Automated Morphological and Morphometric Analysis of Mass Spectrometry Imaging Data: Application to Biomarker Discovery

NASA Astrophysics Data System (ADS)

Picard de Muller, Gaël; Ait-Belkacem, Rima; Bonnel, David; Longuespée, Rémi; Stauber, Jonathan

2017-12-01

Mass spectrometry imaging datasets are mostly analyzed in terms of average intensity in regions of interest. However, biological tissues have different morphologies with several sizes, shapes, and structures. The important biological information, contained in this highly heterogeneous cellular organization, could be hidden by analyzing the average intensities. Finding an analytical process of morphology would help to find such information, describe tissue model, and support identification of biomarkers. This study describes an informatics approach for the extraction and identification of mass spectrometry image features and its application to sample analysis and modeling. For the proof of concept, two different tissue types (healthy kidney and CT-26 xenograft tumor tissues) were imaged and analyzed. A mouse kidney model and tumor model were generated using morphometric - number of objects and total surface - information. The morphometric information was used to identify m/z that have a heterogeneous distribution. It seems to be a worthwhile pursuit as clonal heterogeneity in a tumor is of clinical relevance. This study provides a new approach to find biomarker or support tissue classification with more information. [Figure not available: see fulltext.

Understanding of the Geomorphological Elements in Discrimination of Typical Mediterranean Land Cover Types

NASA Astrophysics Data System (ADS)

Elhag, Mohamed; Boteva, Silvena

2017-12-01

Quantification of geomorphometric features is the keystone concern of the current study. The quantification was based on the statistical approach in term of multivariate analysis of local topographic features. The implemented algorithm utilizes the Digital Elevation Model (DEM) to categorize and extract the geomorphometric features embedded in the topographic dataset. The morphological settings were exercised on the central pixel of 3x3 per-defined convolution kernel to evaluate the surrounding pixels under the right directional pour point model (D8) of the azimuth viewpoints. Realization of unsupervised classification algorithm in term of Iterative Self-Organizing Data Analysis Technique (ISODATA) was carried out on ASTER GDEM within the boundary of the designated study area to distinguish 10 morphometric classes. The morphometric classes expressed spatial distribution variation in the study area. The adopted methodology is successful to appreciate the spatial distribution of the geomorphometric features under investigation. The conducted results verified the superimposition of the delineated geomorphometric elements over a given remote sensing imagery to be further analyzed. Robust relationship between different Land Cover types and the geomorphological elements was established in the context of the study area. The domination and the relative association of different Land Cover types in corresponding to its geomorphological elements were demonstrated.

A new method using multiphoton imaging and morphometric analysis for differentiating chromophobe renal cell carcinoma and oncocytoma kidney tumors

NASA Astrophysics Data System (ADS)

Wu, Binlin; Mukherjee, Sushmita; Jain, Manu

2016-03-01

Distinguishing chromophobe renal cell carcinoma (chRCC) from oncocytoma on hematoxylin and eosin images may be difficult and require time-consuming ancillary procedures. Multiphoton microscopy (MPM), an optical imaging modality, was used to rapidly generate sub-cellular histological resolution images from formalin-fixed unstained tissue sections from chRCC and oncocytoma.Tissues were excited using 780nm wavelength and emission signals (including second harmonic generation and autofluorescence) were collected in different channels between 390 nm and 650 nm. Granular structure in the cell cytoplasm was observed in both chRCC and oncocytoma. Quantitative morphometric analysis was conducted to distinguish chRCC and oncocytoma. To perform the analysis, cytoplasm and granules in tumor cells were segmented from the images. Their area and fluorescence intensity were found in different channels. Multiple features were measured to quantify the morphological and fluorescence properties. Linear support vector machine (SVM) was used for classification. Re-substitution validation, cross validation and receiver operating characteristic (ROC) curve were implemented to evaluate the efficacy of the SVM classifier. A wrapper feature algorithm was used to select the optimal features which provided the best predictive performance in separating the two tissue types (classes). Statistical measures such as sensitivity, specificity, accuracy and area under curve (AUC) of ROC were calculated to evaluate the efficacy of the classification. Over 80% accuracy was achieved as the predictive performance. This method, if validated on a larger and more diverse sample set, may serve as an automated rapid diagnostic tool to differentiate between chRCC and oncocytoma. An advantage of such automated methods are that they are free from investigator bias and variability.

Integration of spectral, spatial and morphometric data into lithological mapping: A comparison of different Machine Learning Algorithms in the Kurdistan Region, NE Iraq

NASA Astrophysics Data System (ADS)

Othman, Arsalan A.; Gloaguen, Richard

2017-09-01

Lithological mapping in mountainous regions is often impeded by limited accessibility due to relief. This study aims to evaluate (1) the performance of different supervised classification approaches using remote sensing data and (2) the use of additional information such as geomorphology. We exemplify the methodology in the Bardi-Zard area in NE Iraq, a part of the Zagros Fold - Thrust Belt, known for its chromite deposits. We highlighted the improvement of remote sensing geological classification by integrating geomorphic features and spatial information in the classification scheme. We performed a Maximum Likelihood (ML) classification method besides two Machine Learning Algorithms (MLA): Support Vector Machine (SVM) and Random Forest (RF) to allow the joint use of geomorphic features, Band Ratio (BR), Principal Component Analysis (PCA), spatial information (spatial coordinates) and multispectral data of the Advanced Space-borne Thermal Emission and Reflection radiometer (ASTER) satellite. The RF algorithm showed reliable results and discriminated serpentinite, talus and terrace deposits, red argillites with conglomerates and limestone, limy conglomerates and limestone conglomerates, tuffites interbedded with basic lavas, limestone and Metamorphosed limestone and reddish green shales. The best overall accuracy (∼80%) was achieved by Random Forest (RF) algorithms in the majority of the sixteen tested combination datasets.

Disentangling diatom species complexes: does morphometry suffice?

PubMed Central

Borrego-Ramos, María; Olenici, Adriana

2017-01-01

Accurate taxonomic resolution in light microscopy analyses of microalgae is essential to achieve high quality, comparable results in both floristic analyses and biomonitoring studies. A number of closely related diatom taxa have been detected to date co-occurring within benthic diatom assemblages, sharing many morphological, morphometrical and ecological characteristics. In this contribution, we analysed the hypothesis that, where a large sample size (number of individuals) is available, common morphometrical parameters (valve length, width and stria density) are sufficient to achieve a correct identification to the species level. We focused on some common diatom taxa belonging to the genus Gomphonema. More than 400 valves and frustules were photographed in valve view and measured using Fiji software. Several statistical tools (mixture and discriminant analysis, k-means clustering, classification trees, etc.) were explored to test whether mere morphometry, independently of other valve features, leads to correct identifications, when compared to identifications made by experts. In view of the results obtained, morphometry-based determination in diatom taxonomy is discouraged. PMID:29250472

Classification of breast cancer cytological specimen using convolutional neural network

NASA Astrophysics Data System (ADS)

Żejmo, Michał; Kowal, Marek; Korbicz, Józef; Monczak, Roman

2017-01-01

The paper presents a deep learning approach for automatic classification of breast tumors based on fine needle cytology. The main aim of the system is to distinguish benign from malignant cases based on microscopic images. Experiment was carried out on cytological samples derived from 50 patients (25 benign cases + 25 malignant cases) diagnosed in Regional Hospital in Zielona Góra. To classify microscopic images, we used convolutional neural networks (CNN) of two types: GoogLeNet and AlexNet. Due to the very large size of images of cytological specimen (on average 200000 × 100000 pixels), they were divided into smaller patches of size 256 × 256 pixels. Breast cancer classification usually is based on morphometric features of nuclei. Therefore, training and validation patches were selected using Support Vector Machine (SVM) so that suitable amount of cell material was depicted. Neural classifiers were tuned using GPU accelerated implementation of gradient descent algorithm. Training error was defined as a cross-entropy classification loss. Classification accuracy was defined as the percentage ratio of successfully classified validation patches to the total number of validation patches. The best accuracy rate of 83% was obtained by GoogLeNet model. We observed that more misclassified patches belong to malignant cases.

Comprehensive evolutionary analysis of the Anthroherpon radiation (Coleoptera, Leiodidae, Leptodirini).

PubMed

Njunjić, Iva; Perrard, Adrien; Hendriks, Kasper; Schilthuizen, Menno; Perreau, Michel; Merckx, Vincent; Baylac, Michel; Deharveng, Louis

2018-01-01

The genus Anthroherpon Reitter, 1889 exhibits the most pronounced troglomorphic characters among Coleoptera, and represents one of the most spectacular radiations of subterranean beetles. However, radiation, diversification, and biogeography of this genus have never been studied in a phylogenetic context. This study provides a comprehensive evolutionary analysis of the Anthroherpon radiation, using a dated molecular phylogeny as a framework for understanding Anthroherpon diversification, reconstructing the ancestral range, and exploring troglomorphic diversity. Based on 16 species and 22 subspecies, i.e. the majority of Anthroherpon diversity, we reconstructed the phylogeny using Bayesian analysis of six loci, both mitochondrial and nuclear, comprising a total of 4143 nucleotides. In parallel, a morphometric analysis was carried out with 79 landmarks on the body that were subjected to geometric morphometrics. We optimized morphometric features to phylogeny, in order to recognize the way troglomorphy was expressed in different clades of the tree, and did character evolution analyses. Finally, we reconstructed the ancestral range of the genus using BioGeoBEARS. Besides further elucidating the suprageneric classification of the East-Mediterranean Leptodirini, our main findings also show that Anthroherpon dates back to the Early Miocene (ca. 22 MYA) and that the genus diversified entirely underground. Biogeographic reconstruction of the ancestral range shows the origin of the genus in the area comprising three high mountains in western Montenegro, which is in the accordance with the available data on the paleogeography of the Balkan Peninsula. Character evolution analysis indicates that troglomorphic morphometric traits in Anthroherpon mostly evolve neutrally but may diverge adaptively under syntopic competition.

A three-dimensional comparison of a morphometric and conventional cephalometric midsagittal planes for craniofacial asymmetry.

PubMed

Damstra, Janalt; Fourie, Zacharias; De Wit, Marnix; Ren, Yijin

2012-02-01

Morphometric methods are used in biology to study object symmetry in living organisms and to determine the true plane of symmetry. The aim of this study was to determine if there are clinical differences between three-dimensional (3D) cephalometric midsagittal planes used to describe craniofacial asymmetry and a true symmetry plane derived from a morphometric method based on visible facial features. The sample consisted of 14 dry skulls (9 symmetric and 5 asymmetric) with metallic markers which were imaged with cone-beam computed tomography. An error study and statistical analysis were performed to validate the morphometric method. The morphometric and conventional cephalometric planes were constructed and compared. The 3D cephalometric planes constructed as perpendiculars to the Frankfort horizontal plane resembled the morphometric plane the most in both the symmetric and asymmetric groups with mean differences of less than 1.00 mm for most variables. However, the standard deviations were often large and clinically significant for these variables. There were clinically relevant differences (>1.00 mm) between the different 3D cephalometric midsagittal planes and the true plane of symmetry determined by the visible facial features. The difference between 3D cephalometric midsagittal planes and the true plane of symmetry determined by the visible facial features were clinically relevant. Care has to be taken using cephalometric midsagittal planes for diagnosis and treatment planning of craniofacial asymmetry as they might differ from the true plane of symmetry as determined by morphometrics.

Differentiation of subspecies and sexes of Beringian Dunlins using morphometric measures

USGS Publications Warehouse

Gates, H. River; Yezerinac, Stephen; Powell, Abby N.; Tomkovich, Pavel S.; Valchuk, Olga P.; Lanctot, Richard B.

2013-01-01

Five subspecies of Dunlins (Calidris alpina) that breed in Beringia are potentially sympatric during the non-breeding season. Studying their ecology during this period requires techniques to distinguish individuals by subspecies. Our objectives were to determine (1) if five morphometric measures (body mass, culmen, head, tarsus, and wing chord) differed between sexes and among subspecies (C. a. actites, arcticola, kistchinski, pacifica, and sakhalina), and (2) if these differences were sufficient to allow for correct classification of individuals using equations derived from discriminant function analyses. We conducted analyses using morphometric data from 10 Dunlin populations breeding in northern Russia and Alaska, USA. Univariate tests revealed significant differences between sexes in most morphometric traits of all subspecies, and discriminant function equations predicted the sex of individuals with an accuracy of 83–100% for each subspecies. We provide equations to determine sex and subspecies of individuals in mixed subspecies groups, including the (1) Western Alaska group of arcticola and pacifica (known to stage together in western Alaska) and (2) East Asia group of arcticola, actites, kistchinski, and sakhalina (known to winter together in East Asia). Equations that predict the sex of individuals in mixed groups had classification accuracies between 75% and 87%, yielding reliable classification equations. We also provide equations that predict the subspecies of individuals with an accuracy of 22–96% for different mixed subspecies groups. When the sex of individuals can be predetermined, the accuracy of these equations is increased substantially. Investigators are cautioned to consider limitations due to age and feather wear when using these equations during the non-breeding season. These equations will allow determination of sexual and subspecies segregation in non-breeding areas, allowing implementation of taxonomic-specific conservation actions.

Computer-assisted sperm morphometry fluorescence-based analysis has potential to determine progeny sex.

PubMed

Santolaria, Pilar; Pauciullo, Alfredo; Silvestre, Miguel A; Vicente-Fiel, Sandra; Villanova, Leyre; Pinton, Alain; Viruel, Juan; Sales, Ester; Yániz, Jesús L

2016-01-01

This study was designed to determine the ability of computer-assisted sperm morphometry analysis (CASA-Morph) with fluorescence to discriminate between spermatozoa carrying different sex chromosomes from the nuclear morphometrics generated and different statistical procedures in the bovine species. The study was divided into two experiments. The first was to study the morphometric differences between X- and Y-chromosome-bearing spermatozoa (SX and SY, respectively). Spermatozoa from eight bulls were processed to assess simultaneously the sex chromosome by FISH and sperm morphometry by fluorescence-based CASA-Morph. SX cells were larger than SY cells on average (P < 0.001) although with important differences between bulls. A simultaneous evaluation of all the measured features by discriminant analysis revealed that nuclear area and average fluorescence intensity were the variables selected by stepwise discriminant function analysis as the best discriminators between SX and SY. In the second experiment, the sperm nuclear morphometric results from CASA-Morph in nonsexed (mixed SX and SY) and sexed (SX) semen samples from four bulls were compared. FISH allowed a successful classification of spermatozoa according to their sex chromosome content. X-sexed spermatozoa displayed a larger size and fluorescence intensity than nonsexed spermatozoa (P < 0.05). We conclude that the CASA-Morph fluorescence-based method has the potential to find differences between X- and Y-chromosome-bearing spermatozoa in bovine species although more studies are needed to increase the precision of sex determination by this technique.

Quantification of Runoff as Influenced by Morphometric Characteristics in a Rural Complex Catchment

NASA Astrophysics Data System (ADS)

Abdulkareem, Jabir Haruna; Pradhan, Biswajeet; Sulaiman, Wan Nor Azmin; Jamil, Nor Rohaizah

2018-05-01

This study addresses the critical scientific question of assessing the relationship between morphometric features and the hydrological factors that increase the risk of flooding in Kelantan River basin, Malaysia. Two hypotheses were developed to achieve this aim, namely: the alternate hypothesis (runoff, is influenced by morphometric characteristics in the study watershed) and the null hypothesis (runoff is not influenced by morphometric characteristics). First, the watershed was delineated into four major catchments, namely: Galas, Pergau, Lebir, and Nenggiri. Next, quantitative morphometric characters such as linear aspects, areal aspects, and relief aspects were determined on each of these catchments. Furthermore, HEC-HMS and flood response analyses were employed to simulate the hydrological response of the catchments. From the results of morphometric analysis, profound spatial changes were observed between runoff features of Kelantan River and the morphometric characteristics. The length of overflow that was related to drainage density and constant channel maintenance was found to be 0.12 in Pergau, 0.04 in both Nenggiri and Lebir, and 0.03 in Galas. Drainage density as influenced by geology and vegetation density was found to be low in all the catchments (0.07-0.24). Results of hydrological response indicated that Lebir, Nenggiri, Galas, and Pergau recorded a flood response factor of 0.75, 0.63, 0.40, and 0.05, respectively. Therefore, Lebir and Nenggiri are more likely to be flooded during a rainstorm. There was no clear indication with regard to the catchment that emerged as the most prevailing in all the morphological features. Hence, the alternate hypothesis was affirmed. This study can be replicated in other catchments with different hydrologic setup.

Quantification of Runoff as Influenced by Morphometric Characteristics in a Rural Complex Catchment

NASA Astrophysics Data System (ADS)

Abdulkareem, Jabir Haruna; Pradhan, Biswajeet; Sulaiman, Wan Nor Azmin; Jamil, Nor Rohaizah

2018-03-01

This study addresses the critical scientific question of assessing the relationship between morphometric features and the hydrological factors that increase the risk of flooding in Kelantan River basin, Malaysia. Two hypotheses were developed to achieve this aim, namely: the alternate hypothesis (runoff, is influenced by morphometric characteristics in the study watershed) and the null hypothesis (runoff is not influenced by morphometric characteristics). First, the watershed was delineated into four major catchments, namely: Galas, Pergau, Lebir, and Nenggiri. Next, quantitative morphometric characters such as linear aspects, areal aspects, and relief aspects were determined on each of these catchments. Furthermore, HEC-HMS and flood response analyses were employed to simulate the hydrological response of the catchments. From the results of morphometric analysis, profound spatial changes were observed between runoff features of Kelantan River and the morphometric characteristics. The length of overflow that was related to drainage density and constant channel maintenance was found to be 0.12 in Pergau, 0.04 in both Nenggiri and Lebir, and 0.03 in Galas. Drainage density as influenced by geology and vegetation density was found to be low in all the catchments (0.07-0.24). Results of hydrological response indicated that Lebir, Nenggiri, Galas, and Pergau recorded a flood response factor of 0.75, 0.63, 0.40, and 0.05, respectively. Therefore, Lebir and Nenggiri are more likely to be flooded during a rainstorm. There was no clear indication with regard to the catchment that emerged as the most prevailing in all the morphological features. Hence, the alternate hypothesis was affirmed. This study can be replicated in other catchments with different hydrologic setup.

Characterization and Biomimcry of Avian Nanostructured Tissues

DTIC Science & Technology

2016-01-19

keratin cortex (Maia et al. 2011) at the outer edge of barbs from TEM images. Geometric morphometrics of barb shape Digitized images of the barb thin...morphological measurements (all P > 0.05; Figure 4C; Table S2). Gloss and Barb Geometric Morphometrics Matte and glossy barbs differed significantly in...barbs and lack of multiple, clear anatomically homologous features, traditional landmark based morphometric techniques (Bookstein, 1982) would be

Tissue Tracking: Applications for Brain MRI Classification

DTIC Science & Technology

2007-01-01

General Hospital, Center for Morphometric Analysis.10,11 The IBSR data-sets are T1-weighted, 3D coronal brain scans after having been positionally...learned priors,” Image Processing, IEEE Transactions on 9(2), pp. 299–301, 2000. 5. P. Olver, G. Sapiro, and A. Tannenbaum, “Invariant Geometric Evolutions...MRI,” NeuroImage 22(3), pp. 1060–1075, 2004. 16. A. Zijdenbos, B. Dawant, R. Margolin, and A. Palmer, “ Morphometric analysis of white matter lesions in

Effects of freezing on white perch Morone americana (Gmelin, 1789): Implications for multivariate morphometrics

USGS Publications Warehouse

Kocovsky, Patrick

2016-01-01

This study tested the hypothesis that duration of freezing differentially affects whole-body morphometrics of a derived teleost. Whole-body morphometrics are frequently analyzed to test hypotheses of different species, or stocks within a species, of fishes. Specimens used for morphometric analyses are typically fixed or preserved prior to analysis, yet little research has been done on how fixation or preservation methods or duration of preservation of specimens might affect outcomes of multivariate statistical analyses of differences in shape. To determine whether whole-body morphometrics changed as a result of freezing, 23 whole-body morphometrics of age-1 white perch (Morone americana) from western Lake Erie (n = 211) were analyzed immediately after capture, after being held on ice overnight, and after freezing for 100 or 200 days. Discriminant function analysis revealed that all four groups differed significantly from one another (P < 0.0001). The first canonical axis reflected long-axis morphometrics, where there was a clear pattern of positive translation along this axis with duration of preservation. Re-classification analysis demonstrated fish were typically assigned to their original preservation class except for fish frozen 100 days, which assigned mostly to frozen 200 days. Morphometric comparisons using frozen fish must be done on fish frozen for identical periods of time to avoid biases related to the length of time they were frozen. Similar experiments should be conducted on other species and also using formalin- and alcohol-preserved specimens.

Differential diagnosis of neurodegenerative diseases using structural MRI data

PubMed Central

Koikkalainen, Juha; Rhodius-Meester, Hanneke; Tolonen, Antti; Barkhof, Frederik; Tijms, Betty; Lemstra, Afina W.; Tong, Tong; Guerrero, Ricardo; Schuh, Andreas; Ledig, Christian; Rueckert, Daniel; Soininen, Hilkka; Remes, Anne M.; Waldemar, Gunhild; Hasselbalch, Steen; Mecocci, Patrizia; van der Flier, Wiesje; Lötjönen, Jyrki

2016-01-01

Different neurodegenerative diseases can cause memory disorders and other cognitive impairments. The early detection and the stratification of patients according to the underlying disease are essential for an efficient approach to this healthcare challenge. This emphasizes the importance of differential diagnostics. Most studies compare patients and controls, or Alzheimer's disease with one other type of dementia. Such a bilateral comparison does not resemble clinical practice, where a clinician is faced with a number of different possible types of dementia. Here we studied which features in structural magnetic resonance imaging (MRI) scans could best distinguish four types of dementia, Alzheimer's disease, frontotemporal dementia, vascular dementia, and dementia with Lewy bodies, and control subjects. We extracted an extensive set of features quantifying volumetric and morphometric characteristics from T1 images, and vascular characteristics from FLAIR images. Classification was performed using a multi-class classifier based on Disease State Index methodology. The classifier provided continuous probability indices for each disease to support clinical decision making. A dataset of 504 individuals was used for evaluation. The cross-validated classification accuracy was 70.6% and balanced accuracy was 69.1% for the five disease groups using only automatically determined MRI features. Vascular dementia patients could be detected with high sensitivity (96%) using features from FLAIR images. Controls (sensitivity 82%) and Alzheimer's disease patients (sensitivity 74%) could be accurately classified using T1-based features, whereas the most difficult group was the dementia with Lewy bodies (sensitivity 32%). These results were notable better than the classification accuracies obtained with visual MRI ratings (accuracy 44.6%, balanced accuracy 51.6%). Different quantification methods provided complementary information, and consequently, the best results were obtained by utilizing several quantification methods. The results prove that automatic quantification methods and computerized decision support methods are feasible for clinical practice and provide comprehensive information that may help clinicians in the diagnosis making. PMID:27104138

Morphometrical study on senile larynx.

PubMed

Zieliński, R

2001-01-01

The aim of the study was a morphometrical macroscopic evaluation of senile larynges, according to its usefulness in ORL diagnostic and operational methods. Larynx preparations were taken from cadavers of both sexes, of age 65 and over, about 24 hours after death. Clinically important laryngeal diameters were collected using common morphometrical methods. A few body features were also being gathered. Computer statistical methods were used in data assessment, including basic statistics and linear correlations between diameters and between diameters and body features. The data presented in the study may be very helpful in evaluation of diagnostic methods. It may also help in selection of right operational tool' sizes, the most appropriate operational technique choice, preoperative preparations and designing and building virtual and plastic models for physicians' training.

MORFOMETRYKA—A NEW WAY OF ESTABLISHING MORPHOLOGICAL CLASSIFICATION OF GALAXIES

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

Ferrari, F.; Carvalho, R. R. de; Trevisan, M., E-mail: fabricio@ferrari.pro.br

We present an extended morphometric system to automatically classify galaxies from astronomical images. The new system includes the original and modified versions of the CASGM coefficients (Concentration C{sub 1}, Asymmetry A{sub 3}, and Smoothness S{sub 3}), and the new parameters entropy, H, and spirality σ{sub ψ}. The new parameters A{sub 3}, S{sub 3}, and H are better to discriminate galaxy classes than A{sub 1}, S{sub 1}, and G, respectively. The new parameter σ{sub ψ} captures the amount of non-radial pattern on the image and is almost linearly dependent on T-type. Using a sample of spiral and elliptical galaxies from themore » Galaxy Zoo project as a training set, we employed the Linear Discriminant Analysis (LDA) technique to classify EFIGI (Baillard et al. 4458 galaxies), Nair and Abraham (14,123 galaxies), and SDSS Legacy (779,235 galaxies) samples. The cross-validation test shows that we can achieve an accuracy of more than 90% with our classification scheme. Therefore, we are able to define a plane in the morphometric parameter space that separates the elliptical and spiral classes with a mismatch between classes smaller than 10%. We use the distance to this plane as a morphometric index (M{sub i}) and we show that it follows the human based T-type index very closely. We calculate morphometric index M{sub i} for ∼780k galaxies from SDSS Legacy Survey–DR7. We discuss how M{sub i} correlates with stellar population parameters obtained using the spectra available from SDSS–DR7.« less

The morphometrics of "masculinity" in human faces.

PubMed

Mitteroecker, Philipp; Windhager, Sonja; Müller, Gerd B; Schaefer, Katrin

2015-01-01

In studies of social inference and human mate preference, a wide but inconsistent array of tools for computing facial masculinity has been devised. Several of these approaches implicitly assumed that the individual expression of sexually dimorphic shape features, which we refer to as maleness, resembles facial shape features perceived as masculine. We outline a morphometric strategy for estimating separately the face shape patterns that underlie perceived masculinity and maleness, and for computing individual scores for these shape patterns. We further show how faces with different degrees of masculinity or maleness can be constructed in a geometric morphometric framework. In an application of these methods to a set of human facial photographs, we found that shape features typically perceived as masculine are wide faces with a wide inter-orbital distance, a wide nose, thin lips, and a large and massive lower face. The individual expressions of this combination of shape features--the masculinity shape scores--were the best predictor of rated masculinity among the compared methods (r = 0.5). The shape features perceived as masculine only partly resembled the average face shape difference between males and females (sexual dimorphism). Discriminant functions and Procrustes distances to the female mean shape were poor predictors of perceived masculinity.

Metrics for comparing neuronal tree shapes based on persistent homology.

PubMed

Li, Yanjie; Wang, Dingkang; Ascoli, Giorgio A; Mitra, Partha; Wang, Yusu

2017-01-01

As more and more neuroanatomical data are made available through efforts such as NeuroMorpho.Org and FlyCircuit.org, the need to develop computational tools to facilitate automatic knowledge discovery from such large datasets becomes more urgent. One fundamental question is how best to compare neuron structures, for instance to organize and classify large collection of neurons. We aim to develop a flexible yet powerful framework to support comparison and classification of large collection of neuron structures efficiently. Specifically we propose to use a topological persistence-based feature vectorization framework. Existing methods to vectorize a neuron (i.e, convert a neuron to a feature vector so as to support efficient comparison and/or searching) typically rely on statistics or summaries of morphometric information, such as the average or maximum local torque angle or partition asymmetry. These simple summaries have limited power in encoding global tree structures. Based on the concept of topological persistence recently developed in the field of computational topology, we vectorize each neuron structure into a simple yet informative summary. In particular, each type of information of interest can be represented as a descriptor function defined on the neuron tree, which is then mapped to a simple persistence-signature. Our framework can encode both local and global tree structure, as well as other information of interest (electrophysiological or dynamical measures), by considering multiple descriptor functions on the neuron. The resulting persistence-based signature is potentially more informative than simple statistical summaries (such as average/mean/max) of morphometric quantities-Indeed, we show that using a certain descriptor function will give a persistence-based signature containing strictly more information than the classical Sholl analysis. At the same time, our framework retains the efficiency associated with treating neurons as points in a simple Euclidean feature space, which would be important for constructing efficient searching or indexing structures over them. We present preliminary experimental results to demonstrate the effectiveness of our persistence-based neuronal feature vectorization framework.

Metrics for comparing neuronal tree shapes based on persistent homology

PubMed Central

Li, Yanjie; Wang, Dingkang; Ascoli, Giorgio A.; Mitra, Partha

2017-01-01

As more and more neuroanatomical data are made available through efforts such as NeuroMorpho.Org and FlyCircuit.org, the need to develop computational tools to facilitate automatic knowledge discovery from such large datasets becomes more urgent. One fundamental question is how best to compare neuron structures, for instance to organize and classify large collection of neurons. We aim to develop a flexible yet powerful framework to support comparison and classification of large collection of neuron structures efficiently. Specifically we propose to use a topological persistence-based feature vectorization framework. Existing methods to vectorize a neuron (i.e, convert a neuron to a feature vector so as to support efficient comparison and/or searching) typically rely on statistics or summaries of morphometric information, such as the average or maximum local torque angle or partition asymmetry. These simple summaries have limited power in encoding global tree structures. Based on the concept of topological persistence recently developed in the field of computational topology, we vectorize each neuron structure into a simple yet informative summary. In particular, each type of information of interest can be represented as a descriptor function defined on the neuron tree, which is then mapped to a simple persistence-signature. Our framework can encode both local and global tree structure, as well as other information of interest (electrophysiological or dynamical measures), by considering multiple descriptor functions on the neuron. The resulting persistence-based signature is potentially more informative than simple statistical summaries (such as average/mean/max) of morphometric quantities—Indeed, we show that using a certain descriptor function will give a persistence-based signature containing strictly more information than the classical Sholl analysis. At the same time, our framework retains the efficiency associated with treating neurons as points in a simple Euclidean feature space, which would be important for constructing efficient searching or indexing structures over them. We present preliminary experimental results to demonstrate the effectiveness of our persistence-based neuronal feature vectorization framework. PMID:28809960

Diabetic peripheral neuropathy assessment through texture based analysis of corneal nerve images

NASA Astrophysics Data System (ADS)

Silva, Susana F.; Gouveia, Sofia; Gomes, Leonor; Negrão, Luís; João Quadrado, Maria; Domingues, José Paulo; Morgado, António Miguel

2015-05-01

Diabetic peripheral neuropathy (DPN) is one common complication of diabetes. Early diagnosis of DPN often fails due to the non-availability of a simple, reliable, non-invasive method. Several published studies show that corneal confocal microscopy (CCM) can identify small nerve fibre damage and quantify the severity of DPN, using nerve morphometric parameters. Here, we used image texture features, extracted from corneal sub-basal nerve plexus images, obtained in vivo by CCM, to identify DPN patients, using classification techniques. A SVM classifier using image texture features was used to identify (DPN vs. No DPN) DPN patients. The accuracies were 80.6%, when excluding diabetic patients without neuropathy, and 73.5%, when including diabetic patients without diabetic neuropathy jointly with healthy controls. The results suggest that texture analysis might be used as a complementing technique for DPN diagnosis, without requiring nerve segmentation in CCM images. The results also suggest that this technique has enough sensitivity to detect early disorders in the corneal nerves of diabetic patients.

Psychoradiologic Utility of MR Imaging for Diagnosis of Attention Deficit Hyperactivity Disorder: A Radiomics Analysis.

PubMed

Sun, Huaiqiang; Chen, Ying; Huang, Qiang; Lui, Su; Huang, Xiaoqi; Shi, Yan; Xu, Xin; Sweeney, John A; Gong, Qiyong

2018-05-01

Purpose To identify cerebral radiomic features related to diagnosis and subtyping of attention deficit hyperactivity disorder (ADHD) and to build and evaluate classification models for ADHD diagnosis and subtyping on the basis of the identified features. Materials and Methods A consecutive cohort of 83 age- and sex-matched children with newly diagnosed and never-treated ADHD (mean age 10.83 years ± 2.30; range, 7-14 years; 71 boys, 40 with ADHD-inattentive [ADHD-I] and 43 with ADHD-combined [ADHD-C, or inattentive and hyperactive]) and 87 healthy control subjects (mean age, 11.21 years ± 2.51; range, 7-15 years; 72 boys) underwent anatomic and diffusion-tensor magnetic resonance (MR) imaging. Features representing the shape properties of gray matter and diffusion properties of white matter were extracted for each participant. The initial feature set was input into an all-relevant feature selection procedure within cross-validation loops to identify features with significant discriminative power for diagnosis and subtyping. Random forest classifiers were constructed and evaluated on the basis of identified features. Results No overall difference was found between children with ADHD and control subjects in total brain volume (1069830.00 mm 3 ± 90743.36 vs 1079 213.00 mm 3 ± 92742.25, respectively; P = .51) or total gray and white matter volume (611978.10 mm 3 ± 51622.81 vs 616960.20 mm 3 ± 51872.93, respectively; P = .53; 413532.00 mm 3 ± 41 114.33 vs 418173.60 mm 3 ± 42395.48, respectively; P = .47). The mean classification accuracy achieved with classifiers to discriminate patients with ADHD from control subjects was 73.7%. Alteration in cortical shape in the left temporal lobe, bilateral cuneus, and regions around the left central sulcus contributed significantly to group discrimination. The mean classification accuracy with classifiers to discriminate ADHD-I from ADHD-C was 80.1%, with significant discriminating features located in the default mode network and insular cortex. Conclusion The results of this study provide preliminary evidence that cerebral morphometric alterations can allow discrimination between patients with ADHD and control subjects and also between the most common ADHD subtypes. By identifying features relevant for diagnosis and subtyping, these findings may advance the understanding of neurodevelopmental alterations related to ADHD. © RSNA, 2017 Online supplemental material is available for this article.

Effect of Quantitative Nuclear Image Features on Recurrence of Ductal Carcinoma In Situ (DCIS) of the Breast

PubMed Central

Axelrod, David E.; Miller, Naomi A.; Lickley, H. Lavina; Qian, Jin; Christens-Barry, William A.; Yuan, Yan; Fu, Yuejiao; Chapman, Judith-Anne W.

2008-01-01

Background Nuclear grade has been associated with breast DCIS recurrence and progression to invasive carcinoma; however, our previous study of a cohort of patients with breast DCIS did not find such an association with outcome. Fifty percent of patients had heterogeneous DCIS with more than one nuclear grade. The aim of the current study was to investigate the effect of quantitative nuclear features assessed with digital image analysis on ipsilateral DCIS recurrence. Methods Hematoxylin and eosin stained slides for a cohort of 80 patients with primary breast DCIS were reviewed and two fields with representative grade (or grades) were identified by a Pathologist and simultaneously used for acquisition of digital images for each field. Van Nuys worst nuclear grade was assigned, as was predominant grade, and heterogeneous grading when present. Patients were grouped by heterogeneity of their nuclear grade: Group A: nuclear grade 1 only, nuclear grades 1 and 2, or nuclear grade 2 only (32 patients), Group B: nuclear grades 1, 2 and 3, or nuclear grades 2 and 3 (31 patients), Group 3: nuclear grade 3 only (17 patients). Nuclear fine structure was assessed by software which captured thirty-nine nuclear feature values describing nuclear morphometry, densitometry, and texture. Step-wise forward Cox regressions were performed with previous clinical and pathologic factors, and the new image analysis features. Results Duplicate measurements were similar for 89.7% to 97.4% of assessed image features. The rate of correct classification of nuclear grading with digital image analysis features was similar in the two fields, and pooled assessment across both fields. In the pooled assessment, a discriminant function with one nuclear morphometric and one texture feature was significantly (p = 0.001) associated with nuclear grading, and provided correct jackknifed classification of a patient’s nuclear grade for Group A (78.1%), Group B (48.4%), and Group C (70.6%). The factors significantly associated with DCIS recurrence were those previously found, type of initial presentation (p = 0.03) and amount of parenchymal involvement (p = 0.05), along with the morphometry image feature of ellipticity (p = 0.04). Conclusion Analysis of nuclear features measured by image cytometry may contribute to the classification and prognosis of breast DCIS patients with more than one nuclear grade. PMID:18779878

Spermiogram and sperm head morphometry assessed by multivariate cluster analysis results during adolescence (12-18 years) and the effect of varicocele

PubMed Central

Vásquez, Fernando; Soler, Carles; Camps, Patricia; Valverde, Anthony; García-Molina, Almudena

2016-01-01

This work evaluates sperm head morphometric characteristics in adolescents from 12 to 18 years of age, and the effect of varicocele. Volunteers between 150 and 224 months of age (mean 191, n = 87), who had reached oigarche by 12 years old, were recruited in the area of Barranquilla, Colombia. Morphometric analysis of sperm heads was performed with principal component (PC) and discriminant analysis. Combining seminal fluid and sperm parameters provided five PCs: two related to sperm morphometry, one to sperm motility, and two to seminal fluid components. Discriminant analysis on the morphometric results of varicocele and nonvaricocele groups did not provide a useful classification matrix. Of the semen-related PCs, the most explanatory (40%) was related to sperm motility. Two PCs, including sperm head elongation and size, were sufficient to evaluate sperm morphometric characteristics. Most of the morphometric variables were correlated with age, with an increase in size and decrease in the elongation of the sperm head. For head size, the entire sperm population could be divided into two morphometric subpopulations, SP1 and SP2, which did not change during adolescence. In general, for varicocele individuals, SP1 had larger and more elongated sperm heads than SP2, which had smaller and more elongated heads than in nonvaricocele men. In summary, sperm head morphometry assessed by CASA-Morph and multivariate cluster analysis provides a better comprehension of the ejaculate structure and possibly sperm function. Morphometric analysis provides much more information than data obtained from conventional semen analysis. PMID:27751986

Looking for Alzheimer's Disease morphometric signatures using machine learning techniques.

PubMed

Donnelly-Kehoe, Patricio Andres; Pascariello, Guido Orlando; Gómez, Juan Carlos

2018-05-15

We present our results in the International challenge for automated prediction of MCI from MRI data. We evaluate the performance of MRI-based neuromorphometrics features (nMF) in the classification of Healthy Controls (HC), Mild Cognitive Impairment (MCI), converters MCI (cMCI) and Alzheimer's Disease (AD) patients. We propose to segregate participants in three groups according to Mini Mental State Examination score (MMSEs), searching for the main nMF in each group. Then we use them to develop a Multi Classifier System (MCS). We compare the MCS against a single classifier scheme using both MMSEs+nMF and nMF only. We repeat this comparison using three state-of-the-art classification algorithms. The MCS showed the best performance on both Accuracy and Area Under the Receiver Operating Curve (AUC) in comparison with single classifiers. The multiclass AUC for the MCS classification on Test Dataset were 0.83 for HC, 0.76 for cMCI, 0.65 for MCI and 0.95 for AD. Furthermore, MCS's optimum accuracy on Neurodegenerative Disease (ND) detection (AD+cMCI vs MCI+HC) was 81.0% (AUC=0.88), while the single classifiers got 71.3% (AUC=0.86) and 63.1% (AUC=0.79) for MMSEs+nMF and only nMF respectively. The proposed MCS showed a better performance than using all nMF into a single state-of-the-art classifier. These findings suggest that using cognitive scoring, e.g. MMSEs, in the design of a Multi Classifier System improves performance by allowing a better selection of MRI-based features. Copyright © 2017 Elsevier B.V. All rights reserved.

Current trends in geomorphological mapping

NASA Astrophysics Data System (ADS)

Seijmonsbergen, A. C.

2012-04-01

Geomorphological mapping is a world currently in motion, driven by technological advances and the availability of new high resolution data. As a consequence, classic (paper) geomorphological maps which were the standard for more than 50 years are rapidly being replaced by digital geomorphological information layers. This is witnessed by the following developments: 1. the conversion of classic paper maps into digital information layers, mainly performed in a digital mapping environment such as a Geographical Information System, 2. updating the location precision and the content of the converted maps, by adding more geomorphological details, taken from high resolution elevation data and/or high resolution image data, 3. (semi) automated extraction and classification of geomorphological features from digital elevation models, broadly separated into unsupervised and supervised classification techniques and 4. New digital visualization / cartographic techniques and reading interfaces. Newly digital geomorphological information layers can be based on manual digitization of polygons using DEMs and/or aerial photographs, or prepared through (semi) automated extraction and delineation of geomorphological features. DEMs are often used as basis to derive Land Surface Parameter information which is used as input for (un) supervised classification techniques. Especially when using high-res data, object-based classification is used as an alternative to traditional pixel-based classifications, to cluster grid cells into homogeneous objects, which can be classified as geomorphological features. Classic map content can also be used as training material for the supervised classification of geomorphological features. In the classification process, rule-based protocols, including expert-knowledge input, are used to map specific geomorphological features or entire landscapes. Current (semi) automated classification techniques are increasingly able to extract morphometric, hydrological, and in the near future also morphogenetic information. As a result, these new opportunities have changed the workflows for geomorphological mapmaking, and their focus have shifted from field-based techniques to using more computer-based techniques: for example, traditional pre-field air-photo based maps are now replaced by maps prepared in a digital mapping environment, and designated field visits using mobile GIS / digital mapping devices now focus on gathering location information and attribute inventories and are strongly time efficient. The resulting 'modern geomorphological maps' are digital collections of geomorphological information layers consisting of georeferenced vector, raster and tabular data which are stored in a digital environment such as a GIS geodatabase, and are easily visualized as e.g. 'birds' eye' views, as animated 3D displays, on virtual globes, or stored as GeoPDF maps in which georeferenced attribute information can be easily exchanged over the internet. Digital geomorphological information layers are increasingly accessed via web-based services distributed through remote servers. Information can be consulted - or even build using remote geoprocessing servers - by the end user. Therefore, it will not only be the geomorphologist anymore, but also the professional end user that dictates the applied use of digital geomorphological information layers.

Morphometric and kinematic sperm subpopulations in split ejaculates of normozoospermic men

PubMed Central

Santolaria, Pilar; Soler, Carles; Recreo, Pilar; Carretero, Teresa; Bono, Araceli; Berné, José M; Yániz, Jesús L

2016-01-01

This study was designed to analyze the sperm kinematic and morphometric subpopulations in the different fractions of the ejaculate in normozoospermic men. Ejaculates from eight normozoospermic men were collected by masturbation in three fractions after 3–5 days of sexual abstinence. Analyses of sperm motility by computer-assisted sperm analysis (CASA-Mot), and of sperm morphometry by computer-assisted sperm morphometry analysis (CASA-Morph) using fluorescence were performed. Clustering and discriminant procedures were performed to identify sperm subpopulations in the kinematic and morphometric data obtained. Clustering procedures resulted in the classification of spermatozoa into three kinematic subpopulations (slow with low ALH [35.6% of all motile spermatozoa], with circular trajectories [32.0%], and rapid with high ALH [32.4%]), and three morphometric subpopulations (large-round [33.9% of all spermatozoa], elongated [32.0%], and small [34.10%]). The distribution of kinematic sperm subpopulations was different among ejaculate fractions (P < 0.001), with higher percentages of spermatozoa exhibiting slow movements with low ALH in the second and third portions, and with a more homogeneous distribution of kinematic sperm subpopulations in the first portion. The distribution of morphometric sperm subpopulations was also different among ejaculate fractions (P < 0.001), with more elongated spermatozoa in the first, and of small spermatozoa in the third, portion. It is concluded that important variations in the distribution of kinematic and morphometric sperm subpopulations exist between ejaculate fractions, with possible functional implications. PMID:27624985

The Morphometrics of “Masculinity” in Human Faces

PubMed Central

Mitteroecker, Philipp; Windhager, Sonja; Müller, Gerd B.; Schaefer, Katrin

2015-01-01

In studies of social inference and human mate preference, a wide but inconsistent array of tools for computing facial masculinity has been devised. Several of these approaches implicitly assumed that the individual expression of sexually dimorphic shape features, which we refer to as maleness, resembles facial shape features perceived as masculine. We outline a morphometric strategy for estimating separately the face shape patterns that underlie perceived masculinity and maleness, and for computing individual scores for these shape patterns. We further show how faces with different degrees of masculinity or maleness can be constructed in a geometric morphometric framework. In an application of these methods to a set of human facial photographs, we found that shape features typically perceived as masculine are wide faces with a wide inter-orbital distance, a wide nose, thin lips, and a large and massive lower face. The individual expressions of this combination of shape features—the masculinity shape scores—were the best predictor of rated masculinity among the compared methods (r = 0.5). The shape features perceived as masculine only partly resembled the average face shape difference between males and females (sexual dimorphism). Discriminant functions and Procrustes distances to the female mean shape were poor predictors of perceived masculinity. PMID:25671667

Object-oriented classification of drumlins from digital elevation models

NASA Astrophysics Data System (ADS)

Saha, Kakoli

Drumlins are common elements of glaciated landscapes which are easily identified by their distinct morphometric characteristics including shape, length/width ratio, elongation ratio, and uniform direction. To date, most researchers have mapped drumlins by tracing contours on maps, or through on-screen digitization directly on top of hillshaded digital elevation models (DEMs). This paper seeks to utilize the unique morphometric characteristics of drumlins and investigates automated extraction of the landforms as objects from DEMs by Definiens Developer software (V.7), using the 30 m United States Geological Survey National Elevation Dataset DEM as input. The Chautauqua drumlin field in Pennsylvania and upstate New York, USA was chosen as a study area. As the study area is huge (approximately covers 2500 sq.km. of area), small test areas were selected for initial testing of the method. Individual polygons representing the drumlins were extracted from the elevation data set by automated recognition, using Definiens' Multiresolution Segmentation tool, followed by rule-based classification. Subsequently parameters such as length, width and length-width ratio, perimeter and area were measured automatically. To test the accuracy of the method, a second base map was produced by manual on-screen digitization of drumlins from topographic maps and the same morphometric parameters were extracted from the mapped landforms using Definiens Developer. Statistical comparison showed a high agreement between the two methods confirming that object-oriented classification for extraction of drumlins can be used for mapping these landforms. The proposed method represents an attempt to solve the problem by providing a generalized rule-set for mass extraction of drumlins. To check that the automated extraction process was next applied to a larger area. Results showed that the proposed method is as successful for the bigger area as it was for the smaller test areas.

The Use of a Geomorphometric Classification to Estimate Subsurface Heterogeneity in the Unconsolidated Sediments of Mountain Watersheds

NASA Astrophysics Data System (ADS)

Cairns, D.; Byrne, J. M.; Jiskoot, H.; McKenzie, J. M.; Johnson, D. L.

2013-12-01

Groundwater controls many aspects of water quantity and quality in mountain watersheds. Groundwater recharge and flow originating in mountain watersheds are often difficult to quantify due to challenges in the characterization of the local geology, as subsurface data are sparse and difficult to collect. Remote sensing data are more readily available and are beneficial for the characterization of watershed hydrodynamics. We present an automated geomorphometric model to identify the approximate spatial distribution of geomorphic features, and to segment each of these features based on relative hydrostratigraphic differences. A digital elevation model (DEM) dataset and predefined indices are used as inputs in a mountain watershed. The model uses periglacial, glacial, fluvial, slope evolution and lacustrine processes to identify regions that are subsequently delineated using morphometric principles. A 10 m cell size DEM from the headwaters of the St. Mary River watershed in Glacier National Park, Montana, was considered sufficient for this research. Morphometric parameters extracted from the DEM that were found to be useful for the calibration of the model were elevation, slope, flow direction, flow accumulation, and surface roughness. Algorithms were developed to utilize these parameters and delineate the distributions of bedrock outcrops, periglacial landscapes, alluvial channels, fans and outwash plains, glacial depositional features, talus slopes, and other mass wasted material. Theoretical differences in sedimentation and hydrofacies associated with each of the geomorphic features were used to segment the watershed into units reflecting similar hydrogeologic properties such as hydraulic conductivity and thickness. The results of the model were verified by comparing the distribution of geomorphic features with published geomorphic maps. Although agreement in semantics between datasets caused difficulties, a consensus yielded a comparison Dice Coefficient of 0.65. The results can be used to assist in groundwater model calibration, or to estimate spatial differences in near-surface groundwater behaviour. Verification of the geomorphometric model would be augmented by evaluating its success after use in the calibration of the groundwater simulation. These results may also be used directly in momentum-based equations to create a stochastic routing routine beneath the soil interface for a hydrometeorological model.

Correlation of DNA content and nucleomorphometric features with World Health Organization grading of meningiomas.

PubMed

Grunewald, J P; Röhl, F W; Kirches, E; Dietzmann, K

1998-02-01

Many studies dealing with extracranial cancer showed a strong correlation of DNA ploidy to a poor clinical outcome, recurrence, or malignancy. In brain tumors, analysis of DNA content did not always provided significant diagnostic information. In this study, DNA density and karyometric parameters of 50 meningiomas (26 Grade I, 10 Grade II, 14 Grade III) were quantitatively evaluated by digital cell image analyses of Feulgen-stained nuclei. In particular, the densitometric parameter SEXT, which describes nuclear DNA content, as well as the morphometric values LENG (a computer-assisted measurement of nuclear circumference), AREA (a computer-assisted measurement of nuclear area), FCON (a parameter that describes nuclear roundness), and CONC (a describing nuclear contour), evaluated with the software IMAGE C, were correlated to World Health Organization (WHO) grading using univariate and multivariate methods. AREA and LENG values showed significant differences between tumors of Grades I and III. FCON values were unable to distinguish WHO Grade III from Grade I/II but were useful in clearly separating Grade II from Grade I tumors. CONC values detected differences between WHO Grades II and I/III tumors but not between the latter. SEXT values clearly distinguished Grade III from Grade I/II tumors. The 1c, 2c, 2.5c, and 5c exceeding rates showed no predictive values. Only the 6c exceeding rate showed a significant difference between Grades I and III. These results outline the characteristic features of the atypical (Grade II) meningiomas, which make them a recognizable tumor entity distinct from benign and anaplastic meningiomas. The combination of DNA densitometric and morphometric findings seems to be a powerful addition to the histopathologic classification of meningiomas, as suggested by the WHO.

Classifying GABAergic interneurons with semi-supervised projected model-based clustering.

PubMed

Mihaljević, Bojan; Benavides-Piccione, Ruth; Guerra, Luis; DeFelipe, Javier; Larrañaga, Pedro; Bielza, Concha

2015-09-01

A recently introduced pragmatic scheme promises to be a useful catalog of interneuron names. We sought to automatically classify digitally reconstructed interneuronal morphologies according to this scheme. Simultaneously, we sought to discover possible subtypes of these types that might emerge during automatic classification (clustering). We also investigated which morphometric properties were most relevant for this classification. A set of 118 digitally reconstructed interneuronal morphologies classified into the common basket (CB), horse-tail (HT), large basket (LB), and Martinotti (MA) interneuron types by 42 of the world's leading neuroscientists, quantified by five simple morphometric properties of the axon and four of the dendrites. We labeled each neuron with the type most commonly assigned to it by the experts. We then removed this class information for each type separately, and applied semi-supervised clustering to those cells (keeping the others' cluster membership fixed), to assess separation from other types and look for the formation of new groups (subtypes). We performed this same experiment unlabeling the cells of two types at a time, and of half the cells of a single type at a time. The clustering model is a finite mixture of Gaussians which we adapted for the estimation of local (per-cluster) feature relevance. We performed the described experiments on three different subsets of the data, formed according to how many experts agreed on type membership: at least 18 experts (the full data set), at least 21 (73 neurons), and at least 26 (47 neurons). Interneurons with more reliable type labels were classified more accurately. We classified HT cells with 100% accuracy, MA cells with 73% accuracy, and CB and LB cells with 56% and 58% accuracy, respectively. We identified three subtypes of the MA type, one subtype of CB and LB types each, and no subtypes of HT (it was a single, homogeneous type). We got maximum (adapted) Silhouette width and ARI values of 1, 0.83, 0.79, and 0.42, when unlabeling the HT, CB, LB, and MA types, respectively, confirming the quality of the formed cluster solutions. The subtypes identified when unlabeling a single type also emerged when unlabeling two types at a time, confirming their validity. Axonal morphometric properties were more relevant that dendritic ones, with the axonal polar histogram length in the [π, 2π) angle interval being particularly useful. The applied semi-supervised clustering method can accurately discriminate among CB, HT, LB, and MA interneuron types while discovering potential subtypes, and is therefore useful for neuronal classification. The discovery of potential subtypes suggests that some of these types are more heterogeneous that previously thought. Finally, axonal variables seem to be more relevant than dendritic ones for distinguishing among the CB, HT, LB, and MA interneuron types. Copyright © 2015 Elsevier B.V. All rights reserved.

Identification of Cichlid Fishes from Lake Malawi Using Computer Vision

PubMed Central

Joo, Deokjin; Kwan, Ye-seul; Song, Jongwoo; Pinho, Catarina; Hey, Jody; Won, Yong-Jin

2013-01-01

Background The explosively radiating evolution of cichlid fishes of Lake Malawi has yielded an amazing number of haplochromine species estimated as many as 500 to 800 with a surprising degree of diversity not only in color and stripe pattern but also in the shape of jaw and body among them. As these morphological diversities have been a central subject of adaptive speciation and taxonomic classification, such high diversity could serve as a foundation for automation of species identification of cichlids. Methodology/Principal Finding Here we demonstrate a method for automatic classification of the Lake Malawi cichlids based on computer vision and geometric morphometrics. For this end we developed a pipeline that integrates multiple image processing tools to automatically extract informative features of color and stripe patterns from a large set of photographic images of wild cichlids. The extracted information was evaluated by statistical classifiers Support Vector Machine and Random Forests. Both classifiers performed better when body shape information was added to the feature of color and stripe. Besides the coloration and stripe pattern, body shape variables boosted the accuracy of classification by about 10%. The programs were able to classify 594 live cichlid individuals belonging to 12 different classes (species and sexes) with an average accuracy of 78%, contrasting to a mere 42% success rate by human eyes. The variables that contributed most to the accuracy were body height and the hue of the most frequent color. Conclusions Computer vision showed a notable performance in extracting information from the color and stripe patterns of Lake Malawi cichlids although the information was not enough for errorless species identification. Our results indicate that there appears an unavoidable difficulty in automatic species identification of cichlid fishes, which may arise from short divergence times and gene flow between closely related species. PMID:24204918

Morphometric comparison by the ISAS® CASA-DNAf system of two techniques for the evaluation of DNA fragmentation in human spermatozoa

PubMed Central

Sadeghi, Sara; García-Molina, Almudena; Celma, Ferran; Valverde, Anthony; Fereidounfar, Sogol; Soler, Carles

2016-01-01

DNA fragmentation has been shown to be one of the causes of male infertility, particularly related to repeated abortions, and different methods have been developed to analyze it. In the present study, two commercial kits based on the SCD technique (Halosperm® and SDFA) were evaluated by the use of the DNA fragmentation module of the ISAS® v1 CASA system. Seven semen samples from volunteers were analyzed. To compare the results between techniques, the Kruskal–Wallis test was used. Data were used for calculation of Principal Components (two PCs were obtained), and subsequent subpopulations were identified using the Halo, Halo/Core Ratio, and PC data. Results from both kits were significantly different (P < 0.001). In each case, four subpopulations were obtained, independently of the classification method used. The distribution of subpopulations differed depending on the kit used. From the PC data, a discriminant analysis matrix was obtained and a good a posteriori classification was obtained (97.1% for Halosperm and 96.6% for SDFA). The present results are the first approach on morphometric evaluation of DNA fragmentation from the SCD technique. This approach could be used for the future definition of a classification matrix surpassing the current subjective evaluation of this important sperm factor. PMID:27678463

Morphometric comparison by the ISAS® CASA-DNAf system of two techniques for the evaluation of DNA fragmentation in human spermatozoa.

PubMed

Sadeghi, Sara; García-Molina, Almudena; Celma, Ferran; Valverde, Anthony; Fereidounfar, Sogol; Soler, Carles

2016-01-01

DNA fragmentation has been shown to be one of the causes of male infertility, particularly related to repeated abortions, and different methods have been developed to analyze it. In the present study, two commercial kits based on the SCD technique (Halosperm ® and SDFA) were evaluated by the use of the DNA fragmentation module of the ISAS ® v1 CASA system. Seven semen samples from volunteers were analyzed. To compare the results between techniques, the Kruskal-Wallis test was used. Data were used for calculation of Principal Components (two PCs were obtained), and subsequent subpopulations were identified using the Halo, Halo/Core Ratio, and PC data. Results from both kits were significantly different (P < 0.001). In each case, four subpopulations were obtained, independently of the classification method used. The distribution of subpopulations differed depending on the kit used. From the PC data, a discriminant analysis matrix was obtained and a good a posteriori classification was obtained (97.1% for Halosperm and 96.6% for SDFA). The present results are the first approach on morphometric evaluation of DNA fragmentation from the SCD technique. This approach could be used for the future definition of a classification matrix surpassing the current subjective evaluation of this important sperm factor.

Morphometric differences of nasopalatine canal based on 3D classifications: descriptive analysis on CBCT.

PubMed

Fernández-Alonso, A; Suárez-Quintanilla, J A; Rapado-González, O; Suárez-Cunqueiro, María Mercedes

2015-09-01

This descriptive retrospective study analyzed differences among sagittal, coronal and axial NC groups based on the dimensions of nasopalatine canal (NC), buccal bone plate (BBP) and palatal bone plate (PBP) to canal. Measurements were made on 224 CBCTs for NC, BBP and PBP on the three anatomic planes at three levels: level 1, when the incisive foramen is completely closed on the axial plane; level 2, at the midpoint of NC length (NCL) on the sagittal plane; and level 3, at the foramina of Stenson on the sagittal plane. ANOVA tests with post hoc tests were used. The intraclass correlation coefficient and Kappa test were used for evaluating the intraobserver agreement. Regarding coronal classification, these significant differences were found: BBP length (BL)level 1 was lower for the two parallel canals group; PBP length (PL)level 1 was lower for single canal group; and NCL was lower for Y-type canal group. Regarding axial classification, these significant differences were found: LPlevel 1 was lower for 3.1-3 group; PBP width (PW)level 3 was the greatest for 3.1-3; and LPlevel 3 was lower for 1.1. Presurgical evaluation with CBCT in premaxillae region should include analysis on coronal and axial planes and not only on sagittal plane seeing as morphometric differences were found on coronal and axial planes. Following the morphological coronal classification, two parallel canals presented a higher NCL, a higher LP and a lower LV at inferior edge of alveolar ridge.

A whole brain morphometric analysis of changes associated with pre-term birth

NASA Astrophysics Data System (ADS)

Thomaz, C. E.; Boardman, J. P.; Counsell, S.; Hill, D. L. G.; Hajnal, J. V.; Edwards, A. D.; Rutherford, M. A.; Gillies, D. F.; Rueckert, D.

2006-03-01

Pre-term birth is strongly associated with subsequent neuropsychiatric impairment. To identify structural differences in preterm infants we have examined a dataset of magnetic resonance (MR) images containing 88 preterm infants and 19 term born controls. We have analyzed these images by combining image registration, deformation based morphometry (DBM), multivariate statistics, and effect size maps (ESM). The methodology described has been performed directly on the MR intensity images rather than on segmented versions of the images. The results indicate that the approach described makes clear the statistical differences between the control and preterm samples, showing a leave-one-out classification accuracy of 94.74% and 95.45% respectively. In addition, finding the most discriminant direction between the groups and using DBM features and ESM we are able to identify not only what are the changes between preterm and term groups but also how relatively relevant they are in terms of volume expansion and contraction.

A revision of chiggers of the minuta species-group (Acari: Trombiculidae: Neotrombicula Hirst, 1925) using multivariate morphometrics.

PubMed

Stekolnikov, Alexandr A; Klimov, Pavel B

2010-09-01

We revise chiggers belonging to the minuta-species group (genus Neotrombicula Hirst, 1925) from the Palaearctic using size-free multivariate morphometrics. This approach allowed us to resolve several diagnostic problems. We show that the widely distributed Neotrombicula scrupulosa Kudryashova, 1993 forms three spatially and ecologically isolated groups different from each other in size or shape (morphometric property) only: specimens from the Caucasus are distinct from those from Asia in shape, whereas the Asian specimens from plains and mountains are different from each other in size. We developed a multivariate classification model to separate three closely related species: N. scrupulosa, N. lubrica Kudryashova, 1993 and N. minuta Schluger, 1966. This model is based on five shape variables selected from an initial 17 variables by a best subset analysis using a custom size-correction subroutine. The variable selection procedure slightly improved the predictive power of the model, suggesting that it not only removed redundancy but also reduced 'noise' in the dataset. The overall classification accuracy of this model is 96.2, 96.2 and 95.5%, as estimated by internal validation, external validation and jackknife statistics, respectively. Our analyses resulted in one new synonymy: N. dimidiata Stekolnikov, 1995 is considered to be a synonym of N. lubrica. Both N. scrupulosa and N. lubrica are recorded from new localities. A key to species of the minuta-group incorporating results from our multivariate analyses is presented.

Quantitative morphometrical characterization of human pronuclear zygotes.

PubMed

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

2008-09-01

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

Correlation of tumor-infiltrating lymphocytes to histopathological features and molecular phenotypes in canine mammary carcinoma: A morphologic and immunohistochemical morphometric study.

PubMed

Kim, Jong-Hyuk; Chon, Seung-Ki; Im, Keum-Soon; Kim, Na-Hyun; Sur, Jung-Hyang

2013-04-01

Abundant lymphocyte infiltration is frequently found in canine malignant mammary tumors, but the pathological features and immunophenotypes associated with the infiltration remain to be elucidated. The aim of the present study was to evaluate the relationship between lymphocyte infiltration, histopathological features, and molecular phenotype in canine mammary carcinoma (MC). The study was done with archived formalin-fixed, paraffin-embedded samples (n = 47) by histologic and immunohistochemical methods. The degree of lymphocyte infiltration was evaluated by morphologic analysis, and the T- and B-cell populations as well as the T/B-cell ratio were evaluated by morphometric analysis; results were compared with the histologic features and molecular phenotypes. The degree of lymphocyte infiltration was significantly higher in MCs with lymphatic invasion than in those without lymphatic invasion (P < 0.0001) and in tumors of high histologic grade compared with those of lower histologic grade (P = 0.045). Morphometric analysis showed a larger amount of T-cells and B-cells in MCs with a higher histologic grade and lymphatic invasion, but the T/B ratio did not change. Lymphocyte infiltration was not associated with histologic type or molecular phenotype, as assessed from the immunohistochemical expression of epidermal growth factor receptor 2, estrogen receptor, cytokeratin 14, and p63. Since intense lymphocyte infiltration was associated with aggressive histologic features, lymphocytes may be important for tumor aggressiveness and greater malignant behavior in the tumor microenvironment.

Reliability of the European Society of Human Reproduction and Embryology/European Society for Gynaecological Endoscopy and American Society for Reproductive Medicine classification systems for congenital uterine anomalies detected using three-dimensional ultrasonography.

PubMed

Ludwin, Artur; Ludwin, Inga; Kudla, Marek; Kottner, Jan

2015-09-01

To estimate the inter-rater/intrarater reliability of the European Society of Human Reproduction and Embryology/European Society for Gynaecological Endoscopy (ESHRE-ESGE) classification of congenital uterine malformations and to compare the results obtained with the reliability of the American Society for Reproductive Medicine (ASRM) classification supplemented with additional morphometric criteria. Reliability/agreement study. Private clinic. Uterine malformations (n = 50 patients, consecutively included) and normal uterus (n = 62 women, randomly selected) constituted the study. These were classified based on real-time three-dimensional ultrasound single volume transvaginal (or transrectal in the case of virgins, 4 cases) ultrasonography findings, which were assessed by an expert rater based on the ESHRE-ESGE criteria. The samples were obtained from women of reproductive age. Unprocessed three-dimensional datasets were independently evaluated offline by two experienced, blinded raters using both classification systems. The κ-values and proportions of agreement. Standardized interpretation indicated that the ESHRE-ESGE system has substantial/good or almost perfect/very good reliability (κ >0.60 and >0.80), but the interpretation of the clinically relevant cutoffs of κ-values showed insufficient reliability for clinical use (κ < 0.90), especially in the diagnosis of septate uterus. The ASRM system had sufficient reliability (κ > 0.95). The low reliability of the ESHRE-ESGE system may lead to a lack of consensus about the management of common uterine malformations and biased research interpretations. The use of the ASRM classification, supplemented with simple morphometric criteria, may be preferred if their sufficient reliability can be confirmed real-time in a large sample size. Copyright © 2015 American Society for Reproductive Medicine. Published by Elsevier Inc. All rights reserved.

Quantitative diagnosis of bladder cancer by morphometric analysis of HE images

NASA Astrophysics Data System (ADS)

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

2015-02-01

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

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.

Morphometry Based on Effective and Accurate Correspondences of Localized Patterns (MEACOLP)

PubMed Central

Wang, Hu; Ren, Yanshuang; Bai, Lijun; Zhang, Wensheng; Tian, Jie

2012-01-01

Local features in volumetric images have been used to identify correspondences of localized anatomical structures for brain morphometry. However, the correspondences are often sparse thus ineffective in reflecting the underlying structures, making it unreliable to evaluate specific morphological differences. This paper presents a morphometry method (MEACOLP) based on correspondences with improved effectiveness and accuracy. A novel two-level scale-invariant feature transform is used to enhance the detection repeatability of local features and to recall the correspondences that might be missed in previous studies. Template patterns whose correspondences could be commonly identified in each group are constructed to serve as the basis for morphometric analysis. A matching algorithm is developed to reduce the identification errors by comparing neighboring local features and rejecting unreliable matches. The two-sample t-test is finally adopted to analyze specific properties of the template patterns. Experiments are performed on the public OASIS database to clinically analyze brain images of Alzheimer's disease (AD) and normal controls (NC). MEACOLP automatically identifies known morphological differences between AD and NC brains, and characterizes the differences well as the scaling and translation of underlying structures. Most of the significant differences are identified in only a single hemisphere, indicating that AD-related structures are characterized by strong anatomical asymmetry. In addition, classification trials to differentiate AD subjects from NC confirm that the morphological differences are reliably related to the groups of interest. PMID:22540000

Differentiating sex and species of Western Grebes (Aechmophorus occidentalis) and Clark's Grebes (Aechmophorus clarkii) and their eggs using external morphometrics and discriminant function analysis

USGS Publications Warehouse

Hartman, C. Alex; Ackerman, Joshua T.; Eagles-Smith, Collin A.; Herzog, Mark

2016-01-01

In birds where males and females are similar in size and plumage, sex determination by alternative means is necessary. Discriminant function analysis based on external morphometrics was used to distinguish males from females in two closely related species: Western Grebe (Aechmophorus occidentalis) and Clark's Grebe (A. clarkii). Additionally, discriminant function analysis was used to evaluate morphometric divergence between Western and Clark's grebe adults and eggs. Aechmophorus grebe adults (n = 576) and eggs (n = 130) were sampled across 29 lakes and reservoirs throughout California, USA, and adult sex was determined using molecular analysis. Both Western and Clark's grebes exhibited considerable sexual size dimorphism. Males averaged 6–26% larger than females among seven morphological measurements, with the greatest sexual size dimorphism occurring for bill morphometrics. Discriminant functions based on bill length, bill depth, and short tarsus length correctly assigned sex to 98% of Western Grebes, and a function based on bill length and bill depth correctly assigned sex to 99% of Clark's Grebes. Further, a simplified discriminant function based only on bill depth correctly assigned sex to 96% of Western Grebes and 98% of Clark's Grebes. In contrast, external morphometrics were not suitable for differentiating between Western and Clark's grebe adults or their eggs, with correct classification rates of discriminant functions of only 60%, 63%, and 61% for adult males, adult females, and eggs, respectively. Our results indicate little divergence in external morphology between species of Aechmophorus grebes, and instead separation is much greater between males and females.

Computer vision approach to morphometric feature analysis of basal cell nuclei for evaluating malignant potentiality of oral submucous fibrosis.

PubMed

Muthu Rama Krishnan, M; Pal, Mousumi; Paul, Ranjan Rashmi; Chakraborty, Chandan; Chatterjee, Jyotirmoy; Ray, Ajoy K

2012-06-01

This research work presents a quantitative approach for analysis of histomorphometric features of the basal cell nuclei in respect to their size, shape and intensity of staining, from surface epithelium of Oral Submucous Fibrosis showing dysplasia (OSFD) to that of the Normal Oral Mucosa (NOM). For all biological activity, the basal cells of the surface epithelium form the proliferative compartment and therefore their morphometric changes will spell the intricate biological behavior pertaining to normal cellular functions as well as in premalignant and malignant status. In view of this, the changes in shape, size and intensity of staining of the nuclei in the basal cell layer of the NOM and OSFD have been studied. Geometric, Zernike moments and Fourier descriptor (FD) based as well as intensity based features are extracted for histomorphometric pattern analysis of the nuclei. All these features are statistically analyzed along with 3D visualization in order to discriminate the groups. Results showed increase in the dimensions (area and perimeter), shape parameters and decreasing mean nuclei intensity of the nuclei in OSFD in respect to NOM. Further, the selected features are fed to the Bayesian classifier to discriminate normal and OSFD. The morphometric and intensity features provide a good sensitivity of 100%, specificity of 98.53% and positive predicative accuracy of 97.35%. This comparative quantitative characterization of basal cell nuclei will be of immense help for oral onco-pathologists, researchers and clinicians to assess the biological behavior of OSFD, specially relating to their premalignant and malignant potentiality. As a future direction more extensive study involving more number of disease subjects is observed.

Variability of morphometric parameters of human trabecular tissue from coxo-arthritis and osteoporotic samples.

PubMed

Marinozzi, Franco; Marinozzi, Andrea; Bini, Fabiano; Zuppante, Francesca; Pecci, Raffaella; Bedini, Rossella

2012-01-01

Morphometric and architectural bone parameters change in diseases such as osteoarthritis and osteoporosis. The mechanical strength of bone is primarily influenced by bone quantity and quality. Bone quality is defined by parameters such as trabecular thickness, trabecular separation, trabecular density and degree of anisotropy that describe the micro-architectural structure of bone. Recently, many studies have validated microtomography as a valuable investigative technique to assess bone morphometry, thanks to micro-CT non-destructive, non-invasive and reliability features, in comparison to traditional techniques such as histology. The aim of this study is the analysis by micro-computed tomography of six specimens, extracted from patients affected by osteoarthritis and osteoporosis, in order to observe the tridimensional structure and calculate several morphometric parameters.

[Morphometric features of the structure of the central nucleus of the amygdala in men and women].

PubMed

Antyukhov, A D

2015-01-01

To identify the interhemispheric asymmetry in the structure of the central nucleus of the amygdala in men and women. Morphometric features of the structure of neurons of the central nucleus amygdala complex were studied in histological sections of the brain of 6 men and 6 women (24 hemispheres), aged 19 to 55 years, with no lifetime diagnosis of mental or neurological disease. The value of the profile fields of neurons of the central nucleus amygdala complex in the left and right hemispheres of the brain were investigated. In women, the average value of neurons in the left hemisphere was somewhat greater than in the right hemisphere, while in men this value was greater in the right hemisphere. The interhemispheric morphometric differences were not significant regardless of gender. In addition, the quantity of relevant fields of neurons in the central nucleus of the amygdala in women was significantly larger than that of men in both hemispheres. The authors attempted to associate the results obtained in the study with emotional perception in men and women.

The crowding effect and morphometric variability in Echinostoma caproni (Digenea: Echinostomatidae) from ICR mice.

PubMed

Stillson, Lindsey L; Platt, Thomas R

2007-04-01

Population density, or crowding, was examined to determine its effect on the morphometric variability of Echinostoma caproni (Digenea) in ICR mice. Six mice were infected with 25 and 100 metacercariae, and a single mouse was infected with 300 metacercariae. All mice were infected at necropsy 22 days postinfection with recoveries of 77%, 69%, and 7.3%, respectively. Whole mounts were prepared, and 31 characters were evaluated (25 direct measurements and 6 ratios). Univariate and multivariate statistical analysis revealed significant differences between adult worms from all 3 groups. Twenty-seven of 31 characters showed significant within-group differences, with the primary differences between worms from 25/100 versus 300 metacercariae infections. Discriminant function analysis yielded a 100% correct classification based on infection size, which is consistent with studies on distinct species of Echinostoma. The low recovery from the mouse infected with 300 metacercariae suggests inflammatory expulsion of juvenile worms and the possibility of immunity as a factor in the crowding effect. These results suggest that external factors may affect morphometric variability of digenetic trematodes to a larger degree than previously recognized.

Effect of sperm concentration in an ejaculate on morphometric traits of spermatozoa in Duroc boars.

PubMed

Kondracki, S; Wysokińska, A; Iwanina, M; Banaszewska, D; Sitarz, D

2011-01-01

The experimental material consisted of 75 ejaculates collected form 8 Duroc boars. The ejaculates were divided into three groups according to sperm concentration in an ejaculate. An ejaculate was obtained from each boar monthly and it was used to make microscopic preparations to examine spermatozoa morphology. In each preparation morphometric measurements were taken of fifteen randomly selected spermatozoa characterized by normal morphology. The following measurements of spermatozoa were taken: length and width of the spermatozoa head, head area, length of the flagellum, perimeter of the spermatozoon head and total spermatozoon length. The results were used to calculate indicators of spermatozoa morphology. Moreover, assessments were made of frequency of morphological defects to isolate spermatozoa with primary and secondary abnormalities following the Blom classification system. It was found that the concentration of spermatozoa in the ejaculate influenced the morphometric characteristics of spermatozoa. Ejaculates with low sperm concentrations are characterized by larger spermatozoa as compared to ejaculates with high sperm concentrations. However, sperm concentration in the ejaculate does not much influence the shape of spermatozoa.

