromyalgia are often associated with symptoms of multisensory hypersensitivity. In this study, female patients with fibromyalgia demonstrated cross-modal hypersensitivity to visual and pressure stimuli compared with age- and sex-matched healthy controls. Functional magnetic resonance imaging revealed that insular activity evoked by an aversive level of visual stimulation was associated with the intensity of fibromyalgia pain. Moreover, attenuation of this insular activity by the analgesic pregabalin was accompanied by concomitant reductions in clinical pain. A multivariate classification method using support vector machines (SVM) applied to visual-evoked brain activity distinguished patients with fibromyalgia from healthy controls with 82% accuracy. A separate SVM classification of treatment effects on visual-evoked activity reliably identified when patients were administered pregabalin as compared with placebo. Both SVM analyses identified significant weights within the insular cortex during aversive visual stimulation. These data suggest that abnormal integration of multisensory and pain pathways within the insula may represent a pathophysiological mechanism in some chronic pain conditions and that insular response to aversive visual stimulation may have utility as a marker for analgesic drug development. PMID:27101425

Internal representations for face detection: an application of noise-based image classification to BOLD responses.

PubMed

Nestor, Adrian; Vettel, Jean M; Tarr, Michael J

2013-11-01

What basic visual structures underlie human face detection and how can we extract such structures directly from the amplitude of neural responses elicited by face processing? Here, we address these issues by investigating an extension of noise-based image classification to BOLD responses recorded in high-level visual areas. First, we assess the applicability of this classification method to such data and, second, we explore its results in connection with the neural processing of faces. To this end, we construct luminance templates from white noise fields based on the response of face-selective areas in the human ventral cortex. Using behaviorally and neurally-derived classification images, our results reveal a family of simple but robust image structures subserving face representation and detection. Thus, we confirm the role played by classical face selective regions in face detection and we help clarify the representational basis of this perceptual function. From a theory standpoint, our findings support the idea of simple but highly diagnostic neurally-coded features for face detection. At the same time, from a methodological perspective, our work demonstrates the ability of noise-based image classification in conjunction with fMRI to help uncover the structure of high-level perceptual representations. Copyright © 2012 Wiley Periodicals, Inc.

Similarity-dissimilarity plot for visualization of high dimensional data in biomedical pattern classification.

PubMed

Arif, Muhammad

2012-06-01

In pattern classification problems, feature extraction is an important step. Quality of features in discriminating different classes plays an important role in pattern classification problems. In real life, pattern classification may require high dimensional feature space and it is impossible to visualize the feature space if the dimension of feature space is greater than four. In this paper, we have proposed a Similarity-Dissimilarity plot which can project high dimensional space to a two dimensional space while retaining important characteristics required to assess the discrimination quality of the features. Similarity-dissimilarity plot can reveal information about the amount of overlap of features of different classes. Separable data points of different classes will also be visible on the plot which can be classified correctly using appropriate classifier. Hence, approximate classification accuracy can be predicted. Moreover, it is possible to know about whom class the misclassified data points will be confused by the classifier. Outlier data points can also be located on the similarity-dissimilarity plot. Various examples of synthetic data are used to highlight important characteristics of the proposed plot. Some real life examples from biomedical data are also used for the analysis. The proposed plot is independent of number of dimensions of the feature space.

Syntactic and semantic restrictions on morphological recomposition: MEG evidence from Greek.

PubMed

Neophytou, K; Manouilidou, C; Stockall, L; Marantz, A

2018-05-16

Complex morphological processing has been extensively studied in the past decades. However, most of this work has either focused on only certain steps involved in this process, or it has been conducted on a few languages, like English. The purpose of the present study is to investigate the spatiotemporal cortical processing profile of the distinct steps previously reported in the literature, from decomposition to re-composition of morphologically complex items, in a relatively understudied language, Greek. Using magnetoencephalography, we confirm the role of the fusiform gyrus in early, form-based morphological decomposition, we relate the syntactic licensing of stem-suffix combinations to the ventral visual processing stream, somewhat independent from lexical access for the stem, and we further elucidate the role of orbitofrontal regions in semantic composition. Thus, the current study offers the most comprehensive test to date of visual morphological processing and additional, crosslinguistic validation of the steps involved in it. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

Visual and tactile interfaces for bi-directional human robot communication

NASA Astrophysics Data System (ADS)

Barber, Daniel; Lackey, Stephanie; Reinerman-Jones, Lauren; Hudson, Irwin

2013-05-01

Seamless integration of unmanned and systems and Soldiers in the operational environment requires robust communication capabilities. Multi-Modal Communication (MMC) facilitates achieving this goal due to redundancy and levels of communication superior to single mode interaction using auditory, visual, and tactile modalities. Visual signaling using arm and hand gestures is a natural method of communication between people. Visual signals standardized within the U.S. Army Field Manual and in use by Soldiers provide a foundation for developing gestures for human to robot communication. Emerging technologies using Inertial Measurement Units (IMU) enable classification of arm and hand gestures for communication with a robot without the requirement of line-of-sight needed by computer vision techniques. These devices improve the robustness of interpreting gestures in noisy environments and are capable of classifying signals relevant to operational tasks. Closing the communication loop between Soldiers and robots necessitates them having the ability to return equivalent messages. Existing visual signals from robots to humans typically require highly anthropomorphic features not present on military vehicles. Tactile displays tap into an unused modality for robot to human communication. Typically used for hands-free navigation and cueing, existing tactile display technologies are used to deliver equivalent visual signals from the U.S. Army Field Manual. This paper describes ongoing research to collaboratively develop tactile communication methods with Soldiers, measure classification accuracy of visual signal interfaces, and provides an integration example including two robotic platforms.

Grammatical number agreement processing using the visual half-field paradigm: an event-related brain potential study.

PubMed

Kemmer, Laura; Coulson, Seana; Kutas, Marta

2014-02-01

Despite indications in the split-brain and lesion literatures that the right hemisphere is capable of some syntactic analysis, few studies have investigated right hemisphere contributions to syntactic processing in people with intact brains. Here we used the visual half-field paradigm in healthy adults to examine each hemisphere's processing of correct and incorrect grammatical number agreement marked either lexically, e.g., antecedent/reflexive pronoun ("The grateful niece asked herself/*themselves…") or morphologically, e.g., subject/verb ("Industrial scientists develop/*develops…"). For reflexives, response times and accuracy of grammaticality decisions suggested similar processing regardless of visual field of presentation. In the subject/verb condition, we observed similar response times and accuracies for central and right visual field (RVF) presentations. For left visual field (LVF) presentation, response times were longer and accuracy rates were reduced relative to RVF presentation. An event-related brain potential (ERP) study using the same materials revealed similar ERP responses to the reflexive pronouns in the two visual fields, but very different ERP effects to the subject/verb violations. For lexically marked violations on reflexives, P600 was elicited by stimuli in both the LVF and RVF; for morphologically marked violations on verbs, P600 was elicited only by RVF stimuli. These data suggest that both hemispheres can process lexically marked pronoun agreement violations, and do so in a similar fashion. Morphologically marked subject/verb agreement errors, however, showed a distinct LH advantage. Copyright © 2013 Elsevier B.V. All rights reserved.

Grammatical number agreement processing using the visual half-field paradigm: An event-related brain potential study

PubMed Central

Kemmer, Laura; Coulson, Seana; Kutas, Marta

2014-01-01

Despite indications in the split-brain and lesion literatures that the right hemisphere is capable of some syntactic analysis, few studies have investigated right hemisphere contributions to syntactic processing in people with intact brains. Here we used the visual half-field paradigm in healthy adults to examine each hemisphere’s processing of correct and incorrect grammatical number agreement marked either lexically, e.g., antecedent/reflexive pronoun (“The grateful niece asked herself/*themselves…”) or morphologically, e.g., subject/verb (“Industrial scientists develop/*develops…”). For reflexives, response times and accuracy of grammaticality decisions suggested similar processing regardless of visual field of presentation. In the subject/verb condition, we observed similar response times and accuracies for central and right visual field (RVF) presentations. For left visual field (LVF) presentation, response times were longer and accuracy rates were reduced relative to RVF presentation. An event-related brain potential (ERP) study using the same materials revealed similar ERP responses to the reflexive pronouns in the two visual fields, but very different ERP effects to the subject/verb violations. For lexically marked violations on reflexives, P600 was elicited by stimuli in both the LVF and RVF; for morphologically marked violations on verbs, P600 was elicited only by RVF stimuli. These data suggest that both hemispheres can process lexically marked pronoun agreement violations, and do so in a similar fashion. Morphologically marked subject/verb agreement errors, however, showed a distinct LH advantage. PMID:24326084

The evolutionary history of Mimosa (Leguminosae): toward a phylogeny of the sensitive plants.

PubMed

Simon, Marcelo F; Grether, Rosaura; de Queiroz, Luciano P; Särkinen, Tiina E; Dutra, Valquíria F; Hughes, Colin E

2011-07-01

Large genera provide remarkable opportunities to investigate patterns of morphological evolution and historical biogeography in plants. A molecular phylogeny of the species-rich and morphologically and ecologically diverse genus Mimosa was generated to evaluate its infrageneric classification, reconstruct the evolution of a set of morphological characters, and establish the relationships of Old World species to the rest of the genus. We used trnD-trnT plastid sequences for 259 species of Mimosa (ca. 50% of the total) to reconstruct the phylogeny of the genus. Six morphological characters (petiolar nectary, inflorescence type, number of stamens, number of petals, pollen type, and seismonasty) were optimized onto the molecular tree. Mimosa was recovered as a monophyletic clade nested within the Piptadenia group and includes the former members of Schrankia, corroborating transfer of that genus to Mimosa. Although we found good support for several infrageneric groups, only one section (Mimadenia) was recovered as monophyletic. All but one of the morphological characters analyzed showed high levels of homoplasy. High levels of geographic structure were found, with species from the same area tending to group together in the phylogeny. Old World species of Mimosa form a monophyletic clade deeply nested within New World groups, indicating recent (6-10 Ma) long-distance dispersal. Although based on a single plastid region, our results establish a preliminary phylogenetic framework for Mimosa that can be used to infer patterns of morphological evolution and relationships and which provides pointers toward a revised infrageneric classification.

Molecular Phylogenies Support Homoplasy of Multiple Morphological Characters Used in the Taxonomy of Heteroscleromorpha (Porifera: Demospongiae)

PubMed Central

Morrow, Christine C.; Redmond, Niamh E.; Picton, Bernard E.; Thacker, Robert W.; Collins, Allen G.; Maggs, Christine A.; Sigwart, Julia D.; Allcock, A. Louise

2013-01-01

Sponge classification has long been based mainly on morphocladistic analyses but is now being greatly challenged by more than 12 years of accumulated analyses of molecular data analyses. The current study used phylogenetic hypotheses based on sequence data from 18S rRNA, 28S rRNA, and the CO1 barcoding fragment, combined with morphology to justify the resurrection of the order Axinellida Lévi, 1953. Axinellida occupies a key position in different morphologically derived topologies. The abandonment of Axinellida and the establishment of Halichondrida Vosmaer, 1887 sensu lato to contain Halichondriidae Gray, 1867, Axinellidae Carter, 1875, Bubaridae Topsent, 1894, Heteroxyidae Dendy, 1905, and a new family Dictyonellidae van Soest et al., 1990 was based on the conclusion that an axially condensed skeleton evolved independently in separate lineages in preference to the less parsimonious assumption that asters (star-shaped spicules), acanthostyles (club-shaped spicules with spines), and sigmata (C-shaped spicules) each evolved more than once. Our new molecular trees are congruent and contrast with the earlier, morphologically based, trees. The results show that axially condensed skeletons, asters, acanthostyles, and sigmata are all homoplasious characters. The unrecognized homoplasious nature of these characters explains much of the incongruence between molecular-based and morphology-based phylogenies. We use the molecular trees presented here as a basis for re-interpreting the morphological characters within Heteroscleromorpha. The implications for the classification of Heteroscleromorpha are discussed and a new order Biemnida ord. nov. is erected. PMID:23753661

Towards a Holistic Cortical Thickness Descriptor: Heat Kernel-Based Grey Matter Morphology Signatures.

PubMed

Wang, Gang; Wang, Yalin

2017-02-15

In this paper, we propose a heat kernel based regional shape descriptor that may be capable of better exploiting volumetric morphological information than other available methods, thereby improving statistical power on brain magnetic resonance imaging (MRI) analysis. The mechanism of our analysis is driven by the graph spectrum and the heat kernel theory, to capture the volumetric geometry information in the constructed tetrahedral meshes. In order to capture profound brain grey matter shape changes, we first use the volumetric Laplace-Beltrami operator to determine the point pair correspondence between white-grey matter and CSF-grey matter boundary surfaces by computing the streamlines in a tetrahedral mesh. Secondly, we propose multi-scale grey matter morphology signatures to describe the transition probability by random walk between the point pairs, which reflects the inherent geometric characteristics. Thirdly, a point distribution model is applied to reduce the dimensionality of the grey matter morphology signatures and generate the internal structure features. With the sparse linear discriminant analysis, we select a concise morphology feature set with improved classification accuracies. In our experiments, the proposed work outperformed the cortical thickness features computed by FreeSurfer software in the classification of Alzheimer's disease and its prodromal stage, i.e., mild cognitive impairment, on publicly available data from the Alzheimer's Disease Neuroimaging Initiative. The multi-scale and physics based volumetric structure feature may bring stronger statistical power than some traditional methods for MRI-based grey matter morphology analysis. Copyright © 2016 Elsevier Inc. All rights reserved.

Cognition, culture and utility: plant classification by Paraguayan immigrant farmers in Misiones, Argentina.

PubMed

Kujawska, Monika; Jiménez-Escobar, N David; Nolan, Justin M; Arias-Mutis, Daniel

2017-07-25

This study was conducted in three rural communities of small farmers of Paraguayan origin living in the province of Misiones, Argentina. These Criollos (Mestizos) hail chiefly from departments located in the east of Paraguay, where the climate and flora have similar characteristics as those in Misiones. These ecological features contribute to the continuation and maintenance of knowledge and practices related to the use of plants. Fieldwork was conducted between September 2014 and August 2015. Forty five informants from three rural localities situated along the Parana River participated in an ethno-classification task. For the classification event, photographs of 30 medicinal and edible plants were chosen, specifically those yielding the highest frequency of mention among the members of that community (based on data obtained in the first stage of research in 2014). Variation in local plant classifications was examined and compared using principal component analysis and cluster analysis. We found that people classify plants according to application or use (primarily medicinal, to a lesser extent as edible). Morphology is rarely taken into account, even for very similar and closely-related species such as varieties of palms. In light of our findings, we highlight a dominant functionality model at work in the process of plant cognition and classification among farmers of Paraguayan origin. Salient cultural beliefs and practices associated with rural Paraguayan plant-based medicine are described. Additionally, the manner by which residents' concepts of plants articulate with local folk epistemology is discussed. Culturally constructed use patterns ultimately override morphological variables in rural Paraguayans' ethnobotanical classification.

[Epidemiological survey of visual impairment in Funing County, Jiangsu].

PubMed

Yang, M; Zhang, J F; Zhu, R R; Kang, L H; Qin, B; Guan, H J

2017-07-11

Objective: To investigate the prevalence of visual impairment and factors associated with visual impairment among people aged 50 years and above in Funing County, Jiangsu Province. Methods: Cross-sectional study. Random cluster sampling was used in selecting individuals aged ≥50 years in 30 clusters, and 5 947 individuals received visual acuity testing and eye examination. Stata 13.0 software was used to analyze the data. Multivariate logistic regression was used to detect possible factors of visual impairment such as age, gender and education. Statistical significance was defined as P< 0.05. Results: A total of 6 145 persons aged 50 years and above were enumerated, and 5 947 (96.8%) participants were examined. Based on the criteria of World Health Organization (WHO) visual impairment classification and presenting visual acuity, 138 persons were diagnosed as blindness, and 1 405 persons were diagnosed as low vision. The prevalence of blindness and low vision was 2.32% and 23.63%, respectively. And the prevalence of visual impairment was 25.95%. Based on the criteria of WHO visual impairment classification and best-corrected visual acuity, 92 persons were diagnosed as blindness, and 383 persons were diagnosed as low vision. The prevalence of blindness and low vision was 1.55% and 6.44%, respectively. And the prevalence of visual impairment was 7.99%. Concerning presenting visual acuity and best-corrected visual acuity, the prevalence of blindness and low vision was higher in old people, females and less educated persons. Cataract (46.63%) was the leading cause of blindness. Uncorrected refractive error (36.51%) was also a main cause of visual impairment. Conclusion: The prevalence of visual impairment is higher in old people, females and less educated persons in Funing County, Jiangsu Province. Cataract is still the leading cause of visual impairment. (Chin J Ophthalmol, 2017, 53: 502-508) .

Unsupervised and self-mapping category formation and semantic object recognition for mobile robot vision used in an actual environment

NASA Astrophysics Data System (ADS)

Madokoro, H.; Tsukada, M.; Sato, K.

2013-07-01

This paper presents an unsupervised learning-based object category formation and recognition method for mobile robot vision. Our method has the following features: detection of feature points and description of features using a scale-invariant feature transform (SIFT), selection of target feature points using one class support vector machines (OC-SVMs), generation of visual words using self-organizing maps (SOMs), formation of labels using adaptive resonance theory 2 (ART-2), and creation and classification of categories on a category map of counter propagation networks (CPNs) for visualizing spatial relations between categories. Classification results of dynamic images using time-series images obtained using two different-size robots and according to movements respectively demonstrate that our method can visualize spatial relations of categories while maintaining time-series characteristics. Moreover, we emphasize the effectiveness of our method for category formation of appearance changes of objects.

Visual gate for brain-computer interfaces.

PubMed

Dias, N S; Jacinto, L R; Mendes, P M; Correia, J H

2009-01-01

Brain-Computer Interfaces (BCI) based on event related potentials (ERP) have been successfully developed for applications like virtual spellers and navigation systems. This study tests the use of visual stimuli unbalanced in the subject's field of view to simultaneously cue mental imagery tasks (left vs. right hand movement) and detect subject attention. The responses to unbalanced cues were compared with the responses to balanced cues in terms of classification accuracy. Subject specific ERP spatial filters were calculated for optimal group separation. The unbalanced cues appear to enhance early ERPs related to cue visuospatial processing that improved the classification accuracy (as low as 6%) of ERPs in response to left vs. right cues soon (150-200 ms) after the cue presentation. This work suggests that such visual interface may be of interest in BCI applications as a gate mechanism for attention estimation and validation of control decisions.

Does patellofemoral congruence following total knee arthroplasty correlate with pain or function? Intraoperative arthroscopic assessment of 30 cases

PubMed Central

Senioris, Antoine; Rahali, Said; Malekpour, Louis; Dujardin, Franck; Courage, Olivier

2016-01-01

Background Anterior knee pain (AKP) is observed in total knee arthroplasty (TKA) both with and without patellar resurfacing, and neither patellar denervation nor secondary resurfacing are effective for treating the symptoms. The exact causes for pain remain unclear, though abnormal patellofemoral forces due to patellar malalignment or inadequate implant design can play an important role. The purpose of this study was to arthroscopically evaluate patellofemoral congruence after wound closure following TKA without patellar resurfacing and correlate it to patellar morphology and postoperative pain and function. Methods The authors prospectively studied 30 patients that received uncemented mobile-bearing TKA. Patellofemoral congruence was assessed arthroscopically after wound closure by estimating the contact area between the native patella and the prosthetic trochlea (> two-thirds, > one-third, < one-third). The findings were correlated to preoperative assessments of patellar geometry (Wiberg classification using X-rays) and clinical outcomes [Knee Society Score (KSS), AKP on Visual Analogic Scale (VAS), and patient satisfaction]. Results Knees of 22 women and 8 men aged 69.8 years (range, 61–84 years) were analyzed at 16 months (range, 12–23 months). Preoperative patellar geometry was Wiberg type A in 11, type B in 12 and type C in 7 knees. Postoperative KSS was 79.1 (range, 50.0–94) and the VAS for AKP was 1.6±1.3 (median, 1; range, 0–5). Patellar congruence was correlated with patellar morphology (P<0.001) but not correlated with any clinical outcomes (KSS, VAS or satisfaction). There were also no statistical correlations between patellar morphology or patellofemoral congruence and patient characteristics. Conclusions While patellar morphology and patellofemoral congruence are strongly related, they are not associated with clinical outcomes or patient demographics. Considering that numerous incongruent patellofemoral joints were pain-free, and conversely, many perfectly congruent patellofemoral joints had anterior pain, the authors suppose that pain is probably caused by mechanisms other than patellofemoral pressures. PMID:27570773

Does patellofemoral congruence following total knee arthroplasty correlate with pain or function? Intraoperative arthroscopic assessment of 30 cases.

PubMed

Senioris, Antoine; Saffarini, Mo; Rahali, Said; Malekpour, Louis; Dujardin, Franck; Courage, Olivier

2016-08-01

Anterior knee pain (AKP) is observed in total knee arthroplasty (TKA) both with and without patellar resurfacing, and neither patellar denervation nor secondary resurfacing are effective for treating the symptoms. The exact causes for pain remain unclear, though abnormal patellofemoral forces due to patellar malalignment or inadequate implant design can play an important role. The purpose of this study was to arthroscopically evaluate patellofemoral congruence after wound closure following TKA without patellar resurfacing and correlate it to patellar morphology and postoperative pain and function. The authors prospectively studied 30 patients that received uncemented mobile-bearing TKA. Patellofemoral congruence was assessed arthroscopically after wound closure by estimating the contact area between the native patella and the prosthetic trochlea (> two-thirds, > one-third, < one-third). The findings were correlated to preoperative assessments of patellar geometry (Wiberg classification using X-rays) and clinical outcomes [Knee Society Score (KSS), AKP on Visual Analogic Scale (VAS), and patient satisfaction]. Knees of 22 women and 8 men aged 69.8 years (range, 61-84 years) were analyzed at 16 months (range, 12-23 months). Preoperative patellar geometry was Wiberg type A in 11, type B in 12 and type C in 7 knees. Postoperative KSS was 79.1 (range, 50.0-94) and the VAS for AKP was 1.6±1.3 (median, 1; range, 0-5). Patellar congruence was correlated with patellar morphology (P<0.001) but not correlated with any clinical outcomes (KSS, VAS or satisfaction). There were also no statistical correlations between patellar morphology or patellofemoral congruence and patient characteristics. While patellar morphology and patellofemoral congruence are strongly related, they are not associated with clinical outcomes or patient demographics. Considering that numerous incongruent patellofemoral joints were pain-free, and conversely, many perfectly congruent patellofemoral joints had anterior pain, the authors suppose that pain is probably caused by mechanisms other than patellofemoral pressures.

Eventogram: A Visual Representation of Main Events in Biomedical Signals.

PubMed

Elgendi, Mohamed

2016-09-22

Biomedical signals carry valuable physiological information and many researchers have difficulty interpreting and analyzing long-term, one-dimensional, quasi-periodic biomedical signals. Traditionally, biomedical signals are analyzed and visualized using periodogram, spectrogram, and wavelet methods. However, these methods do not offer an informative visualization of main events within the processed signal. This paper attempts to provide an event-related framework to overcome the drawbacks of the traditional visualization methods and describe the main events within the biomedical signal in terms of duration and morphology. Electrocardiogram and photoplethysmogram signals are used in the analysis to demonstrate the differences between the traditional visualization methods, and their performance is compared against the proposed method, referred to as the " eventogram " in this paper. The proposed method is based on two event-related moving averages that visualizes the main time-domain events in the processed biomedical signals. The traditional visualization methods were unable to find dominant events in processed signals while the eventogram was able to visualize dominant events in signals in terms of duration and morphology. Moreover, eventogram -based detection algorithms succeeded with detecting main events in different biomedical signals with a sensitivity and positive predictivity >95%. The output of the eventogram captured unique patterns and signatures of physiological events, which could be used to visualize and identify abnormal waveforms in any quasi-periodic signal.

[Relationship between visual field index and visual field morphological stages of glaucoma and their diagnostic value].

PubMed

Hou, X R; Qin, J Y; Ren, Z Q

2017-02-11

Objective: To investigate the rationality of visual field morphological stages of glaucoma, its relationship with visual field index and their diagnostic value. Methods: Retrospective series case study. Two hundred and seventy-four glaucoma patients and 100 normal control received visual field examination by Humphrey perimeter using standard automatic perimetry (SAP) program from March 2014 to September 2014. Glaucoma patients were graded into four stages according to characteristic morphological damage of visual field, distribution of mean defect (MD) and visual field index (VFI) of each stage were plotted and receiver operation characteristic curve (ROC) was used to explore its correlation with MD and VFI. The diagnostic value of MD and VFI was also compared. For the comparison of general data of subjects, categorical variables were compared using χ(2) test, numerical variables were compared using F test. MD and VFI were compared using ANOVA among stages according to visual field, followed by multiple comparisons using LSD method. The correlation between MD and VFI and different stages according to visual field defined their diagnostic value, and compared using area under the curve (AUC) of ROC. Results: No characteristic visual field damage was found in normal control group, and MD and VFI was (-0.06±1.24) dB and (99.15±0.76)%, respectively. Glaucomatous visual field damage was graded into early, medium, late and end stage according to morphological characteristic. MD for each stage were (-2.83±2.00) dB, (-9.70±3.68) dB, (-18.46±2.90) dB, and (-27.96±2.76) dB, respectively. VFI for each stage were (93.84±3.61)%, (75.16±10.85)%, (49.36±11.26)% and (17.65±10.59)%, respectively. MD and VFI of each stage of glaucomatous group and normal control group were all significantly different ( F= 1 165.53 and P <0.01 for MD; F= 1 028.04 and P <0.01 for VFI). AUC of ROC was A(MD)=0.91 and Se(MD)=0.01 (95% confident interval was 0.89-0.94) for MD, and A(VFI)=0.97, Se(VFI)=0.01 (95% confident interval was 0.94-0.10) for VFI. So, AUC(VFI)>AUC(MD) ( P< 0.05). Conclusions: It is feasible and rational of glaucomatous visual field damage to be graded into early, medium, late and end stage using Humphrey perimeter. Distribution of MD and VFI for each stage was relatively concentrative. Both MD and VFI were useful for grading glaucomatous visual field damage with preference for VFI. (Chin J Ophthalmol, 2017, 53: 92-97) .

Hydrologic-Process-Based Soil Texture Classifications for Improved Visualization of Landscape Function

PubMed Central

Groenendyk, Derek G.; Ferré, Ty P.A.; Thorp, Kelly R.; Rice, Amy K.

2015-01-01

Soils lie at the interface between the atmosphere and the subsurface and are a key component that control ecosystem services, food production, and many other processes at the Earth’s surface. There is a long-established convention for identifying and mapping soils by texture. These readily available, georeferenced soil maps and databases are used widely in environmental sciences. Here, we show that these traditional soil classifications can be inappropriate, contributing to bias and uncertainty in applications from slope stability to water resource management. We suggest a new approach to soil classification, with a detailed example from the science of hydrology. Hydrologic simulations based on common meteorological conditions were performed using HYDRUS-1D, spanning textures identified by the United States Department of Agriculture soil texture triangle. We consider these common conditions to be: drainage from saturation, infiltration onto a drained soil, and combined infiltration and drainage events. Using a k-means clustering algorithm, we created soil classifications based on the modeled hydrologic responses of these soils. The hydrologic-process-based classifications were compared to those based on soil texture and a single hydraulic property, Ks. Differences in classifications based on hydrologic response versus soil texture demonstrate that traditional soil texture classification is a poor predictor of hydrologic response. We then developed a QGIS plugin to construct soil maps combining a classification with georeferenced soil data from the Natural Resource Conservation Service. The spatial patterns of hydrologic response were more immediately informative, much simpler, and less ambiguous, for use in applications ranging from trafficability to irrigation management to flood control. The ease with which hydrologic-process-based classifications can be made, along with the improved quantitative predictions of soil responses and visualization of landscape function, suggest that hydrologic-process-based classifications should be incorporated into environmental process models and can be used to define application-specific maps of hydrologic function. PMID:26121466

Hydrologic-Process-Based Soil Texture Classifications for Improved Visualization of Landscape Function.

PubMed

Groenendyk, Derek G; Ferré, Ty P A; Thorp, Kelly R; Rice, Amy K

2015-01-01

Soils lie at the interface between the atmosphere and the subsurface and are a key component that control ecosystem services, food production, and many other processes at the Earth's surface. There is a long-established convention for identifying and mapping soils by texture. These readily available, georeferenced soil maps and databases are used widely in environmental sciences. Here, we show that these traditional soil classifications can be inappropriate, contributing to bias and uncertainty in applications from slope stability to water resource management. We suggest a new approach to soil classification, with a detailed example from the science of hydrology. Hydrologic simulations based on common meteorological conditions were performed using HYDRUS-1D, spanning textures identified by the United States Department of Agriculture soil texture triangle. We consider these common conditions to be: drainage from saturation, infiltration onto a drained soil, and combined infiltration and drainage events. Using a k-means clustering algorithm, we created soil classifications based on the modeled hydrologic responses of these soils. The hydrologic-process-based classifications were compared to those based on soil texture and a single hydraulic property, Ks. Differences in classifications based on hydrologic response versus soil texture demonstrate that traditional soil texture classification is a poor predictor of hydrologic response. We then developed a QGIS plugin to construct soil maps combining a classification with georeferenced soil data from the Natural Resource Conservation Service. The spatial patterns of hydrologic response were more immediately informative, much simpler, and less ambiguous, for use in applications ranging from trafficability to irrigation management to flood control. The ease with which hydrologic-process-based classifications can be made, along with the improved quantitative predictions of soil responses and visualization of landscape function, suggest that hydrologic-process-based classifications should be incorporated into environmental process models and can be used to define application-specific maps of hydrologic function.

Molecular Phylogeny, Recent Radiation and Evolution of Gross Morphology of the Rhubarb genus Rheum (Polygonaceae) Inferred from Chloroplast DNA trnL-F Sequences

PubMed Central

WANG, AILAN; YANG, MEIHUA; LIU, JIANQUAN

2005-01-01

• Background and Aims Rheum, a highly diversified genus with about 60 species, is mainly confined to the mountainous and desert regions of the Qinghai–Tibetan plateau and adjacent areas. This genus represents a good example of the extensive diversification of the temperate genera in the Qinghai–Tibetan plateau, in which the forces to drive diversification remain unknown. To date, the infrageneric classification of Rheum has been mainly based on morphological characters. However, it may have been subject to convergent evolution under habitat pressure, and the systematic position of some sections are unclear, especially Sect. Globulosa, which has globular inflorescences, and Sect. Nobilia, which has semi-translucent bracts. Recent palynological research has found substantial contradictions between exine patterns and the current classification of Rheum. Two specific objectives of this research were (1) to evaluate possible relationships of some ambiguous sections with a unique morphology, and (2) to examine possible occurrence of the radiative speciation with low genetic divergence across the total genus and the correlation between the extensive diversification time of Rheum and past geographical events, especially the recent large-scale uplifts of the Qinghai–Tibetan Plateau. • Methods The chloroplast DNA trnL-F region of 29 individuals representing 26 species of Rheum, belonging to seven out of eight sections, was sequenced and compared. The phylogenetic relationships were further constructed based on the sequences obtained. • Key Results Despite the highly diversified morphology, the genetic variation in this DNA fragment is relatively low. The molecular phylogeny is highly inconsistent with gross morphology, pollen exine patterns and traditional classifications, except for identifying all samples of Sect. Palmata, three species of Sect. Spiciformia and a few species of Sect. Rheum as corresponding monophyletic groups. The monotypic Sect. Globulosa showed a tentative position within the clade comprising five species of Sect. Rheum. All of the analyses revealed the paraphyly of R. nobile and R. alexandrae, the only two species of Sect. Nobilia circumscribed by the possession of large bracts. The crude calibration of lineages based on trnL-F sequence differentiation implied an extensive diversification of Rheum within approx. 7 million years. • Conclusions Based on these results, it is suggested that the rich geological and ecological diversity caused by the recent large-scale uplifts of the Qinghai–Tibetan Plateau since the late Tertiary, coupled with the oscillating climate of the Quaternary stage, might have promoted rapid speciation in small and isolated populations, as well as allowing the fixation of unique or rare morphological characters in Rheum. Such a rapid radiation, combined with introgressive hybridization and reticulate evolution, may have caused the transfer of cpDNA haplotypes between morphologically dissimilar species, and might account for the inconsistency between morphological classification and molecular phylogeny reported here. PMID:15994840

Vietnamese Document Representation and Classification

NASA Astrophysics Data System (ADS)

Nguyen, Giang-Son; Gao, Xiaoying; Andreae, Peter

Vietnamese is very different from English and little research has been done on Vietnamese document classification, or indeed, on any kind of Vietnamese language processing, and only a few small corpora are available for research. We created a large Vietnamese text corpus with about 18000 documents, and manually classified them based on different criteria such as topics and styles, giving several classification tasks of different difficulty levels. This paper introduces a new syllable-based document representation at the morphological level of the language for efficient classification. We tested the representation on our corpus with different classification tasks using six classification algorithms and two feature selection techniques. Our experiments show that the new representation is effective for Vietnamese categorization, and suggest that best performance can be achieved using syllable-pair document representation, an SVM with a polynomial kernel as the learning algorithm, and using Information gain and an external dictionary for feature selection.

Image-based fall detection and classification of a user with a walking support system

NASA Astrophysics Data System (ADS)

Taghvaei, Sajjad; Kosuge, Kazuhiro

2017-10-01

The classification of visual human action is important in the development of systems that interact with humans. This study investigates an image-based classification of the human state while using a walking support system to improve the safety and dependability of these systems.We categorize the possible human behavior while utilizing a walker robot into eight states (i.e., sitting, standing, walking, and five falling types), and propose two different methods, namely, normal distribution and hidden Markov models (HMMs), to detect and recognize these states. The visual feature for the state classification is the centroid position of the upper body, which is extracted from the user's depth images. The first method shows that the centroid position follows a normal distribution while walking, which can be adopted to detect any non-walking state. The second method implements HMMs to detect and recognize these states. We then measure and compare the performance of both methods. The classification results are employed to control the motion of a passive-type walker (called "RT Walker") by activating its brakes in non-walking states. Thus, the system can be used for sit/stand support and fall prevention. The experiments are performed with four subjects, including an experienced physiotherapist. Results show that the algorithm can be adapted to the new user's motion pattern within 40 s, with a fall detection rate of 96.25% and state classification rate of 81.0%. The proposed method can be implemented to other abnormality detection/classification applications that employ depth image-sensing devices.

Diagnostic Accuracy of Optical Coherence Tomography and Scanning Laser Tomography for Identifying Glaucoma in Myopic Eyes.

PubMed

Malik, Rizwan; Belliveau, Anne C; Sharpe, Glen P; Shuba, Lesya M; Chauhan, Balwantray C; Nicolela, Marcelo T

2016-06-01

Ruling out glaucoma in myopic eyes often poses a diagnostic challenge because of atypical optic disc morphology and visual field defects that can mimic glaucoma. We determined whether neuroretinal rim assessment based on Bruch's membrane opening (BMO), rather than conventional optic disc margin (DM)-based assessment or retinal nerve fiber layer (RNFL) thickness, yielded higher diagnostic accuracy in myopic patients with glaucoma. Case-control, cross-sectional study. Myopic patients with glaucoma (n = 56) and myopic normal controls (n = 74). Myopic subjects with refraction error greater than -2 diopters (D) (spherical equivalent) and typical myopic optic disc morphology, with and without glaucoma, were recruited from a glaucoma clinic and a local optometry practice. The final classification of myopic glaucoma or myopic control was based on consensus assessment by 3 clinicians of visual fields and optic disc photographs. Participants underwent imaging with confocal scanning laser tomography for measurement of DM rim area (DM-RA) and with spectral domain optical coherence tomography (SD OCT) for quantification of a BMO-based neuroretinal rim parameter, minimum rim width (BMO-MRW), and RNFL thickness. Sensitivity of DM-RA, BMO-MRW, and RNFL thickness at a fixed specificity of 90% and partial area under the curves (pAUCs) for global and sectoral parameters for specificities ≥90%. Sensitivities at 90% specificity were 30% for DM-RA and 71% for both BMO-MRW and RNFL thickness. The pAUC was higher for the BMO-MRW compared with DM-RA (P < 0.001), but similar to RNFL thickness (P > 0.5). Sectoral values of BMO-MRW tended to have a higher, but nonsignificant, pAUC across all sectors compared with RNFL thickness. Bruch's membrane opening MRW is more sensitive than DM-RA and similar to RNFL thickness for the identification of glaucoma in myopic eyes and offers a valuable diagnostic tool for patients with glaucoma with myopic optic discs. Copyright © 2016 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.

[Analysis of the Effect of Non-phacoemulsification Cataract Operation on Corneal Endothelial Cell Nucleus Division].

PubMed

Huang, Zufeng; Miao, Xiaoqing

2015-09-01

To investigate the effect of non-phacoemulsification cataract operation in two different patterns of nucleus delivery on the quantity and morphology of corneal endothelial cells and postoperative visual acuity. Forty patients diagnosed with cataract underwent cataract surgery and were assigned into the direct nuclear delivery and semi-nuclear delivery groups. Lens density was measured and divided into the hard and soft lenses according to Emery-little lens nucleus grading system. Non-phacoemulsification cataract operation was performed. At 3 d after surgery, the quantity and morphology of corneal endothelium were counted and observed under corneal endothelial microscope. During 3-month postoperative follow-up, the endothelial cell loss rate, morphological changes and visual acuity were compared among four groups. Corneal endothelial cell loss rate in the direct delivery of hard nucleus group significantly differed from those in the other three groups before and 3 months after operation (P < 0.01), whereas no statistical significance was found among the direct delivery of soft nucleus, semi-delivery of hard nucleus and semi-delivery soft nucleus groups (all P > 0.05). Preoperative and postoperative 2-d visual acuity did not differ between the semi-delivery of hard nucleus and direct delivery of soft nucleus groups (P = 0.49), significantly differed from those in the semi-delivery of soft nucleus (P = 0.03) and direct delivery of hard nucleus groups (P = 0.14). Visual acuity at postoperative four months did not differ among four groups (P = 0.067). During non-phacoemulsification cataract surgery, direct delivery of hard nucleus caused severe injury to corneal endothelium and semi-delivery of soft nucleus yielded mild corneal endothelial injury. Slight corneal endothelial injury exerted no apparent effect upon visual acuity and corneal endothelial morphology at three months after surgery.

Relationship between increasing concentrations of two carcinogens and statistical image descriptors of foci morphology in the cell transformation assay.

PubMed

Callegaro, Giulia; Corvi, Raffaella; Salovaara, Susan; Urani, Chiara; Stefanini, Federico M

2017-06-01

Cell Transformation Assays (CTAs) have long been proposed for the identification of chemical carcinogenicity potential. The endpoint of these in vitro assays is represented by the phenotypic alterations in cultured cells, which are characterized by the change from the non-transformed to the transformed phenotype. Despite the wide fields of application and the numerous advantages of CTAs, their use in regulatory toxicology has been limited in part due to concerns about the subjective nature of visual scoring, i.e. the step in which transformed colonies or foci are evaluated through morphological features. An objective evaluation of morphological features has been previously obtained through automated digital processing of foci images to extract the value of three statistical image descriptors. In this study a further potential of the CTA using BALB/c 3T3 cells is addressed by analysing the effect of increasing concentrations of two known carcinogens, benzo[a]pyrene and NiCl 2 , with different modes of action on foci morphology. The main result of our quantitative evaluation shows that the concentration of the considered carcinogens has an effect on foci morphology that is statistically significant for the mean of two among the three selected descriptors. Statistical significance also corresponds to visual relevance. The statistical analysis of variations in foci morphology due to concentration allowed to quantify morphological changes that can be visually appreciated but not precisely determined. Therefore, it has the potential of providing new quantitative parameters in CTAs, and of exploiting all the information encoded in foci. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Genera of the human lineage

PubMed Central

Cela-Conde, Camilo J.; Ayala, Francisco J.