Morphometric and landsliding analyses in chain domain: the Roccella basin, NE Sicily, Italy

NASA Astrophysics Data System (ADS)

Rapisarda, Francesco

2009-10-01

The dynamic interaction of endogenic and exogenic processes in active geodynamic context leads to the deterioration of the physico-mechanical characteristics of the rocks, inducing slopes instability. In such context, the morphometric parameters and the analysis of landslide distribution contribute to appraise the evolutive state of hydrographic basins. The aim of the study is the morphometric characterization of the Roccella Torrent basin (Rtb) located in South Italy. Landsliding and tectonic structure dynamically interact with the drainage pattern that records these effects and permits the definition of the evolutive geomorphic stage of the basin. The Air Photograph Investigation and field surveys permitted to draw the main geomorphic features, the drainage pattern of the Rtb, to calculate the morphometric parameters and to delimit the landslides’ bodies. Detailed analysis about the landslide distribution within a test site 17 km2 wide were carried out to elaborate indicative indexes of the landslides type and to single out the lithotypes that are more involved in slope instability phenomena. The morphometric parameters indicate the rejuvenation state within the Rtb where the stream reaches show the effects of increased energy relief in agreement with the geological settings of this sector of the Apennine-Maghrebian Chain.

Stone loaches of Choman River system, Kurdistan, Iran (Teleostei: Cypriniformes: Nemacheilidae).

PubMed

Kamangar, Barzan Bahrami; Prokofiev, Artem M; Ghaderi, Edris; Nalbant, Theodore T

2014-01-20

For the first time, we present data on species composition and distributions of nemacheilid loaches in the Choman River basin of Kurdistan province, Iran. Two genera and four species are recorded from the area, of which three species are new for science: Oxynoemacheilus kurdistanicus, O. zagrosensis, O. chomanicus spp. nov., and Turcinoemacheilus kosswigi Băn. et Nalb. Detailed and illustrated morphological descriptions and univariate and multivariate analysis of morphometric and meristic features are for each of these species. Forty morphometric and eleven meristic characters were used in multivariate analysis to select characters that could discriminate between the four loach species. Discriminant Function Analysis revealed that sixteen morphometric measures and five meristic characters have the most variability between the loach species. The dendrograms based on cluster analysis of Mahalanobis distances of morphometrics and a combination of both characters confirmed two distinct groups: Oxynoemacheilus spp. and T. kosswigi. Within Oxynoemacheilus, O. zagrosensis and O. chomanicus are more similar to one other rather to either is to O. kurdistanicus.

Further study on Physaloptera clausa Rudolphi, 1819 (Spirurida: Physalopteridae) from the Amur hedgehog Erinaceus amurensis Schrenk (Eulipotyphla: Erinaceidae).

PubMed

Chen, Hui-Xia; Ju, Hui-Dong; Li, Yang; Li, Liang

2017-12-20

In the present study, light and scanning electron microscopy (SEM) were used to further study the detailed morphology of Physaloptera clausa Rudolphi, 1819, based on the material collected from the Amur hedgehog E. amurensis Schrenk in China. The results revealed a few previously unreported morphological features and some morphological and morphometric variability between our specimens and the previous studies. The present supplementary morphological characters and morphometric data could help us to recognize this species more accurately.

Delineation of sympatric morphotypes of lake trout in Lake Superior

USGS Publications Warehouse

Moore, Seth A.; Bronte, Charles R.

2001-01-01

Three morphotypes of lake trout Salvelinus namaycush are recognized in Lake Superior: lean, siscowet, and humper. Absolute morphotype assignment can be difficult. We used a size-free, whole-body morphometric analysis (truss protocol) to determine whether differences in body shape existed among lake trout morphotypes. Our results showed discrimination where traditional morphometric characters and meristic measurements failed to detect differences. Principal components analysis revealed some separation of all three morphotypes based on head and caudal peduncle shape, but it also indicated considerable overlap in score values. Humper lake trout have smaller caudal peduncle widths to head length and depth characters than do lean or siscowet lake trout. Lean lake trout had larger head measures to caudal widths, whereas siscowet had higher caudal peduncle to head measures. Backward stepwise discriminant function analysis retained two head measures, three midbody measures, and four caudal peduncle measures; correct classification rates when using these variables were 83% for leans, 80% for siscowets, and 83% for humpers, which suggests the measures we used for initial classification were consistent. Although clear ecological reasons for these differences are not readily apparent, patterns in misclassification rates may be consistent with evolutionary hypotheses for lake trout within the Laurentian Great Lakes.

Object-based delineation and classification of alluvial fans by application of mean-shift segmentation and support vector machines

NASA Astrophysics Data System (ADS)

Pipaud, Isabel; Lehmkuhl, Frank

2017-09-01

In the field of geomorphology, automated extraction and classification of landforms is one of the most active research areas. Until the late 2000s, this task has primarily been tackled using pixel-based approaches. As these methods consider pixels and pixel neighborhoods as the sole basic entities for analysis, they cannot account for the irregular boundaries of real-world objects. Object-based analysis frameworks emerging from the field of remote sensing have been proposed as an alternative approach, and were successfully applied in case studies falling in the domains of both general and specific geomorphology. In this context, the a-priori selection of scale parameters or bandwidths is crucial for the segmentation result, because inappropriate parametrization will either result in over-segmentation or insufficient segmentation. In this study, we describe a novel supervised method for delineation and classification of alluvial fans, and assess its applicability using a SRTM 1‧‧ DEM scene depicting a section of the north-eastern Mongolian Altai, located in northwest Mongolia. The approach is premised on the application of mean-shift segmentation and the use of a one-class support vector machine (SVM) for classification. To consider variability in terms of alluvial fan dimension and shape, segmentation is performed repeatedly for different weightings of the incorporated morphometric parameters as well as different segmentation bandwidths. The final classification layer is obtained by selecting, for each real-world object, the most appropriate segmentation result according to fuzzy membership values derived from the SVM classification. Our results show that mean-shift segmentation and SVM-based classification provide an effective framework for delineation and classification of a particular landform. Variable bandwidths and terrain parameter weightings were identified as being crucial for consideration of intra-class variability, and, in turn, for a constantly high segmentation quality. Our analysis further reveals that incorporation of morphometric parameters quantifying specific morphological aspects of a landform is indispensable for developing an accurate classification scheme. Alluvial fans exhibiting accentuated composite morphologies were identified as a major challenge for automatic delineation, as they cannot be fully captured by a single segmentation run. There is, however, a high probability that this shortcoming can be overcome by enhancing the presented approach with a routine merging fan sub-entities based on their spatial relationships.

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.

Comparison of geometric morphometric outline methods in the discrimination of age-related differences in feather shape

PubMed Central

Sheets, H David; Covino, Kristen M; Panasiewicz, Joanna M; Morris, Sara R

2006-01-01

Background Geometric morphometric methods of capturing information about curves or outlines of organismal structures may be used in conjunction with canonical variates analysis (CVA) to assign specimens to groups or populations based on their shapes. This methodological paper examines approaches to optimizing the classification of specimens based on their outlines. This study examines the performance of four approaches to the mathematical representation of outlines and two different approaches to curve measurement as applied to a collection of feather outlines. A new approach to the dimension reduction necessary to carry out a CVA on this type of outline data with modest sample sizes is also presented, and its performance is compared to two other approaches to dimension reduction. Results Two semi-landmark-based methods, bending energy alignment and perpendicular projection, are shown to produce roughly equal rates of classification, as do elliptical Fourier methods and the extended eigenshape method of outline measurement. Rates of classification were not highly dependent on the number of points used to represent a curve or the manner in which those points were acquired. The new approach to dimensionality reduction, which utilizes a variable number of principal component (PC) axes, produced higher cross-validation assignment rates than either the standard approach of using a fixed number of PC axes or a partial least squares method. Conclusion Classification of specimens based on feather shape was not highly dependent of the details of the method used to capture shape information. The choice of dimensionality reduction approach was more of a factor, and the cross validation rate of assignment may be optimized using the variable number of PC axes method presented herein. PMID:16978414

Seasonal and gender-related differences in morphometric features and cellular and biochemical parameters of Carcinus aestuarii from the Lagoon of Venice.

PubMed

Matozzo, Valerio; Boscolo, Alice; Marin, Maria Gabriella

2013-08-01

In this study, the seasonal variations in the morphometric features and in the cellular and biochemical parameters of the haemolymph were investigated in both male and female crabs (Carcinus aestuarii). Crabs were seasonally (November 2010-August 2011) collected from the Lagoon of Venice, and the moult stage, weight, width and length of the carapace, and width and length of the bigger chela were evaluated. In addition, the total haemocyte count (THC), haemocyte diameter and volume, haemolymph glucose and total protein levels, and haemolymph phenoloxidase (PO) and N-acetyl-β-glucosaminidase (NAG) activities were measured. The results demonstrated that the collected crabs were all in the intermoult stage and that the males were bigger than the females. A two-way ANOVA revealed a significant effect of season on the THC and the haemocyte volume and a significant influence of gender on the haemocyte diameter. Season and gender significantly affected the haemolymph glucose concentration, whereas haemolymph protein levels were dependent only on the season. In addition, both season and gender significantly influenced the PO and NAG activities in the haemolymph. Overall, the results demonstrated that crab morphometric features as well as haemolymph cellular and biochemical parameters varied markedly as a function of both season and gender. Copyright © 2013 Elsevier Ltd. All rights reserved.

Ejaculate fractioning effect on llama sperm head morphometry as assessed by the ISAS(®) CASA system.

PubMed

Soler, C; Sancho, M; García, A; Fuentes, Mc; Núñez, J; Cucho, H

2014-02-01

South American camelid sperm characteristics are poorly known compared with those of other domestic animals. The long-term duration of ejaculation makes difficult to gather all the seminal fluid, implying possible ejaculation portion losses. Thus, the aim of this research was to evaluate the characteristics of the morphology and morphometry of the spermatozoa change during ejaculation. The morphometric characterization was tested on nine specimens of the Lanuda breed, using a special artificial vagina. In five of the animals, a fractioning of the ejaculate was performed by taking samples every 5 min. for a total of 20 min. Air-dried seminal smears were stained with Hemacolor and mounted permanently with Eukitt. Morphometric analysis was carried out with the morphometry module of the ISAS(®) CASA system. Almost 350 cells were analysed per sample, with a total number of 3207 spermatozoa. Mean values were given as follows: length: 5.51 μm; width: 3.38 μm; area: 17.75 μm(2) ; perimeter: 14.8 μm; ellipticity: 0.24; elongation: 0.56; rugosity: 0.87; regularity: 1.07; and shape factor: 1.41. Different animals showed differences in their morphometric values. When we compared the values from different fractions, only two samples showed differences in morphometric parameter values and four samples showed differences in shape parameters. Multivariate analysis allowed the size classification of the cells into three classes and five classes of shapes. The distribution of classes among fractions showed no differences. Despite the individual morphometric differences observed in some fractions, the characteristics of the sperm head morphometry can be considered constant along the ejaculatory period in the llama. © 2013 Blackwell Verlag GmbH.

Monitoring urban land cover with the use of satellite remote sensing techniques as a means of flood risk assessment in Cyprus

NASA Astrophysics Data System (ADS)

Alexakis, Dimitris; Hadjimitsis, Diofantos; Agapiou, Athos; Themistocleous, Kyriacos; Retalis, Adrianos

2011-11-01

The increase of flood inundation occuring in different regions all over the world have enhanced the need for effective flood risk management. As floods frequency is increasing with a steady rate due to ever increasing human activities on physical floodplains there is a respectively increasing of financial destructive impact of floods. A flood can be determined as a mass of water that produces runoff on land that is not normally covered by water. However, earth observation techniques such as satellite remote sensing can contribute toward a more efficient flood risk mapping according to EU Directives of 2007/60. This study strives to highlight the need of digital mapping of urban sprawl in a catchment area in Cyprus and the assessment of its contribution to flood risk. The Yialias river (Nicosia, Cyprus) was selected as case study where devastating flash floods events took place at 2003 and 2009. In order to search the diachronic land cover regime of the study area multi-temporal satellite imagery was processed and analyzed (e.g Landsat TMETM+, Aster). The land cover regime was examined in detail by using sophisticated post-processing classification algorithms such as Maximum Likelihood, Parallelepiped Algorithm, Minimum Distance, Spectral Angle and Isodata. Texture features were calculated using the Grey Level Co-Occurence Matrix. In addition three classification techniques were compared : multispectral classification, texture based classification and a combination of both. The classification products were compared and evaluated for their accuracy. Moreover, a knowledge-rule method is proposed based on spectral, texture and shape features in order to create efficient land use and land cover maps of the study area. Morphometric parameters such as stream frequency, drainage density and elongation ratio were calculated in order to extract the basic watershed characteristics. In terms of the impacts of land use/cover on flooding, GIS and Fragstats tool were used to detect identifying trends, both visually and statistically, resulting from land use changes in a flood prone area such as Yialias by the use of spatial metrics. The results indicated that there is a considerable increase of urban areas cover during the period of the last 30 years. All these denoted that one of the main driving force of the increasing flood risk in catchment areas in Cyprus is generally associated to human activities.

Genetic and morphological variation in Echinorhynchus gadi Zoega in Müller, 1776 (Acanthocephala: Echinorhynchidae) from Atlantic cod Gadus morhua L.

PubMed

Sobecka, E; Szostakowska, B; MacKenzie, K; Hemmingsen, W; Prajsnar, S; Eydal, M

2012-03-01

Previous studies have shown considerable variability in morphological features and the existence of genetically distinct sibling species in the acanthocephalan Echinorhynchus gadi Zoega in Müller, 1776. The aim of the present study was to follow up and extend those earlier studies by using a combination of DNA analysis and morphometrics to investigate differences between samples of E. gadi from Atlantic cod Gadus morhua L. caught at five fishing grounds in the Baltic Sea and three in different parts of the North Atlantic. Twelve morphological features were measured in 431 specimens of E. gadi, 99 individuals were studied by polymerase chain reaction-restriction fragment length polymorphosm (PCR-RFLP), and selected PCR products were sequenced. The molecular analyses showed the nucleotide sequences of E. gadi rDNA from cod caught at all the sampling sites to be identical. The comparative morphological study, in contrast, revealed significant differences between samples of E. gadi from different sampling sites and showed the separation of E. gadi into two groups corresponding approximately to the systematic classification of cod into the two subspecies, Atlantic G. morhua morhua and Baltic G. morhua callarias. The E. gadi infrapopulation size had a significant effect on some of the morphological features. The results are discussed in relation to cod population biology, the hydrography of the study area and the history of the Baltic Sea formation.

Morphometric comparison of Icelandic lava shield volcanoes versus selected Venusian edifices

NASA Technical Reports Server (NTRS)

Garvin, James B.; Williams, Richard S., Jr.

1993-01-01

Shield volcanoes are common landforms on the silicate planets of the inner Solar System, and a wide variety have recently been documented on Venus by means of Magellan observations. In this report, we emphasize our recently completed morphometric analysis of three representative Icelandic lava shields: the classic Skjaldbreidur edifice, the low-reflief Lambahraun feature, and the monogenetic Sandfellshaed shield, as the basis for comparison with representative venusian edifices (greater than 60 km in diameter). Our detailed morphometric measurements of a representative and well-studied set of Icelandic volcanoes permits us to make comparisons with our measurements of a reasonable subset of shield-like edifices on Venus on the basis of Magellan global radar altimetry. Our study has been restricted to venusian features larger than approximately 60 km in basal diameter, on the basis of the minimum intrinsic spatial resolution (8 km) of the Magellan radar altimetry data. Finally, in order to examine the implications of landform scaling from terrestrial simple and composite shields to larger venusian varieties, we have considered the morphometry of the subaerial component of Mauna Loa, a type-locality for a composite shield edifice on Earth.

Using airborne LiDAR in geoarchaeological contexts: Assessment of an automatic tool for the detection and the morphometric analysis of grazing archaeological structures (French Massif Central).

NASA Astrophysics Data System (ADS)

Roussel, Erwan; Toumazet, Jean-Pierre; Florez, Marta; Vautier, Franck; Dousteyssier, Bertrand

2014-05-01

Airborne laser scanning (ALS) of archaeological regions of interest is nowadays a widely used and established method for accurate topographic and microtopographic survey. The penetration of the vegetation cover by the laser beam allows the reconstruction of reliable digital terrain models (DTM) of forested areas where traditional prospection methods are inefficient, time-consuming and non-exhaustive. The ALS technology provides the opportunity to discover new archaeological features hidden by vegetation and provides a comprehensive survey of cultural heritage sites within their environmental context. However, the post-processing of LiDAR points clouds produces a huge quantity of data in which relevant archaeological features are not easily detectable with common visualizing and analysing tools. Undoubtedly, there is an urgent need for automation of structures detection and morphometric extraction techniques, especially for the "archaeological desert" in densely forested areas. This presentation deals with the development of automatic detection procedures applied to archaeological structures located in the French Massif Central, in the western forested part of the Puy-de-Dôme volcano between 950 and 1100 m a.s.l.. These unknown archaeological sites were discovered by the March 2011 ALS mission and display a high density of subcircular depressions with a corridor access. The spatial organization of these depressions vary from isolated to aggregated or aligned features. Functionally, they appear to be former grazing constructions built from the medieval to the modern period. Similar grazing structures are known in other locations of the French Massif Central (Sancy, Artense, Cézallier) where the ground is vegetation-free. In order to develop a reliable process of automatic detection and mapping of these archaeological structures, a learning zone has been delineated within the ALS surveyed area. The grazing features were mapped and typical morphometric attributes were calculated based on 2 methods: (i) The mapping of the archaeological structures by a human operator using common visualisation tools (DTM, multi-direction hillshading & local relief models) within a GIS environment; (ii) The automatic detection and mapping performed by a recognition algorithm based on a user defined geometric pattern of the grazing structures. The efficiency of the automatic tool has been assessed by comparing the number of structures detected and the morphometric attributes calculated by the two methods. Our results indicate that the algorithm is efficient for the detection and the location of grazing structures. Concerning the morphometric results, there is still a discrepancy between automatic and expert calculations, due to both the expert mapping choices and the algorithm calibration.

Comprehensive morphometric analysis of mononuclear cell infiltration during experimental renal allograft rejection.

PubMed

Hoffmann, Ute; Bergler, Tobias; Jung, Bettina; Steege, Andreas; Pace, Claudia; Rümmele, Petra; Reinhold, Stephan; Krüger, Bernd; Krämer, Bernhard K; Banas, Bernhard

2013-01-01

The role of specific subtypes of infiltrating cells in acute kidney allograft rejection is still not clear and was so far not examined by different analyzing methods under standardized conditions of an experimental kidney transplantation model. Immunohistochemical staining of CD3, CD20 and CD68 was performed in rat allografts, in syngeneically transplanted rats and in control rats with a test duration of 6 and 28 days. The detailed expression and localization of infiltrating cells were analyzed manually in different kidney compartments under light microscope and by the two different morphometric software programs. Data were correlated with the corresponding kidney function as well as with histopathological classification. The information provided by the morphometric software programs on the infiltration of the specific cell types after renal transplantation was in accordance with the manual analysis. Morphometric methods were solid to analyze reliably the induction of cellular infiltrates after renal transplantation. By manual analysis we could clearly demonstrate the detailed localization of the specific cell infiltrates in the different kidney compartments. Besides infiltration of CD3 and CD68 infiltrating cells, a robust infiltration of CD20 B-cells in allogeneically transplanted rats, even at early time points after transplantation was detected. Additionally an MHC class I expression could reliable be seen in allogeneically transplanted rats. The infiltration of B-cells and the reliable antigen presentation might act as a silent subclinical trigger for subsequent chronic rejection and premature graft loss. Copyright © 2012 Elsevier B.V. All rights reserved.

Nuclear morphometry in flat epithelial atypia of the breast as a predictor of malignancy: a digital image-based histopathologic analysis.

PubMed

Williams, Phillip A; Djordjevic, Bojana; Ayroud, Yasmine; Islam, Shahidul; Gravel, Denis; Robertson, Susan J; Parra-Herran, Carlos

2014-12-01

To identify morphometric features unique to flat epithelial atypia associated with cancer using digital image analysis. Cases with diagnosis of flat epithelial atypia were retrieved and divided into 2 groups: flat epithelial atypia associated with invasive or in situ carcinoma (n = 31) and those without malignancy (n = 27). Slides were digitally scanned. Nuclear features were analyzed on representative images at 20x magnification using digital morphometric software. Parameters related to nuclear shape and size (diameter, perimeter) were similar in both groups. However, cases with malignancy had significantly higher densitometric green (p = 0.02), red (p = 0.03), and grey (p = 0.02) scale levels as compared to cases without cancer. A mean grey densitometric level > 119.45 had 71% sensitivity and 70.4% specificity in detecting cases with concomitant carcinoma. Morphometry of features related to nuclear staining appears to be useful in predicting risk of concurrent malignancy in patients with flat epithelial atypia, when added to a comprehensive histopathologic evaluation.

Sex determination by three-dimensional geometric morphometrics of the palate and cranial base.

PubMed

Chovalopoulou, Maria-Eleni; Valakos, Efstratios D; Manolis, Sotiris K

2013-01-01

The purpose of this study is to assess sexual dimorphism in the palate and base of adult crania using three-dimensional geometric morphometric methods. The study sample consisted of 176 crania of known sex (94 males, 82 females) belonging to individuals who lived during the 20th century in Greece. The three-dimensional co-ordinates of 30 ectocranial landmarks were digitized using a MicroScribe 3DX contact digitizer. Generalized Procrustes Analysis (GPA) was used to obtain size and shape variables for statistical analysis. Three discriminant function analyses were carried out: (1) using PC scores from Procrustes shape space, (2) centroid size alone, and (3) PC scores of GPA residuals which includes InCS for analysis in Procrustes form space. Results indicate that there are shape differences between sexes. In males, the palate is deepest and more elongated; the cranial base is shortened. Sex-specific shape differences for the cross-validated data give better classification results in the cranial base (77.2%) compared with the palate (68.9%). Size alone yielded better results for cranial base (82%) in opposition to palate (63.1%). As anticipated, the classification accuracy improves when both size and shape are combined (90.4% for cranial base, and 74.8% for palate).

[CT morphometry for calcaneal fractures and comparison of the Zwipp and Sanders classifications].

PubMed

Andermahr, J; Jesch, A B; Helling, H J; Jubel, A; Fischbach, R; Rehm, K E

2002-01-01

The aim of the study is to correlate the CT-morphological changes of fractured calcaneus and the classifications of Zwipp and Sanders with the clinical outcome. In a retrospective clinical study, the preoperative CT scans of 75 calcaneal fractures were analysed. The morphometry of the fractures was determined by measuring height, length diameter and calcaneo-cuboidal angle in comparison to the intact contralateral side. At a mean of 38 months after trauma 44 patients were clinically followed-up. The data of CT image morphometry were correlated with the severity of fracture classified by Zwipp or Sanders as well as with the functional outcome. There was a good correlation between the fracture classifications and the morphometric data. Both fracture classifying systems have a predictive impact for functional outcome. The more exacting and accurate Zwipp classification considers the most important cofactors like involvement of the calcaneo-cuboidal joint, soft tissue damage, additional fractures etc. The Sanders classification is easier to use during clinical routine. The Zwipp classification includes more relevant cofactors (fracture of the calcaneo-cuboidal-joint, soft tissue swelling, etc.) and presents a higher correlation to the choice of therapy. Both classification systems present a prognostic impact concerning the clinical outcome.

Scaling mimesis: Morphometric and ecomorphological similarities in three sympatric plant-mimetic fish of the family Carangidae (Teleostei).

PubMed

Queiroz, Alexya Cunha de; Vallinoto, Marcelo; Sakai, Yoichi; Giarrizzo, Tommaso; Barros, Breno

2018-01-01

The mimetic juveniles of a number of carangid fish species resemble plant parts floating near the water surface, such as leaves, seeds and other plant debris. The present study is the first to verify the morphological similarities and ecomorphological relationships between three carangids (Oligoplites saurus, Oligoplites palometa and Trachinotus falcatus) and their associated plant models. Behavioral observations were conducted in the estuary of Curuçá River, in northeastern Pará (Brazil) between August 2015 and July 2016. Individual fishes and associated floating objects (models) were sampled for comparative analysis using both geometric and morphometric approaches. While the mimetic fish and their models retain their own distinct, intrinsic morphological features, a high degree of morphological similarity was found between each fish species and its model. The morphometric analyses revealed a general tendency of isometric development in all three fish species, probably related to their pelagic habitats, during all ontogenetic stages.

Morphometric convergence between Proterozoic and post-vegetation rivers

PubMed Central

Ielpi, Alessandro; Rainbird, Robert H.; Ventra, Dario; Ghinassi, Massimiliano

2017-01-01

Proterozoic rivers flowed through barren landscapes, and lacked interactions with macroscopic organisms. It is widely held that, in the absence of vegetation, fluvial systems featured barely entrenched channels that promptly widened over floodplains during floods. This hypothesis has never been tested because of an enduring lack of Precambrian fluvial-channel morphometric data. Here we show, through remote sensing and outcrop sedimentology, that deep rivers were developed in the Proterozoic, and that morphometric parameters for large fluvial channels might have remained within a narrow range over almost 2 billion years. Our data set comprises fluvial-channel forms deposited a few tens to thousands of kilometres from their headwaters, likely the record of basin- to craton-scale systems. Large Proterozoic channel forms present width:thickness ranges matching those of Phanerozoic counterparts, suggesting closer parallels between their fluvial dynamics. This outcome may better inform analyses of extraterrestrial planetary surfaces and related comparisons with pre-vegetation Earth landscapes. PMID:28548109

Morphometric convergence between Proterozoic and post-vegetation rivers.

PubMed

Ielpi, Alessandro; Rainbird, Robert H; Ventra, Dario; Ghinassi, Massimiliano

2017-05-26

Proterozoic rivers flowed through barren landscapes, and lacked interactions with macroscopic organisms. It is widely held that, in the absence of vegetation, fluvial systems featured barely entrenched channels that promptly widened over floodplains during floods. This hypothesis has never been tested because of an enduring lack of Precambrian fluvial-channel morphometric data. Here we show, through remote sensing and outcrop sedimentology, that deep rivers were developed in the Proterozoic, and that morphometric parameters for large fluvial channels might have remained within a narrow range over almost 2 billion years. Our data set comprises fluvial-channel forms deposited a few tens to thousands of kilometres from their headwaters, likely the record of basin- to craton-scale systems. Large Proterozoic channel forms present width:thickness ranges matching those of Phanerozoic counterparts, suggesting closer parallels between their fluvial dynamics. This outcome may better inform analyses of extraterrestrial planetary surfaces and related comparisons with pre-vegetation Earth landscapes.

Left Atrial trajectory impairment in Hypertrophic Cardiomyopathy disclosed by Geometric Morphometrics and Parallel Transport

NASA Astrophysics Data System (ADS)

Piras, Paolo; Torromeo, Concetta; Re, Federica; Evangelista, Antonietta; Gabriele, Stefano; Esposito, Giuseppe; Nardinocchi, Paola; Teresi, Luciano; Madeo, Andrea; Chialastri, Claudia; Schiariti, Michele; Varano, Valerio; Uguccioni, Massimo; Puddu, Paolo E.

2016-10-01

The analysis of full Left Atrium (LA) deformation and whole LA deformational trajectory in time has been poorly investigated and, to the best of our knowledge, seldom discussed in patients with Hypertrophic Cardiomyopathy. Therefore, we considered 22 patients with Hypertrophic Cardiomyopathy (HCM) and 46 healthy subjects, investigated them by three-dimensional Speckle Tracking Echocardiography, and studied the derived landmark clouds via Geometric Morphometrics with Parallel Transport. Trajectory shape and trajectory size were different in Controls versus HCM and their classification powers had high AUC (Area Under the Receiving Operator Characteristic Curve) and accuracy. The two trajectories were much different at the transition between LA conduit and booster pump functions. Full shape and deformation analyses with trajectory analysis enabled a straightforward perception of pathophysiological consequences of HCM condition on LA functioning. It might be worthwhile to apply these techniques to look for novel pathophysiological approaches that may better define atrio-ventricular interaction.

GIS based quantitative morphometric analysis and its consequences: a case study from Shanur River Basin, Maharashtra India

NASA Astrophysics Data System (ADS)

Pande, Chaitanya B.; Moharir, Kanak

2017-05-01

A morphometric analysis of Shanur basin has been carried out using geoprocessing techniques in GIS. These techniques are found relevant for the extraction of river basin and its drainage networks. The extracted drainage network was classified according to Strahler's system of classification and it reveals that the terrain exhibits dendritic to sub-dendritic drainage pattern. Hence, from the study, it is concluded that remote sensing data (SRTM-DEM data of 30 m resolution) coupled with geoprocessing techniques prove to be a competent tool used in morphometric analysis and evaluation of linear, slope, areal and relief aspects of morphometric parameters. The combined outcomes have established the topographical and even recent developmental situations in basin. It will also change the setup of the region. It therefore needs to analyze high level parameters of drainage and environment for suitable planning and management of water resource developmental plan and land resource development plan. The Shanur drainage basin is sprawled over an area of 281.33 km2. The slope of the basin varies from 1 to 10 %, and the slope variation is chiefly controlled by the local geology and erosion cycles. The main stream length ratio of the basin is 14.92 indicating that the study area is elongated with moderate relief and steep slopes. The morphometric parameters of the stream have been analyzed and calculated by applying standard methods and techniques viz. Horton (Trans Am Geophys Union 13:350-361, 1945), Miller (A quantitative geomorphologic study of drainage basin characteristics in the clinch mountain area, Virginia and Tennessee Columbia University, Department of Geology, Technical Report, No. 3, Contract N6 ONR 271-300, 1953), and Strahler (Handbook of applied hydrology, McGraw Hill Book Company, New York, 1964). GIS based on analysis of all morphometric parameters and the erosional development of the area by the streams has been progressed well beyond maturity and lithology is an influence in the drainage development. These studies are very useful for planning of rainwater harvesting and watershed management.

Morphometric computer-assisted image analysis of epithelial cells in different grades of oral squamous cell carcinoma.

PubMed

Ananjan, Chatterjee; Jyothi, Mahadesh; Laxmidevi, B L; Gopinathan, Pillai Arun; Nazir, Salroo Humaira; Pradeep, L

2018-01-01

Oral squamous cell carcinoma (OSCC) accounts 94% of all malignant lesions in the oral cavity. In the assessment of OSCC, nowadays the WHO grading system has been followed widely but due to its subjectivity, investigators applied the sophisticated technique of computer-assisted image analysis in the grading of carcinoma in larynx, lungs, esophagus, and cervix to make it more objective. Access, analyze, and compare the cellular area (CA); cytoplasmic area (Cyt A); nuclear area (NA); nuclear perimeter (NP); nuclear form factor (NF); and nuclear-cytoplasmic ratio (N/C) of the cells in different grades of OSCC. Fifty OSCC cases were obtained and stained with hematoxylin and eosin which were graded according to the WHO classification. The sections were subjected to morphometric analysis to analyze all the morphometric parameters in different grades of OSCC and subjected to one-way ANOVA statistical analysis. CA and Cyt A decreased from normal mucosa with dedifferentiation of OSCC. The NA and NP increased in carcinoma group when compared to normal mucosa but decreased with dedifferentiation of OSCC (P < 0.05). NF had no significance with normal mucosa and different grades of OSCC (P > 0.05), while N/C ratio increased from normal mucosa through increasing grades of OSCC, reaching the highest value in poorly differentiated squamous cell carcinoma (P < 0.05). Both cellular and nuclear variables provide a more accurate indication of tumor aggressiveness than any single parameter. Morphometric analysis can be a reliable tool to determine objectively the degree of malignancy at the invasive tumor front.

A geometric morphometrics comparative analysis of Neandertal humeri (epiphyses-fused) from the El Sidrón cave site (Asturias, Spain).

PubMed

Rosas, Antonio; Pérez-Criado, Laura; Bastir, Markus; Estalrrich, Almudena; Huguet, Rosa; García-Tabernero, Antonio; Pastor, Juan Francisco; de la Rasilla, Marco

2015-05-01

A new collection of 49,000 year old Neandertal fossil humeri from the El Sidrón cave site (Asturias, Spain) is presented. A total of 49 humeral remains were recovered, representing 10 left and 8 right humeri from adults, adolescents, and a juvenile (not included in the analyses). 3D geometric morphometric (GM) methods as well as classic anthropological variables were employed to conduct a broad comparative analysis by means of mean centroid size and shape comparisons, principal components analysis, and cluster studies. Due to the fragmentary nature of the fossils, comparisons were organized in independent analyses according to different humeral portions: distal epiphysis, diaphysis, proximal epiphysis, and the complete humerus. From a multivariate viewpoint, 3D-GM analyses revealed major differences among taxonomic groups, supporting the value of the humerus in systematic classification. Notably, the Australopithecus anamensis (KP-271) and Homo ergaster Nariokotome (KNM-WT 15000) distal humerus consistently clusters close to those of modern humans, which may imply a primitive condition for Homo sapiens morphology. Australopithecus specimens show a high degree of dispersion in the morphospace. The El Sidrón sample perfectly fits into the classic Neandertal pattern, previously described as having a relatively wide olecranon fossa, as well as thin lateral and medial distodorsal pillars. These characteristics were also typical of the Sima de los Huesos (Atapuerca) sample, African mid-Pleistocene Bodo specimen, and Lower Pleistocene TD6-Atapuerca remains and may be considered as a derived state. Finally, we hypothesize that most of the features thought to be different between Neandertals and modern humans might be associated with structural differences in the pectoral girdle and shoulder joint. Copyright © 2015 Elsevier Ltd. All rights reserved.

Application of wavelet transformation and adaptive neighborhood based modified backpropagation (ANMBP) for classification of brain cancer

NASA Astrophysics Data System (ADS)

Werdiningsih, Indah; Zaman, Badrus; Nuqoba, Barry

2017-08-01

This paper presents classification of brain cancer using wavelet transformation and Adaptive Neighborhood Based Modified Backpropagation (ANMBP). Three stages of the processes, namely features extraction, features reduction, and classification process. Wavelet transformation is used for feature extraction and ANMBP is used for classification process. The result of features extraction is feature vectors. Features reduction used 100 energy values per feature and 10 energy values per feature. Classifications of brain cancer are normal, alzheimer, glioma, and carcinoma. Based on simulation results, 10 energy values per feature can be used to classify brain cancer correctly. The correct classification rate of proposed system is 95 %. This research demonstrated that wavelet transformation can be used for features extraction and ANMBP can be used for classification of brain cancer.

Hepatic Histology and Morphometric Measurements in Idiopathic Extrahepatic Portal Vein Thrombosis in Children, Correlated to Clinical Outcome of Meso-Rex Bypass.

PubMed

Tantemsapya, Niramol; Superina, Riccardo; Wang, Deli; Kronauer, Grace; Whitington, Peter F; Melin-Aldana, Hector

2018-06-01

The aim of this study was to correlate clinical, histologic, and morphometric features of the liver in children with extrahepatic portal vein thrombosis (EHPVT), with surgical outcome after Meso-Rex bypass (MRB). Idiopathic EHPVT, a significant cause of portal hypertension, is surgically corrected by MRB. Correlation of histologic and morphometric features of the liver with outcome has not been reported in children. We retrospectively reviewed clinical and intraoperative data of 45 children with idiopathic EHPVT. Liver samples were obtained at the time of MRB. Morphometric measurements of portal tract structures were performed and correlated with surgical outcome. Median follow-up was 3.65 years after surgery (range 1.5 to 10 years). Thirty-seven (82.2%) children had successful MRB. There was no association between age, sex, and suture material with surgical outcome. Average patient age was higher in patients with postoperative complications (P = NS). Portal fibrosis, bridging, parenchymal nodules, portal inflammation, hepatocellular swelling, steatosis, dilatation of portal lymphatics, and periductal fibrosis did not show a significant difference between the 2 groups. Portal vein and bile duct area index were significantly smaller in the unsuccessful group (P = 0.004 and 0.003, respectively). A portal vein area index <0.08 had a lower chance of successful surgical outcome. Hepatic artery area index was not significantly different. Measured intraoperative portal blood inflow was the only significant clinical factor affecting surgical outcome (P = 0.0003). Low portal vein area index and intraoperative portal blood inflow may be negative prognostic factors for MRB outcome in children with idiopathic EHPVT. Average patient age was higher, although not statistically significant, in patients with postoperative complications.

Quantitative pathology in virtual microscopy: history, applications, perspectives.

PubMed

Kayser, Gian; Kayser, Klaus

2013-07-01

With the emerging success of commercially available personal computers and the rapid progress in the development of information technologies, morphometric analyses of static histological images have been introduced to improve our understanding of the biology of diseases such as cancer. First applications have been quantifications of immunohistochemical expression patterns. In addition to object counting and feature extraction, laws of thermodynamics have been applied in morphometric calculations termed syntactic structure analysis. Here, one has to consider that the information of an image can be calculated for separate hierarchical layers such as single pixels, cluster of pixels, segmented small objects, clusters of small objects, objects of higher order composed of several small objects. Using syntactic structure analysis in histological images, functional states can be extracted and efficiency of labor in tissues can be quantified. Image standardization procedures, such as shading correction and color normalization, can overcome artifacts blurring clear thresholds. Morphometric techniques are not only useful to learn more about biological features of growth patterns, they can also be helpful in routine diagnostic pathology. In such cases, entropy calculations are applied in analogy to theoretical considerations concerning information content. Thus, regions with high information content can automatically be highlighted. Analysis of the "regions of high diagnostic value" can deliver in the context of clinical information, site of involvement and patient data (e.g. age, sex), support in histopathological differential diagnoses. It can be expected that quantitative virtual microscopy will open new possibilities for automated histological support. Automated integrated quantification of histological slides also serves for quality assurance. The development and theoretical background of morphometric analyses in histopathology are reviewed, as well as their application and potential future implementation in virtual microscopy. Copyright © 2012 Elsevier GmbH. All rights reserved.

Applications of wavelets in morphometric analysis of medical images

NASA Astrophysics Data System (ADS)

Davatzikos, Christos; Tao, Xiaodong; Shen, Dinggang

2003-11-01

Morphometric analysis of medical images is playing an increasingly important role in understanding brain structure and function, as well as in understanding the way in which these change during development, aging and pathology. This paper presents three wavelet-based methods with related applications in morphometric analysis of magnetic resonance (MR) brain images. The first method handles cases where very limited datasets are available for the training of statistical shape models in the deformable segmentation. The method is capable of capturing a larger range of shape variability than the standard active shape models (ASMs) can, by using the elegant spatial-frequency decomposition of the shape contours provided by wavelet transforms. The second method addresses the difficulty of finding correspondences in anatomical images, which is a key step in shape analysis and deformable registration. The detection of anatomical correspondences is completed by using wavelet-based attribute vectors as morphological signatures of voxels. The third method uses wavelets to characterize the morphological measurements obtained from all voxels in a brain image, and the entire set of wavelet coefficients is further used to build a brain classifier. Since the classification scheme operates in a very-high-dimensional space, it can determine subtle population differences with complex spatial patterns. Experimental results are provided to demonstrate the performance of the proposed methods.

Landmark-based geometric morphometric analysis of wing shape among certain species of Aedes mosquitoes in District Dehradun (Uttarakhand), India.

PubMed

Mondal, Ritwik; Devi, N Pemola; Jauhari, R K

2015-06-01

Insect wing morphology has been used in many studies to describe variations among species and populations using traditional morphometrics, and more recently geometric morphometrics. A landmark-based geometric morphometric analysis of the wings of three species of Aedes (Diptera: Culicidae), viz. Ae. aegypti, Ae. albopictus and Ae. pseudotaeniatus, at District Dehradun was conducted belling on the fact that it can provide insight into the population structure, ecology and taxonomic identification. Adult Aedes mosquito specimens were randomly collected using aerial nets and morphologically examined and identified. The landmarks were identified on the basis of landmark based geometric morphometric analysis thin-plate spline (mainly the software tps-Util 1.28; tps-Dig 1.40; tps-Relw 1.53; and tps-Spline 1.20) and integrated morphometrics programme (mainly twogroup win8 and PCA win8) were utilized. In relative warp (RW) analysis, the first two RW of Ae. aegypti accounted for the highest value (95.82%), followed by Ae. pseudotaeniatus (90.89%), while the lowest (90.12%) being recorded for Ae. albopictus. The bending energies of Ae. aegypti and Ae. pseudotaeniatus were quite identical being 0.1882 and 0.1858 respectively, while Ae. albopictus recorded the highest value of 0.9774. The mean difference values of the distances among Aedes species performing Hotelling's T 2 test were significantly high, predicting major differences among the taxa. In PCA analysis, the horizontal and vertical axis summarized 52.41 and 23.30% of variances respectively. The centroid size exhibited significant differences among populations (non-parametric Kruskal-Wallis test, H = 10.56, p < 0.01). It has been marked out that the geometric morphometrics utilizes powerful and comprehensive statistical procedures to analyze the shape differences of a morphological feature, assuming that the studied mosquitoes may represent different genotypes and probably come from one diverse gene pool.

Grey-matter volume as a potential feature for the classification of Alzheimer's disease and mild cognitive impairment: an exploratory study.

PubMed

Guo, Yane; Zhang, Zengqiang; Zhou, Bo; Wang, Pan; Yao, Hongxiang; Yuan, Minshao; An, Ningyu; Dai, Haitao; Wang, Luning; Zhang, Xi; Liu, Yong

2014-06-01

Specific patterns of brain atrophy may be helpful in the diagnosis of Alzheimer's disease (AD). In the present study, we set out to evaluate the utility of grey-matter volume in the classification of AD and amnestic mild cognitive impairment (aMCI) compared to normal control (NC) individuals. Voxel-based morphometric analyses were performed on structural MRIs from 35 AD patients, 27 aMCI patients, and 27 NC participants. A two-sample two-tailed t-test was computed between the NC and AD groups to create a map of abnormal grey matter in AD. The brain areas with significant differences were extracted as regions of interest (ROIs), and the grey-matter volumes in the ROIs of the aMCI patients were included to evaluate the patterns of change across different disease severities. Next, correlation analyses between the grey-matter volumes in the ROIs and all clinical variables were performed in aMCI and AD patients to determine whether they varied with disease progression. The results revealed significantly decreased grey matter in the bilateral hippocampus/parahippocampus, the bilateral superior/middle temporal gyri, and the right precuneus in AD patients. The grey-matter volumes were positively correlated with clinical variables. Finally, we performed exploratory linear discriminative analyses to assess the classifying capacity of grey-matter volumes in the bilateral hippocampus and parahippocampus among AD, aMCI, and NC. Leave-one-out cross-validation analyses demonstrated that grey-matter volumes in hippocampus and parahippocampus accurately distinguished AD from NC. These findings indicate that grey-matter volumes are useful in the classification of AD.

Radix mesiolingualis and radix distolingualis: a case report of a tooth with an unusual morphology

PubMed Central

Aeran, Himanshu; Singh, Inderpreet

2016-01-01

Variation in the root and canal morphology of the maxillary first molars is quite common. The most common configuration is 3 roots and 3 or 4 canals. Nonetheless, other possibilities still exist. The presence of an additional palatal root is rather uncommon and has been reported to have an incidence of 0.06 - 1.6% in varying populations studied. Whenever two palatal roots exist, one of them is the normal palatal root, the other is a supernumerary structure which can be located either mesiolingually (radix mesiolingualis) or distolingually (radix distolingualis). This case report describes successful endodontic treatment of a maxillary first molar with radix mesiolingualis and radix distolingualis. Identification of this variation was done through clinical examination along with the aid of multiangled radiographs, and an accurate assessment of this morphology was made with the help of a cone-beam computed tomography imaging. In addition to the literature review, this article also discusses the epidemiology, classifications, morphometric features, guidelines for diagnosis, and endodontic management of a maxillary first molar with extra-palatal root. PMID:27847755

Automatic recognition and analysis of synapses. [in brain tissue

NASA Technical Reports Server (NTRS)

Ungerleider, J. A.; Ledley, R. S.; Bloom, F. E.

1976-01-01

An automatic system for recognizing synaptic junctions would allow analysis of large samples of tissue for the possible classification of specific well-defined sets of synapses based upon structural morphometric indices. In this paper the three steps of our system are described: (1) cytochemical tissue preparation to allow easy recognition of the synaptic junctions; (2) transmitting the tissue information to a computer; and (3) analyzing each field to recognize the synapses and make measurements on them.

Sex determination by three-dimensional geometric morphometrics of craniofacial form.

PubMed

Chovalopoulou, Maria-Eleni; Valakos, Efstratios D; Manolis, Sotiris K

The purpose of the present study is to define which regions of the cranium, the upper-face, the orbits and the nasal are the most sexually dimorphic, by using three-dimensional geometric morphometric methods, and investigate the effectiveness of this method in determining sex from the shape of these regions. The study sample consisted of 176 crania of known sex (94 males, 82 females) belonging to individuals who lived in Greece during the 20(th) century. The three-dimensional co-ordinates of 31 ecto-cranial landmarks were digitized using a MicroScribe 3DX contact digitizer. Goodall's F-test was performed in order to compare statistical differences in shape between males and females. Generalized Procrustes Analysis (GPA) was used to obtain size and shape variables for statistical analysis. Shape, Size and Form analyses were carried out by logistic regression and discriminant function analysis. The results indicate that there are shape differences between the sexes in the upper-face and the orbits. The highest shape classification rate was obtained from the upper-face region. The centroid size of the caraniofacial and the orbital regions was smaller in females than males. Moreover, it was found that size is significant for sexual dimorphism in the upper-face region. As anticipated, the classification accuracy improves when both size and shape are combined. The findings presented here constitute a firm basis upon which further research can be conducted.

Morphologic and morphometric studies of impact craters in the northern plains of Mars

NASA Technical Reports Server (NTRS)

Barlow, N. G.

1993-01-01

Fresh impact craters in the northern plains of Mars display a variety of morphologic and morphometric properties. Ejecta morphologies range from radial to fluidized, interior features include central peaks and central pits, fluidized morphologies display a range of sinuosities, and depth-diameter ratios are being measured to determine regional variations. Studies of the martian northern plains over the past five years have concentrated in three areas: (1) determining correlations of ejecta morphologies with crater diameter, latitude, and underlying terrain; (2) determining variations in fluidized ejecta blanket sinuosity across the planet; and (3) measurement of depth-diameter ratios and determination of regional variations in this ratio.

Vertical Feature Mask Feature Classification Flag Extraction

Atmospheric Science Data Center

2013-03-28

  Vertical Feature Mask Feature Classification Flag Extraction This routine demonstrates extraction of the ... in a CALIPSO Lidar Level 2 Vertical Feature Mask feature classification flag value. It is written in Interactive Data Language (IDL) ...

Morphometric and molecular differentiation between quetzal subspecies of Pharomachrus mocinno (Trogoniformes: Trogonidae).

PubMed

Solórzano, Sofía; Oyama, Ken

2010-03-01

The resplendent Quetzal (Pharomachrus mocinno) is an endemic Mesoamerican bird species of conservation concern. Within this species, the subspecies P. m. costaricensis and P. m. mocinno, have been recognized by apparent morphometric differences; however, presently there is no sufficient data for confirmation. We analyzed eight morphometric attributes of the body from 41 quetzals: body length, tarsus and cord wing, as well as the length, wide and depth of the bill, body weight; and in the case of the males, the length of the long upper-tail cover feathers. We used multivariate analyses to discriminate morphometric differences between subspecies and contrasted each morphometric attribute between and within subspecies with paired non-parametric Wilcoxon test. In order to review the intraspecific taxonomic status of this bird, we added phylogenetic analysis, and genetic divergence and differentiation based on nucleotide variations in four sequences of mtDNA. The nucleotide variation was estimated in control region, subunit NDH6, and tRNAGlu and tRNAPhe in 26 quetzals from eight localities distributed in five countries. We estimated the genetic divergence and differentiation between subspecies according to a mutation-drift equilibrium model. We obtained the best mutation nucleotide model following the procedure implemented in model test program. We constructed the phylogenetic relationships between subspecies by maximum parsimony and maximum likelihood using PAUP, as well as with Bayesian statistics. The multivariate analyses showed two different morphometric groups, and individuals clustered according to the subspecies that they belong. The paired comparisons between subspecies showed strong differences in most of the attributes analyzed. Along the four mtDNA sequences, we identified 32 nucleotide positions that have a particular nucleotide according to the quetzals subspecies. The genetic divergence and the differentiation was strong and markedly showed two groups within P. mocinno that corresponded to the quetzals subspecies. The model selected for our data was TVM+G. The three phylogenetic methods here used recovered two clear monophyletic clades corresponding to each subspecies, and evidenced a significant and true partition of P. mocinno species into two different genetic, morphometric and ecologic groups. Additionally, according to our calculations, the gene flow between subspecies is interrupted at least from three million years ago. Thus we propose that P. mocinno be divided in two independent species: P. mocinno (Northern species, from Mexico to Nicaragua) and in P. costaricensis (Southern species, Costa Rica and Panama). This new taxonomic classification of the quetzal subspecies allows us to get well conservation achievements because the evaluation about the kind and magnitude of the threats could be more precise.

Diagnostic and prognostic histopathology system using morphometric indices

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

Parvin, Bahram; Chang, Hang; Han, Ju

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

The Segmental Morphometric Properties of the Horse Cervical Spinal Cord: A Study of Cadaver

PubMed Central

Bahar, Sadullah; Bolat, Durmus; Selcuk, Muhammet Lutfi

2013-01-01

Although the cervical spinal cord (CSC) of the horse has particular importance in diseases of CNS, there is very little information about its segmental morphometry. The objective of the present study was to determine the morphometric features of the CSC segments in the horse and possible relationships among the morphometric features. The segmented CSC from five mature animals was used. Length, weight, diameter, and volume measurements of the segments were performed macroscopically. Lengths and diameters of segments were measured histologically, and area and volume measurements were performed using stereological methods. The length, weight, and volume of the CSC were 61.6 ± 3.2 cm, 107.2 ± 10.4 g, and 95.5 ± 8.3 cm3, respectively. The length of the segments was increased from C 1 to C 3, while it decreased from C 3 to C 8. The gross section (GS), white matter (WM), grey matter (GM), dorsal horn (DH), and ventral horn (VH) had the largest cross-section areas at C 8. The highest volume was found for the total segment and WM at C 4, GM, DH, and VH at C 7, and the central canal (CC) at C 3. The data obtained not only contribute to the knowledge of the normal anatomy of the CSC but may also provide reference data for veterinary pathologists and clinicians. PMID:23476145

Morphometrical differences between resectable and non-resectable pancreatic cancer: a fractal analysis.

PubMed

Vasilescu, Catalin; Giza, Dana Elena; Petrisor, Petre; Dobrescu, Radu; Popescu, Irinel; Herlea, Vlad

2012-01-01

Pancreatic cancer is a highly aggressive cancer with a rising incidence and poor prognosis despite active surgical treatment. Candidates for surgical resection should be carefully selected. In order to avoid unnecessary laparotomy it is useful to identify reliable factors that may predict resectability. Nuclear morphometry and fractal dimension of pancreatic nuclear features could provide important preoperative information in assessing pancreas resectability. Sixty-one patients diagnosed with pancreatic cancer were enrolled in this retrospective study between 2003 and 2005. Patients were divided into two groups: one resectable cancer group and one with non-resectable pancreatic cancer. Morphometric parameters measured were: nuclear area, length of minor axis and length of major axis. Nuclear shape and chromatin distribution of the pancreatic tumor cells were both estimated using fractal dimension. Morphometric measurements have shown significant differences between the nuclear area of the resectable group and the non-resectable group (61.9 ± 19.8µm vs. 42.2 ± 15.6µm). Fractal dimension of the nuclear outlines and chromatin distribution was found to have a higher value in the non-resectable group (p<0.05). Objective measurements should be performed to improve risk assessment and therapeutic decisions in pancreatic cancer. Nuclear morphometry of the pancreatic nuclear features can provide important pre-operative information in resectability assessment. The fractal dimension of the nuclear shape and chromatin distribution may be considered a new promising adjunctive tool for conventional pathological analysis.

Scaling mimesis: Morphometric and ecomorphological similarities in three sympatric plant-mimetic fish of the family Carangidae (Teleostei)

PubMed Central

de Queiroz, Alexya Cunha; Vallinoto, Marcelo; Sakai, Yoichi; Giarrizzo, Tommaso

2018-01-01

The mimetic juveniles of a number of carangid fish species resemble plant parts floating near the water surface, such as leaves, seeds and other plant debris. The present study is the first to verify the morphological similarities and ecomorphological relationships between three carangids (Oligoplites saurus, Oligoplites palometa and Trachinotus falcatus) and their associated plant models. Behavioral observations were conducted in the estuary of Curuçá River, in northeastern Pará (Brazil) between August 2015 and July 2016. Individual fishes and associated floating objects (models) were sampled for comparative analysis using both geometric and morphometric approaches. While the mimetic fish and their models retain their own distinct, intrinsic morphological features, a high degree of morphological similarity was found between each fish species and its model. The morphometric analyses revealed a general tendency of isometric development in all three fish species, probably related to their pelagic habitats, during all ontogenetic stages. PMID:29558476

Three-dimensional (3D) geometric morphometric analysis of human premolars to assess sexual dimorphism and biological ancestry in Australian populations.

PubMed

Yong, Robin; Ranjitkar, Sarbin; Lekkas, Dimitra; Halazonetis, Demetrios; Evans, Alistair; Brook, Alan; Townsend, Grant

2018-06-01

This study aimed to investigate size and shape variation of human premolars between Indigenous Australians and Australians of European ancestry, and to assess whether sex and ancestry could be differentiated between these groups using 3D geometric morphometrics. Seventy dental casts from each group, equally subdivided by sex, were scanned using a structured-light scanner. The 3D meshes of upper and lower premolars were processed using geometric morphometric methods. Seventy-two landmarks were recorded for upper premolars and 50 landmarks for lower premolars. For each tooth type, two-way ANOVA was used to assess group differences in centroid size. Shape variations were explored using principal component analysis and visualized using 3D morphing. Two-way Procrustes ANOVA was applied to test group differences for ancestry and sex, and a "leave-one-out" discriminant function was applied to assess group assignment. Centroid size and shape did not display significant difference between the sexes. Centroid size was larger in Indigenous Australians for upper premolars and lower second premolars compared to the Australians of European ancestry. Significant shape variation was noted between the two ancestral groups for upper premolars and the lower first premolar. Correct group assignment of individual teeth to their ancestral groups ranged between 80.0 and 92.8% for upper premolars and 60.0 and 75.7% for lower premolars. Our findings provide evidence of significant size and shape variation in human premolars between the two ancestral groups. High classification rates based on shape analysis of upper premolars highlight potential application of geometric morphometrics in anthropological, bioarcheological and forensic contexts. © 2018 Wiley Periodicals, Inc.

Geometric morphometrics in primatology: craniofacial variation in Homo sapiens and Pan troglodytes.

PubMed

Lynch, J M; Wood, C G; Luboga, S A

1996-01-01

Traditionally, morphometric studies have relied on statistical analysis of distances, angles or ratios to investigate morphometric variation among taxa. Recently, geometric techniques have been developed for the direct analysis of landmark data. In this paper, we offer a summary (with examples) of three of these newer techniques, namely shape coordinate, thin-plate spline and relative warp analyses. Shape coordinate analysis detected significant craniofacial variation between 4 modern human populations, with African and Australian Aboriginal specimens being relatively prognathous compared with their Eurasian counterparts. In addition, the Australian specimens exhibited greater basicranial flexion than all other samples. The observed relationships between size and craniofacial shape were weak. The decomposition of shape variation into affine and non-affine components is illustrated via a thin-plate spline analysis of Homo and Pan cranial landmarks. We note differences between Homo and Pan in the degree of prognathism and basicranial flexion and the position and orientation of the foramen magnum. We compare these results with previous studies of these features in higher primates and discuss the utility of geometric morphometrics as a tool in primatology and physical anthropology. We conclude that many studies of morphological variation, both within and between taxa, would benefit from the graphical nature of these techniques.

Biological effects of ultraviolet irradiation on bees

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

Es`kov, E.K.

1995-09-01

The influence of natural solar and artificial ultraviolet irradiation on developing bees was studied. Lethal exposures to irradiation at different stages of development were determined. The influence of irradiation on the variability of the morphometric features of bees was revealed. 5 refs., 1 fig.

Comparison of Feature Selection Techniques in Machine Learning for Anatomical Brain MRI in Dementia.

PubMed

Tohka, Jussi; Moradi, Elaheh; Huttunen, Heikki

2016-07-01

We present a comparative split-half resampling analysis of various data driven feature selection and classification methods for the whole brain voxel-based classification analysis of anatomical magnetic resonance images. We compared support vector machines (SVMs), with or without filter based feature selection, several embedded feature selection methods and stability selection. While comparisons of the accuracy of various classification methods have been reported previously, the variability of the out-of-training sample classification accuracy and the set of selected features due to independent training and test sets have not been previously addressed in a brain imaging context. We studied two classification problems: 1) Alzheimer's disease (AD) vs. normal control (NC) and 2) mild cognitive impairment (MCI) vs. NC classification. In AD vs. NC classification, the variability in the test accuracy due to the subject sample did not vary between different methods and exceeded the variability due to different classifiers. In MCI vs. NC classification, particularly with a large training set, embedded feature selection methods outperformed SVM-based ones with the difference in the test accuracy exceeding the test accuracy variability due to the subject sample. The filter and embedded methods produced divergent feature patterns for MCI vs. NC classification that suggests the utility of the embedded feature selection for this problem when linked with the good generalization performance. The stability of the feature sets was strongly correlated with the number of features selected, weakly correlated with the stability of classification accuracy, and uncorrelated with the average classification accuracy.

Effects of dietary Tenebrio molitor meal inclusion in free-range chickens.

PubMed

Biasato, I; De Marco, M; Rotolo, L; Renna, M; Lussiana, C; Dabbou, S; Capucchio, M T; Biasibetti, E; Costa, P; Gai, F; Pozzo, L; Dezzutto, D; Bergagna, S; Martínez, S; Tarantola, M; Gasco, L; Schiavone, A

2016-12-01

Insects are currently being considered as a novel protein source for animal feeds, because they contain a large amount of protein. The larvae of Tenebrio molitor (TM) have been shown to be an acceptable protein source for broiler chickens in terms of growth performance, but till now, no data on histological or intestinal morphometric features have been reported. This study has had the aim of evaluating the effects of dietary TM inclusion on the performance, welfare, intestinal morphology and histological features of free-range chickens. A total of 140 medium-growing hybrid female chickens were free-range reared and randomly allotted to two dietary treatments: (i) a control group and (ii) a TM group, in which TM meal was included at 75 g/kg. Each group consisted of five pens as replicates, with 14 chicks per pen. Growth performance, haematological and serum parameters and welfare indicators were evaluated, and the animals were slaughtered at the age of 97 days. Two birds per pen (10 birds/treatment) were submitted to histological (liver, spleen, thymus, bursa of Fabricius, kidney, heart, glandular stomach and gut) and morphometric (duodenum, jejunum and ileum) investigations. The inclusion of TM did not affect the growth performance, haematological or serum parameters. The morphometric and histological features were not significantly affected either, thus suggesting no influence on nutrient metabolization, performance or animal health. Glandular stomach alterations (chronic flogosis with epithelial squamous metaplasia) were considered paraphysiological in relation to free-range farming. The observed chronic intestinal flogosis, with concomitant activation of the lymphoid tissue, was probably due to previous parasitic infections, which are very frequently detected in free-range chickens. In conclusion, the findings of this study show that yellow mealworm inclusion does not affect the welfare, productive performances or morphological features of free-range chickens, thus confirming that TM can be used safely in poultry diets. Journal of Animal Physiology and Animal Nutrition © 2016 Blackwell Verlag GmbH.

Ancestry Estimation in Forensic Anthropology: Geometric Morphometric versus Standard and Nonstandard Interlandmark Distances.

PubMed

Katherine Spradley, M; Jantz, Richard L

2016-07-01

Standard cranial measurements are commonly used for ancestry estimation; however, 3D digitizers have made cranial landmark data collection and geometric morphometric (GM) analyses more popular within forensic anthropology. Yet there has been little focus on which data type works best. The goal of the present research is to test the discrimination ability of standard and nonstandard craniometric measurements and data derived from GM analysis. A total of 31 cranial landmarks were used to generate 465 interlandmark distances, including a subset of 20 commonly used measurements, and to generate principal component scores from procrustes coordinates. All were subjected to discriminant function analysis to ascertain which type of data performed best for ancestry estimation of American Black and White and Hispanic males and females. The nonstandard interlandmark distances generated the highest classification rates for females (90.5%) and males (88.2%). Using nonstandard interlandmark distances over more commonly used measurements leads to better ancestry estimates for our current population structure. © 2016 American Academy of Forensic Sciences.

Multi-Feature Classification of Multi-Sensor Satellite Imagery Based on Dual-Polarimetric Sentinel-1A, Landsat-8 OLI, and Hyperion Images for Urban Land-Cover Classification.

PubMed

Zhou, Tao; Li, Zhaofu; Pan, Jianjun

2018-01-27

This paper focuses on evaluating the ability and contribution of using backscatter intensity, texture, coherence, and color features extracted from Sentinel-1A data for urban land cover classification and comparing different multi-sensor land cover mapping methods to improve classification accuracy. Both Landsat-8 OLI and Hyperion images were also acquired, in combination with Sentinel-1A data, to explore the potential of different multi-sensor urban land cover mapping methods to improve classification accuracy. The classification was performed using a random forest (RF) method. The results showed that the optimal window size of the combination of all texture features was 9 × 9, and the optimal window size was different for each individual texture feature. For the four different feature types, the texture features contributed the most to the classification, followed by the coherence and backscatter intensity features; and the color features had the least impact on the urban land cover classification. Satisfactory classification results can be obtained using only the combination of texture and coherence features, with an overall accuracy up to 91.55% and a kappa coefficient up to 0.8935, respectively. Among all combinations of Sentinel-1A-derived features, the combination of the four features had the best classification result. Multi-sensor urban land cover mapping obtained higher classification accuracy. The combination of Sentinel-1A and Hyperion data achieved higher classification accuracy compared to the combination of Sentinel-1A and Landsat-8 OLI images, with an overall accuracy of up to 99.12% and a kappa coefficient up to 0.9889. When Sentinel-1A data was added to Hyperion images, the overall accuracy and kappa coefficient were increased by 4.01% and 0.0519, respectively.

Automated simultaneous multiple feature classification of MTI data

NASA Astrophysics Data System (ADS)

Harvey, Neal R.; Theiler, James P.; Balick, Lee K.; Pope, Paul A.; Szymanski, John J.; Perkins, Simon J.; Porter, Reid B.; Brumby, Steven P.; Bloch, Jeffrey J.; David, Nancy A.; Galassi, Mark C.

2002-08-01

Los Alamos National Laboratory has developed and demonstrated a highly capable system, GENIE, for the two-class problem of detecting a single feature against a background of non-feature. In addition to the two-class case, however, a commonly encountered remote sensing task is the segmentation of multispectral image data into a larger number of distinct feature classes or land cover types. To this end we have extended our existing system to allow the simultaneous classification of multiple features/classes from multispectral data. The technique builds on previous work and its core continues to utilize a hybrid evolutionary-algorithm-based system capable of searching for image processing pipelines optimized for specific image feature extraction tasks. We describe the improvements made to the GENIE software to allow multiple-feature classification and describe the application of this system to the automatic simultaneous classification of multiple features from MTI image data. We show the application of the multiple-feature classification technique to the problem of classifying lava flows on Mauna Loa volcano, Hawaii, using MTI image data and compare the classification results with standard supervised multiple-feature classification techniques.

Multi-source remotely sensed data fusion for improving land cover classification

NASA Astrophysics Data System (ADS)

Chen, Bin; Huang, Bo; Xu, Bing

2017-02-01

Although many advances have been made in past decades, land cover classification of fine-resolution remotely sensed (RS) data integrating multiple temporal, angular, and spectral features remains limited, and the contribution of different RS features to land cover classification accuracy remains uncertain. We proposed to improve land cover classification accuracy by integrating multi-source RS features through data fusion. We further investigated the effect of different RS features on classification performance. The results of fusing Landsat-8 Operational Land Imager (OLI) data with Moderate Resolution Imaging Spectroradiometer (MODIS), China Environment 1A series (HJ-1A), and Advanced Spaceborne Thermal Emission and Reflection (ASTER) digital elevation model (DEM) data, showed that the fused data integrating temporal, spectral, angular, and topographic features achieved better land cover classification accuracy than the original RS data. Compared with the topographic feature, the temporal and angular features extracted from the fused data played more important roles in classification performance, especially those temporal features containing abundant vegetation growth information, which markedly increased the overall classification accuracy. In addition, the multispectral and hyperspectral fusion successfully discriminated detailed forest types. Our study provides a straightforward strategy for hierarchical land cover classification by making full use of available RS data. All of these methods and findings could be useful for land cover classification at both regional and global scales.

SVM-RFE based feature selection and Taguchi parameters optimization for multiclass SVM classifier.

PubMed

Huang, Mei-Ling; Hung, Yung-Hsiang; Lee, W M; Li, R K; Jiang, Bo-Ru

2014-01-01

Recently, support vector machine (SVM) has excellent performance on classification and prediction and is widely used on disease diagnosis or medical assistance. However, SVM only functions well on two-group classification problems. This study combines feature selection and SVM recursive feature elimination (SVM-RFE) to investigate the classification accuracy of multiclass problems for Dermatology and Zoo databases. Dermatology dataset contains 33 feature variables, 1 class variable, and 366 testing instances; and the Zoo dataset contains 16 feature variables, 1 class variable, and 101 testing instances. The feature variables in the two datasets were sorted in descending order by explanatory power, and different feature sets were selected by SVM-RFE to explore classification accuracy. Meanwhile, Taguchi method was jointly combined with SVM classifier in order to optimize parameters C and γ to increase classification accuracy for multiclass classification. The experimental results show that the classification accuracy can be more than 95% after SVM-RFE feature selection and Taguchi parameter optimization for Dermatology and Zoo databases.

SVM-RFE Based Feature Selection and Taguchi Parameters Optimization for Multiclass SVM Classifier

PubMed Central

Huang, Mei-Ling; Hung, Yung-Hsiang; Lee, W. M.; Li, R. K.; Jiang, Bo-Ru

2014-01-01

Recently, support vector machine (SVM) has excellent performance on classification and prediction and is widely used on disease diagnosis or medical assistance. However, SVM only functions well on two-group classification problems. This study combines feature selection and SVM recursive feature elimination (SVM-RFE) to investigate the classification accuracy of multiclass problems for Dermatology and Zoo databases. Dermatology dataset contains 33 feature variables, 1 class variable, and 366 testing instances; and the Zoo dataset contains 16 feature variables, 1 class variable, and 101 testing instances. The feature variables in the two datasets were sorted in descending order by explanatory power, and different feature sets were selected by SVM-RFE to explore classification accuracy. Meanwhile, Taguchi method was jointly combined with SVM classifier in order to optimize parameters C and γ to increase classification accuracy for multiclass classification. The experimental results show that the classification accuracy can be more than 95% after SVM-RFE feature selection and Taguchi parameter optimization for Dermatology and Zoo databases. PMID:25295306

Learning about the internal structure of categories through classification and feature inference.

PubMed

Jee, Benjamin D; Wiley, Jennifer

2014-01-01

Previous research on category learning has found that classification tasks produce representations that are skewed toward diagnostic feature dimensions, whereas feature inference tasks lead to richer representations of within-category structure. Yet, prior studies often measure category knowledge through tasks that involve identifying only the typical features of a category. This neglects an important aspect of a category's internal structure: how typical and atypical features are distributed within a category. The present experiments tested the hypothesis that inference learning results in richer knowledge of internal category structure than classification learning. We introduced several new measures to probe learners' representations of within-category structure. Experiment 1 found that participants in the inference condition learned and used a wider range of feature dimensions than classification learners. Classification learners, however, were more sensitive to the presence of atypical features within categories. Experiment 2 provided converging evidence that classification learners were more likely to incorporate atypical features into their representations. Inference learners were less likely to encode atypical category features, even in a "partial inference" condition that focused learners' attention on the feature dimensions relevant to classification. Overall, these results are contrary to the hypothesis that inference learning produces superior knowledge of within-category structure. Although inference learning promoted representations that included a broad range of category-typical features, classification learning promoted greater sensitivity to the distribution of typical and atypical features within categories.

Feature ranking and rank aggregation for automatic sleep stage classification: a comparative study.

PubMed

Najdi, Shirin; Gharbali, Ali Abdollahi; Fonseca, José Manuel

2017-08-18

Nowadays, sleep quality is one of the most important measures of healthy life, especially considering the huge number of sleep-related disorders. Identifying sleep stages using polysomnographic (PSG) signals is the traditional way of assessing sleep quality. However, the manual process of sleep stage classification is time-consuming, subjective and costly. Therefore, in order to improve the accuracy and efficiency of the sleep stage classification, researchers have been trying to develop automatic classification algorithms. Automatic sleep stage classification mainly consists of three steps: pre-processing, feature extraction and classification. Since classification accuracy is deeply affected by the extracted features, a poor feature vector will adversely affect the classifier and eventually lead to low classification accuracy. Therefore, special attention should be given to the feature extraction and selection process. In this paper the performance of seven feature selection methods, as well as two feature rank aggregation methods, were compared. Pz-Oz EEG, horizontal EOG and submental chin EMG recordings of 22 healthy males and females were used. A comprehensive feature set including 49 features was extracted from these recordings. The extracted features are among the most common and effective features used in sleep stage classification from temporal, spectral, entropy-based and nonlinear categories. The feature selection methods were evaluated and compared using three criteria: classification accuracy, stability, and similarity. Simulation results show that MRMR-MID achieves the highest classification performance while Fisher method provides the most stable ranking. In our simulations, the performance of the aggregation methods was in the average level, although they are known to generate more stable results and better accuracy. The Borda and RRA rank aggregation methods could not outperform significantly the conventional feature ranking methods. Among conventional methods, some of them slightly performed better than others, although the choice of a suitable technique is dependent on the computational complexity and accuracy requirements of the user.

Determining the saliency of feature measurements obtained from images of sedimentary organic matter for use in its classification

NASA Astrophysics Data System (ADS)

Weller, Andrew F.; Harris, Anthony J.; Ware, J. Andrew; Jarvis, Paul S.

2006-11-01

The classification of sedimentary organic matter (OM) images can be improved by determining the saliency of image analysis (IA) features measured from them. Knowing the saliency of IA feature measurements means that only the most significant discriminating features need be used in the classification process. This is an important consideration for classification techniques such as artificial neural networks (ANNs), where too many features can lead to the 'curse of dimensionality'. The classification scheme adopted in this work is a hybrid of morphologically and texturally descriptive features from previous manual classification schemes. Some of these descriptive features are assigned to IA features, along with several others built into the IA software (Halcon) to ensure that a valid cross-section is available. After an image is captured and segmented, a total of 194 features are measured for each particle. To reduce this number to a more manageable magnitude, the SPSS AnswerTree Exhaustive CHAID (χ 2 automatic interaction detector) classification tree algorithm is used to establish each measurement's saliency as a classification discriminator. In the case of continuous data as used here, the F-test is used as opposed to the published algorithm. The F-test checks various statistical hypotheses about the variance of groups of IA feature measurements obtained from the particles to be classified. The aim is to reduce the number of features required to perform the classification without reducing its accuracy. In the best-case scenario, 194 inputs are reduced to 8, with a subsequent multi-layer back-propagation ANN recognition rate of 98.65%. This paper demonstrates the ability of the algorithm to reduce noise, help overcome the curse of dimensionality, and facilitate an understanding of the saliency of IA features as discriminators for sedimentary OM classification.