2003-01-01

Human fossils dated between 3.5 and nearly 7 million years old discovered during the last 8 years have been assigned to as many as four new genera of the family Hominidae: Ardipithecus, Orrorin, Kenyanthropus, and Sahelanthropus. These specimens are described as having morphological traits that justify placing them in the family Hominidae while creating a new genus for the classification of each. The discovery of these fossils pushed backward by >2 million years the date of the oldest hominids known. Only two or three hominid genera, Australopithecus, Paranthropus, and Homo, had been previously accepted, with Paranthropus considered a subgenus of Australopithecus by some authors. Two questions arise from the classification of the newly discovered fossils: (i) Should each one of these specimens be placed in the family Hominidae? (ii) Are these specimens sufficiently distinct to justify the creation of four new genera? The answers depend, in turn, on the concepts of what is a hominid and how the genus category is defined. These specimens seem to possess a sufficient number of morphological traits to be placed in the Hominidae. However, the nature of the morphological evidence and the adaptation-rooted concept of what a genus is do not justify the establishment of four new genera. We propose a classification that includes four well defined genera: Praeanthropus, Ardipithecus, Australopithecus, and Homo, plus one tentative incertae sedis genus: Sahelanthropus. PMID:12794185

[Evaluation of traditional pathological classification at molecular classification era for gastric cancer].

PubMed

Yu, Yingyan

2014-01-01

Histopathological classification is in a pivotal position in both basic research and clinical diagnosis and treatment of gastric cancer. Currently, there are different classification systems in basic science and clinical application. In medical literatures, different classifications are used including Lauren and WHO systems, which have confused many researchers. Lauren classification has been proposed for half a century, but is still used worldwide. It shows many advantages of simple, easy handling with prognostic significance. The WHO classification scheme is better than Lauren classification in that it is continuously being revised according to the progress of gastric cancer, and is always used in the clinical and pathological diagnosis of common scenarios. Along with the progression of genomics, transcriptomics, proteomics, metabolomics researches, molecular classification of gastric cancer becomes the current hot topics. The traditional therapeutic approach based on phenotypic characteristics of gastric cancer will most likely be replaced with a gene variation mode. The gene-targeted therapy against the same molecular variation seems more reasonable than traditional chemical treatment based on the same morphological change.

Toward Biological Subtyping of Papillary Renal Cell Carcinoma With Clinical Implications Through Histologic, Immunohistochemical, and Molecular Analysis.

PubMed

Saleeb, Rola M; Brimo, Fadi; Farag, Mina; Rompré-Brodeur, Alexis; Rotondo, Fabio; Beharry, Vidya; Wala, Samantha; Plant, Pamela; Downes, Michelle R; Pace, Kenneth; Evans, Andrew; Bjarnason, Georg; Bartlett, John M S; Yousef, George M

2017-12-01

Papillary renal cell carcinoma (PRCC) has 2 histologic subtypes. Almost half of the cases fail to meet all morphologic criteria for either type, hence are characterized as PRCC not otherwise specified (NOS). There are yet no markers to resolve the PRCC NOS category. Accurate classification can better guide the management of these patients. In our previous PRCC study we identified markers that can distinguish between the subtypes. A PRCC patient cohort of 108 cases was selected for the current study. A panel of potentially distinguishing markers was chosen from our previous genomic analysis, and assessed by immunohistochemistry. The panel exhibited distinct staining patterns between the 2 classic PRCC subtypes; and successfully reclassified the NOS (45%) cases. Moreover, these immunomarkers revealed a third subtype, PRCC3 (35% of the cohort). Molecular testing using miRNA expression and copy number variation analysis confirmed the presence of 3 distinct molecular signatures corresponding to the 3 subtypes. Disease-free survival was significantly enhanced in PRCC1 versus 2 and 3 (P=0.047) on univariate analysis. The subtypes stratification was also significant on multivariate analysis (P=0.025; hazard ratio, 6; 95% confidence interval, 1.25-32.2). We propose a new classification system of PRCC integrating morphologic, immunophenotypical, and molecular analysis. The newly described PRCC3 has overlapping morphology between PRCC1 and PRCC2, hence would be subtyped as NOS in the current classification. Molecularly PRCC3 has a distinct signature and clinically it behaves similar to PRCC2. The new classification stratifies PRCC patients into clinically relevant subgroups and has significant implications on the management of PRCC.

Sexing adult black-legged kittiwakes by DNA, behavior, and morphology

USGS Publications Warehouse

Jodice, P.G.R.; Lanctot, Richard B.; Gill, V.A.; Roby, D.D.; Hatch, Shyla A.

2000-01-01

We sexed adult Black-legged Kittiwakes (Rissa tridactyla) using DNA-based genetic techniques, behavior and morphology and compared results from these techniques. Genetic and morphology data were collected on 605 breeding kittiwakes and sex-specific behaviors were recorded for a sub-sample of 285 of these individuals. We compared sex classification based on both genetic and behavioral techniques for this sub-sample to assess the accuracy of the genetic technique. DNA-based techniques correctly sexed 97.2% and sex-specific behaviors, 96.5% of this sub-sample. We used the corrected genetic classifications from this sub-sample and the genetic classifications for the remaining birds, under the assumption they were correct, to develop predictive morphometric discriminant function models for all 605 birds. These models accurately predicted the sex of 73-96% of individuals examined, depending on the sample of birds used and the characters included. The most accurate single measurement for determining sex was length of head plus bill, which correctly classified 88% of individuals tested. When both members of a pair were measured, classification levels improved and approached the accuracy of both behavioral observations and genetic analyses. Morphometric techniques were only slightly less accurate than genetic techniques but were easier to implement in the field and less costly. Behavioral observations, while highly accurate, required that birds be easily observable during the breeding season and that birds be identifiable. As such, sex-specific behaviors may best be applied as a confirmation of sex for previously marked birds. All three techniques thus have the potential to be highly accurate, and the selection of one or more will depend on the circumstances of any particular field study.

Mining the Galaxy Zoo Database: Machine Learning Applications

NASA Astrophysics Data System (ADS)

Borne, Kirk D.; Wallin, J.; Vedachalam, A.; Baehr, S.; Lintott, C.; Darg, D.; Smith, A.; Fortson, L.

2010-01-01

The new Zooniverse initiative is addressing the data flood in the sciences through a transformative partnership between professional scientists, volunteer citizen scientists, and machines. As part of this project, we are exploring the application of machine learning techniques to data mining problems associated with the large and growing database of volunteer science results gathered by the Galaxy Zoo citizen science project. We will describe the basic challenge, some machine learning approaches, and early results. One of the motivators for this study is the acquisition (through the Galaxy Zoo results database) of approximately 100 million classification labels for roughly one million galaxies, yielding a tremendously large and rich set of training examples for improving automated galaxy morphological classification algorithms. In our first case study, the goal is to learn which morphological and photometric features in the Sloan Digital Sky Survey (SDSS) database correlate most strongly with user-selected galaxy morphological class. As a corollary to this study, we are also aiming to identify which galaxy parameters in the SDSS database correspond to galaxies that have been the most difficult to classify (based upon large dispersion in their volunter-provided classifications). Our second case study will focus on similar data mining analyses and machine leaning algorithms applied to the Galaxy Zoo catalog of merging and interacting galaxies. The outcomes of this project will have applications in future large sky surveys, such as the LSST (Large Synoptic Survey Telescope) project, which will generate a catalog of 20 billion galaxies and will produce an additional astronomical alert database of approximately 100 thousand events each night for 10 years -- the capabilities and algorithms that we are exploring will assist in the rapid characterization and classification of such massive data streams. This research has been supported in part through NSF award #0941610.

Functional traits and root morphology of alpine plants

PubMed Central

Pohl, Mandy; Stroude, Raphaël; Buttler, Alexandre; Rixen, Christian

2011-01-01

Background and Aims Vegetation has long been recognized to protect the soil from erosion. Understanding species differences in root morphology and functional traits is an important step to assess which species and species mixtures may provide erosion control. Furthermore, extending classification of plant functional types towards root traits may be a useful procedure in understanding important root functions. Methods In this study, pioneer data on traits of alpine plant species, i.e. plant height and shoot biomass, root depth, horizontal root spreading, root length, diameter, tensile strength, plant age and root biomass, from a disturbed site in the Swiss Alps are presented. The applicability of three classifications of plant functional types (PFTs), i.e. life form, growth form and root type, was examined for above- and below-ground plant traits. Key Results Plant traits differed considerably among species even of the same life form, e.g. in the case of total root length by more than two orders of magnitude. Within the same root diameter, species differed significantly in tensile strength: some species (Geum reptans and Luzula spicata) had roots more than twice as strong as those of other species. Species of different life forms provided different root functions (e.g. root depth and horizontal root spreading) that may be important for soil physical processes. All classifications of PFTs were helpful to categorize plant traits; however, the PFTs according to root type explained total root length far better than the other PFTs. Conclusions The results of the study illustrate the remarkable differences between root traits of alpine plants, some of which cannot be assessed from simple morphological inspection, e.g. tensile strength. PFT classification based on root traits seems useful to categorize plant traits, even though some patterns are better explained at the individual species level. PMID:21795278

Chemometric classification of morphologically similar Umbelliferae medicinal herbs by DART-TOF-MS fingerprint.

PubMed

Lee, Sang Min; Kim, Hye-Jin; Jang, Young Pyo

2012-01-01

It needs many years of special training to gain expertise on the organoleptic classification of botanical raw materials and, even for those experts, discrimination among Umbelliferae medicinal herbs remains an intricate challenge due to their morphological similarity. To develop a new chemometric classification method using a direct analysis in real time-time of flight-mass spectrometry (DART-TOF-MS) fingerprinting for Umbelliferae medicinal herbs and to provide a platform for its application to the discrimination of other herbal medicines. Angelica tenuissima, Angelica gigas, Angelica dahurica and Cnidium officinale were chosen for this study and ten samples of each species were purchased from various Korean markets. DART-TOF-MS was employed on powdered raw materials to obtain a chemical fingerprint of each sample and the orthogonal partial-least squares method in discriminant analysis (OPLS-DA) was used for multivariate analysis. All samples of collected species were successfully discriminated from each other according to their characteristic DART-TOF-MS fingerprint. Decursin (or decursinol angelate) and byakangelicol were identified as marker molecules for Angelica gigas and A. dahurica, respectively. Using the OPLS method for discriminant analysis, Angelica tenuissima and Cnidium officinale were clearly separated into two groups. Angelica tenuissima was characterised by the presence of ligustilide and unidentified molecular ions of m/z 239 and 283, while senkyunolide A together with signals with m/z 387 and 389 were the marker compounds for Cnidium officinale. Elaborating with chemoinformatics, DART-TOF-MS fingerprinting with chemoinformatic tools results in a powerful method for the classification of morphologically similar Umbelliferae medicinal herbs and quality control of medicinal herbal products, including the extracts of these crude drugs. Copyright © 2012 John Wiley & Sons, Ltd.

Opportunities and challenges for digital morphology

PubMed Central

2010-01-01

Advances in digital data acquisition, analysis, and storage have revolutionized the work in many biological disciplines such as genomics, molecular phylogenetics, and structural biology, but have not yet found satisfactory acceptance in morphology. Improvements in non-invasive imaging and three-dimensional visualization techniques, however, permit high-throughput analyses also of whole biological specimens, including museum material. These developments pave the way towards a digital era in morphology. Using sea urchins (Echinodermata: Echinoidea), we provide examples illustrating the power of these techniques. However, remote visualization, the creation of a specialized database, and the implementation of standardized, world-wide accepted data deposition practices prior to publication are essential to cope with the foreseeable exponential increase in digital morphological data. Reviewers This article was reviewed by Marc D. Sutton (nominated by Stephan Beck), Gonzalo Giribet (nominated by Lutz Walter), and Lennart Olsson (nominated by Purificación López-García). PMID:20604956

Analysis of the star formation histories of galaxies in different environments: from low to high density

NASA Astrophysics Data System (ADS)

Ortega-Minakata, René A.

2015-11-01

In this thesis, a value-added cataloge of 403,372 SDSS-DR7 galaxies is presented. This catalogue incorporates information on their stellar populations, including their star formation histories, their dominant emission-line activity type, inferred morphology and a measurement of their environmental density. The sample that formed this catalogue was selected from the SDSS-DR7 (Legacy) spectroscopic catalogue of galaxies in the Northern Galactic Cap, selecting only galaxies with high-quality spectra and redshift determination, and photometric measurements with small errors. Also, galaxies near the edge of the photometric survey footprint were excluded to avoid errors in the determination of their environment. Only galaxies in the 0.03-0.30 redshift range were considered. Starlight fits of the spectra of these galaxies were used to obtain information on their star formation history and stellar mass, velocity dispersion and mean age. From the fit residuals, emission-line fluxes were measured and used to obtain the dominant activity type of these galaxies using the BPT diagnostic diagram. A neighbour search code was written and applied to the catalogue to measure the local environmental density of these galaxies. This code counts the number of neighbours within a fixed search radius and a radial velocity range centered at each galaxy's radial velocity. A projected radius of 1.5 Mpc and a range of ± 2,500 km/s, both centered at the redshift of the target galaxy, were used to search and count all the neighbours of each galaxy in the catalogue. The neighbours were counted from the photometric catalogue of the SDSS-DR7 using photometric redshifts, to avoid incompleteness of the spectroscopic catalogue. The morphology of the galaxies in the catalogue was inferred by inverting previously found relations between subsamples of galaxies with visual morphology classification and their optical colours and concentration of light. The galaxies in the catalogue were matched to six other catalogues, creating six subsamples from these matches used to characterize these new value-added catalogue. These catalogues are: the 2MIG catalogue of isolated galaxies with visual morphology; a catalogue of galaxies with visual morphology; a catalogue of galaxies with automated morphology; the first Galaxy Zoo catalogue of galaxies with visual morphology based on general public participation (citizen science); the MaxBCG catalogue of Brightest Cluster Galaxies found with an automatic method; and a compilation of galaxies in rich clusters maintained by H. Andernach. Using the information from the catalogue presented here, strong evidence of a downsizing effect in the formation of galaxies was found, with high mass galaxies showing older stellar populations and mean stellar ages at any redshift in the 0.03-0.30 range than low mass galaxies, which show increasing stellar ages with decreasing redshifts. A strong relation between the dominant activity type and the inferred morphologies of galaxies was also found, with star-forming galaxies having the latest morphologies, Sy2-dominated galaxies being of intermediate types, LINER-like galaxies having earlier morphologies, and passive galaxies showing the earliest morphologies. This relation is observed regardless of the environment of the galaxies and for both high and low stellar mass galaxies. This implies that the morphology and emission-line activity of galaxies are tightly linked to their evolution, and mostly determined by their stellar mass. Also, the morphology-density relation was recovered for galaxies in clusters, but was only observed weakly for the general galaxy population. This confirms that the processes that may change the morphology of individual galaxies are more common in the cluster environment but are mostly absent in other environments, and also implies that secular evolution may change the morphology of galaxies only for less massive galaxies that are still building up their mass. The star formation histories of galaxies in our catalogue were found to be strongly dependent on their morphology, and consequently on their emission-line activity. Star-forming galaxies are late types that have the youngest populations; Sy2-dominated galaxies show a mixture of young and old populations, while LINER-likes have older populations; and passive, early-type galaxies have the oldest populations and have no current star formation. This is consistent with the idea that the processes that fix or change the morphology of galaxies are more internal and modulated by their mass, and are tightly related to how much gas is available to stimulate or stop star formation or AGN activity. In contrast, the star formation histories of galaxies were found to be only weakly dependent on their environmental density, with isolated galaxies showing somewhat younger populations than galaxies in high-density environments. This is consistent with the weak morphology-density relation found for the general population of galaxies, and supports the idea that morphology and formation history are tightly related and, while the processes that change the morphology are more common in the cluster environment, the environmental density itself does not directly affect much the formation history of galaxies. The stellar mass of galaxies seems to modulate their activity and morphology: massive galaxies formed more rapidly in the past, efficiently converting their gas into stars, leaving little or no gas to form stars or fuel AGN activity later on, thus making them low-intensity active galaxies or passive galaxies. The formation of these massive galaxies would then only depend on the local density of protogalaxies, so a high merger rate in environments similar to compact groups of galaxies in the past would result in an early-type galaxy, effectively explaining the relation between mass, activity, morphology, stellar mean age and velocity dispersion of galaxies. The catalogue presented here is useful and relevant because it is a publicly available catalogue of galaxies with consistent, homogeneous information that allows for direct comparisons between samples defined from galaxies in the catalogue, thus providing less biased results, and the large number of galaxies within it allows for statistically significant results, increasing their reliability.

Flow Classification and Cave Discharge Characteristics in Unsaturated Karst Formation

NASA Astrophysics Data System (ADS)

Mariethoz, G.; Mahmud, K.; Baker, A.; Treble, P. C.

2015-12-01

In this study we utilize the spatial array of automated cave drip monitoring in two large chambers of the Golgotha Cave, SW Australia, developed in Quaternary aeolianite (dune limestone), with the aim of understanding infiltration water movement via the relationships between infiltration, stalactite morphology and groundwater recharge. Mahmud et al. (2015) used the Terrestrial LiDAR measurements to analyze stalactite morphology and to characterize possible flow locations in this cave. Here we identify the stalactites feeding the drip loggers and classify each as matrix (soda straw or icicle), fracture or combined-flow. These morphology-based classifications are compared with flow characteristics from the drip logger time series and the discharge from each stalactite is calculated. The total estimated discharge from each area is compared with infiltration estimates to better understand flow from the surface to the cave ceilings of the studied areas. The drip discharge data agrees with the morphology-based flow classification in terms of flow and geometrical characteristics of cave ceiling stalactites. No significant relationships were observed between the drip logger discharge, skewness and coefficient of variation with overburden thickness, due to the possibility of potential vadose-zone storage volume and increasing complexity of the karst architecture. However, these properties can be used to characterize different flow categories. A correlation matrix demonstrates that similar flow categories are positively correlated, implying significant influence of spatial distribution. The infiltration water comes from a larger surface area, suggesting that infiltration is being focused to the studied ceiling areas of each chamber. Most of the ceiling in the cave site is dry, suggesting the possibility of capillary effects with water moving around the cave rather than passing through it. Reference:Mahmud et al. (2015), Terrestrial Lidar Survey and Morphological Analysis to Identify Infiltration Properties in the Tamala Limestone, Western Australia, IEEE JSTARS, DOI: 10.1109/JSTARS.2015.2451088, in Press.

Changing Places: A Cross-Language Perspective on Frequency and Family Size in Dutch and Hebrew

ERIC Educational Resources Information Center

Moscoso del Prado Martin, Fermin; Deutsch, Avital; Frost, Ram; Schreuder, Robert; De Jong, Nivja H.; Baayen, R. Harald

2005-01-01

This study uses the morphological family size effect as a tool for exploring the degree of isomorphism in the networks of morphologically related words in the Hebrew and Dutch mental lexicon. Hebrew and Dutch are genetically unrelated, and they structure their morphologically complex words in very different ways. Two visual lexical decision…

Visual field defects of the contralateral eye of non-arteritic ischemic anterior optic neuropathy: are they related to sleep apnea?

PubMed

Aptel, Florent; Aryal-Charles, Nischal; Tamisier, Renaud; Pépin, Jean-Louis; Lesoin, Antoine; Chiquet, Christophe

2017-06-01

To evaluate whether obstructive sleep apnea (OSA) is responsible for the visual field defects found in the fellow eyes of patients with non-arteritic ischemic optic neuropathy (NAION). Prospective cross-sectional study. The visual fields of the fellow eyes of NAION subjects with OSA were compared to the visual fields of control OSA patients matched for OSA severity. All patients underwent comprehensive ophthalmological and general examination including Humphrey 24.2 SITA-Standard visual field and polysomnography. Visual field defects were classified according the Ischemic Optic Neuropathy Decompression Trial (IONDT) classification. From a cohort of 78 consecutive subjects with NAION, 34 unaffected fellow eyes were compared to 34 control eyes of subjects matched for OSA severity (apnea-hypopnea index [AHI] 35.5 ± 11.6 vs 35.4 ± 9.4 events per hour, respectively, p = 0.63). After adjustment for age and body mass index, all visual field parameters were significantly different between the NAION fellow eyes and those of the control OSA groups, including mean deviation (-4.5 ± 3.7 vs -1.3 ± 1.8 dB, respectively, p < 0.05), visual field index (91.6 ± 10 vs 97.4 ± 3.5%, respectively, p = 0.002), pattern standard deviation (3.7 ± 2.3 vs 2.5 ± 2 dB, respectively, p = 0.015), and number of subjects with at least one defect on the IONDT classification (20 vs 10, respectively, p < 0.05). OSA alone does not explain the visual field defects frequently found in the fellow eyes of NAION patients.

Decoding complex flow-field patterns in visual working memory.

PubMed

Christophel, Thomas B; Haynes, John-Dylan

2014-05-01

There has been a long history of research on visual working memory. Whereas early studies have focused on the role of lateral prefrontal cortex in the storage of sensory information, this has been challenged by research in humans that has directly assessed the encoding of perceptual contents, pointing towards a role of visual and parietal regions during storage. In a previous study we used pattern classification to investigate the storage of complex visual color patterns across delay periods. This revealed coding of such contents in early visual and parietal brain regions. Here we aim to investigate whether the involvement of visual and parietal cortex is also observable for other types of complex, visuo-spatial pattern stimuli. Specifically, we used a combination of fMRI and multivariate classification to investigate the retention of complex flow-field stimuli defined by the spatial patterning of motion trajectories of random dots. Subjects were trained to memorize the precise spatial layout of these stimuli and to retain this information during an extended delay. We used a multivariate decoding approach to identify brain regions where spatial patterns of activity encoded the memorized stimuli. Content-specific memory signals were observable in motion sensitive visual area MT+ and in posterior parietal cortex that might encode spatial information in a modality independent manner. Interestingly, we also found information about the memorized visual stimulus in somatosensory cortex, suggesting a potential crossmodal contribution to memory. Our findings thus indicate that working memory storage of visual percepts might be distributed across unimodal, multimodal and even crossmodal brain regions. Copyright © 2014 Elsevier Inc. All rights reserved.

Molecular Pathological Classification of Neurodegenerative Diseases: Turning towards Precision Medicine.

PubMed

Kovacs, Gabor G

2016-02-02

Neurodegenerative diseases (NDDs) are characterized by selective dysfunction and loss of neurons associated with pathologically altered proteins that deposit in the human brain but also in peripheral organs. These proteins and their biochemical modifications can be potentially targeted for therapy or used as biomarkers. Despite a plethora of modifications demonstrated for different neurodegeneration-related proteins, such as amyloid-β, prion protein, tau, α-synuclein, TAR DNA-binding protein 43 (TDP-43), or fused in sarcoma protein (FUS), molecular classification of NDDs relies on detailed morphological evaluation of protein deposits, their distribution in the brain, and their correlation to clinical symptoms together with specific genetic alterations. A further facet of the neuropathology-based classification is the fact that many protein deposits show a hierarchical involvement of brain regions. This has been shown for Alzheimer and Parkinson disease and some forms of tauopathies and TDP-43 proteinopathies. The present paper aims to summarize current molecular classification of NDDs, focusing on the most relevant biochemical and morphological aspects. Since the combination of proteinopathies is frequent, definition of novel clusters of patients with NDDs needs to be considered in the era of precision medicine. Optimally, neuropathological categorizing of NDDs should be translated into in vivo detectable biomarkers to support better prediction of prognosis and stratification of patients for therapy trials.

Classification of microscopic images of breast tissue

NASA Astrophysics Data System (ADS)

Ballerini, Lucia; Franzen, Lennart

2004-05-01

Breast cancer is the most common form of cancer among women. The diagnosis is usually performed by the pathologist, that subjectively evaluates tissue samples. The aim of our research is to develop techniques for the automatic classification of cancerous tissue, by analyzing histological samples of intact tissue taken with a biopsy. In our study, we considered 200 images presenting four different conditions: normal tissue, fibroadenosis, ductal cancer and lobular cancer. Methods to extract features have been investigated and described. One method is based on granulometries, which are size-shape descriptors widely used in mathematical morphology. Applications of granulometries lead to distribution functions whose moments are used as features. A second method is based on fractal geometry, that seems very suitable to quantify biological structures. The fractal dimension of binary images has been computed using the euclidean distance mapping. Image classification has then been performed using the extracted features as input of a back-propagation neural network. A new method that combines genetic algorithms and morphological filters has been also investigated. In this case, the classification is based on a correlation measure. Very encouraging results have been obtained with pilot experiments using a small subset of images as training set. Experimental results indicate the effectiveness of the proposed methods. Cancerous tissue was correctly classified in 92.5% of the cases.

Morphology of Mesiobuccal Root Canals of Maxillary First Molars: a comparison of CBCT scanning and Cross-sectioning.

PubMed

Lyra, Carina Maria; Delai, Débora; Pereira, Keila Cristina Rausch; Pereira, Guy Martins; Pasternak Júnior, Bráulio; Oliveira, César Augusto Pereira

2015-10-01

The aim of this study was to evaluate the mesiobuccal root of maxillary first molars, according to the root canal configuration, prevalence and location of isthmuses at 3 and 6 mm from the apex, comparing cone-beam computed tomography (CBCT) analysis and cross sectioning of roots by thirds. Images of the mesiobuccal root of 100 maxillary first molars were acquired by CBCT and then roots were cross-sectioned into two parts, starting at 3 mm from the apex. Data were recorded and analyzed according to Weine's classification for root canal configuration, and Hsu and Kim's classification for isthmuses. In the analysis of CBCT images, 8 root canals were classified as type I, 57 as type II, 35 as type III. In the cross-sectioning technique, 19 root canals were classified as type I, 60 as type II, 20 as type III and 1 as type IV. The classification of isthmuses was predominantly type I in both CBCT and cross-sectioning evaluations for sections at 3 mm from the apex, while for sections at 6 mm from the apex, the classification of isthmuses was predominantly types V and II in CBCT and cross-sectioning evaluations, respectively. The cross-sectioning technique showed better results in detection of the internal morphology of root canals than CBCT scanning.

Molecular Pathological Classification of Neurodegenerative Diseases: Turning towards Precision Medicine

PubMed Central

Kovacs, Gabor G.

2016-01-01

Neurodegenerative diseases (NDDs) are characterized by selective dysfunction and loss of neurons associated with pathologically altered proteins that deposit in the human brain but also in peripheral organs. These proteins and their biochemical modifications can be potentially targeted for therapy or used as biomarkers. Despite a plethora of modifications demonstrated for different neurodegeneration-related proteins, such as amyloid-β, prion protein, tau, α-synuclein, TAR DNA-binding protein 43 (TDP-43), or fused in sarcoma protein (FUS), molecular classification of NDDs relies on detailed morphological evaluation of protein deposits, their distribution in the brain, and their correlation to clinical symptoms together with specific genetic alterations. A further facet of the neuropathology-based classification is the fact that many protein deposits show a hierarchical involvement of brain regions. This has been shown for Alzheimer and Parkinson disease and some forms of tauopathies and TDP-43 proteinopathies. The present paper aims to summarize current molecular classification of NDDs, focusing on the most relevant biochemical and morphological aspects. Since the combination of proteinopathies is frequent, definition of novel clusters of patients with NDDs needs to be considered in the era of precision medicine. Optimally, neuropathological categorizing of NDDs should be translated into in vivo detectable biomarkers to support better prediction of prognosis and stratification of patients for therapy trials. PMID:26848654

Effect of Eight Weekly Aerobic Training Program on Auditory Reaction Time and MaxVO[subscript 2] in Visual Impairments

ERIC Educational Resources Information Center

Taskin, Cengiz

2016-01-01

The aim of study was to examine the effect of eight weekly aerobic exercises on auditory reaction time and MaxVO[subscript 2] in visual impairments. Forty visual impairment children that have blind 3 classification from the Turkey, experimental group; (age = 15.60 ± 1.10 years; height = 164.15 ± 4.88 cm; weight = 66.60 ± 4.77 kg) for twenty…

Spatial scale and distribution of neurovascular signals underlying decoding of orientation and eye of origin from fMRI data

PubMed Central

Harrison, Charlotte; Jackson, Jade; Oh, Seung-Mock; Zeringyte, Vaida

2016-01-01

Multivariate pattern analysis of functional magnetic resonance imaging (fMRI) data is widely used, yet the spatial scales and origin of neurovascular signals underlying such analyses remain unclear. We compared decoding performance for stimulus orientation and eye of origin from fMRI measurements in human visual cortex with predictions based on the columnar organization of each feature and estimated the spatial scales of patterns driving decoding. Both orientation and eye of origin could be decoded significantly above chance in early visual areas (V1–V3). Contrary to predictions based on a columnar origin of response biases, decoding performance for eye of origin in V2 and V3 was not significantly lower than that in V1, nor did decoding performance for orientation and eye of origin differ significantly. Instead, response biases for both features showed large-scale organization, evident as a radial bias for orientation, and a nasotemporal bias for eye preference. To determine whether these patterns could drive classification, we quantified the effect on classification performance of binning voxels according to visual field position. Consistent with large-scale biases driving classification, binning by polar angle yielded significantly better decoding performance for orientation than random binning in V1–V3. Similarly, binning by hemifield significantly improved decoding performance for eye of origin. Patterns of orientation and eye preference bias in V2 and V3 showed a substantial degree of spatial correlation with the corresponding patterns in V1, suggesting that response biases in these areas originate in V1. Together, these findings indicate that multivariate classification results need not reflect the underlying columnar organization of neuronal response selectivities in early visual areas. NEW & NOTEWORTHY Large-scale response biases can account for decoding of orientation and eye of origin in human early visual areas V1–V3. For eye of origin this pattern is a nasotemporal bias; for orientation it is a radial bias. Differences in decoding performance across areas and stimulus features are not well predicted by differences in columnar-scale organization of each feature. Large-scale biases in extrastriate areas are spatially correlated with those in V1, suggesting biases originate in primary visual cortex. PMID:27903637

Believing is Seeing: Visual Conventions in Barr's Classification of the "Feeble-Minded"

ERIC Educational Resources Information Center

Elks, Martin A.

2004-01-01

The eugenics era (c. 1900?1930) produced a strong desire among mental retardation professionals to recognize and control "the feeble-minded." Some eugenicists believed it was possible to classify individuals visually by learning to recognize what they believed to be observable characteristics of idiocy and imbecility. In this paper I used…

Deep learning for EEG-Based preference classification

NASA Astrophysics Data System (ADS)

Teo, Jason; Hou, Chew Lin; Mountstephens, James

2017-10-01

Electroencephalogram (EEG)-based emotion classification is rapidly becoming one of the most intensely studied areas of brain-computer interfacing (BCI). The ability to passively identify yet accurately correlate brainwaves with our immediate emotions opens up truly meaningful and previously unattainable human-computer interactions such as in forensic neuroscience, rehabilitative medicine, affective entertainment and neuro-marketing. One particularly useful yet rarely explored areas of EEG-based emotion classification is preference recognition [1], which is simply the detection of like versus dislike. Within the limited investigations into preference classification, all reported studies were based on musically-induced stimuli except for a single study which used 2D images. The main objective of this study is to apply deep learning, which has been shown to produce state-of-the-art results in diverse hard problems such as in computer vision, natural language processing and audio recognition, to 3D object preference classification over a larger group of test subjects. A cohort of 16 users was shown 60 bracelet-like objects as rotating visual stimuli on a computer display while their preferences and EEGs were recorded. After training a variety of machine learning approaches which included deep neural networks, we then attempted to classify the users' preferences for the 3D visual stimuli based on their EEGs. Here, we show that that deep learning outperforms a variety of other machine learning classifiers for this EEG-based preference classification task particularly in a highly challenging dataset with large inter- and intra-subject variability.

Building a robust vehicle detection and classification module

NASA Astrophysics Data System (ADS)

Grigoryev, Anton; Khanipov, Timur; Koptelov, Ivan; Bocharov, Dmitry; Postnikov, Vassily; Nikolaev, Dmitry

2015-12-01

The growing adoption of intelligent transportation systems (ITS) and autonomous driving requires robust real-time solutions for various event and object detection problems. Most of real-world systems still cannot rely on computer vision algorithms and employ a wide range of costly additional hardware like LIDARs. In this paper we explore engineering challenges encountered in building a highly robust visual vehicle detection and classification module that works under broad range of environmental and road conditions. The resulting technology is competitive to traditional non-visual means of traffic monitoring. The main focus of the paper is on software and hardware architecture, algorithm selection and domain-specific heuristics that help the computer vision system avoid implausible answers.

Refractory anemia with ringed sideroblasts and chronic myelomonocytic leukemia: myelodysplastic/myeloproliferative disease.

PubMed

D'Angelo, Guido

2005-01-01

Here is reported the case of an elderly woman that, after surgical intervention, showed an important anemia, leucocytosis and thrombocytopenia. The leucocytosis was accompanied with clean increase of the monocytes. The morphological appearances, both peripheral blood and bone marrow, showed an evident overlapping of myelodysplastic and myeloproliferative picture, characterized from the presence of refractory anemia with ringed sideroblasts (RARS) and chronic myelomonocytic leukemia (CMML). The case has been reported because it is not frequent, besides, the CMML, until from the beginning of French-American-British (FAB) classification application, has raised problems of classification. Currently, the World Health Organization (WHO) has given an arrangement to the hematological picture with myelodysplastic and myeloproliferative morphological appearances, including this pathology in a new category: myelodysplastic/myeloproliferative diseases (MDS/MPD).

[Classification of prosthetic loosening and determination of wear particles].

PubMed

Otto, M

2008-11-01

Nowaday, loosening of orthopaedic implants implies important medical and socioeconomic problems. Implant loosening is caused by implant infections as well as aseptic loosening, due to particle disease and mechanical alterations. Clinically we divide the implant infection into early and late infections. Morphologically it is possible to reliably detect the infection by quantification of neutrophil granulocytes. Additionally molecular methods are suitable to detect micro-organisms which are responsible for the prosthetic joint infection including their resistance to antibiotics. Particle disease may be reproducibly classified by the detection of different types of wear particles, particularly polyethylene, metal, ceramic and cement. The aetiology of the indeterminate type of the periprosthetic membrane is obscure, but may be associated with osteopathies. This classification of the periprosthetic membrane morphology provides clinically significant information concerning clinical management of implant loosening.

Sleep staging with movement-related signals.

PubMed

Jansen, B H; Shankar, K

1993-05-01

Body movement related signals (i.e., activity due to postural changes and the ballistocardiac effort) were recorded from six normal volunteers using the static-charge-sensitive bed (SCSB). Visual sleep staging was performed on the basis of simultaneously recorded EEG, EMG and EOG signals. A statistical classification technique was used to determine if reliable sleep staging could be performed using only the SCSB signal. A classification rate of between 52% and 75% was obtained for sleep staging in the five conventional sleep stages and the awake state. These rates improved from 78% to 89% for classification between awake, REM and non-REM sleep and from 86% to 98% for awake versus asleep classification.

Characterization of virus-like particles by atomic force microscopy in ambient conditions

NASA Astrophysics Data System (ADS)

Oropesa, Reinier; Ramos, Jorge R.; Falcón, Viviana; Felipe, Ariel

2013-06-01

Recombinant virus-like particles (VLPs) are attractive candidates for vaccine design since they resemble native viroids in size and morphology, but they are non-infectious due to the absence of a viral genome. The visualization of surface morphologies and structures can be used to deepen the understanding of physical, chemical, and biological phenomena. Atomic force microscopy (AFM) is a useful tool for the visualization of soft biological samples in a nanoscale resolution. In this work we have investigated the morphology of recombinant surface antigens of hepatitis B (rHBsAg) VLPs from Cuban vaccine against hepatitis B. The rHBsAg VLPs sizes estimated by AFM between 15 and 30 nm are similar to those reported on previous transmission electron microscopy (TEM) studies.

Data Mining Technologies Inspired from Visual Principle

NASA Astrophysics Data System (ADS)

Xu, Zongben

In this talk we review the recent work done by our group on data mining (DM) technologies deduced from simulating visual principle. Through viewing a DM problem as a cognition problems and treading a data set as an image with each light point located at a datum position, we developed a series of high efficient algorithms for clustering, classification and regression via mimicking visual principles. In pattern recognition, human eyes seem to possess a singular aptitude to group objects and find important structure in an efficient way. Thus, a DM algorithm simulating visual system may solve some basic problems in DM research. From this point of view, we proposed a new approach for data clustering by modeling the blurring effect of lateral retinal interconnections based on scale space theory. In this approach, as the data image blurs, smaller light blobs merge into large ones until the whole image becomes one light blob at a low enough level of resolution. By identifying each blob with a cluster, the blurring process then generates a family of clustering along the hierarchy. The proposed approach provides unique solutions to many long standing problems, such as the cluster validity and the sensitivity to initialization problems, in clustering. We extended such an approach to classification and regression problems, through combatively employing the Weber's law in physiology and the cell response classification facts. The resultant classification and regression algorithms are proven to be very efficient and solve the problems of model selection and applicability to huge size of data set in DM technologies. We finally applied the similar idea to the difficult parameter setting problem in support vector machine (SVM). Viewing the parameter setting problem as a recognition problem of choosing a visual scale at which the global and local structures of a data set can be preserved, and the difference between the two structures be maximized in the feature space, we derived a direct parameter setting formula for the Gaussian SVM. The simulations and applications show that the suggested formula significantly outperforms the known model selection methods in terms of efficiency and precision.

Molecular approaches for classifying endometrial carcinoma.

PubMed

Piulats, Josep M; Guerra, Esther; Gil-Martín, Marta; Roman-Canal, Berta; Gatius, Sonia; Sanz-Pamplona, Rebeca; Velasco, Ana; Vidal, August; Matias-Guiu, Xavier

2017-04-01

Endometrial carcinoma is the most common cancer of the female genital tract. This review article discusses the usefulness of molecular techniques to classify endometrial carcinoma. Any proposal for molecular classification of neoplasms should integrate morphological features of the tumors. For that reason, we start with the current histological classification of endometrial carcinoma, by discussing the correlation between genotype and phenotype, and the most significant recent improvements. Then, we comment on some of the possible flaws of this classification, by discussing also the value of molecular pathology in improving them, including interobserver variation in pathologic interpretation of high grade tumors. Third, we discuss the importance of applying TCGA molecular approach to clinical practice. We also comment on the impact of intratumor heterogeneity in classification, and finally, we will discuss briefly, the usefulness of TCGA classification in tailoring immunotherapy in endometrial cancer patients. We suggest combining pathologic classification and the surrogate TCGA molecular classification for high-grade endometrial carcinomas, as an option to improve assessment of prognosis. Copyright © 2016 Elsevier Inc. All rights reserved.

The research on medical image classification algorithm based on PLSA-BOW model.

PubMed

Cao, C H; Cao, H L

2016-04-29

With the rapid development of modern medical imaging technology, medical image classification has become more important for medical diagnosis and treatment. To solve the existence of polysemous words and synonyms problem, this study combines the word bag model with PLSA (Probabilistic Latent Semantic Analysis) and proposes the PLSA-BOW (Probabilistic Latent Semantic Analysis-Bag of Words) model. In this paper we introduce the bag of words model in text field to image field, and build the model of visual bag of words model. The method enables the word bag model-based classification method to be further improved in accuracy. The experimental results show that the PLSA-BOW model for medical image classification can lead to a more accurate classification.