Multi-Feature Classification of Multi-Sensor Satellite Imagery Based on Dual-Polarimetric Sentinel-1A, Landsat-8 OLI, and Hyperion Images for Urban Land-Cover Classification

PubMed Central

Pan, Jianjun

2018-01-01

This paper focuses on evaluating the ability and contribution of using backscatter intensity, texture, coherence, and color features extracted from Sentinel-1A data for urban land cover classification and comparing different multi-sensor land cover mapping methods to improve classification accuracy. Both Landsat-8 OLI and Hyperion images were also acquired, in combination with Sentinel-1A data, to explore the potential of different multi-sensor urban land cover mapping methods to improve classification accuracy. The classification was performed using a random forest (RF) method. The results showed that the optimal window size of the combination of all texture features was 9 × 9, and the optimal window size was different for each individual texture feature. For the four different feature types, the texture features contributed the most to the classification, followed by the coherence and backscatter intensity features; and the color features had the least impact on the urban land cover classification. Satisfactory classification results can be obtained using only the combination of texture and coherence features, with an overall accuracy up to 91.55% and a kappa coefficient up to 0.8935, respectively. Among all combinations of Sentinel-1A-derived features, the combination of the four features had the best classification result. Multi-sensor urban land cover mapping obtained higher classification accuracy. The combination of Sentinel-1A and Hyperion data achieved higher classification accuracy compared to the combination of Sentinel-1A and Landsat-8 OLI images, with an overall accuracy of up to 99.12% and a kappa coefficient up to 0.9889. When Sentinel-1A data was added to Hyperion images, the overall accuracy and kappa coefficient were increased by 4.01% and 0.0519, respectively. PMID:29382073

Gene masking - a technique to improve accuracy for cancer classification with high dimensionality in microarray data.

PubMed

Saini, Harsh; Lal, Sunil Pranit; Naidu, Vimal Vikash; Pickering, Vincel Wince; Singh, Gurmeet; Tsunoda, Tatsuhiko; Sharma, Alok

2016-12-05

High dimensional feature space generally degrades classification in several applications. In this paper, we propose a strategy called gene masking, in which non-contributing dimensions are heuristically removed from the data to improve classification accuracy. Gene masking is implemented via a binary encoded genetic algorithm that can be integrated seamlessly with classifiers during the training phase of classification to perform feature selection. It can also be used to discriminate between features that contribute most to the classification, thereby, allowing researchers to isolate features that may have special significance. This technique was applied on publicly available datasets whereby it substantially reduced the number of features used for classification while maintaining high accuracies. The proposed technique can be extremely useful in feature selection as it heuristically removes non-contributing features to improve the performance of classifiers.

High Dimensional Classification Using Features Annealed Independence Rules.

PubMed

Fan, Jianqing; Fan, Yingying

2008-01-01

Classification using high-dimensional features arises frequently in many contemporary statistical studies such as tumor classification using microarray or other high-throughput data. The impact of dimensionality on classifications is largely poorly understood. In a seminal paper, Bickel and Levina (2004) show that the Fisher discriminant performs poorly due to diverging spectra and they propose to use the independence rule to overcome the problem. We first demonstrate that even for the independence classification rule, classification using all the features can be as bad as the random guessing due to noise accumulation in estimating population centroids in high-dimensional feature space. In fact, we demonstrate further that almost all linear discriminants can perform as bad as the random guessing. Thus, it is paramountly important to select a subset of important features for high-dimensional classification, resulting in Features Annealed Independence Rules (FAIR). The conditions under which all the important features can be selected by the two-sample t-statistic are established. The choice of the optimal number of features, or equivalently, the threshold value of the test statistics are proposed based on an upper bound of the classification error. Simulation studies and real data analysis support our theoretical results and demonstrate convincingly the advantage of our new classification procedure.

Automated Feature Identification and Classification Using Automated Feature Weighted Self Organizing Map (FWSOM)

NASA Astrophysics Data System (ADS)

Starkey, Andrew; Usman Ahmad, Aliyu; Hamdoun, Hassan

2017-10-01

This paper investigates the application of a novel method for classification called Feature Weighted Self Organizing Map (FWSOM) that analyses the topology information of a converged standard Self Organizing Map (SOM) to automatically guide the selection of important inputs during training for improved classification of data with redundant inputs, examined against two traditional approaches namely neural networks and Support Vector Machines (SVM) for the classification of EEG data as presented in previous work. In particular, the novel method looks to identify the features that are important for classification automatically, and in this way the important features can be used to improve the diagnostic ability of any of the above methods. The paper presents the results and shows how the automated identification of the important features successfully identified the important features in the dataset and how this results in an improvement of the classification results for all methods apart from linear discriminatory methods which cannot separate the underlying nonlinear relationship in the data. The FWSOM in addition to achieving higher classification accuracy has given insights into what features are important in the classification of each class (left and right-hand movements), and these are corroborated by already published work in this area.

Correlation between three-dimensional power Doppler and morphometric measurement of endometrial vascularity at the time of embryo implantation in women with unexplained recurrent miscarriage.

PubMed

Chen, Xiaoyan; Saravelos, Sotirios H; Liu, Yingyu; Huang, Jin; Wang, Chi Chiu; Li, Tin Chiu

2017-06-01

Power Doppler in combination with three-dimensional (3D-PD) ultrasonography has been used as a noninvasive tool to evaluate the vascularity. However, it is unclear whether 3D-PD can accurately reflect endometrial vascularization and replace the invasive endometrial biopsy. This study aims to investigate the correlation between 3D-PD and micro vessel morphometric measurement of endometrial vascularity. Twenty-five women with unexplained recurrent miscarriage were recruited for 3D-PD and endometrial biopsy on precisely day LH + 7. Immunohistochemistry using vWF was employed to identify micro vessels in endometrial biopsy specimens followed by the use of morphometric technique to measure the mean vessel diameter and volume fractions. The vascularization index (VI), flow index (FI) and vascularization flow index (VFI) assessed by 3D-PD were calculated for both the endometrial and sub-endometrial regions. There were no significant correlations between any of the ultrasonographic measurements (endometrial thickness, endometrial volume, endometrial VI/FI/VFI, sub-endometrial volume, sub-endometrial VI/FI/VFI) and morphometric features (number of micro vessel, mean diameter of micro vessel and volume fraction measurement of vessel). This study indicates that endometrial vascularity assessed by 3D-PD could not be used to reflect changes in micro vessels of the endometrium at the time of embryo implantation in women with unexplained recurrent miscarriage.

Hydrological inferences through morphometric analysis of lower Kosi river basin of India for water resource management based on remote sensing data

NASA Astrophysics Data System (ADS)

Rai, Praveen Kumar; Chandel, Rajeev Singh; Mishra, Varun Narayan; Singh, Prafull

2018-03-01

Satellite based remote sensing technology has proven to be an effectual tool in analysis of drainage networks, study of surface morphological features and their correlation with groundwater management prospect at basin level. The present study highlights the effectiveness and advantage of remote sensing and GIS-based analysis for quantitative and qualitative assessment of flood plain region of lower Kosi river basin based on morphometric analysis. In this study, ASTER DEM is used to extract the vital hydrological parameters of lower Kosi river basin in ARC GIS software. Morphometric parameters, e.g., stream order, stream length, bifurcation ratio, drainage density, drainage frequency, drainage texture, form factor, circularity ratio, elongation ratio, etc., have been calculated for the Kosi basin and their hydrological inferences were discussed. Most of the morphometric parameters such as bifurcation ratio, drainage density, drainage frequency, drainage texture concluded that basin has good prospect for water management program for various purposes and also generated data base that can provide scientific information for site selection of water-harvesting structures and flood management activities in the basin. Land use land cover (LULC) of the basin were also prepared from Landsat data of 2005, 2010 and 2015 to assess the change in dynamic of the basin and these layers are very noteworthy for further watershed prioritization.

Classification of radiolarian images with hand-crafted and deep features

NASA Astrophysics Data System (ADS)

Keçeli, Ali Seydi; Kaya, Aydın; Keçeli, Seda Uzunçimen

2017-12-01

Radiolarians are planktonic protozoa and are important biostratigraphic and paleoenvironmental indicators for paleogeographic reconstructions. Radiolarian paleontology still remains as a low cost and the one of the most convenient way to obtain dating of deep ocean sediments. Traditional methods for identifying radiolarians are time-consuming and cannot scale to the granularity or scope necessary for large-scale studies. Automated image classification will allow making these analyses promptly. In this study, a method for automatic radiolarian image classification is proposed on Scanning Electron Microscope (SEM) images of radiolarians to ease species identification of fossilized radiolarians. The proposed method uses both hand-crafted features like invariant moments, wavelet moments, Gabor features, basic morphological features and deep features obtained from a pre-trained Convolutional Neural Network (CNN). Feature selection is applied over deep features to reduce high dimensionality. Classification outcomes are analyzed to compare hand-crafted features, deep features, and their combinations. Results show that the deep features obtained from a pre-trained CNN are more discriminative comparing to hand-crafted ones. Additionally, feature selection utilizes to the computational cost of classification algorithms and have no negative effect on classification accuracy.

Chinese wine classification system based on micrograph using combination of shape and structure features

NASA Astrophysics Data System (ADS)

Wan, Yi

2011-06-01

Chinese wines can be classification or graded by the micrographs. Micrographs of Chinese wines show floccules, stick and granule of variant shape and size. Different wines have variant microstructure and micrographs, we study the classification of Chinese wines based on the micrographs. Shape and structure of wines' particles in microstructure is the most important feature for recognition and classification of wines. So we introduce a feature extraction method which can describe the structure and region shape of micrograph efficiently. First, the micrographs are enhanced using total variation denoising, and segmented using a modified Otsu's method based on the Rayleigh Distribution. Then features are extracted using proposed method in the paper based on area, perimeter and traditional shape feature. Eight kinds total 26 features are selected. Finally, Chinese wine classification system based on micrograph using combination of shape and structure features and BP neural network have been presented. We compare the recognition results for different choices of features (traditional shape features or proposed features). The experimental results show that the better classification rate have been achieved using the combinational features proposed in this paper.

Integration of heterogeneous features for remote sensing scene classification

NASA Astrophysics Data System (ADS)

Wang, Xin; Xiong, Xingnan; Ning, Chen; Shi, Aiye; Lv, Guofang

2018-01-01

Scene classification is one of the most important issues in remote sensing (RS) image processing. We find that features from different channels (shape, spectral, texture, etc.), levels (low-level and middle-level), or perspectives (local and global) could provide various properties for RS images, and then propose a heterogeneous feature framework to extract and integrate heterogeneous features with different types for RS scene classification. The proposed method is composed of three modules (1) heterogeneous features extraction, where three heterogeneous feature types, called DS-SURF-LLC, mean-Std-LLC, and MS-CLBP, are calculated, (2) heterogeneous features fusion, where the multiple kernel learning (MKL) is utilized to integrate the heterogeneous features, and (3) an MKL support vector machine classifier for RS scene classification. The proposed method is extensively evaluated on three challenging benchmark datasets (a 6-class dataset, a 12-class dataset, and a 21-class dataset), and the experimental results show that the proposed method leads to good classification performance. It produces good informative features to describe the RS image scenes. Moreover, the integration of heterogeneous features outperforms some state-of-the-art features on RS scene classification tasks.

Morphometric analysis with open source software to explore shallow hydrogeological features in Senegal and Guinea

NASA Astrophysics Data System (ADS)

Fussi, Fabio; Di Leo, Margherita; Bonomi, Tullia; Di Mauro, Biagio; Fava, Francesco; Fumagalli, Letizia; Hamidou Kane, Cheikh; Faye, Gayane; Niang, Magatte; Wade, Souleye; Hamidou, Barry; Colombo, Roberto

2015-04-01

Water represents a vital resource for everyone on this Planet, but, for some populations, the access to potable water is not given for granted. Recently, the interest in low cost technical solutions to improve access to ground water in developing countries, especially for people located in remote areas, has increased. Manual drilling (techniques to drill boreholes for water using human or animal power) is well known and practiced for centuries in many countries and represents a valid alternative to increase water access. Lately, this practice has raised the attention of national governments and international organizations. This technique is applicable only where hydrogeological conditions are suitable, namely in presence of thick layers of unconsolidated sediments and a shallow water table Aim of this study is exploring the potential of morphometric analysis to improve the methodology to identify areas with suitable hydrogeological conditions for manual drilling, supporting the implementation of water supply programs that can have great impact on living condition of the population. The characteristics of shallow geological layers are strongly dependent from geomorphological processes and are usually reflected in the morphological characteristics of landforms. Under these hypotheses, we have been investigating the geo-statistical correlation between several morphometric variables and a set of hydrogeological variables used in the estimation of suitability for manual drilling: thickness of unconsolidated sediments, texture, hydraulic conductivity of shallow aquifer, depth of water table. The morphology of two study areas with different landscape characteristics in Guinea and Senegal has been investigated coupling the Free and Open Source Software GRASS GIS and R. Several morphometric parameters have been extracted from ASTER GDEM digital elevation model, and have been compared with a set of hydrogeological characteristics obtained from semi-automatic analysis of stratigraphic logs from water boreholes. We observed the relationships between the spatial distribution of hydrogeological features and the morphology, applying multivariate statistical analysis. The ultimate goal of this study is to infer hydrogeological information of shallow aquifers, exploiting morphometric parameters (together with other layers of information from existing thematic maps and remote sensing) and to reconstruct the geometry and the characteristic of shallow porous aquifer. This research is part of a larger project financed by NERC (National Environment Research Council, UK) in the framework of the program UPGRO (Unlocking the Potential of Groundwater for the Poors), with the collaboration of different partners from Italy, Senegal and Guinea

A semi-automatic method for analysis of landscape elements using Shuttle Radar Topography Mission and Landsat ETM+ data

NASA Astrophysics Data System (ADS)

Ehsani, Amir Houshang; Quiel, Friedrich

2009-02-01

In this paper, we demonstrate artificial neural networks—self-organizing map (SOM)—as a semi-automatic method for extraction and analysis of landscape elements in the man and biosphere reserve "Eastern Carpathians". The Shuttle Radar Topography Mission (SRTM) collected data to produce generally available digital elevation models (DEM). Together with Landsat Thematic Mapper data, this provides a unique, consistent and nearly worldwide data set. To integrate the DEM with Landsat data, it was re-projected from geographic coordinates to UTM with 28.5 m spatial resolution using cubic convolution interpolation. To provide quantitative morphometric parameters, first-order (slope) and second-order derivatives of the DEM—minimum curvature, maximum curvature and cross-sectional curvature—were calculated by fitting a bivariate quadratic surface with a window size of 9×9 pixels. These surface curvatures are strongly related to landform features and geomorphological processes. Four morphometric parameters and seven Landsat-enhanced thematic mapper (ETM+) bands were used as input for the SOM algorithm. Once the network weights have been randomly initialized, different learning parameter sets, e.g. initial radius, final radius and number of iterations, were investigated. An optimal SOM with 20 classes using 1000 iterations and a final neighborhood radius of 0.05 provided a low average quantization error of 0.3394 and was used for further analysis. The effect of randomization of initial weights for optimal SOM was also studied. Feature space analysis, three-dimensional inspection and auxiliary data facilitated the assignment of semantic meaning to the output classes in terms of landform, based on morphometric analysis, and land use, based on spectral properties. Results were displayed as thematic map of landscape elements according to form, cover and slope. Spectral and morphometric signature analysis with corresponding zoom samples superimposed by contour lines were compared in detail to clarify the role of morphometric parameters to separate landscape elements. The results revealed the efficiency of SOM to integrate SRTM and Landsat data in landscape analysis. Despite the stochastic nature of SOM, the results in this particular study are not sensitive to randomization of initial weight vectors if many iterations are used. This procedure is reproducible for the same application with consistent results.

Effective and extensible feature extraction method using genetic algorithm-based frequency-domain feature search for epileptic EEG multiclassification

PubMed Central

Wen, Tingxi; Zhang, Zhongnan

2017-01-01

Abstract In this paper, genetic algorithm-based frequency-domain feature search (GAFDS) method is proposed for the electroencephalogram (EEG) analysis of epilepsy. In this method, frequency-domain features are first searched and then combined with nonlinear features. Subsequently, these features are selected and optimized to classify EEG signals. The extracted features are analyzed experimentally. The features extracted by GAFDS show remarkable independence, and they are superior to the nonlinear features in terms of the ratio of interclass distance and intraclass distance. Moreover, the proposed feature search method can search for features of instantaneous frequency in a signal after Hilbert transformation. The classification results achieved using these features are reasonable; thus, GAFDS exhibits good extensibility. Multiple classical classifiers (i.e., k-nearest neighbor, linear discriminant analysis, decision tree, AdaBoost, multilayer perceptron, and Naïve Bayes) achieve satisfactory classification accuracies by using the features generated by the GAFDS method and the optimized feature selection. The accuracies for 2-classification and 3-classification problems may reach up to 99% and 97%, respectively. Results of several cross-validation experiments illustrate that GAFDS is effective in the extraction of effective features for EEG classification. Therefore, the proposed feature selection and optimization model can improve classification accuracy. PMID:28489789

Effective and extensible feature extraction method using genetic algorithm-based frequency-domain feature search for epileptic EEG multiclassification.

PubMed

Wen, Tingxi; Zhang, Zhongnan

2017-05-01

In this paper, genetic algorithm-based frequency-domain feature search (GAFDS) method is proposed for the electroencephalogram (EEG) analysis of epilepsy. In this method, frequency-domain features are first searched and then combined with nonlinear features. Subsequently, these features are selected and optimized to classify EEG signals. The extracted features are analyzed experimentally. The features extracted by GAFDS show remarkable independence, and they are superior to the nonlinear features in terms of the ratio of interclass distance and intraclass distance. Moreover, the proposed feature search method can search for features of instantaneous frequency in a signal after Hilbert transformation. The classification results achieved using these features are reasonable; thus, GAFDS exhibits good extensibility. Multiple classical classifiers (i.e., k-nearest neighbor, linear discriminant analysis, decision tree, AdaBoost, multilayer perceptron, and Naïve Bayes) achieve satisfactory classification accuracies by using the features generated by the GAFDS method and the optimized feature selection. The accuracies for 2-classification and 3-classification problems may reach up to 99% and 97%, respectively. Results of several cross-validation experiments illustrate that GAFDS is effective in the extraction of effective features for EEG classification. Therefore, the proposed feature selection and optimization model can improve classification accuracy.

Phenotypes of intermediate forms of Fasciola hepatica and F. gigantica in buffaloes from Central Punjab, Pakistan.

PubMed

Afshan, K; Valero, M A; Qayyum, M; Peixoto, R V; Magraner, A; Mas-Coma, S

2014-12-01

Fascioliasis is an important food-borne parasitic disease caused by the two trematode species, Fasciola hepatica and Fasciola gigantica. The phenotypic features of fasciolid adults and eggs infecting buffaloes inhabiting the Central Punjab area, Pakistan, have been studied to characterize fasciolid populations involved. Morphometric analyses were made with a computer image analysis system (CIAS) applied on the basis of standardized measurements. Since it is the first study of this kind undertaken in Pakistan, the results are compared to pure fasciolid populations: (a) F. hepatica from the European Mediterranean area; and (b) F. gigantica from Burkina Faso; i.e. geographical areas where both species do not co-exist. Only parasites obtained from bovines were used. The multivariate analysis showed that the characteristics, including egg morphometrics, of fasciolids from Central Punjab, Pakistan, are between F. hepatica and F. gigantica standard populations. Similarly, the morphometric measurements of fasciolid eggs from Central Punjab are also between F. hepatica and F. gigantica standard populations. These results demonstrate the existence of fasciolid intermediate forms in endemic areas in Pakistan.

A support vector machine approach for classification of welding defects from ultrasonic signals

NASA Astrophysics Data System (ADS)

Chen, Yuan; Ma, Hong-Wei; Zhang, Guang-Ming

2014-07-01

Defect classification is an important issue in ultrasonic non-destructive evaluation. A layered multi-class support vector machine (LMSVM) classification system, which combines multiple SVM classifiers through a layered architecture, is proposed in this paper. The proposed LMSVM classification system is applied to the classification of welding defects from ultrasonic test signals. The measured ultrasonic defect echo signals are first decomposed into wavelet coefficients by the wavelet packet transform. The energy of the wavelet coefficients at different frequency channels are used to construct the feature vectors. The bees algorithm (BA) is then used for feature selection and SVM parameter optimisation for the LMSVM classification system. The BA-based feature selection optimises the energy feature vectors. The optimised feature vectors are input to the LMSVM classification system for training and testing. Experimental results of classifying welding defects demonstrate that the proposed technique is highly robust, precise and reliable for ultrasonic defect classification.

Automatic sleep stage classification of single-channel EEG by using complex-valued convolutional neural network.

PubMed

Zhang, Junming; Wu, Yan

2018-03-28

Many systems are developed for automatic sleep stage classification. However, nearly all models are based on handcrafted features. Because of the large feature space, there are so many features that feature selection should be used. Meanwhile, designing handcrafted features is a difficult and time-consuming task because the feature designing needs domain knowledge of experienced experts. Results vary when different sets of features are chosen to identify sleep stages. Additionally, many features that we may be unaware of exist. However, these features may be important for sleep stage classification. Therefore, a new sleep stage classification system, which is based on the complex-valued convolutional neural network (CCNN), is proposed in this study. Unlike the existing sleep stage methods, our method can automatically extract features from raw electroencephalography data and then classify sleep stage based on the learned features. Additionally, we also prove that the decision boundaries for the real and imaginary parts of a complex-valued convolutional neuron intersect orthogonally. The classification performances of handcrafted features are compared with those of learned features via CCNN. Experimental results show that the proposed method is comparable to the existing methods. CCNN obtains a better classification performance and considerably faster convergence speed than convolutional neural network. Experimental results also show that the proposed method is a useful decision-support tool for automatic sleep stage classification.

Aneurysms of the anterior and posterior cerebral circulation: comparison of the morphometric features.

PubMed

Tykocki, Tomasz; Kostkiewicz, Bogusław

2014-09-01

Intracranial aneurysms (IAs) located in the posterior circulation are considered to have higher annual bleed rates than those in the anterior circulation. The aim of the study was to compare the morphometric factors differentiating between IAs located in the anterior and posterior cerebral circulation. A total number of 254 IAs diagnosed between 2009 and 2012 were retrospectively analyzed. All patients qualified for diagnostic, three-dimensional rotational angiography. IAs were assigned to either the anterior or posterior cerebral circulation subsets for the analysis. Means were compared with a t-test. The univariate and stepwise logistic regression analyses were used to determine the predictors of morphometric differences between the groups. For the defined predictors, ROC (receiver-operating characteristic) curves and interactive dot diagrams were calculated with the cutoff values of the morphometric factors. The number of anterior cerebral circulation IAs was 179 (70.5 %); 141 (55.5 %) aneurysms were ruptured. Significant differences between anterior and posterior circulation IAs were found for: the parent artery size (5.08 ± 1.8 mm vs. 3.95 ± 1.5 mm; p < 0.05), size ratio (2.22 ± 0.9 vs. 3.19 ± 1.8; p < 0.045) and aspect ratio (AR) (1.91 ± 0.8 vs. 2.75 ± 1.8; p = 0.02). Predicting factors differentiating anterior and posterior circulation IAs were: the AR (OR = 2.20; 95 % CI 1.80-270; Is 270 correct or should it be 2.70 and parent artery size (OR = 0.44; 95 % CI 0.38-0.54). The cutoff point in the ROC curve was 2.185 for the AR and 4.89 mm for parent artery size. Aspect ratio and parent artery size were found to be predictive morphometric factors in differentiating between anterior and posterior cerebral IAs.

Toward optimal feature and time segment selection by divergence method for EEG signals classification.

PubMed

Wang, Jie; Feng, Zuren; Lu, Na; Luo, Jing

2018-06-01

Feature selection plays an important role in the field of EEG signals based motor imagery pattern classification. It is a process that aims to select an optimal feature subset from the original set. Two significant advantages involved are: lowering the computational burden so as to speed up the learning procedure and removing redundant and irrelevant features so as to improve the classification performance. Therefore, feature selection is widely employed in the classification of EEG signals in practical brain-computer interface systems. In this paper, we present a novel statistical model to select the optimal feature subset based on the Kullback-Leibler divergence measure, and automatically select the optimal subject-specific time segment. The proposed method comprises four successive stages: a broad frequency band filtering and common spatial pattern enhancement as preprocessing, features extraction by autoregressive model and log-variance, the Kullback-Leibler divergence based optimal feature and time segment selection and linear discriminate analysis classification. More importantly, this paper provides a potential framework for combining other feature extraction models and classification algorithms with the proposed method for EEG signals classification. Experiments on single-trial EEG signals from two public competition datasets not only demonstrate that the proposed method is effective in selecting discriminative features and time segment, but also show that the proposed method yields relatively better classification results in comparison with other competitive methods. Copyright © 2018 Elsevier Ltd. All rights reserved.

Analysis of Morphological Features of Benign and Malignant Breast Cell Extracted From FNAC Microscopic Image Using the Pearsonian System of Curves.

PubMed

Rajbongshi, Nijara; Bora, Kangkana; Nath, Dilip C; Das, Anup K; Mahanta, Lipi B

2018-01-01

Cytological changes in terms of shape and size of nuclei are some of the common morphometric features to study breast cancer, which can be observed by careful screening of fine needle aspiration cytology (FNAC) images. This study attempts to categorize a collection of FNAC microscopic images into benign and malignant classes based on family of probability distribution using some morphometric features of cell nuclei. For this study, features namely area, perimeter, eccentricity, compactness, and circularity of cell nuclei were extracted from FNAC images of both benign and malignant samples using an image processing technique. All experiments were performed on a generated FNAC image database containing 564 malignant (cancerous) and 693 benign (noncancerous) cell level images. The five-set extracted features were reduced to three-set (area, perimeter, and circularity) based on the mean statistic. Finally, the data were fitted to the generalized Pearsonian system of frequency curve, so that the resulting distribution can be used as a statistical model. Pearsonian system is a family of distributions where kappa (κ) is the selection criteria computed as functions of the first four central moments. For the benign group, kappa (κ) corresponding to area, perimeter, and circularity was -0.00004, 0.0000, and 0.04155 and for malignant group it was 1016942, 0.01464, and -0.3213, respectively. Thus, the family of distribution related to these features for the benign and malignant group were different, and therefore, characterization of their probability curve will also be different.

Improving Classification of Protein Interaction Articles Using Context Similarity-Based Feature Selection.

PubMed

Chen, Yifei; Sun, Yuxing; Han, Bing-Qing

2015-01-01

Protein interaction article classification is a text classification task in the biological domain to determine which articles describe protein-protein interactions. Since the feature space in text classification is high-dimensional, feature selection is widely used for reducing the dimensionality of features to speed up computation without sacrificing classification performance. Many existing feature selection methods are based on the statistical measure of document frequency and term frequency. One potential drawback of these methods is that they treat features separately. Hence, first we design a similarity measure between the context information to take word cooccurrences and phrase chunks around the features into account. Then we introduce the similarity of context information to the importance measure of the features to substitute the document and term frequency. Hence we propose new context similarity-based feature selection methods. Their performance is evaluated on two protein interaction article collections and compared against the frequency-based methods. The experimental results reveal that the context similarity-based methods perform better in terms of the F1 measure and the dimension reduction rate. Benefiting from the context information surrounding the features, the proposed methods can select distinctive features effectively for protein interaction article classification.

Analysing the floral elements of the lost tree of Easter Island: a morphometric comparison between the remaining ex-situ lines of the endemic extinct species Sophora toromiro.

PubMed

Püschel, Thomas A; Espejo, Jaime; Sanzana, María-José; Benítez, Hugo A

2014-01-01

Sophora toromiro (Phil) Skottsb. is a species that has been extinct in its natural habitat Easter Island (Rapa Nui) for over 50 years. However, seed collections carried out before its extinction have allowed its persistence ex-situ in different botanical gardens and private collections around the world. The progenies of these diverse collections have been classified in different lines, most of them exhibiting high similarity as corroborated by molecular markers. In spite of this resemblance observed between the different lines, one of them (Titze) has dissimilar floral elements, thus generating doubts regarding its species classification. The floral elements (wing, standard and keel) belonging to three different S. toromiro lines and two related species were analyzed using geometric morphometrics. This method was applied in order to quantify the floral shape variation of the standard, wing, and keel between the different lines and control species. Geometric morphometrics analyses were able to distinguish the floral elements at both intra (lines) and inter-specific levels. The present results are on line with the cumulative evidence that supports the Titze line as not being a proper member of the S. toromiro species, but probably a hybridization product or even another species of the Edwardsia section. The reintroduction programs of S. toromiro should consider this information when assessing the authenticity and origin of the lines that will be used to repopulate the island.

Cyprinid fishes of the genus Neolissochilus in Peninsular Malaysia.

PubMed

Khaironizam, M Z; Akaria-Ismail, M; Armbruster, Jonathan W

2015-05-22

Meristic, morphometric and distributional patterns of cyprinid fishes of the genus Neolissochilus found in Peninsular Malaysia are presented. Based on the current concept of Neolissochilus, only two species are present: N. soroides and N. hendersoni. Neolissochilus hendersoni differs from N. soroides by having lower scale and gill raker counts. Neolissochilus soroides has three mouth types (normal with a rounded snout, snout with a truncate edge, and lobe with a comparatively thick lower lip). A PCA of log-transformed measurements did not reveal significant differences between N. hendersoni and N. soroides, or between any of the morphotypes of N. soroides; however, a CVA of log-transformed measurements successfully classified 87.1% of all specimens. Removing body size by running a CVA on all of the principal components except PC1 (which was correlated with length) only slightly decreased the successful classification rate to 86.1%. Differences in morphometrics were as great between the three morphotypes of N. soroides as between any of the morphotypes and N. hendersoni suggesting that the morphotypes should be examined in greater detail with genetic tools. The PCA of morphometrics revealed separate clouds for N. hendersoni and N. soroides, but no differences between the N. soroides morphotypes. This study revealed that N. hendersoni is recorded for the first time in the mainland area of Peninsular Malaysia. Other nominal species of Neolissochilus reported to occur in the river systems of Peninsular Malaysia are discussed. Lissochilus tweediei Herre in Herre & Myers 1937 and Tor soro Bishop 1973 are synonyms of Neolissochilus soroides.

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.

Joint Feature Selection and Classification for Multilabel Learning.

PubMed

Huang, Jun; Li, Guorong; Huang, Qingming; Wu, Xindong

2018-03-01

Multilabel learning deals with examples having multiple class labels simultaneously. It has been applied to a variety of applications, such as text categorization and image annotation. A large number of algorithms have been proposed for multilabel learning, most of which concentrate on multilabel classification problems and only a few of them are feature selection algorithms. Current multilabel classification models are mainly built on a single data representation composed of all the features which are shared by all the class labels. Since each class label might be decided by some specific features of its own, and the problems of classification and feature selection are often addressed independently, in this paper, we propose a novel method which can perform joint feature selection and classification for multilabel learning, named JFSC. Different from many existing methods, JFSC learns both shared features and label-specific features by considering pairwise label correlations, and builds the multilabel classifier on the learned low-dimensional data representations simultaneously. A comparative study with state-of-the-art approaches manifests a competitive performance of our proposed method both in classification and feature selection for multilabel learning.

Ensemble methods with simple features for document zone classification

NASA Astrophysics Data System (ADS)

Obafemi-Ajayi, Tayo; Agam, Gady; Xie, Bingqing

2012-01-01

Document layout analysis is of fundamental importance for document image understanding and information retrieval. It requires the identification of blocks extracted from a document image via features extraction and block classification. In this paper, we focus on the classification of the extracted blocks into five classes: text (machine printed), handwriting, graphics, images, and noise. We propose a new set of features for efficient classifications of these blocks. We present a comparative evaluation of three ensemble based classification algorithms (boosting, bagging, and combined model trees) in addition to other known learning algorithms. Experimental results are demonstrated for a set of 36503 zones extracted from 416 document images which were randomly selected from the tobacco legacy document collection. The results obtained verify the robustness and effectiveness of the proposed set of features in comparison to the commonly used Ocropus recognition features. When used in conjunction with the Ocropus feature set, we further improve the performance of the block classification system to obtain a classification accuracy of 99.21%.

Multi-Temporal Classification and Change Detection Using Uav Images

NASA Astrophysics Data System (ADS)

Makuti, S.; Nex, F.; Yang, M. Y.

2018-05-01

In this paper different methodologies for the classification and change detection of UAV image blocks are explored. UAV is not only the cheapest platform for image acquisition but it is also the easiest platform to operate in repeated data collections over a changing area like a building construction site. Two change detection techniques have been evaluated in this study: the pre-classification and the post-classification algorithms. These methods are based on three main steps: feature extraction, classification and change detection. A set of state of the art features have been used in the tests: colour features (HSV), textural features (GLCM) and 3D geometric features. For classification purposes Conditional Random Field (CRF) has been used: the unary potential was determined using the Random Forest algorithm while the pairwise potential was defined by the fully connected CRF. In the performed tests, different feature configurations and settings have been considered to assess the performance of these methods in such challenging task. Experimental results showed that the post-classification approach outperforms the pre-classification change detection method. This was analysed using the overall accuracy, where by post classification have an accuracy of up to 62.6 % and the pre classification change detection have an accuracy of 46.5 %. These results represent a first useful indication for future works and developments.

Automatic topic identification of health-related messages in online health community using text classification.

PubMed

Lu, Yingjie

2013-01-01

To facilitate patient involvement in online health community and obtain informative support and emotional support they need, a topic identification approach was proposed in this paper for identifying automatically topics of the health-related messages in online health community, thus assisting patients in reaching the most relevant messages for their queries efficiently. Feature-based classification framework was presented for automatic topic identification in our study. We first collected the messages related to some predefined topics in a online health community. Then we combined three different types of features, n-gram-based features, domain-specific features and sentiment features to build four feature sets for health-related text representation. Finally, three different text classification techniques, C4.5, Naïve Bayes and SVM were adopted to evaluate our topic classification model. By comparing different feature sets and different classification techniques, we found that n-gram-based features, domain-specific features and sentiment features were all considered to be effective in distinguishing different types of health-related topics. In addition, feature reduction technique based on information gain was also effective to improve the topic classification performance. In terms of classification techniques, SVM outperformed C4.5 and Naïve Bayes significantly. The experimental results demonstrated that the proposed approach could identify the topics of online health-related messages efficiently.

Feature Selection for Chemical Sensor Arrays Using Mutual Information

PubMed Central

Wang, X. Rosalind; Lizier, Joseph T.; Nowotny, Thomas; Berna, Amalia Z.; Prokopenko, Mikhail; Trowell, Stephen C.

2014-01-01

We address the problem of feature selection for classifying a diverse set of chemicals using an array of metal oxide sensors. Our aim is to evaluate a filter approach to feature selection with reference to previous work, which used a wrapper approach on the same data set, and established best features and upper bounds on classification performance. We selected feature sets that exhibit the maximal mutual information with the identity of the chemicals. The selected features closely match those found to perform well in the previous study using a wrapper approach to conduct an exhaustive search of all permitted feature combinations. By comparing the classification performance of support vector machines (using features selected by mutual information) with the performance observed in the previous study, we found that while our approach does not always give the maximum possible classification performance, it always selects features that achieve classification performance approaching the optimum obtained by exhaustive search. We performed further classification using the selected feature set with some common classifiers and found that, for the selected features, Bayesian Networks gave the best performance. Finally, we compared the observed classification performances with the performance of classifiers using randomly selected features. We found that the selected features consistently outperformed randomly selected features for all tested classifiers. The mutual information filter approach is therefore a computationally efficient method for selecting near optimal features for chemical sensor arrays. PMID:24595058

Morphometrics applied to medical entomology.

PubMed

Dujardin, Jean-Pierre

2008-12-01

Morphometrics underwent a revolution more than one decade ago. In the modern morphometrics, the estimate of size is now contained in a single variable reflecting variation in many directions, as many as there are landmarks under study, and shape is defined as their relative positions after correcting for size, position and orientation. With these informative data, and the corresponding software freely available to conduct complex analyses, significant biological and epidemiological features can be quantified more accurately. We discuss the evolutionary significance of the environmental impact on metric variability, mentioning the importance of concepts like genetic assimilation, genetic accommodation, and epigenetics. We provide examples of measuring the effect of selection on metric variation by comparing (unpublished) Qst values with corresponding (published) Fst. The primary needs of medical entomologists are to distinguish species, especially cryptic species, and to detect them where they are not expected. We explain how geometric morphometrics could apply to these questions, and where there are deficiencies preventing the approach from being utilized at its maximum potential. Medical entomologists in connection with control programs aim to identify isolated populations where the risk of reinfestation after treatment would be low ("biogeographical islands"). Identifying them can be obtained from estimating the number of migrants per generation. Direct assessment of movement remains the most valid approach, but it scores active movement only. Genetic methods estimating gene flow levels among interbreeding populations are commonly used, but gene flow does not necessarily mean the current flow of migrants. Methods using the morphometric variation are neither suited to evaluate gene flow, nor are they adapted to estimate the flow of migrants. They may provide, however, the information needed to create a preliminary map pointing to relevant areas where one could invest in using molecular machinery. In case of reinfesting specimens after treatment, the question relates to the likely source of reinfesting specimens: are they a residual sample not affected by the control measures, or are they individuals migrating from surrounding, untreated foci? We explain why the morphometric approach may be adapted to answer such question. Thus, we describe the differences between estimating the flow of migrants and identifying the source of reinfestation after treatment: although morphometrics is not suited to deal with the former, it may be an appropriate tool to address the latter.

Integrated feature extraction and selection for neuroimage classification

NASA Astrophysics Data System (ADS)

Fan, Yong; Shen, Dinggang

2009-02-01

Feature extraction and selection are of great importance in neuroimage classification for identifying informative features and reducing feature dimensionality, which are generally implemented as two separate steps. This paper presents an integrated feature extraction and selection algorithm with two iterative steps: constrained subspace learning based feature extraction and support vector machine (SVM) based feature selection. The subspace learning based feature extraction focuses on the brain regions with higher possibility of being affected by the disease under study, while the possibility of brain regions being affected by disease is estimated by the SVM based feature selection, in conjunction with SVM classification. This algorithm can not only take into account the inter-correlation among different brain regions, but also overcome the limitation of traditional subspace learning based feature extraction methods. To achieve robust performance and optimal selection of parameters involved in feature extraction, selection, and classification, a bootstrapping strategy is used to generate multiple versions of training and testing sets for parameter optimization, according to the classification performance measured by the area under the ROC (receiver operating characteristic) curve. The integrated feature extraction and selection method is applied to a structural MR image based Alzheimer's disease (AD) study with 98 non-demented and 100 demented subjects. Cross-validation results indicate that the proposed algorithm can improve performance of the traditional subspace learning based classification.

10 CFR 1045.17 - Classification levels.

Code of Federal Regulations, 2014 CFR

2014-01-01

... classification include detailed technical descriptions of critical features of a nuclear explosive design that... classification include designs for specific weapon components (not revealing critical features), key features of uranium enrichment technologies, or specifications of weapon materials. (3) Confidential. The Director of...

10 CFR 1045.17 - Classification levels.

Code of Federal Regulations, 2013 CFR

2013-01-01

... classification include detailed technical descriptions of critical features of a nuclear explosive design that... classification include designs for specific weapon components (not revealing critical features), key features of uranium enrichment technologies, or specifications of weapon materials. (3) Confidential. The Director of...

10 CFR 1045.17 - Classification levels.

Code of Federal Regulations, 2011 CFR

2011-01-01

... classification include detailed technical descriptions of critical features of a nuclear explosive design that... classification include designs for specific weapon components (not revealing critical features), key features of uranium enrichment technologies, or specifications of weapon materials. (3) Confidential. The Director of...

10 CFR 1045.17 - Classification levels.

Code of Federal Regulations, 2012 CFR

2012-01-01

... classification include detailed technical descriptions of critical features of a nuclear explosive design that... classification include designs for specific weapon components (not revealing critical features), key features of uranium enrichment technologies, or specifications of weapon materials. (3) Confidential. The Director of...

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.

Clinical, morphologic, and morphometric features of cranial thoracic spinal stenosis in large and giant breed dogs.

PubMed

Johnson, Philippa; De Risio, Luisa; Sparkes, Andrew; McConnell, Fraser; Holloway, Andrew

2012-01-01

The clinical, morphologic, and morphometric features of cranial thoracic spinal stenosis were investigated in large and giant breed dogs. Seventy-nine magnetic resonance imaging studies of the cranial thoracic spine were assessed. Twenty-six were retrieved retrospectively and 53 were acquired prospectively using the same inclusion criteria. Images were evaluated using a modified compression scale as: no osseous stenosis (grade 0), osseous stenosis without spinal cord compression (grade 1), and osseous stenosis with spinal cord compression (grade 2). Morphometric analysis was performed and compared to the subjective grading system. Grades 1 and 2 cranial thoracic spinal stenosis were identified on 24 imaging studies in 23 dogs. Sixteen of 23 dogs had a conformation typified by Molosser breeds and 21/23 were male. The most common sites of stenosis were T2-3 and T3-4. The articular process joints were enlarged with abnormal oblique orientation. Stenosis was dorsolateral, lateralized, or dorsoventral. Concurrent osseous cervical spondylomyelopathy was recognized in six dogs and other neurologic disease in five dogs. Cranial thoracic spinal stenosis was the only finding in 12 dogs. In 9 of these 12 dogs (all grade 2) neurolocalization was to the T3-L3 spinal segment. The median age of these dogs was 9.5 months. In the remaining three dogs neurologic signs were not present. Stenosis ratios were of limited benefit in detecting stenotic sites. Grade 2 cranial thoracic spinal stenosis causing direct spinal cord compression may lead to neurologic signs, however milder stenosis (grade 1) is likely to be subclinical or incidental. © 2012 Veterinary Radiology & Ultrasound.

Vessel Classification in Cosmo-Skymed SAR Data Using Hierarchical Feature Selection

NASA Astrophysics Data System (ADS)

Makedonas, A.; Theoharatos, C.; Tsagaris, V.; Anastasopoulos, V.; Costicoglou, S.

2015-04-01

SAR based ship detection and classification are important elements of maritime monitoring applications. Recently, high-resolution SAR data have opened new possibilities to researchers for achieving improved classification results. In this work, a hierarchical vessel classification procedure is presented based on a robust feature extraction and selection scheme that utilizes scale, shape and texture features in a hierarchical way. Initially, different types of feature extraction algorithms are implemented in order to form the utilized feature pool, able to represent the structure, material, orientation and other vessel type characteristics. A two-stage hierarchical feature selection algorithm is utilized next in order to be able to discriminate effectively civilian vessels into three distinct types, in COSMO-SkyMed SAR images: cargos, small ships and tankers. In our analysis, scale and shape features are utilized in order to discriminate smaller types of vessels present in the available SAR data, or shape specific vessels. Then, the most informative texture and intensity features are incorporated in order to be able to better distinguish the civilian types with high accuracy. A feature selection procedure that utilizes heuristic measures based on features' statistical characteristics, followed by an exhaustive research with feature sets formed by the most qualified features is carried out, in order to discriminate the most appropriate combination of features for the final classification. In our analysis, five COSMO-SkyMed SAR data with 2.2m x 2.2m resolution were used to analyse the detailed characteristics of these types of ships. A total of 111 ships with available AIS data were used in the classification process. The experimental results show that this method has good performance in ship classification, with an overall accuracy reaching 83%. Further investigation of additional features and proper feature selection is currently in progress.

Data Field Modeling and Spectral-Spatial Feature Fusion for Hyperspectral Data Classification.

PubMed

Liu, Da; Li, Jianxun

2016-12-16

Classification is a significant subject in hyperspectral remote sensing image processing. This study proposes a spectral-spatial feature fusion algorithm for the classification of hyperspectral images (HSI). Unlike existing spectral-spatial classification methods, the influences and interactions of the surroundings on each measured pixel were taken into consideration in this paper. Data field theory was employed as the mathematical realization of the field theory concept in physics, and both the spectral and spatial domains of HSI were considered as data fields. Therefore, the inherent dependency of interacting pixels was modeled. Using data field modeling, spatial and spectral features were transformed into a unified radiation form and further fused into a new feature by using a linear model. In contrast to the current spectral-spatial classification methods, which usually simply stack spectral and spatial features together, the proposed method builds the inner connection between the spectral and spatial features, and explores the hidden information that contributed to classification. Therefore, new information is included for classification. The final classification result was obtained using a random forest (RF) classifier. The proposed method was tested with the University of Pavia and Indian Pines, two well-known standard hyperspectral datasets. The experimental results demonstrate that the proposed method has higher classification accuracies than those obtained by the traditional approaches.

Sequence-based genotyping clarifies conflicting historical morphometric and biological data for 5 Eimeria species infecting turkeys.

PubMed

El-Sherry, S; Ogedengbe, M E; Hafeez, M A; Sayf-Al-Din, M; Gad, N; Barta, J R

2015-02-01

Unlike with Eimeria species infecting chickens, specific identification and nomenclature of Eimeria species infecting turkeys is complicated, and in the absence of molecular data, imprecise. In an attempt to reconcile contradictory data reported on oocyst morphometrics and biological descriptions of various Eimeria species infecting turkey, we established single oocyst derived lines of 5 important Eimeria species infecting turkeys, Eimeria meleagrimitis (USMN08-01 strain), Eimeria adenoeides (Guelph strain), Eimeria gallopavonis (Weybridge strain), Eimeria meleagridis (USAR97-01 strain), and Eimeria dispersa (Briston strain). Short portions (514 bp) of mitochondrial cytochrome c oxidase subunit I gene (mt COI) from each were amplified and sequenced. Comparison of these sequences showed sufficient species-specific sequence variation to recommend these short mt COI sequences as species-specific markers. Uniformity of oocyst features (dimensions and oocyst structure) of each pure line was observed. Additional morphological features of the oocysts of these species are described as useful for the microscopic differentiation of these Eimeria species. Combined molecular and morphometric data on these single species lines compared with the original species descriptions and more recent data have helped to clarify some confusing, and sometimes conflicting, features associated with these Eimeria spp. For example, these new data suggest that the KCH and KR strains of E. adenoeides reported previously represent 2 distinct species, E. adenoeides and E. meleagridis, respectively. Likewise, analysis of the Weybridge strain of E. adenoeides, which has long been used as a reference strain in various studies conducted on the pathogenicity of E. adenoeides, indicates that this coccidium is actually a strain of E. gallopavonis. We highly recommend mt COI sequence-based genotyping be incorporated into all studies using Eimeria spp. of turkeys to confirm species identifications and so that any resulting data can be associated correctly with a single named Eimeria species. © 2015 Poultry Science Association Inc.

A New Direction of Cancer Classification: Positive Effect of Low-Ranking MicroRNAs.

PubMed

Li, Feifei; Piao, Minghao; Piao, Yongjun; Li, Meijing; Ryu, Keun Ho

2014-10-01

Many studies based on microRNA (miRNA) expression profiles showed a new aspect of cancer classification. Because one characteristic of miRNA expression data is the high dimensionality, feature selection methods have been used to facilitate dimensionality reduction. The feature selection methods have one shortcoming thus far: they just consider the problem of where feature to class is 1:1 or n:1. However, because one miRNA may influence more than one type of cancer, human miRNA is considered to be ranked low in traditional feature selection methods and are removed most of the time. In view of the limitation of the miRNA number, low-ranking miRNAs are also important to cancer classification. We considered both high- and low-ranking features to cover all problems (1:1, n:1, 1:n, and m:n) in cancer classification. First, we used the correlation-based feature selection method to select the high-ranking miRNAs, and chose the support vector machine, Bayes network, decision tree, k-nearest-neighbor, and logistic classifier to construct cancer classification. Then, we chose Chi-square test, information gain, gain ratio, and Pearson's correlation feature selection methods to build the m:n feature subset, and used the selected miRNAs to determine cancer classification. The low-ranking miRNA expression profiles achieved higher classification accuracy compared with just using high-ranking miRNAs in traditional feature selection methods. Our results demonstrate that the m:n feature subset made a positive impression of low-ranking miRNAs in cancer classification.

Feature selection and classification of multiparametric medical images using bagging and SVM

NASA Astrophysics Data System (ADS)

Fan, Yong; Resnick, Susan M.; Davatzikos, Christos

2008-03-01

This paper presents a framework for brain classification based on multi-parametric medical images. This method takes advantage of multi-parametric imaging to provide a set of discriminative features for classifier construction by using a regional feature extraction method which takes into account joint correlations among different image parameters; in the experiments herein, MRI and PET images of the brain are used. Support vector machine classifiers are then trained based on the most discriminative features selected from the feature set. To facilitate robust classification and optimal selection of parameters involved in classification, in view of the well-known "curse of dimensionality", base classifiers are constructed in a bagging (bootstrap aggregating) framework for building an ensemble classifier and the classification parameters of these base classifiers are optimized by means of maximizing the area under the ROC (receiver operating characteristic) curve estimated from their prediction performance on left-out samples of bootstrap sampling. This classification system is tested on a sex classification problem, where it yields over 90% classification rates for unseen subjects. The proposed classification method is also compared with other commonly used classification algorithms, with favorable results. These results illustrate that the methods built upon information jointly extracted from multi-parametric images have the potential to perform individual classification with high sensitivity and specificity.

New feature extraction method for classification of agricultural products from x-ray images

NASA Astrophysics Data System (ADS)

Talukder, Ashit; Casasent, David P.; Lee, Ha-Woon; Keagy, Pamela M.; Schatzki, Thomas F.

1999-01-01

Classification of real-time x-ray images of randomly oriented touching pistachio nuts is discussed. The ultimate objective is the development of a system for automated non- invasive detection of defective product items on a conveyor belt. We discuss the extraction of new features that allow better discrimination between damaged and clean items. This feature extraction and classification stage is the new aspect of this paper; our new maximum representation and discrimination between damaged and clean items. This feature extraction and classification stage is the new aspect of this paper; our new maximum representation and discriminating feature (MRDF) extraction method computes nonlinear features that are used as inputs to a new modified k nearest neighbor classifier. In this work the MRDF is applied to standard features. The MRDF is robust to various probability distributions of the input class and is shown to provide good classification and new ROC data.

PCA based feature reduction to improve the accuracy of decision tree c4.5 classification

NASA Astrophysics Data System (ADS)

Nasution, M. Z. F.; Sitompul, O. S.; Ramli, M.

2018-03-01

Splitting attribute is a major process in Decision Tree C4.5 classification. However, this process does not give a significant impact on the establishment of the decision tree in terms of removing irrelevant features. It is a major problem in decision tree classification process called over-fitting resulting from noisy data and irrelevant features. In turns, over-fitting creates misclassification and data imbalance. Many algorithms have been proposed to overcome misclassification and overfitting on classifications Decision Tree C4.5. Feature reduction is one of important issues in classification model which is intended to remove irrelevant data in order to improve accuracy. The feature reduction framework is used to simplify high dimensional data to low dimensional data with non-correlated attributes. In this research, we proposed a framework for selecting relevant and non-correlated feature subsets. We consider principal component analysis (PCA) for feature reduction to perform non-correlated feature selection and Decision Tree C4.5 algorithm for the classification. From the experiments conducted using available data sets from UCI Cervical cancer data set repository with 858 instances and 36 attributes, we evaluated the performance of our framework based on accuracy, specificity and precision. Experimental results show that our proposed framework is robust to enhance classification accuracy with 90.70% accuracy rates.

Feature extraction based on extended multi-attribute profiles and sparse autoencoder for remote sensing image classification

NASA Astrophysics Data System (ADS)

Teffahi, Hanane; Yao, Hongxun; Belabid, Nasreddine; Chaib, Souleyman

2018-02-01

The satellite images with very high spatial resolution have been recently widely used in image classification topic as it has become challenging task in remote sensing field. Due to a number of limitations such as the redundancy of features and the high dimensionality of the data, different classification methods have been proposed for remote sensing images classification particularly the methods using feature extraction techniques. This paper propose a simple efficient method exploiting the capability of extended multi-attribute profiles (EMAP) with sparse autoencoder (SAE) for remote sensing image classification. The proposed method is used to classify various remote sensing datasets including hyperspectral and multispectral images by extracting spatial and spectral features based on the combination of EMAP and SAE by linking them to kernel support vector machine (SVM) for classification. Experiments on new hyperspectral image "Huston data" and multispectral image "Washington DC data" shows that this new scheme can achieve better performance of feature learning than the primitive features, traditional classifiers and ordinary autoencoder and has huge potential to achieve higher accuracy for classification in short running time.

Closed depressions in the European loess belt - Natural or anthropogenic origin?

NASA Astrophysics Data System (ADS)

Kołodyńska-Gawrysiak, Renata; Poesen, Jean

2017-07-01

Closed depressions (CDs) are typical geomorphological features of the loess belt in Europe. CDs have been reported in several regions of the European loess belt, where they are described as hollows, mardeles, wymoki, crovuri, bludtsa and zapadiny. The natural and anthropogenic origins of CDs are debated in literature. Moreover, no comprehensive review of the geomorphic properties or the evolution of these depressions exists. Therefore this paper reviews the characteristics of CDs in the European loess belt and attempts to better understand their genesis based on detailed case studies. The main morphometric features as well as sediment deposits within CDs in several sub-regions of Europe were analysed and compared. Morphometric properties of CDs from the West European and East European loess belt were investigated through a comparison of CDs from two representative regions, i.e. East Poland and Central Belgium. In both study areas, CDs under cropland are similar. However, a comparison of morphological features of CDs under forest, revealed clear differences, suggesting a different origin of CDs from both regions. Infilled sediments in CDs show various litho-genetical features in different regions of the European loess belt. The morphometric features, ages and stratigraphy of infillings clearly indicate that both anthropogenic and natural processes have shaped these landforms within the loess belt of Europe. CDs observed in Eastern Europe may have a very different origin than those documented in Western Europe. Detailed analysis of CDs in Poland and in neighbouring regions of East Europe, suggest that CDs are natural landforms: thermokarst, deflation and dissolution of loess are reported as probable genetic processes. In contrast, several studies in Western Europe indicate a dominant anthropogenic origin (i.e. digging of calcareous loess or marls, bomb and mining craters, collapse of underground limestone quarries), although CDs formed by natural processes (i.e. piping, dissolution of limestone and salt lenses below the loess cover) are reported as well. CDs act as important archives, allowing one to reconstruct both natural and anthropogenic processes operating in the past. As CDs store most sediments eroded within their catchment they provide ideal sediment traps to assess long-term erosion rates in these environments which have hitherto been under-researched. More research is needed to unravel the genesis and evolution of these depressions to better understand the importance of the Late Glacial and Holocene stages for the morphogenesis of the loess belt in Europe.

A Features Selection for Crops Classification

NASA Astrophysics Data System (ADS)

Liu, Yifan; Shao, Luyi; Yin, Qiang; Hong, Wen

2016-08-01

The components of the polarimetric target decomposition reflect the differences of target since they linked with the scattering properties of the target and can be imported into SVM as the classification features. The result of decomposition usually concentrate on part of the components. Selecting a combination of components can reduce the features that importing into the SVM. The features reduction can lead to less calculation and targeted classification of one target when we classify a multi-class area. In this research, we import different combinations of features into the SVM and find a better combination for classification with a data of AGRISAR.

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

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 spectrum fractal feature classification algorithm for agriculture crops with hyper spectrum image

NASA Astrophysics Data System (ADS)

Su, Junying

2011-11-01

A fractal dimension feature analysis method in spectrum domain for hyper spectrum image is proposed for agriculture crops classification. Firstly, a fractal dimension calculation algorithm in spectrum domain is presented together with the fast fractal dimension value calculation algorithm using the step measurement method. Secondly, the hyper spectrum image classification algorithm and flowchart is presented based on fractal dimension feature analysis in spectrum domain. Finally, the experiment result of the agricultural crops classification with FCL1 hyper spectrum image set with the proposed method and SAM (spectral angle mapper). The experiment results show it can obtain better classification result than the traditional SAM feature analysis which can fulfill use the spectrum information of hyper spectrum image to realize precision agricultural crops classification.

Spectral dependence of texture features integrated with hyperspectral data for area target classification improvement

NASA Astrophysics Data System (ADS)

Bangs, Corey F.; Kruse, Fred A.; Olsen, Chris R.

2013-05-01

Hyperspectral data were assessed to determine the effect of integrating spectral data and extracted texture feature data on classification accuracy. Four separate spectral ranges (hundreds of spectral bands total) were used from the Visible and Near Infrared (VNIR) and Shortwave Infrared (SWIR) portions of the electromagnetic spectrum. Haralick texture features (contrast, entropy, and correlation) were extracted from the average gray-level image for each of the four spectral ranges studied. A maximum likelihood classifier was trained using a set of ground truth regions of interest (ROIs) and applied separately to the spectral data, texture data, and a fused dataset containing both. Classification accuracy was measured by comparison of results to a separate verification set of test ROIs. Analysis indicates that the spectral range (source of the gray-level image) used to extract the texture feature data has a significant effect on the classification accuracy. This result applies to texture-only classifications as well as the classification of integrated spectral data and texture feature data sets. Overall classification improvement for the integrated data sets was near 1%. Individual improvement for integrated spectral and texture classification of the "Urban" class showed approximately 9% accuracy increase over spectral-only classification. Texture-only classification accuracy was highest for the "Dirt Path" class at approximately 92% for the spectral range from 947 to 1343nm. This research demonstrates the effectiveness of texture feature data for more accurate analysis of hyperspectral data and the importance of selecting the correct spectral range to be used for the gray-level image source to extract these features.

Classification of product inspection items using nonlinear features

NASA Astrophysics Data System (ADS)

Talukder, Ashit; Casasent, David P.; Lee, H.-W.

1998-03-01

Automated processing and classification of real-time x-ray images of randomly oriented touching pistachio nuts is discussed. The ultimate objective is the development of a system for automated non-invasive detection of defective product items on a conveyor belt. This approach involves two main steps: preprocessing and classification. Preprocessing locates individual items and segments ones that touch using a modified watershed algorithm. The second stage involves extraction of features that allow discrimination between damaged and clean items (pistachio nuts). This feature extraction and classification stage is the new aspect of this paper. We use a new nonlinear feature extraction scheme called the maximum representation and discriminating feature (MRDF) extraction method to compute nonlinear features that are used as inputs to a classifier. The MRDF is shown to provide better classification and a better ROC (receiver operating characteristic) curve than other methods.

Gender classification under extended operating conditions

NASA Astrophysics Data System (ADS)

Rude, Howard N.; Rizki, Mateen

2014-06-01

Gender classification is a critical component of a robust image security system. Many techniques exist to perform gender classification using facial features. In contrast, this paper explores gender classification using body features extracted from clothed subjects. Several of the most effective types of features for gender classification identified in literature were implemented and applied to the newly developed Seasonal Weather And Gender (SWAG) dataset. SWAG contains video clips of approximately 2000 samples of human subjects captured over a period of several months. The subjects are wearing casual business attire and outer garments appropriate for the specific weather conditions observed in the Midwest. The results from a series of experiments are presented that compare the classification accuracy of systems that incorporate various types and combinations of features applied to multiple looks at subjects at different image resolutions to determine a baseline performance for gender classification.

Exploring Deep Learning and Transfer Learning for Colonic Polyp Classification

PubMed Central

Uhl, Andreas; Wimmer, Georg; Häfner, Michael

2016-01-01

Recently, Deep Learning, especially through Convolutional Neural Networks (CNNs) has been widely used to enable the extraction of highly representative features. This is done among the network layers by filtering, selecting, and using these features in the last fully connected layers for pattern classification. However, CNN training for automated endoscopic image classification still provides a challenge due to the lack of large and publicly available annotated databases. In this work we explore Deep Learning for the automated classification of colonic polyps using different configurations for training CNNs from scratch (or full training) and distinct architectures of pretrained CNNs tested on 8-HD-endoscopic image databases acquired using different modalities. We compare our results with some commonly used features for colonic polyp classification and the good results suggest that features learned by CNNs trained from scratch and the “off-the-shelf” CNNs features can be highly relevant for automated classification of colonic polyps. Moreover, we also show that the combination of classical features and “off-the-shelf” CNNs features can be a good approach to further improve the results. PMID:27847543

Generalizing roughness: experiments with flow-oriented roughness

NASA Astrophysics Data System (ADS)

Trevisani, Sebastiano

2015-04-01

Surface texture analysis applied to High Resolution Digital Terrain Models (HRDTMs) improves the capability to characterize fine-scale morphology and permits the derivation of useful morphometric indexes. An important indicator to be taken into account in surface texture analysis is surface roughness, which can have a discriminant role in the detection of different geomorphic processes and factors. The evaluation of surface roughness is generally performed considering it as an isotropic surface parameter (e.g., Cavalli, 2008; Grohmann, 2011). However, surface texture has often an anisotropic character, which means that surface roughness could change according to the considered direction. In some applications, for example involving surface flow processes, the anisotropy of roughness should be taken into account (e.g., Trevisani, 2012; Smith, 2014). Accordingly, we test the application of a flow-oriented directional measure of roughness, computed considering surface gravity-driven flow. For the calculation of flow-oriented roughness we use both classical variogram-based roughness (e.g., Herzfeld,1996; Atkinson, 2000) as well as an ad-hoc developed robust modification of variogram (i.e. MAD, Trevisani, 2014). The presented approach, based on a D8 algorithm, shows the potential impact of considering directionality in the calculation of roughness indexes. The use of flow-oriented roughness could improve the definition of effective proxies of impedance to flow. Preliminary results on the integration of directional roughness operators with morphometric-based models, are promising and can be extended to more complex approaches. Atkinson, P.M., Lewis, P., 2000. Geostatistical classification for remote sensing: an introduction. Computers & Geosciences 26, 361-371. Cavalli, M. & Marchi, L. 2008, "Characterization of the surface morphology of an alpine alluvial fan using airborne LiDAR", Natural Hazards and Earth System Science, vol. 8, no. 2, pp. 323-333. Grohmann, C.H., Smith, M.J., Riccomini, C., 2011. Multiscale Analysis of Topographic Surface Roughness in the Midland Valley, Scotland. IEEE Transactions on Geoscience and Remote Sensing 49, 1220-1213. Herzfeld, U.C., Higginson, C.A., 1996. Automated geostatistical seafloor classification - Principles, parameters, feature vectors, and discrimination criteria. Computers and Geosciences, 22 (1), pp. 35-52. Smith, M.W. 2014, "Roughness in the Earth Sciences", Earth-Science Reviews, vol. 136, pp. 202-225. Trevisani, S., Cavalli, M. & Marchi, L. 2012, "Surface texture analysis of a high-resolution DTM: Interpreting an alpine basin", Geomorphology, vol. 161-162, pp. 26-39. Trevisani S., Rocca M., 2014. Geomorphometric analysis of fine-scale morphology for extensive areas: a new surface-texture operator. Geophysical Research Abstracts, Vol. 16, EGU2014-5612, 2014. EGU General Assembly 2014.

A liver cirrhosis classification on B-mode ultrasound images by the use of higher order local autocorrelation features

NASA Astrophysics Data System (ADS)

Sasaki, Kenya; Mitani, Yoshihiro; Fujita, Yusuke; Hamamoto, Yoshihiko; Sakaida, Isao

2017-02-01

In this paper, in order to classify liver cirrhosis on regions of interest (ROIs) images from B-mode ultrasound images, we have proposed to use the higher order local autocorrelation (HLAC) features. In a previous study, we tried to classify liver cirrhosis by using a Gabor filter based approach. However, the classification performance of the Gabor feature was poor from our preliminary experimental results. In order accurately to classify liver cirrhosis, we examined to use the HLAC features for liver cirrhosis classification. The experimental results show the effectiveness of HLAC features compared with the Gabor feature. Furthermore, by using a binary image made by an adaptive thresholding method, the classification performance of HLAC features has improved.

Protective Effects of Trehalose on the Corneal Epithelial Cells

PubMed Central

Aragona, Pasquale; Colosi, Pietro; Colosi, Francesca; Pisani, Antonina; Puzzolo, Domenico; Micali, Antonio

2014-01-01

Purpose. Aim of the present work was to evaluate the effects of the trehalose on the corneal epithelium undergoing alcohol delamination. Methods. Twelve patients undergoing laser subepithelial keratomileusis (LASEK) were consecutively included in the study. The right eyes were pretreated with 3% trehalose eye drops, whilst left eyes were used as control. Epithelial specimens were processed for cells vitality assessment, apoptosis, and light and transmission electron microscopy; a morphometric analysis was performed in both groups. Results. In both trehalose-untreated eyes (TUE) and trehalose-treated eyes (TTE), the percentage of vital cells was similar and no apoptotic cells were observed. In TUE, the corneal epithelium showed superficial cells with reduced microfolds, wing cells with vesicles and dilated intercellular spaces, and dark basal cells with vesicles and wide clefts. In TTE, superficial and wing cells were better preserved, and basal cells were generally clear with intracytoplasmatic vesicles. The morphometric analysis showed statistically significant differences between the two groups: the TTE epithelial height was higher, the basal cells showed larger area and clearer cytoplasm. The distribution of desmosomes and hemidesmosomes was significantly different between the groups. Conclusions. Trehalose administration better preserved morphological and morphometric features of alcohol-treated corneal epithelium, when compared to controls. PMID:25045743

Protective effects of trehalose on the corneal epithelial cells.

PubMed

Aragona, Pasquale; Colosi, Pietro; Rania, Laura; Colosi, Francesca; Pisani, Antonina; Puzzolo, Domenico; Micali, Antonio

2014-01-01

Aim of the present work was to evaluate the effects of the trehalose on the corneal epithelium undergoing alcohol delamination. Twelve patients undergoing laser subepithelial keratomileusis (LASEK) were consecutively included in the study. The right eyes were pretreated with 3% trehalose eye drops, whilst left eyes were used as control. Epithelial specimens were processed for cells vitality assessment, apoptosis, and light and transmission electron microscopy; a morphometric analysis was performed in both groups. In both trehalose-untreated eyes (TUE) and trehalose-treated eyes (TTE), the percentage of vital cells was similar and no apoptotic cells were observed. In TUE, the corneal epithelium showed superficial cells with reduced microfolds, wing cells with vesicles and dilated intercellular spaces, and dark basal cells with vesicles and wide clefts. In TTE, superficial and wing cells were better preserved, and basal cells were generally clear with intracytoplasmatic vesicles. The morphometric analysis showed statistically significant differences between the two groups: the TTE epithelial height was higher, the basal cells showed larger area and clearer cytoplasm. The distribution of desmosomes and hemidesmosomes was significantly different between the groups. Trehalose administration better preserved morphological and morphometric features of alcohol-treated corneal epithelium, when compared to controls.

Comparison of hand-craft feature based SVM and CNN based deep learning framework for automatic polyp classification.

PubMed

Younghak Shin; Balasingham, Ilangko

2017-07-01

Colonoscopy is a standard method for screening polyps by highly trained physicians. Miss-detected polyps in colonoscopy are potential risk factor for colorectal cancer. In this study, we investigate an automatic polyp classification framework. We aim to compare two different approaches named hand-craft feature method and convolutional neural network (CNN) based deep learning method. Combined shape and color features are used for hand craft feature extraction and support vector machine (SVM) method is adopted for classification. For CNN approach, three convolution and pooling based deep learning framework is used for classification purpose. The proposed framework is evaluated using three public polyp databases. From the experimental results, we have shown that the CNN based deep learning framework shows better classification performance than the hand-craft feature based methods. It achieves over 90% of classification accuracy, sensitivity, specificity and precision.

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

PubMed

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

2017-12-28

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

Unsupervised feature learning for autonomous rock image classification

NASA Astrophysics Data System (ADS)

Shu, Lei; McIsaac, Kenneth; Osinski, Gordon R.; Francis, Raymond

2017-09-01

Autonomous rock image classification can enhance the capability of robots for geological detection and enlarge the scientific returns, both in investigation on Earth and planetary surface exploration on Mars. Since rock textural images are usually inhomogeneous and manually hand-crafting features is not always reliable, we propose an unsupervised feature learning method to autonomously learn the feature representation for rock images. In our tests, rock image classification using the learned features shows that the learned features can outperform manually selected features. Self-taught learning is also proposed to learn the feature representation from a large database of unlabelled rock images of mixed class. The learned features can then be used repeatedly for classification of any subclass. This takes advantage of the large dataset of unlabelled rock images and learns a general feature representation for many kinds of rocks. We show experimental results supporting the feasibility of self-taught learning on rock images.

Prediction of cause of death from forensic autopsy reports using text classification techniques: A comparative study.

PubMed

Mujtaba, Ghulam; Shuib, Liyana; Raj, Ram Gopal; Rajandram, Retnagowri; Shaikh, Khairunisa

2018-07-01

Automatic text classification techniques are useful for classifying plaintext medical documents. This study aims to automatically predict the cause of death from free text forensic autopsy reports by comparing various schemes for feature extraction, term weighing or feature value representation, text classification, and feature reduction. For experiments, the autopsy reports belonging to eight different causes of death were collected, preprocessed and converted into 43 master feature vectors using various schemes for feature extraction, representation, and reduction. The six different text classification techniques were applied on these 43 master feature vectors to construct a classification model that can predict the cause of death. Finally, classification model performance was evaluated using four performance measures i.e. overall accuracy, macro precision, macro-F-measure, and macro recall. From experiments, it was found that that unigram features obtained the highest performance compared to bigram, trigram, and hybrid-gram features. Furthermore, in feature representation schemes, term frequency, and term frequency with inverse document frequency obtained similar and better results when compared with binary frequency, and normalized term frequency with inverse document frequency. Furthermore, the chi-square feature reduction approach outperformed Pearson correlation, and information gain approaches. Finally, in text classification algorithms, support vector machine classifier outperforms random forest, Naive Bayes, k-nearest neighbor, decision tree, and ensemble-voted classifier. Our results and comparisons hold practical importance and serve as references for future works. Moreover, the comparison outputs will act as state-of-art techniques to compare future proposals with existing automated text classification techniques. Copyright © 2017 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.

Space Object Classification Using Fused Features of Time Series Data

NASA Astrophysics Data System (ADS)

Jia, B.; Pham, K. D.; Blasch, E.; Shen, D.; Wang, Z.; Chen, G.

In this paper, a fused feature vector consisting of raw time series and texture feature information is proposed for space object classification. The time series data includes historical orbit trajectories and asteroid light curves. The texture feature is derived from recurrence plots using Gabor filters for both unsupervised learning and supervised learning algorithms. The simulation results show that the classification algorithms using the fused feature vector achieve better performance than those using raw time series or texture features only.

Acoustic⁻Seismic Mixed Feature Extraction Based on Wavelet Transform for Vehicle Classification in Wireless Sensor Networks.

PubMed

Zhang, Heng; Pan, Zhongming; Zhang, Wenna

2018-06-07

An acoustic⁻seismic mixed feature extraction method based on the wavelet coefficient energy ratio (WCER) of the target signal is proposed in this study for classifying vehicle targets in wireless sensor networks. The signal was decomposed into a set of wavelet coefficients using the à trous algorithm, which is a concise method used to implement the wavelet transform of a discrete signal sequence. After the wavelet coefficients of the target acoustic and seismic signals were obtained, the energy ratio of each layer coefficient was calculated as the feature vector of the target signals. Subsequently, the acoustic and seismic features were merged into an acoustic⁻seismic mixed feature to improve the target classification accuracy after the acoustic and seismic WCER features of the target signal were simplified using the hierarchical clustering method. We selected the support vector machine method for classification and utilized the data acquired from a real-world experiment to validate the proposed method. The calculated results show that the WCER feature extraction method can effectively extract the target features from target signals. Feature simplification can reduce the time consumption of feature extraction and classification, with no effect on the target classification accuracy. The use of acoustic⁻seismic mixed features effectively improved target classification accuracy by approximately 12% compared with either acoustic signal or seismic signal alone.

Classification of large-scale fundus image data sets: a cloud-computing framework.

PubMed

Roychowdhury, Sohini

2016-08-01

Large medical image data sets with high dimensionality require substantial amount of computation time for data creation and data processing. This paper presents a novel generalized method that finds optimal image-based feature sets that reduce computational time complexity while maximizing overall classification accuracy for detection of diabetic retinopathy (DR). First, region-based and pixel-based features are extracted from fundus images for classification of DR lesions and vessel-like structures. Next, feature ranking strategies are used to distinguish the optimal classification feature sets. DR lesion and vessel classification accuracies are computed using the boosted decision tree and decision forest classifiers in the Microsoft Azure Machine Learning Studio platform, respectively. For images from the DIARETDB1 data set, 40 of its highest-ranked features are used to classify four DR lesion types with an average classification accuracy of 90.1% in 792 seconds. Also, for classification of red lesion regions and hemorrhages from microaneurysms, accuracies of 85% and 72% are observed, respectively. For images from STARE data set, 40 high-ranked features can classify minor blood vessels with an accuracy of 83.5% in 326 seconds. Such cloud-based fundus image analysis systems can significantly enhance the borderline classification performances in automated screening systems.

Machine learning approach for automated screening of malaria parasite using light microscopic images.

PubMed

Das, Dev Kumar; Ghosh, Madhumala; Pal, Mallika; Maiti, Asok K; Chakraborty, Chandan

2013-02-01

The aim of this paper is to address the development of computer assisted malaria parasite characterization and classification using machine learning approach based on light microscopic images of peripheral blood smears. In doing this, microscopic image acquisition from stained slides, illumination correction and noise reduction, erythrocyte segmentation, feature extraction, feature selection and finally classification of different stages of malaria (Plasmodium vivax and Plasmodium falciparum) have been investigated. The erythrocytes are segmented using marker controlled watershed transformation and subsequently total ninety six features describing shape-size and texture of erythrocytes are extracted in respect to the parasitemia infected versus non-infected cells. Ninety four features are found to be statistically significant in discriminating six classes. Here a feature selection-cum-classification scheme has been devised by combining F-statistic, statistical learning techniques i.e., Bayesian learning and support vector machine (SVM) in order to provide the higher classification accuracy using best set of discriminating features. Results show that Bayesian approach provides the highest accuracy i.e., 84% for malaria classification by selecting 19 most significant features while SVM provides highest accuracy i.e., 83.5% with 9 most significant features. Finally, the performance of these two classifiers under feature selection framework has been compared toward malaria parasite classification. Copyright © 2012 Elsevier Ltd. All rights reserved.

Speech Music Discrimination Using Class-Specific Features

DTIC Science & Technology

2004-08-01

Speech Music Discrimination Using Class-Specific Features Thomas Beierholm...between speech and music . Feature extraction is class-specific and can therefore be tailored to each class meaning that segment size, model orders...interest. Some of the applications of audio signal classification are speech/ music classification [1], acoustical environmental classification [2][3

Automated feature extraction and classification from image sources

USGS Publications Warehouse

,

1995-01-01

The U.S. Department of the Interior, U.S. Geological Survey (USGS), and Unisys Corporation have completed a cooperative research and development agreement (CRADA) to explore automated feature extraction and classification from image sources. The CRADA helped the USGS define the spectral and spatial resolution characteristics of airborne and satellite imaging sensors necessary to meet base cartographic and land use and land cover feature classification requirements and help develop future automated geographic and cartographic data production capabilities. The USGS is seeking a new commercial partner to continue automated feature extraction and classification research and development.

Classification of subsurface objects using singular values derived from signal frames

DOEpatents

Chambers, David H; Paglieroni, David W

2014-05-06

The classification system represents a detected object with a feature vector derived from the return signals acquired by an array of N transceivers operating in multistatic mode. The classification system generates the feature vector by transforming the real-valued return signals into complex-valued spectra, using, for example, a Fast Fourier Transform. The classification system then generates a feature vector of singular values for each user-designated spectral sub-band by applying a singular value decomposition (SVD) to the N.times.N square complex-valued matrix formed from sub-band samples associated with all possible transmitter-receiver pairs. The resulting feature vector of singular values may be transformed into a feature vector of singular value likelihoods and then subjected to a multi-category linear or neural network classifier for object classification.

Feature selection for elderly faller classification based on wearable sensors.

PubMed

Howcroft, Jennifer; Kofman, Jonathan; Lemaire, Edward D

2017-05-30

Wearable sensors can be used to derive numerous gait pattern features for elderly fall risk and faller classification; however, an appropriate feature set is required to avoid high computational costs and the inclusion of irrelevant features. The objectives of this study were to identify and evaluate smaller feature sets for faller classification from large feature sets derived from wearable accelerometer and pressure-sensing insole gait data. A convenience sample of 100 older adults (75.5 ± 6.7 years; 76 non-fallers, 24 fallers based on 6 month retrospective fall occurrence) walked 7.62 m while wearing pressure-sensing insoles and tri-axial accelerometers at the head, pelvis, left and right shanks. Feature selection was performed using correlation-based feature selection (CFS), fast correlation based filter (FCBF), and Relief-F algorithms. Faller classification was performed using multi-layer perceptron neural network, naïve Bayesian, and support vector machine classifiers, with 75:25 single stratified holdout and repeated random sampling. The best performing model was a support vector machine with 78% accuracy, 26% sensitivity, 95% specificity, 0.36 F1 score, and 0.31 MCC and one posterior pelvis accelerometer input feature (left acceleration standard deviation). The second best model achieved better sensitivity (44%) and used a support vector machine with 74% accuracy, 83% specificity, 0.44 F1 score, and 0.29 MCC. This model had ten input features: maximum, mean and standard deviation posterior acceleration; maximum, mean and standard deviation anterior acceleration; mean superior acceleration; and three impulse features. The best multi-sensor model sensitivity (56%) was achieved using posterior pelvis and both shank accelerometers and a naïve Bayesian classifier. The best single-sensor model sensitivity (41%) was achieved using the posterior pelvis accelerometer and a naïve Bayesian classifier. Feature selection provided models with smaller feature sets and improved faller classification compared to faller classification without feature selection. CFS and FCBF provided the best feature subset (one posterior pelvis accelerometer feature) for faller classification. However, better sensitivity was achieved by the second best model based on a Relief-F feature subset with three pressure-sensing insole features and seven head accelerometer features. Feature selection should be considered as an important step in faller classification using wearable sensors.

Sex determination of the Acadian Flycatcher using discriminant analysis

USGS Publications Warehouse

Wilson, R.R.

1999-01-01

I used five morphometric variables from 114 individuals captured in Arkansas to develop a discriminant model to predict the sex of Acadian Flycatchers (Empidonax virescens). Stepwise discriminant function analyses selected wing chord and tail length as the most parsimonious subset of variables for discriminating sex. This two-variable model correctly classified 80% of females and 97% of males used to develop the model. Validation of the model using 19 individuals from Louisiana and Virginia resulted in 100% correct classification of males and females. This model provides criteria for sexing monomorphic Acadian Flycatchers during the breeding season and possibly during the winter.

Object-Based Paddy Rice Mapping Using HJ-1A/B Data and Temporal Features Extracted from Time Series MODIS NDVI Data

PubMed Central

Singha, Mrinal; Wu, Bingfang; Zhang, Miao

2016-01-01

Accurate and timely mapping of paddy rice is vital for food security and environmental sustainability. This study evaluates the utility of temporal features extracted from coarse resolution data for object-based paddy rice classification of fine resolution data. The coarse resolution vegetation index data is first fused with the fine resolution data to generate the time series fine resolution data. Temporal features are extracted from the fused data and added with the multi-spectral data to improve the classification accuracy. Temporal features provided the crop growth information, while multi-spectral data provided the pattern variation of paddy rice. The achieved overall classification accuracy and kappa coefficient were 84.37% and 0.68, respectively. The results indicate that the use of temporal features improved the overall classification accuracy of a single-date multi-spectral image by 18.75% from 65.62% to 84.37%. The minimum sensitivity (MS) of the paddy rice classification has also been improved. The comparison showed that the mapped paddy area was analogous to the agricultural statistics at the district level. This work also highlighted the importance of feature selection to achieve higher classification accuracies. These results demonstrate the potential of the combined use of temporal and spectral features for accurate paddy rice classification. PMID:28025525

Object-Based Paddy Rice Mapping Using HJ-1A/B Data and Temporal Features Extracted from Time Series MODIS NDVI Data.

PubMed

Singha, Mrinal; Wu, Bingfang; Zhang, Miao

2016-12-22

Accurate and timely mapping of paddy rice is vital for food security and environmental sustainability. This study evaluates the utility of temporal features extracted from coarse resolution data for object-based paddy rice classification of fine resolution data. The coarse resolution vegetation index data is first fused with the fine resolution data to generate the time series fine resolution data. Temporal features are extracted from the fused data and added with the multi-spectral data to improve the classification accuracy. Temporal features provided the crop growth information, while multi-spectral data provided the pattern variation of paddy rice. The achieved overall classification accuracy and kappa coefficient were 84.37% and 0.68, respectively. The results indicate that the use of temporal features improved the overall classification accuracy of a single-date multi-spectral image by 18.75% from 65.62% to 84.37%. The minimum sensitivity (MS) of the paddy rice classification has also been improved. The comparison showed that the mapped paddy area was analogous to the agricultural statistics at the district level. This work also highlighted the importance of feature selection to achieve higher classification accuracies. These results demonstrate the potential of the combined use of temporal and spectral features for accurate paddy rice classification.

Joint Concept Correlation and Feature-Concept Relevance Learning for Multilabel Classification.

PubMed

Zhao, Xiaowei; Ma, Zhigang; Li, Zhi; Li, Zhihui

2018-02-01

In recent years, multilabel classification has attracted significant attention in multimedia annotation. However, most of the multilabel classification methods focus only on the inherent correlations existing among multiple labels and concepts and ignore the relevance between features and the target concepts. To obtain more robust multilabel classification results, we propose a new multilabel classification method aiming to capture the correlations among multiple concepts by leveraging hypergraph that is proved to be beneficial for relational learning. Moreover, we consider mining feature-concept relevance, which is often overlooked by many multilabel learning algorithms. To better show the feature-concept relevance, we impose a sparsity constraint on the proposed method. We compare the proposed method with several other multilabel classification methods and evaluate the classification performance by mean average precision on several data sets. The experimental results show that the proposed method outperforms the state-of-the-art methods.

More than one kind of inference: re-examining what's learned in feature inference and classification.

PubMed

Sweller, Naomi; Hayes, Brett K

2010-08-01

Three studies examined how task demands that impact on attention to typical or atypical category features shape the category representations formed through classification learning and inference learning. During training categories were learned via exemplar classification or by inferring missing exemplar features. In the latter condition inferences were made about missing typical features alone (typical feature inference) or about both missing typical and atypical features (mixed feature inference). Classification and mixed feature inference led to the incorporation of typical and atypical features into category representations, with both kinds of features influencing inferences about familiar (Experiments 1 and 2) and novel (Experiment 3) test items. Those in the typical inference condition focused primarily on typical features. Together with formal modelling, these results challenge previous accounts that have characterized inference learning as producing a focus on typical category features. The results show that two different kinds of inference learning are possible and that these are subserved by different kinds of category representations.

WND-CHARM: Multi-purpose image classification using compound image transforms

PubMed Central

Orlov, Nikita; Shamir, Lior; Macura, Tomasz; Johnston, Josiah; Eckley, D. Mark; Goldberg, Ilya G.

2008-01-01

We describe a multi-purpose image classifier that can be applied to a wide variety of image classification tasks without modifications or fine-tuning, and yet provide classification accuracy comparable to state-of-the-art task-specific image classifiers. The proposed image classifier first extracts a large set of 1025 image features including polynomial decompositions, high contrast features, pixel statistics, and textures. These features are computed on the raw image, transforms of the image, and transforms of transforms of the image. The feature values are then used to classify test images into a set of pre-defined image classes. This classifier was tested on several different problems including biological image classification and face recognition. Although we cannot make a claim of universality, our experimental results show that this classifier performs as well or better than classifiers developed specifically for these image classification tasks. Our classifier’s high performance on a variety of classification problems is attributed to (i) a large set of features extracted from images; and (ii) an effective feature selection and weighting algorithm sensitive to specific image classification problems. The algorithms are available for free download from openmicroscopy.org. PMID:18958301

Natural image classification driven by human brain activity

NASA Astrophysics Data System (ADS)

Zhang, Dai; Peng, Hanyang; Wang, Jinqiao; Tang, Ming; Xue, Rong; Zuo, Zhentao

2016-03-01

Natural image classification has been a hot topic in computer vision and pattern recognition research field. Since the performance of an image classification system can be improved by feature selection, many image feature selection methods have been developed. However, the existing supervised feature selection methods are typically driven by the class label information that are identical for different samples from the same class, ignoring with-in class image variability and therefore degrading the feature selection performance. In this study, we propose a novel feature selection method, driven by human brain activity signals collected using fMRI technique when human subjects were viewing natural images of different categories. The fMRI signals associated with subjects viewing different images encode the human perception of natural images, and therefore may capture image variability within- and cross- categories. We then select image features with the guidance of fMRI signals from brain regions with active response to image viewing. Particularly, bag of words features based on GIST descriptor are extracted from natural images for classification, and a sparse regression base feature selection method is adapted to select image features that can best predict fMRI signals. Finally, a classification model is built on the select image features to classify images without fMRI signals. The validation experiments for classifying images from 4 categories of two subjects have demonstrated that our method could achieve much better classification performance than the classifiers built on image feature selected by traditional feature selection methods.

Automatic migraine classification via feature selection committee and machine learning techniques over imaging and questionnaire data.

PubMed

Garcia-Chimeno, Yolanda; Garcia-Zapirain, Begonya; Gomez-Beldarrain, Marian; Fernandez-Ruanova, Begonya; Garcia-Monco, Juan Carlos

2017-04-13

Feature selection methods are commonly used to identify subsets of relevant features to facilitate the construction of models for classification, yet little is known about how feature selection methods perform in diffusion tensor images (DTIs). In this study, feature selection and machine learning classification methods were tested for the purpose of automating diagnosis of migraines using both DTIs and questionnaire answers related to emotion and cognition - factors that influence of pain perceptions. We select 52 adult subjects for the study divided into three groups: control group (15), subjects with sporadic migraine (19) and subjects with chronic migraine and medication overuse (18). These subjects underwent magnetic resonance with diffusion tensor to see white matter pathway integrity of the regions of interest involved in pain and emotion. The tests also gather data about pathology. The DTI images and test results were then introduced into feature selection algorithms (Gradient Tree Boosting, L1-based, Random Forest and Univariate) to reduce features of the first dataset and classification algorithms (SVM (Support Vector Machine), Boosting (Adaboost) and Naive Bayes) to perform a classification of migraine group. Moreover we implement a committee method to improve the classification accuracy based on feature selection algorithms. When classifying the migraine group, the greatest improvements in accuracy were made using the proposed committee-based feature selection method. Using this approach, the accuracy of classification into three types improved from 67 to 93% when using the Naive Bayes classifier, from 90 to 95% with the support vector machine classifier, 93 to 94% in boosting. The features that were determined to be most useful for classification included are related with the pain, analgesics and left uncinate brain (connected with the pain and emotions). The proposed feature selection committee method improved the performance of migraine diagnosis classifiers compared to individual feature selection methods, producing a robust system that achieved over 90% accuracy in all classifiers. The results suggest that the proposed methods can be used to support specialists in the classification of migraines in patients undergoing magnetic resonance imaging.

Structural MRI-based detection of Alzheimer's disease using feature ranking and classification error.

PubMed

Beheshti, Iman; Demirel, Hasan; Farokhian, Farnaz; Yang, Chunlan; Matsuda, Hiroshi

2016-12-01

This paper presents an automatic computer-aided diagnosis (CAD) system based on feature ranking for detection of Alzheimer's disease (AD) using structural magnetic resonance imaging (sMRI) data. The proposed CAD system is composed of four systematic stages. First, global and local differences in the gray matter (GM) of AD patients compared to the GM of healthy controls (HCs) are analyzed using a voxel-based morphometry technique. The aim is to identify significant local differences in the volume of GM as volumes of interests (VOIs). Second, the voxel intensity values of the VOIs are extracted as raw features. Third, the raw features are ranked using a seven-feature ranking method, namely, statistical dependency (SD), mutual information (MI), information gain (IG), Pearson's correlation coefficient (PCC), t-test score (TS), Fisher's criterion (FC), and the Gini index (GI). The features with higher scores are more discriminative. To determine the number of top features, the estimated classification error based on training set made up of the AD and HC groups is calculated, with the vector size that minimized this error selected as the top discriminative feature. Fourth, the classification is performed using a support vector machine (SVM). In addition, a data fusion approach among feature ranking methods is introduced to improve the classification performance. The proposed method is evaluated using a data-set from ADNI (130 AD and 130 HC) with 10-fold cross-validation. The classification accuracy of the proposed automatic system for the diagnosis of AD is up to 92.48% using the sMRI data. An automatic CAD system for the classification of AD based on feature-ranking method and classification errors is proposed. In this regard, seven-feature ranking methods (i.e., SD, MI, IG, PCC, TS, FC, and GI) are evaluated. The optimal size of top discriminative features is determined by the classification error estimation in the training phase. The experimental results indicate that the performance of the proposed system is comparative to that of state-of-the-art classification models. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Application of quantum-behaved particle swarm optimization to motor imagery EEG classification.

PubMed

Hsu, Wei-Yen

2013-12-01

In this study, we propose a recognition system for single-trial analysis of motor imagery (MI) electroencephalogram (EEG) data. Applying event-related brain potential (ERP) data acquired from the sensorimotor cortices, the system chiefly consists of automatic artifact elimination, feature extraction, feature selection and classification. In addition to the use of independent component analysis, a similarity measure is proposed to further remove the electrooculographic (EOG) artifacts automatically. Several potential features, such as wavelet-fractal features, are then extracted for subsequent classification. Next, quantum-behaved particle swarm optimization (QPSO) is used to select features from the feature combination. Finally, selected sub-features are classified by support vector machine (SVM). Compared with without artifact elimination, feature selection using a genetic algorithm (GA) and feature classification with Fisher's linear discriminant (FLD) on MI data from two data sets for eight subjects, the results indicate that the proposed method is promising in brain-computer interface (BCI) applications.

Alexnet Feature Extraction and Multi-Kernel Learning for Objectoriented Classification

NASA Astrophysics Data System (ADS)

Ding, L.; Li, H.; Hu, C.; Zhang, W.; Wang, S.

2018-04-01

In view of the fact that the deep convolutional neural network has stronger ability of feature learning and feature expression, an exploratory research is done on feature extraction and classification for high resolution remote sensing images. Taking the Google image with 0.3 meter spatial resolution in Ludian area of Yunnan Province as an example, the image segmentation object was taken as the basic unit, and the pre-trained AlexNet deep convolution neural network model was used for feature extraction. And the spectral features, AlexNet features and GLCM texture features are combined with multi-kernel learning and SVM classifier, finally the classification results were compared and analyzed. The results show that the deep convolution neural network can extract more accurate remote sensing image features, and significantly improve the overall accuracy of classification, and provide a reference value for earthquake disaster investigation and remote sensing disaster evaluation.

Scaling laws for coastal overwash morphology

NASA Astrophysics Data System (ADS)

Lazarus, Eli D.

2016-12-01

Overwash is a physical process of coastal sediment transport driven by storm events and is essential to landscape resilience in low-lying barrier environments. This work establishes a comprehensive set of scaling laws for overwash morphology: unifying quantitative descriptions with which to compare overwash features by their morphological attributes across case examples. Such scaling laws also help relate overwash features to other morphodynamic phenomena. Here morphometric data from a physical experiment are compared with data from natural examples of overwash features. The resulting scaling relationships indicate scale invariance spanning several orders of magnitude. Furthermore, these new relationships for overwash morphology align with classic scaling laws for fluvial drainages and alluvial fans.

Vitamin B12 deficiency: Characterization of psychometrics and MRI morphometrics.

PubMed

Hsu, Yen-Hsuan; Huang, Ching-Feng; Lo, Chung-Ping; Wang, Tzu-Lan; Tu, Min-Chien

2016-01-01

Vitamin B12 is essential for the integrity of the central nervous system. However, performances in different cognitive domains relevant to vitamin B12 deficiency remain to be detailed. To date, there have been limited studies that examined the relationships between cognitions and structural neuroimaging in a single cohort of low-vitamin B12 status. The present study aimed to depict psychometrics and magnetic resonance imaging (MRI) morphometrics among patients with vitamin B12 deficiency, and to examine their inter-relations. We compared 34 consecutive patients with vitamin B12 deficiency (serum level ≤ 250 pg/ml) to 34 demographically matched controls by their cognitive performances and morphometric indices of brain MRI. The correlations between psychometrics and morphometrics were analyzed. The vitamin B12 deficiency group had lower scores than the controls on total scores of Mini-Mental Status Examination (MMSE) and Cognitive Abilities Screening Instrument (CASI) (both P < 0.05), language (P < 0.01), orientation (P < 0.01), and mental manipulation (P < 0.05). The patients also showed a greater frontal horn ratio than the controls (P < 0.05). Bicaudate ratio, fronto-occipital ratio, uncotemporal index, and normalized interuncal distance all showed a strong correlation with the total score of MMSE and CASI (all P < 0.01). Among these psychometric and morphometric indices, pronounced correlations between bicaudate ratio and long-term memory, mental manipulation, orientation, language, and verbal fluency were noted (all P < 0.01). Vitamin B12 deficiency is associated with a global cognition decline with language, orientation, and mental manipulation selectively impaired. Preferential atrophy in frontal regions is the main neuroimaging feature. Although the frontal ratio highlights the relevant atrophy among patients, the bicaudate ratio might be the best index on the basis of its strong association with global cognition and related cognitive domains, implying dysfunction of fronto-subcortical circuits as the fundamental pathogenesis related to vitamin B12 deficiency.

A Two-Stream Deep Fusion Framework for High-Resolution Aerial Scene Classification

PubMed Central

Liu, Fuxian

2018-01-01

One of the challenging problems in understanding high-resolution remote sensing images is aerial scene classification. A well-designed feature representation method and classifier can improve classification accuracy. In this paper, we construct a new two-stream deep architecture for aerial scene classification. First, we use two pretrained convolutional neural networks (CNNs) as feature extractor to learn deep features from the original aerial image and the processed aerial image through saliency detection, respectively. Second, two feature fusion strategies are adopted to fuse the two different types of deep convolutional features extracted by the original RGB stream and the saliency stream. Finally, we use the extreme learning machine (ELM) classifier for final classification with the fused features. The effectiveness of the proposed architecture is tested on four challenging datasets: UC-Merced dataset with 21 scene categories, WHU-RS dataset with 19 scene categories, AID dataset with 30 scene categories, and NWPU-RESISC45 dataset with 45 challenging scene categories. The experimental results demonstrate that our architecture gets a significant classification accuracy improvement over all state-of-the-art references. PMID:29581722

A Two-Stream Deep Fusion Framework for High-Resolution Aerial Scene Classification.

PubMed

Yu, Yunlong; Liu, Fuxian

2018-01-01

One of the challenging problems in understanding high-resolution remote sensing images is aerial scene classification. A well-designed feature representation method and classifier can improve classification accuracy. In this paper, we construct a new two-stream deep architecture for aerial scene classification. First, we use two pretrained convolutional neural networks (CNNs) as feature extractor to learn deep features from the original aerial image and the processed aerial image through saliency detection, respectively. Second, two feature fusion strategies are adopted to fuse the two different types of deep convolutional features extracted by the original RGB stream and the saliency stream. Finally, we use the extreme learning machine (ELM) classifier for final classification with the fused features. The effectiveness of the proposed architecture is tested on four challenging datasets: UC-Merced dataset with 21 scene categories, WHU-RS dataset with 19 scene categories, AID dataset with 30 scene categories, and NWPU-RESISC45 dataset with 45 challenging scene categories. The experimental results demonstrate that our architecture gets a significant classification accuracy improvement over all state-of-the-art references.

Morphological characters of the thickbody skate Amblyraja frerichsi (Krefft 1968) (Rajiformes: Rajidae), with notes on its biology.

PubMed

Bustamante, Carlos; Lamilla, Julio; Concha, Francisco; Ebert, David A; Bennett, Michael B

2012-01-01

Detailed descriptions of morphological features, morphometrics, neurocranium anatomy, clasper structure and egg case descriptions are provided for the thickbody skate Amblyraja frerichsi; a rare, deep-water species from Chile, Argentina and Falkland Islands. The species diagnosis is complemented from new observations and aspects such as colour, size and distribution are described. Geographic and bathymetric distributional ranges are discussed as relevant features of this taxońs biology. Additionally, the conservation status is assessed including bycatch records from Chilean fisheries.

Morphological Characters of the Thickbody Skate Amblyraja frerichsi (Krefft 1968) (Rajiformes: Rajidae), with Notes on Its Biology

PubMed Central

Bustamante, Carlos; Lamilla, Julio; Concha, Francisco; Ebert, David A.; Bennett, Michael B.

2012-01-01

Detailed descriptions of morphological features, morphometrics, neurocranium anatomy, clasper structure and egg case descriptions are provided for the thickbody skate Amblyraja frerichsi; a rare, deep-water species from Chile, Argentina and Falkland Islands. The species diagnosis is complemented from new observations and aspects such as colour, size and distribution are described. Geographic and bathymetric distributional ranges are discussed as relevant features of this taxońs biology. Additionally, the conservation status is assessed including bycatch records from Chilean fisheries. PMID:22768186

Interspecific variation in the tetradactyl manus of modern tapirs (Perissodactyla: Tapirus) exposed using geometric morphometrics.

PubMed

MacLaren, Jamie A; Nauwelaerts, Sandra

2017-11-01

The distal forelimb (autopodium) of quadrupedal mammals is a key morphological unit involved in locomotion, body support, and interaction with the substrate. The manus of the tapir (Perissodactyla: Tapirus) is unique within modern perissodactyls, as it retains the plesiomorphic tetradactyl (four-toed) condition also exhibited by basal equids and rhinoceroses. Tapirs are known to exhibit anatomical mesaxonic symmetry in the manus, although interspecific differences and biomechanical mesaxony have yet to be rigorously tested. Here, we investigate variation in the manus morphology of four modern tapir species (Tapirus indicus, Tapirus bairdii, Tapirus pinchaque, and Tapirus terrestris) using a geometric morphometric approach. Autopodial bones were laser scanned to capture surface shape and morphology was quantified using 3D-landmark analysis. Landmarks were aligned using Generalised Procrustes Analysis, with discriminant function and partial least square analyses performed on aligned coordinate data to identify features that significantly separate tapir species. Overall, our results support the previously held hypothesis that T. indicus is morphologically separate from neotropical tapirs; however, previous conclusions regarding function from morphological differences are shown to require reassessment. We find evidence indicating that T. bairdii exhibits reduced reliance on the lateral fifth digit compared to other tapirs. Morphometric assessment of the metacarpophalangeal joint and the morphology of the distal facets of the lunate lend evidence toward high loading on the lateral digits of both the large T. indicus (large body mass) and the small, long limbed T. pinchaque (ground impact). Our results support other recent studies on T. pinchaque, suggesting subtle but important adaptations to a compliant but inclined habitat. In conclusion, we demonstrate further evidence that the modern tapir forelimb is a variable locomotor unit with a range of interspecific features tailored to habitual and biomechanical needs of each species. © 2017 Wiley Periodicals, Inc.

Adult Neandertal clavicles from the El Sidrón site (Asturias, Spain) in the context of Homo pectoral girdle evolution.

PubMed

Rosas, Antonio; Rodriguez-Perez, Francisco Javier; Bastir, Markus; Estalrrich, Almudena; Huguet, Rosa; García-Tabernero, Antonio; Pastor, Juan Francisco; de la Rasilla, Marco

2016-06-01

We undertook a three-dimensional geometric morphometric (3DGM) analysis on 12 new Neandertal clavicle specimens from the El Sidrón site (Spain), dated to 49,000 years ago. The 3DGM methods were applied in a comparative framework in order to improve our understanding of trait polarity in features related to Homo pectoral girdle evolution, using other Neandertals, Homo sapiens, Pan, ATD6-50 (Homo antecessor), and KNM-WT 15000 (Homo ergaster/erectus) in the reference collection. Twenty-nine homologous landmarks were measured for each clavicle. Variation and morphological similarities were assessed through principal component analysis, conducted separately for the complete clavicle and the diaphysis. On average, Neandertal clavicles had significantly larger muscular entheses, double dorsal curvature, clavicle torsion, and cranial orientation of the acromial end than non-Neandertal clavicles; the El Sidrón clavicles fit this pattern. Variation within the samples was large, with extensive overlap between Homo species; only chimpanzee specimens clearly differed from the other specimens in morphometric terms. Taken together, our morphometric analyses are consistent with the following phylogenetic sequence. The primitive condition of the clavicle is manifest in the cranial orientation of both the acromial and sternal ends. The derived condition expressed in the H. sapiens + Neandertal clade is defined by caudal rotation of both the sternal and acromial ends, but with variation in the number of acromia remaining in a certain cranial orientation. Finally, the autapomorphic Neandertal condition is defined by secondarily acquired primitive cranial re-orientation of the acromial end, which varies from individual to individual. These results suggest that the pace of phylogenetic change in the pectoral girdle does not seem to follow that of other postcranial skeletal features. Copyright © 2016 Elsevier Ltd. All rights reserved.

Optimization of breast mass classification using sequential forward floating selection (SFFS) and a support vector machine (SVM) model

PubMed Central

Tan, Maxine; Pu, Jiantao; Zheng, Bin

2014-01-01

Purpose: Improving radiologists’ performance in classification between malignant and benign breast lesions is important to increase cancer detection sensitivity and reduce false-positive recalls. For this purpose, developing computer-aided diagnosis (CAD) schemes has been attracting research interest in recent years. In this study, we investigated a new feature selection method for the task of breast mass classification. Methods: We initially computed 181 image features based on mass shape, spiculation, contrast, presence of fat or calcifications, texture, isodensity, and other morphological features. From this large image feature pool, we used a sequential forward floating selection (SFFS)-based feature selection method to select relevant features, and analyzed their performance using a support vector machine (SVM) model trained for the classification task. On a database of 600 benign and 600 malignant mass regions of interest (ROIs), we performed the study using a ten-fold cross-validation method. Feature selection and optimization of the SVM parameters were conducted on the training subsets only. Results: The area under the receiver operating characteristic curve (AUC) = 0.805±0.012 was obtained for the classification task. The results also showed that the most frequently-selected features by the SFFS-based algorithm in 10-fold iterations were those related to mass shape, isodensity and presence of fat, which are consistent with the image features frequently used by radiologists in the clinical environment for mass classification. The study also indicated that accurately computing mass spiculation features from the projection mammograms was difficult, and failed to perform well for the mass classification task due to tissue overlap within the benign mass regions. Conclusions: In conclusion, this comprehensive feature analysis study provided new and valuable information for optimizing computerized mass classification schemes that may have potential to be useful as a “second reader” in future clinical practice. PMID:24664267

Hystricognathy vs Sciurognathy in the Rodent Jaw: A New Morphometric Assessment of Hystricognathy Applied to the Living Fossil Laonastes (Diatomyidae)

PubMed Central

Hautier, Lionel; Lebrun, Renaud; Saksiri, Soonchan; Michaux, Jacques; Vianey-Liaud, Monique; Marivaux, Laurent

2011-01-01

While exceptional for an intense diversification of lineages, the evolutionary history of the order Rodentia comprises only a limited number of morphological morphotypes for the mandible. This situation could partly explain the intense debates about the taxonomic position of the latest described member of this clade, the Laotian rock rat Laonastes aenigmamus (Diatomyidae). This discovery has re-launched the debate on the definition of the Hystricognathi suborder identified using the angle of the jaw relative to the plane of the incisors. Our study aims to end this ambiguity. For clarity, it became necessary to revisit the entire morphological diversity of the mandible in extant and extinct rodents. However, current and past rodent diversity brings out the limitations of the qualitative descriptive approach and highlights the need for a quantitative approach. Here, we present the first descriptive comparison of the masticatory apparatus within the Ctenohystrica clade, in combining classic comparative anatomy with morphometrical methods. First, we quantified the shape of the mandible in rodents using 3D landmarks. Then, the analysis of osteological features was compared to myological features in order to understand the biomechanical origin of this morphological diversity. Among the morphological variation observed, the mandible of Laonastes aenigmamus displays an intermediate association of features that could be considered neither as sciurognathous nor as hystricognathous. PMID:21490933

Sexual dimorphic features within extant great ape faciodental skeletal anatomy and testing the single species hypothesis.

PubMed

Cameron, D W

1997-01-01

This paper examines sexually dimorphic skeletal characters within the face and upper dentition of extant hominids (great ape), not including members of the Hominini. Specimens of Pan paniscus, Pan troglodytes, Gorilla gorilla, and Pongo pygmaeus are used to help identify likely sex specific characters for the Hominidae. The aim of this paper is to identify extant hominid faciodental sexual features which can be used to help sex fossil specimens. A morphometric and skeletal study of sexual variability demonstrates relatively diverse patterns of sexual variability within the extant hominids. In terms of morphometrics, P. paniscus is relatively non-dimorphic, while P. troglodytes, Gorilla and Pongo display a large degree of sexual dimorphism. In their respective skeletal anatomies, however, each has specific characters which tend to differentiate between the sexes. Some faciodental sex features are shown to be common amongst all four taxa and as such are likely to be important criteria for determining the sex of Miocene and Plio-Pleistocene fossil hominid specimens. The construction of extant great ape sexual ranges of variability are also important in helping to test the fossil ape single species hypotheses. The testing of sex and species ranges of variability should employ range based statistics not only because they are sample size independent, (relative to C.V.) but also because they are of low power.

Biomarker selection and classification of "-omics" data using a two-step bayes classification framework.

PubMed

Assawamakin, Anunchai; Prueksaaroon, Supakit; Kulawonganunchai, Supasak; Shaw, Philip James; Varavithya, Vara; Ruangrajitpakorn, Taneth; Tongsima, Sissades

2013-01-01

Identification of suitable biomarkers for accurate prediction of phenotypic outcomes is a goal for personalized medicine. However, current machine learning approaches are either too complex or perform poorly. Here, a novel two-step machine-learning framework is presented to address this need. First, a Naïve Bayes estimator is used to rank features from which the top-ranked will most likely contain the most informative features for prediction of the underlying biological classes. The top-ranked features are then used in a Hidden Naïve Bayes classifier to construct a classification prediction model from these filtered attributes. In order to obtain the minimum set of the most informative biomarkers, the bottom-ranked features are successively removed from the Naïve Bayes-filtered feature list one at a time, and the classification accuracy of the Hidden Naïve Bayes classifier is checked for each pruned feature set. The performance of the proposed two-step Bayes classification framework was tested on different types of -omics datasets including gene expression microarray, single nucleotide polymorphism microarray (SNParray), and surface-enhanced laser desorption/ionization time-of-flight (SELDI-TOF) proteomic data. The proposed two-step Bayes classification framework was equal to and, in some cases, outperformed other classification methods in terms of prediction accuracy, minimum number of classification markers, and computational time.

A Novel Hybrid Classification Model of Genetic Algorithms, Modified k-Nearest Neighbor and Developed Backpropagation Neural Network

PubMed Central

Salari, Nader; Shohaimi, Shamarina; Najafi, Farid; Nallappan, Meenakshii; Karishnarajah, Isthrinayagy

2014-01-01

Among numerous artificial intelligence approaches, k-Nearest Neighbor algorithms, genetic algorithms, and artificial neural networks are considered as the most common and effective methods in classification problems in numerous studies. In the present study, the results of the implementation of a novel hybrid feature selection-classification model using the above mentioned methods are presented. The purpose is benefitting from the synergies obtained from combining these technologies for the development of classification models. Such a combination creates an opportunity to invest in the strength of each algorithm, and is an approach to make up for their deficiencies. To develop proposed model, with the aim of obtaining the best array of features, first, feature ranking techniques such as the Fisher's discriminant ratio and class separability criteria were used to prioritize features. Second, the obtained results that included arrays of the top-ranked features were used as the initial population of a genetic algorithm to produce optimum arrays of features. Third, using a modified k-Nearest Neighbor method as well as an improved method of backpropagation neural networks, the classification process was advanced based on optimum arrays of the features selected by genetic algorithms. The performance of the proposed model was compared with thirteen well-known classification models based on seven datasets. Furthermore, the statistical analysis was performed using the Friedman test followed by post-hoc tests. The experimental findings indicated that the novel proposed hybrid model resulted in significantly better classification performance compared with all 13 classification methods. Finally, the performance results of the proposed model was benchmarked against the best ones reported as the state-of-the-art classifiers in terms of classification accuracy for the same data sets. The substantial findings of the comprehensive comparative study revealed that performance of the proposed model in terms of classification accuracy is desirable, promising, and competitive to the existing state-of-the-art classification models. PMID:25419659

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

NASA Astrophysics Data System (ADS)

Li, Qing; Zhang, Yujin; Dai, Shengyang

2001-12-01

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

Deep feature extraction and combination for synthetic aperture radar target classification

NASA Astrophysics Data System (ADS)

Amrani, Moussa; Jiang, Feng

2017-10-01

Feature extraction has always been a difficult problem in the classification performance of synthetic aperture radar automatic target recognition (SAR-ATR). It is very important to select discriminative features to train a classifier, which is a prerequisite. Inspired by the great success of convolutional neural network (CNN), we address the problem of SAR target classification by proposing a feature extraction method, which takes advantage of exploiting the extracted deep features from CNNs on SAR images to introduce more powerful discriminative features and robust representation ability for them. First, the pretrained VGG-S net is fine-tuned on moving and stationary target acquisition and recognition (MSTAR) public release database. Second, after a simple preprocessing is performed, the fine-tuned network is used as a fixed feature extractor to extract deep features from the processed SAR images. Third, the extracted deep features are fused by using a traditional concatenation and a discriminant correlation analysis algorithm. Finally, for target classification, K-nearest neighbors algorithm based on LogDet divergence-based metric learning triplet constraints is adopted as a baseline classifier. Experiments on MSTAR are conducted, and the classification accuracy results demonstrate that the proposed method outperforms the state-of-the-art methods.

Feature selection for the classification of traced neurons.

PubMed

López-Cabrera, José D; Lorenzo-Ginori, Juan V

2018-06-01

The great availability of computational tools to calculate the properties of traced neurons leads to the existence of many descriptors which allow the automated classification of neurons from these reconstructions. This situation determines the necessity to eliminate irrelevant features as well as making a selection of the most appropriate among them, in order to improve the quality of the classification obtained. The dataset used contains a total of 318 traced neurons, classified by human experts in 192 GABAergic interneurons and 126 pyramidal cells. The features were extracted by means of the L-measure software, which is one of the most used computational tools in neuroinformatics to quantify traced neurons. We review some current feature selection techniques as filter, wrapper, embedded and ensemble methods. The stability of the feature selection methods was measured. For the ensemble methods, several aggregation methods based on different metrics were applied to combine the subsets obtained during the feature selection process. The subsets obtained applying feature selection methods were evaluated using supervised classifiers, among which Random Forest, C4.5, SVM, Naïve Bayes, Knn, Decision Table and the Logistic classifier were used as classification algorithms. Feature selection methods of types filter, embedded, wrappers and ensembles were compared and the subsets returned were tested in classification tasks for different classification algorithms. L-measure features EucDistanceSD, PathDistanceSD, Branch_pathlengthAve, Branch_pathlengthSD and EucDistanceAve were present in more than 60% of the selected subsets which provides evidence about their importance in the classification of this neurons. Copyright © 2018 Elsevier B.V. All rights reserved.

[The forensic medical evaluation of the injuries inflicted inside the passenger compartment of a moving car equipped with the modern personal safety systems].

PubMed

Pigolkin, Yu I; Dubrovin, I A; Mosoyan, A S; Bychkov, A A

The objective of the present study was to elucidate the characteristic features of the injuries inflicted to the victims of a road traffic accident inside the passenger compartment of a moving car equipped with the modern personal safety systems. The materials available for the present work included the lesions documented in 210 drivers and 150 occupants of the car passenger compartments. Both comparative, morphometric and statistical methods were used to analyze the data obtained. The morphometric analysis included identification of the form of the injury, such as extravasation, wounds, fractures, and lesions of the internal organs (e.g. hemorrhages, ruptures, etc.), their number and localization. Special attention was given to the specific features of the injuries to the occupants of the cars equipped with the modern personal safety systems. The study has demonstrated that the form, frequency, and localization of the injuries inflicted to the victims of a road traffic accident inside the passenger car compartment (including the drivers and other occupants) can be used for determining the positions of the victims at the moment of the accident.

Statistical analysis and data mining of digital reconstructions of dendritic morphologies.

PubMed

Polavaram, Sridevi; Gillette, Todd A; Parekh, Ruchi; Ascoli, Giorgio A

2014-01-01

Neuronal morphology is diverse among animal species, developmental stages, brain regions, and cell types. The geometry of individual neurons also varies substantially even within the same cell class. Moreover, specific histological, imaging, and reconstruction methodologies can differentially affect morphometric measures. The quantitative characterization of neuronal arbors is necessary for in-depth understanding of the structure-function relationship in nervous systems. The large collection of community-contributed digitally reconstructed neurons available at NeuroMorpho.Org constitutes a "big data" research opportunity for neuroscience discovery beyond the approaches typically pursued in single laboratories. To illustrate these potential and related challenges, we present a database-wide statistical analysis of dendritic arbors enabling the quantification of major morphological similarities and differences across broadly adopted metadata categories. Furthermore, we adopt a complementary unsupervised approach based on clustering and dimensionality reduction to identify the main morphological parameters leading to the most statistically informative structural classification. We find that specific combinations of measures related to branching density, overall size, tortuosity, bifurcation angles, arbor flatness, and topological asymmetry can capture anatomically and functionally relevant features of dendritic trees. The reported results only represent a small fraction of the relationships available for data exploration and hypothesis testing enabled by sharing of digital morphological reconstructions.

A comparative study for chest radiograph image retrieval using binary texture and deep learning classification.

PubMed

Anavi, Yaron; Kogan, Ilya; Gelbart, Elad; Geva, Ofer; Greenspan, Hayit

2015-08-01

In this work various approaches are investigated for X-ray image retrieval and specifically chest pathology retrieval. Given a query image taken from a data set of 443 images, the objective is to rank images according to similarity. Different features, including binary features, texture features, and deep learning (CNN) features are examined. In addition, two approaches are investigated for the retrieval task. One approach is based on the distance of image descriptors using the above features (hereon termed the "descriptor"-based approach); the second approach ("classification"-based approach) is based on a probability descriptor, generated by a pair-wise classification of each two classes (pathologies) and their decision values using an SVM classifier. Best results are achieved using deep learning features in a classification scheme.

Analysis of mandibular second molars with fused roots and shallow radicular grooves by using micro-computed tomography.

PubMed

Amoroso-Silva, Pablo; De Moraes, Ivaldo Gomes; Marceliano-Alves, Marilia; Bramante, Clovis Monteiro; Zapata, Ronald Ordinola; Hungaro Duarte, Marco Antonio

2018-01-01

This study aimed to describe the morphological and morphometric aspects of fused mandibular second molars with radicular shallow grooves using micro-computed tomography (CT). Eighty-eight mandibular second molars with fused roots were scanned in a micro-CT scanner at a voxel size of 19.6 μm. After reconstruction, only molars without C-shaped roots and presenting shallow radicular grooves were selected. 30 molars were chosen for further analysis. Canal cross-sections were classified according to Fan's modified classification (C1, C2, C3, and C4) and morphometric parameters at the apical region, examination of accessory foramina and tridimensional configuration were evaluated. Three-dimensional reconstructions indicated a higher prevalence of merging type ( n = 22). According to Fan's modified classification, the C4 configuration was predominant in the 3 apical mm. Roundness median values revealed a more round-shaped canals at 3 mm (0.72) than at 2 (0.63) and 1 (0.61) mm from the apex. High values of major and minor diameters were observed in the canals of these evaluated sections. In addition, few accessory apical foramina were observed at 1 and 2 mm from the apex. The average distance between last accessory foramina and the anatomic apex was 1.17 mm. A less complex internal anatomy is found when a mandibular second molar presents fused roots with shallow radicular grooves. The merging type canal was frequently observed. Moreover, the C4 configuration was predominant at a point 3 mm from the apex and presented rounded canals, large apical diameters, and few accessory foramina. The cervical and middle thirds presented C3 and C1 canal configurations most frequently. A minor morphological complexity is found when fused mandibular second molars present shallow radicular grooves.

Insights from Integrative Systematics Reveal Cryptic Diversity in Pristimantis Frogs (Anura: Craugastoridae) from the Upper Amazon Basin

PubMed Central

Ortega-Andrade, H. Mauricio; Rojas-Soto, Octavio R.; Valencia, Jorge H.; Espinosa de los Monteros, Alejandro; Morrone, Juan J.; Ron, Santiago R.; Cannatella, David C.

2015-01-01

Pluralistic approaches to taxonomy facilitate a more complete appraisal of biodiversity, especially the diversification of cryptic species. Although species delimitation has traditionally been based primarily on morphological differences, the integration of new methods allows diverse lines of evidence to solve the problem. Robber frogs (Pristimantis) are exemplary, as many of the species show high morphological variation within populations, but few traits that are diagnostic of species. We used a combination of DNA sequences from three mitochondrial genes, morphometric data, and comparisons of ecological niche models (ENMs) to infer a phylogenetic hypothesis for the Pristimantis acuminatus complex. Molecular phylogenetic analyses revealed a close relationship between three new species—Pristimantis enigmaticus sp. nov., P. limoncochensis sp. nov. and P. omeviridis sp. nov.—originally confused with Pristimantis acuminatus. In combination with morphometric data and geographic distributions, several morphological characters such as degree of tympanum exposure, skin texture, ulnar/tarsal tubercles and sexual secondary characters (vocal slits and nuptial pads in males) were found to be useful for diagnosing species in the complex. Multivariate discriminant analyses provided a successful classification rate for 83–100% of specimens. Discriminant analysis of localities in environmental niche space showed a successful classification rate of 75–98%. Identity tests of ENMs rejected hypotheses of niche equivalency, although not strongly because the high values on niche overlap. Pristimantis acuminatus and P. enigmaticus sp. nov. are distributed along the lowlands of central–southern Ecuador and northern Peru, in contrast with P. limoncochensis sp. nov. and P. omeviridis sp. nov., which are found in northern Ecuador and southern Colombia, up to 1200 m in the upper Amazon Basin. The methods used herein provide an integrated framework for inventorying the greatly underestimated biodiversity in Amazonia. PMID:26600198

Predicting meat yields and commercial meat cuts from carcasses of young bulls of Spanish breeds by the SEUROP method and an image analysis system.

PubMed

Oliver, A; Mendizabal, J A; Ripoll, G; Albertí, P; Purroy, A

2010-04-01

The SEUROP system is currently in use for carcass classification in Europe. Image analysis and other new technologies are being developed to enhance and supplement this classification system. After slaughtering, 91 carcasses of local Spanish beef breeds were weighed and classified according to the SEUROP system. Two digital photographs (a side and a dorsal view) were taken of the left carcass sides, and a total of 33 morphometric measurements (lengths, perimeters, areas) were made. Commercial butchering of these carcasses took place 24 h postmortem, and the different cuts were grouped according to four commercial meat cut quality categories: extra, first, second, and third. Multiple regression analysis of carcass weight and the SEUROP conformation score (x variables) on meat yield and the four commercial cut quality category yields (y variables) was performed as a measure of the accuracy of the SEUROP system. Stepwise regression analysis of carcass weight and the 33 morphometric image analysis measurements (x variables) and meat yield and yields of the four commercial cut quality categories (y variables) was carried out. Higher accuracy was achieved using image analysis than using only the current SEUROP conformation score. The regression coefficient values were between R(2)=0.66 and R(2)=0.93 (P<0.001) for the SEUROP system and between R(2)=0.81 and R(2)=0.94 (P<0.001) for the image analysis method. These results suggest that the image analysis method should be helpful as a means of supplementing and enhancing the SEUROP system for grading beef carcasses. 2009 Elsevier Ltd. All rights reserved.

Sex determination by three-dimensional geometric morphometrics of the vault and midsagittal curve of the neurocranium in a modern Greek population sample.

PubMed

Chovalopoulou, Maria-Eleni; Valakos, Efstratios D; Manolis, Sotiris K

2016-06-01

The aim of this study is to assess sexual dimorphism of adult crania in the vault and midsagittal curve of the vault using three-dimensional geometric morphometric methods. The study sample consisted of 176 crania of known sex (94 males, 82 females) belonging to individuals who lived during the 20th century in Greece. The three-dimensional co-ordinates of 31 ecto-cranial landmarks and 30 semi-landmarks were digitized using a MicroScribe 3DX contact digitizer. Generalized Procrustes analysis (GPA) was used to obtain size and shape variables for statistical analysis. Shape, size and form analyses were carried out by logistic regression and three discriminant function analyses. Results indicate that there are shape differences between sexes. Females in the region of the parietal bones are narrower and the axis forming the frontal and occipital bones is more elongated; the frontal bone is more vertical. Sex-specific shape differences give better classification results in the vault (79%) compared with the midsagittal curve of the neurocranium (68.8%). Size alone yielded better results for cranial vault (82%), while for the midsagittal curve of the vault the result is poorer (68.1%). As anticipated, the classification accuracy improves when both size and shape are combined (89.2% for vault, and 79.4% for midsagittal curve of the vault). These latter findings imply that, in contrast to the midsagittal curve of the neurocranium, the shape of the cranial vault can be used as an indicator of sex in the modern Greek population. Copyright © 2016. Published by Elsevier GmbH.

Insights from Integrative Systematics Reveal Cryptic Diversity in Pristimantis Frogs (Anura: Craugastoridae) from the Upper Amazon Basin.

PubMed

Ortega-Andrade, H Mauricio; Rojas-Soto, Octavio R; Valencia, Jorge H; Espinosa de Los Monteros, Alejandro; Morrone, Juan J; Ron, Santiago R; Cannatella, David C

2015-01-01

Pluralistic approaches to taxonomy facilitate a more complete appraisal of biodiversity, especially the diversification of cryptic species. Although species delimitation has traditionally been based primarily on morphological differences, the integration of new methods allows diverse lines of evidence to solve the problem. Robber frogs (Pristimantis) are exemplary, as many of the species show high morphological variation within populations, but few traits that are diagnostic of species. We used a combination of DNA sequences from three mitochondrial genes, morphometric data, and comparisons of ecological niche models (ENMs) to infer a phylogenetic hypothesis for the Pristimantis acuminatus complex. Molecular phylogenetic analyses revealed a close relationship between three new species-Pristimantis enigmaticus sp. nov., P. limoncochensis sp. nov. and P. omeviridis sp. nov.-originally confused with Pristimantis acuminatus. In combination with morphometric data and geographic distributions, several morphological characters such as degree of tympanum exposure, skin texture, ulnar/tarsal tubercles and sexual secondary characters (vocal slits and nuptial pads in males) were found to be useful for diagnosing species in the complex. Multivariate discriminant analyses provided a successful classification rate for 83-100% of specimens. Discriminant analysis of localities in environmental niche space showed a successful classification rate of 75-98%. Identity tests of ENMs rejected hypotheses of niche equivalency, although not strongly because the high values on niche overlap. Pristimantis acuminatus and P. enigmaticus sp. nov. are distributed along the lowlands of central-southern Ecuador and northern Peru, in contrast with P. limoncochensis sp. nov. and P. omeviridis sp. nov., which are found in northern Ecuador and southern Colombia, up to 1200 m in the upper Amazon Basin. The methods used herein provide an integrated framework for inventorying the greatly underestimated biodiversity in Amazonia.

Rule-guided human classification of Volunteered Geographic Information

NASA Astrophysics Data System (ADS)

Ali, Ahmed Loai; Falomir, Zoe; Schmid, Falko; Freksa, Christian

2017-05-01

During the last decade, web technologies and location sensing devices have evolved generating a form of crowdsourcing known as Volunteered Geographic Information (VGI). VGI acted as a platform of spatial data collection, in particular, when a group of public participants are involved in collaborative mapping activities: they work together to collect, share, and use information about geographic features. VGI exploits participants' local knowledge to produce rich data sources. However, the resulting data inherits problematic data classification. In VGI projects, the challenges of data classification are due to the following: (i) data is likely prone to subjective classification, (ii) remote contributions and flexible contribution mechanisms in most projects, and (iii) the uncertainty of spatial data and non-strict definitions of geographic features. These factors lead to various forms of problematic classification: inconsistent, incomplete, and imprecise data classification. This research addresses classification appropriateness. Whether the classification of an entity is appropriate or inappropriate is related to quantitative and/or qualitative observations. Small differences between observations may be not recognizable particularly for non-expert participants. Hence, in this paper, the problem is tackled by developing a rule-guided classification approach. This approach exploits data mining techniques of Association Classification (AC) to extract descriptive (qualitative) rules of specific geographic features. The rules are extracted based on the investigation of qualitative topological relations between target features and their context. Afterwards, the extracted rules are used to develop a recommendation system able to guide participants to the most appropriate classification. The approach proposes two scenarios to guide participants towards enhancing the quality of data classification. An empirical study is conducted to investigate the classification of grass-related features like forest, garden, park, and meadow. The findings of this study indicate the feasibility of the proposed approach.

A 3-D morphometric analysis of erosional features in a contourite drift from offshore SE Brazil

NASA Astrophysics Data System (ADS)

Alves, Tiago M.

2010-12-01

A contourite drift from offshore Brazil is mapped in detail and investigated using state-of-the-art 3-D seismic data. The aim was to review the relevance of erosional features in contourite drifts accumulated on continental slopes. Topographically confined by growing salt diapirs, the mapped contourite ridge is limited by two erosional features, a contourite moat and a turbidite channel, showing multiple slide scars on it flanks. Associated with the latter features are thick accumulations of high-amplitude strata, probably comprising sandy/silty sediment of Miocene to Holocene age. The erosional unconformities are mostly observed in a region averaging 3.75km away from the axes of a channel and a moat, whose deposits interfinger with continuous strata in central parts of the contourite drift. The multiple unconformities observed are mostly related to slide scars and local erosion on the flanks of the drift. This work demonstrates that the existence of widespread unconformities within contourite drifts on continental slopes: (1) may not be as prominent as often documented, (2) are often diachronic and interfinger with correlative hiatuses or aggraded strata in axial regions of contourite drifts. Although less widespread than regional, or ocean-scale unconformities, these diachronous features result in significant hiatuses within contourite drifts and are, therefore, potentially mappable as relevant (regional-scale) unconformities on 2-D/3-D seismic data. Thus, without a full 3-D morphometric analysis of contourite drifts, significant errors may occur when estimating major changes in the dynamics of principal geostrophic currents based on single-site core data, or on direct correlations between stratigraphic surfaces of distinct contourite bodies.

Feature selection for neural network based defect classification of ceramic components using high frequency ultrasound.

PubMed

Kesharaju, Manasa; Nagarajah, Romesh

2015-09-01

The motivation for this research stems from a need for providing a non-destructive testing method capable of detecting and locating any defects and microstructural variations within armour ceramic components before issuing them to the soldiers who rely on them for their survival. The development of an automated ultrasonic inspection based classification system would make possible the checking of each ceramic component and immediately alert the operator about the presence of defects. Generally, in many classification problems a choice of features or dimensionality reduction is significant and simultaneously very difficult, as a substantial computational effort is required to evaluate possible feature subsets. In this research, a combination of artificial neural networks and genetic algorithms are used to optimize the feature subset used in classification of various defects in reaction-sintered silicon carbide ceramic components. Initially wavelet based feature extraction is implemented from the region of interest. An Artificial Neural Network classifier is employed to evaluate the performance of these features. Genetic Algorithm based feature selection is performed. Principal Component Analysis is a popular technique used for feature selection and is compared with the genetic algorithm based technique in terms of classification accuracy and selection of optimal number of features. The experimental results confirm that features identified by Principal Component Analysis lead to improved performance in terms of classification percentage with 96% than Genetic algorithm with 94%. Copyright © 2015 Elsevier B.V. All rights reserved.

Derivation of an artificial gene to improve classification accuracy upon gene selection.

PubMed

Seo, Minseok; Oh, Sejong

2012-02-01

Classification analysis has been developed continuously since 1936. This research field has advanced as a result of development of classifiers such as KNN, ANN, and SVM, as well as through data preprocessing areas. Feature (gene) selection is required for very high dimensional data such as microarray before classification work. The goal of feature selection is to choose a subset of informative features that reduces processing time and provides higher classification accuracy. In this study, we devised a method of artificial gene making (AGM) for microarray data to improve classification accuracy. Our artificial gene was derived from a whole microarray dataset, and combined with a result of gene selection for classification analysis. We experimentally confirmed a clear improvement of classification accuracy after inserting artificial gene. Our artificial gene worked well for popular feature (gene) selection algorithms and classifiers. The proposed approach can be applied to any type of high dimensional dataset. Copyright © 2011 Elsevier Ltd. All rights reserved.

Pattern classification of kinematic and kinetic running data to distinguish gender, shod/barefoot and injury groups with feature ranking.

PubMed

Eskofier, Bjoern M; Kraus, Martin; Worobets, Jay T; Stefanyshyn, Darren J; Nigg, Benno M

2012-01-01

The identification of differences between groups is often important in biomechanics. This paper presents group classification tasks using kinetic and kinematic data from a prospective running injury study. Groups composed of gender, of shod/barefoot running and of runners who developed patellofemoral pain syndrome (PFPS) during the study, and asymptotic runners were classified. The features computed from the biomechanical data were deliberately chosen to be generic. Therefore, they were suited for different biomechanical measurements and classification tasks without adaptation to the input signals. Feature ranking was applied to reveal the relevance of each feature to the classification task. Data from 80 runners were analysed for gender and shod/barefoot classification, while 12 runners were investigated in the injury classification task. Gender groups could be differentiated with 84.7%, shod/barefoot running with 98.3%, and PFPS with 100% classification rate. For the latter group, one single variable could be identified that alone allowed discrimination.

[Combining speech sample and feature bilateral selection algorithm for classification of Parkinson's disease].

PubMed

Zhang, Xiaoheng; Wang, Lirui; Cao, Yao; Wang, Pin; Zhang, Cheng; Yang, Liuyang; Li, Yongming; Zhang, Yanling; Cheng, Oumei

2018-02-01

Diagnosis of Parkinson's disease (PD) based on speech data has been proved to be an effective way in recent years. However, current researches just care about the feature extraction and classifier design, and do not consider the instance selection. Former research by authors showed that the instance selection can lead to improvement on classification accuracy. However, no attention is paid on the relationship between speech sample and feature until now. Therefore, a new diagnosis algorithm of PD is proposed in this paper by simultaneously selecting speech sample and feature based on relevant feature weighting algorithm and multiple kernel method, so as to find their synergy effects, thereby improving classification accuracy. Experimental results showed that this proposed algorithm obtained apparent improvement on classification accuracy. It can obtain mean classification accuracy of 82.5%, which was 30.5% higher than the relevant algorithm. Besides, the proposed algorithm detected the synergy effects of speech sample and feature, which is valuable for speech marker extraction.

Per-field crop classification in irrigated agricultural regions in middle Asia using random forest and support vector machine ensemble

NASA Astrophysics Data System (ADS)

Löw, Fabian; Schorcht, Gunther; Michel, Ulrich; Dech, Stefan; Conrad, Christopher

2012-10-01

Accurate crop identification and crop area estimation are important for studies on irrigated agricultural systems, yield and water demand modeling, and agrarian policy development. In this study a novel combination of Random Forest (RF) and Support Vector Machine (SVM) classifiers is presented that (i) enhances crop classification accuracy and (ii) provides spatial information on map uncertainty. The methodology was implemented over four distinct irrigated sites in Middle Asia using RapidEye time series data. The RF feature importance statistics was used as feature-selection strategy for the SVM to assess possible negative effects on classification accuracy caused by an oversized feature space. The results of the individual RF and SVM classifications were combined with rules based on posterior classification probability and estimates of classification probability entropy. SVM classification performance was increased by feature selection through RF. Further experimental results indicate that the hybrid classifier improves overall classification accuracy in comparison to the single classifiers as well as useŕs and produceŕs accuracy.

Ensemble of sparse classifiers for high-dimensional biological data.

PubMed

Kim, Sunghan; Scalzo, Fabien; Telesca, Donatello; Hu, Xiao

2015-01-01

Biological data are often high in dimension while the number of samples is small. In such cases, the performance of classification can be improved by reducing the dimension of data, which is referred to as feature selection. Recently, a novel feature selection method has been proposed utilising the sparsity of high-dimensional biological data where a small subset of features accounts for most variance of the dataset. In this study we propose a new classification method for high-dimensional biological data, which performs both feature selection and classification within a single framework. Our proposed method utilises a sparse linear solution technique and the bootstrap aggregating algorithm. We tested its performance on four public mass spectrometry cancer datasets along with two other conventional classification techniques such as Support Vector Machines and Adaptive Boosting. The results demonstrate that our proposed method performs more accurate classification across various cancer datasets than those conventional classification techniques.

A geometric morphometric analysis of hominin upper premolars. Shape variation and morphological integration.

PubMed

Gómez-Robles, Aida; Martinón-Torres, María; Bermúdez de Castro, José María; Prado-Simón, Leyre; Arsuaga, Juan Luis

2011-12-01

This paper continues the series of articles initiated in 2006 that analyse hominin dental crown morphology by means of geometric morphometric techniques. The detailed study of both upper premolar occlusal morphologies in a comprehensive sample of hominin fossils, including those coming from the Gran Dolina-TD6 and Sima de los Huesos sites from Atapuerca, Spain, complement previous works on lower first and second premolars and upper first molars. A morphological gradient consisting of the change from asymmetric to symmetric upper premolars and a marked reduction of the lingual cusp in recent Homo species has been observed in both premolars. Although percentages of correct classification based on upper premolar morphologies are not very high, significant morphological differences between Neanderthals (and European middle Pleistocene fossils) and modern humans have been identified, especially in upper second premolars. The study of morphological integration between premolar morphologies reveals significant correlations that are weaker between upper premolars than between lower ones and significant correlations between antagonists. These results have important implications for understanding the genetic and functional factors underlying dental phenotypic variation and covariation. Copyright © 2011 Elsevier Ltd. All rights reserved.

Semantic and topological classification of images in magnetically guided capsule endoscopy

NASA Astrophysics Data System (ADS)

Mewes, P. W.; Rennert, P.; Juloski, A. L.; Lalande, A.; Angelopoulou, E.; Kuth, R.; Hornegger, J.

2012-03-01

Magnetically-guided capsule endoscopy (MGCE) is a nascent technology with the goal to allow the steering of a capsule endoscope inside a water filled stomach through an external magnetic field. We developed a classification cascade for MGCE images with groups images in semantic and topological categories. Results can be used in a post-procedure review or as a starting point for algorithms classifying pathologies. The first semantic classification step discards over-/under-exposed images as well as images with a large amount of debris. The second topological classification step groups images with respect to their position in the upper gastrointestinal tract (mouth, esophagus, stomach, duodenum). In the third stage two parallel classifications steps distinguish topologically different regions inside the stomach (cardia, fundus, pylorus, antrum, peristaltic view). For image classification, global image features and local texture features were applied and their performance was evaluated. We show that the third classification step can be improved by a bubble and debris segmentation because it limits feature extraction to discriminative areas only. We also investigated the impact of segmenting intestinal folds on the identification of different semantic camera positions. The results of classifications with a support-vector-machine show the significance of color histogram features for the classification of corrupted images (97%). Features extracted from intestinal fold segmentation lead only to a minor improvement (3%) in discriminating different camera positions.

Mutual information criterion for feature selection with application to classification of breast microcalcifications

NASA Astrophysics Data System (ADS)

Diamant, Idit; Shalhon, Moran; Goldberger, Jacob; Greenspan, Hayit

2016-03-01

Classification of clustered breast microcalcifications into benign and malignant categories is an extremely challenging task for computerized algorithms and expert radiologists alike. In this paper we present a novel method for feature selection based on mutual information (MI) criterion for automatic classification of microcalcifications. We explored the MI based feature selection for various texture features. The proposed method was evaluated on a standardized digital database for screening mammography (DDSM). Experimental results demonstrate the effectiveness and the advantage of using the MI-based feature selection to obtain the most relevant features for the task and thus to provide for improved performance as compared to using all features.

Classification of Informal Settlements Through the Integration of 2d and 3d Features Extracted from Uav Data

NASA Astrophysics Data System (ADS)

Gevaert, C. M.; Persello, C.; Sliuzas, R.; Vosselman, G.

2016-06-01

Unmanned Aerial Vehicles (UAVs) are capable of providing very high resolution and up-to-date information to support informal settlement upgrading projects. In order to provide accurate basemaps, urban scene understanding through the identification and classification of buildings and terrain is imperative. However, common characteristics of informal settlements such as small, irregular buildings with heterogeneous roof material and large presence of clutter challenge state-of-the-art algorithms. Especially the dense buildings and steeply sloped terrain cause difficulties in identifying elevated objects. This work investigates how 2D radiometric and textural features, 2.5D topographic features, and 3D geometric features obtained from UAV imagery can be integrated to obtain a high classification accuracy in challenging classification problems for the analysis of informal settlements. It compares the utility of pixel-based and segment-based features obtained from an orthomosaic and DSM with point-based and segment-based features extracted from the point cloud to classify an unplanned settlement in Kigali, Rwanda. Findings show that the integration of 2D and 3D features leads to higher classification accuracies.

An ant colony optimization based feature selection for web page classification.

PubMed

Saraç, Esra; Özel, Selma Ayşe

2014-01-01

The increased popularity of the web has caused the inclusion of huge amount of information to the web, and as a result of this explosive information growth, automated web page classification systems are needed to improve search engines' performance. Web pages have a large number of features such as HTML/XML tags, URLs, hyperlinks, and text contents that should be considered during an automated classification process. The aim of this study is to reduce the number of features to be used to improve runtime and accuracy of the classification of web pages. In this study, we used an ant colony optimization (ACO) algorithm to select the best features, and then we applied the well-known C4.5, naive Bayes, and k nearest neighbor classifiers to assign class labels to web pages. We used the WebKB and Conference datasets in our experiments, and we showed that using the ACO for feature selection improves both accuracy and runtime performance of classification. We also showed that the proposed ACO based algorithm can select better features with respect to the well-known information gain and chi square feature selection methods.

Question analysis for Indonesian comparative question

NASA Astrophysics Data System (ADS)

Saelan, A.; Purwarianti, A.; Widyantoro, D. H.

2017-01-01

Information seeking is one of human needs today. Comparing things using search engine surely take more times than search only one thing. In this paper, we analyzed comparative questions for comparative question answering system. Comparative question is a question that comparing two or more entities. We grouped comparative questions into 5 types: selection between mentioned entities, selection between unmentioned entities, selection between any entity, comparison, and yes or no question. Then we extracted 4 types of information from comparative questions: entity, aspect, comparison, and constraint. We built classifiers for classification task and information extraction task. Features used for classification task are bag of words, whether for information extraction, we used lexical, 2 previous and following words lexical, and previous label as features. We tried 2 scenarios: classification first and extraction first. For classification first, we used classification result as a feature for extraction. Otherwise, for extraction first, we used extraction result as features for classification. We found that the result would be better if we do extraction first before classification. For the extraction task, classification using SMO gave the best result (88.78%), while for classification, it is better to use naïve bayes (82.35%).

Feature Selection for Ridge Regression with Provable Guarantees.

PubMed

Paul, Saurabh; Drineas, Petros

2016-04-01

We introduce single-set spectral sparsification as a deterministic sampling-based feature selection technique for regularized least-squares classification, which is the classification analog to ridge regression. The method is unsupervised and gives worst-case guarantees of the generalization power of the classification function after feature selection with respect to the classification function obtained using all features. We also introduce leverage-score sampling as an unsupervised randomized feature selection method for ridge regression. We provide risk bounds for both single-set spectral sparsification and leverage-score sampling on ridge regression in the fixed design setting and show that the risk in the sampled space is comparable to the risk in the full-feature space. We perform experiments on synthetic and real-world data sets; a subset of TechTC-300 data sets, to support our theory. Experimental results indicate that the proposed methods perform better than the existing feature selection methods.

Neandertal talus bones from El Sidrón site (Asturias, Spain): A 3D geometric morphometrics analysis.

PubMed

Rosas, Antonio; Ferrando, Anabel; Bastir, Markus; García-Tabernero, Antonio; Estalrrich, Almudena; Huguet, Rosa; García-Martínez, Daniel; Pastor, Juan Francisco; de la Rasilla, Marco

2017-10-01

The El Sidrón tali sample is assessed in an evolutionary framework. We aim to explore the relationship between Neandertal talus morphology and body size/shape. We test the hypothesis 1: talar Neandertal traits are influenced by body size, and the hypothesis 2: shape variables independent of body size correspond to inherited primitive features. We quantify 35 landmarks through 3D geometric morphometrics techniques to describe H. neanderthalensis-H. sapiens shape variation, by Mean Shape Comparisons, Principal Component, Phenetic Clusters, Minimum spanning tree analyses and partial least square and regression of talus shape on body variables. Shape variation correlated to body size is compared to Neandertals-Modern Humans (MH) evolutionary shape variation. The Neandertal sample is compared to early hominins. Neandertal talus presents trochlear hypertrophy, a larger equality of trochlear rims, a shorter neck, a more expanded head, curvature and an anterior location of the medial malleolar facet, an expanded and projected lateral malleolar facet and laterally expanded posterior calcaneal facet compared to MH. The Neandertal talocrural joint morphology is influenced by body size. The other Neandertal talus traits do not co-vary with it or not follow the same co-variation pattern as MH. Besides, the trochlear hypertrophy, the trochlear rims equality and the short neck could be inherited primitive features; the medial malleolar facet morphology could be an inherited primitive feature or a secondarily primitive trait; and the calcaneal posterior facet would be an autapomorphic feature of the Neandertal lineage. © 2017 Wiley Periodicals, Inc.

Feature Selection Has a Large Impact on One-Class Classification Accuracy for MicroRNAs in Plants.

PubMed

Yousef, Malik; Saçar Demirci, Müşerref Duygu; Khalifa, Waleed; Allmer, Jens

2016-01-01

MicroRNAs (miRNAs) are short RNA sequences involved in posttranscriptional gene regulation. Their experimental analysis is complicated and, therefore, needs to be supplemented with computational miRNA detection. Currently computational miRNA detection is mainly performed using machine learning and in particular two-class classification. For machine learning, the miRNAs need to be parametrized and more than 700 features have been described. Positive training examples for machine learning are readily available, but negative data is hard to come by. Therefore, it seems prerogative to use one-class classification instead of two-class classification. Previously, we were able to almost reach two-class classification accuracy using one-class classifiers. In this work, we employ feature selection procedures in conjunction with one-class classification and show that there is up to 36% difference in accuracy among these feature selection methods. The best feature set allowed the training of a one-class classifier which achieved an average accuracy of ~95.6% thereby outperforming previous two-class-based plant miRNA detection approaches by about 0.5%. We believe that this can be improved upon in the future by rigorous filtering of the positive training examples and by improving current feature clustering algorithms to better target pre-miRNA feature selection.

Particle Swarm Optimization approach to defect detection in armour ceramics.

PubMed

Kesharaju, Manasa; Nagarajah, Romesh

2017-03-01

In this research, various extracted features were used in the development of an automated ultrasonic sensor based inspection system that enables defect classification in each ceramic component prior to despatch to the field. Classification is an important task and large number of irrelevant, redundant features commonly introduced to a dataset reduces the classifiers performance. Feature selection aims to reduce the dimensionality of the dataset while improving the performance of a classification system. In the context of a multi-criteria optimization problem (i.e. to minimize classification error rate and reduce number of features) such as one discussed in this research, the literature suggests that evolutionary algorithms offer good results. Besides, it is noted that Particle Swarm Optimization (PSO) has not been explored especially in the field of classification of high frequency ultrasonic signals. Hence, a binary coded Particle Swarm Optimization (BPSO) technique is investigated in the implementation of feature subset selection and to optimize the classification error rate. In the proposed method, the population data is used as input to an Artificial Neural Network (ANN) based classification system to obtain the error rate, as ANN serves as an evaluator of PSO fitness function. Copyright © 2016. Published by Elsevier B.V.

a Two-Step Classification Approach to Distinguishing Similar Objects in Mobile LIDAR Point Clouds

NASA Astrophysics Data System (ADS)

He, H.; Khoshelham, K.; Fraser, C.

2017-09-01

Nowadays, lidar is widely used in cultural heritage documentation, urban modeling, and driverless car technology for its fast and accurate 3D scanning ability. However, full exploitation of the potential of point cloud data for efficient and automatic object recognition remains elusive. Recently, feature-based methods have become very popular in object recognition on account of their good performance in capturing object details. Compared with global features describing the whole shape of the object, local features recording the fractional details are more discriminative and are applicable for object classes with considerable similarity. In this paper, we propose a two-step classification approach based on point feature histograms and the bag-of-features method for automatic recognition of similar objects in mobile lidar point clouds. Lamp post, street light and traffic sign are grouped as one category in the first-step classification for their inter similarity compared with tree and vehicle. A finer classification of the lamp post, street light and traffic sign based on the result of the first-step classification is implemented in the second step. The proposed two-step classification approach is shown to yield a considerable improvement over the conventional one-step classification approach.

Cloud field classification based on textural features

NASA Technical Reports Server (NTRS)

Sengupta, Sailes Kumar

1989-01-01

An essential component in global climate research is accurate cloud cover and type determination. Of the two approaches to texture-based classification (statistical and textural), only the former is effective in the classification of natural scenes such as land, ocean, and atmosphere. In the statistical approach that was adopted, parameters characterizing the stochastic properties of the spatial distribution of grey levels in an image are estimated and then used as features for cloud classification. Two types of textural measures were used. One is based on the distribution of the grey level difference vector (GLDV), and the other on a set of textural features derived from the MaxMin cooccurrence matrix (MMCM). The GLDV method looks at the difference D of grey levels at pixels separated by a horizontal distance d and computes several statistics based on this distribution. These are then used as features in subsequent classification. The MaxMin tectural features on the other hand are based on the MMCM, a matrix whose (I,J)th entry give the relative frequency of occurrences of the grey level pair (I,J) that are consecutive and thresholded local extremes separated by a given pixel distance d. Textural measures are then computed based on this matrix in much the same manner as is done in texture computation using the grey level cooccurrence matrix. The database consists of 37 cloud field scenes from LANDSAT imagery using a near IR visible channel. The classification algorithm used is the well known Stepwise Discriminant Analysis. The overall accuracy was estimated by the percentage or correct classifications in each case. It turns out that both types of classifiers, at their best combination of features, and at any given spatial resolution give approximately the same classification accuracy. A neural network based classifier with a feed forward architecture and a back propagation training algorithm is used to increase the classification accuracy, using these two classes of features. Preliminary results based on the GLDV textural features alone look promising.