Classification in Astronomy: Past and Present

NASA Astrophysics Data System (ADS)

Feigelson, Eric

2012-03-01

Astronomers have always classified celestial objects. The ancient Greeks distinguished between asteros, the fixed stars, and planetos, the roving stars. The latter were associated with the Gods and, starting with Plato in his dialog Timaeus, provided the first mathematical models of celestial phenomena. Giovanni Hodierna classified nebulous objects, seen with a Galilean refractor telescope in the mid-seventeenth century into three classes: "Luminosae," "Nebulosae," and "Occultae." A century later, Charles Messier compiled a larger list of nebulae, star clusters and galaxies, but did not attempt a classification. Classification of comets was a significant enterprise in the 19th century: Alexander (1850) considered two groups based on orbit sizes, Lardner (1853) proposed three groups of orbits, and Barnard (1891) divided them into two classes based on morphology. Aside from the segmentation of the bright stars into constellations, most stellar classifications were based on colors and spectral properties. During the 1860s, the pioneering spectroscopist Angelo Secchi classified stars into five classes: white, yellow, orange, carbon stars, and emission line stars. After many debates, the stellar spectral sequence was refined by the group at Harvard into the familiar OBAFGKM spectral types, later found to be a sequence on surface temperature (Cannon 1926). The spectral classification is still being extended with recent additions of O2 hot stars (Walborn et al. 2002) and L and T brown dwarfs (Kirkpatrick 2005). Townley (1913) reviews 30 years of variable star classification, emerging with six classes with five subclasses. The modern classification of variable stars has about 80 (sub)classes, and is still under debate (Samus 2009). Shortly after his confirmation that some nebulae are external galaxies, Edwin Hubble (1926) proposed his famous bifurcated classification of galaxy morphologies with three classes: ellipticals, spirals, and irregulars. These classes are still used today with many refinements by Gerard de Vaucouleurs and others. Supernovae, nearly all of which are found in external galaxies, have a complicated classification scheme:Type I with subtypes Ia, Ib, Ic, Ib/c pec and Type II with subtypes IIb, IIL, IIP, and IIn (Turatto 2003). The classification is based on elemental abundances in optical spectra and on optical light curve shapes. Tadhunter (2009) presents a three-dimensional classification of active galactic nuclei involving radio power, emission line width, and nuclear luminosity. These taxonomies have played enormously important roles in the development of astronomy, yet all were developed using heuristic methods. Many are based on qualitative and subjective assessments of spatial, temporal, or spectral properties. A qualitative, morphological approach to astronomical studies was explicitly promoted by Zwicky (1957). Other classifications are based on quantitative criteria, but these criteria were developed by subjective examination of training datasets. For example, starburst galaxies are discriminated from narrow-line Seyfert galaxies by a curved line in a diagramof the ratios of four emission lines (Veilleux and Osterbrock 1987). Class II young stellar objects have been defined by a rectangular region in a mid-infrared color-color diagram (Allen et al. 2004). Short and hard gamma-ray bursts are discriminated by a dip in the distribution of burst durations (Kouveliotou et al. 2000). In no case was a statistical or algorithmic procedure used to define the classes.

Extracting Urban Morphology for Atmospheric Modeling from Multispectral and SAR Satellite Imagery

NASA Astrophysics Data System (ADS)

Wittke, S.; Karila, K.; Puttonen, E.; Hellsten, A.; Auvinen, M.; Karjalainen, M.

2017-05-01

This paper presents an approach designed to derive an urban morphology map from satellite data while aiming to minimize the cost of data and user interference. The approach will help to provide updates to the current morphological databases around the world. The proposed urban morphology maps consist of two layers: 1) Digital Elevation Model (DEM) and 2) land cover map. Sentinel-2 data was used to create a land cover map, which was realized through image classification using optical range indices calculated from image data. For the purpose of atmospheric modeling, the most important classes are water and vegetation areas. The rest of the area includes bare soil and built-up areas among others, and they were merged into one class in the end. The classification result was validated with ground truth data collected both from field measurements and aerial imagery. The overall classification accuracy for the three classes is 91 %. TanDEM-X data was processed into two DEMs with different grid sizes using interferometric SAR processing. The resulting DEM has a RMSE of 3.2 meters compared to a high resolution DEM, which was estimated through 20 control points in flat areas. Comparing the derived DEM with the ground truth DEM from airborne LIDAR data, it can be seen that the street canyons, that are of high importance for urban atmospheric modeling are not detectable in the TanDEM-X DEM. However, the derived DEM is suitable for a class of urban atmospheric models. Based on the numerical modeling needs for regional atmospheric pollutant dispersion studies, the generated files enable the extraction of relevant parametrizations, such as Urban Canopy Parameters (UCP).

Phylogeny of Comatulidae (Echinodermata: Crinoidea: Comatulida): a new classification and an assessment of morphological characters for crinoid taxonomy.

PubMed

Summers, Mindi M; Messing, Charles G; Rouse, Greg W

2014-11-01

Comatulidae Fleming, 1828 (previously, and incorrectly, Comasteridae A.H. Clark, 1908a), is a group of feather star crinoids currently divided into four accepted subfamilies, 21 genera and approximately 95 nominal species. Comatulidae is the most commonly-encountered and species-rich crinoid group on shallow tropical coral reefs, particularly in the Indo-western Pacific region (IWP). We conducted a molecular phylogenetic analysis of the group with concatenated data from up to seven genes for 43 nominal species spanning 17 genera and all subfamilies. Basal nodes returned low support, but maximum likelihood, maximum parsimony, and Bayesian analyses were largely congruent, permitting an evaluation of current taxonomy and analysis of morphological character transformations. Two of the four current subfamilies were paraphyletic, whereas 15 of the 17 included genera returned as monophyletic. We provide a new classification with two subfamilies, Comatulinae and Comatellinae n. subfamily Summers, Messing, & Rouse, the former containing five tribes. We revised membership of analyzed genera to make them all clades and erected Anneissia n. gen. Summers, Messing, & Rouse. Transformation analyses for morphological features generally used in feather star classification (e.g., ray branching patterns, articulations) and those specifically for Comatulidae (e.g., comb pinnule form, mouth placement) were labile with considerable homoplasy. These traditional characters, in combination, allow for generic diagnoses, but in most cases we did not recover apomorphies for subfamilies, tribes, and genera. New morphological characters that will be informative for crinoid taxonomy and identification are still needed. DNA sequence data currently provides the most reliable method of identification to the species-level for many taxa of Comatulidae. Copyright © 2014 Elsevier Inc. All rights reserved.

Congenital Differences of the Upper Extremity: Classification and Treatment Principles

PubMed Central

2011-01-01

For hand surgeons, the treatment of children with congenital differences of the upper extremity is challenging because of the diverse spectrum of conditions encountered, but the task is also rewarding because it provides surgeons with the opportunity to impact a child's growth and development. An ideal classification of congenital differences of the upper extremity would reflect the full spectrum of morphologic abnormalities and encompass etiology, a guide to treatment, and provide prognoses. In this report, I review current classification systems and discuss their contradictions and limitations. In addition, I present a modified classification system and provide treatment principles. As our understanding of the etiology of congenital differences of the upper extremity increases and as experience of treating difficult cases accumulates, even an ideal classification system and optimal treatment strategies will undoubtedly continue to evolve. PMID:21909463

Texture classification using non-Euclidean Minkowski dilation

NASA Astrophysics Data System (ADS)

Florindo, Joao B.; Bruno, Odemir M.

2018-03-01

This study presents a new method to extract meaningful descriptors of gray-scale texture images using Minkowski morphological dilation based on the Lp metric. The proposed approach is motivated by the success previously achieved by Bouligand-Minkowski fractal descriptors on texture classification. In essence, such descriptors are directly derived from the morphological dilation of a three-dimensional representation of the gray-level pixels using the classical Euclidean metric. In this way, we generalize the dilation for different values of p in the Lp metric (Euclidean is a particular case when p = 2) and obtain the descriptors from the cumulated distribution of the distance transform computed over the texture image. The proposed method is compared to other state-of-the-art approaches (such as local binary patterns and textons for example) in the classification of two benchmark data sets (UIUC and Outex). The proposed descriptors outperformed all the other approaches in terms of rate of images correctly classified. The interesting results suggest the potential of these descriptors in this type of task, with a wide range of possible applications to real-world problems.

Mid-level image representations for real-time heart view plane classification of echocardiograms.

PubMed

Penatti, Otávio A B; Werneck, Rafael de O; de Almeida, Waldir R; Stein, Bernardo V; Pazinato, Daniel V; Mendes Júnior, Pedro R; Torres, Ricardo da S; Rocha, Anderson

2015-11-01

In this paper, we explore mid-level image representations for real-time heart view plane classification of 2D echocardiogram ultrasound images. The proposed representations rely on bags of visual words, successfully used by the computer vision community in visual recognition problems. An important element of the proposed representations is the image sampling with large regions, drastically reducing the execution time of the image characterization procedure. Throughout an extensive set of experiments, we evaluate the proposed approach against different image descriptors for classifying four heart view planes. The results show that our approach is effective and efficient for the target problem, making it suitable for use in real-time setups. The proposed representations are also robust to different image transformations, e.g., downsampling, noise filtering, and different machine learning classifiers, keeping classification accuracy above 90%. Feature extraction can be performed in 30 fps or 60 fps in some cases. This paper also includes an in-depth review of the literature in the area of automatic echocardiogram view classification giving the reader a through comprehension of this field of study. Copyright © 2015 Elsevier Ltd. All rights reserved.

Modality-Driven Classification and Visualization of Ensemble Variance

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

Bensema, Kevin; Gosink, Luke; Obermaier, Harald

Advances in computational power now enable domain scientists to address conceptual and parametric uncertainty by running simulations multiple times in order to sufficiently sample the uncertain input space. While this approach helps address conceptual and parametric uncertainties, the ensemble datasets produced by this technique present a special challenge to visualization researchers as the ensemble dataset records a distribution of possible values for each location in the domain. Contemporary visualization approaches that rely solely on summary statistics (e.g., mean and variance) cannot convey the detailed information encoded in ensemble distributions that are paramount to ensemble analysis; summary statistics provide no informationmore » about modality classification and modality persistence. To address this problem, we propose a novel technique that classifies high-variance locations based on the modality of the distribution of ensemble predictions. Additionally, we develop a set of confidence metrics to inform the end-user of the quality of fit between the distribution at a given location and its assigned class. We apply a similar method to time-varying ensembles to illustrate the relationship between peak variance and bimodal or multimodal behavior. These classification schemes enable a deeper understanding of the behavior of the ensemble members by distinguishing between distributions that can be described by a single tendency and distributions which reflect divergent trends in the ensemble.« less

Combining features from ERP components in single-trial EEG for discriminating four-category visual objects.

PubMed

Wang, Changming; Xiong, Shi; Hu, Xiaoping; Yao, Li; Zhang, Jiacai

2012-10-01

Categorization of images containing visual objects can be successfully recognized using single-trial electroencephalograph (EEG) measured when subjects view images. Previous studies have shown that task-related information contained in event-related potential (ERP) components could discriminate two or three categories of object images. In this study, we investigated whether four categories of objects (human faces, buildings, cats and cars) could be mutually discriminated using single-trial EEG data. Here, the EEG waveforms acquired while subjects were viewing four categories of object images were segmented into several ERP components (P1, N1, P2a and P2b), and then Fisher linear discriminant analysis (Fisher-LDA) was used to classify EEG features extracted from ERP components. Firstly, we compared the classification results using features from single ERP components, and identified that the N1 component achieved the highest classification accuracies. Secondly, we discriminated four categories of objects using combining features from multiple ERP components, and showed that combination of ERP components improved four-category classification accuracies by utilizing the complementarity of discriminative information in ERP components. These findings confirmed that four categories of object images could be discriminated with single-trial EEG and could direct us to select effective EEG features for classifying visual objects.

Will it Blend? Visualization and Accuracy Evaluation of High-Resolution Fuzzy Vegetation Maps

NASA Astrophysics Data System (ADS)

Zlinszky, A.; Kania, A.

2016-06-01

Instead of assigning every map pixel to a single class, fuzzy classification includes information on the class assigned to each pixel but also the certainty of this class and the alternative possible classes based on fuzzy set theory. The advantages of fuzzy classification for vegetation mapping are well recognized, but the accuracy and uncertainty of fuzzy maps cannot be directly quantified with indices developed for hard-boundary categorizations. The rich information in such a map is impossible to convey with a single map product or accuracy figure. Here we introduce a suite of evaluation indices and visualization products for fuzzy maps generated with ensemble classifiers. We also propose a way of evaluating classwise prediction certainty with "dominance profiles" visualizing the number of pixels in bins according to the probability of the dominant class, also showing the probability of all the other classes. Together, these data products allow a quantitative understanding of the rich information in a fuzzy raster map both for individual classes and in terms of variability in space, and also establish the connection between spatially explicit class certainty and traditional accuracy metrics. These map products are directly comparable to widely used hard boundary evaluation procedures, support active learning-based iterative classification and can be applied for operational use.

AutoBD: Automated Bi-Level Description for Scalable Fine-Grained Visual Categorization.

PubMed

Yao, Hantao; Zhang, Shiliang; Yan, Chenggang; Zhang, Yongdong; Li, Jintao; Tian, Qi

Compared with traditional image classification, fine-grained visual categorization is a more challenging task, because it targets to classify objects belonging to the same species, e.g. , classify hundreds of birds or cars. In the past several years, researchers have made many achievements on this topic. However, most of them are heavily dependent on the artificial annotations, e.g., bounding boxes, part annotations, and so on . The requirement of artificial annotations largely hinders the scalability and application. Motivated to release such dependence, this paper proposes a robust and discriminative visual description named Automated Bi-level Description (AutoBD). "Bi-level" denotes two complementary part-level and object-level visual descriptions, respectively. AutoBD is "automated," because it only requires the image-level labels of training images and does not need any annotations for testing images. Compared with the part annotations labeled by the human, the image-level labels can be easily acquired, which thus makes AutoBD suitable for large-scale visual categorization. Specifically, the part-level description is extracted by identifying the local region saliently representing the visual distinctiveness. The object-level description is extracted from object bounding boxes generated with a co-localization algorithm. Although only using the image-level labels, AutoBD outperforms the recent studies on two public benchmark, i.e. , classification accuracy achieves 81.6% on CUB-200-2011 and 88.9% on Car-196, respectively. On the large-scale Birdsnap data set, AutoBD achieves the accuracy of 68%, which is currently the best performance to the best of our knowledge.Compared with traditional image classification, fine-grained visual categorization is a more challenging task, because it targets to classify objects belonging to the same species, e.g. , classify hundreds of birds or cars. In the past several years, researchers have made many achievements on this topic. However, most of them are heavily dependent on the artificial annotations, e.g., bounding boxes, part annotations, and so on . The requirement of artificial annotations largely hinders the scalability and application. Motivated to release such dependence, this paper proposes a robust and discriminative visual description named Automated Bi-level Description (AutoBD). "Bi-level" denotes two complementary part-level and object-level visual descriptions, respectively. AutoBD is "automated," because it only requires the image-level labels of training images and does not need any annotations for testing images. Compared with the part annotations labeled by the human, the image-level labels can be easily acquired, which thus makes AutoBD suitable for large-scale visual categorization. Specifically, the part-level description is extracted by identifying the local region saliently representing the visual distinctiveness. The object-level description is extracted from object bounding boxes generated with a co-localization algorithm. Although only using the image-level labels, AutoBD outperforms the recent studies on two public benchmark, i.e. , classification accuracy achieves 81.6% on CUB-200-2011 and 88.9% on Car-196, respectively. On the large-scale Birdsnap data set, AutoBD achieves the accuracy of 68%, which is currently the best performance to the best of our knowledge.

Accurate micro-computed tomography imaging of pore spaces in collagen-based scaffold.

PubMed

Zidek, Jan; Vojtova, Lucy; Abdel-Mohsen, A M; Chmelik, Jiri; Zikmund, Tomas; Brtnikova, Jana; Jakubicek, Roman; Zubal, Lukas; Jan, Jiri; Kaiser, Jozef

2016-06-01

In this work we have used X-ray micro-computed tomography (μCT) as a method to observe the morphology of 3D porous pure collagen and collagen-composite scaffolds useful in tissue engineering. Two aspects of visualizations were taken into consideration: improvement of the scan and investigation of its sensitivity to the scan parameters. Due to the low material density some parts of collagen scaffolds are invisible in a μCT scan. Therefore, here we present different contrast agents, which increase the contrast of the scanned biopolymeric sample for μCT visualization. The increase of contrast of collagenous scaffolds was performed with ceramic hydroxyapatite microparticles (HAp), silver ions (Ag(+)) and silver nanoparticles (Ag-NPs). Since a relatively small change in imaging parameters (e.g. in 3D volume rendering, threshold value and μCT acquisition conditions) leads to a completely different visualized pattern, we have optimized these parameters to obtain the most realistic picture for visual and qualitative evaluation of the biopolymeric scaffold. Moreover, scaffold images were stereoscopically visualized in order to better see the 3D biopolymer composite scaffold morphology. However, the optimized visualization has some discontinuities in zoomed view, which can be problematic for further analysis of interconnected pores by commonly used numerical methods. Therefore, we applied the locally adaptive method to solve discontinuities issue. The combination of contrast agent and imaging techniques presented in this paper help us to better understand the structure and morphology of the biopolymeric scaffold that is crucial in the design of new biomaterials useful in tissue engineering.

The Fruitless Effort of Growing a Fruitless Tree: Early Morpho-Orthographic and Morpho-Semantic Effects in Sentence Reading

ERIC Educational Resources Information Center

Amenta, Simona; Marelli, Marco; Crepaldi, Davide

2015-01-01

In this eye-tracking study, we investigated how semantics inform morphological analysis at the early stages of visual word identification in sentence reading. We exploited a feature of several derived Italian words, that is, that they can be read in a "morphologically transparent" way or in a "morphologically opaque" way…

Applications of UAS-SfM for coastal vulnerability assessment: Geomorphic feature extraction and land cover classification from fine-scale elevation and imagery data

NASA Astrophysics Data System (ADS)

Sturdivant, E. J.; Lentz, E. E.; Thieler, E. R.; Remsen, D.; Miner, S.

2016-12-01

Characterizing the vulnerability of coastal systems to storm events, chronic change and sea-level rise can be improved with high-resolution data that capture timely snapshots of biogeomorphology. Imagery acquired with unmanned aerial systems (UAS) coupled with structure from motion (SfM) photogrammetry can produce high-resolution topographic and visual reflectance datasets that rival or exceed lidar and orthoimagery. Here we compare SfM-derived data to lidar and visual imagery for their utility in a) geomorphic feature extraction and b) land cover classification for coastal habitat assessment. At a beach and wetland site on Cape Cod, Massachusetts, we used UAS to capture photographs over a 15-hectare coastal area with a resulting pixel resolution of 2.5 cm. We used standard SfM processing in Agisoft PhotoScan to produce an elevation point cloud, an orthomosaic, and a digital elevation model (DEM). The SfM-derived products have a horizontal uncertainty of +/- 2.8 cm. Using the point cloud in an extraction routine developed for lidar data, we determined the position of shorelines, dune crests, and dune toes. We used the output imagery and DEM to map land cover with a pixel-based supervised classification. The dense and highly precise SfM point cloud enabled extraction of geomorphic features with greater detail than with lidar. The feature positions are reported with near-continuous coverage and sub-meter accuracy. The orthomosaic image produced with SfM provides visual reflectance with higher resolution than those available from aerial flight surveys, which enables visual identification of small features and thus aids the training and validation of the automated classification. We find that the high-resolution and correspondingly high density of UAS data requires some simple modifications to existing measurement techniques and processing workflows, and that the types of data and the quality provided is equivalent to, and in some cases surpasses, that of data collected using other methods.

An integration of minimum local feature representation methods to recognize large variation of foods

NASA Astrophysics Data System (ADS)

Razali, Mohd Norhisham bin; Manshor, Noridayu; Halin, Alfian Abdul; Mustapha, Norwati; Yaakob, Razali

2017-10-01

Local invariant features have shown to be successful in describing object appearances for image classification tasks. Such features are robust towards occlusion and clutter and are also invariant against scale and orientation changes. This makes them suitable for classification tasks with little inter-class similarity and large intra-class difference. In this paper, we propose an integrated representation of the Speeded-Up Robust Feature (SURF) and Scale Invariant Feature Transform (SIFT) descriptors, using late fusion strategy. The proposed representation is used for food recognition from a dataset of food images with complex appearance variations. The Bag of Features (BOF) approach is employed to enhance the discriminative ability of the local features. Firstly, the individual local features are extracted to construct two kinds of visual vocabularies, representing SURF and SIFT. The visual vocabularies are then concatenated and fed into a Linear Support Vector Machine (SVM) to classify the respective food categories. Experimental results demonstrate impressive overall recognition at 82.38% classification accuracy based on the challenging UEC-Food100 dataset.

When mental fatigue maybe characterized by Event Related Potential (P300) during virtual wheelchair navigation.

PubMed

Lamti, Hachem A; Gorce, Philippe; Ben Khelifa, Mohamed Moncef; Alimi, Adel M

2016-12-01

The goal of this study is to investigate the influence of mental fatigue on the event related potential P300 features (maximum pick, minimum amplitude, latency and period) during virtual wheelchair navigation. For this purpose, an experimental environment was set up based on customizable environmental parameters (luminosity, number of obstacles and obstacles velocities). A correlation study between P300 and fatigue ratings was conducted. Finally, the best correlated features supplied three classification algorithms which are MLP (Multi Layer Perceptron), Linear Discriminate Analysis and Support Vector Machine. The results showed that the maximum feature over visual and temporal regions as well as period feature over frontal, fronto-central and visual regions were correlated with mental fatigue levels. In the other hand, minimum amplitude and latency features didn't show any correlation. Among classification techniques, MLP showed the best performance although the differences between classification techniques are minimal. Those findings can help us in order to design suitable mental fatigue based wheelchair control.

A three-parameter asteroid taxonomy

NASA Technical Reports Server (NTRS)

Tedesco, Edward F.; Williams, James G.; Matson, Dennis L.; Veeder, Glenn J.; Gradie, Jonathan C.

1989-01-01

Broadband U, V, and x photometry together with IRAS asteroid albedos have been used to construct an asteroid classification system. The system is based on three parameters (U-V and v-x color indices and visual geometric albedo), and it is able to place 96 percent of the present sample of 357 asteroids into 11 taxonomic classes. It is noted that all but one of these classes are analogous to those previously found using other classification schemes. The algorithm is shown to account for the observational uncertainties in each of the classification parameters.

DEEP MOTIF DASHBOARD: VISUALIZING AND UNDERSTANDING GENOMIC SEQUENCES USING DEEP NEURAL NETWORKS.

PubMed

Lanchantin, Jack; Singh, Ritambhara; Wang, Beilun; Qi, Yanjun

2017-01-01

Deep neural network (DNN) models have recently obtained state-of-the-art prediction accuracy for the transcription factor binding (TFBS) site classification task. However, it remains unclear how these approaches identify meaningful DNA sequence signals and give insights as to why TFs bind to certain locations. In this paper, we propose a toolkit called the Deep Motif Dashboard (DeMo Dashboard) which provides a suite of visualization strategies to extract motifs, or sequence patterns from deep neural network models for TFBS classification. We demonstrate how to visualize and understand three important DNN models: convolutional, recurrent, and convolutional-recurrent networks. Our first visualization method is finding a test sequence's saliency map which uses first-order derivatives to describe the importance of each nucleotide in making the final prediction. Second, considering recurrent models make predictions in a temporal manner (from one end of a TFBS sequence to the other), we introduce temporal output scores, indicating the prediction score of a model over time for a sequential input. Lastly, a class-specific visualization strategy finds the optimal input sequence for a given TFBS positive class via stochastic gradient optimization. Our experimental results indicate that a convolutional-recurrent architecture performs the best among the three architectures. The visualization techniques indicate that CNN-RNN makes predictions by modeling both motifs as well as dependencies among them.

Deep Motif Dashboard: Visualizing and Understanding Genomic Sequences Using Deep Neural Networks

PubMed Central

Lanchantin, Jack; Singh, Ritambhara; Wang, Beilun; Qi, Yanjun

2018-01-01

Deep neural network (DNN) models have recently obtained state-of-the-art prediction accuracy for the transcription factor binding (TFBS) site classification task. However, it remains unclear how these approaches identify meaningful DNA sequence signals and give insights as to why TFs bind to certain locations. In this paper, we propose a toolkit called the Deep Motif Dashboard (DeMo Dashboard) which provides a suite of visualization strategies to extract motifs, or sequence patterns from deep neural network models for TFBS classification. We demonstrate how to visualize and understand three important DNN models: convolutional, recurrent, and convolutional-recurrent networks. Our first visualization method is finding a test sequence’s saliency map which uses first-order derivatives to describe the importance of each nucleotide in making the final prediction. Second, considering recurrent models make predictions in a temporal manner (from one end of a TFBS sequence to the other), we introduce temporal output scores, indicating the prediction score of a model over time for a sequential input. Lastly, a class-specific visualization strategy finds the optimal input sequence for a given TFBS positive class via stochastic gradient optimization. Our experimental results indicate that a convolutional-recurrent architecture performs the best among the three architectures. The visualization techniques indicate that CNN-RNN makes predictions by modeling both motifs as well as dependencies among them. PMID:27896980

Melanocytoma-like melanoma may be the missing link between benign and malignant uveal melanocytic lesions in humans and dogs: a comparative study.

PubMed

Zoroquiain, Pablo; Mayo-Goldberg, Erin; Alghamdi, Sarah; Alhumaid, Sulaiman; Perlmann, Eduardo; Barros, Paulo; Mayo, Nancy; Burnier, Miguel N

2016-12-01

The cutoff presented in the current classification of canine melanocytic lesions by Wilcock and Pfeiffer is based on the clinical outcome rather than morphological concepts. Classification of tumors based on morphology or molecular signatures is the key to identifying new therapies or prognostic factors. Therefore, the aim of this study was to analyze morphological findings in canine melanocytic lesions based on classic malignant morphologic principles of neoplasia and to compare these features with human uveal melanoma (HUM) samples. In total, 64 canine and 111 human morphologically malignant melanocytic lesions were classified into two groups (melanocytoma-like or classic melanoma) based on the presence or absence of M cells, respectively. Histopathological characteristics were compared between the two groups using the χ-test, t-test, and multivariate discriminant analysis. Among the 64 canine tumors, 28 (43.7%) were classic and 36 (56.3%) were melanocytoma-like melanomas. Smaller tumor size, a higher degree of pigmentation, and lower mitotic activity distinguished melanocytoma-like from classic tumors with an accuracy of 100% for melanocytoma-like lesions. From the human series, only one case showed melanocytoma-like features and had a low risk for metastasis characteristics. Canine uveal melanoma showed a morphological spectrum with features similar to the HUM counterpart (classic melanoma) and overlapped features between uveal melanoma and melanocytoma (melanocytoma-like melanoma). Recognition that the subgroup of melanocytoma-like melanoma may represent the missing link between benign and malignant lesions could help explain the progression of uveal melanoma in dogs; these findings can potentially be translated to HUM.

Preplanetary Nebulae: A Hubble Space Telescope Imaging Survey and a New Morphological Classification System

NASA Technical Reports Server (NTRS)

Sahai, Raghvendra; Morris, Mark; Sanchez Contreras, Carmen; Claussen, Mark

2007-01-01

Using the Hubble Space Telescope (HST ), we have carried out a survey of candidate preplanetary nebulae (PPNs). We report here our discoveries of objects having well-resolved geometric structures, and we use the large sample of PPNs now imaged with HST (including previously studied objects in this class) to devise a comprehensive morphological classification system for this category of objects. The wide variety of aspherical morphologies which we have found for PPNs are qualitatively similar to those found for young planetary nebulae (PNs) in previous surveys. We also find prominent halos surrounding the central aspherical shapes in many of our objects; these are direct signatures of the undisturbed circumstellar envelopes of the progenitor AGB stars. Although the majority of these have surface brightness distributions consistent with a constant mass-loss rate with a constant expansion velocity, there are also examples of objects with varying mass-loss rates. As in our surveys of young PNs, we find no round PPNs. The similarities in morphologies between our survey objects and young PNs supports the view that the former are the progenitors of aspherical PNs. This suggests that the primary shaping of a PN does not occur during the PN phase via the fast radiative wind of the hot central star, but significantly earlier in its evolution.

A Visual mining based framework for classification accuracy estimation

NASA Astrophysics Data System (ADS)

Arun, Pattathal Vijayakumar

2013-12-01

Classification techniques have been widely used in different remote sensing applications and correct classification of mixed pixels is a tedious task. Traditional approaches adopt various statistical parameters, however does not facilitate effective visualisation. Data mining tools are proving very helpful in the classification process. We propose a visual mining based frame work for accuracy assessment of classification techniques using open source tools such as WEKA and PREFUSE. These tools in integration can provide an efficient approach for getting information about improvements in the classification accuracy and helps in refining training data set. We have illustrated framework for investigating the effects of various resampling methods on classification accuracy and found that bilinear (BL) is best suited for preserving radiometric characteristics. We have also investigated the optimal number of folds required for effective analysis of LISS-IV images. Techniki klasyfikacji są szeroko wykorzystywane w różnych aplikacjach teledetekcyjnych, w których poprawna klasyfikacja pikseli stanowi poważne wyzwanie. Podejście tradycyjne wykorzystujące różnego rodzaju parametry statystyczne nie zapewnia efektywnej wizualizacji. Wielce obiecujące wydaje się zastosowanie do klasyfikacji narzędzi do eksploracji danych. W artykule zaproponowano podejście bazujące na wizualnej analizie eksploracyjnej, wykorzystujące takie narzędzia typu open source jak WEKA i PREFUSE. Wymienione narzędzia ułatwiają korektę pół treningowych i efektywnie wspomagają poprawę dokładności klasyfikacji. Działanie metody sprawdzono wykorzystując wpływ różnych metod resampling na zachowanie dokładności radiometrycznej i uzyskując najlepsze wyniki dla metody bilinearnej (BL).

Tactile and Visual Identification of the XM106 Bursting Smoke Grenade: Limited User Evaluation

DTIC Science & Technology

2010-12-01

situations representing the typical handwear and eyewear configurations of dismounted Warfighters. Thirty-six test Soldiers participated in the evaluation...all handwear and eyewear conditions. 15. SUBJECT TERMS XM106, smoke grenade, tactile/visual identification 16. SECURITY CLASSIFICATION OF: 17...1.3.2  Eyewear Compatibility ........................................................................................3  1.3.3  Physical Load

Exploring Students' Visual Conception of Matter: Towards Developing a Teaching Framework Using Models

ERIC Educational Resources Information Center

Espinosa, Allen A.; Marasigan, Arlyne C.; Datukan, Janir T.

2016-01-01

This study explored how students visualise the states and classifications of matter with the use of scientific models. Misconceptions of students in using scientific models were also identified to formulate a teaching framework. To elicit data in the study, a Visual Conception Questionnaire was administered to thirty-four (34), firstyear, general…

Congenital basis of posterior fossa anomalies

PubMed Central

Cotes, Claudia; Bonfante, Eliana; Lazor, Jillian; Jadhav, Siddharth; Caldas, Maria; Swischuk, Leonard

2015-01-01

The classification of posterior fossa congenital anomalies has been a controversial topic. Advances in genetics and imaging have allowed a better understanding of the embryologic development of these abnormalities. A new classification schema correlates the embryologic, morphologic, and genetic bases of these anomalies in order to better distinguish and describe them. Although they provide a better understanding of the clinical aspects and genetics of these disorders, it is crucial for the radiologist to be able to diagnose the congenital posterior fossa anomalies based on their morphology, since neuroimaging is usually the initial step when these disorders are suspected. We divide the most common posterior fossa congenital anomalies into two groups: 1) hindbrain malformations, including diseases with cerebellar or vermian agenesis, aplasia or hypoplasia and cystic posterior fossa anomalies; and 2) cranial vault malformations. In addition, we will review the embryologic development of the posterior fossa and, from the perspective of embryonic development, will describe the imaging appearance of congenital posterior fossa anomalies. Knowledge of the developmental bases of these malformations facilitates detection of the morphological changes identified on imaging, allowing accurate differentiation and diagnosis of congenital posterior fossa anomalies. PMID:26246090

Visual fields and eye morphology support color vision in a color-changing crab-spider.

PubMed

Insausti, Teresita C; Defrize, Jérémy; Lazzari, Claudio R; Casas, Jérôme

2012-03-01

Vision plays a major role in many spiders, being involved in prey hunting, orientation or substrate choice, among others. In Misumena vatia, which experiences morphological color changes, vision has been reported to be involved in substrate color matching. Electrophysiological evidence reveals that at least two types of photoreceptors are present in this species, but these data are not backed up by morphological evidence. This work analyzes the functional structure of the eyes of this spider and relates it to its color-changing abilities. A broad superposition of the visual field of the different eyes was observed, even between binocular regions of principal and secondary eyes. The frontal space is simultaneously analyzed by four eyes. This superposition supports the integration of the visual information provided by the different eye types. The mobile retina of the principal eyes of this spider is organized in three layers of three different types of rhabdoms. The third and deepest layer is composed by just one large rhabdom surrounded by dark screening pigments that limit the light entry. The three pairs of secondary eyes have all a single layer of rhabdoms. Our findings provide strong support for an involvement of the visual system in color matching in this spider. Copyright © 2011 Elsevier Ltd. All rights reserved.

Polsar Land Cover Classification Based on Hidden Polarimetric Features in Rotation Domain and Svm Classifier

NASA Astrophysics Data System (ADS)

Tao, C.-S.; Chen, S.-W.; Li, Y.-Z.; Xiao, S.-P.

2017-09-01

Land cover classification is an important application for polarimetric synthetic aperture radar (PolSAR) data utilization. Rollinvariant polarimetric features such as H / Ani / α / Span are commonly adopted in PolSAR land cover classification. However, target orientation diversity effect makes PolSAR images understanding and interpretation difficult. Only using the roll-invariant polarimetric features may introduce ambiguity in the interpretation of targets' scattering mechanisms and limit the followed classification accuracy. To address this problem, this work firstly focuses on hidden polarimetric feature mining in the rotation domain along the radar line of sight using the recently reported uniform polarimetric matrix rotation theory and the visualization and characterization tool of polarimetric coherence pattern. The former rotates the acquired polarimetric matrix along the radar line of sight and fully describes the rotation characteristics of each entry of the matrix. Sets of new polarimetric features are derived to describe the hidden scattering information of the target in the rotation domain. The latter extends the traditional polarimetric coherence at a given rotation angle to the rotation domain for complete interpretation. A visualization and characterization tool is established to derive new polarimetric features for hidden information exploration. Then, a classification scheme is developed combing both the selected new hidden polarimetric features in rotation domain and the commonly used roll-invariant polarimetric features with a support vector machine (SVM) classifier. Comparison experiments based on AIRSAR and multi-temporal UAVSAR data demonstrate that compared with the conventional classification scheme which only uses the roll-invariant polarimetric features, the proposed classification scheme achieves both higher classification accuracy and better robustness. For AIRSAR data, the overall classification accuracy with the proposed classification scheme is 94.91 %, while that with the conventional classification scheme is 93.70 %. Moreover, for multi-temporal UAVSAR data, the averaged overall classification accuracy with the proposed classification scheme is up to 97.08 %, which is much higher than the 87.79 % from the conventional classification scheme. Furthermore, for multitemporal PolSAR data, the proposed classification scheme can achieve better robustness. The comparison studies also clearly demonstrate that mining and utilization of hidden polarimetric features and information in the rotation domain can gain the added benefits for PolSAR land cover classification and provide a new vision for PolSAR image interpretation and application.

Land use in the Paraiba Valley through remotely sensed data. [Brazil

NASA Technical Reports Server (NTRS)

Dejesusparada, N. (Principal Investigator); Lombardo, M. A.; Novo, E. M. L. D.; Niero, M.; Foresti, C.

1980-01-01

A methodology for land use survey was developed and land use modification rates were determined using LANDSAT imagery of the Paraiba Valley (state of Sao Paulo). Both visual and automatic interpretation methods were employed to analyze seven land use classes: urban area, industrial area, bare soil, cultivated area, pastureland, reforestation and natural vegetation. By means of visual interpretation, little spectral differences are observed among those classes. The automatic classification of LANDSAT MSS data using maximum likelihood algorithm shows a 39% average error of omission and a 3.4% error of inclusion for the seven classes. The complexity of land uses in the study area, the large spectral variations of analyzed classes, and the low resolution of LANDSAT data influenced the classification results.

Characterization of limes (Citrus aurantifolia) grown in Bhutan and Indonesia using high-throughput sequencing

PubMed Central

Penjor, Tshering; Mimura, Takashi; Matsumoto, Ryoji; Yamamoto, Masashi; Nagano, Yukio

2014-01-01

Lime [Citrus aurantifolia (Cristm.) Swingle] is a Citrus species that is a popular ingredient in many cuisines. Some citrus plants are known to originate in the area ranging from northeastern India to southwestern China. In the current study, we characterized and compared limes grown in Bhutan (n = 5 accessions) and Indonesia (n = 3 accessions). The limes were separated into two groups based on their morphology. Restriction site-associated DNA sequencing (RAD-seq) separated the eight accessions into two clusters. One cluster contained four accessions from Bhutan, whereas the other cluster contained one accession from Bhutan and the three accessions from Indonesia. This genetic classification supported the morphological classification of limes. The analysis suggests that the properties associated with asexual reproduction, and somatic homologous recombination, have contributed to the genetic diversification of limes. PMID:24781859

A multi-gene phylogeny of Chlorophyllum (Agaricaceae, Basidiomycota): new species, new combination and infrageneric classification

PubMed Central

Ge, Zai-Wei; Jacobs, Adriaana; Vellinga, Else C.; Sysouphanthong, Phongeun; van der Walt, Retha; Lavorato, Carmine; An, Yi-Feng; Yang, Zhu L.

2018-01-01

Abstract Taxonomic and phylogenetic studies of Chlorophyllum were carried out on the basis of morphological differences and molecular phylogenetic analyses. Based on the phylogeny inferred from the internal transcribed spacer (ITS), the partial large subunit nuclear ribosomal DNA (nrLSU), the second largest subunit of RNA polymerase II (rpb2) and translation elongation factor 1-α (tef1) sequences, six well-supported clades and 17 phylogenetic species are recognised. Within this phylogenetic framework and considering the diagnostic morphological characters, two new species, C. africanum and C. palaeotropicum, are described. In addition, a new infrageneric classification of Chlorophyllum is proposed, in which the genus is divided into six sections. One new combination is also made. This study provides a robust basis for a more detailed investigation of diversity and biogeography of Chlorophyllum. PMID:29681738

Urothelial dysplasia and other flat lesions of the urinary bladder: clinicopathologic and molecular features.

PubMed

Hodges, Kurt B; Lopez-Beltran, Antonio; Davidson, Darrell D; Montironi, Rodolfo; Cheng, Liang

2010-02-01

The 2004 World Health Organization classification system for urothelial neoplasia classifies flat-related preneoplastic lesions as urothelial hyperplasia (flat and papillary), reactive urothelial atypia, urothelial atypia of unknown significance, urothelial dysplasia (low-grade intraurothelial neoplasia), and urothelial carcinoma in situ (high-grade intraurothelial neoplasia). Each lesion is defined with precise nomenclature and strict morphologic criteria. In many cases, morphologic features alone suffice for diagnosis. Other cases may require a panel of immunohistochemical antibodies consisting of cytokeratin 20, p53, and CD44 for diagnosis. Recent molecular studies have provided further insight into the premalignant potential of these urothelial lesions. Herein, we present a review of flat urothelial lesions of the urinary bladder as defined by the 2004 World Health Organization classification with focus on the clinicopathologic, immunohistochemical, and molecular features. Copyright 2010 Elsevier Inc. All rights reserved.