Research on Remote Sensing Image Classification Based on Feature Level Fusion

NASA Astrophysics Data System (ADS)

Yuan, L.; Zhu, G.

2018-04-01

Remote sensing image classification, as an important direction of remote sensing image processing and application, has been widely studied. However, in the process of existing classification algorithms, there still exists the phenomenon of misclassification and missing points, which leads to the final classification accuracy is not high. In this paper, we selected Sentinel-1A and Landsat8 OLI images as data sources, and propose a classification method based on feature level fusion. Compare three kind of feature level fusion algorithms (i.e., Gram-Schmidt spectral sharpening, Principal Component Analysis transform and Brovey transform), and then select the best fused image for the classification experimental. In the classification process, we choose four kinds of image classification algorithms (i.e. Minimum distance, Mahalanobis distance, Support Vector Machine and ISODATA) to do contrast experiment. We use overall classification precision and Kappa coefficient as the classification accuracy evaluation criteria, and the four classification results of fused image are analysed. The experimental results show that the fusion effect of Gram-Schmidt spectral sharpening is better than other methods. In four kinds of classification algorithms, the fused image has the best applicability to Support Vector Machine classification, the overall classification precision is 94.01 % and the Kappa coefficients is 0.91. The fused image with Sentinel-1A and Landsat8 OLI is not only have more spatial information and spectral texture characteristics, but also enhances the distinguishing features of the images. The proposed method is beneficial to improve the accuracy and stability of remote sensing image classification.

Improved classification accuracy by feature extraction using genetic algorithms

NASA Astrophysics Data System (ADS)

Patriarche, Julia; Manduca, Armando; Erickson, Bradley J.

2003-05-01

A feature extraction algorithm has been developed for the purposes of improving classification accuracy. The algorithm uses a genetic algorithm / hill-climber hybrid to generate a set of linearly recombined features, which may be of reduced dimensionality compared with the original set. The genetic algorithm performs the global exploration, and a hill climber explores local neighborhoods. Hybridizing the genetic algorithm with a hill climber improves both the rate of convergence, and the final overall cost function value; it also reduces the sensitivity of the genetic algorithm to parameter selection. The genetic algorithm includes the operators: crossover, mutation, and deletion / reactivation - the last of these effects dimensionality reduction. The feature extractor is supervised, and is capable of deriving a separate feature space for each tissue (which are reintegrated during classification). A non-anatomical digital phantom was developed as a gold standard for testing purposes. In tests with the phantom, and with images of multiple sclerosis patients, classification with feature extractor derived features yielded lower error rates than using standard pulse sequences, and with features derived using principal components analysis. Using the multiple sclerosis patient data, the algorithm resulted in a mean 31% reduction in classification error of pure tissues.

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.

Feature Inference Learning and Eyetracking

ERIC Educational Resources Information Center

Rehder, Bob; Colner, Robert M.; Hoffman, Aaron B.

2009-01-01

Besides traditional supervised classification learning, people can learn categories by inferring the missing features of category members. It has been proposed that feature inference learning promotes learning a category's internal structure (e.g., its typical features and interfeature correlations) whereas classification promotes the learning of…

Improved wavelet packet classification algorithm for vibrational intrusions in distributed fiber-optic monitoring systems

NASA Astrophysics Data System (ADS)

Wang, Bingjie; Pi, Shaohua; Sun, Qi; Jia, Bo

2015-05-01

An improved classification algorithm that considers multiscale wavelet packet Shannon entropy is proposed. Decomposition coefficients at all levels are obtained to build the initial Shannon entropy feature vector. After subtracting the Shannon entropy map of the background signal, components of the strongest discriminating power in the initial feature vector are picked out to rebuild the Shannon entropy feature vector, which is transferred to radial basis function (RBF) neural network for classification. Four types of man-made vibrational intrusion signals are recorded based on a modified Sagnac interferometer. The performance of the improved classification algorithm has been evaluated by the classification experiments via RBF neural network under different diffusion coefficients. An 85% classification accuracy rate is achieved, which is higher than the other common algorithms. The classification results show that this improved classification algorithm can be used to classify vibrational intrusion signals in an automatic real-time monitoring system.

Quantitative analysis of histopathological findings using image processing software.

PubMed

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

2017-10-01

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

[The morphometric characteristics of the main structural components of renal nephrons in the white rats with experimentally induced acute and chronic alcohol intoxication].

PubMed

Shcherbakova, V M

2016-01-01

The objective of the present work was to study the morphometric characteristics of the main structural components of renal nephrons in the white rats with the experimentally induced acute and chronic alcohol intoxication. We undertook the morphometric examination of the structural elements of rat kidneys with the subsequent statistical analysis of the data obtained. The results of the study give evidence of the toxic action of ethanol on all structural components of the nephron in the case of both acute and chronic alcohol intoxication. The study revealed some specific features of the development of pathological process in the renal tissue structures at different stages of alcohol intoxication. The most pronounced morphological changes were observed in the renal proximal tubules and the least pronounced ones in the structure of the renal glomeruli. The earliest morphological changes become apparent in distal convoluted tubules of the nephron; in the case of persistent alcoholemia, they first develop in the renal corpuscles and thereafter in the distal proximal tubules. The maximum changes occur in the case of acute alcohol intoxication and between 2 weeks and 2 months of chronic intoxication; they become less conspicuous during a later period.

First metatarsophalangeal joint motion in Homo sapiens: theoretical association of two-axis kinematics and specific morphometrics.

PubMed

Durrant, Michael N; McElroy, Tucker; Durrant, Lara

2012-01-01

The metatarsal head and proximal phalanx exhibit considerable asymmetry in their shape and geometry, but there is little documentation in the literature regarding the prevalence of structural characteristics that occur in a given population. Although there is a considerable volume of in vivo and in vitro experiments demonstrating first metatarsal inversion around its longitudinal axis with dorsiflexion, little is known regarding the applicability of specific morphometrics to these motions. Nine distinctive osseous characteristics in the metatarsal head and phalanx were selected based on their location, geometry, and perceived functional relationship to previous studies describing metatarsal motion as inversion with dorsiflexion. The prevalences of the chosen characteristics were determined in a cohort of 21 randomly selected skeletal specimens, 19 of which were provided by the anatomical preparation office at the University of California, San Diego, and two of which were in the possession of one of us (M.D.). The frequency of occurrence of each selected morphological characteristic in this sample and the relevant summary statistics confirm a strong association between the selected features and a conceptual two-axis kinematic model of the metatarsophalangeal joint. The selected morphometrics are consistent with inversion of the metatarsal around its longitudinal axis as it dorsiflexes.

Automatic Identification of Messages Related to Adverse Drug Reactions from Online User Reviews using Feature-based Classification.

PubMed

Liu, Jingfang; Zhang, Pengzhu; Lu, Yingjie

2014-11-01

User-generated medical messages on Internet contain extensive information related to adverse drug reactions (ADRs) and are known as valuable resources for post-marketing drug surveillance. The aim of this study was to find an effective method to identify messages related to ADRs automatically from online user reviews. We conducted experiments on online user reviews using different feature set and different classification technique. Firstly, the messages from three communities, allergy community, schizophrenia community and pain management community, were collected, the 3000 messages were annotated. Secondly, the N-gram-based features set and medical domain-specific features set were generated. Thirdly, three classification techniques, SVM, C4.5 and Naïve Bayes, were used to perform classification tasks separately. Finally, we evaluated the performance of different method using different feature set and different classification technique by comparing the metrics including accuracy and F-measure. In terms of accuracy, the accuracy of SVM classifier was higher than 0.8, the accuracy of C4.5 classifier or Naïve Bayes classifier was lower than 0.8; meanwhile, the combination feature sets including n-gram-based feature set and domain-specific feature set consistently outperformed single feature set. In terms of F-measure, the highest F-measure is 0.895 which was achieved by using combination feature sets and a SVM classifier. In all, we can get the best classification performance by using combination feature sets and SVM classifier. By using combination feature sets and SVM classifier, we can get an effective method to identify messages related to ADRs automatically from online user reviews.

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.

SoFoCles: feature filtering for microarray classification based on gene ontology.

PubMed

Papachristoudis, Georgios; Diplaris, Sotiris; Mitkas, Pericles A

2010-02-01

Marker gene selection has been an important research topic in the classification analysis of gene expression data. Current methods try to reduce the "curse of dimensionality" by using statistical intra-feature set calculations, or classifiers that are based on the given dataset. In this paper, we present SoFoCles, an interactive tool that enables semantic feature filtering in microarray classification problems with the use of external, well-defined knowledge retrieved from the Gene Ontology. The notion of semantic similarity is used to derive genes that are involved in the same biological path during the microarray experiment, by enriching a feature set that has been initially produced with legacy methods. Among its other functionalities, SoFoCles offers a large repository of semantic similarity methods that are used in order to derive feature sets and marker genes. The structure and functionality of the tool are discussed in detail, as well as its ability to improve classification accuracy. Through experimental evaluation, SoFoCles is shown to outperform other classification schemes in terms of classification accuracy in two real datasets using different semantic similarity computation approaches.

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

A new interpretation of the bee fossil Melitta willardi Cockerell (Hymenoptera, Melittidae) based on geometric morphometrics of the wing.

PubMed

Dewulf, Alexandre; De Meulemeester, Thibaut; Dehon, Manuel; Engel, Michael S; Michez, Denis

2014-01-01

Although bees are one of the major lineages of pollinators and are today quite diverse, few well-preserved fossils are available from which to establish the tempo of their diversification/extinction since the Early Cretaceous. Here we present a reassessment of the taxonomic affinities of Melitta willardiCockerell 1909, preserved as a compression fossil from the Florissant shales of Colorado, USA. Based on geometric morphometric wing shape analyses M. willardi cannot be confidently assigned to the genus Melitta Kirby (Anthophila, Melittidae). Instead, the species exhibits phenotypic affinity with the subfamily Andreninae (Anthophila, Andrenidae), but does not appear to belong to any of the known genera therein. Accordingly, we describe a new genus, Andrenopteryx gen. n., based on wing shape as well as additional morphological features and to accommodate M. willardi. The new combination Andrenopteryx willardi (Cockerell) is established.

A new interpretation of the bee fossil Melitta willardi Cockerell (Hymenoptera, Melittidae) based on geometric morphometrics of the wing

PubMed Central

Dewulf, Alexandre; De Meulemeester, Thibaut; Dehon, Manuel; Engel, Michael S.; Michez, Denis

2014-01-01

Abstract Although bees are one of the major lineages of pollinators and are today quite diverse, few well-preserved fossils are available from which to establish the tempo of their diversification/extinction since the Early Cretaceous. Here we present a reassessment of the taxonomic affinities of Melitta willardi Cockerell 1909, preserved as a compression fossil from the Florissant shales of Colorado, USA. Based on geometric morphometric wing shape analyses M. willardi cannot be confidently assigned to the genus Melitta Kirby (Anthophila, Melittidae). Instead, the species exhibits phenotypic affinity with the subfamily Andreninae (Anthophila, Andrenidae), but does not appear to belong to any of the known genera therein. Accordingly, we describe a new genus, Andrenopteryx gen. n., based on wing shape as well as additional morphological features and to accommodate M. willardi. The new combination Andrenopteryx willardi (Cockerell) is established. PMID:24715773

Novel chromatin texture features for the classification of pap smears

NASA Astrophysics Data System (ADS)

Bejnordi, Babak E.; Moshavegh, Ramin; Sujathan, K.; Malm, Patrik; Bengtsson, Ewert; Mehnert, Andrew

2013-03-01

This paper presents a set of novel structural texture features for quantifying nuclear chromatin patterns in cells on a conventional Pap smear. The features are derived from an initial segmentation of the chromatin into bloblike texture primitives. The results of a comprehensive feature selection experiment, including the set of proposed structural texture features and a range of different cytology features drawn from the literature, show that two of the four top ranking features are structural texture features. They also show that a combination of structural and conventional features yields a classification performance of 0.954±0.019 (AUC±SE) for the discrimination of normal (NILM) and abnormal (LSIL and HSIL) slides. The results of a second classification experiment, using only normal-appearing cells from both normal and abnormal slides, demonstrates that a single structural texture feature measuring chromatin margination yields a classification performance of 0.815±0.019. Overall the results demonstrate the efficacy of the proposed structural approach and that it is possible to detect malignancy associated changes (MACs) in Papanicoloau stain.

A Quantum Hybrid PSO Combined with Fuzzy k-NN Approach to Feature Selection and Cell Classification in Cervical Cancer Detection.

PubMed

Iliyasu, Abdullah M; Fatichah, Chastine

2017-12-19

A quantum hybrid (QH) intelligent approach that blends the adaptive search capability of the quantum-behaved particle swarm optimisation (QPSO) method with the intuitionistic rationality of traditional fuzzy k -nearest neighbours (Fuzzy k -NN) algorithm (known simply as the Q-Fuzzy approach) is proposed for efficient feature selection and classification of cells in cervical smeared (CS) images. From an initial multitude of 17 features describing the geometry, colour, and texture of the CS images, the QPSO stage of our proposed technique is used to select the best subset features (i.e., global best particles) that represent a pruned down collection of seven features. Using a dataset of almost 1000 images, performance evaluation of our proposed Q-Fuzzy approach assesses the impact of our feature selection on classification accuracy by way of three experimental scenarios that are compared alongside two other approaches: the All-features (i.e., classification without prior feature selection) and another hybrid technique combining the standard PSO algorithm with the Fuzzy k -NN technique (P-Fuzzy approach). In the first and second scenarios, we further divided the assessment criteria in terms of classification accuracy based on the choice of best features and those in terms of the different categories of the cervical cells. In the third scenario, we introduced new QH hybrid techniques, i.e., QPSO combined with other supervised learning methods, and compared the classification accuracy alongside our proposed Q-Fuzzy approach. Furthermore, we employed statistical approaches to establish qualitative agreement with regards to the feature selection in the experimental scenarios 1 and 3. The synergy between the QPSO and Fuzzy k -NN in the proposed Q-Fuzzy approach improves classification accuracy as manifest in the reduction in number cell features, which is crucial for effective cervical cancer detection and diagnosis.

Micro-computed Tomographic Analysis of Mandibular Second Molars with C-shaped Root Canals.

PubMed

Amoroso-Silva, Pablo Andrés; Ordinola-Zapata, Ronald; Duarte, Marco Antonio Hungaro; Gutmann, James L; del Carpio-Perochena, Aldo; Bramante, Clovis Monteiro; de Moraes, Ivaldo Gomes

2015-06-01

The goal of the present study was to evaluate the morphometric aspects of the internal anatomy of the root canal system of mandibular second molars with C-shaped canals. Fifty-two extracted second mandibular molars with C-shaped canals, fused roots, and radicular grooves were selected from a Brazilian population. The samples were scanned with a micro-computed tomographic scanner at a voxel size of 19.6 μm. The root canal cross sections were recorded as C1, C2, C3, and C4 root canal configurations according to the modified Melton classification. Morphometric parameters, including the major and minor diameters of the root canals, the aspect ratio, the roundness, and the tridimensional configuration (merging, symmetric, and asymmetric), were evaluated. The 3-dimensional reconstruction images of the teeth indicated an even distribution within the sample. The analysis of the prevalence of the different cross-sectional configurations of the C-shaped molars revealed that these were predominantly of the C4 and C3 configurations (1 mm from the apex) and the C1 and C2 configurations in the cervical third. According to the morphometric parameters, the C1 and the distal aspect of the C2 configurations exhibited the lowest roundness values and higher values for the area, major diameter, and aspect ratio in the apical third. Mandibular molars with C-shaped root canals exhibited similar distributions of symmetric, asymmetric, and merging type canals. The C1 configuration and the distal aspect of the C2 configuration exhibited the highest area values, low roundness values, and large apical diameters. Copyright © 2015 American Association of Endodontists. Published by Elsevier Inc. All rights reserved.

Semi-automated extraction of longitudinal subglacial bedforms from digital terrain models - Two new methods

NASA Astrophysics Data System (ADS)

Jorge, Marco G.; Brennand, Tracy A.

2017-07-01

Relict drumlin and mega-scale glacial lineation (positive relief, longitudinal subglacial bedforms - LSBs) morphometry has been used as a proxy for paleo ice-sheet dynamics. LSB morphometric inventories have relied on manual mapping, which is slow and subjective and thus potentially difficult to reproduce. Automated methods are faster and reproducible, but previous methods for LSB semi-automated mapping have not been highly successful. Here, two new object-based methods for the semi-automated extraction of LSBs (footprints) from digital terrain models are compared in a test area in the Puget Lowland, Washington, USA. As segmentation procedures to create LSB-candidate objects, the normalized closed contour method relies on the contouring of a normalized local relief model addressing LSBs on slopes, and the landform elements mask method relies on the classification of landform elements derived from the digital terrain model. For identifying which LSB-candidate objects correspond to LSBs, both methods use the same LSB operational definition: a ruleset encapsulating expert knowledge, published morphometric data, and the morphometric range of LSBs in the study area. The normalized closed contour method was separately applied to four different local relief models, two computed in moving windows and two hydrology-based. Overall, the normalized closed contour method outperformed the landform elements mask method. The normalized closed contour method performed on a hydrological relief model from a multiple direction flow routing algorithm performed best. For an assessment of its transferability, the normalized closed contour method was evaluated on a second area, the Chautauqua drumlin field, Pennsylvania and New York, USA where it performed better than in the Puget Lowland. A broad comparison to previous methods suggests that the normalized relief closed contour method may be the most capable method to date, but more development is required.

Emotional textile image classification based on cross-domain convolutional sparse autoencoders with feature selection

NASA Astrophysics Data System (ADS)

Li, Zuhe; Fan, Yangyu; Liu, Weihua; Yu, Zeqi; Wang, Fengqin

2017-01-01

We aim to apply sparse autoencoder-based unsupervised feature learning to emotional semantic analysis for textile images. To tackle the problem of limited training data, we present a cross-domain feature learning scheme for emotional textile image classification using convolutional autoencoders. We further propose a correlation-analysis-based feature selection method for the weights learned by sparse autoencoders to reduce the number of features extracted from large size images. First, we randomly collect image patches on an unlabeled image dataset in the source domain and learn local features with a sparse autoencoder. We then conduct feature selection according to the correlation between different weight vectors corresponding to the autoencoder's hidden units. We finally adopt a convolutional neural network including a pooling layer to obtain global feature activations of textile images in the target domain and send these global feature vectors into logistic regression models for emotional image classification. The cross-domain unsupervised feature learning method achieves 65% to 78% average accuracy in the cross-validation experiments corresponding to eight emotional categories and performs better than conventional methods. Feature selection can reduce the computational cost of global feature extraction by about 50% while improving classification performance.

Plant species classification using flower images—A comparative study of local feature representations

PubMed Central

Seeland, Marco; Rzanny, Michael; Alaqraa, Nedal; Wäldchen, Jana; Mäder, Patrick

2017-01-01

Steady improvements of image description methods induced a growing interest in image-based plant species classification, a task vital to the study of biodiversity and ecological sensitivity. Various techniques have been proposed for general object classification over the past years and several of them have already been studied for plant species classification. However, results of these studies are selective in the evaluated steps of a classification pipeline, in the utilized datasets for evaluation, and in the compared baseline methods. No study is available that evaluates the main competing methods for building an image representation on the same datasets allowing for generalized findings regarding flower-based plant species classification. The aim of this paper is to comparatively evaluate methods, method combinations, and their parameters towards classification accuracy. The investigated methods span from detection, extraction, fusion, pooling, to encoding of local features for quantifying shape and color information of flower images. We selected the flower image datasets Oxford Flower 17 and Oxford Flower 102 as well as our own Jena Flower 30 dataset for our experiments. Findings show large differences among the various studied techniques and that their wisely chosen orchestration allows for high accuracies in species classification. We further found that true local feature detectors in combination with advanced encoding methods yield higher classification results at lower computational costs compared to commonly used dense sampling and spatial pooling methods. Color was found to be an indispensable feature for high classification results, especially while preserving spatial correspondence to gray-level features. In result, our study provides a comprehensive overview of competing techniques and the implications of their main parameters for flower-based plant species classification. PMID:28234999

Histomorphometric analysis of collagen architecture of auricular keloids in an Asian population.

PubMed

Chong, Yosep; Park, Tae Hwan; Seo, Sang won; Chang, Choong Hyun

2015-03-01

Keloids are a pathologic condition of the reparative process, which present as excessive scar formation that involves various cells and cytokines. Many studies focusing on the histologic feature of keloids, however, have shown discordant results without consideration of architectural aspect of collagen structure. The purpose of this study was to demonstrate a schematic illustration of collagen architecture of keloids, specifically auricular keloids, and to analyze each part on the histomorphologic and morphometric basis. Thirty-nine surgically excised auricular keloids were retrieved from the file of Kangbuk Samsung Hospital. After exhaustive histomorphologic analysis, 3 distinctive structural parts, keloidal collagen, organizing collagen, and proliferating core collagen, were identified and mapped in every case. Cellularity of fibroblasts, blood vessel density, degree of inflammatory cell infiltration, and mast cells counts using Masson trichrome stain, Van Gieson stain, toluidine blue stain, and immunohistochemical stains for CD31 and smooth muscle actin were analyzed in each part of each case. Morphometric analysis on these parameters using ImageJ software was performed using 3 representative images of each part. Three parts were histomorphologically distinct by shape and array of collagen bundles, fibroblasts cellularity, blood vessel density, degree of inflammatory cells, and mast cell infiltration. Morphometric analysis revealed statistically significant difference between each part in fibroblasts cellularity, blood vessel density, degree of inflammatory cell infiltration, and mast cells count. All parameters were exceedingly high in whorling hypercellular fibrous nodules in proliferating core collagen showing simultaneous changes in other parts. Morphologically and morphometrically, 3 distinctive parts were identified in auricular keloids. Mast cell infiltrations, blood vessel density, and fibroblast cellularity are simultaneously increased or decreased according to these parts. Proliferating core collagen might serve as a proliferating center of keloids and might be a key portion for tumor growth and recurrence.

Hyperspectral image classification based on local binary patterns and PCANet

NASA Astrophysics Data System (ADS)

Yang, Huizhen; Gao, Feng; Dong, Junyu; Yang, Yang

2018-04-01

Hyperspectral image classification has been well acknowledged as one of the challenging tasks of hyperspectral data processing. In this paper, we propose a novel hyperspectral image classification framework based on local binary pattern (LBP) features and PCANet. In the proposed method, linear prediction error (LPE) is first employed to select a subset of informative bands, and LBP is utilized to extract texture features. Then, spectral and texture features are stacked into a high dimensional vectors. Next, the extracted features of a specified position are transformed to a 2-D image. The obtained images of all pixels are fed into PCANet for classification. Experimental results on real hyperspectral dataset demonstrate the effectiveness of the proposed method.

Malay sentiment analysis based on combined classification approaches and Senti-lexicon algorithm.

PubMed

Al-Saffar, Ahmed; Awang, Suryanti; Tao, Hai; Omar, Nazlia; Al-Saiagh, Wafaa; Al-Bared, Mohammed

2018-01-01

Sentiment analysis techniques are increasingly exploited to categorize the opinion text to one or more predefined sentiment classes for the creation and automated maintenance of review-aggregation websites. In this paper, a Malay sentiment analysis classification model is proposed to improve classification performances based on the semantic orientation and machine learning approaches. First, a total of 2,478 Malay sentiment-lexicon phrases and words are assigned with a synonym and stored with the help of more than one Malay native speaker, and the polarity is manually allotted with a score. In addition, the supervised machine learning approaches and lexicon knowledge method are combined for Malay sentiment classification with evaluating thirteen features. Finally, three individual classifiers and a combined classifier are used to evaluate the classification accuracy. In experimental results, a wide-range of comparative experiments is conducted on a Malay Reviews Corpus (MRC), and it demonstrates that the feature extraction improves the performance of Malay sentiment analysis based on the combined classification. However, the results depend on three factors, the features, the number of features and the classification approach.

Malay sentiment analysis based on combined classification approaches and Senti-lexicon algorithm

PubMed Central

Awang, Suryanti; Tao, Hai; Omar, Nazlia; Al-Saiagh, Wafaa; Al-bared, Mohammed

2018-01-01

Sentiment analysis techniques are increasingly exploited to categorize the opinion text to one or more predefined sentiment classes for the creation and automated maintenance of review-aggregation websites. In this paper, a Malay sentiment analysis classification model is proposed to improve classification performances based on the semantic orientation and machine learning approaches. First, a total of 2,478 Malay sentiment-lexicon phrases and words are assigned with a synonym and stored with the help of more than one Malay native speaker, and the polarity is manually allotted with a score. In addition, the supervised machine learning approaches and lexicon knowledge method are combined for Malay sentiment classification with evaluating thirteen features. Finally, three individual classifiers and a combined classifier are used to evaluate the classification accuracy. In experimental results, a wide-range of comparative experiments is conducted on a Malay Reviews Corpus (MRC), and it demonstrates that the feature extraction improves the performance of Malay sentiment analysis based on the combined classification. However, the results depend on three factors, the features, the number of features and the classification approach. PMID:29684036

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

An Ant Colony Optimization Based Feature Selection for Web Page Classification

PubMed Central

2014-01-01

The increased popularity of the web has caused the inclusion of huge amount of information to the web, and as a result of this explosive information growth, automated web page classification systems are needed to improve search engines' performance. Web pages have a large number of features such as HTML/XML tags, URLs, hyperlinks, and text contents that should be considered during an automated classification process. The aim of this study is to reduce the number of features to be used to improve runtime and accuracy of the classification of web pages. In this study, we used an ant colony optimization (ACO) algorithm to select the best features, and then we applied the well-known C4.5, naive Bayes, and k nearest neighbor classifiers to assign class labels to web pages. We used the WebKB and Conference datasets in our experiments, and we showed that using the ACO for feature selection improves both accuracy and runtime performance of classification. We also showed that the proposed ACO based algorithm can select better features with respect to the well-known information gain and chi square feature selection methods. PMID:25136678

Unsupervised semantic indoor scene classification for robot vision based on context of features using Gist and HSV-SIFT

NASA Astrophysics Data System (ADS)

Madokoro, H.; Yamanashi, A.; Sato, K.

2013-08-01

This paper presents an unsupervised scene classification method for actualizing semantic recognition of indoor scenes. Background and foreground features are respectively extracted using Gist and color scale-invariant feature transform (SIFT) as feature representations based on context. We used hue, saturation, and value SIFT (HSV-SIFT) because of its simple algorithm with low calculation costs. Our method creates bags of features for voting visual words created from both feature descriptors to a two-dimensional histogram. Moreover, our method generates labels as candidates of categories for time-series images while maintaining stability and plasticity together. Automatic labeling of category maps can be realized using labels created using adaptive resonance theory (ART) as teaching signals for counter propagation networks (CPNs). We evaluated our method for semantic scene classification using KTH's image database for robot localization (KTH-IDOL), which is popularly used for robot localization and navigation. The mean classification accuracies of Gist, gray SIFT, one class support vector machines (OC-SVM), position-invariant robust features (PIRF), and our method are, respectively, 39.7, 58.0, 56.0, 63.6, and 79.4%. The result of our method is 15.8% higher than that of PIRF. Moreover, we applied our method for fine classification using our original mobile robot. We obtained mean classification accuracy of 83.2% for six zones.

Classification of tumor based on magnetic resonance (MR) brain images using wavelet energy feature and neuro-fuzzy model

NASA Astrophysics Data System (ADS)

Damayanti, A.; Werdiningsih, I.

2018-03-01

The brain is the organ that coordinates all the activities that occur in our bodies. Small abnormalities in the brain will affect body activity. Tumor of the brain is a mass formed a result of cell growth not normal and unbridled in the brain. MRI is a non-invasive medical test that is useful for doctors in diagnosing and treating medical conditions. The process of classification of brain tumor can provide the right decision and correct treatment and right on the process of treatment of brain tumor. In this study, the classification process performed to determine the type of brain tumor disease, namely Alzheimer’s, Glioma, Carcinoma and normal, using energy coefficient and ANFIS. Process stages in the classification of images of MR brain are the extraction of a feature, reduction of a feature, and process of classification. The result of feature extraction is a vector approximation of each wavelet decomposition level. The feature reduction is a process of reducing the feature by using the energy coefficients of the vector approximation. The feature reduction result for energy coefficient of 100 per feature is 1 x 52 pixels. This vector will be the input on the classification using ANFIS with Fuzzy C-Means and FLVQ clustering process and LM back-propagation. Percentage of success rate of MR brain images recognition using ANFIS-FLVQ, ANFIS, and LM back-propagation was obtained at 100%.

EEG Sleep Stages Classification Based on Time Domain Features and Structural Graph Similarity.

PubMed

Diykh, Mohammed; Li, Yan; Wen, Peng

2016-11-01

The electroencephalogram (EEG) signals are commonly used in diagnosing and treating sleep disorders. Many existing methods for sleep stages classification mainly depend on the analysis of EEG signals in time or frequency domain to obtain a high classification accuracy. In this paper, the statistical features in time domain, the structural graph similarity and the K-means (SGSKM) are combined to identify six sleep stages using single channel EEG signals. Firstly, each EEG segment is partitioned into sub-segments. The size of a sub-segment is determined empirically. Secondly, statistical features are extracted, sorted into different sets of features and forwarded to the SGSKM to classify EEG sleep stages. We have also investigated the relationships between sleep stages and the time domain features of the EEG data used in this paper. The experimental results show that the proposed method yields better classification results than other four existing methods and the support vector machine (SVM) classifier. A 95.93% average classification accuracy is achieved by using the proposed method.

CNN universal machine as classificaton platform: an art-like clustering algorithm.

PubMed

Bálya, David

2003-12-01

Fast and robust classification of feature vectors is a crucial task in a number of real-time systems. A cellular neural/nonlinear network universal machine (CNN-UM) can be very efficient as a feature detector. The next step is to post-process the results for object recognition. This paper shows how a robust classification scheme based on adaptive resonance theory (ART) can be mapped to the CNN-UM. Moreover, this mapping is general enough to include different types of feed-forward neural networks. The designed analogic CNN algorithm is capable of classifying the extracted feature vectors keeping the advantages of the ART networks, such as robust, plastic and fault-tolerant behaviors. An analogic algorithm is presented for unsupervised classification with tunable sensitivity and automatic new class creation. The algorithm is extended for supervised classification. The presented binary feature vector classification is implemented on the existing standard CNN-UM chips for fast classification. The experimental evaluation shows promising performance after 100% accuracy on the training set.

Land Covers Classification Based on Random Forest Method Using Features from Full-Waveform LIDAR Data

NASA Astrophysics Data System (ADS)

Ma, L.; Zhou, M.; Li, C.

2017-09-01

In this study, a Random Forest (RF) based land covers classification method is presented to predict the types of land covers in Miyun area. The returned full-waveforms which were acquired by a LiteMapper 5600 airborne LiDAR system were processed, including waveform filtering, waveform decomposition and features extraction. The commonly used features that were distance, intensity, Full Width at Half Maximum (FWHM), skewness and kurtosis were extracted. These waveform features were used as attributes of training data for generating the RF prediction model. The RF prediction model was applied to predict the types of land covers in Miyun area as trees, buildings, farmland and ground. The classification results of these four types of land covers were obtained according to the ground truth information acquired from CCD image data of the same region. The RF classification results were compared with that of SVM method and show better results. The RF classification accuracy reached 89.73% and the classification Kappa was 0.8631.

Gynecomastia Classification for Surgical Management: A Systematic Review and Novel Classification System.

PubMed

Waltho, Daniel; Hatchell, Alexandra; Thoma, Achilleas

2017-03-01

Gynecomastia is a common deformity of the male breast, where certain cases warrant surgical management. There are several surgical options, which vary depending on the breast characteristics. To guide surgical management, several classification systems for gynecomastia have been proposed. A systematic review was performed to (1) identify all classification systems for the surgical management of gynecomastia, and (2) determine the adequacy of these classification systems to appropriately categorize the condition for surgical decision-making. The search yielded 1012 articles, and 11 articles were included in the review. Eleven classification systems in total were ascertained, and a total of 10 unique features were identified: (1) breast size, (2) skin redundancy, (3) breast ptosis, (4) tissue predominance, (5) upper abdominal laxity, (6) breast tuberosity, (7) nipple malposition, (8) chest shape, (9) absence of sternal notch, and (10) breast skin elasticity. On average, classification systems included two or three of these features. Breast size and ptosis were the most commonly included features. Based on their review of the current classification systems, the authors believe the ideal classification system should be universal and cater to all causes of gynecomastia; be surgically useful and easy to use; and should include a comprehensive set of clinically appropriate patient-related features, such as breast size, breast ptosis, tissue predominance, and skin redundancy. None of the current classification systems appears to fulfill these criteria.

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.

Artificial neural network model to distinguish follicular adenoma from follicular carcinoma on fine needle aspiration of thyroid.

PubMed

Savala, Rajiv; Dey, Pranab; Gupta, Nalini

2018-03-01

To distinguish follicular adenoma (FA) and follicular carcinoma (FC) of thyroid in fine needle aspiration cytology (FNAC) is a challenging problem. In this article, we attempted to build an artificial neural network (ANN) model from the cytological and morphometric features of the FNAC smears of thyroid to distinguish FA from FC. The cytological features and morphometric analysis were done on the FNAC smears of histology proven cases of FA (26) and FC (31). The cytological features were analysed semi-quantitatively by two independent observers (RS and PD). These data were used to make an ANN model to differentiate FA versus FC on FNAC material. The performance of this ANN model was assessed by analysing the confusion matrix and receiving operator curve. There were 39 cases in training set, 9 cases each in validation and test sets. In the test group, ANN model successfully distinguished all cases (9/9) of FA and FC. The area under receiver operating curve was 1. The present ANN model is efficient to diagnose follicular adenoma and carcinoma cases on cytology smears without any error. In future, this ANN model will be able to diagnose follicular adenoma and carcinoma cases on thyroid aspirate. This study has immense potential in future. This is an open ended ANN model and more parameters and more cases can be included to make the model much stronger. © 2017 Wiley Periodicals, Inc.

Nonlinear features for classification and pose estimation of machined parts from single views

NASA Astrophysics Data System (ADS)

Talukder, Ashit; Casasent, David P.

1998-10-01

A new nonlinear feature extraction method is presented for classification and pose estimation of objects from single views. The feature extraction method is called the maximum representation and discrimination feature (MRDF) method. The nonlinear MRDF transformations to use are obtained in closed form, and offer significant advantages compared to nonlinear neural network implementations. The features extracted are useful for both object discrimination (classification) and object representation (pose estimation). We consider MRDFs on image data, provide a new 2-stage nonlinear MRDF solution, and show it specializes to well-known linear and nonlinear image processing transforms under certain conditions. We show the use of MRDF in estimating the class and pose of images of rendered solid CAD models of machine parts from single views using a feature-space trajectory neural network classifier. We show new results with better classification and pose estimation accuracy than are achieved by standard principal component analysis and Fukunaga-Koontz feature extraction methods.

Effective Feature Selection for Classification of Promoter Sequences.

PubMed

K, Kouser; P G, Lavanya; Rangarajan, Lalitha; K, Acharya Kshitish

2016-01-01

Exploring novel computational methods in making sense of biological data has not only been a necessity, but also productive. A part of this trend is the search for more efficient in silico methods/tools for analysis of promoters, which are parts of DNA sequences that are involved in regulation of expression of genes into other functional molecules. Promoter regions vary greatly in their function based on the sequence of nucleotides and the arrangement of protein-binding short-regions called motifs. In fact, the regulatory nature of the promoters seems to be largely driven by the selective presence and/or the arrangement of these motifs. Here, we explore computational classification of promoter sequences based on the pattern of motif distributions, as such classification can pave a new way of functional analysis of promoters and to discover the functionally crucial motifs. We make use of Position Specific Motif Matrix (PSMM) features for exploring the possibility of accurately classifying promoter sequences using some of the popular classification techniques. The classification results on the complete feature set are low, perhaps due to the huge number of features. We propose two ways of reducing features. Our test results show improvement in the classification output after the reduction of features. The results also show that decision trees outperform SVM (Support Vector Machine), KNN (K Nearest Neighbor) and ensemble classifier LibD3C, particularly with reduced features. The proposed feature selection methods outperform some of the popular feature transformation methods such as PCA and SVD. Also, the methods proposed are as accurate as MRMR (feature selection method) but much faster than MRMR. Such methods could be useful to categorize new promoters and explore regulatory mechanisms of gene expressions in complex eukaryotic species.

Some new classification methods for hyperspectral remote sensing

NASA Astrophysics Data System (ADS)

Du, Pei-jun; Chen, Yun-hao; Jones, Simon; Ferwerda, Jelle G.; Chen, Zhi-jun; Zhang, Hua-peng; Tan, Kun; Yin, Zuo-xia

2006-10-01

Hyperspectral Remote Sensing (HRS) is one of the most significant recent achievements of Earth Observation Technology. Classification is the most commonly employed processing methodology. In this paper three new hyperspectral RS image classification methods are analyzed. These methods are: Object-oriented FIRS image classification, HRS image classification based on information fusion and HSRS image classification by Back Propagation Neural Network (BPNN). OMIS FIRS image is used as the example data. Object-oriented techniques have gained popularity for RS image classification in recent years. In such method, image segmentation is used to extract the regions from the pixel information based on homogeneity criteria at first, and spectral parameters like mean vector, texture, NDVI and spatial/shape parameters like aspect ratio, convexity, solidity, roundness and orientation for each region are calculated, finally classification of the image using the region feature vectors and also using suitable classifiers such as artificial neural network (ANN). It proves that object-oriented methods can improve classification accuracy since they utilize information and features both from the point and the neighborhood, and the processing unit is a polygon (in which all pixels are homogeneous and belong to the class). HRS image classification based on information fusion, divides all bands of the image into different groups initially, and extracts features from every group according to the properties of each group. Three levels of information fusion: data level fusion, feature level fusion and decision level fusion are used to HRS image classification. Artificial Neural Network (ANN) can perform well in RS image classification. In order to promote the advances of ANN used for HIRS image classification, Back Propagation Neural Network (BPNN), the most commonly used neural network, is used to HRS image classification.

Object-Based Random Forest Classification of Land Cover from Remotely Sensed Imagery for Industrial and Mining Reclamation

NASA Astrophysics Data System (ADS)

Chen, Y.; Luo, M.; Xu, L.; Zhou, X.; Ren, J.; Zhou, J.

2018-04-01

The RF method based on grid-search parameter optimization could achieve a classification accuracy of 88.16 % in the classification of images with multiple feature variables. This classification accuracy was higher than that of SVM and ANN under the same feature variables. In terms of efficiency, the RF classification method performs better than SVM and ANN, it is more capable of handling multidimensional feature variables. The RF method combined with object-based analysis approach could highlight the classification accuracy further. The multiresolution segmentation approach on the basis of ESP scale parameter optimization was used for obtaining six scales to execute image segmentation, when the segmentation scale was 49, the classification accuracy reached the highest value of 89.58 %. The classification accuracy of object-based RF classification was 1.42 % higher than that of pixel-based classification (88.16 %), and the classification accuracy was further improved. Therefore, the RF classification method combined with object-based analysis approach could achieve relatively high accuracy in the classification and extraction of land use information for industrial and mining reclamation areas. Moreover, the interpretation of remotely sensed imagery using the proposed method could provide technical support and theoretical reference for remotely sensed monitoring land reclamation.

Craters on Earth, Moon, and Mars: Multivariate classification and mode of origin

USGS Publications Warehouse

Pike, R.J.

1974-01-01

Testing extraterrestrial craters and candidate terrestrial analogs for morphologic similitude is treated as a problem in numerical taxonomy. According to a principal-components solution and a cluster analysis, 402 representative craters on the Earth, the Moon, and Mars divide into two major classes of contrasting shapes and modes of origin. Craters of net accumulation of material (cratered lunar domes, Martian "calderas," and all terrestrial volcanoes except maars and tuff rings) group apart from craters of excavation (terrestrial meteorite impact and experimental explosion craters, typical Martian craters, and all other lunar craters). Maars and tuff rings belong to neither group but are transitional. The classification criteria are four independent attributes of topographic geometry derived from seven descriptive variables by the principal-components transformation. Morphometric differences between crater bowl and raised rim constitute the strongest of the four components. Although single topographic variables cannot confidently predict the genesis of individual extraterrestrial craters, multivariate statistical models constructed from several variables can distinguish consistently between large impact craters and volcanoes. ?? 1974.

Waveform fitting and geometry analysis for full-waveform lidar feature extraction

NASA Astrophysics Data System (ADS)

Tsai, Fuan; Lai, Jhe-Syuan; Cheng, Yi-Hsiu

2016-10-01

This paper presents a systematic approach that integrates spline curve fitting and geometry analysis to extract full-waveform LiDAR features for land-cover classification. The cubic smoothing spline algorithm is used to fit the waveform curve of the received LiDAR signals. After that, the local peak locations of the waveform curve are detected using a second derivative method. According to the detected local peak locations, commonly used full-waveform features such as full width at half maximum (FWHM) and amplitude can then be obtained. In addition, the number of peaks, time difference between the first and last peaks, and the average amplitude are also considered as features of LiDAR waveforms with multiple returns. Based on the waveform geometry, dynamic time-warping (DTW) is applied to measure the waveform similarity. The sum of the absolute amplitude differences that remain after time-warping can be used as a similarity feature in a classification procedure. An airborne full-waveform LiDAR data set was used to test the performance of the developed feature extraction method for land-cover classification. Experimental results indicate that the developed spline curve- fitting algorithm and geometry analysis can extract helpful full-waveform LiDAR features to produce better land-cover classification than conventional LiDAR data and feature extraction methods. In particular, the multiple-return features and the dynamic time-warping index can improve the classification results significantly.

Recent development of feature extraction and classification multispectral/hyperspectral images: a systematic literature review

NASA Astrophysics Data System (ADS)

Setiyoko, A.; Dharma, I. G. W. S.; Haryanto, T.

2017-01-01

Multispectral data and hyperspectral data acquired from satellite sensor have the ability in detecting various objects on the earth ranging from low scale to high scale modeling. These data are increasingly being used to produce geospatial information for rapid analysis by running feature extraction or classification process. Applying the most suited model for this data mining is still challenging because there are issues regarding accuracy and computational cost. This research aim is to develop a better understanding regarding object feature extraction and classification applied for satellite image by systematically reviewing related recent research projects. A method used in this research is based on PRISMA statement. After deriving important points from trusted sources, pixel based and texture-based feature extraction techniques are promising technique to be analyzed more in recent development of feature extraction and classification.

Multi-task linear programming discriminant analysis for the identification of progressive MCI individuals.

PubMed

Yu, Guan; Liu, Yufeng; Thung, Kim-Han; Shen, Dinggang

2014-01-01

Accurately identifying mild cognitive impairment (MCI) individuals who will progress to Alzheimer's disease (AD) is very important for making early interventions. Many classification methods focus on integrating multiple imaging modalities such as magnetic resonance imaging (MRI) and fluorodeoxyglucose positron emission tomography (FDG-PET). However, the main challenge for MCI classification using multiple imaging modalities is the existence of a lot of missing data in many subjects. For example, in the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, almost half of the subjects do not have PET images. In this paper, we propose a new and flexible binary classification method, namely Multi-task Linear Programming Discriminant (MLPD) analysis, for the incomplete multi-source feature learning. Specifically, we decompose the classification problem into different classification tasks, i.e., one for each combination of available data sources. To solve all different classification tasks jointly, our proposed MLPD method links them together by constraining them to achieve the similar estimated mean difference between the two classes (under classification) for those shared features. Compared with the state-of-the-art incomplete Multi-Source Feature (iMSF) learning method, instead of constraining different classification tasks to choose a common feature subset for those shared features, MLPD can flexibly and adaptively choose different feature subsets for different classification tasks. Furthermore, our proposed MLPD method can be efficiently implemented by linear programming. To validate our MLPD method, we perform experiments on the ADNI baseline dataset with the incomplete MRI and PET images from 167 progressive MCI (pMCI) subjects and 226 stable MCI (sMCI) subjects. We further compared our method with the iMSF method (using incomplete MRI and PET images) and also the single-task classification method (using only MRI or only subjects with both MRI and PET images). Experimental results show very promising performance of our proposed MLPD method.

Multi-Task Linear Programming Discriminant Analysis for the Identification of Progressive MCI Individuals

PubMed Central

Yu, Guan; Liu, Yufeng; Thung, Kim-Han; Shen, Dinggang

2014-01-01

Accurately identifying mild cognitive impairment (MCI) individuals who will progress to Alzheimer's disease (AD) is very important for making early interventions. Many classification methods focus on integrating multiple imaging modalities such as magnetic resonance imaging (MRI) and fluorodeoxyglucose positron emission tomography (FDG-PET). However, the main challenge for MCI classification using multiple imaging modalities is the existence of a lot of missing data in many subjects. For example, in the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, almost half of the subjects do not have PET images. In this paper, we propose a new and flexible binary classification method, namely Multi-task Linear Programming Discriminant (MLPD) analysis, for the incomplete multi-source feature learning. Specifically, we decompose the classification problem into different classification tasks, i.e., one for each combination of available data sources. To solve all different classification tasks jointly, our proposed MLPD method links them together by constraining them to achieve the similar estimated mean difference between the two classes (under classification) for those shared features. Compared with the state-of-the-art incomplete Multi-Source Feature (iMSF) learning method, instead of constraining different classification tasks to choose a common feature subset for those shared features, MLPD can flexibly and adaptively choose different feature subsets for different classification tasks. Furthermore, our proposed MLPD method can be efficiently implemented by linear programming. To validate our MLPD method, we perform experiments on the ADNI baseline dataset with the incomplete MRI and PET images from 167 progressive MCI (pMCI) subjects and 226 stable MCI (sMCI) subjects. We further compared our method with the iMSF method (using incomplete MRI and PET images) and also the single-task classification method (using only MRI or only subjects with both MRI and PET images). Experimental results show very promising performance of our proposed MLPD method. PMID:24820966

Uav-Based Crops Classification with Joint Features from Orthoimage and Dsm Data

NASA Astrophysics Data System (ADS)

Liu, B.; Shi, Y.; Duan, Y.; Wu, W.

2018-04-01

Accurate crops classification remains a challenging task due to the same crop with different spectra and different crops with same spectrum phenomenon. Recently, UAV-based remote sensing approach gains popularity not only for its high spatial and temporal resolution, but also for its ability to obtain spectraand spatial data at the same time. This paper focus on how to take full advantages of spatial and spectrum features to improve crops classification accuracy, based on an UAV platform equipped with a general digital camera. Texture and spatial features extracted from the RGB orthoimage and the digital surface model of the monitoring area are analysed and integrated within a SVM classification framework. Extensive experiences results indicate that the overall classification accuracy is drastically improved from 72.9 % to 94.5 % when the spatial features are combined together, which verified the feasibility and effectiveness of the proposed method.

Deep Learning for ECG Classification

NASA Astrophysics Data System (ADS)

Pyakillya, B.; Kazachenko, N.; Mikhailovsky, N.

2017-10-01

The importance of ECG classification is very high now due to many current medical applications where this problem can be stated. Currently, there are many machine learning (ML) solutions which can be used for analyzing and classifying ECG data. However, the main disadvantages of these ML results is use of heuristic hand-crafted or engineered features with shallow feature learning architectures. The problem relies in the possibility not to find most appropriate features which will give high classification accuracy in this ECG problem. One of the proposing solution is to use deep learning architectures where first layers of convolutional neurons behave as feature extractors and in the end some fully-connected (FCN) layers are used for making final decision about ECG classes. In this work the deep learning architecture with 1D convolutional layers and FCN layers for ECG classification is presented and some classification results are showed.

Do size, shape, and alignment parameters of the femoral condyle affect the trochlear groove tracking? A morphometric study based on 3D- computed tomography models in Chinese people.

PubMed

Du, Zhe; Chen, Shichang; Yan, Mengning; Yue, Bing; Zeng, Yiming; Wang, You

2017-01-06

Our study aimed to investigate whether geometrical features (size, shape, or alignment parameters) of the femoral condyle affect the morphology of the trochlear groove. Computed tomography models of 195 femurs (97 and 98 knees from male and female subjects, respectively) were reconstructed into three-dimensional models and categorised into four types of trochlear groove morphology based on the position of the turning point in relation to the mechanical axis (types 45°, 60°, 75°, and 90°). Only subjects with healthy knees were included, whereas individuals with previous knee trauma or knee pain, soft tissue injury, osteoarthritis, or other chronic diseases of the musculoskeletal system were excluded. The size parameters were: radius of the best-fit cylinder, anteroposterior dimension of the lateral condyles (AP), and distal mediolateral dimension (ML). The shape parameters were: aspect ratio (AP/ML), arc angle, and proximal- and distal- end angles. The alignment parameters were: knee valgus physiologic angle (KVPA), mechanical medial distal femoral angle (mMDFA), and hip-knee-ankle angle (HKA). All variables were measured in the femoral condyle models, and the means for each groove type were compared using one-way analysis of variance. No significant difference among groove types was observed regarding size parameters. There were significant differences when comparing type 45° with types 60°, 75°, and 90° regarding aspect ratio and distal-end angle (p < 0.05), but not regarding proximal-end angle. There were significant differences when comparing type 90° with types 45°, 60°, and 75° regarding KVPA, mMDFA, and HKA (p < 0.05). Among size, shape, and alignment parameters, the latter two exhibited partial influence on the morphology of the trochlear groove. Shape parameters affected the trochlear groove for trochlear type 45°, for which the femoral condyle was relatively flat, whereas alignment parameters affected the trochlear groove for trochlear type 90°, showing that knees in type 90° tend to be valgus. The morphometric analysis based on trochlear groove classification may be helpful for the future design of individualized prostheses.

Network-based high level data classification.

PubMed

Silva, Thiago Christiano; Zhao, Liang

2012-06-01

Traditional supervised data classification considers only physical features (e.g., distance or similarity) of the input data. Here, this type of learning is called low level classification. On the other hand, the human (animal) brain performs both low and high orders of learning and it has facility in identifying patterns according to the semantic meaning of the input data. Data classification that considers not only physical attributes but also the pattern formation is, here, referred to as high level classification. In this paper, we propose a hybrid classification technique that combines both types of learning. The low level term can be implemented by any classification technique, while the high level term is realized by the extraction of features of the underlying network constructed from the input data. Thus, the former classifies the test instances by their physical features or class topologies, while the latter measures the compliance of the test instances to the pattern formation of the data. Our study shows that the proposed technique not only can realize classification according to the pattern formation, but also is able to improve the performance of traditional classification techniques. Furthermore, as the class configuration's complexity increases, such as the mixture among different classes, a larger portion of the high level term is required to get correct classification. This feature confirms that the high level classification has a special importance in complex situations of classification. Finally, we show how the proposed technique can be employed in a real-world application, where it is capable of identifying variations and distortions of handwritten digit images. As a result, it supplies an improvement in the overall pattern recognition rate.

Regression-Based Approach For Feature Selection In Classification Issues. Application To Breast Cancer Detection And Recurrence

NASA Astrophysics Data System (ADS)

Belciug, Smaranda; Serbanescu, Mircea-Sebastian

2015-09-01

Feature selection is considered a key factor in classifications/decision problems. It is currently used in designing intelligent decision systems to choose the best features which allow the best performance. This paper proposes a regression-based approach to select the most important predictors to significantly increase the classification performance. Application to breast cancer detection and recurrence using publically available datasets proved the efficiency of this technique.

Mammogram classification scheme using 2D-discrete wavelet and local binary pattern for detection of breast cancer

NASA Astrophysics Data System (ADS)

Adi Putra, Januar

2018-04-01

In this paper, we propose a new mammogram classification scheme to classify the breast tissues as normal or abnormal. Feature matrix is generated using Local Binary Pattern to all the detailed coefficients from 2D-DWT of the region of interest (ROI) of a mammogram. Feature selection is done by selecting the relevant features that affect the classification. Feature selection is used to reduce the dimensionality of data and features that are not relevant, in this paper the F-test and Ttest will be performed to the results of the feature extraction dataset to reduce and select the relevant feature. The best features are used in a Neural Network classifier for classification. In this research we use MIAS and DDSM database. In addition to the suggested scheme, the competent schemes are also simulated for comparative analysis. It is observed that the proposed scheme has a better say with respect to accuracy, specificity and sensitivity. Based on experiments, the performance of the proposed scheme can produce high accuracy that is 92.71%, while the lowest accuracy obtained is 77.08%.

Classification of pulmonary pathology from breath sounds using the wavelet packet transform and an extreme learning machine.

PubMed

Palaniappan, Rajkumar; Sundaraj, Kenneth; Sundaraj, Sebastian; Huliraj, N; Revadi, S S

2017-06-08

Auscultation is a medical procedure used for the initial diagnosis and assessment of lung and heart diseases. From this perspective, we propose assessing the performance of the extreme learning machine (ELM) classifiers for the diagnosis of pulmonary pathology using breath sounds. Energy and entropy features were extracted from the breath sound using the wavelet packet transform. The statistical significance of the extracted features was evaluated by one-way analysis of variance (ANOVA). The extracted features were inputted into the ELM classifier. The maximum classification accuracies obtained for the conventional validation (CV) of the energy and entropy features were 97.36% and 98.37%, respectively, whereas the accuracies obtained for the cross validation (CRV) of the energy and entropy features were 96.80% and 97.91%, respectively. In addition, maximum classification accuracies of 98.25% and 99.25% were obtained for the CV and CRV of the ensemble features, respectively. The results indicate that the classification accuracy obtained with the ensemble features was higher than those obtained with the energy and entropy features.

Homo floresiensis Contextualized: A Geometric Morphometric Comparative Analysis of Fossil and Pathological Human Samples

PubMed Central

Baab, Karen L.; McNulty, Kieran P.; Harvati, Katerina

2013-01-01

The origin of hominins found on the remote Indonesian island of Flores remains highly contentious. These specimens may represent a new hominin species, Homo floresiensis, descended from a local population of Homo erectus or from an earlier (pre-H. erectus) migration of a small-bodied and small-brained hominin out of Africa. Alternatively, some workers suggest that some or all of the specimens recovered from Liang Bua are pathological members of a small-bodied modern human population. Pathological conditions proposed to explain their documented anatomical features include microcephaly, myxoedematous endemic hypothyroidism (“cretinism”) and Laron syndrome (primary growth hormone insensitivity). This study evaluates evolutionary and pathological hypotheses through comparative analysis of cranial morphology. Geometric morphometric analyses of landmark data show that the sole Flores cranium (LB1) is clearly distinct from healthy modern humans and from those exhibiting hypothyroidism and Laron syndrome. Modern human microcephalic specimens converge, to some extent, on crania of extinct species of Homo. However in the features that distinguish these two groups, LB1 consistently groups with fossil hominins and is most similar to H. erectus. Our study provides further support for recognizing the Flores hominins as a distinct species, H. floresiensis, whose affinities lie with archaic Homo. PMID:23874886

Exploring Eucladoceros ecomorphology using geometric morphometrics.

PubMed

Curran, Sabrina C

2015-01-01

An increasingly common method for reconstructing paleoenvironmental parameters of hominin sites is ecological functional morphology (ecomorphology). This study provides a geometric morphometric study of cervid rearlimb morphology as it relates to phylogeny, size, and ecomorphology. These methods are then applied to an extinct Pleistocene cervid, Eucladoceros, which is found in some of the earliest hominin-occupied sites in Eurasia. Variation in cervid postcranial functional morphology associated with different habitats can be summarized as trade-offs between joint stability versus mobility and rapid movement versus power-generation. Cervids in open habitats emphasize limb stability to avoid joint dislocation during rapid flight from predators. Closed-adapted cervids require more joint mobility to rapidly switch directions in complex habitats. Two skeletal features (of the tibia and calcaneus) have significant phylogenetic signals, while two (the femur and third phalanx) do not. Additionally, morphology of two of these features (tibia and third phalanx) were correlated with body size. For the tibial analysis (but not the third phalanx) this correlation was ameliorated when phylogeny was taken into account. Eucladoceros specimens from France and Romania fall on the more open side of the habitat continuum, a result that is at odds with reconstructions of their diet as browsers, suggesting that they may have had a behavioral regime unlike any extant cervid. © 2014 Wiley Periodicals, Inc.

GENIE: a hybrid genetic algorithm for feature classification in multispectral images

NASA Astrophysics Data System (ADS)

Perkins, Simon J.; Theiler, James P.; Brumby, Steven P.; Harvey, Neal R.; Porter, Reid B.; Szymanski, John J.; Bloch, Jeffrey J.

2000-10-01

We consider the problem of pixel-by-pixel classification of a multi- spectral image using supervised learning. Conventional spuervised classification techniques such as maximum likelihood classification and less conventional ones s uch as neural networks, typically base such classifications solely on the spectral components of each pixel. It is easy to see why: the color of a pixel provides a nice, bounded, fixed dimensional space in which these classifiers work well. It is often the case however, that spectral information alone is not sufficient to correctly classify a pixel. Maybe spatial neighborhood information is required as well. Or maybe the raw spectral components do not themselves make for easy classification, but some arithmetic combination of them would. In either of these cases we have the problem of selecting suitable spatial, spectral or spatio-spectral features that allow the classifier to do its job well. The number of all possible such features is extremely large. How can we select a suitable subset? We have developed GENIE, a hybrid learning system that combines a genetic algorithm that searches a space of image processing operations for a set that can produce suitable feature planes, and a more conventional classifier which uses those feature planes to output a final classification. In this paper we show that the use of a hybrid GA provides significant advantages over using either a GA alone or more conventional classification methods alone. We present results using high-resolution IKONOS data, looking for regions of burned forest and for roads.

Remote sensing imagery classification using multi-objective gravitational search algorithm

NASA Astrophysics Data System (ADS)

Zhang, Aizhu; Sun, Genyun; Wang, Zhenjie

2016-10-01

Simultaneous optimization of different validity measures can capture different data characteristics of remote sensing imagery (RSI) and thereby achieving high quality classification results. In this paper, two conflicting cluster validity indices, the Xie-Beni (XB) index and the fuzzy C-means (FCM) (Jm) measure, are integrated with a diversity-enhanced and memory-based multi-objective gravitational search algorithm (DMMOGSA) to present a novel multi-objective optimization based RSI classification method. In this method, the Gabor filter method is firstly implemented to extract texture features of RSI. Then, the texture features are syncretized with the spectral features to construct the spatial-spectral feature space/set of the RSI. Afterwards, cluster of the spectral-spatial feature set is carried out on the basis of the proposed method. To be specific, cluster centers are randomly generated initially. After that, the cluster centers are updated and optimized adaptively by employing the DMMOGSA. Accordingly, a set of non-dominated cluster centers are obtained. Therefore, numbers of image classification results of RSI are produced and users can pick up the most promising one according to their problem requirements. To quantitatively and qualitatively validate the effectiveness of the proposed method, the proposed classification method was applied to classifier two aerial high-resolution remote sensing imageries. The obtained classification results are compared with that produced by two single cluster validity index based and two state-of-the-art multi-objective optimization algorithms based classification results. Comparison results show that the proposed method can achieve more accurate RSI classification.

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.

An EEG-based functional connectivity measure for automatic detection of alcohol use disorder.

PubMed

Mumtaz, Wajid; Saad, Mohamad Naufal B Mohamad; Kamel, Nidal; Ali, Syed Saad Azhar; Malik, Aamir Saeed

2018-01-01

The abnormal alcohol consumption could cause toxicity and could alter the human brain's structure and function, termed as alcohol used disorder (AUD). Unfortunately, the conventional screening methods for AUD patients are subjective and manual. Hence, to perform automatic screening of AUD patients, objective methods are needed. The electroencephalographic (EEG) data have been utilized to study the differences of brain signals between alcoholics and healthy controls that could further developed as an automatic screening tool for alcoholics. In this work, resting-state EEG-derived features were utilized as input data to the proposed feature selection and classification method. The aim was to perform automatic classification of AUD patients and healthy controls. The validation of the proposed method involved real-EEG data acquired from 30 AUD patients and 30 age-matched healthy controls. The resting-state EEG-derived features such as synchronization likelihood (SL) were computed involving 19 scalp locations resulted into 513 features. Furthermore, the features were rank-ordered to select the most discriminant features involving a rank-based feature selection method according to a criterion, i.e., receiver operating characteristics (ROC). Consequently, a reduced set of most discriminant features was identified and utilized further during classification of AUD patients and healthy controls. In this study, three different classification models such as Support Vector Machine (SVM), Naïve Bayesian (NB), and Logistic Regression (LR) were used. The study resulted into SVM classification accuracy=98%, sensitivity=99.9%, specificity=95%, and f-measure=0.97; LR classification accuracy=91.7%, sensitivity=86.66%, specificity=96.6%, and f-measure=0.90; NB classification accuracy=93.6%, sensitivity=100%, specificity=87.9%, and f-measure=0.95. The SL features could be utilized as objective markers to screen the AUD patients and healthy controls. Copyright © 2017 Elsevier B.V. All rights reserved.

System Complexity Reduction via Feature Selection

ERIC Educational Resources Information Center

Deng, Houtao

2011-01-01

This dissertation transforms a set of system complexity reduction problems to feature selection problems. Three systems are considered: classification based on association rules, network structure learning, and time series classification. Furthermore, two variable importance measures are proposed to reduce the feature selection bias in tree…

Medical X-ray Image Hierarchical Classification Using a Merging and Splitting Scheme in Feature Space.

PubMed

Fesharaki, Nooshin Jafari; Pourghassem, Hossein

2013-07-01

Due to the daily mass production and the widespread variation of medical X-ray images, it is necessary to classify these for searching and retrieving proposes, especially for content-based medical image retrieval systems. In this paper, a medical X-ray image hierarchical classification structure based on a novel merging and splitting scheme and using shape and texture features is proposed. In the first level of the proposed structure, to improve the classification performance, similar classes with regard to shape contents are grouped based on merging measures and shape features into the general overlapped classes. In the next levels of this structure, the overlapped classes split in smaller classes based on the classification performance of combination of shape and texture features or texture features only. Ultimately, in the last levels, this procedure is also continued forming all the classes, separately. Moreover, to optimize the feature vector in the proposed structure, we use orthogonal forward selection algorithm according to Mahalanobis class separability measure as a feature selection and reduction algorithm. In other words, according to the complexity and inter-class distance of each class, a sub-space of the feature space is selected in each level and then a supervised merging and splitting scheme is applied to form the hierarchical classification. The proposed structure is evaluated on a database consisting of 2158 medical X-ray images of 18 classes (IMAGECLEF 2005 database) and accuracy rate of 93.6% in the last level of the hierarchical structure for an 18-class classification problem is obtained.

Automated system for characterization and classification of malaria-infected stages using light microscopic images of thin blood smears.

PubMed

Das, D K; Maiti, A K; Chakraborty, C

2015-03-01

In this paper, we propose a comprehensive image characterization cum classification framework for malaria-infected stage detection using microscopic images of thin blood smears. The methodology mainly includes microscopic imaging of Leishman stained blood slides, noise reduction and illumination correction, erythrocyte segmentation, feature selection followed by machine classification. Amongst three-image segmentation algorithms (namely, rule-based, Chan-Vese-based and marker-controlled watershed methods), marker-controlled watershed technique provides better boundary detection of erythrocytes specially in overlapping situations. Microscopic features at intensity, texture and morphology levels are extracted to discriminate infected and noninfected erythrocytes. In order to achieve subgroup of potential features, feature selection techniques, namely, F-statistic and information gain criteria are considered here for ranking. Finally, five different classifiers, namely, Naive Bayes, multilayer perceptron neural network, logistic regression, classification and regression tree (CART), RBF neural network have been trained and tested by 888 erythrocytes (infected and noninfected) for each features' subset. Performance evaluation of the proposed methodology shows that multilayer perceptron network provides higher accuracy for malaria-infected erythrocytes recognition and infected stage classification. Results show that top 90 features ranked by F-statistic (specificity: 98.64%, sensitivity: 100%, PPV: 99.73% and overall accuracy: 96.84%) and top 60 features ranked by information gain provides better results (specificity: 97.29%, sensitivity: 100%, PPV: 99.46% and overall accuracy: 96.73%) for malaria-infected stage classification. © 2014 The Authors Journal of Microscopy © 2014 Royal Microscopical Society.

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.

A discrete wavelet based feature extraction and hybrid classification technique for microarray data analysis.

PubMed

Bennet, Jaison; Ganaprakasam, Chilambuchelvan Arul; Arputharaj, Kannan

2014-01-01

Cancer classification by doctors and radiologists was based on morphological and clinical features and had limited diagnostic ability in olden days. The recent arrival of DNA microarray technology has led to the concurrent monitoring of thousands of gene expressions in a single chip which stimulates the progress in cancer classification. In this paper, we have proposed a hybrid approach for microarray data classification based on nearest neighbor (KNN), naive Bayes, and support vector machine (SVM). Feature selection prior to classification plays a vital role and a feature selection technique which combines discrete wavelet transform (DWT) and moving window technique (MWT) is used. The performance of the proposed method is compared with the conventional classifiers like support vector machine, nearest neighbor, and naive Bayes. Experiments have been conducted on both real and benchmark datasets and the results indicate that the ensemble approach produces higher classification accuracy than conventional classifiers. This paper serves as an automated system for the classification of cancer and can be applied by doctors in real cases which serve as a boon to the medical community. This work further reduces the misclassification of cancers which is highly not allowed in cancer detection.

Land use and land cover classification for rural residential areas in China using soft-probability cascading of multifeatures

NASA Astrophysics Data System (ADS)

Zhang, Bin; Liu, Yueyan; Zhang, Zuyu; Shen, Yonglin

2017-10-01

A multifeature soft-probability cascading scheme to solve the problem of land use and land cover (LULC) classification using high-spatial-resolution images to map rural residential areas in China is proposed. The proposed method is used to build midlevel LULC features. Local features are frequently considered as low-level feature descriptors in a midlevel feature learning method. However, spectral and textural features, which are very effective low-level features, are neglected. The acquisition of the dictionary of sparse coding is unsupervised, and this phenomenon reduces the discriminative power of the midlevel feature. Thus, we propose to learn supervised features based on sparse coding, a support vector machine (SVM) classifier, and a conditional random field (CRF) model to utilize the different effective low-level features and improve the discriminability of midlevel feature descriptors. First, three kinds of typical low-level features, namely, dense scale-invariant feature transform, gray-level co-occurrence matrix, and spectral features, are extracted separately. Second, combined with sparse coding and the SVM classifier, the probabilities of the different LULC classes are inferred to build supervised feature descriptors. Finally, the CRF model, which consists of two parts: unary potential and pairwise potential, is employed to construct an LULC classification map. Experimental results show that the proposed classification scheme can achieve impressive performance when the total accuracy reached about 87%.

High Accuracy Human Activity Recognition Based on Sparse Locality Preserving Projections.

PubMed

Zhu, Xiangbin; Qiu, Huiling

2016-01-01

Human activity recognition(HAR) from the temporal streams of sensory data has been applied to many fields, such as healthcare services, intelligent environments and cyber security. However, the classification accuracy of most existed methods is not enough in some applications, especially for healthcare services. In order to improving accuracy, it is necessary to develop a novel method which will take full account of the intrinsic sequential characteristics for time-series sensory data. Moreover, each human activity may has correlated feature relationship at different levels. Therefore, in this paper, we propose a three-stage continuous hidden Markov model (TSCHMM) approach to recognize human activities. The proposed method contains coarse, fine and accurate classification. The feature reduction is an important step in classification processing. In this paper, sparse locality preserving projections (SpLPP) is exploited to determine the optimal feature subsets for accurate classification of the stationary-activity data. It can extract more discriminative activities features from the sensor data compared with locality preserving projections. Furthermore, all of the gyro-based features are used for accurate classification of the moving data. Compared with other methods, our method uses significantly less number of features, and the over-all accuracy has been obviously improved.

High Accuracy Human Activity Recognition Based on Sparse Locality Preserving Projections

PubMed Central

2016-01-01

Human activity recognition(HAR) from the temporal streams of sensory data has been applied to many fields, such as healthcare services, intelligent environments and cyber security. However, the classification accuracy of most existed methods is not enough in some applications, especially for healthcare services. In order to improving accuracy, it is necessary to develop a novel method which will take full account of the intrinsic sequential characteristics for time-series sensory data. Moreover, each human activity may has correlated feature relationship at different levels. Therefore, in this paper, we propose a three-stage continuous hidden Markov model (TSCHMM) approach to recognize human activities. The proposed method contains coarse, fine and accurate classification. The feature reduction is an important step in classification processing. In this paper, sparse locality preserving projections (SpLPP) is exploited to determine the optimal feature subsets for accurate classification of the stationary-activity data. It can extract more discriminative activities features from the sensor data compared with locality preserving projections. Furthermore, all of the gyro-based features are used for accurate classification of the moving data. Compared with other methods, our method uses significantly less number of features, and the over-all accuracy has been obviously improved. PMID:27893761

Pattern classification using an olfactory model with PCA feature selection in electronic noses: study and application.

PubMed

Fu, Jun; Huang, Canqin; Xing, Jianguo; Zheng, Junbao

2012-01-01

Biologically-inspired models and algorithms are considered as promising sensor array signal processing methods for electronic noses. Feature selection is one of the most important issues for developing robust pattern recognition models in machine learning. This paper describes an investigation into the classification performance of a bionic olfactory model with the increase of the dimensions of input feature vector (outer factor) as well as its parallel channels (inner factor). The principal component analysis technique was applied for feature selection and dimension reduction. Two data sets of three classes of wine derived from different cultivars and five classes of green tea derived from five different provinces of China were used for experiments. In the former case the results showed that the average correct classification rate increased as more principal components were put in to feature vector. In the latter case the results showed that sufficient parallel channels should be reserved in the model to avoid pattern space crowding. We concluded that 6~8 channels of the model with principal component feature vector values of at least 90% cumulative variance is adequate for a classification task of 3~5 pattern classes considering the trade-off between time consumption and classification rate.

Mental Task Classification Scheme Utilizing Correlation Coefficient Extracted from Interchannel Intrinsic Mode Function.

PubMed

Rahman, Md Mostafizur; Fattah, Shaikh Anowarul

2017-01-01

In view of recent increase of brain computer interface (BCI) based applications, the importance of efficient classification of various mental tasks has increased prodigiously nowadays. In order to obtain effective classification, efficient feature extraction scheme is necessary, for which, in the proposed method, the interchannel relationship among electroencephalogram (EEG) data is utilized. It is expected that the correlation obtained from different combination of channels will be different for different mental tasks, which can be exploited to extract distinctive feature. The empirical mode decomposition (EMD) technique is employed on a test EEG signal obtained from a channel, which provides a number of intrinsic mode functions (IMFs), and correlation coefficient is extracted from interchannel IMF data. Simultaneously, different statistical features are also obtained from each IMF. Finally, the feature matrix is formed utilizing interchannel correlation features and intrachannel statistical features of the selected IMFs of EEG signal. Different kernels of the support vector machine (SVM) classifier are used to carry out the classification task. An EEG dataset containing ten different combinations of five different mental tasks is utilized to demonstrate the classification performance and a very high level of accuracy is achieved by the proposed scheme compared to existing methods.

Additional studies of forest classification accuracy as influenced by multispectral scanner spatial resolution

NASA Technical Reports Server (NTRS)

Sadowski, F. E.; Sarno, J. E.

1976-01-01

First, an analysis of forest feature signatures was used to help explain the large variation in classification accuracy that can occur among individual forest features for any one case of spatial resolution and the inconsistent changes in classification accuracy that were demonstrated among features as spatial resolution was degraded. Second, the classification rejection threshold was varied in an effort to reduce the large proportion of unclassified resolution elements that previously appeared in the processing of coarse resolution data when a constant rejection threshold was used for all cases of spatial resolution. For the signature analysis, two-channel ellipse plots showing the feature signature distributions for several cases of spatial resolution indicated that the capability of signatures to correctly identify their respective features is dependent on the amount of statistical overlap among signatures. Reductions in signature variance that occur in data of degraded spatial resolution may not necessarily decrease the amount of statistical overlap among signatures having large variance and small mean separations. Features classified by such signatures may thus continue to have similar amounts of misclassified elements in coarser resolution data, and thus, not necessarily improve in classification accuracy.