Radiological assessment of breast density by visual classification (BI-RADS) compared to automated volumetric digital software (Quantra): implications for clinical practice.

PubMed

Regini, Elisa; Mariscotti, Giovanna; Durando, Manuela; Ghione, Gianluca; Luparia, Andrea; Campanino, Pier Paolo; Bianchi, Caterina Chiara; Bergamasco, Laura; Fonio, Paolo; Gandini, Giovanni

2014-10-01

This study was done to assess breast density on digital mammography and digital breast tomosynthesis according to the visual Breast Imaging Reporting and Data System (BI-RADS) classification, to compare visual assessment with Quantra software for automated density measurement, and to establish the role of the software in clinical practice. We analysed 200 digital mammograms performed in 2D and 3D modality, 100 of which positive for breast cancer and 100 negative. Radiological density was assessed with the BI-RADS classification; a Quantra density cut-off value was sought on the 2D images only to discriminate between BI-RADS categories 1-2 and BI-RADS 3-4. Breast density was correlated with age, use of hormone therapy, and increased risk of disease. The agreement between the 2D and 3D assessments of BI-RADS density was high (K 0.96). A cut-off value of 21% is that which allows us to best discriminate between BI-RADS categories 1-2 and 3-4. Breast density was negatively correlated to age (r = -0.44) and positively to use of hormone therapy (p = 0.0004). Quantra density was higher in breasts with cancer than in healthy breasts. There is no clear difference between the visual assessments of density on 2D and 3D images. Use of the automated system requires the adoption of a cut-off value (set at 21%) to effectively discriminate BI-RADS 1-2 and 3-4, and could be useful in clinical practice.

Demographics of Isolated Galaxies along the Hubble Sequence

NASA Astrophysics Data System (ADS)

Khim, Hong-geun; Park, Jongwon; Seo, Seong-Woo; Lee, Jaehyun; Smith, Rory; Yi, Sukyoung K.

2015-09-01

Isolated galaxies in low-density regions are significant in the sense that they are least affected by the hierarchical pattern of galaxy growth and interactions with perturbers, at least for the last few gigayears. To form a comprehensive picture of the star-formation history of isolated galaxies, we constructed a catalog of isolated galaxies and their comparison sample in relatively denser environments. The galaxies are drawn from the Sloan Digital Sky Survey Data Release 7 in the redshift range of 0.025\\lt z\\lt 0.044. We performed a visual inspection and classified their morphology following the Hubble classification scheme. For the spectroscopic study, we make use of the catalog provided by Oh et al. in 2011. We confirm most of the earlier understanding on isolated galaxies. The most remarkable additional results are as follows. Isolated galaxies are dominantly late type with the morphology distribution (E:S0:S:Irr) = (9.9:11.3:77.6:1.2)%. The frequency of elliptical galaxies among isolated galaxies is only a third of that of the comparison sample. Most of the photometric and spectroscopic properties are surprisingly similar between the isolated and comparison samples. However, early-type isolated galaxies are less massive by 50% and younger (by Hβ) by 20% than their counterparts in the comparison sample. This can be explained as a result of different merger and star-formation histories for differing environments in the hierarchical merger paradigm. We provide an online catalog for the list and properties of our sample galaxies.

Multi-viewpoint Coronal Mass Ejection Catalog Based on STEREO COR2 Observations

NASA Astrophysics Data System (ADS)

Vourlidas, Angelos; Balmaceda, Laura A.; Stenborg, Guillermo; Dal Lago, Alisson

2017-04-01

We present the first multi-viewpoint coronal mass ejection (CME) catalog. The events are identified visually in simultaneous total brightness observations from the twin SECCHI/COR2 coronagraphs on board the Solar Terrestrial Relations Observatory mission. The Multi-View CME Catalog differs from past catalogs in three key aspects: (1) all events between the two viewpoints are cross-linked, (2) each event is assigned a physics-motivated morphological classification (e.g., jet, wave, and flux rope), and (3) kinematic and geometric information is extracted semi-automatically via a supervised image segmentation algorithm. The database extends from the beginning of the COR2 synoptic program (2007 March) to the end of dual-viewpoint observations (2014 September). It contains 4473 unique events with 3358 events identified in both COR2s. Kinematic properties exist currently for 1747 events (26% of COR2-A events and 17% of COR2-B events). We examine several issues, made possible by this cross-linked CME database, including the role of projection on the perceived morphology of events, the missing CME rate, the existence of cool material in CMEs, the solar cycle dependence on CME rate, speeds and width, and the existence of flux rope within CMEs. We discuss the implications for past single-viewpoint studies and for Space Weather research. The database is publicly available on the web including all available measurements. We hope that it will become a useful resource for the community.

Sonic morphology: Aesthetic dimensional auditory spatial awareness

NASA Astrophysics Data System (ADS)

Whitehouse, Martha M.

The sound and ceramic sculpture installation, " Skirting the Edge: Experiences in Sound & Form," is an integration of art and science demonstrating the concept of sonic morphology. "Sonic morphology" is herein defined as aesthetic three-dimensional auditory spatial awareness. The exhibition explicates my empirical phenomenal observations that sound has a three-dimensional form. Composed of ceramic sculptures that allude to different social and physical situations, coupled with sound compositions that enhance and create a three-dimensional auditory and visual aesthetic experience (see accompanying DVD), the exhibition supports the research question, "What is the relationship between sound and form?" Precisely how people aurally experience three-dimensional space involves an integration of spatial properties, auditory perception, individual history, and cultural mores. People also utilize environmental sound events as a guide in social situations and in remembering their personal history, as well as a guide in moving through space. Aesthetically, sound affects the fascination, meaning, and attention one has within a particular space. Sonic morphology brings art forms such as a movie, video, sound composition, and musical performance into the cognitive scope by generating meaning from the link between the visual and auditory senses. This research examined sonic morphology as an extension of musique concrete, sound as object, originating in Pierre Schaeffer's work in the 1940s. Pointing, as John Cage did, to the corporeal three-dimensional experience of "all sound," I composed works that took their total form only through the perceiver-participant's participation in the exhibition. While contemporary artist Alvin Lucier creates artworks that draw attention to making sound visible, "Skirting the Edge" engages the perceiver-participant visually and aurally, leading to recognition of sonic morphology.

Mapping Landslides in Lunar Impact Craters Using Chebyshev Polynomials and Dem's

NASA Astrophysics Data System (ADS)

Yordanov, V.; Scaioni, M.; Brunetti, M. T.; Melis, M. T.; Zinzi, A.; Giommi, P.

2016-06-01

Geological slope failure processes have been observed on the Moon surface for decades, nevertheless a detailed and exhaustive lunar landslide inventory has not been produced yet. For a preliminary survey, WAC images and DEM maps from LROC at 100 m/pixels have been exploited in combination with the criteria applied by Brunetti et al. (2015) to detect the landslides. These criteria are based on the visual analysis of optical images to recognize mass wasting features. In the literature, Chebyshev polynomials have been applied to interpolate crater cross-sections in order to obtain a parametric characterization useful for classification into different morphological shapes. Here a new implementation of Chebyshev polynomial approximation is proposed, taking into account some statistical testing of the results obtained during Least-squares estimation. The presence of landslides in lunar craters is then investigated by analyzing the absolute values off odd coefficients of estimated Chebyshev polynomials. A case study on the Cassini A crater has demonstrated the key-points of the proposed methodology and outlined the required future development to carry out.

Development of a high-content screening assay panel to accelerate mechanism of action studies for oncology research.

PubMed

Towne, Danli L; Nicholl, Emily E; Comess, Kenneth M; Galasinski, Scott C; Hajduk, Philip J; Abraham, Vivek C

2012-09-01

Efficient elucidation of the biological mechanism of action of novel compounds remains a major bottleneck in the drug discovery process. To address this need in the area of oncology, we report the development of a multiparametric high-content screening assay panel at the level of single cells to dramatically accelerate understanding the mechanism of action of cell growth-inhibiting compounds on a large scale. Our approach is based on measuring 10 established end points associated with mitochondrial apoptosis, cell cycle disruption, DNA damage, and cellular morphological changes in the same experiment, across three multiparametric assays. The data from all of the measurements taken together are expected to help increase our current understanding of target protein functions, constrain the list of possible targets for compounds identified using phenotypic screens, and identify off-target effects. We have also developed novel data visualization and phenotypic classification approaches for detailed interpretation of individual compound effects and navigation of large collections of multiparametric cellular responses. We expect this general approach to be valuable for drug discovery across multiple therapeutic areas.

Sustainable Biosphere Initiative Project

NASA Technical Reports Server (NTRS)

1997-01-01

The goal of the Advanced Technology in Ecological Sciences project is to gain broad participation within the environmental scientific community in developing a research agenda addressing the development and refinement of technologies instrumental to research that responds to these challenges (e.g. global climate change, unsustainable resource use, and threats to biological diversity). The following activities have been completed: (1) A listserve 'eco-tech was set up to serve as a clearinghouse of information about activities and events relating to advanced technologies; (2) A series of conference calls were organized on specific topics including data visualization and spatial analysis, and remote sensing; and (3) Two meetings were organized at the 19% ESA Annual Meeting in Providence, Rhode Island. Topics covered included concerns about tool and data sharing; interest in expanded development of ground-based remote sensing technologies for monitoring; issues involved in training for using new technologies and increasing data streams, and- associated implications of data processing capabilities; questions about how to develop appropriate standards (i.e. surface morphology classification standards) that facilitate the exchange and comparison of analytical results; and some thoughts about remote sensing platforms and vehicles.

Lexicality, Morphological Structure, and Semantic Transparency in the Processing of German Ver-Verbs: The Complementarity of On-Line and Off-Line Evidence

ERIC Educational Resources Information Center

Schirmeier, Matthias K.; Derwing, Bruce L.; Libben, Gary

2004-01-01

Two types of experiments investigate the visual on-line and off-line processing of German ver-verbs (e.g., verbittern "to embitte"). In Experiments 1 and 2 (morphological priming), latency patterns revealed the existence of facilitation effects for the morphological conditions (BITTER-VERBITTERN and BITTERN-VERBITTERN) as compared to the neutral…

[The establishment, development and application of classification approach of freshwater phytoplankton based on the functional group: a review].

PubMed

Yang, Wen; Zhu, Jin-Yong; Lu, Kai-Hong; Wan, Li; Mao, Xiao-Hua

2014-06-01

Appropriate schemes for classification of freshwater phytoplankton are prerequisites and important tools for revealing phytoplanktonic succession and studying freshwater ecosystems. An alternative approach, functional group of freshwater phytoplankton, has been proposed and developed due to the deficiencies of Linnaean and molecular identification in ecological applications. The functional group of phytoplankton is a classification scheme based on autoecology. In this study, the theoretical basis and classification criterion of functional group (FG), morpho-functional group (MFG) and morphology-based functional group (MBFG) were summarized, as well as their merits and demerits. FG was considered as the optimal classification approach for the aquatic ecology research and aquatic environment evaluation. The application status of FG was introduced, with the evaluation standards and problems of two approaches to assess water quality on the basis of FG, index methods of Q and QR, being briefly discussed.

Pathological and Molecular Evaluation of Pancreatic Neoplasms

PubMed Central

Rishi, Arvind; Goggins, Michael; Wood, Laura D.; Hruban, Ralph H.

2015-01-01

Pancreatic neoplasms are morphologically and genetically heterogeneous and include wide variety of neoplasms ranging from benign to malignant with an extremely poor clinical outcome. Our understanding of these pancreatic neoplasms has improved significantly with recent advances in cancer sequencing. Awareness of molecular pathogenesis brings in new opportunities for early detection, improved prognostication, and personalized gene-specific therapies. Here we review the pathological classification of pancreatic neoplasms from their molecular and genetic perspective. All of the major tumor types that arise in the pancreas have been sequenced, and a new classification that incorporates molecular findings together with pathological findings is now possible (Table 1). This classification has significant implications for our understanding of why tumors aggregate in some families, for the development of early detection tests, and for the development of personalized therapies for patients with established cancers. Here we describe this new classification using the framework of the standard histological classification. PMID:25726050

Image patch-based method for automated classification and detection of focal liver lesions on CT

NASA Astrophysics Data System (ADS)

Safdari, Mustafa; Pasari, Raghav; Rubin, Daniel; Greenspan, Hayit

2013-03-01

We developed a method for automated classification and detection of liver lesions in CT images based on image patch representation and bag-of-visual-words (BoVW). BoVW analysis has been extensively used in the computer vision domain to analyze scenery images. In the current work we discuss how it can be used for liver lesion classification and detection. The methodology includes building a dictionary for a training set using local descriptors and representing a region in the image using a visual word histogram. Two tasks are described: a classification task, for lesion characterization, and a detection task in which a scan window moves across the image and is determined to be normal liver tissue or a lesion. Data: In the classification task 73 CT images of liver lesions were used, 25 images having cysts, 24 having metastasis and 24 having hemangiomas. A radiologist circumscribed the lesions, creating a region of interest (ROI), in each of the images. He then provided the diagnosis, which was established either by biopsy or clinical follow-up. Thus our data set comprises 73 images and 73 ROIs. In the detection task, a radiologist drew ROIs around each liver lesion and two regions of normal liver, for a total of 159 liver lesion ROIs and 146 normal liver ROIs. The radiologist also demarcated the liver boundary. Results: Classification results of more than 95% were obtained. In the detection task, F1 results obtained is 0.76. Recall is 84%, with precision of 73%. Results show the ability to detect lesions, regardless of shape.

Root Morphology and Canal Configuration of First and Second Maxillary Molars in a Selected Iranian Population: A Cone-Beam Computed Tomography Evaluation

PubMed Central

Khademi, Abbasali; Zamani Naser, Asieh; Bahreinian, Zahra; Mehdizadeh, Mojdeh; Najarian, Mojtaba; Khazaei, Saber

2017-01-01

Introduction: The aim of this investigation was to evaluate root canal morphology of maxillary first and second molars and also to assess the prevalence and morphology of the second mesiobuccal canal (MB2) in these teeth, using cone-beam computed tomography (CBCT). Methods and Materials: In this cross-sectional study, the total of 470 CBCT images from the archive of Radiology Department of Isfahan University of Medical Sciences (IUMS), Iran, was evaluated and 295 images were selected. The number of roots, and canal configuration were determined based on Vertucci’s classification system. The data was analyzed using SPSS 20, and P-values less than 0.05 were considered significant. Results: A total of 295 images from 295 patients (165 females and 130 males), including 389 maxillary first (197 right and 192 left) and 460 maxillary second (235 right and 225 left) molars were evaluated. The prevalence of MB2 canals were 70.2% and 43.4% in the maxillary first and second molars, respectively. The most common type of Vertucci’s classification was type II (53.1%), followed by type I. Conclusion: The second mesiobuccal canal was present in almost two thirds of first and less than half of second molars. The morphology and canal configuration of a maxillary molar can almost predict the morphology of contralateral molar. However, it does not relate to the ipsilateral molar. PMID:28808452

Aircraft target detection algorithm based on high resolution spaceborne SAR imagery

NASA Astrophysics Data System (ADS)

Zhang, Hui; Hao, Mengxi; Zhang, Cong; Su, Xiaojing

2018-03-01

In this paper, an image classification algorithm for airport area is proposed, which based on the statistical features of synthetic aperture radar (SAR) images and the spatial information of pixels. The algorithm combines Gamma mixture model and MRF. The algorithm using Gamma mixture model to obtain the initial classification result. Pixel space correlation based on the classification results are optimized by the MRF technique. Additionally, morphology methods are employed to extract airport (ROI) region where the suspected aircraft target samples are clarified to reduce the false alarm and increase the detection performance. Finally, this paper presents the plane target detection, which have been verified by simulation test.

The evaluative imaging of mental models - Visual representations of complexity

NASA Technical Reports Server (NTRS)

Dede, Christopher

1989-01-01

The paper deals with some design issues involved in building a system that could visually represent the semantic structures of training materials and their underlying mental models. In particular, hypermedia-based semantic networks that instantiate classification problem solving strategies are thought to be a useful formalism for such representations; the complexity of these web structures can be best managed through visual depictions. It is also noted that a useful approach to implement in these hypermedia models would be some metrics of conceptual distance.

Hierarchical Modelling Of Mobile, Seeing Robots

NASA Astrophysics Data System (ADS)

Luh, Cheng-Jye; Zeigler, Bernard P.

1990-03-01

This paper describes the implementation of a hierarchical robot simulation which supports the design of robots with vision and mobility. A seeing robot applies a classification expert system for visual identification of laboratory objects. The visual data acquisition algorithm used by the robot vision system has been developed to exploit multiple viewing distances and perspectives. Several different simulations have been run testing the visual logic in a laboratory environment. Much work remains to integrate the vision system with the rest of the robot system.

Hierarchical modelling of mobile, seeing robots

NASA Technical Reports Server (NTRS)

Luh, Cheng-Jye; Zeigler, Bernard P.

1990-01-01

This paper describes the implementation of a hierarchical robot simulation which supports the design of robots with vision and mobility. A seeing robot applies a classification expert system for visual identification of laboratory objects. The visual data acquisition algorithm used by the robot vision system has been developed to exploit multiple viewing distances and perspectives. Several different simulations have been run testing the visual logic in a laboratory environment. Much work remains to integrate the vision system with the rest of the robot system.

Immunological classification of high grade non-Hodgkin's lymphomas (NHL) in children.

PubMed

Pituch-Noworolska, A; Miezyński, W

1994-01-01

The immunological classification of 28 high grade non-Hodgkin's lymphomas (NHL) in children was shown. The morphological classification was based on Working Formulation, the immunological classification--on acute lymphoblastic leukemia subtypes. The phenotypes were assayed cytofluorometrically with monoclonal antibodies and compared to ontogenic stages in B and T cell development. Small non-cleaved cell lymphoma (Burkitt's type) was seen in 13 patients, lymphoblastic lymphoma in 12 patients, low differentiated in 3 patients. Immunological classification showed B-lymphocyte origin of blast cells in 15 patients including 11 small non-cleaved Burkitt's lymphoma (mature B and cALL phenotype), 3 undifferentiated cases (pro-B and mature B cell) and 1 case of lymphoblastic lymphoma (cALL type). T-cell origin of blast cells was demonstrated in 13 patients. The immunological classification used routinely was helpful in selection of patients with unfavourable prognosis. The more precise description of blast cells was valuable for better adjustment of therapy and better prognosis.

Orthographic units in the absence of visual processing: Evidence from sublexical structure in braille.

PubMed

Fischer-Baum, Simon; Englebretson, Robert

2016-08-01

Reading relies on the recognition of units larger than single letters and smaller than whole words. Previous research has linked sublexical structures in reading to properties of the visual system, specifically on the parallel processing of letters that the visual system enables. But whether the visual system is essential for this to happen, or whether the recognition of sublexical structures may emerge by other means, is an open question. To address this question, we investigate braille, a writing system that relies exclusively on the tactile rather than the visual modality. We provide experimental evidence demonstrating that adult readers of (English) braille are sensitive to sublexical units. Contrary to prior assumptions in the braille research literature, we find strong evidence that braille readers do indeed access sublexical structure, namely the processing of multi-cell contractions as single orthographic units and the recognition of morphemes within morphologically-complex words. Therefore, we conclude that the recognition of sublexical structure is not exclusively tied to the visual system. However, our findings also suggest that there are aspects of morphological processing on which braille and print readers differ, and that these differences may, crucially, be related to reading using the tactile rather than the visual sensory modality. Copyright © 2016 Elsevier B.V. All rights reserved.