Label-aligned Multi-task Feature Learning for Multimodal Classification of Alzheimer’s Disease and Mild Cognitive Impairment

PubMed Central

Zu, Chen; Jie, Biao; Liu, Mingxia; Chen, Songcan

2015-01-01

Multimodal classification methods using different modalities of imaging and non-imaging data have recently shown great advantages over traditional single-modality-based ones for diagnosis and prognosis of Alzheimer’s disease (AD), as well as its prodromal stage, i.e., mild cognitive impairment (MCI). However, to the best of our knowledge, most existing methods focus on mining the relationship across multiple modalities of the same subjects, while ignoring the potentially useful relationship across different subjects. Accordingly, in this paper, we propose a novel learning method for multimodal classification of AD/MCI, by fully exploring the relationships across both modalities and subjects. Specifically, our proposed method includes two subsequent components, i.e., label-aligned multi-task feature selection and multimodal classification. In the first step, the feature selection learning from multiple modalities are treated as different learning tasks and a group sparsity regularizer is imposed to jointly select a subset of relevant features. Furthermore, to utilize the discriminative information among labeled subjects, a new label-aligned regularization term is added into the objective function of standard multi-task feature selection, where label-alignment means that all multi-modality subjects with the same class labels should be closer in the new feature-reduced space. In the second step, a multi-kernel support vector machine (SVM) is adopted to fuse the selected features from multi-modality data for final classification. To validate our method, we perform experiments on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database using baseline MRI and FDG-PET imaging data. The experimental results demonstrate that our proposed method achieves better classification performance compared with several state-of-the-art methods for multimodal classification of AD/MCI. PMID:26572145

Comparing the Behavior of Polarimetric SAR Imagery (TerraSAR-X and Radarsat-2) for Automated Sea Ice Classification

NASA Astrophysics Data System (ADS)

Ressel, Rudolf; Singha, Suman; Lehner, Susanne

2016-08-01

Arctic Sea ice monitoring has attracted increasing attention over the last few decades. Besides the scientific interest in sea ice, the operational aspect of ice charting is becoming more important due to growing navigational possibilities in an increasingly ice free Arctic. For this purpose, satellite borne SAR imagery has become an invaluable tool. In past, mostly single polarimetric datasets were investigated with supervised or unsupervised classification schemes for sea ice investigation. Despite proven sea ice classification achievements on single polarimetric data, a fully automatic, general purpose classifier for single-pol data has not been established due to large variation of sea ice manifestations and incidence angle impact. Recently, through the advent of polarimetric SAR sensors, polarimetric features have moved into the focus of ice classification research. The higher information content four polarimetric channels promises to offer greater insight into sea ice scattering mechanism and overcome some of the shortcomings of single- polarimetric classifiers. Two spatially and temporally coincident pairs of fully polarimetric acquisitions from the TerraSAR-X/TanDEM-X and RADARSAT-2 satellites are investigated. Proposed supervised classification algorithm consists of two steps: The first step comprises a feature extraction, the results of which are ingested into a neural network classifier in the second step. Based on the common coherency and covariance matrix, we extract a number of features and analyze the relevance and redundancy by means of mutual information for the purpose of sea ice classification. Coherency matrix based features which require an eigendecomposition are found to be either of low relevance or redundant to other covariance matrix based features. Among the most useful features for classification are matrix invariant based features (Geometric Intensity, Scattering Diversity, Surface Scattering Fraction).

Multisensor multiresolution data fusion for improvement in classification

NASA Astrophysics Data System (ADS)

Rubeena, V.; Tiwari, K. C.

2016-04-01

The rapid advancements in technology have facilitated easy availability of multisensor and multiresolution remote sensing data. Multisensor, multiresolution data contain complementary information and fusion of such data may result in application dependent significant information which may otherwise remain trapped within. The present work aims at improving classification by fusing features of coarse resolution hyperspectral (1 m) LWIR and fine resolution (20 cm) RGB data. The classification map comprises of eight classes. The class names are Road, Trees, Red Roof, Grey Roof, Concrete Roof, Vegetation, bare Soil and Unclassified. The processing methodology for hyperspectral LWIR data comprises of dimensionality reduction, resampling of data by interpolation technique for registering the two images at same spatial resolution, extraction of the spatial features to improve classification accuracy. In the case of fine resolution RGB data, the vegetation index is computed for classifying the vegetation class and the morphological building index is calculated for buildings. In order to extract the textural features, occurrence and co-occurence statistics is considered and the features will be extracted from all the three bands of RGB data. After extracting the features, Support Vector Machine (SVMs) has been used for training and classification. To increase the classification accuracy, post processing steps like removal of any spurious noise such as salt and pepper noise is done which is followed by filtering process by majority voting within the objects for better object classification.

Classification and pose estimation of objects using nonlinear features

NASA Astrophysics Data System (ADS)

Talukder, Ashit; Casasent, David P.

1998-03-01

A new nonlinear feature extraction method called the maximum representation and discrimination feature (MRDF) method is presented for extraction of features from input image data. It implements transformations similar to the Sigma-Pi neural network. However, the weights of the MRDF are obtained in closed form, and offer advantages compared to nonlinear neural network implementations. The features extracted are useful for both object discrimination (classification) and object representation (pose estimation). We show its use in estimating the class and pose of images of real objects and rendered solid CAD models of machine parts from single views using a feature-space trajectory (FST) neural network classifier. We show more accurate classification and pose estimation results than are achieved by standard principal component analysis (PCA) and Fukunaga-Koontz (FK) feature extraction methods.

An Evaluation of Feature Learning Methods for High Resolution Image Classification

NASA Astrophysics Data System (ADS)

Tokarczyk, P.; Montoya, J.; Schindler, K.

2012-07-01

Automatic image classification is one of the fundamental problems of remote sensing research. The classification problem is even more challenging in high-resolution images of urban areas, where the objects are small and heterogeneous. Two questions arise, namely which features to extract from the raw sensor data to capture the local radiometry and image structure at each pixel or segment, and which classification method to apply to the feature vectors. While classifiers are nowadays well understood, selecting the right features remains a largely empirical process. Here we concentrate on the features. Several methods are evaluated which allow one to learn suitable features from unlabelled image data by analysing the image statistics. In a comparative study, we evaluate unsupervised feature learning with different linear and non-linear learning methods, including principal component analysis (PCA) and deep belief networks (DBN). We also compare these automatically learned features with popular choices of ad-hoc features including raw intensity values, standard combinations like the NDVI, a few PCA channels, and texture filters. The comparison is done in a unified framework using the same images, the target classes, reference data and a Random Forest classifier.

Stacked sparse autoencoder in hyperspectral data classification using spectral-spatial, higher order statistics and multifractal spectrum features

NASA Astrophysics Data System (ADS)

Wan, Xiaoqing; Zhao, Chunhui; Wang, Yanchun; Liu, Wu

2017-11-01

This paper proposes a novel classification paradigm for hyperspectral image (HSI) using feature-level fusion and deep learning-based methodologies. Operation is carried out in three main steps. First, during a pre-processing stage, wave atoms are introduced into bilateral filter to smooth HSI, and this strategy can effectively attenuate noise and restore texture information. Meanwhile, high quality spectral-spatial features can be extracted from HSI by taking geometric closeness and photometric similarity among pixels into consideration simultaneously. Second, higher order statistics techniques are firstly introduced into hyperspectral data classification to characterize the phase correlations of spectral curves. Third, multifractal spectrum features are extracted to characterize the singularities and self-similarities of spectra shapes. To this end, a feature-level fusion is applied to the extracted spectral-spatial features along with higher order statistics and multifractal spectrum features. Finally, stacked sparse autoencoder is utilized to learn more abstract and invariant high-level features from the multiple feature sets, and then random forest classifier is employed to perform supervised fine-tuning and classification. Experimental results on two real hyperspectral data sets demonstrate that the proposed method outperforms some traditional alternatives.

Documentation of procedures for textural/spatial pattern recognition techniques

NASA Technical Reports Server (NTRS)

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

1976-01-01

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

An Extended Spectral-Spatial Classification Approach for Hyperspectral Data

NASA Astrophysics Data System (ADS)

Akbari, D.

2017-11-01

In this paper an extended classification approach for hyperspectral imagery based on both spectral and spatial information is proposed. The spatial information is obtained by an enhanced marker-based minimum spanning forest (MSF) algorithm. Three different methods of dimension reduction are first used to obtain the subspace of hyperspectral data: (1) unsupervised feature extraction methods including principal component analysis (PCA), independent component analysis (ICA), and minimum noise fraction (MNF); (2) supervised feature extraction including decision boundary feature extraction (DBFE), discriminate analysis feature extraction (DAFE), and nonparametric weighted feature extraction (NWFE); (3) genetic algorithm (GA). The spectral features obtained are then fed into the enhanced marker-based MSF classification algorithm. In the enhanced MSF algorithm, the markers are extracted from the classification maps obtained by both SVM and watershed segmentation algorithm. To evaluate the proposed approach, the Pavia University hyperspectral data is tested. Experimental results show that the proposed approach using GA achieves an approximately 8 % overall accuracy higher than the original MSF-based algorithm.

Intelligence, Surveillance, and Reconnaissance Fusion for Coalition Operations

DTIC Science & Technology

2008-07-01

classification of the targets of interest. The MMI features extracted in this manner have two properties that provide a sound justification for...are generalizations of well- known feature extraction methods such as Principal Components Analysis (PCA) and Independent Component Analysis (ICA...augment (without degrading performance) a large class of generic fusion processes. Ontologies Classifications Feature extraction Feature analysis

Two Algorithms for High-throughput and Multi-parametric Quantification of Drosophila Neuromuscular Junction Morphology.

PubMed

Castells-Nobau, Anna; Nijhof, Bonnie; Eidhof, Ilse; Wolf, Louis; Scheffer-de Gooyert, Jolanda M; Monedero, Ignacio; Torroja, Laura; van der Laak, Jeroen A W M; Schenck, Annette

2017-05-03

Synaptic morphology is tightly related to synaptic efficacy, and in many cases morphological synapse defects ultimately lead to synaptic malfunction. The Drosophila larval neuromuscular junction (NMJ), a well-established model for glutamatergic synapses, has been extensively studied for decades. Identification of mutations causing NMJ morphological defects revealed a repertoire of genes that regulate synapse development and function. Many of these were identified in large-scale studies that focused on qualitative approaches to detect morphological abnormalities of the Drosophila NMJ. A drawback of qualitative analyses is that many subtle players contributing to NMJ morphology likely remain unnoticed. Whereas quantitative analyses are required to detect the subtler morphological differences, such analyses are not yet commonly performed because they are laborious. This protocol describes in detail two image analysis algorithms "Drosophila NMJ Morphometrics" and "Drosophila NMJ Bouton Morphometrics", available as Fiji-compatible macros, for quantitative, accurate and objective morphometric analysis of the Drosophila NMJ. This methodology is developed to analyze NMJ terminals immunolabeled with the commonly used markers Dlg-1 and Brp. Additionally, its wider application to other markers such as Hrp, Csp and Syt is presented in this protocol. The macros are able to assess nine morphological NMJ features: NMJ area, NMJ perimeter, number of boutons, NMJ length, NMJ longest branch length, number of islands, number of branches, number of branching points and number of active zones in the NMJ terminal.

Model of human recurrent respiratory papilloma on chicken embryo chorioallantoic membrane for tumor angiogenesis research.

PubMed

Uloza, Virgilijus; Kuzminienė, Alina; Palubinskienė, Jolita; Balnytė, Ingrida; Ulozienė, Ingrida; Valančiūtė, Angelija

2017-07-01

We aimed to develop a chick embryo chorioallantoic membrane (CAM) model of recurrent respiratory papilloma (RPP) and to evaluate its morphological and morphometric characteristics, together with angiogenic features. Fresh RRP tissue samples obtained from 13 patients were implanted in 174 chick embryo CAMs. Morphological, morphometric, and angiogenic changes in the CAM and chorionic epithelium were evaluated up until 7 days after the implantation. Immunohistochemical analysis (34βE12, Ki-67, MMP-9, PCNA, and Sambucus nigra staining) was performed to detect cytokeratins and endothelial cells and to evaluate proliferative capacity of the RRP before and after implantation on the CAM. The implanted RRP tissue samples survived on CAM in 73% of cases while retaining their essential morphologic characteristics and proliferative capacity of the original tumor. Implants induced thickening of both the CAM (241-560%, p=0.001) and the chorionic epithelium (107-151%, p=0.001), while the number of blood vessels (37-85%, p=0.001) in the CAM increased. The results of the present study confirmed that chick embryo CAM is a relevant host for serving as a medium for RRP fresh tissue implantation. The CAM assay demonstrated the specific RRP tumor growth pattern after implantation and provided the first morphological and morphometric characterization of the RRP CAM model that opens new horizons in studying this disease.

A minimally invasive methodology based on morphometric parameters for day 2 embryo quality assessment.

PubMed

Molina, Inmaculada; Lázaro-Ibáñez, Elisa; Pertusa, Jose; Debón, Ana; Martínez-Sanchís, Juan Vicente; Pellicer, Antonio

2014-10-01

The risk of multiple pregnancy to maternal-fetal health can be minimized by reducing the number of embryos transferred. New tools for selecting embryos with the highest implantation potential should be developed. The aim of this study was to evaluate the ability of morphological and morphometric variables to predict implantation by analysing images of embryos. This was a retrospective study of 135 embryo photographs from 112 IVF-ICSI cycles carried out between January and March 2011. The embryos were photographed immediately before transfer using Cronus 3 software. Their images were analysed using the public program ImageJ. Significant effects (P < 0.05), and higher discriminant power to predict implantation were observed for the morphometric embryo variables compared with morphological ones. The features for successfully implanted embryos were as follows: four cells on day 2 of development; all blastomeres with circular shape (roundness factor greater than 0.9), an average zona pellucida thickness of 13 µm and an average of 17695.1 µm² for the embryo area. Embryo size, which is described by its area and the average roundness factor for each cell, provides two objective variables to consider when predicting implantation. This approach should be further investigated for its potential ability to improve embryo scoring. Copyright © 2014 Reproductive Healthcare Ltd. Published by Elsevier Ltd. All rights reserved.

Pyramidal neurons in the septal and temporal CA1 field of the human and hedgehog tenrec hippocampus.

PubMed

Liagkouras, Ioannis; Michaloudi, Helen; Batzios, Christos; Psaroulis, Dimitrios; Georgiadis, Marios; Künzle, Heinz; Papadopoulos, Georgios C

2008-07-07

The present study examines comparatively the cellular density of disector-counted/Nissl-stained CA1 pyramidal neurons and the morphometric characteristics (dendritic number/length, spine number/density and Sholl-counted dendritic branch points/20 microm) of the basal and apical dendritic systems of Golgi-impregnated CA1 neurons, in the septal and temporal hippocampus of the human and hedgehog tenrec brain. The obtained results indicate that in both hippocampal parts the cellular density of the CA1 pyramidal neurons is lower in human than in tenrec. However, while the human pyramidal cell density is higher in the septal hippocampal part than in the temporal one, in the tenrec the density of these cells is higher in the temporal part. The dendritic tree of the CA1 pyramidal cells, more developed in the septal than in temporal hippocampus in both species studied, is in general more complex in the human hippocampus. The basal and the apical dendritic systems exhibit species related morphometric differences, while dendrites of different orders exhibit differences in their number and length, and in their spine density. Finally, in both species, as well as hippocampal parts and dendritic systems, changes of dendritic morphometric features along ascending dendritic orders fluctuate in a similar way, as do the number of dendritic branch points in relation to the distance from the neuron soma.

Efficient Feature Selection and Classification of Protein Sequence Data in Bioinformatics

PubMed Central

Faye, Ibrahima; Samir, Brahim Belhaouari; Md Said, Abas

2014-01-01

Bioinformatics has been an emerging area of research for the last three decades. The ultimate aims of bioinformatics were to store and manage the biological data, and develop and analyze computational tools to enhance their understanding. The size of data accumulated under various sequencing projects is increasing exponentially, which presents difficulties for the experimental methods. To reduce the gap between newly sequenced protein and proteins with known functions, many computational techniques involving classification and clustering algorithms were proposed in the past. The classification of protein sequences into existing superfamilies is helpful in predicting the structure and function of large amount of newly discovered proteins. The existing classification results are unsatisfactory due to a huge size of features obtained through various feature encoding methods. In this work, a statistical metric-based feature selection technique has been proposed in order to reduce the size of the extracted feature vector. The proposed method of protein classification shows significant improvement in terms of performance measure metrics: accuracy, sensitivity, specificity, recall, F-measure, and so forth. PMID:25045727

Intelligent Color Vision System for Ripeness Classification of Oil Palm Fresh Fruit Bunch

PubMed Central

Fadilah, Norasyikin; Mohamad-Saleh, Junita; Halim, Zaini Abdul; Ibrahim, Haidi; Ali, Syed Salim Syed

2012-01-01

Ripeness classification of oil palm fresh fruit bunches (FFBs) during harvesting is important to ensure that they are harvested during optimum stage for maximum oil production. This paper presents the application of color vision for automated ripeness classification of oil palm FFB. Images of oil palm FFBs of type DxP Yangambi were collected and analyzed using digital image processing techniques. Then the color features were extracted from those images and used as the inputs for Artificial Neural Network (ANN) learning. The performance of the ANN for ripeness classification of oil palm FFB was investigated using two methods: training ANN with full features and training ANN with reduced features based on the Principal Component Analysis (PCA) data reduction technique. Results showed that compared with using full features in ANN, using the ANN trained with reduced features can improve the classification accuracy by 1.66% and is more effective in developing an automated ripeness classifier for oil palm FFB. The developed ripeness classifier can act as a sensor in determining the correct oil palm FFB ripeness category. PMID:23202043

Yarn-dyed fabric defect classification based on convolutional neural network

NASA Astrophysics Data System (ADS)

Jing, Junfeng; Dong, Amei; Li, Pengfei

2017-07-01

Considering that the manual inspection of the yarn-dyed fabric can be time consuming and less efficient, a convolutional neural network (CNN) solution based on the modified AlexNet structure for the classification of the yarn-dyed fabric defect is proposed. CNN has powerful ability of feature extraction and feature fusion which can simulate the learning mechanism of the human brain. In order to enhance computational efficiency and detection accuracy, the local response normalization (LRN) layers in AlexNet are replaced by the batch normalization (BN) layers. In the process of the network training, through several convolution operations, the characteristics of the image are extracted step by step, and the essential features of the image can be obtained from the edge features. And the max pooling layers, the dropout layers, the fully connected layers are also employed in the classification model to reduce the computation cost and acquire more precise features of fabric defect. Finally, the results of the defect classification are predicted by the softmax function. The experimental results show the capability of defect classification via the modified Alexnet model and indicate its robustness.

Intelligent color vision system for ripeness classification of oil palm fresh fruit bunch.

PubMed

Fadilah, Norasyikin; Mohamad-Saleh, Junita; Abdul Halim, Zaini; Ibrahim, Haidi; Syed Ali, Syed Salim

2012-10-22

Ripeness classification of oil palm fresh fruit bunches (FFBs) during harvesting is important to ensure that they are harvested during optimum stage for maximum oil production. This paper presents the application of color vision for automated ripeness classification of oil palm FFB. Images of oil palm FFBs of type DxP Yangambi were collected and analyzed using digital image processing techniques. Then the color features were extracted from those images and used as the inputs for Artificial Neural Network (ANN) learning. The performance of the ANN for ripeness classification of oil palm FFB was investigated using two methods: training ANN with full features and training ANN with reduced features based on the Principal Component Analysis (PCA) data reduction technique. Results showed that compared with using full features in ANN, using the ANN trained with reduced features can improve the classification accuracy by 1.66% and is more effective in developing an automated ripeness classifier for oil palm FFB. The developed ripeness classifier can act as a sensor in determining the correct oil palm FFB ripeness category.

Arrhythmia Classification Based on Multi-Domain Feature Extraction for an ECG Recognition System.

PubMed

Li, Hongqiang; Yuan, Danyang; Wang, Youxi; Cui, Dianyin; Cao, Lu

2016-10-20

Automatic recognition of arrhythmias is particularly important in the diagnosis of heart diseases. This study presents an electrocardiogram (ECG) recognition system based on multi-domain feature extraction to classify ECG beats. An improved wavelet threshold method for ECG signal pre-processing is applied to remove noise interference. A novel multi-domain feature extraction method is proposed; this method employs kernel-independent component analysis in nonlinear feature extraction and uses discrete wavelet transform to extract frequency domain features. The proposed system utilises a support vector machine classifier optimized with a genetic algorithm to recognize different types of heartbeats. An ECG acquisition experimental platform, in which ECG beats are collected as ECG data for classification, is constructed to demonstrate the effectiveness of the system in ECG beat classification. The presented system, when applied to the MIT-BIH arrhythmia database, achieves a high classification accuracy of 98.8%. Experimental results based on the ECG acquisition experimental platform show that the system obtains a satisfactory classification accuracy of 97.3% and is able to classify ECG beats efficiently for the automatic identification of cardiac arrhythmias.

Arrhythmia Classification Based on Multi-Domain Feature Extraction for an ECG Recognition System

PubMed Central

Li, Hongqiang; Yuan, Danyang; Wang, Youxi; Cui, Dianyin; Cao, Lu

2016-01-01

Automatic recognition of arrhythmias is particularly important in the diagnosis of heart diseases. This study presents an electrocardiogram (ECG) recognition system based on multi-domain feature extraction to classify ECG beats. An improved wavelet threshold method for ECG signal pre-processing is applied to remove noise interference. A novel multi-domain feature extraction method is proposed; this method employs kernel-independent component analysis in nonlinear feature extraction and uses discrete wavelet transform to extract frequency domain features. The proposed system utilises a support vector machine classifier optimized with a genetic algorithm to recognize different types of heartbeats. An ECG acquisition experimental platform, in which ECG beats are collected as ECG data for classification, is constructed to demonstrate the effectiveness of the system in ECG beat classification. The presented system, when applied to the MIT-BIH arrhythmia database, achieves a high classification accuracy of 98.8%. Experimental results based on the ECG acquisition experimental platform show that the system obtains a satisfactory classification accuracy of 97.3% and is able to classify ECG beats efficiently for the automatic identification of cardiac arrhythmias. PMID:27775596

The Costs of Supervised Classification: The Effect of Learning Task on Conceptual Flexibility

ERIC Educational Resources Information Center

Hoffman, Aaron B.; Rehder, Bob

2010-01-01

Research has shown that learning a concept via standard supervised classification leads to a focus on diagnostic features, whereas learning by inferring missing features promotes the acquisition of within-category information. Accordingly, we predicted that classification learning would produce a deficit in people's ability to draw "novel…

A web system of virtual morphometric globes for Mars and the Moon

NASA Astrophysics Data System (ADS)

Florinsky, I. V.; Garov, A. S.; Karachevtseva, I. P.

2018-09-01

We developed a web system of virtual morphometric globes for Mars and the Moon. As the initial data, we used 15-arc-minutes gridded global digital elevation models (DEMs) extracted from the Mars Orbiter Laser Altimeter (MOLA) and the Lunar Orbiter Laser Altimeter (LOLA) gridded archives. We derived global digital models of sixteen morphometric variables including horizontal, vertical, minimal, and maximal curvatures, as well as catchment area and topographic index. The morphometric models were integrated into the web system developed as a distributed application consisting of a client front-end and a server back-end. The following main functions are implemented in the system: (1) selection of a morphometric variable; (2) two-dimensional visualization of a calculated global morphometric model; (3) 3D visualization of a calculated global morphometric model on the sphere surface; (4) change of a globe scale; and (5) globe rotation by an arbitrary angle. Free, real-time web access to the system is provided. The web system of virtual morphometric globes can be used for geological and geomorphological studies of Mars and the Moon at the global, continental, and regional scales.

Application of machine learning on brain cancer multiclass classification

NASA Astrophysics Data System (ADS)

Panca, V.; Rustam, Z.

2017-07-01

Classification of brain cancer is a problem of multiclass classification. One approach to solve this problem is by first transforming it into several binary problems. The microarray gene expression dataset has the two main characteristics of medical data: extremely many features (genes) and only a few number of samples. The application of machine learning on microarray gene expression dataset mainly consists of two steps: feature selection and classification. In this paper, the features are selected using a method based on support vector machine recursive feature elimination (SVM-RFE) principle which is improved to solve multiclass classification, called multiple multiclass SVM-RFE. Instead of using only the selected features on a single classifier, this method combines the result of multiple classifiers. The features are divided into subsets and SVM-RFE is used on each subset. Then, the selected features on each subset are put on separate classifiers. This method enhances the feature selection ability of each single SVM-RFE. Twin support vector machine (TWSVM) is used as the method of the classifier to reduce computational complexity. While ordinary SVM finds single optimum hyperplane, the main objective Twin SVM is to find two non-parallel optimum hyperplanes. The experiment on the brain cancer microarray gene expression dataset shows this method could classify 71,4% of the overall test data correctly, using 100 and 1000 genes selected from multiple multiclass SVM-RFE feature selection method. Furthermore, the per class results show that this method could classify data of normal and MD class with 100% accuracy.

Problems of stock definition in estimating relative contributions of Atlantic striped bass to the coastal fishery

USGS Publications Warehouse

Waldman, John R.; Fabrizio, Mary C.

1994-01-01

Stock contribution studies of mixed-stock fisheries rely on the application of classification algorithms to samples of unknown origin. Although the performance of these algorithms can be assessed, there are no guidelines regarding decisions about including minor stocks, pooling stocks into regional groups, or sampling discrete substocks to adequately characterize a stock. We examined these questions for striped bass Morone saxatilis of the U.S. Atlantic coast by applying linear discriminant functions to meristic and morphometric data from fish collected from spawning areas. Some of our samples were from the Hudson and Roanoke rivers and four tributaries of the Chesapeake Bay. We also collected fish of mixed-stock origin from the Atlantic Ocean near Montauk, New York. Inclusion of the minor stock from the Roanoke River in the classification algorithm decreased the correct-classification rate, whereas grouping of the Roanoke River and Chesapeake Bay stock into a regional (''southern'') group increased the overall resolution. The increased resolution was offset by our inability to obtain separate contribution estimates of the groups that were pooled. Although multivariate analysis of variance indicated significant differences among Chesapeake Bay substocks, increasing the number of substocks in the discriminant analysis decreased the overall correct-classification rate. Although the inclusion of one, two, three, or four substocks in the classification algorithm did not greatly affect the overall correct-classification rates, the specific combination of substocks significantly affected the relative contribution estimates derived from the mixed-stock sample. Future studies of this kind must balance the costs and benefits of including minor stocks and would profit from examination of the variation in discriminant characters among all Chesapeake Bay substocks.

Comparison of Naive Bayes and Decision Tree on Feature Selection Using Genetic Algorithm for Classification Problem

NASA Astrophysics Data System (ADS)

Rahmadani, S.; Dongoran, A.; Zarlis, M.; Zakarias

2018-03-01

This paper discusses the problem of feature selection using genetic algorithms on a dataset for classification problems. The classification model used is the decicion tree (DT), and Naive Bayes. In this paper we will discuss how the Naive Bayes and Decision Tree models to overcome the classification problem in the dataset, where the dataset feature is selectively selected using GA. Then both models compared their performance, whether there is an increase in accuracy or not. From the results obtained shows an increase in accuracy if the feature selection using GA. The proposed model is referred to as GADT (GA-Decision Tree) and GANB (GA-Naive Bayes). The data sets tested in this paper are taken from the UCI Machine Learning repository.

A Review of Major Nursing Vocabularies and the Extent to Which They Have the Characteristics Required for Implementation in Computer-based Systems

PubMed Central

Henry, Suzanne Bakken; Warren, Judith J.; Lange, Linda; Button, Patricia

1998-01-01

Building on the work of previous authors, the Computer-based Patient Record Institute (CPRI) Work Group on Codes and Structures has described features of a classification scheme for implementation within a computer-based patient record. The authors of the current study reviewed the evaluation literature related to six major nursing vocabularies (the North American Nursing Diagnosis Association Taxonomy 1, the Nursing Interventions Classification, the Nursing Outcomes Classification, the Home Health Care Classification, the Omaha System, and the International Classification for Nursing Practice) to determine the extent to which the vocabularies include the CPRI features. None of the vocabularies met all criteria. The Omaha System, Home Health Care Classification, and International Classification for Nursing Practice each included five features. Criteria not fully met by any systems were clear and non-redundant representation of concepts, administrative cross-references, syntax and grammar, synonyms, uncertainty, context-free identifiers, and language independence. PMID:9670127

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

Glaciated valleys in Europe and western Asia

PubMed Central

Prasicek, Günther; Otto, Jan-Christoph; Montgomery, David R.; Schrott, Lothar

2015-01-01

In recent years, remote sensing, morphometric analysis, and other computational concepts and tools have invigorated the field of geomorphological mapping. Automated interpretation of digital terrain data based on impartial rules holds substantial promise for large dataset processing and objective landscape classification. However, the geomorphological realm presents tremendous complexity and challenges in the translation of qualitative descriptions into geomorphometric semantics. Here, the simple, conventional distinction of V-shaped fluvial and U-shaped glacial valleys was analyzed quantitatively using multi-scale curvature and a novel morphometric variable termed Difference of Minimum Curvature (DMC). We used this automated terrain analysis approach to produce a raster map at a scale of 1:6,000,000 showing the distribution of glaciated valleys across Europe and western Asia. The data set has a cell size of 3 arc seconds and consists of more than 40 billion grid cells. Glaciated U-shaped valleys commonly associated with erosion by warm-based glaciers are abundant in the alpine regions of mid Europe and western Asia but also occur at the margins of mountain ice sheets in Scandinavia. The high-level correspondence with field mapping and the fully transferable semantics validate this approach for automated analysis of yet unexplored terrain around the globe and qualify for potential applications on other planetary bodies like Mars. PMID:27019665

Taxonomic revision of the Dasypus kappleri complex, with revalidations of Dasypus pastasae (Thomas, 1901) and Dasypus beniensis Lönnberg, 1942 (Cingulata, Dasypodidae).

PubMed

Feijó, Anderson; Cordeiro-Estrela, Pedro

2016-09-23

Dasypus kappleri is the largest species of the genus Dasypus and is restricted to the Amazonian rainforest biome. Over the last century, related taxa have been described and synonymized without comprehensive analyses, and the current classification involving two subspecies, Dasypus k. kappleri and Dasypus k. pastasae, has never been revised. The aim of this work is to clarify the taxonomy of Dasypus kappleri through integrative morphological and morphometric analyses. We examined 70 specimens housed in scientific collections as well as photographs of the type specimens of five nominal taxa. Three methodologies (discrete characters, linear and geometric morphometrics) were employed. All results converged on the recognition of three allopatric groups, each with diagnostic qualitative and quantitative traits, that we recognize as full species: Dasypus kappleri Krauss, 1862, occurs in the Guiana shield; Dasypus pastasae (Thomas, 1901) is distributed from the eastern Andes of Peru, Ecuador, Colombia, and Venezuela south of the Orinoco River into the western Brazilian Amazon; and Dasypus beniensis Lönnberg, 1942, occurs in the lowlands of Amazonian Brazil and Bolivia to the south of the Madre de Dios, Madeira, and lower Amazon rivers. This revision raises to nine the number of living species of Dasypus.

Characteristics of terrestrial basaltic rock populations: Implications for Mars lander and rover science and safety

NASA Astrophysics Data System (ADS)

Craddock, Robert A.; Golombek, Matthew P.

2016-08-01

We analyzed the morphometry of basaltic rock populations that have been emplaced or affected by a variety of geologic processes, including explosive volcanic eruptions (as a proxy for impact cratering), catastrophic flooding, frost shattering, salt weathering, alluvial deposition, and chemical weathering. Morphometric indices for these rock populations were compared to an unmodified population of rocks that had broken off a solidified lava flow to understand how different geologic processes change rock shape. We found that a majority of rocks have an sphericity described as either a disc or sphere in the Zingg classification system and posit that this is a function of cooling fractures in the basalt (Zingg [1935] Schweiz. Miner. Petrogr. Mitt., 15, 39-140). Angularity (roundness) is the most diagnostic morphometric index, but the Corey Shape Factor (CSF), Oblate-Prolate Index (OPI) and deviation from compactness (D) also sometimes distinguished weathering processes. Comparison of our results to prior analyses of rock populations found at the Mars Pathfinder, Spirit, and Curiosity landing sites support previous conclusions. The observation that the size-frequency distribution of terrestrial rock populations follow exponential functions similar to lander and orbital measurements of rocks on Mars, which is expected from fracture and fragmentation theory, indicates that these distributions are being dominantly controlled by the initial fracture and fragmentation of the basalt.

Evolutionary lineages of marine snails identified using molecular phylogenetics and geometric morphometric analysis of shells.

PubMed

Vaux, Felix; Trewick, Steven A; Crampton, James S; Marshall, Bruce A; Beu, Alan G; Hills, Simon F K; Morgan-Richards, Mary

2018-06-15

The relationship between morphology and inheritance is of perennial interest in evolutionary biology and palaeontology. Using three marine snail genera Penion, Antarctoneptunea and Kelletia, we investigate whether systematics based on shell morphology accurately reflect evolutionary lineages indicated by molecular phylogenetics. Members of these gastropod genera have been a taxonomic challenge due to substantial variation in shell morphology, conservative radular and soft tissue morphology, few known ecological differences, and geographical overlap between numerous species. Sampling all sixteen putative taxa identified across the three genera, we infer mitochondrial and nuclear ribosomal DNA phylogenetic relationships within the group, and compare this to variation in adult shell shape and size. Results of phylogenetic analysis indicate that each genus is monophyletic, although the status of some phylogenetically derived and likely more recently evolved taxa within Penion is uncertain. The recently described species P. lineatus is supported by genetic evidence. Morphology, captured using geometric morphometric analysis, distinguishes the genera and matches the molecular phylogeny, although using the same dataset, species and phylogenetic subclades are not identified with high accuracy. Overall, despite abundant variation, we find that shell morphology accurately reflects genus-level classification and the corresponding deep phylogenetic splits identified in this group of marine snails. Copyright © 2018 Elsevier Inc. All rights reserved.

Vertisols and vertic soils of the middle and lower Volga regions

NASA Astrophysics Data System (ADS)

Khitrov, N. B.; Rogovneva, L. V.

2014-12-01

In addition to the earlier known vertic alluvial soils (slitozems) of the Volga-Akhtuba floodplain, 44 new areas of Vertisols and vertic soils (according to the WRB), or dark slitozems (according to the new Russian soil classification system), have been found in the Middle and Lower Volga regions from the forest-steppe to the semidesert zones. Though these soils occupy relatively small areas, they are regularly found in the studied regions. Vertisols developed from the clayey alluvial sediments occur in widened parts of the central floodplain in the areas of strong meandering of the river downstream from the areas, where it washes out ancient swelling clay sediments. Many areas of Vertisols and vertic soils are confined to the second Khvalyn terrace of the Volga River composed of the chocolate-brown swelling Khvalyn clay. These soils do not occupy the entire terrace. They have an insular-type distribution and highly diverse in their properties. In the soils developed from the eluvium of the microlayered chocolate-brown marine clay within the Privolzhskaya Upland, vertic features are absent. The destruction of the lithogenic layering in the course of the redeposition of the marine clay with the formation of the new Quaternary clayey sediments creates conditions for the development of vertic soils. The northernmost area of Vertisols proper has been found in the area of the Samara Arc (53.231° N, 049.322° E). The soils with vertic features have been found in Mordovia and Samara oblast even further to the north (up to 54.2° N). Morphometric data on the slickensides, wedge-shaped structure, and depth of the soil cracking are presented.

An approach for automatic classification of grouper vocalizations with passive acoustic monitoring.

PubMed

Ibrahim, Ali K; Chérubin, Laurent M; Zhuang, Hanqi; Schärer Umpierre, Michelle T; Dalgleish, Fraser; Erdol, Nurgun; Ouyang, B; Dalgleish, A

2018-02-01

Grouper, a family of marine fishes, produce distinct vocalizations associated with their reproductive behavior during spawning aggregation. These low frequencies sounds (50-350 Hz) consist of a series of pulses repeated at a variable rate. In this paper, an approach is presented for automatic classification of grouper vocalizations from ambient sounds recorded in situ with fixed hydrophones based on weighted features and sparse classifier. Group sounds were labeled initially by humans for training and testing various feature extraction and classification methods. In the feature extraction phase, four types of features were used to extract features of sounds produced by groupers. Once the sound features were extracted, three types of representative classifiers were applied to categorize the species that produced these sounds. Experimental results showed that the overall percentage of identification using the best combination of the selected feature extractor weighted mel frequency cepstral coefficients and sparse classifier achieved 82.7% accuracy. The proposed algorithm has been implemented in an autonomous platform (wave glider) for real-time detection and classification of group vocalizations.

Comparing Features for Classification of MEG Responses to Motor Imagery.

PubMed

Halme, Hanna-Leena; Parkkonen, Lauri

2016-01-01

Motor imagery (MI) with real-time neurofeedback could be a viable approach, e.g., in rehabilitation of cerebral stroke. Magnetoencephalography (MEG) noninvasively measures electric brain activity at high temporal resolution and is well-suited for recording oscillatory brain signals. MI is known to modulate 10- and 20-Hz oscillations in the somatomotor system. In order to provide accurate feedback to the subject, the most relevant MI-related features should be extracted from MEG data. In this study, we evaluated several MEG signal features for discriminating between left- and right-hand MI and between MI and rest. MEG was measured from nine healthy participants imagining either left- or right-hand finger tapping according to visual cues. Data preprocessing, feature extraction and classification were performed offline. The evaluated MI-related features were power spectral density (PSD), Morlet wavelets, short-time Fourier transform (STFT), common spatial patterns (CSP), filter-bank common spatial patterns (FBCSP), spatio-spectral decomposition (SSD), and combined SSD+CSP, CSP+PSD, CSP+Morlet, and CSP+STFT. We also compared four classifiers applied to single trials using 5-fold cross-validation for evaluating the classification accuracy and its possible dependence on the classification algorithm. In addition, we estimated the inter-session left-vs-right accuracy for each subject. The SSD+CSP combination yielded the best accuracy in both left-vs-right (mean 73.7%) and MI-vs-rest (mean 81.3%) classification. CSP+Morlet yielded the best mean accuracy in inter-session left-vs-right classification (mean 69.1%). There were large inter-subject differences in classification accuracy, and the level of the 20-Hz suppression correlated significantly with the subjective MI-vs-rest accuracy. Selection of the classification algorithm had only a minor effect on the results. We obtained good accuracy in sensor-level decoding of MI from single-trial MEG data. Feature extraction methods utilizing both the spatial and spectral profile of MI-related signals provided the best classification results, suggesting good performance of these methods in an online MEG neurofeedback system.

Statistical Distribution Analysis of Lineated Bands on Europa

NASA Astrophysics Data System (ADS)

Chen, T.; Phillips, C. B.; Pappalardo, R. T.

2016-12-01

Tina Chen, Cynthia B. Phillips, Robert T. Pappalardo Europa's surface is covered with intriguing linear and disrupted features, including lineated bands that range in scale and size. Previous studies have shown the possibility of an icy shell at the surface that may be concealing a liquid ocean with the potential to harboring life (Pappalardo et al., 1999). Utilizing the high-resolution imaging data from the Galileo spacecraft, we examined bands through a morphometric and morphologic approach. Greeley et al. (2000) and Procktor et al. (2002) have defined bands as wide, hummocky to lineated features that have distinctive surface texture and albedo compared to its surrounding terrain. We took morphometric measurements of lineated bands to find correlations in properties such as size, location, and orientation, and to shed light on formation models. We will present our measurements of over 100 bands on Europa that was mapped on the USGS Europa Global Mosaic Base Map (2002). We also conducted a statistical analysis to understand the distribution of lineated bands globally, and whether the widths of the bands differ by location. Our preliminary analysis from our statistical distribution evaluation, combined with the morphometric measurements, supports a uniform ice shell thickness for Europa rather than one that varies geographically. References: Greeley, Ronald, et al. "Geologic mapping of Europa." Journal of Geophysical Research: Planets 105.E9 (2000): 22559-22578.; Pappalardo, R. T., et al. "Does Europa have a subsurface ocean? Evaluation of the geological evidence." Journal of Geophysical Research: Planets 104.E10 (1999): 24015-24055.; Prockter, Louise M., et al. "Morphology of Europan bands at high resolution: A mid-ocean ridge-type rift mechanism." Journal of Geophysical Research: Planets 107.E5 (2002).; U.S. Geological Survey, 2002, Controlled photomosaic map of Europa, Je 15M CMN: U.S. Geological Survey Geologic Investigations Series I-2757, available at http://pubs.usgs.gov/imap/i2757/

Prevalence, topographic and morphometric features of femoral cam-type deformity: changes in relation to age and gender.

PubMed

Morales-Avalos, R; Leyva-Villegas, J I; Sánchez-Mejorada, G; Reynaga-Obregón, J; Galindo-de León, S; Vílchez-Cavazos, F; Espinosa-Uribe, A G; Acosta-Olivo, C; de la Garza-Castro, O; Guzmán-Avilan, R I; Elizondo-Omaña, R E; Guzmán-López, S

2016-09-01

Femoroacetabular impingement (FAI) syndrome is a frequent cause of pain and in recent years considered to be a precursor of premature hip osteoarthritis. The structural abnormalities which characterize FAI syndrome, such as the cam-type deformity, are associated with morphological alterations that may lead to hip osteoarthritis. The aim of this study was to determine the prevalence and topographic and morphometric features of the cam deformity in a series of 326 femur specimens obtained from a Mexican population, as well as changes in prevalence in relation to age and gender. The specimens were subdivided into groups according to gender and age. A standardized photograph of the proximal femur of each specimen was taken, and the photograph was used to determine the alpha angle using a computer program; the location of the lesion was determined by quadrant and the morphometric characteristics were determined by direct observation. The overall prevalence of cam deformities in the femur specimens was 29.8 % (97/326), with a prevalence by gender of 35.2 % (64/182) in men and 22.9 % (33/144) in women. The mean alpha angle was 54.6° ± 8.5° in all of the osteological specimens and 65.6° ± 7.5° in those specimens exhibiting a cam deformity. Cam deformities were found topographically in the anterior-superior quadrant of the femoral head-neck junction in 86.6 % (84/97) of the femurs. Deformities were found in 28.2 % of the right femurs and 31.3 % of the left femurs. The prevalence of cam deformity was higher in the femur specimens of young men and in those of middle-aged and older women. There were no significant differences in this deformity in relation to the alpha angle according to age and gender.

Deconvolution single shot multibox detector for supermarket commodity detection and classification

NASA Astrophysics Data System (ADS)

Li, Dejian; Li, Jian; Nie, Binling; Sun, Shouqian

2017-07-01

This paper proposes an image detection model to detect and classify supermarkets shelves' commodity. Based on the principle of the features directly affects the accuracy of the final classification, feature maps are performed to combine high level features with bottom level features. Then set some fixed anchors on those feature maps, finally the label and the position of commodity is generated by doing a box regression and classification. In this work, we proposed a model named Deconvolutiuon Single Shot MultiBox Detector, we evaluated the model using 300 images photographed from real supermarket shelves. Followed the same protocol in other recent methods, the results showed that our model outperformed other baseline methods.

Classification of weld defect based on information fusion technology for radiographic testing system

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

Jiang, Hongquan; Liang, Zeming, E-mail: heavenlzm@126.com; Gao, Jianmin

Improving the efficiency and accuracy of weld defect classification is an important technical problem in developing the radiographic testing system. This paper proposes a novel weld defect classification method based on information fusion technology, Dempster–Shafer evidence theory. First, to characterize weld defects and improve the accuracy of their classification, 11 weld defect features were defined based on the sub-pixel level edges of radiographic images, four of which are presented for the first time in this paper. Second, we applied information fusion technology to combine different features for weld defect classification, including a mass function defined based on the weld defectmore » feature information and the quartile-method-based calculation of standard weld defect class which is to solve a sample problem involving a limited number of training samples. A steam turbine weld defect classification case study is also presented herein to illustrate our technique. The results show that the proposed method can increase the correct classification rate with limited training samples and address the uncertainties associated with weld defect classification.« less

Classification of weld defect based on information fusion technology for radiographic testing system.

PubMed

Jiang, Hongquan; Liang, Zeming; Gao, Jianmin; Dang, Changying

2016-03-01

Improving the efficiency and accuracy of weld defect classification is an important technical problem in developing the radiographic testing system. This paper proposes a novel weld defect classification method based on information fusion technology, Dempster-Shafer evidence theory. First, to characterize weld defects and improve the accuracy of their classification, 11 weld defect features were defined based on the sub-pixel level edges of radiographic images, four of which are presented for the first time in this paper. Second, we applied information fusion technology to combine different features for weld defect classification, including a mass function defined based on the weld defect feature information and the quartile-method-based calculation of standard weld defect class which is to solve a sample problem involving a limited number of training samples. A steam turbine weld defect classification case study is also presented herein to illustrate our technique. The results show that the proposed method can increase the correct classification rate with limited training samples and address the uncertainties associated with weld defect classification.

Handwriting Classification in Forensic Science.

ERIC Educational Resources Information Center

Ansell, Michael

1979-01-01

Considers systems for the classification of handwriting features, discusses computer storage of information about handwriting features, and summarizes recent studies that give an idea of the range of forensic handwriting research. (GT)

Multiclass Classification for the Differential Diagnosis on the ADHD Subtypes Using Recursive Feature Elimination and Hierarchical Extreme Learning Machine: Structural MRI Study

PubMed Central

Qureshi, Muhammad Naveed Iqbal; Min, Beomjun; Jo, Hang Joon; Lee, Boreom

2016-01-01

The classification of neuroimaging data for the diagnosis of certain brain diseases is one of the main research goals of the neuroscience and clinical communities. In this study, we performed multiclass classification using a hierarchical extreme learning machine (H-ELM) classifier. We compared the performance of this classifier with that of a support vector machine (SVM) and basic extreme learning machine (ELM) for cortical MRI data from attention deficit/hyperactivity disorder (ADHD) patients. We used 159 structural MRI images of children from the publicly available ADHD-200 MRI dataset. The data consisted of three types, namely, typically developing (TDC), ADHD-inattentive (ADHD-I), and ADHD-combined (ADHD-C). We carried out feature selection by using standard SVM-based recursive feature elimination (RFE-SVM) that enabled us to achieve good classification accuracy (60.78%). In this study, we found the RFE-SVM feature selection approach in combination with H-ELM to effectively enable the acquisition of high multiclass classification accuracy rates for structural neuroimaging data. In addition, we found that the most important features for classification were the surface area of the superior frontal lobe, and the cortical thickness, volume, and mean surface area of the whole cortex. PMID:27500640

Multiclass Classification for the Differential Diagnosis on the ADHD Subtypes Using Recursive Feature Elimination and Hierarchical Extreme Learning Machine: Structural MRI Study.

PubMed

Qureshi, Muhammad Naveed Iqbal; Min, Beomjun; Jo, Hang Joon; Lee, Boreom

2016-01-01

The classification of neuroimaging data for the diagnosis of certain brain diseases is one of the main research goals of the neuroscience and clinical communities. In this study, we performed multiclass classification using a hierarchical extreme learning machine (H-ELM) classifier. We compared the performance of this classifier with that of a support vector machine (SVM) and basic extreme learning machine (ELM) for cortical MRI data from attention deficit/hyperactivity disorder (ADHD) patients. We used 159 structural MRI images of children from the publicly available ADHD-200 MRI dataset. The data consisted of three types, namely, typically developing (TDC), ADHD-inattentive (ADHD-I), and ADHD-combined (ADHD-C). We carried out feature selection by using standard SVM-based recursive feature elimination (RFE-SVM) that enabled us to achieve good classification accuracy (60.78%). In this study, we found the RFE-SVM feature selection approach in combination with H-ELM to effectively enable the acquisition of high multiclass classification accuracy rates for structural neuroimaging data. In addition, we found that the most important features for classification were the surface area of the superior frontal lobe, and the cortical thickness, volume, and mean surface area of the whole cortex.

Identification of terrain cover using the optimum polarimetric classifier

NASA Technical Reports Server (NTRS)

Kong, J. A.; Swartz, A. A.; Yueh, H. A.; Novak, L. M.; Shin, R. T.

1988-01-01

A systematic approach for the identification of terrain media such as vegetation canopy, forest, and snow-covered fields is developed using the optimum polarimetric classifier. The covariance matrices for various terrain cover are computed from theoretical models of random medium by evaluating the scattering matrix elements. The optimal classification scheme makes use of a quadratic distance measure and is applied to classify a vegetation canopy consisting of both trees and grass. Experimentally measured data are used to validate the classification scheme. Analytical and Monte Carlo simulated classification errors using the fully polarimetric feature vector are compared with classification based on single features which include the phase difference between the VV and HH polarization returns. It is shown that the full polarimetric results are optimal and provide better classification performance than single feature measurements.

Geomorphology and seismic risk

NASA Astrophysics Data System (ADS)

Panizza, Mario

1991-07-01

The author analyses the contributions provided by geomorphology in studies suited to the assessment of seismic risk: this is defined as function of the seismic hazard, of the seismic susceptibility, and of the vulnerability. The geomorphological studies applicable to seismic risk assessment can be divided into two sectors: (a) morpho-neotectonic investigations conducted to identify active tectonic structures; (b) geomorphological and morphometric analyses aimed at identifying the particular situations that amplify or reduce seismic susceptibility. The morpho-neotectonic studies lead to the identification, selection and classification of the lineaments that can be linked with active tectonic structures. The most important geomorphological situations that can condition seismic susceptibility are: slope angle, debris, morphology, degradational slopes, paleo-landslides and underground cavities.

Classification and conservation priority of five Deccani sheep ecotypes of Maharashtra, India

PubMed Central

Arora, Reena; Jain, Anand

2017-01-01

Characterization of Indian livestock breeds has mostly been limited to single breed/population focused on either physical description of traditionally recognized breeds/populations or to their genetic description. Usually, morphological and genetic characterization has taken place in isolation. A parallel morphological characterization of genetically identified breeds or genetic characterization of morphologically described breeds is mostly missing, and their conservation priorities have largely been based on solely considering degree of endangerment. This study uses parallel approach based on morphometric and genetic differentiation for classification of five sheep ecotypes of Maharashtra state, and sets their conservation priority using threat parameters, current utilities/merits and contribution to genetic diversity. A total of 1101 animals were described for 7 body measurements for morphometric characterization. From this sample set, 456 animals were genotyped for 25 microsatellite markers for genetic characterization. Conservation priorities were assessed combining genetic and non-genetic factors. All studied traits varied significantly among ecotypes (p<0.05). All morphometric traits exhibited substantial sexual dimorphism except ear length. Males were 42% heavier than females. Madgyal sheep were the largest amongst the five ecotypes. In the stepwise discriminant analysis, all measured traits were significant and were found to have potential discriminatory power. Tail length was the most discriminatory trait. The Mahalanobis distance of the morphological traits between Kolhapuri and Madgyal was maximum (12.07) while the least differentiation was observed between Madgyal and Solapuri (1.50). Discriminant analysis showed that 68.12% sheep were classified into their source population. The Sangamneri sheep showed least assignment error (22%) whilst Solapuri exhibited maximum error level (41%). A total of 407 alleles were observed, with an average of 16.28 alleles per locus. Sufficient levels of genetic diversity were observed in all the ecotypes with observed heterozygosity values exceeding 0.47 and gene diversity values exceeding 0.76. About 6% of the total genetic variation was explained by population differences (FST = 0.059). Pairwise FST values indicated least differentiation between Solapuri and Madgyal (0.025). In terms of genetic distances, Kolhapuri and Lonand were most closely related (Ds = 0.177). The most probable structure clustering of the five studied populations was at K = 5. The study showed a fair congruence between the dendrogram constructed on the basis of Mahalanobis distances and Nei’s as well as Reynolds genetic distances. The findings gave highest conservation priority to Lonand and least to Solapuri ecotype. PMID:28910329

A new species of Aspidoras Ihering (Siluriformes: Callichthyidae: Corydoradinae) from the Rio Xingu Basin, Pará, Brazil.

PubMed

Leão, Manuela D V; Britto, Marcelo R; Wosiacki, Wolmar B

2015-07-21

A new species of Aspidoras is described from an unnamed stream in the Rio Xingu Basin, Castelo de Sonhos municipality, Pará State, representing the northernmost record of the genus along the edge of the Brazilian Shield in the Amazon Basin. Aspidoras marianae is easily distinguished from all congeners in having minute odontode-bearing platelets scattered over the surface of the snout region, minute platelets between the parieto-supraoccipital process and the nuchal plate, and other shared features related to color pattern, morphometrics, meristics and morphological data. Comments about exclusive and shared features are presented.

Somatostatinoma: collision with neurofibroma and ultrastructural features.

PubMed

Varikatt, W; Yong, J L C; Killingsworth, M C

2006-11-01

The clinical presentation, histopathology and immunoelectron microscopic features of two cases of duodenal somatostatinoma are described, one of which is a hitherto unreported example of a collision tumour with a neurofibroma. Ultrastructural morphometric immunoelectron microscopy studies revealed the presence of four types of cells in both tumours, but there was no difference in the proportions of these cells between the collision tumour and the non-collision tumour. Neurosecretory granules ranging in size from 255-815 nm were generally larger than those previously reported for somatostatinomas and somatostatin was identified in granules of all sizes across this range. Neither tumour was associated with the somatostatinoma syndrome comprising associated diabetes mellitis, steatorrhoea and cholelithiasis.

Ensemble support vector machine classification of dementia using structural MRI and mini-mental state examination.

PubMed

Sørensen, Lauge; Nielsen, Mads

2018-05-15

The International Challenge for Automated Prediction of MCI from MRI data offered independent, standardized comparison of machine learning algorithms for multi-class classification of normal control (NC), mild cognitive impairment (MCI), converting MCI (cMCI), and Alzheimer's disease (AD) using brain imaging and general cognition. We proposed to use an ensemble of support vector machines (SVMs) that combined bagging without replacement and feature selection. SVM is the most commonly used algorithm in multivariate classification of dementia, and it was therefore valuable to evaluate the potential benefit of ensembling this type of classifier. The ensemble SVM, using either a linear or a radial basis function (RBF) kernel, achieved multi-class classification accuracies of 55.6% and 55.0% in the challenge test set (60 NC, 60 MCI, 60 cMCI, 60 AD), resulting in a third place in the challenge. Similar feature subset sizes were obtained for both kernels, and the most frequently selected MRI features were the volumes of the two hippocampal subregions left presubiculum and right subiculum. Post-challenge analysis revealed that enforcing a minimum number of selected features and increasing the number of ensemble classifiers improved classification accuracy up to 59.1%. The ensemble SVM outperformed single SVM classifications consistently in the challenge test set. Ensemble methods using bagging and feature selection can improve the performance of the commonly applied SVM classifier in dementia classification. This resulted in competitive classification accuracies in the International Challenge for Automated Prediction of MCI from MRI data. Copyright © 2018 Elsevier B.V. All rights reserved.

Automated retinal image quality assessment on the UK Biobank dataset for epidemiological studies.

PubMed

Welikala, R A; Fraz, M M; Foster, P J; Whincup, P H; Rudnicka, A R; Owen, C G; Strachan, D P; Barman, S A

2016-04-01

Morphological changes in the retinal vascular network are associated with future risk of many systemic and vascular diseases. However, uncertainty over the presence and nature of some of these associations exists. Analysis of data from large population based studies will help to resolve these uncertainties. The QUARTZ (QUantitative Analysis of Retinal vessel Topology and siZe) retinal image analysis system allows automated processing of large numbers of retinal images. However, an image quality assessment module is needed to achieve full automation. In this paper, we propose such an algorithm, which uses the segmented vessel map to determine the suitability of retinal images for use in the creation of vessel morphometric data suitable for epidemiological studies. This includes an effective 3-dimensional feature set and support vector machine classification. A random subset of 800 retinal images from UK Biobank (a large prospective study of 500,000 middle aged adults; where 68,151 underwent retinal imaging) was used to examine the performance of the image quality algorithm. The algorithm achieved a sensitivity of 95.33% and a specificity of 91.13% for the detection of inadequate images. The strong performance of this image quality algorithm will make rapid automated analysis of vascular morphometry feasible on the entire UK Biobank dataset (and other large retinal datasets), with minimal operator involvement, and at low cost. Copyright © 2016 Elsevier Ltd. All rights reserved.

Many local pattern texture features: which is better for image-based multilabel human protein subcellular localization classification?

PubMed

Yang, Fan; Xu, Ying-Ying; Shen, Hong-Bin

2014-01-01

Human protein subcellular location prediction can provide critical knowledge for understanding a protein's function. Since significant progress has been made on digital microscopy, automated image-based protein subcellular location classification is urgently needed. In this paper, we aim to investigate more representative image features that can be effectively used for dealing with the multilabel subcellular image samples. We prepared a large multilabel immunohistochemistry (IHC) image benchmark from the Human Protein Atlas database and tested the performance of different local texture features, including completed local binary pattern, local tetra pattern, and the standard local binary pattern feature. According to our experimental results from binary relevance multilabel machine learning models, the completed local binary pattern, and local tetra pattern are more discriminative for describing IHC images when compared to the traditional local binary pattern descriptor. The combination of these two novel local pattern features and the conventional global texture features is also studied. The enhanced performance of final binary relevance classification model trained on the combined feature space demonstrates that different features are complementary to each other and thus capable of improving the accuracy of classification.

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.

Deep Learning Methods for Underwater Target Feature Extraction and Recognition

PubMed Central

Peng, Yuan; Qiu, Mengran; Shi, Jianfei; Liu, Liangliang

2018-01-01

The classification and recognition technology of underwater acoustic signal were always an important research content in the field of underwater acoustic signal processing. Currently, wavelet transform, Hilbert-Huang transform, and Mel frequency cepstral coefficients are used as a method of underwater acoustic signal feature extraction. In this paper, a method for feature extraction and identification of underwater noise data based on CNN and ELM is proposed. An automatic feature extraction method of underwater acoustic signals is proposed using depth convolution network. An underwater target recognition classifier is based on extreme learning machine. Although convolution neural networks can execute both feature extraction and classification, their function mainly relies on a full connection layer, which is trained by gradient descent-based; the generalization ability is limited and suboptimal, so an extreme learning machine (ELM) was used in classification stage. Firstly, CNN learns deep and robust features, followed by the removing of the fully connected layers. Then ELM fed with the CNN features is used as the classifier to conduct an excellent classification. Experiments on the actual data set of civil ships obtained 93.04% recognition rate; compared to the traditional Mel frequency cepstral coefficients and Hilbert-Huang feature, recognition rate greatly improved. PMID:29780407

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

Jing, Yaqi; Meng, Qinghao, E-mail: qh-meng@tju.edu.cn; Qi, Peifeng

An electronic nose (e-nose) was designed to classify Chinese liquors of the same aroma style. A new method of feature reduction which combined feature selection with feature extraction was proposed. Feature selection method used 8 feature-selection algorithms based on information theory and reduced the dimension of the feature space to 41. Kernel entropy component analysis was introduced into the e-nose system as a feature extraction method and the dimension of feature space was reduced to 12. Classification of Chinese liquors was performed by using back propagation artificial neural network (BP-ANN), linear discrimination analysis (LDA), and a multi-linear classifier. The classificationmore » rate of the multi-linear classifier was 97.22%, which was higher than LDA and BP-ANN. Finally the classification of Chinese liquors according to their raw materials and geographical origins was performed using the proposed multi-linear classifier and classification rate was 98.75% and 100%, respectively.« less

Classification of Urban Feature from Unmanned Aerial Vehicle Images Using Gasvm Integration and Multi-Scale Segmentation

NASA Astrophysics Data System (ADS)

Modiri, M.; Salehabadi, A.; Mohebbi, M.; Hashemi, A. M.; Masumi, M.

2015-12-01

The use of UAV in the application of photogrammetry to obtain cover images and achieve the main objectives of the photogrammetric mapping has been a boom in the region. The images taken from REGGIOLO region in the province of, Italy Reggio -Emilia by UAV with non-metric camera Canon Ixus and with an average height of 139.42 meters were used to classify urban feature. Using the software provided SURE and cover images of the study area, to produce dense point cloud, DSM and Artvqvtv spatial resolution of 10 cm was prepared. DTM area using Adaptive TIN filtering algorithm was developed. NDSM area was prepared with using the difference between DSM and DTM and a separate features in the image stack. In order to extract features, using simultaneous occurrence matrix features mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment, and correlation for each of the RGB band image was used Orthophoto area. Classes used to classify urban problems, including buildings, trees and tall vegetation, grass and vegetation short, paved road and is impervious surfaces. Class consists of impervious surfaces such as pavement conditions, the cement, the car, the roof is stored. In order to pixel-based classification and selection of optimal features of classification was GASVM pixel basis. In order to achieve the classification results with higher accuracy and spectral composition informations, texture, and shape conceptual image featureOrthophoto area was fencing. The segmentation of multi-scale segmentation method was used.it belonged class. Search results using the proposed classification of urban feature, suggests the suitability of this method of classification complications UAV is a city using images. The overall accuracy and kappa coefficient method proposed in this study, respectively, 47/93% and 84/91% was.

Wavelet-based energy features for glaucomatous image classification.

PubMed

Dua, Sumeet; Acharya, U Rajendra; Chowriappa, Pradeep; Sree, S Vinitha

2012-01-01

Texture features within images are actively pursued for accurate and efficient glaucoma classification. Energy distribution over wavelet subbands is applied to find these important texture features. In this paper, we investigate the discriminatory potential of wavelet features obtained from the daubechies (db3), symlets (sym3), and biorthogonal (bio3.3, bio3.5, and bio3.7) wavelet filters. We propose a novel technique to extract energy signatures obtained using 2-D discrete wavelet transform, and subject these signatures to different feature ranking and feature selection strategies. We have gauged the effectiveness of the resultant ranked and selected subsets of features using a support vector machine, sequential minimal optimization, random forest, and naïve Bayes classification strategies. We observed an accuracy of around 93% using tenfold cross validations to demonstrate the effectiveness of these methods.

[Severity classification of chronic obstructive pulmonary disease based on deep learning].

PubMed

Ying, Jun; Yang, Ceyuan; Li, Quanzheng; Xue, Wanguo; Li, Tanshi; Cao, Wenzhe

2017-12-01

In this paper, a deep learning method has been raised to build an automatic classification algorithm of severity of chronic obstructive pulmonary disease. Large sample clinical data as input feature were analyzed for their weights in classification. Through feature selection, model training, parameter optimization and model testing, a classification prediction model based on deep belief network was built to predict severity classification criteria raised by the Global Initiative for Chronic Obstructive Lung Disease (GOLD). We get accuracy over 90% in prediction for two different standardized versions of severity criteria raised in 2007 and 2011 respectively. Moreover, we also got the contribution ranking of different input features through analyzing the model coefficient matrix and confirmed that there was a certain degree of agreement between the more contributive input features and the clinical diagnostic knowledge. The validity of the deep belief network model was proved by this result. This study provides an effective solution for the application of deep learning method in automatic diagnostic decision making.

Tissue classification using depth-dependent ultrasound time series analysis: in-vitro animal study

NASA Astrophysics Data System (ADS)

Imani, Farhad; Daoud, Mohammad; Moradi, Mehdi; Abolmaesumi, Purang; Mousavi, Parvin

2011-03-01

Time series analysis of ultrasound radio-frequency (RF) signals has been shown to be an effective tissue classification method. Previous studies of this method for tissue differentiation at high and clinical-frequencies have been reported. In this paper, analysis of RF time series is extended to improve tissue classification at the clinical frequencies by including novel features extracted from the time series spectrum. The primary feature examined is the Mean Central Frequency (MCF) computed for regions of interest (ROIs) in the tissue extending along the axial axis of the transducer. In addition, the intercept and slope of a line fitted to the MCF-values of the RF time series as a function of depth have been included. To evaluate the accuracy of the new features, an in vitro animal study is performed using three tissue types: bovine muscle, bovine liver, and chicken breast, where perfect two-way classification is achieved. The results show statistically significant improvements over the classification accuracies with previously reported features.

Atmosphere-based image classification through luminance and hue

NASA Astrophysics Data System (ADS)

Xu, Feng; Zhang, Yujin

2005-07-01

In this paper a novel image classification system is proposed. Atmosphere serves an important role in generating the scene"s topic or in conveying the message behind the scene"s story, which belongs to abstract attribute level in semantic levels. At first, five atmosphere semantic categories are defined according to rules of photo and film grammar, followed by global luminance and hue features. Then the hierarchical SVM classifiers are applied. In each classification stage, corresponding features are extracted and the trained linear SVM is implemented, resulting in two classes. After three stages of classification, five atmosphere categories are obtained. At last, the text annotation of the atmosphere semantics and the corresponding features by Extensible Markup Language (XML) in MPEG-7 is defined, which can be integrated into more multimedia applications (such as searching, indexing and accessing of multimedia content). The experiment is performed on Corel images and film frames. The classification results prove the effectiveness of the definition of atmosphere semantic classes and the corresponding features.

Local curvature analysis for classifying breast tumors: Preliminary analysis in dedicated breast CT

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

Lee, Juhun, E-mail: leej15@upmc.edu; Nishikawa, Robert M.; Reiser, Ingrid

2015-09-15

Purpose: The purpose of this study is to measure the effectiveness of local curvature measures as novel image features for classifying breast tumors. Methods: A total of 119 breast lesions from 104 noncontrast dedicated breast computed tomography images of women were used in this study. Volumetric segmentation was done using a seed-based segmentation algorithm and then a triangulated surface was extracted from the resulting segmentation. Total, mean, and Gaussian curvatures were then computed. Normalized curvatures were used as classification features. In addition, traditional image features were also extracted and a forward feature selection scheme was used to select the optimalmore » feature set. Logistic regression was used as a classifier and leave-one-out cross-validation was utilized to evaluate the classification performances of the features. The area under the receiver operating characteristic curve (AUC, area under curve) was used as a figure of merit. Results: Among curvature measures, the normalized total curvature (C{sub T}) showed the best classification performance (AUC of 0.74), while the others showed no classification power individually. Five traditional image features (two shape, two margin, and one texture descriptors) were selected via the feature selection scheme and its resulting classifier achieved an AUC of 0.83. Among those five features, the radial gradient index (RGI), which is a margin descriptor, showed the best classification performance (AUC of 0.73). A classifier combining RGI and C{sub T} yielded an AUC of 0.81, which showed similar performance (i.e., no statistically significant difference) to the classifier with the above five traditional image features. Additional comparisons in AUC values between classifiers using different combinations of traditional image features and C{sub T} were conducted. The results showed that C{sub T} was able to replace the other four image features for the classification task. Conclusions: The normalized curvature measure contains useful information in classifying breast tumors. Using this, one can reduce the number of features in a classifier, which may result in more robust classifiers for different datasets.« less

Automatic ICD-10 multi-class classification of cause of death from plaintext autopsy reports through expert-driven feature selection.

PubMed

Mujtaba, Ghulam; Shuib, Liyana; Raj, Ram Gopal; Rajandram, Retnagowri; Shaikh, Khairunisa; Al-Garadi, Mohammed Ali

2017-01-01

Widespread implementation of electronic databases has improved the accessibility of plaintext clinical information for supplementary use. Numerous machine learning techniques, such as supervised machine learning approaches or ontology-based approaches, have been employed to obtain useful information from plaintext clinical data. This study proposes an automatic multi-class classification system to predict accident-related causes of death from plaintext autopsy reports through expert-driven feature selection with supervised automatic text classification decision models. Accident-related autopsy reports were obtained from one of the largest hospital in Kuala Lumpur. These reports belong to nine different accident-related causes of death. Master feature vector was prepared by extracting features from the collected autopsy reports by using unigram with lexical categorization. This master feature vector was used to detect cause of death [according to internal classification of disease version 10 (ICD-10) classification system] through five automated feature selection schemes, proposed expert-driven approach, five subset sizes of features, and five machine learning classifiers. Model performance was evaluated using precisionM, recallM, F-measureM, accuracy, and area under ROC curve. Four baselines were used to compare the results with the proposed system. Random forest and J48 decision models parameterized using expert-driven feature selection yielded the highest evaluation measure approaching (85% to 90%) for most metrics by using a feature subset size of 30. The proposed system also showed approximately 14% to 16% improvement in the overall accuracy compared with the existing techniques and four baselines. The proposed system is feasible and practical to use for automatic classification of ICD-10-related cause of death from autopsy reports. The proposed system assists pathologists to accurately and rapidly determine underlying cause of death based on autopsy findings. Furthermore, the proposed expert-driven feature selection approach and the findings are generally applicable to other kinds of plaintext clinical reports.

Automatic ICD-10 multi-class classification of cause of death from plaintext autopsy reports through expert-driven feature selection

PubMed Central

Mujtaba, Ghulam; Shuib, Liyana; Raj, Ram Gopal; Rajandram, Retnagowri; Shaikh, Khairunisa; Al-Garadi, Mohammed Ali

2017-01-01

Objectives Widespread implementation of electronic databases has improved the accessibility of plaintext clinical information for supplementary use. Numerous machine learning techniques, such as supervised machine learning approaches or ontology-based approaches, have been employed to obtain useful information from plaintext clinical data. This study proposes an automatic multi-class classification system to predict accident-related causes of death from plaintext autopsy reports through expert-driven feature selection with supervised automatic text classification decision models. Methods Accident-related autopsy reports were obtained from one of the largest hospital in Kuala Lumpur. These reports belong to nine different accident-related causes of death. Master feature vector was prepared by extracting features from the collected autopsy reports by using unigram with lexical categorization. This master feature vector was used to detect cause of death [according to internal classification of disease version 10 (ICD-10) classification system] through five automated feature selection schemes, proposed expert-driven approach, five subset sizes of features, and five machine learning classifiers. Model performance was evaluated using precisionM, recallM, F-measureM, accuracy, and area under ROC curve. Four baselines were used to compare the results with the proposed system. Results Random forest and J48 decision models parameterized using expert-driven feature selection yielded the highest evaluation measure approaching (85% to 90%) for most metrics by using a feature subset size of 30. The proposed system also showed approximately 14% to 16% improvement in the overall accuracy compared with the existing techniques and four baselines. Conclusion The proposed system is feasible and practical to use for automatic classification of ICD-10-related cause of death from autopsy reports. The proposed system assists pathologists to accurately and rapidly determine underlying cause of death based on autopsy findings. Furthermore, the proposed expert-driven feature selection approach and the findings are generally applicable to other kinds of plaintext clinical reports. PMID:28166263

Nonlinear features for product inspection

NASA Astrophysics Data System (ADS)

Talukder, Ashit; Casasent, David P.

1999-03-01

Classification of real-time X-ray images of randomly oriented touching pistachio nuts is discussed. The ultimate objective is the development of a system for automated non-invasive detection of defective product items on a conveyor belt. We discuss the extraction of new features that allow better discrimination between damaged and clean items (pistachio nuts). This feature extraction and classification stage is the new aspect of this paper; our new maximum representation and discriminating feature (MRDF) extraction method computes nonlinear features that are used as inputs to a new modified k nearest neighbor classifier. In this work, the MRDF is applied to standard features (rather than iconic data). The MRDF is robust to various probability distributions of the input class and is shown to provide good classification and new ROC (receiver operating characteristic) data.

Combining various types of classifiers and features extracted from magnetic resonance imaging data in schizophrenia recognition.

PubMed

Janousova, Eva; Schwarz, Daniel; Kasparek, Tomas

2015-06-30

We investigated a combination of three classification algorithms, namely the modified maximum uncertainty linear discriminant analysis (mMLDA), the centroid method, and the average linkage, with three types of features extracted from three-dimensional T1-weighted magnetic resonance (MR) brain images, specifically MR intensities, grey matter densities, and local deformations for distinguishing 49 first episode schizophrenia male patients from 49 healthy male subjects. The feature sets were reduced using intersubject principal component analysis before classification. By combining the classifiers, we were able to obtain slightly improved results when compared with single classifiers. The best classification performance (81.6% accuracy, 75.5% sensitivity, and 87.8% specificity) was significantly better than classification by chance. We also showed that classifiers based on features calculated using more computation-intensive image preprocessing perform better; mMLDA with classification boundary calculated as weighted mean discriminative scores of the groups had improved sensitivity but similar accuracy compared to the original MLDA; reducing a number of eigenvectors during data reduction did not always lead to higher classification accuracy, since noise as well as the signal important for classification were removed. Our findings provide important information for schizophrenia research and may improve accuracy of computer-aided diagnostics of neuropsychiatric diseases. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Pattern Classification Using an Olfactory Model with PCA Feature Selection in Electronic Noses: Study and Application

PubMed Central

Fu, Jun; Huang, Canqin; Xing, Jianguo; Zheng, Junbao

2012-01-01

Biologically-inspired models and algorithms are considered as promising sensor array signal processing methods for electronic noses. Feature selection is one of the most important issues for developing robust pattern recognition models in machine learning. This paper describes an investigation into the classification performance of a bionic olfactory model with the increase of the dimensions of input feature vector (outer factor) as well as its parallel channels (inner factor). The principal component analysis technique was applied for feature selection and dimension reduction. Two data sets of three classes of wine derived from different cultivars and five classes of green tea derived from five different provinces of China were used for experiments. In the former case the results showed that the average correct classification rate increased as more principal components were put in to feature vector. In the latter case the results showed that sufficient parallel channels should be reserved in the model to avoid pattern space crowding. We concluded that 6∼8 channels of the model with principal component feature vector values of at least 90% cumulative variance is adequate for a classification task of 3∼5 pattern classes considering the trade-off between time consumption and classification rate. PMID:22736979

Classifying Human Voices by Using Hybrid SFX Time-Series Preprocessing and Ensemble Feature Selection

PubMed Central

Wong, Raymond

2013-01-01

Voice biometrics is one kind of physiological characteristics whose voice is different for each individual person. Due to this uniqueness, voice classification has found useful applications in classifying speakers' gender, mother tongue or ethnicity (accent), emotion states, identity verification, verbal command control, and so forth. In this paper, we adopt a new preprocessing method named Statistical Feature Extraction (SFX) for extracting important features in training a classification model, based on piecewise transformation treating an audio waveform as a time-series. Using SFX we can faithfully remodel statistical characteristics of the time-series; together with spectral analysis, a substantial amount of features are extracted in combination. An ensemble is utilized in selecting only the influential features to be used in classification model induction. We focus on the comparison of effects of various popular data mining algorithms on multiple datasets. Our experiment consists of classification tests over four typical categories of human voice data, namely, Female and Male, Emotional Speech, Speaker Identification, and Language Recognition. The experiments yield encouraging results supporting the fact that heuristically choosing significant features from both time and frequency domains indeed produces better performance in voice classification than traditional signal processing techniques alone, like wavelets and LPC-to-CC. PMID:24288684

Classification of Microarray Data Using Kernel Fuzzy Inference System

PubMed Central

Kumar Rath, Santanu

2014-01-01

The DNA microarray classification technique has gained more popularity in both research and practice. In real data analysis, such as microarray data, the dataset contains a huge number of insignificant and irrelevant features that tend to lose useful information. Classes with high relevance and feature sets with high significance are generally referred for the selected features, which determine the samples classification into their respective classes. In this paper, kernel fuzzy inference system (K-FIS) algorithm is applied to classify the microarray data (leukemia) using t-test as a feature selection method. Kernel functions are used to map original data points into a higher-dimensional (possibly infinite-dimensional) feature space defined by a (usually nonlinear) function ϕ through a mathematical process called the kernel trick. This paper also presents a comparative study for classification using K-FIS along with support vector machine (SVM) for different set of features (genes). Performance parameters available in the literature such as precision, recall, specificity, F-measure, ROC curve, and accuracy are considered to analyze the efficiency of the classification model. From the proposed approach, it is apparent that K-FIS model obtains similar results when compared with SVM model. This is an indication that the proposed approach relies on kernel function. PMID:27433543

Assessing the performance of multiple spectral-spatial features of a hyperspectral image for classification of urban land cover classes using support vector machines and artificial neural network

NASA Astrophysics Data System (ADS)

Pullanagari, Reddy; Kereszturi, Gábor; Yule, Ian J.; Ghamisi, Pedram

2017-04-01

Accurate and spatially detailed mapping of complex urban environments is essential for land managers. Classifying high spectral and spatial resolution hyperspectral images is a challenging task because of its data abundance and computational complexity. Approaches with a combination of spectral and spatial information in a single classification framework have attracted special attention because of their potential to improve the classification accuracy. We extracted multiple features from spectral and spatial domains of hyperspectral images and evaluated them with two supervised classification algorithms; support vector machines (SVM) and an artificial neural network. The spatial features considered are produced by a gray level co-occurrence matrix and extended multiattribute profiles. All of these features were stacked, and the most informative features were selected using a genetic algorithm-based SVM. After selecting the most informative features, the classification model was integrated with a segmentation map derived using a hidden Markov random field. We tested the proposed method on a real application of a hyperspectral image acquired from AisaFENIX and on widely used hyperspectral images. From the results, it can be concluded that the proposed framework significantly improves the results with different spectral and spatial resolutions over different instrumentation.

Multiband tangent space mapping and feature selection for classification of EEG during motor imagery.

PubMed

Islam, Md Rabiul; Tanaka, Toshihisa; Molla, Md Khademul Islam

2018-05-08

When designing multiclass motor imagery-based brain-computer interface (MI-BCI), a so-called tangent space mapping (TSM) method utilizing the geometric structure of covariance matrices is an effective technique. This paper aims to introduce a method using TSM for finding accurate operational frequency bands related brain activities associated with MI tasks. A multichannel electroencephalogram (EEG) signal is decomposed into multiple subbands, and tangent features are then estimated on each subband. A mutual information analysis-based effective algorithm is implemented to select subbands containing features capable of improving motor imagery classification accuracy. Thus obtained features of selected subbands are combined to get feature space. A principal component analysis-based approach is employed to reduce the features dimension and then the classification is accomplished by a support vector machine (SVM). Offline analysis demonstrates the proposed multiband tangent space mapping with subband selection (MTSMS) approach outperforms state-of-the-art methods. It acheives the highest average classification accuracy for all datasets (BCI competition dataset 2a, IIIa, IIIb, and dataset JK-HH1). The increased classification accuracy of MI tasks with the proposed MTSMS approach can yield effective implementation of BCI. The mutual information-based subband selection method is implemented to tune operation frequency bands to represent actual motor imagery tasks.

A Novel Feature Selection Technique for Text Classification Using Naïve Bayes.

PubMed

Dey Sarkar, Subhajit; Goswami, Saptarsi; Agarwal, Aman; Aktar, Javed

2014-01-01

With the proliferation of unstructured data, text classification or text categorization has found many applications in topic classification, sentiment analysis, authorship identification, spam detection, and so on. There are many classification algorithms available. Naïve Bayes remains one of the oldest and most popular classifiers. On one hand, implementation of naïve Bayes is simple and, on the other hand, this also requires fewer amounts of training data. From the literature review, it is found that naïve Bayes performs poorly compared to other classifiers in text classification. As a result, this makes the naïve Bayes classifier unusable in spite of the simplicity and intuitiveness of the model. In this paper, we propose a two-step feature selection method based on firstly a univariate feature selection and then feature clustering, where we use the univariate feature selection method to reduce the search space and then apply clustering to select relatively independent feature sets. We demonstrate the effectiveness of our method by a thorough evaluation and comparison over 13 datasets. The performance improvement thus achieved makes naïve Bayes comparable or superior to other classifiers. The proposed algorithm is shown to outperform other traditional methods like greedy search based wrapper or CFS.

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

Honorio, J.; Goldstein, R.; Honorio, J.

We propose a simple, well grounded classification technique which is suited for group classification on brain fMRI data sets that have high dimensionality, small number of subjects, high noise level, high subject variability, imperfect registration and capture subtle cognitive effects. We propose threshold-split region as a new feature selection method and majority voteas the classification technique. Our method does not require a predefined set of regions of interest. We use average acros ssessions, only one feature perexperimental condition, feature independence assumption, and simple classifiers. The seeming counter-intuitive approach of using a simple design is supported by signal processing and statisticalmore » theory. Experimental results in two block design data sets that capture brain function under distinct monetary rewards for cocaine addicted and control subjects, show that our method exhibits increased generalization accuracy compared to commonly used feature selection and classification techniques.« less

Hierarchical learning architecture with automatic feature selection for multiclass protein fold classification.

PubMed

Huang, Chuen-Der; Lin, Chin-Teng; Pal, Nikhil Ranjan

2003-12-01

The structure classification of proteins plays a very important role in bioinformatics, since the relationships and characteristics among those known proteins can be exploited to predict the structure of new proteins. The success of a classification system depends heavily on two things: the tools being used and the features considered. For the bioinformatics applications, the role of appropriate features has not been paid adequate importance. In this investigation we use three novel ideas for multiclass protein fold classification. First, we use the gating neural network, where each input node is associated with a gate. This network can select important features in an online manner when the learning goes on. At the beginning of the training, all gates are almost closed, i.e., no feature is allowed to enter the network. Through the training, gates corresponding to good features are completely opened while gates corresponding to bad features are closed more tightly, and some gates may be partially open. The second novel idea is to use a hierarchical learning architecture (HLA). The classifier in the first level of HLA classifies the protein features into four major classes: all alpha, all beta, alpha + beta, and alpha/beta. And in the next level we have another set of classifiers, which further classifies the protein features into 27 folds. The third novel idea is to induce the indirect coding features from the amino-acid composition sequence of proteins based on the N-gram concept. This provides us with more representative and discriminative new local features of protein sequences for multiclass protein fold classification. The proposed HLA with new indirect coding features increases the protein fold classification accuracy by about 12%. Moreover, the gating neural network is found to reduce the number of features drastically. Using only half of the original features selected by the gating neural network can reach comparable test accuracy as that using all the original features. The gating mechanism also helps us to get a better insight into the folding process of proteins. For example, tracking the evolution of different gates we can find which characteristics (features) of the data are more important for the folding process. And, of course, it also reduces the computation time.

Time-frequency feature representation using multi-resolution texture analysis and acoustic activity detector for real-life speech emotion recognition.

PubMed

Wang, Kun-Ching

2015-01-14

The classification of emotional speech is mostly considered in speech-related research on human-computer interaction (HCI). In this paper, the purpose is to present a novel feature extraction based on multi-resolutions texture image information (MRTII). The MRTII feature set is derived from multi-resolution texture analysis for characterization and classification of different emotions in a speech signal. The motivation is that we have to consider emotions have different intensity values in different frequency bands. In terms of human visual perceptual, the texture property on multi-resolution of emotional speech spectrogram should be a good feature set for emotion classification in speech. Furthermore, the multi-resolution analysis on texture can give a clearer discrimination between each emotion than uniform-resolution analysis on texture. In order to provide high accuracy of emotional discrimination especially in real-life, an acoustic activity detection (AAD) algorithm must be applied into the MRTII-based feature extraction. Considering the presence of many blended emotions in real life, in this paper make use of two corpora of naturally-occurring dialogs recorded in real-life call centers. Compared with the traditional Mel-scale Frequency Cepstral Coefficients (MFCC) and the state-of-the-art features, the MRTII features also can improve the correct classification rates of proposed systems among different language databases. Experimental results show that the proposed MRTII-based feature information inspired by human visual perception of the spectrogram image can provide significant classification for real-life emotional recognition in speech.

Analysis of the human female foot in two different measurement systems: from geometric morphometrics to functional morphology.

PubMed

Bookstein, Fred L; Domjanić, Jacqueline

2014-09-01

The relationship of geometric morphometrics (GMM) to functional analysis of the same morphological resources is currently a topic of active interest among functional morphologists. Although GMM is typically advertised as free of prior assumptions about shape features or morphological theories, it is common for GMM findings to be concordant with findings from studies based on a-priori lists of shape features whenever prior insights or theories have been properly accounted for in the study design. The present paper demonstrates this happy possibility by revisiting a previously published GMM analysis of footprint outlines for which there is also functionally relevant information in the form of a-pri-ori foot measurements. We show how to convert the conventional measurements into the language of shape, thereby affording two parallel statistical analyses. One is the classic multivariate analysis of "shape features", the other the equally classic GMM of semilandmark coordinates. In this example, the two data sets, analyzed by protocols that are remarkably different in both their geometry and their algebra, nevertheless result in one common biometrical summary: wearing high heels is bad for women inasmuch as it leads to the need for orthotic devices to treat the consequently flattened arch. This concordance bears implications for other branches of applied anthropology. To carry out a good biomedical analysis of applied anthropometric data it may not matter whether one uses GMM or instead an adequate assortment of conventional measurements. What matters is whether the conventional measurements have been selected in order to match the natural spectrum of functional variation.

Classification of motor imagery tasks for BCI with multiresolution analysis and multiobjective feature selection.

PubMed

Ortega, Julio; Asensio-Cubero, Javier; Gan, John Q; Ortiz, Andrés

2016-07-15

Brain-computer interfacing (BCI) applications based on the classification of electroencephalographic (EEG) signals require solving high-dimensional pattern classification problems with such a relatively small number of training patterns that curse of dimensionality problems usually arise. Multiresolution analysis (MRA) has useful properties for signal analysis in both temporal and spectral analysis, and has been broadly used in the BCI field. However, MRA usually increases the dimensionality of the input data. Therefore, some approaches to feature selection or feature dimensionality reduction should be considered for improving the performance of the MRA based BCI. This paper investigates feature selection in the MRA-based frameworks for BCI. Several wrapper approaches to evolutionary multiobjective feature selection are proposed with different structures of classifiers. They are evaluated by comparing with baseline methods using sparse representation of features or without feature selection. The statistical analysis, by applying the Kolmogorov-Smirnoff and Kruskal-Wallis tests to the means of the Kappa values evaluated by using the test patterns in each approach, has demonstrated some advantages of the proposed approaches. In comparison with the baseline MRA approach used in previous studies, the proposed evolutionary multiobjective feature selection approaches provide similar or even better classification performances, with significant reduction in the number of features that need to be computed.

Classification of fresh and frozen-thawed pork muscles using visible and near infrared hyperspectral imaging and textural analysis.

PubMed

Pu, Hongbin; Sun, Da-Wen; Ma, Ji; Cheng, Jun-Hu

2015-01-01

The potential of visible and near infrared hyperspectral imaging was investigated as a rapid and nondestructive technique for classifying fresh and frozen-thawed meats by integrating critical spectral and image features extracted from hyperspectral images in the region of 400-1000 nm. Six feature wavelengths (400, 446, 477, 516, 592 and 686 nm) were identified using uninformative variable elimination and successive projections algorithm. Image textural features of the principal component images from hyperspectral images were obtained using histogram statistics (HS), gray level co-occurrence matrix (GLCM) and gray level-gradient co-occurrence matrix (GLGCM). By these spectral and textural features, probabilistic neural network (PNN) models for classification of fresh and frozen-thawed pork meats were established. Compared with the models using the optimum wavelengths only, optimum wavelengths with HS image features, and optimum wavelengths with GLCM image features, the model integrating optimum wavelengths with GLGCM gave the highest classification rate of 93.14% and 90.91% for calibration and validation sets, respectively. Results indicated that the classification accuracy can be improved by combining spectral features with textural features and the fusion of critical spectral and textural features had better potential than single spectral extraction in classifying fresh and frozen-thawed pork meat. Copyright © 2014 Elsevier Ltd. All rights reserved.

High throughput, detailed, cell-specific neuroanatomy of dendritic spines using microinjection and confocal microscopy

PubMed Central

Dumitriu, Dani; Rodriguez, Alfredo; Morrison, John H.

2012-01-01

Morphological features such as size, shape and density of dendritic spines have been shown to reflect important synaptic functional attributes and potential for plasticity. Here we describe in detail a protocol for obtaining detailed morphometric analysis of spines using microinjection of fluorescent dyes, high resolution confocal microscopy, deconvolution and image analysis using NeuronStudio. Recent technical advancements include better preservation of tissue resulting in prolonged ability to microinject, and algorithmic improvements that compensate for the residual Z-smear inherent in all optical imaging. Confocal imaging parameters were probed systematically for the identification of both optimal resolution as well as highest efficiency. When combined, our methods yield size and density measurements comparable to serial section transmission electron microscopy in a fraction of the time. An experiment containing 3 experimental groups with 8 subjects in each can take as little as one month if optimized for speed, or approximately 4 to 5 months if the highest resolution and morphometric detail is sought. PMID:21886104

Karstic terrain in the equatorial layered deposits within a crater in northern Sinus Meridiani, Mars.

NASA Astrophysics Data System (ADS)

Baioni, Davide

2017-04-01

This work investigates the equatorial layered deposits (ELDs) located within a crater located in northern Sinus Meridiani, Mars (4.430 N, 3.320 W), which display traits that are consistent with formation by karst-driven processes. Here, shallow depressions showing a variety of plan forms ranging from rounded, circular, elongated, polygonal and drop-like to elliptical can be observed. The morphologic and morphometric analyses performed, highlight that these depressions display strong morphometric (sizes) and morphologic (shapes, bottoms, walls) similarities with the karst depressions that are common on limestone and evaporite terrains on the Earth and other regions on Mars. On the basis of the characteristics of the investigated landforms and the similarities of features on Earth and Mars, and after discarding other possible origins such as, aeolian, periglacial, volcanic or impact related processes, it has been inferred that the depressions are karstic dolines formed polygenetically by corrosion and solution-related intra-crater processes.

A machine learning approach to identify functional biomarkers in human prefrontal cortex for individuals with traumatic brain injury using functional near-infrared spectroscopy.

PubMed

Karamzadeh, Nader; Amyot, Franck; Kenney, Kimbra; Anderson, Afrouz; Chowdhry, Fatima; Dashtestani, Hadis; Wassermann, Eric M; Chernomordik, Victor; Boccara, Claude; Wegman, Edward; Diaz-Arrastia, Ramon; Gandjbakhche, Amir H

2016-11-01

We have explored the potential prefrontal hemodynamic biomarkers to characterize subjects with Traumatic Brain Injury (TBI) by employing the multivariate machine learning approach and introducing a novel task-related hemodynamic response detection followed by a heuristic search for optimum set of hemodynamic features. To achieve this goal, the hemodynamic response from a group of 31 healthy controls and 30 chronic TBI subjects were recorded as they performed a complexity task. To determine the optimum hemodynamic features, we considered 11 features and their combinations in characterizing TBI subjects. We investigated the significance of the features by utilizing a machine learning classification algorithm to score all the possible combinations of features according to their predictive power. The identified optimum feature elements resulted in classification accuracy, sensitivity, and specificity of 85%, 85%, and 84%, respectively. Classification improvement was achieved for TBI subject classification through feature combination. It signified the major advantage of the multivariate analysis over the commonly used univariate analysis suggesting that the features that are individually irrelevant in characterizing the data may become relevant when used in combination. We also conducted a spatio-temporal classification to identify regions within the prefrontal cortex (PFC) that contribute in distinguishing between TBI and healthy subjects. As expected, Brodmann areas (BA) 10 within the PFC were isolated as the region that healthy subjects (unlike subjects with TBI), showed major hemodynamic activity in response to the High Complexity task. Overall, our results indicate that identified temporal and spatio-temporal features from PFC's hemodynamic activity are promising biomarkers in classifying subjects with TBI.

Comparison of EEG-Features and Classification Methods for Motor Imagery in Patients with Disorders of Consciousness

PubMed Central

Höller, Yvonne; Bergmann, Jürgen; Thomschewski, Aljoscha; Kronbichler, Martin; Höller, Peter; Crone, Julia S.; Schmid, Elisabeth V.; Butz, Kevin; Nardone, Raffaele; Trinka, Eugen

2013-01-01

Current research aims at identifying voluntary brain activation in patients who are behaviorally diagnosed as being unconscious, but are able to perform commands by modulating their brain activity patterns. This involves machine learning techniques and feature extraction methods such as applied in brain computer interfaces. In this study, we try to answer the question if features/classification methods which show advantages in healthy participants are also accurate when applied to data of patients with disorders of consciousness. A sample of healthy participants (N = 22), patients in a minimally conscious state (MCS; N = 5), and with unresponsive wakefulness syndrome (UWS; N = 9) was examined with a motor imagery task which involved imagery of moving both hands and an instruction to hold both hands firm. We extracted a set of 20 features from the electroencephalogram and used linear discriminant analysis, k-nearest neighbor classification, and support vector machines (SVM) as classification methods. In healthy participants, the best classification accuracies were seen with coherences (mean = .79; range = .53−.94) and power spectra (mean = .69; range = .40−.85). The coherence patterns in healthy participants did not match the expectation of central modulated -rhythm. Instead, coherence involved mainly frontal regions. In healthy participants, the best classification tool was SVM. Five patients had at least one feature-classifier outcome with p0.05 (none of which were coherence or power spectra), though none remained significant after false-discovery rate correction for multiple comparisons. The present work suggests the use of coherences in patients with disorders of consciousness because they show high reliability among healthy subjects and patient groups. However, feature extraction and classification is a challenging task in unresponsive patients because there is no ground truth to validate the results. PMID:24282545

Analysis of swallowing sounds using hidden Markov models.

PubMed

Aboofazeli, Mohammad; Moussavi, Zahra

2008-04-01

In recent years, acoustical analysis of the swallowing mechanism has received considerable attention due to its diagnostic potentials. This paper presents a hidden Markov model (HMM) based method for the swallowing sound segmentation and classification. Swallowing sound signals of 15 healthy and 11 dysphagic subjects were studied. The signals were divided into sequences of 25 ms segments each of which were represented by seven features. The sequences of features were modeled by HMMs. Trained HMMs were used for segmentation of the swallowing sounds into three distinct phases, i.e., initial quiet period, initial discrete sounds (IDS) and bolus transit sounds (BTS). Among the seven features, accuracy of segmentation by the HMM based on multi-scale product of wavelet coefficients was higher than that of the other HMMs and the linear prediction coefficient (LPC)-based HMM showed the weakest performance. In addition, HMMs were used for classification of the swallowing sounds of healthy subjects and dysphagic patients. Classification accuracy of different HMM configurations was investigated. When we increased the number of states of the HMMs from 4 to 8, the classification error gradually decreased. In most cases, classification error for N=9 was higher than that of N=8. Among the seven features used, root mean square (RMS) and waveform fractal dimension (WFD) showed the best performance in the HMM-based classification of swallowing sounds. When the sequences of the features of IDS segment were modeled separately, the accuracy reached up to 85.5%. As a second stage classification, a screening algorithm was used which correctly classified all the subjects but one healthy subject when RMS was used as characteristic feature of the swallowing sounds and the number of states was set to N=8.

Optimal number of features as a function of sample size for various classification rules.

PubMed

Hua, Jianping; Xiong, Zixiang; Lowey, James; Suh, Edward; Dougherty, Edward R

2005-04-15

Given the joint feature-label distribution, increasing the number of features always results in decreased classification error; however, this is not the case when a classifier is designed via a classification rule from sample data. Typically (but not always), for fixed sample size, the error of a designed classifier decreases and then increases as the number of features grows. The potential downside of using too many features is most critical for small samples, which are commonplace for gene-expression-based classifiers for phenotype discrimination. For fixed sample size and feature-label distribution, the issue is to find an optimal number of features. Since only in rare cases is there a known distribution of the error as a function of the number of features and sample size, this study employs simulation for various feature-label distributions and classification rules, and across a wide range of sample and feature-set sizes. To achieve the desired end, finding the optimal number of features as a function of sample size, it employs massively parallel computation. Seven classifiers are treated: 3-nearest-neighbor, Gaussian kernel, linear support vector machine, polynomial support vector machine, perceptron, regular histogram and linear discriminant analysis. Three Gaussian-based models are considered: linear, nonlinear and bimodal. In addition, real patient data from a large breast-cancer study is considered. To mitigate the combinatorial search for finding optimal feature sets, and to model the situation in which subsets of genes are co-regulated and correlation is internal to these subsets, we assume that the covariance matrix of the features is blocked, with each block corresponding to a group of correlated features. Altogether there are a large number of error surfaces for the many cases. These are provided in full on a companion website, which is meant to serve as resource for those working with small-sample classification. For the companion website, please visit http://public.tgen.org/tamu/ofs/ e-dougherty@ee.tamu.edu.

Multi-stage classification method oriented to aerial image based on low-rank recovery and multi-feature fusion sparse representation.

PubMed

Ma, Xu; Cheng, Yongmei; Hao, Shuai

2016-12-10

Automatic classification of terrain surfaces from an aerial image is essential for an autonomous unmanned aerial vehicle (UAV) landing at an unprepared site by using vision. Diverse terrain surfaces may show similar spectral properties due to the illumination and noise that easily cause poor classification performance. To address this issue, a multi-stage classification algorithm based on low-rank recovery and multi-feature fusion sparse representation is proposed. First, color moments and Gabor texture feature are extracted from training data and stacked as column vectors of a dictionary. Then we perform low-rank matrix recovery for the dictionary by using augmented Lagrange multipliers and construct a multi-stage terrain classifier. Experimental results on an aerial map database that we prepared verify the classification accuracy and robustness of the proposed method.

Objected-oriented remote sensing image classification method based on geographic ontology model

NASA Astrophysics Data System (ADS)

Chu, Z.; Liu, Z. J.; Gu, H. Y.

2016-11-01

Nowadays, with the development of high resolution remote sensing image and the wide application of laser point cloud data, proceeding objected-oriented remote sensing classification based on the characteristic knowledge of multi-source spatial data has been an important trend on the field of remote sensing image classification, which gradually replaced the traditional method through improving algorithm to optimize image classification results. For this purpose, the paper puts forward a remote sensing image classification method that uses the he characteristic knowledge of multi-source spatial data to build the geographic ontology semantic network model, and carries out the objected-oriented classification experiment to implement urban features classification, the experiment uses protégé software which is developed by Stanford University in the United States, and intelligent image analysis software—eCognition software as the experiment platform, uses hyperspectral image and Lidar data that is obtained through flight in DaFeng City of JiangSu as the main data source, first of all, the experiment uses hyperspectral image to obtain feature knowledge of remote sensing image and related special index, the second, the experiment uses Lidar data to generate nDSM(Normalized DSM, Normalized Digital Surface Model),obtaining elevation information, the last, the experiment bases image feature knowledge, special index and elevation information to build the geographic ontology semantic network model that implement urban features classification, the experiment results show that, this method is significantly higher than the traditional classification algorithm on classification accuracy, especially it performs more evidently on the respect of building classification. The method not only considers the advantage of multi-source spatial data, for example, remote sensing image, Lidar data and so on, but also realizes multi-source spatial data knowledge integration and application of the knowledge to the field of remote sensing image classification, which provides an effective way for objected-oriented remote sensing image classification in the future.

A spectral-structural bag-of-features scene classifier for very high spatial resolution remote sensing imagery

NASA Astrophysics Data System (ADS)

Zhao, Bei; Zhong, Yanfei; Zhang, Liangpei

2016-06-01

Land-use classification of very high spatial resolution remote sensing (VHSR) imagery is one of the most challenging tasks in the field of remote sensing image processing. However, the land-use classification is hard to be addressed by the land-cover classification techniques, due to the complexity of the land-use scenes. Scene classification is considered to be one of the expected ways to address the land-use classification issue. The commonly used scene classification methods of VHSR imagery are all derived from the computer vision community that mainly deal with terrestrial image recognition. Differing from terrestrial images, VHSR images are taken by looking down with airborne and spaceborne sensors, which leads to the distinct light conditions and spatial configuration of land cover in VHSR imagery. Considering the distinct characteristics, two questions should be answered: (1) Which type or combination of information is suitable for the VHSR imagery scene classification? (2) Which scene classification algorithm is best for VHSR imagery? In this paper, an efficient spectral-structural bag-of-features scene classifier (SSBFC) is proposed to combine the spectral and structural information of VHSR imagery. SSBFC utilizes the first- and second-order statistics (the mean and standard deviation values, MeanStd) as the statistical spectral descriptor for the spectral information of the VHSR imagery, and uses dense scale-invariant feature transform (SIFT) as the structural feature descriptor. From the experimental results, the spectral information works better than the structural information, while the combination of the spectral and structural information is better than any single type of information. Taking the characteristic of the spatial configuration into consideration, SSBFC uses the whole image scene as the scope of the pooling operator, instead of the scope generated by a spatial pyramid (SP) commonly used in terrestrial image classification. The experimental results show that the whole image as the scope of the pooling operator performs better than the scope generated by SP. In addition, SSBFC codes and pools the spectral and structural features separately to avoid mutual interruption between the spectral and structural features. The coding vectors of spectral and structural features are then concatenated into a final coding vector. Finally, SSBFC classifies the final coding vector by support vector machine (SVM) with a histogram intersection kernel (HIK). Compared with the latest scene classification methods, the experimental results with three VHSR datasets demonstrate that the proposed SSBFC performs better than the other classification methods for VHSR image scenes.

Tabu search and binary particle swarm optimization for feature selection using microarray data.

PubMed

Chuang, Li-Yeh; Yang, Cheng-Huei; Yang, Cheng-Hong

2009-12-01

Gene expression profiles have great potential as a medical diagnosis tool because they represent the state of a cell at the molecular level. In the classification of cancer type research, available training datasets generally have a fairly small sample size compared to the number of genes involved. This fact poses an unprecedented challenge to some classification methodologies due to training data limitations. Therefore, a good selection method for genes relevant for sample classification is needed to improve the predictive accuracy, and to avoid incomprehensibility due to the large number of genes investigated. In this article, we propose to combine tabu search (TS) and binary particle swarm optimization (BPSO) for feature selection. BPSO acts as a local optimizer each time the TS has been run for a single generation. The K-nearest neighbor method with leave-one-out cross-validation and support vector machine with one-versus-rest serve as evaluators of the TS and BPSO. The proposed method is applied and compared to the 11 classification problems taken from the literature. Experimental results show that our method simplifies features effectively and either obtains higher classification accuracy or uses fewer features compared to other feature selection methods.

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

Random-Forest Classification of High-Resolution Remote Sensing Images and Ndsm Over Urban Areas

NASA Astrophysics Data System (ADS)

Sun, X. F.; Lin, X. G.

2017-09-01

As an intermediate step between raw remote sensing data and digital urban maps, remote sensing data classification has been a challenging and long-standing research problem in the community of remote sensing. In this work, an effective classification method is proposed for classifying high-resolution remote sensing data over urban areas. Starting from high resolution multi-spectral images and 3D geometry data, our method proceeds in three main stages: feature extraction, classification, and classified result refinement. First, we extract color, vegetation index and texture features from the multi-spectral image and compute the height, elevation texture and differential morphological profile (DMP) features from the 3D geometry data. Then in the classification stage, multiple random forest (RF) classifiers are trained separately, then combined to form a RF ensemble to estimate each sample's category probabilities. Finally the probabilities along with the feature importance indicator outputted by RF ensemble are used to construct a fully connected conditional random field (FCCRF) graph model, by which the classification results are refined through mean-field based statistical inference. Experiments on the ISPRS Semantic Labeling Contest dataset show that our proposed 3-stage method achieves 86.9% overall accuracy on the test data.

Retinal vasculature classification using novel multifractal features

NASA Astrophysics Data System (ADS)

Ding, Y.; Ward, W. O. C.; Duan, Jinming; Auer, D. P.; Gowland, Penny; Bai, L.

2015-11-01

Retinal blood vessels have been implicated in a large number of diseases including diabetic retinopathy and cardiovascular diseases, which cause damages to retinal blood vessels. The availability of retinal vessel imaging provides an excellent opportunity for monitoring and diagnosis of retinal diseases, and automatic analysis of retinal vessels will help with the processes. However, state of the art vascular analysis methods such as counting the number of branches or measuring the curvature and diameter of individual vessels are unsuitable for the microvasculature. There has been published research using fractal analysis to calculate fractal dimensions of retinal blood vessels, but so far there has been no systematic research extracting discriminant features from retinal vessels for classifications. This paper introduces new methods for feature extraction from multifractal spectra of retinal vessels for classification. Two publicly available retinal vascular image databases are used for the experiments, and the proposed methods have produced accuracies of 85.5% and 77% for classification of healthy and diabetic retinal vasculatures. Experiments show that classification with multiple fractal features produces better rates compared with methods using a single fractal dimension value. In addition to this, experiments also show that classification accuracy can be affected by the accuracy of vessel segmentation algorithms.

A Real-Time Infrared Ultra-Spectral Signature Classification Method via Spatial Pyramid Matching

PubMed Central

Mei, Xiaoguang; Ma, Yong; Li, Chang; Fan, Fan; Huang, Jun; Ma, Jiayi

2015-01-01

The state-of-the-art ultra-spectral sensor technology brings new hope for high precision applications due to its high spectral resolution. However, it also comes with new challenges, such as the high data dimension and noise problems. In this paper, we propose a real-time method for infrared ultra-spectral signature classification via spatial pyramid matching (SPM), which includes two aspects. First, we introduce an infrared ultra-spectral signature similarity measure method via SPM, which is the foundation of the matching-based classification method. Second, we propose the classification method with reference spectral libraries, which utilizes the SPM-based similarity for the real-time infrared ultra-spectral signature classification with robustness performance. Specifically, instead of matching with each spectrum in the spectral library, our method is based on feature matching, which includes a feature library-generating phase. We calculate the SPM-based similarity between the feature of the spectrum and that of each spectrum of the reference feature library, then take the class index of the corresponding spectrum having the maximum similarity as the final result. Experimental comparisons on two publicly-available datasets demonstrate that the proposed method effectively improves the real-time classification performance and robustness to noise. PMID:26205263

Classifier dependent feature preprocessing methods

NASA Astrophysics Data System (ADS)

Rodriguez, Benjamin M., II; Peterson, Gilbert L.

2008-04-01

In mobile applications, computational complexity is an issue that limits sophisticated algorithms from being implemented on these devices. This paper provides an initial solution to applying pattern recognition systems on mobile devices by combining existing preprocessing algorithms for recognition. In pattern recognition systems, it is essential to properly apply feature preprocessing tools prior to training classification models in an attempt to reduce computational complexity and improve the overall classification accuracy. The feature preprocessing tools extended for the mobile environment are feature ranking, feature extraction, data preparation and outlier removal. Most desktop systems today are capable of processing a majority of the available classification algorithms without concern of processing while the same is not true on mobile platforms. As an application of pattern recognition for mobile devices, the recognition system targets the problem of steganalysis, determining if an image contains hidden information. The measure of performance shows that feature preprocessing increases the overall steganalysis classification accuracy by an average of 22%. The methods in this paper are tested on a workstation and a Nokia 6620 (Symbian operating system) camera phone with similar results.

Shift-invariant discrete wavelet transform analysis for retinal image classification.

PubMed

Khademi, April; Krishnan, Sridhar

2007-12-01

This work involves retinal image classification and a novel analysis system was developed. From the compressed domain, the proposed scheme extracts textural features from wavelet coefficients, which describe the relative homogeneity of localized areas of the retinal images. Since the discrete wavelet transform (DWT) is shift-variant, a shift-invariant DWT was explored to ensure that a robust feature set was extracted. To combat the small database size, linear discriminant analysis classification was used with the leave one out method. 38 normal and 48 abnormal (exudates, large drusens, fine drusens, choroidal neovascularization, central vein and artery occlusion, histoplasmosis, arteriosclerotic retinopathy, hemi-central retinal vein occlusion and more) were used and a specificity of 79% and sensitivity of 85.4% were achieved (the average classification rate is 82.2%). The success of the system can be accounted to the highly robust feature set which included translation, scale and semi-rotational, features. Additionally, this technique is database independent since the features were specifically tuned to the pathologies of the human eye.

Classification of Medical Datasets Using SVMs with Hybrid Evolutionary Algorithms Based on Endocrine-Based Particle Swarm Optimization and Artificial Bee Colony Algorithms.

PubMed

Lin, Kuan-Cheng; Hsieh, Yi-Hsiu

2015-10-01

The classification and analysis of data is an important issue in today's research. Selecting a suitable set of features makes it possible to classify an enormous quantity of data quickly and efficiently. Feature selection is generally viewed as a problem of feature subset selection, such as combination optimization problems. Evolutionary algorithms using random search methods have proven highly effective in obtaining solutions to problems of optimization in a diversity of applications. In this study, we developed a hybrid evolutionary algorithm based on endocrine-based particle swarm optimization (EPSO) and artificial bee colony (ABC) algorithms in conjunction with a support vector machine (SVM) for the selection of optimal feature subsets for the classification of datasets. The results of experiments using specific UCI medical datasets demonstrate that the accuracy of the proposed hybrid evolutionary algorithm is superior to that of basic PSO, EPSO and ABC algorithms, with regard to classification accuracy using subsets with a reduced number of features.

Discriminative least squares regression for multiclass classification and feature selection.

PubMed

Xiang, Shiming; Nie, Feiping; Meng, Gaofeng; Pan, Chunhong; Zhang, Changshui

2012-11-01

This paper presents a framework of discriminative least squares regression (LSR) for multiclass classification and feature selection. The core idea is to enlarge the distance between different classes under the conceptual framework of LSR. First, a technique called ε-dragging is introduced to force the regression targets of different classes moving along opposite directions such that the distances between classes can be enlarged. Then, the ε-draggings are integrated into the LSR model for multiclass classification. Our learning framework, referred to as discriminative LSR, has a compact model form, where there is no need to train two-class machines that are independent of each other. With its compact form, this model can be naturally extended for feature selection. This goal is achieved in terms of L2,1 norm of matrix, generating a sparse learning model for feature selection. The model for multiclass classification and its extension for feature selection are finally solved elegantly and efficiently. Experimental evaluation over a range of benchmark datasets indicates the validity of our method.

Discriminative Nonlinear Analysis Operator Learning: When Cosparse Model Meets Image Classification.

PubMed

Wen, Zaidao; Hou, Biao; Jiao, Licheng

2017-05-03

Linear synthesis model based dictionary learning framework has achieved remarkable performances in image classification in the last decade. Behaved as a generative feature model, it however suffers from some intrinsic deficiencies. In this paper, we propose a novel parametric nonlinear analysis cosparse model (NACM) with which a unique feature vector will be much more efficiently extracted. Additionally, we derive a deep insight to demonstrate that NACM is capable of simultaneously learning the task adapted feature transformation and regularization to encode our preferences, domain prior knowledge and task oriented supervised information into the features. The proposed NACM is devoted to the classification task as a discriminative feature model and yield a novel discriminative nonlinear analysis operator learning framework (DNAOL). The theoretical analysis and experimental performances clearly demonstrate that DNAOL will not only achieve the better or at least competitive classification accuracies than the state-of-the-art algorithms but it can also dramatically reduce the time complexities in both training and testing phases.

Fuzzy membership functions for analysis of high-resolution CT images of diffuse pulmonary diseases.

PubMed

Almeida, Eliana; Rangayyan, Rangaraj M; Azevedo-Marques, Paulo M

2015-08-01

We propose the use of fuzzy membership functions to analyze images of diffuse pulmonary diseases (DPDs) based on fractal and texture features. The features were extracted from preprocessed regions of interest (ROIs) selected from high-resolution computed tomography images. The ROIs represent five different patterns of DPDs and normal lung tissue. A Gaussian mixture model (GMM) was constructed for each feature, with six Gaussians modeling the six patterns. Feature selection was performed and the GMMs of the five significant features were used. From the GMMs, fuzzy membership functions were obtained by a probability-possibility transformation and further statistical analysis was performed. An average classification accuracy of 63.5% was obtained for the six classes. For four of the six classes, the classification accuracy was superior to 65%, and the best classification accuracy was 75.5% for one class. The use of fuzzy membership functions to assist in pattern classification is an alternative to deterministic approaches to explore strategies for medical diagnosis.

Diabetic Rethinopathy Screening by Bright Lesions Extraction from Fundus Images

NASA Astrophysics Data System (ADS)

Hanđsková, Veronika; Pavlovičova, Jarmila; Oravec, Miloš; Blaško, Radoslav

2013-09-01

Retinal images are nowadays widely used to diagnose many diseases, for example diabetic retinopathy. In our work, we propose the algorithm for the screening application, which identifies the patients with such severe diabetic complication as diabetic retinopathy is, in early phase. In the application we use the patient's fundus photography without any additional examination by an ophtalmologist. After this screening identification, other examination methods should be considered and the patient's follow-up by a doctor is necessary. Our application is composed of three principal modules including fundus image preprocessing, feature extraction and feature classification. Image preprocessing module has the role of luminance normalization, contrast enhancement and optical disk masking. Feature extraction module includes two stages: bright lesions candidates localization and candidates feature extraction. We selected 16 statistical and structural features. For feature classification, we use multilayer perceptron (MLP) with one hidden layer. We classify images into two classes. Feature classification efficiency is about 93 percent.

A new computer approach to mixed feature classification for forestry application

NASA Technical Reports Server (NTRS)

Kan, E. P.

1976-01-01

A computer approach for mapping mixed forest features (i.e., types, classes) from computer classification maps is discussed. Mixed features such as mixed softwood/hardwood stands are treated as admixtures of softwood and hardwood areas. Large-area mixed features are identified and small-area features neglected when the nominal size of a mixed feature can be specified. The computer program merges small isolated areas into surrounding areas by the iterative manipulation of the postprocessing algorithm that eliminates small connected sets. For a forestry application, computer-classified LANDSAT multispectral scanner data of the Sam Houston National Forest were used to demonstrate the proposed approach. The technique was successful in cleaning the salt-and-pepper appearance of multiclass classification maps and in mapping admixtures of softwood areas and hardwood areas. However, the computer-mapped mixed areas matched very poorly with the ground truth because of inadequate resolution and inappropriate definition of mixed features.

New nonlinear features for inspection, robotics, and face recognition

NASA Astrophysics Data System (ADS)

Casasent, David P.; Talukder, Ashit

1999-10-01

Classification of real-time X-ray images of randomly oriented touching pistachio nuts is discussed. The ultimate objective is the development of a system for automated non- invasive detection of defective product items on a conveyor belt. We discuss the extraction of new features that allow better discrimination between damaged and clean items (pistachio nuts). This feature extraction and classification stage is the new aspect of this paper; our new maximum representation and discriminating feature (MRDF) extraction method computes nonlinear features that are used as inputs to a new modified k nearest neighbor classifier. In this work, the MRDF is applied to standard features (rather than iconic data). The MRDF is robust to various probability distributions of the input class and is shown to provide good classification and new ROC (receiver operating characteristic) data. Other applications of these new feature spaces in robotics and face recognition are also noted.

Creation of a virtual cutaneous tissue bank

NASA Astrophysics Data System (ADS)

LaFramboise, William A.; Shah, Sujal; Hoy, R. W.; Letbetter, D.; Petrosko, P.; Vennare, R.; Johnson, Peter C.

2000-04-01

Cellular and non-cellular constituents of skin contain fundamental morphometric features and structural patterns that correlate with tissue function. High resolution digital image acquisitions performed using an automated system and proprietary software to assemble adjacent images and create a contiguous, lossless, digital representation of individual microscope slide specimens. Serial extraction, evaluation and statistical analysis of cutaneous feature is performed utilizing an automated analysis system, to derive normal cutaneous parameters comprising essential structural skin components. Automated digital cutaneous analysis allows for fast extraction of microanatomic dat with accuracy approximating manual measurement. The process provides rapid assessment of feature both within individual specimens and across sample populations. The images, component data, and statistical analysis comprise a bioinformatics database to serve as an architectural blueprint for skin tissue engineering and as a diagnostic standard of comparison for pathologic specimens.

Combining morphometric features and convolutional networks fusion for glaucoma diagnosis

NASA Astrophysics Data System (ADS)

Perdomo, Oscar; Arevalo, John; González, Fabio A.

2017-11-01

Glaucoma is an eye condition that leads to loss of vision and blindness. Ophthalmoscopy exam evaluates the shape, color and proportion between the optic disc and physiologic cup, but the lack of agreement among experts is still the main diagnosis problem. The application of deep convolutional neural networks combined with automatic extraction of features such as: the cup-to-disc distance in the four quadrants, the perimeter, area, eccentricity, the major radio, the minor radio in optic disc and cup, in addition to all the ratios among the previous parameters may help with a better automatic grading of glaucoma. This paper presents a strategy to merge morphological features and deep convolutional neural networks as a novel methodology to support the glaucoma diagnosis in eye fundus images.

Long-term penile morphometric alterations in patients treated with robot-assisted versus open radical prostatectomy.

PubMed

Capogrosso, P; Ventimiglia, E; Cazzaniga, W; Stabile, A; Pederzoli, F; Boeri, L; Gandaglia, G; Dehò, F; Briganti, A; Montorsi, F; Salonia, A

2018-01-01

Neglected side effects after radical prostatectomy have been previously reported. In this context, the prevalence of penile morphometric alterations has never been assessed in robot-assisted radical prostatectomy series. We aimed to assess prevalence of and predictors of penile morphometric alterations (i.e. penile shortening or penile morphometric deformation) at long-term follow-up in patients submitted to either robot-assisted (robot-assisted radical prostatectomy) or open radical prostatectomy. Sexually active patients after either robot-assisted radical prostatectomy or open radical prostatectomy prospectively completed a 28-item questionnaire, with sensitive issues regarding sexual function, namely orgasmic functioning, climacturia and changes in morphometric characteristics of the penis. Only patients with a post-operative follow-up ≥ 24 months were included. Patients submitted to either adjuvant or salvage therapies or those who refused to comprehensively complete the questionnaire were excluded from the analyses. A propensity-score matching analysis was implemented to control for baseline differences between groups. Logistic regression models tested potential predictors of penile morphometric alterations at long-term post-operative follow-up. Overall, 67 (50%) and 67 (50%) patients were included after open radical prostatectomy or robot-assisted radical prostatectomy, respectively. Self-rated post-operative penile shortening and penile morphometric deformation were reported by 75 (56%) and 29 (22.8%) patients, respectively. Rates of penile shortening and penile morphometric deformation were not different after open radical prostatectomy and robot-assisted radical prostatectomy [all p > 0.5]. At univariable analysis, self-reported penile morphometric alterations (either penile shortening or penile morphometric deformation) were significantly associated with baseline international index of erectile function-erectile function scores, body mass index, post-operative erectile function recovery, year of surgery and type of surgery (all p < 0.05). At multivariable analysis, robot-assisted radical prostatectomy was independently associated with a lower risk of post-operative penile morphometric alterations (OR: 0.38; 95% CI: 0.16-0.93). Self-perceived penile morphometric alterations were reported in one of two patients after radical prostatectomy at long-term follow-up, with open surgery associated with a potential higher risk of this self-perception. © 2017 American Society of Andrology and European Academy of Andrology.

Morphometric assessment of in vitro matured dromedary camel oocytes determines the developmental competence after parthenogenetic activation.

PubMed

Saadeldin, Islam M; Swelum, Ayman Abdel-Aziz; Yaqoob, Syed Hilal; Alowaimer, Abdullah Nasser

2017-06-01

The aim of the current study was to improve the selection method of camel oocytes after in vitro maturation by reducing exclusion criteria that were based only on the presence of the first polar body. A combined nuclear and morphometric assessment of camel oocytes after in vitro maturation was included to perform a judgment. The nuclear status of the oocytes, including the presence of the first polar body, meiosis I stage, and lack of nuclear materials, was investigated. The morphometric criteria that comprised the dimensions of each oocyte were as follows: diameter of the whole oocyte, including the zona pellucida (ZPO), zona pellucida thickness (ZPT), ooplasm diameter (OD), the perivitelline space (PVS) area, and PVS diameter. Among the oocytes with different nuclear status, there were no differences in ZPO and ZPT. However, oocytes with no nuclear material showed a significant reduction in OD (110.19 ± 1.4 μm) and a significant increase in PVS area (2139 ± 324.6 μm 2 ) and PVS diameter (13.9 ± 1.96 μm) when compared with oocytes in the meiosis I stage (117.41 ± 2.85 μm, 1287.4 ± 123.4 μm 2 , and 8.56 ± 0.65 μm, respectively). To simplify the selection, the major difference between meiosis I and degenerated oocytes was the diameter of the PVS, which was greater than the ZPT in degenerated oocytes. Therefore, three groups were morphologically differentiated into oocytes with polar bodies (PB1), meiosis I (MI) oocytes, and degenerated oocytes. MI oocytes were able to extrude the polar body after activation but were not able to develop into blastocysts. In contrast, MI oocytes were able to develop into blastocysts after a biphasic activation protocol in which the oocytes were electrically activated and treated with ionomycin after 2 h. In conclusion, the results obtained by the morphometric assessment allowed us to develop a simple and objective classification system for in vitro matured dromedary camel oocytes, which will lead to accurate oocyte selection for the support of subsequent embryonic development. Copyright © 2017 Elsevier Inc. All rights reserved.

Does expert knowledge improve automatic probabilistic classification of gait joint motion patterns in children with cerebral palsy?

PubMed Central

Papageorgiou, Eirini; Nieuwenhuys, Angela; Desloovere, Kaat

2017-01-01

Background This study aimed to improve the automatic probabilistic classification of joint motion gait patterns in children with cerebral palsy by using the expert knowledge available via a recently developed Delphi-consensus study. To this end, this study applied both Naïve Bayes and Logistic Regression classification with varying degrees of usage of the expert knowledge (expert-defined and discretized features). A database of 356 patients and 1719 gait trials was used to validate the classification performance of eleven joint motions. Hypotheses Two main hypotheses stated that: (1) Joint motion patterns in children with CP, obtained through a Delphi-consensus study, can be automatically classified following a probabilistic approach, with an accuracy similar to clinical expert classification, and (2) The inclusion of clinical expert knowledge in the selection of relevant gait features and the discretization of continuous features increases the performance of automatic probabilistic joint motion classification. Findings This study provided objective evidence supporting the first hypothesis. Automatic probabilistic gait classification using the expert knowledge available from the Delphi-consensus study resulted in accuracy (91%) similar to that obtained with two expert raters (90%), and higher accuracy than that obtained with non-expert raters (78%). Regarding the second hypothesis, this study demonstrated that the use of more advanced machine learning techniques such as automatic feature selection and discretization instead of expert-defined and discretized features can result in slightly higher joint motion classification performance. However, the increase in performance is limited and does not outweigh the additional computational cost and the higher risk of loss of clinical interpretability, which threatens the clinical acceptance and applicability. PMID:28570616

Specific-age group sex estimation of infants through geometric morphometrics analysis of pubis and ischium.

PubMed

Estévez Campo, Enrique José; López-Lázaro, Sandra; López-Morago Rodríguez, Claudia; Alemán Aguilera, Inmaculada; Botella López, Miguel Cecilio

2018-05-01

Sex determination of unknown individuals is one of the primary goals of Physical and Forensic Anthropology. The adult skeleton can be sexed using both morphological and metric traits on a large number of bones. The human pelvis is often used as an important element of adult sex determination. However, studies carried out about the pelvic bone in subadult individuals present several limitations due the absence of sexually dimorphic characteristics. In this study, we analyse the sexual dimorphism of the immature pubis and ischium bones, attending to their shape (Procrustes residuals) and size (centroid size), using an identified sample of subadult individuals composed of 58 individuals for the pubis and 83 for the ischium, aged between birth and 1year of life, from the Granada osteological collection of identified infants (Granada, Spain). Geometric morphometric methods and discriminant analysis were applied to this study. The results of intra- and inter-observer error showed good and excellent agreement in the location of coordinates of landmarks and semilandmarks, respectively. Principal component analysis performed on shape and size variables showed superposition of the two sexes, suggesting a low degree of sexual dimorphism. Canonical variable analysis did not show significant changes between the male and female shapes. As a consequence, discriminant analysis with leave-one-out cross validation provided low classification accuracy. The results suggested a low degree of sexual dimorphism supported by significant sexual dimorphism in the subadult sample and poor cross-validated classification accuracy. The inclusion of centroid size as a discriminant variable does not imply a significant improvement in the results of the analysis. The similarities found between the sexes prevent consideration of pubic and ischial morphology as a sex estimator in early stages of development. The authors suggest extending this study by analysing the different trajectories of shape and size in later ontogeny between males and females. Copyright © 2018 Elsevier B.V. All rights reserved.

CNN-BLPred: a Convolutional neural network based predictor for β-Lactamases (BL) and their classes.

PubMed

White, Clarence; Ismail, Hamid D; Saigo, Hiroto; Kc, Dukka B

2017-12-28

The β-Lactamase (BL) enzyme family is an important class of enzymes that plays a key role in bacterial resistance to antibiotics. As the newly identified number of BL enzymes is increasing daily, it is imperative to develop a computational tool to classify the newly identified BL enzymes into one of its classes. There are two types of classification of BL enzymes: Molecular Classification and Functional Classification. Existing computational methods only address Molecular Classification and the performance of these existing methods is unsatisfactory. We addressed the unsatisfactory performance of the existing methods by implementing a Deep Learning approach called Convolutional Neural Network (CNN). We developed CNN-BLPred, an approach for the classification of BL proteins. The CNN-BLPred uses Gradient Boosted Feature Selection (GBFS) in order to select the ideal feature set for each BL classification. Based on the rigorous benchmarking of CCN-BLPred using both leave-one-out cross-validation and independent test sets, CCN-BLPred performed better than the other existing algorithms. Compared with other architectures of CNN, Recurrent Neural Network, and Random Forest, the simple CNN architecture with only one convolutional layer performs the best. After feature extraction, we were able to remove ~95% of the 10,912 features using Gradient Boosted Trees. During 10-fold cross validation, we increased the accuracy of the classic BL predictions by 7%. We also increased the accuracy of Class A, Class B, Class C, and Class D performance by an average of 25.64%. The independent test results followed a similar trend. We implemented a deep learning algorithm known as Convolutional Neural Network (CNN) to develop a classifier for BL classification. Combined with feature selection on an exhaustive feature set and using balancing method such as Random Oversampling (ROS), Random Undersampling (RUS) and Synthetic Minority Oversampling Technique (SMOTE), CNN-BLPred performs significantly better than existing algorithms for BL classification.

Comparison of Single and Multi-Scale Method for Leaf and Wood Points Classification from Terrestrial Laser Scanning Data

NASA Astrophysics Data System (ADS)

Wei, Hongqiang; Zhou, Guiyun; Zhou, Junjie

2018-04-01

The classification of leaf and wood points is an essential preprocessing step for extracting inventory measurements and canopy characterization of trees from the terrestrial laser scanning (TLS) data. The geometry-based approach is one of the widely used classification method. In the geometry-based method, it is common practice to extract salient features at one single scale before the features are used for classification. It remains unclear how different scale(s) used affect the classification accuracy and efficiency. To assess the scale effect on the classification accuracy and efficiency, we extracted the single-scale and multi-scale salient features from the point clouds of two oak trees of different sizes and conducted the classification on leaf and wood. Our experimental results show that the balanced accuracy of the multi-scale method is higher than the average balanced accuracy of the single-scale method by about 10 % for both trees. The average speed-up ratio of single scale classifiers over multi-scale classifier for each tree is higher than 30.

Yarn-dyed fabric defect classification based on convolutional neural network

NASA Astrophysics Data System (ADS)

Jing, Junfeng; Dong, Amei; Li, Pengfei; Zhang, Kaibing

2017-09-01

Considering that manual inspection of the yarn-dyed fabric can be time consuming and inefficient, we propose a yarn-dyed fabric defect classification method by using a convolutional neural network (CNN) based on a modified AlexNet. CNN shows powerful ability in performing feature extraction and fusion by simulating the learning mechanism of human brain. The local response normalization layers in AlexNet are replaced by the batch normalization layers, which can enhance both the computational efficiency and classification accuracy. In the training process of the network, the characteristics of the defect are extracted step by step and the essential features of the image can be obtained from the fusion of the edge details with several convolution operations. Then the max-pooling layers, the dropout layers, and the fully connected layers are employed in the classification model to reduce the computation cost and extract more precise features of the defective fabric. Finally, the results of the defect classification are predicted by the softmax function. The experimental results show promising performance with an acceptable average classification rate and strong robustness on yarn-dyed fabric defect classification.

Pattern recognition and image processing for environmental monitoring

NASA Astrophysics Data System (ADS)

Siddiqui, Khalid J.; Eastwood, DeLyle

1999-12-01

Pattern recognition (PR) and signal/image processing methods are among the most powerful tools currently available for noninvasively examining spectroscopic and other chemical data for environmental monitoring. Using spectral data, these systems have found a variety of applications employing analytical techniques for chemometrics such as gas chromatography, fluorescence spectroscopy, etc. An advantage of PR approaches is that they make no a prior assumption regarding the structure of the patterns. However, a majority of these systems rely on human judgment for parameter selection and classification. A PR problem is considered as a composite of four subproblems: pattern acquisition, feature extraction, feature selection, and pattern classification. One of the basic issues in PR approaches is to determine and measure the features useful for successful classification. Selection of features that contain the most discriminatory information is important because the cost of pattern classification is directly related to the number of features used in the decision rules. The state of the spectral techniques as applied to environmental monitoring is reviewed. A spectral pattern classification system combining the above components and automatic decision-theoretic approaches for classification is developed. It is shown how such a system can be used for analysis of large data sets, warehousing, and interpretation. In a preliminary test, the classifier was used to classify synchronous UV-vis fluorescence spectra of relatively similar petroleum oils with reasonable success.

Low complexity feature extraction for classification of harmonic signals

NASA Astrophysics Data System (ADS)

William, Peter E.

In this dissertation, feature extraction algorithms have been developed for extraction of characteristic features from harmonic signals. The common theme for all developed algorithms is the simplicity in generating a significant set of features directly from the time domain harmonic signal. The features are a time domain representation of the composite, yet sparse, harmonic signature in the spectral domain. The algorithms are adequate for low-power unattended sensors which perform sensing, feature extraction, and classification in a standalone scenario. The first algorithm generates the characteristic features using only the duration between successive zero-crossing intervals. The second algorithm estimates the harmonics' amplitudes of the harmonic structure employing a simplified least squares method without the need to estimate the true harmonic parameters of the source signal. The third algorithm, resulting from a collaborative effort with Daniel White at the DSP Lab, University of Nebraska-Lincoln, presents an analog front end approach that utilizes a multichannel analog projection and integration to extract the sparse spectral features from the analog time domain signal. Classification is performed using a multilayer feedforward neural network. Evaluation of the proposed feature extraction algorithms for classification through the processing of several acoustic and vibration data sets (including military vehicles and rotating electric machines) with comparison to spectral features shows that, for harmonic signals, time domain features are simpler to extract and provide equivalent or improved reliability over the spectral features in both the detection probabilities and false alarm rate.

Mammographic mass classification based on possibility theory

NASA Astrophysics Data System (ADS)

Hmida, Marwa; Hamrouni, Kamel; Solaiman, Basel; Boussetta, Sana

2017-03-01

Shape and margin features are very important for differentiating between benign and malignant masses in mammographic images. In fact, benign masses are usually round and oval and have smooth contours. However, malignant tumors have generally irregular shape and appear lobulated or speculated in margins. This knowledge suffers from imprecision and ambiguity. Therefore, this paper deals with the problem of mass classification by using shape and margin features while taking into account the uncertainty linked to the degree of truth of the available information and the imprecision related to its content. Thus, in this work, we proposed a novel mass classification approach which provides a possibility based representation of the extracted shape features and builds a possibility knowledge basis in order to evaluate the possibility degree of malignancy and benignity for each mass. For experimentation, the MIAS database was used and the classification results show the great performance of our approach in spite of using simple features.

Decoding memory features from hippocampal spiking activities using sparse classification models.

PubMed

Dong Song; Hampson, Robert E; Robinson, Brian S; Marmarelis, Vasilis Z; Deadwyler, Sam A; Berger, Theodore W

2016-08-01

To understand how memory information is encoded in the hippocampus, we build classification models to decode memory features from hippocampal CA3 and CA1 spatio-temporal patterns of spikes recorded from epilepsy patients performing a memory-dependent delayed match-to-sample task. The classification model consists of a set of B-spline basis functions for extracting memory features from the spike patterns, and a sparse logistic regression classifier for generating binary categorical output of memory features. Results show that classification models can extract significant amount of memory information with respects to types of memory tasks and categories of sample images used in the task, despite the high level of variability in prediction accuracy due to the small sample size. These results support the hypothesis that memories are encoded in the hippocampal activities and have important implication to the development of hippocampal memory prostheses.

Deep Convolutional Neural Networks for Classifying Body Constitution Based on Face Image.

PubMed

Huan, Er-Yang; Wen, Gui-Hua; Zhang, Shi-Jun; Li, Dan-Yang; Hu, Yang; Chang, Tian-Yuan; Wang, Qing; Huang, Bing-Lin

2017-01-01

Body constitution classification is the basis and core content of traditional Chinese medicine constitution research. It is to extract the relevant laws from the complex constitution phenomenon and finally build the constitution classification system. Traditional identification methods have the disadvantages of inefficiency and low accuracy, for instance, questionnaires. This paper proposed a body constitution recognition algorithm based on deep convolutional neural network, which can classify individual constitution types according to face images. The proposed model first uses the convolutional neural network to extract the features of face image and then combines the extracted features with the color features. Finally, the fusion features are input to the Softmax classifier to get the classification result. Different comparison experiments show that the algorithm proposed in this paper can achieve the accuracy of 65.29% about the constitution classification. And its performance was accepted by Chinese medicine practitioners.

Sparse Coding for N-Gram Feature Extraction and Training for File Fragment Classification

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

Wang, Felix; Quach, Tu-Thach; Wheeler, Jason

File fragment classification is an important step in the task of file carving in digital forensics. In file carving, files must be reconstructed based on their content as a result of their fragmented storage on disk or in memory. Existing methods for classification of file fragments typically use hand-engineered features such as byte histograms or entropy measures. In this paper, we propose an approach using sparse coding that enables automated feature extraction. Sparse coding, or sparse dictionary learning, is an unsupervised learning algorithm, and is capable of extracting features based simply on how well those features can be used tomore » reconstruct the original data. With respect to file fragments, we learn sparse dictionaries for n-grams, continuous sequences of bytes, of different sizes. These dictionaries may then be used to estimate n-gram frequencies for a given file fragment, but for significantly larger n-gram sizes than are typically found in existing methods which suffer from combinatorial explosion. To demonstrate the capability of our sparse coding approach, we used the resulting features to train standard classifiers such as support vector machines (SVMs) over multiple file types. Experimentally, we achieved significantly better classification results with respect to existing methods, especially when the features were used in supplement to existing hand-engineered features.« less

Sparse Coding for N-Gram Feature Extraction and Training for File Fragment Classification

DOE PAGES

Wang, Felix; Quach, Tu-Thach; Wheeler, Jason; ...

2018-04-05

File fragment classification is an important step in the task of file carving in digital forensics. In file carving, files must be reconstructed based on their content as a result of their fragmented storage on disk or in memory. Existing methods for classification of file fragments typically use hand-engineered features such as byte histograms or entropy measures. In this paper, we propose an approach using sparse coding that enables automated feature extraction. Sparse coding, or sparse dictionary learning, is an unsupervised learning algorithm, and is capable of extracting features based simply on how well those features can be used tomore » reconstruct the original data. With respect to file fragments, we learn sparse dictionaries for n-grams, continuous sequences of bytes, of different sizes. These dictionaries may then be used to estimate n-gram frequencies for a given file fragment, but for significantly larger n-gram sizes than are typically found in existing methods which suffer from combinatorial explosion. To demonstrate the capability of our sparse coding approach, we used the resulting features to train standard classifiers such as support vector machines (SVMs) over multiple file types. Experimentally, we achieved significantly better classification results with respect to existing methods, especially when the features were used in supplement to existing hand-engineered features.« less

Photometric Supernova Classification with Machine Learning

NASA Astrophysics Data System (ADS)

Lochner, Michelle; McEwen, Jason D.; Peiris, Hiranya V.; Lahav, Ofer; Winter, Max K.

2016-08-01

Automated photometric supernova classification has become an active area of research in recent years in light of current and upcoming imaging surveys such as the Dark Energy Survey (DES) and the Large Synoptic Survey Telescope, given that spectroscopic confirmation of type for all supernovae discovered will be impossible. Here, we develop a multi-faceted classification pipeline, combining existing and new approaches. Our pipeline consists of two stages: extracting descriptive features from the light curves and classification using a machine learning algorithm. Our feature extraction methods vary from model-dependent techniques, namely SALT2 fits, to more independent techniques that fit parametric models to curves, to a completely model-independent wavelet approach. We cover a range of representative machine learning algorithms, including naive Bayes, k-nearest neighbors, support vector machines, artificial neural networks, and boosted decision trees (BDTs). We test the pipeline on simulated multi-band DES light curves from the Supernova Photometric Classification Challenge. Using the commonly used area under the curve (AUC) of the Receiver Operating Characteristic as a metric, we find that the SALT2 fits and the wavelet approach, with the BDTs algorithm, each achieve an AUC of 0.98, where 1 represents perfect classification. We find that a representative training set is essential for good classification, whatever the feature set or algorithm, with implications for spectroscopic follow-up. Importantly, we find that by using either the SALT2 or the wavelet feature sets with a BDT algorithm, accurate classification is possible purely from light curve data, without the need for any redshift information.

Composite Biomarkers Derived from Micro-Electrode Array Measurements and Computer Simulations Improve the Classification of Drug-Induced Channel Block.

PubMed

Tixier, Eliott; Raphel, Fabien; Lombardi, Damiano; Gerbeau, Jean-Frédéric

2017-01-01

The Micro-Electrode Array (MEA) device enables high-throughput electrophysiology measurements that are less labor-intensive than patch-clamp based techniques. Combined with human-induced pluripotent stem cells cardiomyocytes (hiPSC-CM), it represents a new and promising paradigm for automated and accurate in vitro drug safety evaluation. In this article, the following question is addressed: which features of the MEA signals should be measured to better classify the effects of drugs? A framework for the classification of drugs using MEA measurements is proposed. The classification is based on the ion channels blockades induced by the drugs. It relies on an in silico electrophysiology model of the MEA, a feature selection algorithm and automatic classification tools. An in silico model of the MEA is developed and is used to generate synthetic measurements. An algorithm that extracts MEA measurements features designed to perform well in a classification context is described. These features are called composite biomarkers. A state-of-the-art machine learning program is used to carry out the classification of drugs using experimental MEA measurements. The experiments are carried out using five different drugs: mexiletine, flecainide, diltiazem, moxifloxacin, and dofetilide. We show that the composite biomarkers outperform the classical ones in different classification scenarios. We show that using both synthetic and experimental MEA measurements improves the robustness of the composite biomarkers and that the classification scores are increased.

Geometric Morphometrics on Gene Expression Patterns Within Phenotypes: A Case Example on Limb Development

PubMed Central

Martínez-Abadías, Neus; Mateu, Roger; Niksic, Martina; Russo, Lucia; Sharpe, James

2016-01-01

How the genotype translates into the phenotype through development is critical to fully understand the evolution of phenotypes. We propose a novel approach to directly assess how changes in gene expression patterns are associated with changes in morphology using the limb as a case example. Our method combines molecular biology techniques, such as whole-mount in situ hybridization, with image and shape analysis, extending the use of Geometric Morphometrics to the analysis of nonanatomical shapes, such as gene expression domains. Elliptical Fourier and Procrustes-based semilandmark analyses were used to analyze the variation and covariation patterns of the limb bud shape with the expression patterns of two relevant genes for limb morphogenesis, Hoxa11 and Hoxa13. We devised a multiple thresholding method to semiautomatically segment gene domains at several expression levels in large samples of limb buds from C57Bl6 mouse embryos between 10 and 12 postfertilization days. Besides providing an accurate phenotyping tool to quantify the spatiotemporal dynamics of gene expression patterns within developing structures, our morphometric analyses revealed high, non-random, and gene-specific variation undergoing canalization during limb development. Our results demonstrate that Hoxa11 and Hoxa13, despite being paralogs with analogous functions in limb patterning, show clearly distinct dynamic patterns, both in shape and size, and are associated differently with the limb bud shape. The correspondence between our results and already well-established molecular processes underlying limb development confirms that this morphometric approach is a powerful tool to extract features of development regulating morphogenesis. Such multilevel analyses are promising in systems where not so much molecular information is available and will advance our understanding of the genotype–phenotype map. In systematics, this knowledge will increase our ability to infer how evolution modified a common developmental pattern to generate a wide diversity of morphologies, as in the vertebrate limb. PMID:26377442

Choice matters: incipient speciation in Gyrodactylus corydori (Monogenoidea: Gyrodactylidae).

PubMed

Bueno-Silva, Marlus; Boeger, Walter A; Pie, Marcio R

2011-05-01

We investigated how Gyrodactylus corydoriBueno-Silva and Boeger, 2009 exploits two sympatric host species, Corydoras paleatus (Jenyns, 1842) and Corydoras ehrhardti Steindachner, 1910. Specimens of G. corydori were collected from the Piraquara and Miringuava Rivers, State of Paraná, Brazil, between 2005 and 2006. A total of 167 parasites was measured from both host species. Nine morphometric features of the haptoral sclerites were measured and analyzed by discriminant analysis, cluster analysis and multivariate analysis of variance. A fragment of the mitochondrial cytochrome oxidase I gene (COI) (∼740 bp) and the rDNA internal transcribed spacers (ITS) (∼1200 bp) of G. corydori were sequenced. Bayesian and parsimony analyses of COI recognized two genetically structured clades of G. corydori, which corresponded closely with the two species of Corydoras. Twenty-eight haplotypes were detected (18 were exclusive to C. ehrhardti and seven were exclusive to C. paleatus). The same general pattern between parasites and host species was observed in the morphometric analyses. Nevertheless, poor correlation of genetic and morphometric variation strongly supports the plastic nature of the morphological variation of haptoral sclerites. The existence of two clades with limited gene flow would suggest that G. corydori already represents two cryptic species. However, the morphometric and molecular data showed that there is insufficient evidence to support two valid species. The low COI (0.1-6.2%) and ITS (0.09-3.5%) divergence within G. corydori suggest a recent separation of the lineages between distinct host species (less than 1 million years). As the hypothesis of secondary contact of the parasite demographic history was rejected, our results point to the possibility of sympatric incipient ongoing speciation of G. corydori to form distinct parasite lineages adapted to C. ehrhardti and C. paleatus. This may be a common event within the Gyrodactylidae, adding a yet unreported mode of adaptive speciation that helps to understand its rate of diversification. Copyright © 2011 Australian Society for Parasitology Inc. Published by Elsevier Ltd. All rights reserved.

Robust spike classification based on frequency domain neural waveform features.

PubMed

Yang, Chenhui; Yuan, Yuan; Si, Jennie

2013-12-01

We introduce a new spike classification algorithm based on frequency domain features of the spike snippets. The goal for the algorithm is to provide high classification accuracy, low false misclassification, ease of implementation, robustness to signal degradation, and objectivity in classification outcomes. In this paper, we propose a spike classification algorithm based on frequency domain features (CFDF). It makes use of frequency domain contents of the recorded neural waveforms for spike classification. The self-organizing map (SOM) is used as a tool to determine the cluster number intuitively and directly by viewing the SOM output map. After that, spike classification can be easily performed using clustering algorithms such as the k-Means. In conjunction with our previously developed multiscale correlation of wavelet coefficient (MCWC) spike detection algorithm, we show that the MCWC and CFDF detection and classification system is robust when tested on several sets of artificial and real neural waveforms. The CFDF is comparable to or outperforms some popular automatic spike classification algorithms with artificial and real neural data. The detection and classification of neural action potentials or neural spikes is an important step in single-unit-based neuroscientific studies and applications. After the detection of neural snippets potentially containing neural spikes, a robust classification algorithm is applied for the analysis of the snippets to (1) extract similar waveforms into one class for them to be considered coming from one unit, and to (2) remove noise snippets if they do not contain any features of an action potential. Usually, a snippet is a small 2 or 3 ms segment of the recorded waveform, and differences in neural action potentials can be subtle from one unit to another. Therefore, a robust, high performance classification system like the CFDF is necessary. In addition, the proposed algorithm does not require any assumptions on statistical properties of the noise and proves to be robust under noise contamination.

Comparing Features for Classification of MEG Responses to Motor Imagery

PubMed Central

Halme, Hanna-Leena; Parkkonen, Lauri

2016-01-01

Background Motor imagery (MI) with real-time neurofeedback could be a viable approach, e.g., in rehabilitation of cerebral stroke. Magnetoencephalography (MEG) noninvasively measures electric brain activity at high temporal resolution and is well-suited for recording oscillatory brain signals. MI is known to modulate 10- and 20-Hz oscillations in the somatomotor system. In order to provide accurate feedback to the subject, the most relevant MI-related features should be extracted from MEG data. In this study, we evaluated several MEG signal features for discriminating between left- and right-hand MI and between MI and rest. Methods MEG was measured from nine healthy participants imagining either left- or right-hand finger tapping according to visual cues. Data preprocessing, feature extraction and classification were performed offline. The evaluated MI-related features were power spectral density (PSD), Morlet wavelets, short-time Fourier transform (STFT), common spatial patterns (CSP), filter-bank common spatial patterns (FBCSP), spatio—spectral decomposition (SSD), and combined SSD+CSP, CSP+PSD, CSP+Morlet, and CSP+STFT. We also compared four classifiers applied to single trials using 5-fold cross-validation for evaluating the classification accuracy and its possible dependence on the classification algorithm. In addition, we estimated the inter-session left-vs-right accuracy for each subject. Results The SSD+CSP combination yielded the best accuracy in both left-vs-right (mean 73.7%) and MI-vs-rest (mean 81.3%) classification. CSP+Morlet yielded the best mean accuracy in inter-session left-vs-right classification (mean 69.1%). There were large inter-subject differences in classification accuracy, and the level of the 20-Hz suppression correlated significantly with the subjective MI-vs-rest accuracy. Selection of the classification algorithm had only a minor effect on the results. Conclusions We obtained good accuracy in sensor-level decoding of MI from single-trial MEG data. Feature extraction methods utilizing both the spatial and spectral profile of MI-related signals provided the best classification results, suggesting good performance of these methods in an online MEG neurofeedback system. PMID:27992574

Behavioral state classification in epileptic brain using intracranial electrophysiology

NASA Astrophysics Data System (ADS)

Kremen, Vaclav; Duque, Juliano J.; Brinkmann, Benjamin H.; Berry, Brent M.; Kucewicz, Michal T.; Khadjevand, Fatemeh; Van Gompel, Jamie; Stead, Matt; St. Louis, Erik K.; Worrell, Gregory A.