Importance of recurrence rating, morphology, hernial gap size, and risk factors in ventral and incisional hernia classification.

PubMed

Dietz, U A; Winkler, M S; Härtel, R W; Fleischhacker, A; Wiegering, A; Isbert, C; Jurowich, Ch; Heuschmann, P; Germer, C-T

2014-02-01

There is limited evidence on the natural course of ventral and incisional hernias and the results of hernia repair, what might partially be explained by the lack of an accepted classification system. The aim of the present study is to investigate the association of the criteria included in the Wuerzburg classification system of ventral and incisional hernias with postoperative complications and long-term recurrence. In a retrospective cohort study, the data on 330 consecutive patients who underwent surgery to repair ventral and incisional hernias were analyzed. The following four classification criteria were applied: (a) recurrence rating (ventral, incisional or incisional recurrent); (b) morphology (location); (c) size of the hernial gap; and (d) risk factors. The primary endpoint was the occurrence of a recurrence during follow-up. Secondary endpoints were incidence of postoperative complications. Independent association between classification criteria, type of surgical procedures and postoperative complications was calculated by multivariate logistic regression analysis and between classification criteria, type of surgical procedures and risk of long-term recurrence by Cox regression analysis. Follow-up lasted a mean 47.7 ± 23.53 months (median 45 months) or 3.9 ± 1.96 years. The criterion "recurrence rating" was found as predictive factor for postoperative complications in the multivariate analysis (OR 2.04; 95 % CI 1.09-3.84; incisional vs. ventral hernia). The criterion "morphology" had influence neither on the incidence of the critical event "recurrence during follow-up" nor on the incidence of postoperative complications. Hernial gap "width" predicted postoperative complications in the multivariate analysis (OR 1.98; 95 % CI 1.19-3.29; ≤5 vs. >5 cm). Length of the hernial gap was found to be an independent prognostic factor for the critical event "recurrence during follow-up" (HR 2.05; 95 % CI 1.25-3.37; ≤5 vs. >5 cm). The presence of 3 or more risk factors was a consistent predictor for "recurrence during follow-up" (HR 2.25; 95 % CI 1.28-9.92). Mesh repair was an independent protective factor for "recurrence during follow-up" compared to suture (HR 0.53; 95 % CI 0.32-0.86). The ventral and incisional hernia classification of Dietz et al. employs a clinically proven terminology and has an open classification structure. Hernial gap size and the number of risk factors are independent predictors for "recurrence during follow-up", whereas recurrence rating and hernial gap size correlated significantly with the incidence of postoperative complications. We propose the application of these criteria for future clinical research, as larger patient numbers will be needed to refine the results.

Visualization of chorioretinal vasculature in mice in vivo using a combined OCT/SLO imaging system

NASA Astrophysics Data System (ADS)

Goswami, Mayank; Zhang, Pengfei; Pugh, Edward N.; Zawadzki, Robert J.

2016-03-01

Chorioretinal blood vessel morphology in mice is of great interest to researchers studying eye disease mechanisms in animal models. Two leading retinal imaging modalities -- Optical Coherence Tomography (OCT) and Scanning Laser Ophthalmoscopy (SLO) -- have offered much insight into vascular morphology and blood flow. OCT "flow-contrast" methods have provided detailed mapping of vascular morphology with micrometer depth resolution, while OCT Doppler methods have enabled the measurement of local flow velocities. SLO remains indispensable in studying blood leakage, microaneurysms, and the clearance time of contrast agents of different sizes. In this manuscript we present results obtained with a custom OCT/SLO system applied to visualize the chorioretinal vascular morphology of pigmented C57Bl/6J and albino nude (Nu/Nu) mice. Blood perfusion maps of choroidal vessels and choricapillaris created by OCT and SLO are presented, along with detailed evaluation of different OCT imaging parameters, including the use of the scattering contrast agent Intralipid. Future applications are discussed.

A partial list of southern clusters of galaxies

NASA Technical Reports Server (NTRS)

Quintana, H.; White, R. A.

1990-01-01

An inspection of 34 SRC/ESO J southern sky fields is the basis of the present list of clusters of galaxies and their approximate classifications in terms of cluster concentration, defined independently of richness and shape-symmetry. Where possible, an estimate of the cluster morphological population is provided. The Bautz-Morgan classification was applied using a strict comparison with clusters on the Palomar Sky Survey. Magnitudes were estimated on the basis of galaxies with photoelectric or photographic magnitudes.

[Analysis of morphologic classifications of testicular lesions in male sterility].

PubMed

Lysenko, A I; Kirpatovskiĭ, I D

1991-01-01

An analysis of the authors' own data based on the examination of 697 testis biopsies is given. The material is subdivided into two groups depending on the presence or absence of germ cells: 1) hypospermatogenesis and 2) aspermatogenesis. Hypospermatogenesis is related to any tubule changes resulting in the under-production of spermatozoa. Criteria of tubule changes and a method for determination of the hypospermatogenesis degree are given. Insolvency of some existing histologic classifications is shown.

Image classification of human carcinoma cells using complex wavelet-based covariance descriptors.

PubMed

Keskin, Furkan; Suhre, Alexander; Kose, Kivanc; Ersahin, Tulin; Cetin, A Enis; Cetin-Atalay, Rengul

2013-01-01

Cancer cell lines are widely used for research purposes in laboratories all over the world. Computer-assisted classification of cancer cells can alleviate the burden of manual labeling and help cancer research. In this paper, we present a novel computerized method for cancer cell line image classification. The aim is to automatically classify 14 different classes of cell lines including 7 classes of breast and 7 classes of liver cancer cells. Microscopic images containing irregular carcinoma cell patterns are represented by subwindows which correspond to foreground pixels. For each subwindow, a covariance descriptor utilizing the dual-tree complex wavelet transform (DT-[Formula: see text]WT) coefficients and several morphological attributes are computed. Directionally selective DT-[Formula: see text]WT feature parameters are preferred primarily because of their ability to characterize edges at multiple orientations which is the characteristic feature of carcinoma cell line images. A Support Vector Machine (SVM) classifier with radial basis function (RBF) kernel is employed for final classification. Over a dataset of 840 images, we achieve an accuracy above 98%, which outperforms the classical covariance-based methods. The proposed system can be used as a reliable decision maker for laboratory studies. Our tool provides an automated, time- and cost-efficient analysis of cancer cell morphology to classify different cancer cell lines using image-processing techniques, which can be used as an alternative to the costly short tandem repeat (STR) analysis. The data set used in this manuscript is available as supplementary material through http://signal.ee.bilkent.edu.tr/cancerCellLineClassificationSampleImages.html.

Image Classification of Human Carcinoma Cells Using Complex Wavelet-Based Covariance Descriptors

PubMed Central

Keskin, Furkan; Suhre, Alexander; Kose, Kivanc; Ersahin, Tulin; Cetin, A. Enis; Cetin-Atalay, Rengul

2013-01-01

Cancer cell lines are widely used for research purposes in laboratories all over the world. Computer-assisted classification of cancer cells can alleviate the burden of manual labeling and help cancer research. In this paper, we present a novel computerized method for cancer cell line image classification. The aim is to automatically classify 14 different classes of cell lines including 7 classes of breast and 7 classes of liver cancer cells. Microscopic images containing irregular carcinoma cell patterns are represented by subwindows which correspond to foreground pixels. For each subwindow, a covariance descriptor utilizing the dual-tree complex wavelet transform (DT-WT) coefficients and several morphological attributes are computed. Directionally selective DT-WT feature parameters are preferred primarily because of their ability to characterize edges at multiple orientations which is the characteristic feature of carcinoma cell line images. A Support Vector Machine (SVM) classifier with radial basis function (RBF) kernel is employed for final classification. Over a dataset of 840 images, we achieve an accuracy above 98%, which outperforms the classical covariance-based methods. The proposed system can be used as a reliable decision maker for laboratory studies. Our tool provides an automated, time- and cost-efficient analysis of cancer cell morphology to classify different cancer cell lines using image-processing techniques, which can be used as an alternative to the costly short tandem repeat (STR) analysis. The data set used in this manuscript is available as supplementary material through http://signal.ee.bilkent.edu.tr/cancerCellLineClassificationSampleImages.html. PMID:23341908

Comprehensive Materials and Morphologies Study of Ion Traps (COMMIT) for Scalable Quantum Computation

DTIC Science & Technology

2012-04-21

the photoelectric effect. The typical shortest wavelengths needed for ion traps range from 194 nm for Hg+ to 493 nm for Ba +, corresponding to 6.4-2.5...REPORT Comprehensive Materials and Morphologies Study of Ion Traps (COMMIT) for scalable Quantum Computation - Final Report 14. ABSTRACT 16. SECURITY...CLASSIFICATION OF: Trapped ion systems, are extremely promising for large-scale quantum computation, but face a vexing problem, with motional quantum

Image Classification Using Biomimetic Pattern Recognition with Convolutional Neural Networks Features

PubMed Central

Huo, Guanying

2017-01-01

As a typical deep-learning model, Convolutional Neural Networks (CNNs) can be exploited to automatically extract features from images using the hierarchical structure inspired by mammalian visual system. For image classification tasks, traditional CNN models employ the softmax function for classification. However, owing to the limited capacity of the softmax function, there are some shortcomings of traditional CNN models in image classification. To deal with this problem, a new method combining Biomimetic Pattern Recognition (BPR) with CNNs is proposed for image classification. BPR performs class recognition by a union of geometrical cover sets in a high-dimensional feature space and therefore can overcome some disadvantages of traditional pattern recognition. The proposed method is evaluated on three famous image classification benchmarks, that is, MNIST, AR, and CIFAR-10. The classification accuracies of the proposed method for the three datasets are 99.01%, 98.40%, and 87.11%, respectively, which are much higher in comparison with the other four methods in most cases. PMID:28316614

Feasibility and Reliability of the Modified Berg Balance Scale in Persons with Severe Intellectual and Visual Disabilities

ERIC Educational Resources Information Center

Waninge, A.; van Wijck, R.; Steenbergen, B.; van der Schans, C. P.

2011-01-01

Background: The purpose of this study was to determine the feasibility and reliability of the modified Berg Balance Scale (mBBS) in persons with severe intellectual and visual disabilities (severe multiple disabilities, SMD) assigned Gross Motor Function Classification System (GMFCS) grades I and II. Method: Thirty-nine participants with SMD and…

Crossmodal Congruency Benefits of Tactile and Visual Signalling

DTIC Science & Technology

2013-11-12

modal information format seemed to produce faster and more accurate performance. The question of learning complex tactile communication signals...SECURITY CLASSIFICATION OF: We conducted an experiment in which tactile messages were created based on five common military arm and hand signals. We...compared response times and accuracy rates of novice individuals responding to visual and tactile representations of these messages, which were

Enhancing navigation in biomedical databases by community voting and database-driven text classification

PubMed Central

Duchrow, Timo; Shtatland, Timur; Guettler, Daniel; Pivovarov, Misha; Kramer, Stefan; Weissleder, Ralph

2009-01-01

Background The breadth of biological databases and their information content continues to increase exponentially. Unfortunately, our ability to query such sources is still often suboptimal. Here, we introduce and apply community voting, database-driven text classification, and visual aids as a means to incorporate distributed expert knowledge, to automatically classify database entries and to efficiently retrieve them. Results Using a previously developed peptide database as an example, we compared several machine learning algorithms in their ability to classify abstracts of published literature results into categories relevant to peptide research, such as related or not related to cancer, angiogenesis, molecular imaging, etc. Ensembles of bagged decision trees met the requirements of our application best. No other algorithm consistently performed better in comparative testing. Moreover, we show that the algorithm produces meaningful class probability estimates, which can be used to visualize the confidence of automatic classification during the retrieval process. To allow viewing long lists of search results enriched by automatic classifications, we added a dynamic heat map to the web interface. We take advantage of community knowledge by enabling users to cast votes in Web 2.0 style in order to correct automated classification errors, which triggers reclassification of all entries. We used a novel framework in which the database "drives" the entire vote aggregation and reclassification process to increase speed while conserving computational resources and keeping the method scalable. In our experiments, we simulate community voting by adding various levels of noise to nearly perfectly labelled instances, and show that, under such conditions, classification can be improved significantly. Conclusion Using PepBank as a model database, we show how to build a classification-aided retrieval system that gathers training data from the community, is completely controlled by the database, scales well with concurrent change events, and can be adapted to add text classification capability to other biomedical databases. The system can be accessed at . PMID:19799796

Texture analysis applied to second harmonic generation image data for disease classification and development of a multi-view second harmonic generation imaging platform

NASA Astrophysics Data System (ADS)

Wen, Lianggong

Many diseases, e.g. ovarian cancer, breast cancer and pulmonary fibrosis, are commonly associated with drastic alterations in surrounding connective tissue, and changes in the extracellular matrix (ECM) are associated with the vast majority of cellular processes in disease progression and carcinogenesis: cell differentiation, proliferation, biosynthetic ability, polarity, and motility. We use second harmonic generation (SHG) microscopy for imaging the ECM because it is a non-invasive, non-linear laser scanning technique with high sensitivity and specificity for visualizing fibrillar collagen. In this thesis, we are interested in developing imaging techniques to understand how the ECM, especially the collagen architecture, is remodeled in diseases. To quantitate remodeling, we implement a 3D texture analysis to delineate the collagen fibrillar morphology observed in SHG microscopy images of human normal and high grade malignant ovarian tissues. In the learning stage, a dictionary of "textons"---frequently occurring texture features that are identified by measuring the image response to a filter bank of various shapes, sizes, and orientations---is created. By calculating a representative model based on the texton distribution for each tissue type using a training set of respective mages, we then perform classification between normal and high grade malignant ovarian tissues classification based on the area under receiver operating characteristic curves (true positives versus false positives). The local analysis algorithm is a more general method to probe rapidly changing fibrillar morphologies than global analyses such as FFT. It is also more versatile than other texture approaches as the filter bank can be highly tailored to specific applications (e.g., different disease states) by creating customized libraries based on common image features. Further, we describe the development of a multi-view 3D SHG imaging platform. Unlike fluorescence microscopy, SHG excites intrinsic characteristics of collagen, bypassing the need for additional primary and secondary imaging labels. However, single view image collection from endogenous SHG contrast of collagen molecules is not "a true 3D technique", because collagen fibers oriented along the plane of the lasers used to excite them are invisible to the excitation The loss of information means that researchers cannot resolve the 3D structure of the ECM using this technique. We are developing a new, multi-view approach that involves rotation of agarose embedded sample in FEP tubing, so that the excitation beam path travels to from multiple angles, to reveal new insight in understanding the 3D collagen structure and its role in normal and diseased tissue.

Computer vision cracks the leaf code

PubMed Central

Wilf, Peter; Zhang, Shengping; Chikkerur, Sharat; Little, Stefan A.; Wing, Scott L.; Serre, Thomas

2016-01-01

Understanding the extremely variable, complex shape and venation characters of angiosperm leaves is one of the most challenging problems in botany. Machine learning offers opportunities to analyze large numbers of specimens, to discover novel leaf features of angiosperm clades that may have phylogenetic significance, and to use those characters to classify unknowns. Previous computer vision approaches have primarily focused on leaf identification at the species level. It remains an open question whether learning and classification are possible among major evolutionary groups such as families and orders, which usually contain hundreds to thousands of species each and exhibit many times the foliar variation of individual species. Here, we tested whether a computer vision algorithm could use a database of 7,597 leaf images from 2,001 genera to learn features of botanical families and orders, then classify novel images. The images are of cleared leaves, specimens that are chemically bleached, then stained to reveal venation. Machine learning was used to learn a codebook of visual elements representing leaf shape and venation patterns. The resulting automated system learned to classify images into families and orders with a success rate many times greater than chance. Of direct botanical interest, the responses of diagnostic features can be visualized on leaf images as heat maps, which are likely to prompt recognition and evolutionary interpretation of a wealth of novel morphological characters. With assistance from computer vision, leaves are poised to make numerous new contributions to systematic and paleobotanical studies. PMID:26951664

Diagnostic reproducibility of hydatidiform moles: ancillary techniques (p57 immunohistochemistry and molecular genotyping) improve morphologic diagnosis for both recently trained and experienced gynecologic pathologists.

PubMed

Gupta, Mamta; Vang, Russell; Yemelyanova, Anna V; Kurman, Robert J; Li, Fanghong Rose; Maambo, Emily C; Murphy, Kathleen M; DeScipio, Cheryl; Thompson, Carol B; Ronnett, Brigitte M

2012-12-01

Distinction of hydatidiform moles from nonmolar specimens (NMs) and subclassification of hydatidiform moles as complete hydatidiform mole (CHM) and partial hydatidiform mole (PHM) are important for clinical practice and investigational studies; however, diagnosis based solely on morphology is affected by interobserver variability. Molecular genotyping can distinguish these entities by discerning androgenetic diploidy, diandric triploidy, and biparental diploidy to diagnose CHMs, PHMs, and NMs, respectively. Eighty genotyped cases (27 CHMs, 27 PHMs, 26 NMs) were selected from a series of 200 potentially molar specimens previously diagnosed using p57 immunohistochemistry and genotyping. Cases were classified by 6 pathologists (3 faculty level gynecologic pathologists and 3 fellows) on the basis of morphology, masked to p57 immunostaining and genotyping results, into 1 of 3 categories (CHM, PHM, or NM) during 2 diagnostic rounds; a third round incorporating p57 immunostaining results was also conducted. Consensus diagnoses (those rendered by 2 of 3 pathologists in each group) were also determined. Performance of experienced gynecologic pathologists versus fellow pathologists was compared, using genotyping results as the gold standard. Correct classification of CHMs ranged from 59% to 100%; there were no statistically significant differences in performance of faculty versus fellows in any round (P-values of 0.13, 0.67, and 0.54 for rounds 1 to 3, respectively). Correct classification of PHMs ranged from 26% to 93%, with statistically significantly better performance of faculty versus fellows in each round (P-values of 0.04, <0.01, and <0.01 for rounds 1 to 3, respectively). Correct classification of NMs ranged from 31% to 92%, with statistically significantly better performance of faculty only in round 2 (P-values of 1.0, <0.01, and 0.61 for rounds 1 to 3, respectively). Correct classification of all cases combined ranged from 51% to 75% by morphology and 70% to 80% with p57, with statistically significantly better performance of faculty only in round 2 (P-values of 0.69, <0.01, and 0.15 for rounds 1 to 3, respectively). p57 immunostaining significantly improved recognition of CHMs (P<0.01) and had high reproducibility (κ=0.93 to 0.96) but had no impact on distinction of PHMs and NMs. Genotyping provides a definitive diagnosis for the ∼25% to 50% of cases that are misclassified by morphology, especially those that are also unresolved by p57 immunostaining.

Investigating the effects of visual distractors on the performance of a motor imagery brain-computer interface.

PubMed

Emami, Zahra; Chau, Tom

2018-06-01

Brain-computer interfaces (BCIs) allow users to operate a device or application by means of cognitive activity. This technology will ultimately be used in real-world environments which include the presence of distractors. The purpose of the study was to determine the effect of visual distractors on BCI performance. Sixteen able-bodied participants underwent neurofeedback training to achieve motor imagery-guided BCI control in an online paradigm using electroencephalography (EEG) to measure neural signals. Participants then completed two sessions of the motor imagery EEG-BCI protocol in the presence of infrequent, small visual distractors. BCI performance was determined based on classification accuracy. The presence of distractors was found to affect motor imagery-specific patterns in mu and beta power. However, the distractors did not significantly affect the BCI classification accuracy; across participants, the mean classification accuracy was 81.5 ± 14% for non-distractor trials, and 78.3 ± 17% for distractor trials. This minimal consequence suggests that the BCI was robust to distractor effects, despite motor imagery-related brain activity being attenuated amid distractors. A BCI system that mitigates distraction-related effects may improve the ease of its use and ultimately facilitate the effective translation of the technology from the lab to the home. Copyright © 2018 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.