2017-04-01

Objective. Automated behavioral state classification can benefit next generation implantable epilepsy devices. In this study we explored the feasibility of automated awake (AW) and slow wave sleep (SWS) classification using wide bandwidth intracranial EEG (iEEG) in patients undergoing evaluation for epilepsy surgery. Approach. Data from seven patients (age 34+/- 12 , 4 women) who underwent intracranial depth electrode implantation for iEEG monitoring were included. Spectral power features (0.1-600 Hz) spanning several frequency bands from a single electrode were used to train and test a support vector machine classifier. Main results. Classification accuracy of 97.8  ±  0.3% (normal tissue) and 89.4  ±  0.8% (epileptic tissue) across seven subjects using multiple spectral power features from a single electrode was achieved. Spectral power features from electrodes placed in normal temporal neocortex were found to be more useful (accuracy 90.8  ±  0.8%) for sleep-wake state classification than electrodes located in normal hippocampus (87.1  ±  1.6%). Spectral power in high frequency band features (Ripple (80-250 Hz), Fast Ripple (250-600 Hz)) showed comparable performance for AW and SWS classification as the best performing Berger bands (Alpha, Beta, low Gamma) with accuracy  ⩾90% using a single electrode contact and single spectral feature. Significance. Automated classification of wake and SWS should prove useful for future implantable epilepsy devices with limited computational power, memory, and number of electrodes. Applications include quantifying patient sleep patterns and behavioral state dependent detection, prediction, and electrical stimulation therapies.

Terrain-Moisture Classification Using GPS Surface-Reflected Signals

NASA Technical Reports Server (NTRS)

Grant, Michael S.; Acton, Scott T.; Katzberg, Stephen J.

2006-01-01

In this study we present a novel method of land surface classification using surface-reflected GPS signals in combination with digital imagery. Two GPS-derived classification features are merged with visible image data to create terrain-moisture (TM) classes, defined here as visibly identifiable terrain or landcover classes containing a surface/soil moisture component. As compared to using surface imagery alone, classification accuracy is significantly improved for a number of visible classes when adding the GPS-based signal features. Since the strength of the reflected GPS signal is proportional to the amount of moisture in the surface, use of these GPS features provides information about the surface that is not obtainable using visible wavelengths alone. Application areas include hydrology, precision agriculture, and wetlands mapping.

Alzheimer's disease detection via automatic 3D caudate nucleus segmentation using coupled dictionary learning with level set formulation.

PubMed

Al-Shaikhli, Saif Dawood Salman; Yang, Michael Ying; Rosenhahn, Bodo

2016-12-01

This paper presents a novel method for Alzheimer's disease classification via an automatic 3D caudate nucleus segmentation. The proposed method consists of segmentation and classification steps. In the segmentation step, we propose a novel level set cost function. The proposed cost function is constrained by a sparse representation of local image features using a dictionary learning method. We present coupled dictionaries: a feature dictionary of a grayscale brain image and a label dictionary of a caudate nucleus label image. Using online dictionary learning, the coupled dictionaries are learned from the training data. The learned coupled dictionaries are embedded into a level set function. In the classification step, a region-based feature dictionary is built. The region-based feature dictionary is learned from shape features of the caudate nucleus in the training data. The classification is based on the measure of the similarity between the sparse representation of region-based shape features of the segmented caudate in the test image and the region-based feature dictionary. The experimental results demonstrate the superiority of our method over the state-of-the-art methods by achieving a high segmentation (91.5%) and classification (92.5%) accuracy. In this paper, we find that the study of the caudate nucleus atrophy gives an advantage over the study of whole brain structure atrophy to detect Alzheimer's disease. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Classifying EEG for Brain-Computer Interface: Learning Optimal Filters for Dynamical System Features

PubMed Central

Song, Le; Epps, Julien

2007-01-01

Classification of multichannel EEG recordings during motor imagination has been exploited successfully for brain-computer interfaces (BCI). In this paper, we consider EEG signals as the outputs of a networked dynamical system (the cortex), and exploit synchronization features from the dynamical system for classification. Herein, we also propose a new framework for learning optimal filters automatically from the data, by employing a Fisher ratio criterion. Experimental evaluations comparing the proposed dynamical system features with the CSP and the AR features reveal their competitive performance during classification. Results also show the benefits of employing the spatial and the temporal filters optimized using the proposed learning approach. PMID:18364986

Time-Frequency Feature Representation Using Multi-Resolution Texture Analysis and Acoustic Activity Detector for Real-Life Speech Emotion Recognition

PubMed Central

Wang, Kun-Ching

2015-01-01

The classification of emotional speech is mostly considered in speech-related research on human-computer interaction (HCI). In this paper, the purpose is to present a novel feature extraction based on multi-resolutions texture image information (MRTII). The MRTII feature set is derived from multi-resolution texture analysis for characterization and classification of different emotions in a speech signal. The motivation is that we have to consider emotions have different intensity values in different frequency bands. In terms of human visual perceptual, the texture property on multi-resolution of emotional speech spectrogram should be a good feature set for emotion classification in speech. Furthermore, the multi-resolution analysis on texture can give a clearer discrimination between each emotion than uniform-resolution analysis on texture. In order to provide high accuracy of emotional discrimination especially in real-life, an acoustic activity detection (AAD) algorithm must be applied into the MRTII-based feature extraction. Considering the presence of many blended emotions in real life, in this paper make use of two corpora of naturally-occurring dialogs recorded in real-life call centers. Compared with the traditional Mel-scale Frequency Cepstral Coefficients (MFCC) and the state-of-the-art features, the MRTII features also can improve the correct classification rates of proposed systems among different language databases. Experimental results show that the proposed MRTII-based feature information inspired by human visual perception of the spectrogram image can provide significant classification for real-life emotional recognition in speech. PMID:25594590

Multi-class computational evolution: development, benchmark evaluation and application to RNA-Seq biomarker discovery.

PubMed

Crabtree, Nathaniel M; Moore, Jason H; Bowyer, John F; George, Nysia I

2017-01-01

A computational evolution system (CES) is a knowledge discovery engine that can identify subtle, synergistic relationships in large datasets. Pareto optimization allows CESs to balance accuracy with model complexity when evolving classifiers. Using Pareto optimization, a CES is able to identify a very small number of features while maintaining high classification accuracy. A CES can be designed for various types of data, and the user can exploit expert knowledge about the classification problem in order to improve discrimination between classes. These characteristics give CES an advantage over other classification and feature selection algorithms, particularly when the goal is to identify a small number of highly relevant, non-redundant biomarkers. Previously, CESs have been developed only for binary class datasets. In this study, we developed a multi-class CES. The multi-class CES was compared to three common feature selection and classification algorithms: support vector machine (SVM), random k-nearest neighbor (RKNN), and random forest (RF). The algorithms were evaluated on three distinct multi-class RNA sequencing datasets. The comparison criteria were run-time, classification accuracy, number of selected features, and stability of selected feature set (as measured by the Tanimoto distance). The performance of each algorithm was data-dependent. CES performed best on the dataset with the smallest sample size, indicating that CES has a unique advantage since the accuracy of most classification methods suffer when sample size is small. The multi-class extension of CES increases the appeal of its application to complex, multi-class datasets in order to identify important biomarkers and features.

Protein classification using modified n-grams and skip-grams.

PubMed

Islam, S M Ashiqul; Heil, Benjamin J; Kearney, Christopher Michel; Baker, Erich J

2018-05-01

Classification by supervised machine learning greatly facilitates the annotation of protein characteristics from their primary sequence. However, the feature generation step in this process requires detailed knowledge of attributes used to classify the proteins. Lack of this knowledge risks the selection of irrelevant features, resulting in a faulty model. In this study, we introduce a supervised protein classification method with a novel means of automating the work-intensive feature generation step via a Natural Language Processing (NLP)-dependent model, using a modified combination of n-grams and skip-grams (m-NGSG). A meta-comparison of cross-validation accuracy with twelve training datasets from nine different published studies demonstrates a consistent increase in accuracy of m-NGSG when compared to contemporary classification and feature generation models. We expect this model to accelerate the classification of proteins from primary sequence data and increase the accessibility of protein characteristic prediction to a broader range of scientists. m-NGSG is freely available at Bitbucket: https://bitbucket.org/sm_islam/mngsg/src. A web server is available at watson.ecs.baylor.edu/ngsg. erich_baker@baylor.edu. Supplementary data are available at Bioinformatics online.

On feature augmentation for semantic argument classification of the Quran English translation using support vector machine

NASA Astrophysics Data System (ADS)

Khaira Batubara, Dina; Arif Bijaksana, Moch; Adiwijaya

2018-03-01

Research on the semantic argument classification requires semantically labeled data in large numbers, called corpus. Because building a corpus is costly and time-consuming, recently many studies have used existing corpus as the training data to conduct semantic argument classification research on new domain. But previous studies have proven that there is a significant decrease in performance when classifying semantic arguments on different domain between the training and the testing data. The main problem is when there is a new argument that found in the testing data but it is not found in the training data. This research carries on semantic argument classification on a new domain that is Quran English Translation by utilizing Propbank corpus as the training data. To recognize the new argument in the training data, this research proposes four new features for extending the argument features in the training data. By using SVM Linear, the experiment has proven that augmenting the proposed features to the baseline system with some combinations option improve the performance of semantic argument classification on Quran data using Propbank Corpus as training data.

Hyperspectral Image Enhancement and Mixture Deep-Learning Classification of Corneal Epithelium Injuries.

PubMed

Noor, Siti Salwa Md; Michael, Kaleena; Marshall, Stephen; Ren, Jinchang

2017-11-16

In our preliminary study, the reflectance signatures obtained from hyperspectral imaging (HSI) of normal and abnormal corneal epithelium tissues of porcine show similar morphology with subtle differences. Here we present image enhancement algorithms that can be used to improve the interpretability of data into clinically relevant information to facilitate diagnostics. A total of 25 corneal epithelium images without the application of eye staining were used. Three image feature extraction approaches were applied for image classification: (i) image feature classification from histogram using a support vector machine with a Gaussian radial basis function (SVM-GRBF); (ii) physical image feature classification using deep-learning Convolutional Neural Networks (CNNs) only; and (iii) the combined classification of CNNs and SVM-Linear. The performance results indicate that our chosen image features from the histogram and length-scale parameter were able to classify with up to 100% accuracy; particularly, at CNNs and CNNs-SVM, by employing 80% of the data sample for training and 20% for testing. Thus, in the assessment of corneal epithelium injuries, HSI has high potential as a method that could surpass current technologies regarding speed, objectivity, and reliability.

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.

Artificial bee colony algorithm for single-trial electroencephalogram analysis.

PubMed

Hsu, Wei-Yen; Hu, Ya-Ping

2015-04-01

In this study, we propose an analysis system combined with feature selection to further improve the classification accuracy of single-trial electroencephalogram (EEG) data. Acquiring event-related brain potential data from the sensorimotor cortices, the system comprises artifact and background noise removal, feature extraction, feature selection, and feature classification. First, the artifacts and background noise are removed automatically by means of independent component analysis and surface Laplacian filter, respectively. Several potential features, such as band power, autoregressive model, and coherence and phase-locking value, are then extracted for subsequent classification. Next, artificial bee colony (ABC) algorithm is used to select features from the aforementioned feature combination. Finally, selected subfeatures are classified by support vector machine. Comparing with and without artifact removal and feature selection, using a genetic algorithm on single-trial EEG data for 6 subjects, the results indicate that the proposed system is promising and suitable for brain-computer interface applications. © EEG and Clinical Neuroscience Society (ECNS) 2014.

Improving Naive Bayes with Online Feature Selection for Quick Adaptation to Evolving Feature Usefulness

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

Pon, R K; Cardenas, A F; Buttler, D J

The definition of what makes an article interesting varies from user to user and continually evolves even for a single user. As a result, for news recommendation systems, useless document features can not be determined a priori and all features are usually considered for interestingness classification. Consequently, the presence of currently useless features degrades classification performance [1], particularly over the initial set of news articles being classified. The initial set of document is critical for a user when considering which particular news recommendation system to adopt. To address these problems, we introduce an improved version of the naive Bayes classifiermore » with online feature selection. We use correlation to determine the utility of each feature and take advantage of the conditional independence assumption used by naive Bayes for online feature selection and classification. The augmented naive Bayes classifier performs 28% better than the traditional naive Bayes classifier in recommending news articles from the Yahoo! RSS feeds.« less

Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2's q2-feature-classifier plugin.

PubMed

Bokulich, Nicholas A; Kaehler, Benjamin D; Rideout, Jai Ram; Dillon, Matthew; Bolyen, Evan; Knight, Rob; Huttley, Gavin A; Gregory Caporaso, J

2018-05-17

Taxonomic classification of marker-gene sequences is an important step in microbiome analysis. We present q2-feature-classifier ( https://github.com/qiime2/q2-feature-classifier ), a QIIME 2 plugin containing several novel machine-learning and alignment-based methods for taxonomy classification. We evaluated and optimized several commonly used classification methods implemented in QIIME 1 (RDP, BLAST, UCLUST, and SortMeRNA) and several new methods implemented in QIIME 2 (a scikit-learn naive Bayes machine-learning classifier, and alignment-based taxonomy consensus methods based on VSEARCH, and BLAST+) for classification of bacterial 16S rRNA and fungal ITS marker-gene amplicon sequence data. The naive-Bayes, BLAST+-based, and VSEARCH-based classifiers implemented in QIIME 2 meet or exceed the species-level accuracy of other commonly used methods designed for classification of marker gene sequences that were evaluated in this work. These evaluations, based on 19 mock communities and error-free sequence simulations, including classification of simulated "novel" marker-gene sequences, are available in our extensible benchmarking framework, tax-credit ( https://github.com/caporaso-lab/tax-credit-data ). Our results illustrate the importance of parameter tuning for optimizing classifier performance, and we make recommendations regarding parameter choices for these classifiers under a range of standard operating conditions. q2-feature-classifier and tax-credit are both free, open-source, BSD-licensed packages available on GitHub.

Mapping the Proxies of Memory and Learning Function in Senior Adults with High-performing, Normal Aging and Neurocognitive Disorders.

PubMed

Lu, Hanna; Xi, Ni; Fung, Ada W T; Lam, Linda C W

2018-06-09

Memory and learning, as the core brain function, shows controversial results across studies focusing on aging and dementia. One of the reasons is because of the multi-faceted nature of memory and learning. However, there is still a dearth of comparable proxies with psychometric and morphometric portrait in clinical and non-clinical populations. We aim to investigate the proxies of memory and learning function with direct and derived measures and examine their associations with morphometric features in senior adults with different cognitive status. Based on two modality-driven tests, we assessed the component-specific memory and learning in the individuals with high performing (HP), normal aging, and neurocognitive disorders (NCD) (n = 488). Structural magnetic resonance imaging was used to measure the regional cortical thickness with surface-based morphometry analysis in a subsample (n = 52). Compared with HP elderly, the ones with normal aging and minor NCD showed declined recognition memory and working memory, whereas had better learning performance (derived scores). Meanwhile, major NCD patients showed more breakdowns of memory and learning function. The correlation between proxies of memory and learning and cortical thickness exhibited the overlapped and unique neural underpinnings. The proxies of memory and learning could be characterized by component-specific constructs with psychometric and morphometric bases. Overall, the constructs of memory are more likely related to the pathological changes, and the constructs of learning tend to reflect the cognitive abilities of compensation.

Morphological and morphometric features of nematode-cysts in Gymnotus inaequilabiatus liver in the Brazilian Pantanal.

PubMed

Galindo, Gizela Melina; Rodrigues, Robson Andrade; Marcondes, Sandriely Fernanda; Soares, Priscilla; Tavares, Luiz Eduardo Roland; Fernandes, Carlos Eurico

2017-01-01

The aim of this study was to determine the morphometric measures and morphological aspects of nematode-cysts in Gymnotus inaequilabiatus, and the presence of melanomacrophage centers (MMCs) associated with the periphery of cysts and in the liver parenchyma. Adult specimens, 34 female (123.1 ± 43.9g) and 45 male (135.5 ± 43.4g), from Paraguay River, Corumbá, Brazil, were used. The number of nematode-cysts was determined in 79 livers and 25 of them randomly selected for histopathological analysis and morphometric measures of nematode-cysts (mean diameter, thickness of collagen layer, and cyst-wall layer). The percentage of cysts with MMCs on the periphery and density in the liver parenchyma was estimated. The average number of macroscopic cysts was of 48.7 ± 2.78. Granulomatous reaction was observed surrounding the cysts. Diameter, collagen layer and cyst-wall measurements were 293.0 ± 75.18 (µm), 17.72 ± 6.01 (µm) and 12.21 ± 9.51 (µm), respectively. The number of nematode-cysts was correlated with hepatosomatic index, (r=0.26, P<0.05). Collagen layer was correlated with cyst diameter (r=0.62, P<0.01). Pericystic and parenchymatous MMCs were moderately (r=0.48) and highly (r=0.90) correlated with nematode-cysts number. Morphological characteristics of hepatic tissue and cysts-nematodes measures suggest that G. inaequilabiatus acts as a paratenic host to nematodes in the larval stage.

Geometric morphometric footprint analysis of young women

PubMed Central

2013-01-01

Background Most published attempts to quantify footprint shape are based on a small number of measurements. We applied geometric morphometric methods to study shape variation of the complete footprint outline in a sample of 83 adult women. Methods The outline of the footprint, including the toes, was represented by a comprehensive set of 85 landmarks and semilandmarks. Shape coordinates were computed by Generalized Procrustes Analysis. Results The first four principal components represented the major axes of variation in foot morphology: low-arched versus high-arched feet, long and narrow versus short and wide feet, the relative length of the hallux, and the relative length of the forefoot. These shape features varied across the measured individuals without any distinct clusters or discrete types of footprint shape. A high body mass index (BMI) was associated with wide and flat feet, and a high frequency of wearing high-heeled shoes was associated with a larger forefoot area of the footprint and a relatively long hallux. Larger feet had an increased length-to-width ratio of the footprint, a lower-arched foot, and longer toes relative to the remaining foot. Footprint shape differed on average between left and right feet, and the variability of footprint asymmetry increased with BMI. Conclusions Foot shape is affected by lifestyle factors even in a sample of young women (median age 23 years). Geometric morphometrics proved to be a powerful tool for the detailed analysis of footprint shape that is applicable in various scientific disciplines, including forensics, orthopedics, and footwear design. PMID:23886074

Arabidopsis phenotyping through Geometric Morphometrics.

PubMed

Manacorda, Carlos A; Asurmendi, Sebastian

2018-06-18

Recently, much technical progress was achieved in the field of plant phenotyping. High-throughput platforms and the development of improved algorithms for rosette image segmentation make it now possible to extract shape and size parameters for genetic, physiological and environmental studies on a large scale. The development of low-cost phenotyping platforms and freeware resources make it possible to widely expand phenotypic analysis tools for Arabidopsis. However, objective descriptors of shape parameters that could be used independently of platform and segmentation software used are still lacking and shape descriptions still rely on ad hoc or even sometimes contradictory descriptors, which could make comparisons difficult and perhaps inaccurate. Modern geometric morphometrics is a family of methods in quantitative biology proposed to be the main source of data and analytical tools in the emerging field of phenomics studies. Based on the location of landmarks (corresponding points) over imaged specimens and by combining geometry, multivariate analysis and powerful statistical techniques, these tools offer the possibility to reproducibly and accurately account for shape variations amongst groups and measure them in shape distance units. Here, a particular scheme of landmarks placement on Arabidopsis rosette images is proposed to study shape variation in the case of viral infection processes. Shape differences between controls and infected plants are quantified throughout the infectious process and visualized. Quantitative comparisons between two unrelated ssRNA+ viruses are shown and reproducibility issues are assessed. Combined with the newest automated platforms and plant segmentation procedures, geometric morphometric tools could boost phenotypic features extraction and processing in an objective, reproducible manner.

BMI and WHR Are Reflected in Female Facial Shape and Texture: A Geometric Morphometric Image Analysis.

PubMed

Mayer, Christine; Windhager, Sonja; Schaefer, Katrin; Mitteroecker, Philipp

2017-01-01

Facial markers of body composition are frequently studied in evolutionary psychology and are important in computational and forensic face recognition. We assessed the association of body mass index (BMI) and waist-to-hip ratio (WHR) with facial shape and texture (color pattern) in a sample of young Middle European women by a combination of geometric morphometrics and image analysis. Faces of women with high BMI had a wider and rounder facial outline relative to the size of the eyes and lips, and relatively lower eyebrows. Furthermore, women with high BMI had a brighter and more reddish skin color than women with lower BMI. The same facial features were associated with WHR, even though BMI and WHR were only moderately correlated. Yet BMI was better predictable than WHR from facial attributes. After leave-one-out cross-validation, we were able to predict 25% of variation in BMI and 10% of variation in WHR by facial shape. Facial texture predicted only about 3-10% of variation in BMI and WHR. This indicates that facial shape primarily reflects total fat proportion, rather than the distribution of fat within the body. The association of reddish facial texture in high-BMI women may be mediated by increased blood pressure and superficial blood flow as well as diet. Our study elucidates how geometric morphometric image analysis serves to quantify the effect of biological factors such as BMI and WHR to facial shape and color, which in turn contributes to social perception.

A Fast, Open EEG Classification Framework Based on Feature Compression and Channel Ranking

PubMed Central

Han, Jiuqi; Zhao, Yuwei; Sun, Hongji; Chen, Jiayun; Ke, Ang; Xu, Gesen; Zhang, Hualiang; Zhou, Jin; Wang, Changyong

2018-01-01

Superior feature extraction, channel selection and classification methods are essential for designing electroencephalography (EEG) classification frameworks. However, the performance of most frameworks is limited by their improper channel selection methods and too specifical design, leading to high computational complexity, non-convergent procedure and narrow expansibility. In this paper, to remedy these drawbacks, we propose a fast, open EEG classification framework centralized by EEG feature compression, low-dimensional representation, and convergent iterative channel ranking. First, to reduce the complexity, we use data clustering to compress the EEG features channel-wise, packing the high-dimensional EEG signal, and endowing them with numerical signatures. Second, to provide easy access to alternative superior methods, we structurally represent each EEG trial in a feature vector with its corresponding numerical signature. Thus, the recorded signals of many trials shrink to a low-dimensional structural matrix compatible with most pattern recognition methods. Third, a series of effective iterative feature selection approaches with theoretical convergence is introduced to rank the EEG channels and remove redundant ones, further accelerating the EEG classification process and ensuring its stability. Finally, a classical linear discriminant analysis (LDA) model is employed to classify a single EEG trial with selected channels. Experimental results on two real world brain-computer interface (BCI) competition datasets demonstrate the promising performance of the proposed framework over state-of-the-art methods. PMID:29713262

Feature Selection for Classification of Polar Regions Using a Fuzzy Expert System

NASA Technical Reports Server (NTRS)

Penaloza, Mauel A.; Welch, Ronald M.

1996-01-01

Labeling, feature selection, and the choice of classifier are critical elements for classification of scenes and for image understanding. This study examines several methods for feature selection in polar regions, including the list, of a fuzzy logic-based expert system for further refinement of a set of selected features. Six Advanced Very High Resolution Radiometer (AVHRR) Local Area Coverage (LAC) arctic scenes are classified into nine classes: water, snow / ice, ice cloud, land, thin stratus, stratus over water, cumulus over water, textured snow over water, and snow-covered mountains. Sixty-seven spectral and textural features are computed and analyzed by the feature selection algorithms. The divergence, histogram analysis, and discriminant analysis approaches are intercompared for their effectiveness in feature selection. The fuzzy expert system method is used not only to determine the effectiveness of each approach in classifying polar scenes, but also to further reduce the features into a more optimal set. For each selection method,features are ranked from best to worst, and the best half of the features are selected. Then, rules using these selected features are defined. The results of running the fuzzy expert system with these rules show that the divergence method produces the best set features, not only does it produce the highest classification accuracy, but also it has the lowest computation requirements. A reduction of the set of features produced by the divergence method using the fuzzy expert system results in an overall classification accuracy of over 95 %. However, this increase of accuracy has a high computation cost.

A Dimensionally Aligned Signal Projection for Classification of Unintended Radiated Emissions

DOE PAGES

Vann, Jason Michael; Karnowski, Thomas P.; Kerekes, Ryan; ...

2017-04-24

Characterization of unintended radiated emissions (URE) from electronic devices plays an important role in many research areas from electromagnetic interference to nonintrusive load monitoring to information system security. URE can provide insights for applications ranging from load disaggregation and energy efficiency to condition-based maintenance of equipment-based upon detected fault conditions. URE characterization often requires subject matter expertise to tailor transforms and feature extractors for the specific electrical devices of interest. We present a novel approach, named dimensionally aligned signal projection (DASP), for projecting aligned signal characteristics that are inherent to the physical implementation of many commercial electronic devices. These projectionsmore » minimize the need for an intimate understanding of the underlying physical circuitry and significantly reduce the number of features required for signal classification. We present three possible DASP algorithms that leverage frequency harmonics, modulation alignments, and frequency peak spacings, along with a two-dimensional image manipulation method for statistical feature extraction. To demonstrate the ability of DASP to generate relevant features from URE, we measured the conducted URE from 14 residential electronic devices using a 2 MS/s collection system. Furthermore, a linear discriminant analysis classifier was trained using DASP generated features and was blind tested resulting in a greater than 90% classification accuracy for each of the DASP algorithms and an accuracy of 99.1% when DASP features are used in combination. Furthermore, we show that a rank reduced feature set of the combined DASP algorithms provides a 98.9% classification accuracy with only three features and outperforms a set of spectral features in terms of general classification as well as applicability across a broad number of devices.« less

Intelligent feature selection techniques for pattern classification of Lamb wave signals

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

Hinders, Mark K.; Miller, Corey A.

2014-02-18

Lamb wave interaction with flaws is a complex, three-dimensional phenomenon, which often frustrates signal interpretation schemes based on mode arrival time shifts predicted by dispersion curves. As the flaw severity increases, scattering and mode conversion effects will often dominate the time-domain signals, obscuring available information about flaws because multiple modes may arrive on top of each other. Even for idealized flaw geometries the scattering and mode conversion behavior of Lamb waves is very complex. Here, multi-mode Lamb waves in a metal plate are propagated across a rectangular flat-bottom hole in a sequence of pitch-catch measurements corresponding to the double crossholemore » tomography geometry. The flaw is sequentially deepened, with the Lamb wave measurements repeated at each flaw depth. Lamb wave tomography reconstructions are used to identify which waveforms have interacted with the flaw and thereby carry information about its depth. Multiple features are extracted from each of the Lamb wave signals using wavelets, which are then fed to statistical pattern classification algorithms that identify flaw severity. In order to achieve the highest classification accuracy, an optimal feature space is required but it’s never known a priori which features are going to be best. For structural health monitoring we make use of the fact that physical flaws, such as corrosion, will only increase over time. This allows us to identify feature vectors which are topologically well-behaved by requiring that sequential classes “line up” in feature vector space. An intelligent feature selection routine is illustrated that identifies favorable class distributions in multi-dimensional feature spaces using computational homology theory. Betti numbers and formal classification accuracies are calculated for each feature space subset to establish a correlation between the topology of the class distribution and the corresponding classification accuracy.« less

A Dimensionally Aligned Signal Projection for Classification of Unintended Radiated Emissions

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

Vann, Jason Michael; Karnowski, Thomas P.; Kerekes, Ryan

Characterization of unintended radiated emissions (URE) from electronic devices plays an important role in many research areas from electromagnetic interference to nonintrusive load monitoring to information system security. URE can provide insights for applications ranging from load disaggregation and energy efficiency to condition-based maintenance of equipment-based upon detected fault conditions. URE characterization often requires subject matter expertise to tailor transforms and feature extractors for the specific electrical devices of interest. We present a novel approach, named dimensionally aligned signal projection (DASP), for projecting aligned signal characteristics that are inherent to the physical implementation of many commercial electronic devices. These projectionsmore » minimize the need for an intimate understanding of the underlying physical circuitry and significantly reduce the number of features required for signal classification. We present three possible DASP algorithms that leverage frequency harmonics, modulation alignments, and frequency peak spacings, along with a two-dimensional image manipulation method for statistical feature extraction. To demonstrate the ability of DASP to generate relevant features from URE, we measured the conducted URE from 14 residential electronic devices using a 2 MS/s collection system. Furthermore, a linear discriminant analysis classifier was trained using DASP generated features and was blind tested resulting in a greater than 90% classification accuracy for each of the DASP algorithms and an accuracy of 99.1% when DASP features are used in combination. Furthermore, we show that a rank reduced feature set of the combined DASP algorithms provides a 98.9% classification accuracy with only three features and outperforms a set of spectral features in terms of general classification as well as applicability across a broad number of devices.« less

Large Margin Multi-Modal Multi-Task Feature Extraction for Image Classification.

PubMed

Yong Luo; Yonggang Wen; Dacheng Tao; Jie Gui; Chao Xu

2016-01-01

The features used in many image analysis-based applications are frequently of very high dimension. Feature extraction offers several advantages in high-dimensional cases, and many recent studies have used multi-task feature extraction approaches, which often outperform single-task feature extraction approaches. However, most of these methods are limited in that they only consider data represented by a single type of feature, even though features usually represent images from multiple modalities. We, therefore, propose a novel large margin multi-modal multi-task feature extraction (LM3FE) framework for handling multi-modal features for image classification. In particular, LM3FE simultaneously learns the feature extraction matrix for each modality and the modality combination coefficients. In this way, LM3FE not only handles correlated and noisy features, but also utilizes the complementarity of different modalities to further help reduce feature redundancy in each modality. The large margin principle employed also helps to extract strongly predictive features, so that they are more suitable for prediction (e.g., classification). An alternating algorithm is developed for problem optimization, and each subproblem can be efficiently solved. Experiments on two challenging real-world image data sets demonstrate the effectiveness and superiority of the proposed method.

Machine Learning Classification Combining Multiple Features of A Hyper-Network of fMRI Data in Alzheimer's Disease

PubMed Central

Guo, Hao; Zhang, Fan; Chen, Junjie; Xu, Yong; Xiang, Jie

2017-01-01

Exploring functional interactions among various brain regions is helpful for understanding the pathological underpinnings of neurological disorders. Brain networks provide an important representation of those functional interactions, and thus are widely applied in the diagnosis and classification of neurodegenerative diseases. Many mental disorders involve a sharp decline in cognitive ability as a major symptom, which can be caused by abnormal connectivity patterns among several brain regions. However, conventional functional connectivity networks are usually constructed based on pairwise correlations among different brain regions. This approach ignores higher-order relationships, and cannot effectively characterize the high-order interactions of many brain regions working together. Recent neuroscience research suggests that higher-order relationships between brain regions are important for brain network analysis. Hyper-networks have been proposed that can effectively represent the interactions among brain regions. However, this method extracts the local properties of brain regions as features, but ignores the global topology information, which affects the evaluation of network topology and reduces the performance of the classifier. This problem can be compensated by a subgraph feature-based method, but it is not sensitive to change in a single brain region. Considering that both of these feature extraction methods result in the loss of information, we propose a novel machine learning classification method that combines multiple features of a hyper-network based on functional magnetic resonance imaging in Alzheimer's disease. The method combines the brain region features and subgraph features, and then uses a multi-kernel SVM for classification. This retains not only the global topological information, but also the sensitivity to change in a single brain region. To certify the proposed method, 28 normal control subjects and 38 Alzheimer's disease patients were selected to participate in an experiment. The proposed method achieved satisfactory classification accuracy, with an average of 91.60%. The abnormal brain regions included the bilateral precuneus, right parahippocampal gyrus\\hippocampus, right posterior cingulate gyrus, and other regions that are known to be important in Alzheimer's disease. Machine learning classification combining multiple features of a hyper-network of functional magnetic resonance imaging data in Alzheimer's disease obtains better classification performance. PMID:29209156

Morphometricity as a measure of the neuroanatomical signature of a trait.

PubMed

Sabuncu, Mert R; Ge, Tian; Holmes, Avram J; Smoller, Jordan W; Buckner, Randy L; Fischl, Bruce

2016-09-27

Complex physiological and behavioral traits, including neurological and psychiatric disorders, often associate with distributed anatomical variation. This paper introduces a global metric, called morphometricity, as a measure of the anatomical signature of different traits. Morphometricity is defined as the proportion of phenotypic variation that can be explained by macroscopic brain morphology. We estimate morphometricity via a linear mixed-effects model that uses an anatomical similarity matrix computed based on measurements derived from structural brain MRI scans. We examined over 3,800 unique MRI scans from nine large-scale studies to estimate the morphometricity of a range of phenotypes, including clinical diagnoses such as Alzheimer's disease, and nonclinical traits such as measures of cognition. Our results demonstrate that morphometricity can provide novel insights about the neuroanatomical correlates of a diverse set of traits, revealing associations that might not be detectable through traditional statistical techniques.

Morphometricity as a measure of the neuroanatomical signature of a trait

PubMed Central

Sabuncu, Mert R.; Ge, Tian; Holmes, Avram J.; Smoller, Jordan W.; Buckner, Randy L.; Fischl, Bruce

2016-01-01

Complex physiological and behavioral traits, including neurological and psychiatric disorders, often associate with distributed anatomical variation. This paper introduces a global metric, called morphometricity, as a measure of the anatomical signature of different traits. Morphometricity is defined as the proportion of phenotypic variation that can be explained by macroscopic brain morphology. We estimate morphometricity via a linear mixed-effects model that uses an anatomical similarity matrix computed based on measurements derived from structural brain MRI scans. We examined over 3,800 unique MRI scans from nine large-scale studies to estimate the morphometricity of a range of phenotypes, including clinical diagnoses such as Alzheimer’s disease, and nonclinical traits such as measures of cognition. Our results demonstrate that morphometricity can provide novel insights about the neuroanatomical correlates of a diverse set of traits, revealing associations that might not be detectable through traditional statistical techniques. PMID:27613854

Geomorphometric comparative analysis of Latin-American volcanoes

NASA Astrophysics Data System (ADS)

Camiz, Sergio; Poscolieri, Maurizio; Roverato, Matteo

2017-07-01

The geomorphometric classifications of three groups of volcanoes situated in the Andes Cordillera, Central America, and Mexico are performed and compared. Input data are eight local topographic gradients (i.e. elevation differences) obtained by processing each volcano raster ASTER-GDEM data. The pixels of each volcano DEM have been classified into 17 classes through a K-means clustering procedure following principal component analysis of the gradients. The spatial distribution of the classes, representing homogeneous terrain units, is shown on thematic colour maps, where colours are assigned according to mean slope and aspect class values. The interpretation of the geomorphometric classification of the volcanoes is based on the statistics of both gradients and morphometric parameters (slope, aspect and elevation). The latter were used for a comparison of the volcanoes, performed through classes' slope/aspect scatterplots and multidimensional methods. In this paper, we apply the mentioned methodology on 21 volcanoes, randomly chosen from Mexico to Patagonia, to show how it may contribute to detect geomorphological similarities and differences among them. As such, both its descriptive and graphical abilities may be a useful complement to future volcanological studies.

Validation of Morphometric Analyses of Small-Intestinal Biopsy Readouts in Celiac Disease

PubMed Central

Taavela, Juha; Koskinen, Outi; Huhtala, Heini; Lähdeaho, Marja-Leena; Popp, Alina; Laurila, Kaija; Collin, Pekka; Kaukinen, Katri; Kurppa, Kalle; Mäki, Markku

2013-01-01

Background Assessment of the gluten-induced small-intestinal mucosal injury remains the cornerstone of celiac disease diagnosis. Usually the injury is evaluated using grouped classifications (e.g. Marsh groups), but this is often too imprecise and ignores minor but significant changes in the mucosa. Consequently, there is a need for validated continuous variables in everyday practice and in academic and pharmacological research. Methods We studied the performance of our standard operating procedure (SOP) on 93 selected biopsy specimens from adult celiac disease patients and non-celiac disease controls. The specimens, which comprised different grades of gluten-induced mucosal injury, were evaluated by morphometric measurements. Specimens with tangential cutting resulting from poorly oriented biopsies were included. Two accredited evaluators performed the measurements in blinded fashion. The intraobserver and interobserver variations for villus height and crypt depth ratio (VH:CrD) and densities of intraepithelial lymphocytes (IELs) were analyzed by the Bland-Altman method and intraclass correlation. Results Unevaluable biopsies according to our SOP were correctly identified. The intraobserver analysis of VH:CrD showed a mean difference of 0.087 with limits of agreement from −0.398 to 0.224; the standard deviation (SD) was 0.159. The mean difference in interobserver analysis was 0.070, limits of agreement −0.516 to 0.375, and SD 0.227. The intraclass correlation coefficient in intraobserver variation was 0.983 and that in interobserver variation 0.978. CD3+ IEL density countings in the paraffin-embedded and frozen biopsies showed SDs of 17.1% and 16.5%; the intraclass correlation coefficients were 0.961 and 0.956, respectively. Conclusions Using our SOP, quantitative, reliable and reproducible morphometric results can be obtained on duodenal biopsy specimens with different grades of gluten-induced injury. Clinically significant changes were defined according to the error margins (2SD) of the analyses in VH:CrD as 0.4 and in CD3+-stained IELs as 30%. PMID:24146832

Integrated Change Detection and Classification in Urban Areas Based on Airborne Laser Scanning Point Clouds.

PubMed

Tran, Thi Huong Giang; Ressl, Camillo; Pfeifer, Norbert

2018-02-03

This paper suggests a new approach for change detection (CD) in 3D point clouds. It combines classification and CD in one step using machine learning. The point cloud data of both epochs are merged for computing features of four types: features describing the point distribution, a feature relating to relative terrain elevation, features specific for the multi-target capability of laser scanning, and features combining the point clouds of both epochs to identify the change. All these features are merged in the points and then training samples are acquired to create the model for supervised classification, which is then applied to the whole study area. The final results reach an overall accuracy of over 90% for both epochs of eight classes: lost tree, new tree, lost building, new building, changed ground, unchanged building, unchanged tree, and unchanged ground.

Comparison of the ESHRE–ESGE and ASRM classifications of Müllerian duct anomalies in everyday practice

PubMed Central

Ludwin, A.; Ludwin, I.

2015-01-01

STUDY QUESTION Does the European Society of Human Reproduction and Embryology–European Society for Gynaecological Endoscopy (ESHRE–ESGE) classification of female genital tract malformations significantly increase the frequency of septate uterus diagnosis relative to the American Society for Reproductive Medicine (ASRM) classification? SUMMARY ANSWER Use of the ESHRE–ESGE classification, compared with the ASRM classification, significantly increased the frequency of septate uterus recognition. WHAT IS KNOWN ALREADY The ESHRE–ESGE criteria were supposed to eliminate the subjective diagnoses of septate uterus by the ASRM criteria and replace the complementary absolute morphometric criteria. However, the clinical value of the ESHRE–ESGE classification in daily practice is difficult to appreciate. The application of the ESHRE–ESGE criteria has resulted in a significantly increased recognition of residual septum after hysteroscopic metroplasty, with a possible risk of overdiagnosis of septate uterus and problems for its management. STUDY DESIGN, SIZE, AND DURATION A prospective observational study was performed with 261 women consecutively enrolled between June and September 2013. PARTICIPANTS/MATERIALS, SETTING, AND METHODS Non-pregnant women of reproductive age presented for evaluation to a private medical center. A gynecological examination and 3D ultrasonography were performed to assess the anatomy of the uterus, cervix and vagina. Congenital anomalies were diagnosed using the ASRM classification with additional morphometric criteria as well as with the ESHRE–ESGE classification. We compared the frequency and concordance of diagnoses of septate uterus and all congenital malformations of the uterus according to both classifications. The morphological characteristics of septate uterus recognized by both criteria were compared. MAIN RESULTS AND ROLE OF CHANCE Of the 261 patients enrolled in this study, septate uterus was diagnosed in 44 (16.9%) and 16 (6.1%) patients using the ESGE–ESHRE and ASRM criteria, respectively [relative risk (RR)ESHRE–ESGE:ASRM 2.74; 95% confidence interval (CI), 1.6–4.72; P < 0.01]. At least one congenital anomaly were diagnosed in 58 (22.2%) and 43 (16.5%) patients using the ESHRE–ESGE and ASRM classifications (RRESHRE–ESGE:ASRM, 1.35; 95% CI, 0.95–1.92, P = 0.1), respectively. The two criteria had moderate strength of agreement in the diagnosis of septate uterus (κ = 0.45, P < 0.01). There was good agreement in differentiation between anomaly and norm between the two assessment criteria (κ = 0.79, P < 0.01). The percentages of all congenital malformations and results of the differentiation between the anomaly and norm were obtained after excluding the confounding original ESHRE–ESGE criterion of dysmorphic uterus (internal indentation <50% uterine wall thickness). The morphology of septa identified by the ESHRE–ESGE [length of internal fundal indentation (mm): median 10.7; lower–upper quartile, 8.1–20] significantly differed (P < 0.01) from that identified by the ASRM criteria [length of internal fundal indentation (mm): median, 21.1; lower–upper quartile, 18.8–33.1]. Internal fundal indentation in 16 out of 44 (36.4%) cases was <1 cm in the septate uterus by ESHRE–ESGE and met the criteria for normal uterus by ASRM. LIMITATIONS AND REASONS FOR CAUTION The study participants were women who visited a diagnostic and treatment center specialized in uterine congenital malformations for a medical assessment, not from the general public. WIDER IMPLICATIONS OF THE FINDINGS Septate uterus diagnosis by ESHRE–ESGE was quantitatively dominated by morphological states corresponding to arcuate uterus or cases that were not diagnosed as congenital malformations by ASRM. Relative overdiagnosis of septate uterus by ESHRE–ESGE in these cases may lead to unnecessary overtreatment without the expected benefits. The ESHRE–ESGE classification criteria should be redefined due to confusions in the methodology. Until the criteria are revised, septate uterus should not be diagnosed using this classification system and it should not be used as an eligibility criterion for hysteroscopic metroplasty. STUDY FUNDING/COMPETING INTEREST(S) This work was supported by Jagiellonian University (grant no. K/ZDS/003821). The authors have no competing interests to declare. PMID:25534461

Integration of adaptive guided filtering, deep feature learning, and edge-detection techniques for hyperspectral image classification

NASA Astrophysics Data System (ADS)

Wan, Xiaoqing; Zhao, Chunhui; Gao, Bing

2017-11-01

The integration of an edge-preserving filtering technique in the classification of a hyperspectral image (HSI) has been proven effective in enhancing classification performance. This paper proposes an ensemble strategy for HSI classification using an edge-preserving filter along with a deep learning model and edge detection. First, an adaptive guided filter is applied to the original HSI to reduce the noise in degraded images and to extract powerful spectral-spatial features. Second, the extracted features are fed as input to a stacked sparse autoencoder to adaptively exploit more invariant and deep feature representations; then, a random forest classifier is applied to fine-tune the entire pretrained network and determine the classification output. Third, a Prewitt compass operator is further performed on the HSI to extract the edges of the first principal component after dimension reduction. Moreover, the regional growth rule is applied to the resulting edge logical image to determine the local region for each unlabeled pixel. Finally, the categories of the corresponding neighborhood samples are determined in the original classification map; then, the major voting mechanism is implemented to generate the final output. Extensive experiments proved that the proposed method achieves competitive performance compared with several traditional approaches.

Genetic Programming and Frequent Itemset Mining to Identify Feature Selection Patterns of iEEG and fMRI Epilepsy Data

PubMed Central

Smart, Otis; Burrell, Lauren

2014-01-01

Pattern classification for intracranial electroencephalogram (iEEG) and functional magnetic resonance imaging (fMRI) signals has furthered epilepsy research toward understanding the origin of epileptic seizures and localizing dysfunctional brain tissue for treatment. Prior research has demonstrated that implicitly selecting features with a genetic programming (GP) algorithm more effectively determined the proper features to discern biomarker and non-biomarker interictal iEEG and fMRI activity than conventional feature selection approaches. However for each the iEEG and fMRI modalities, it is still uncertain whether the stochastic properties of indirect feature selection with a GP yield (a) consistent results within a patient data set and (b) features that are specific or universal across multiple patient data sets. We examined the reproducibility of implicitly selecting features to classify interictal activity using a GP algorithm by performing several selection trials and subsequent frequent itemset mining (FIM) for separate iEEG and fMRI epilepsy patient data. We observed within-subject consistency and across-subject variability with some small similarity for selected features, indicating a clear need for patient-specific features and possible need for patient-specific feature selection or/and classification. For the fMRI, using nearest-neighbor classification and 30 GP generations, we obtained over 60% median sensitivity and over 60% median selectivity. For the iEEG, using nearest-neighbor classification and 30 GP generations, we obtained over 65% median sensitivity and over 65% median selectivity except one patient. PMID:25580059

Land use classification using texture information in ERTS-A MSS imagery

NASA Technical Reports Server (NTRS)

Haralick, R. M. (Principal Investigator); Shanmugam, K. S.; Bosley, R.

1973-01-01

The author has identified the following significant results. Preliminary digital analysis of ERTS-1 MSS imagery reveals that the textural features of the imagery are very useful for land use classification. A procedure for extracting the textural features of ERTS-1 imagery is presented and the results of a land use classification scheme based on the textural features are also presented. The land use classification algorithm using textural features was tested on a 5100 square mile area covered by part of an ERTS-1 MSS band 5 image over the California coastline. The image covering this area was blocked into 648 subimages of size 8.9 square miles each. Based on a color composite of the image set, a total of 7 land use categories were identified. These land use categories are: coastal forest, woodlands, annual grasslands, urban areas, large irrigated fields, small irrigated fields, and water. The automatic classifier was trained to identify the land use categories using only the textural characteristics of the subimages; 75 percent of the subimages were assigned correct identifications. Since texture and spectral features provide completely different kinds of information, a significant increase in identification accuracy will take place when both features are used together.

Informal settlement classification using point-cloud and image-based features from UAV data

NASA Astrophysics Data System (ADS)

Gevaert, C. M.; Persello, C.; Sliuzas, R.; Vosselman, G.

2017-03-01

Unmanned Aerial Vehicles (UAVs) are capable of providing very high resolution and up-to-date information to support informal settlement upgrading projects. In order to provide accurate basemaps, urban scene understanding through the identification and classification of buildings and terrain is imperative. However, common characteristics of informal settlements such as small, irregular buildings with heterogeneous roof material and large presence of clutter challenge state-of-the-art algorithms. Furthermore, it is of interest to analyse which fundamental attributes are suitable for describing these objects in different geographic locations. This work investigates how 2D radiometric and textural features, 2.5D topographic features, and 3D geometric features obtained from UAV imagery can be integrated to obtain a high classification accuracy in challenging classification problems for the analysis of informal settlements. UAV datasets from informal settlements in two different countries are compared in order to identify salient features for specific objects in heterogeneous urban environments. Findings show that the integration of 2D and 3D features leads to an overall accuracy of 91.6% and 95.2% respectively for informal settlements in Kigali, Rwanda and Maldonado, Uruguay.

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.

Convolutional neural network for high-accuracy functional near-infrared spectroscopy in a brain-computer interface: three-class classification of rest, right-, and left-hand motor execution.

PubMed

Trakoolwilaiwan, Thanawin; Behboodi, Bahareh; Lee, Jaeseok; Kim, Kyungsoo; Choi, Ji-Woong

2018-01-01

The aim of this work is to develop an effective brain-computer interface (BCI) method based on functional near-infrared spectroscopy (fNIRS). In order to improve the performance of the BCI system in terms of accuracy, the ability to discriminate features from input signals and proper classification are desired. Previous studies have mainly extracted features from the signal manually, but proper features need to be selected carefully. To avoid performance degradation caused by manual feature selection, we applied convolutional neural networks (CNNs) as the automatic feature extractor and classifier for fNIRS-based BCI. In this study, the hemodynamic responses evoked by performing rest, right-, and left-hand motor execution tasks were measured on eight healthy subjects to compare performances. Our CNN-based method provided improvements in classification accuracy over conventional methods employing the most commonly used features of mean, peak, slope, variance, kurtosis, and skewness, classified by support vector machine (SVM) and artificial neural network (ANN). Specifically, up to 6.49% and 3.33% improvement in classification accuracy was achieved by CNN compared with SVM and ANN, respectively.

Discriminative Dictionary Learning With Two-Level Low Rank and Group Sparse Decomposition for Image Classification.

PubMed

Wen, Zaidao; Hou, Zaidao; Jiao, Licheng

2017-11-01

Discriminative dictionary learning (DDL) framework has been widely used in image classification which aims to learn some class-specific feature vectors as well as a representative dictionary according to a set of labeled training samples. However, interclass similarities and intraclass variances among input samples and learned features will generally weaken the representability of dictionary and the discrimination of feature vectors so as to degrade the classification performance. Therefore, how to explicitly represent them becomes an important issue. In this paper, we present a novel DDL framework with two-level low rank and group sparse decomposition model. In the first level, we learn a class-shared and several class-specific dictionaries, where a low rank and a group sparse regularization are, respectively, imposed on the corresponding feature matrices. In the second level, the class-specific feature matrix will be further decomposed into a low rank and a sparse matrix so that intraclass variances can be separated to concentrate the corresponding feature vectors. Extensive experimental results demonstrate the effectiveness of our model. Compared with the other state-of-the-arts on several popular image databases, our model can achieve a competitive or better performance in terms of the classification accuracy.

Feature generation and representations for protein-protein interaction classification.

PubMed

Lan, Man; Tan, Chew Lim; Su, Jian

2009-10-01

Automatic detecting protein-protein interaction (PPI) relevant articles is a crucial step for large-scale biological database curation. The previous work adopted POS tagging, shallow parsing and sentence splitting techniques, but they achieved worse performance than the simple bag-of-words representation. In this paper, we generated and investigated multiple types of feature representations in order to further improve the performance of PPI text classification task. Besides the traditional domain-independent bag-of-words approach and the term weighting methods, we also explored other domain-dependent features, i.e. protein-protein interaction trigger keywords, protein named entities and the advanced ways of incorporating Natural Language Processing (NLP) output. The integration of these multiple features has been evaluated on the BioCreAtIvE II corpus. The experimental results showed that both the advanced way of using NLP output and the integration of bag-of-words and NLP output improved the performance of text classification. Specifically, in comparison with the best performance achieved in the BioCreAtIvE II IAS, the feature-level and classifier-level integration of multiple features improved the performance of classification 2.71% and 3.95%, respectively.

A stereo remote sensing feature selection method based on artificial bee colony algorithm

NASA Astrophysics Data System (ADS)

Yan, Yiming; Liu, Pigang; Zhang, Ye; Su, Nan; Tian, Shu; Gao, Fengjiao; Shen, Yi

2014-05-01

To improve the efficiency of stereo information for remote sensing classification, a stereo remote sensing feature selection method is proposed in this paper presents, which is based on artificial bee colony algorithm. Remote sensing stereo information could be described by digital surface model (DSM) and optical image, which contain information of the three-dimensional structure and optical characteristics, respectively. Firstly, three-dimensional structure characteristic could be analyzed by 3D-Zernike descriptors (3DZD). However, different parameters of 3DZD could descript different complexity of three-dimensional structure, and it needs to be better optimized selected for various objects on the ground. Secondly, features for representing optical characteristic also need to be optimized. If not properly handled, when a stereo feature vector composed of 3DZD and image features, that would be a lot of redundant information, and the redundant information may not improve the classification accuracy, even cause adverse effects. To reduce information redundancy while maintaining or improving the classification accuracy, an optimized frame for this stereo feature selection problem is created, and artificial bee colony algorithm is introduced for solving this optimization problem. Experimental results show that the proposed method can effectively improve the computational efficiency, improve the classification accuracy.

Optimal Non-Invasive Fault Classification Model for Packaged Ceramic Tile Quality Monitoring Using MMW Imaging

NASA Astrophysics Data System (ADS)

Agarwal, Smriti; Singh, Dharmendra

2016-04-01

Millimeter wave (MMW) frequency has emerged as an efficient tool for different stand-off imaging applications. In this paper, we have dealt with a novel MMW imaging application, i.e., non-invasive packaged goods quality estimation for industrial quality monitoring applications. An active MMW imaging radar operating at 60 GHz has been ingeniously designed for concealed fault estimation. Ceramic tiles covered with commonly used packaging cardboard were used as concealed targets for undercover fault classification. A comparison of computer vision-based state-of-the-art feature extraction techniques, viz, discrete Fourier transform (DFT), wavelet transform (WT), principal component analysis (PCA), gray level co-occurrence texture (GLCM), and histogram of oriented gradient (HOG) has been done with respect to their efficient and differentiable feature vector generation capability for undercover target fault classification. An extensive number of experiments were performed with different ceramic tile fault configurations, viz., vertical crack, horizontal crack, random crack, diagonal crack along with the non-faulty tiles. Further, an independent algorithm validation was done demonstrating classification accuracy: 80, 86.67, 73.33, and 93.33 % for DFT, WT, PCA, GLCM, and HOG feature-based artificial neural network (ANN) classifier models, respectively. Classification results show good capability for HOG feature extraction technique towards non-destructive quality inspection with appreciably low false alarm as compared to other techniques. Thereby, a robust and optimal image feature-based neural network classification model has been proposed for non-invasive, automatic fault monitoring for a financially and commercially competent industrial growth.

D Object Classification Based on Thermal and Visible Imagery in Urban Area

NASA Astrophysics Data System (ADS)

Hasani, H.; Samadzadegan, F.

2015-12-01

The spatial distribution of land cover in the urban area especially 3D objects (buildings and trees) is a fundamental dataset for urban planning, ecological research, disaster management, etc. According to recent advances in sensor technologies, several types of remotely sensed data are available from the same area. Data fusion has been widely investigated for integrating different source of data in classification of urban area. Thermal infrared imagery (TIR) contains information on emitted radiation and has unique radiometric properties. However, due to coarse spatial resolution of thermal data, its application has been restricted in urban areas. On the other hand, visible image (VIS) has high spatial resolution and information in visible spectrum. Consequently, there is a complementary relation between thermal and visible imagery in classification of urban area. This paper evaluates the potential of aerial thermal hyperspectral and visible imagery fusion in classification of urban area. In the pre-processing step, thermal imagery is resampled to the spatial resolution of visible image. Then feature level fusion is applied to construct hybrid feature space include visible bands, thermal hyperspectral bands, spatial and texture features and moreover Principle Component Analysis (PCA) transformation is applied to extract PCs. Due to high dimensionality of feature space, dimension reduction method is performed. Finally, Support Vector Machines (SVMs) classify the reduced hybrid feature space. The obtained results show using thermal imagery along with visible imagery, improved the classification accuracy up to 8% respect to visible image classification.

Dissimilarity representations in lung parenchyma classification

NASA Astrophysics Data System (ADS)

Sørensen, Lauge; de Bruijne, Marleen

2009-02-01

A good problem representation is important for a pattern recognition system to be successful. The traditional approach to statistical pattern recognition is feature representation. More specifically, objects are represented by a number of features in a feature vector space, and classifiers are built in this representation. This is also the general trend in lung parenchyma classification in computed tomography (CT) images, where the features often are measures on feature histograms. Instead, we propose to build normal density based classifiers in dissimilarity representations for lung parenchyma classification. This allows for the classifiers to work on dissimilarities between objects, which might be a more natural way of representing lung parenchyma. In this context, dissimilarity is defined between CT regions of interest (ROI)s. ROIs are represented by their CT attenuation histogram and ROI dissimilarity is defined as a histogram dissimilarity measure between the attenuation histograms. In this setting, the full histograms are utilized according to the chosen histogram dissimilarity measure. We apply this idea to classification of different emphysema patterns as well as normal, healthy tissue. Two dissimilarity representation approaches as well as different histogram dissimilarity measures are considered. The approaches are evaluated on a set of 168 CT ROIs using normal density based classifiers all showing good performance. Compared to using histogram dissimilarity directly as distance in a emph{k} nearest neighbor classifier, which achieves a classification accuracy of 92.9%, the best dissimilarity representation based classifier is significantly better with a classification accuracy of 97.0% (text{emph{p" border="0" class="imgtopleft"> = 0.046).

Computerized Classification of Pneumoconiosis on Digital Chest Radiography Artificial Neural Network with Three Stages.

PubMed

Okumura, Eiichiro; Kawashita, Ikuo; Ishida, Takayuki

2017-08-01

It is difficult for radiologists to classify pneumoconiosis from category 0 to category 3 on chest radiographs. Therefore, we have developed a computer-aided diagnosis (CAD) system based on a three-stage artificial neural network (ANN) method for classification based on four texture features. The image database consists of 36 chest radiographs classified as category 0 to category 3. Regions of interest (ROIs) with a matrix size of 32 × 32 were selected from chest radiographs. We obtained a gray-level histogram, histogram of gray-level difference, gray-level run-length matrix (GLRLM) feature image, and gray-level co-occurrence matrix (GLCOM) feature image in each ROI. For ROI-based classification, the first ANN was trained with each texture feature. Next, the second ANN was trained with output patterns obtained from the first ANN. Finally, we obtained a case-based classification for distinguishing among four categories with the third ANN method. We determined the performance of the third ANN by receiver operating characteristic (ROC) analysis. The areas under the ROC curve (AUC) of the highest category (severe pneumoconiosis) case and the lowest category (early pneumoconiosis) case were 0.89 ± 0.09 and 0.84 ± 0.12, respectively. The three-stage ANN with four texture features showed the highest performance for classification among the four categories. Our CAD system would be useful for assisting radiologists in classification of pneumoconiosis from category 0 to category 3.

Pancreatic abnormalities detected by endoscopic ultrasound (EUS) in patients without clinical signs of pancreatic disease: any difference between standard and Rosemont classification scoring?

PubMed

Petrone, Maria Chiara; Terracciano, Fulvia; Perri, Francesco; Carrara, Silvia; Cavestro, Giulia Martina; Mariani, Alberto; Testoni, Pier Alberto; Arcidiacono, Paolo Giorgio

2014-01-01

The prevalence of nine EUS features of chronic pancreatitis (CP) according to the standard Wiersema classification has been investigated in 489 patients undergoing EUS for an indication not related to pancreatico-biliary disease. We showed that 82 subjects (16.8%) had at least one ductular or parenchymal abnormality. Among them, 18 (3.7% of study population) had ≥3 Wiersema criteria suggestive of CP. Recently, a new classification (Rosemont) of EUS findings consistent, suggestive or indeterminate for CP has been proposed. To stratify healthy subjects into different subgroups on the basis of EUS features of CP according to the Wiersema and Rosemont classifications and to evaluate the agreement in the diagnosis of CP with the two scoring systems. Weighted kappa statistics was computed to evaluate the strength of agreement between the two scoring systems. Univariate and multivariate analysis between any EUS abnormality and habits were performed. Eighty-two EUS videos were reviewed. Using the Wiersema classification, 18 subjects showed ≥3 EUS features suggestive of CP. The EUS diagnosis of CP in these 18 subjects was considered as consistent in only one patient, according to Rosemont classification. Weighted Kappa statistics was 0.34 showing that the strength of agreement was 'fair'. Alcohol use and smoking were identified as risk factors for having pancreatic abnormalities on EUS. The prevalence of EUS features consistent or suggestive of CP in healthy subjects according to the Rosemont classification is lower than that assessed by Wiersema criteria. In that regard the Rosemont classification seems to be more accurate in excluding clinically relevant CP. Overall agreement between the two classifications is fair. Copyright © 2014 IAP and EPC. Published by Elsevier B.V. All rights reserved.

Genetic algorithm for the optimization of features and neural networks in ECG signals classification

NASA Astrophysics Data System (ADS)

Li, Hongqiang; Yuan, Danyang; Ma, Xiangdong; Cui, Dianyin; Cao, Lu

2017-01-01

Feature extraction and classification of electrocardiogram (ECG) signals are necessary for the automatic diagnosis of cardiac diseases. In this study, a novel method based on genetic algorithm-back propagation neural network (GA-BPNN) for classifying ECG signals with feature extraction using wavelet packet decomposition (WPD) is proposed. WPD combined with the statistical method is utilized to extract the effective features of ECG signals. The statistical features of the wavelet packet coefficients are calculated as the feature sets. GA is employed to decrease the dimensions of the feature sets and to optimize the weights and biases of the back propagation neural network (BPNN). Thereafter, the optimized BPNN classifier is applied to classify six types of ECG signals. In addition, an experimental platform is constructed for ECG signal acquisition to supply the ECG data for verifying the effectiveness of the proposed method. The GA-BPNN method with the MIT-BIH arrhythmia database achieved a dimension reduction of nearly 50% and produced good classification results with an accuracy of 97.78%. The experimental results based on the established acquisition platform indicated that the GA-BPNN method achieved a high classification accuracy of 99.33% and could be efficiently applied in the automatic identification of cardiac arrhythmias.

FSR: feature set reduction for scalable and accurate multi-class cancer subtype classification based on copy number.

PubMed

Wong, Gerard; Leckie, Christopher; Kowalczyk, Adam

2012-01-15

Feature selection is a key concept in machine learning for microarray datasets, where features represented by probesets are typically several orders of magnitude larger than the available sample size. Computational tractability is a key challenge for feature selection algorithms in handling very high-dimensional datasets beyond a hundred thousand features, such as in datasets produced on single nucleotide polymorphism microarrays. In this article, we present a novel feature set reduction approach that enables scalable feature selection on datasets with hundreds of thousands of features and beyond. Our approach enables more efficient handling of higher resolution datasets to achieve better disease subtype classification of samples for potentially more accurate diagnosis and prognosis, which allows clinicians to make more informed decisions in regards to patient treatment options. We applied our feature set reduction approach to several publicly available cancer single nucleotide polymorphism (SNP) array datasets and evaluated its performance in terms of its multiclass predictive classification accuracy over different cancer subtypes, its speedup in execution as well as its scalability with respect to sample size and array resolution. Feature Set Reduction (FSR) was able to reduce the dimensions of an SNP array dataset by more than two orders of magnitude while achieving at least equal, and in most cases superior predictive classification performance over that achieved on features selected by existing feature selection methods alone. An examination of the biological relevance of frequently selected features from FSR-reduced feature sets revealed strong enrichment in association with cancer. FSR was implemented in MATLAB R2010b and is available at http://ww2.cs.mu.oz.au/~gwong/FSR.

Developing a radiomics framework for classifying non-small cell lung carcinoma subtypes

NASA Astrophysics Data System (ADS)

Yu, Dongdong; Zang, Yali; Dong, Di; Zhou, Mu; Gevaert, Olivier; Fang, Mengjie; Shi, Jingyun; Tian, Jie

2017-03-01

Patient-targeted treatment of non-small cell lung carcinoma (NSCLC) has been well documented according to the histologic subtypes over the past decade. In parallel, recent development of quantitative image biomarkers has recently been highlighted as important diagnostic tools to facilitate histological subtype classification. In this study, we present a radiomics analysis that classifies the adenocarcinoma (ADC) and squamous cell carcinoma (SqCC). We extract 52-dimensional, CT-based features (7 statistical features and 45 image texture features) to represent each nodule. We evaluate our approach on a clinical dataset including 324 ADCs and 110 SqCCs patients with CT image scans. Classification of these features is performed with four different machine-learning classifiers including Support Vector Machines with Radial Basis Function kernel (RBF-SVM), Random forest (RF), K-nearest neighbor (KNN), and RUSBoost algorithms. To improve the classifiers' performance, optimal feature subset is selected from the original feature set by using an iterative forward inclusion and backward eliminating algorithm. Extensive experimental results demonstrate that radiomics features achieve encouraging classification results on both complete feature set (AUC=0.89) and optimal feature subset (AUC=0.91).

A comparative analysis of swarm intelligence techniques for feature selection in cancer classification.

PubMed

Gunavathi, Chellamuthu; Premalatha, Kandasamy

2014-01-01

Feature selection in cancer classification is a central area of research in the field of bioinformatics and used to select the informative genes from thousands of genes of the microarray. The genes are ranked based on T-statistics, signal-to-noise ratio (SNR), and F-test values. The swarm intelligence (SI) technique finds the informative genes from the top-m ranked genes. These selected genes are used for classification. In this paper the shuffled frog leaping with Lévy flight (SFLLF) is proposed for feature selection. In SFLLF, the Lévy flight is included to avoid premature convergence of shuffled frog leaping (SFL) algorithm. The SI techniques such as particle swarm optimization (PSO), cuckoo search (CS), SFL, and SFLLF are used for feature selection which identifies informative genes for classification. The k-nearest neighbour (k-NN) technique is used to classify the samples. The proposed work is applied on 10 different benchmark datasets and examined with SI techniques. The experimental results show that the results obtained from k-NN classifier through SFLLF feature selection method outperform PSO, CS, and SFL.

Better physical activity classification using smartphone acceleration sensor.

PubMed

Arif, Muhammad; Bilal, Mohsin; Kattan, Ahmed; Ahamed, S Iqbal

2014-09-01

Obesity is becoming one of the serious problems for the health of worldwide population. Social interactions on mobile phones and computers via internet through social e-networks are one of the major causes of lack of physical activities. For the health specialist, it is important to track the record of physical activities of the obese or overweight patients to supervise weight loss control. In this study, acceleration sensor present in the smartphone is used to monitor the physical activity of the user. Physical activities including Walking, Jogging, Sitting, Standing, Walking upstairs and Walking downstairs are classified. Time domain features are extracted from the acceleration data recorded by smartphone during different physical activities. Time and space complexity of the whole framework is done by optimal feature subset selection and pruning of instances. Classification results of six physical activities are reported in this paper. Using simple time domain features, 99 % classification accuracy is achieved. Furthermore, attributes subset selection is used to remove the redundant features and to minimize the time complexity of the algorithm. A subset of 30 features produced more than 98 % classification accuracy for the six physical activities.

Evaluation of different distortion correction methods and interpolation techniques for an automated classification of celiac disease☆

PubMed Central

Gadermayr, M.; Liedlgruber, M.; Uhl, A.; Vécsei, A.

2013-01-01

Due to the optics used in endoscopes, a typical degradation observed in endoscopic images are barrel-type distortions. In this work we investigate the impact of methods used to correct such distortions in images on the classification accuracy in the context of automated celiac disease classification. For this purpose we compare various different distortion correction methods and apply them to endoscopic images, which are subsequently classified. Since the interpolation used in such methods is also assumed to have an influence on the resulting classification accuracies, we also investigate different interpolation methods and their impact on the classification performance. In order to be able to make solid statements about the benefit of distortion correction we use various different feature extraction methods used to obtain features for the classification. Our experiments show that it is not possible to make a clear statement about the usefulness of distortion correction methods in the context of an automated diagnosis of celiac disease. This is mainly due to the fact that an eventual benefit of distortion correction highly depends on the feature extraction method used for the classification. PMID:23981585

Medical image classification based on multi-scale non-negative sparse coding.

PubMed

Zhang, Ruijie; Shen, Jian; Wei, Fushan; Li, Xiong; Sangaiah, Arun Kumar

2017-11-01

With the rapid development of modern medical imaging technology, medical image classification has become more and more important in medical diagnosis and clinical practice. Conventional medical image classification algorithms usually neglect the semantic gap problem between low-level features and high-level image semantic, which will largely degrade the classification performance. To solve this problem, we propose a multi-scale non-negative sparse coding based medical image classification algorithm. Firstly, Medical images are decomposed into multiple scale layers, thus diverse visual details can be extracted from different scale layers. Secondly, for each scale layer, the non-negative sparse coding model with fisher discriminative analysis is constructed to obtain the discriminative sparse representation of medical images. Then, the obtained multi-scale non-negative sparse coding features are combined to form a multi-scale feature histogram as the final representation for a medical image. Finally, SVM classifier is combined to conduct medical image classification. The experimental results demonstrate that our proposed algorithm can effectively utilize multi-scale and contextual spatial information of medical images, reduce the semantic gap in a large degree and improve medical image classification performance. Copyright © 2017 Elsevier B.V. All rights reserved.

The influence of multispectral scanner spatial resolution on forest feature classification

NASA Technical Reports Server (NTRS)

Sadowski, F. G.; Malila, W. A.; Sarno, J. E.; Nalepka, R. F.

1977-01-01

Inappropriate spatial resolution and corresponding data processing techniques may be major causes for non-optimal forest classification results frequently achieved from multispectral scanner (MSS) data. Procedures and results of empirical investigations are studied to determine the influence of MSS spatial resolution on the classification of forest features into levels of detail or hierarchies of information that might be appropriate for nationwide forest surveys and detailed in-place inventories. Two somewhat different, but related studies are presented. The first consisted of establishing classification accuracies for several hierarchies of features as spatial resolution was progressively coarsened from (2 meters) squared to (64 meters) squared. The second investigated the capabilities for specialized processing techniques to improve upon the results of conventional processing procedures for both coarse and fine resolution data.

An improved arteriovenous classification method for the early diagnostics of various diseases in retinal image.

PubMed

Xu, Xiayu; Ding, Wenxiang; Abràmoff, Michael D; Cao, Ruofan

2017-04-01

Retinal artery and vein classification is an important task for the automatic computer-aided diagnosis of various eye diseases and systemic diseases. This paper presents an improved supervised artery and vein classification method in retinal image. Intra-image regularization and inter-subject normalization is applied to reduce the differences in feature space. Novel features, including first-order and second-order texture features, are utilized to capture the discriminating characteristics of arteries and veins. The proposed method was tested on the DRIVE dataset and achieved an overall accuracy of 0.923. This retinal artery and vein classification algorithm serves as a potentially important tool for the early diagnosis of various diseases, including diabetic retinopathy and cardiovascular diseases. Copyright © 2017 Elsevier B.V. All rights reserved.

PHOTOMETRIC SUPERNOVA CLASSIFICATION WITH MACHINE LEARNING

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

Lochner, Michelle; Peiris, Hiranya V.; Lahav, Ofer

Automated photometric supernova classification has become an active area of research in recent years in light of current and upcoming imaging surveys such as the Dark Energy Survey (DES) and the Large Synoptic Survey Telescope, given that spectroscopic confirmation of type for all supernovae discovered will be impossible. Here, we develop a multi-faceted classification pipeline, combining existing and new approaches. Our pipeline consists of two stages: extracting descriptive features from the light curves and classification using a machine learning algorithm. Our feature extraction methods vary from model-dependent techniques, namely SALT2 fits, to more independent techniques that fit parametric models tomore » curves, to a completely model-independent wavelet approach. We cover a range of representative machine learning algorithms, including naive Bayes, k -nearest neighbors, support vector machines, artificial neural networks, and boosted decision trees (BDTs). We test the pipeline on simulated multi-band DES light curves from the Supernova Photometric Classification Challenge. Using the commonly used area under the curve (AUC) of the Receiver Operating Characteristic as a metric, we find that the SALT2 fits and the wavelet approach, with the BDTs algorithm, each achieve an AUC of 0.98, where 1 represents perfect classification. We find that a representative training set is essential for good classification, whatever the feature set or algorithm, with implications for spectroscopic follow-up. Importantly, we find that by using either the SALT2 or the wavelet feature sets with a BDT algorithm, accurate classification is possible purely from light curve data, without the need for any redshift information.« less

Integrated Low-Rank-Based Discriminative Feature Learning for Recognition.

PubMed

Zhou, Pan; Lin, Zhouchen; Zhang, Chao

2016-05-01

Feature learning plays a central role in pattern recognition. In recent years, many representation-based feature learning methods have been proposed and have achieved great success in many applications. However, these methods perform feature learning and subsequent classification in two separate steps, which may not be optimal for recognition tasks. In this paper, we present a supervised low-rank-based approach for learning discriminative features. By integrating latent low-rank representation (LatLRR) with a ridge regression-based classifier, our approach combines feature learning with classification, so that the regulated classification error is minimized. In this way, the extracted features are more discriminative for the recognition tasks. Our approach benefits from a recent discovery on the closed-form solutions to noiseless LatLRR. When there is noise, a robust Principal Component Analysis (PCA)-based denoising step can be added as preprocessing. When the scale of a problem is large, we utilize a fast randomized algorithm to speed up the computation of robust PCA. Extensive experimental results demonstrate the effectiveness and robustness of our method.

Classification of speech dysfluencies using LPC based parameterization techniques.

PubMed

Hariharan, M; Chee, Lim Sin; Ai, Ooi Chia; Yaacob, Sazali

2012-06-01

The goal of this paper is to discuss and compare three feature extraction methods: Linear Predictive Coefficients (LPC), Linear Prediction Cepstral Coefficients (LPCC) and Weighted Linear Prediction Cepstral Coefficients (WLPCC) for recognizing the stuttered events. Speech samples from the University College London Archive of Stuttered Speech (UCLASS) were used for our analysis. The stuttered events were identified through manual segmentation and were used for feature extraction. Two simple classifiers namely, k-nearest neighbour (kNN) and Linear Discriminant Analysis (LDA) were employed for speech dysfluencies classification. Conventional validation method was used for testing the reliability of the classifier results. The study on the effect of different frame length, percentage of overlapping, value of ã in a first order pre-emphasizer and different order p were discussed. The speech dysfluencies classification accuracy was found to be improved by applying statistical normalization before feature extraction. The experimental investigation elucidated LPC, LPCC and WLPCC features can be used for identifying the stuttered events and WLPCC features slightly outperforms LPCC features and LPC features.

Capability of geometric features to classify ships in SAR imagery

NASA Astrophysics Data System (ADS)

Lang, Haitao; Wu, Siwen; Lai, Quan; Ma, Li

2016-10-01

Ship classification in synthetic aperture radar (SAR) imagery has become a new hotspot in remote sensing community for its valuable potential in many maritime applications. Several kinds of ship features, such as geometric features, polarimetric features, and scattering features have been widely applied on ship classification tasks. Compared with polarimetric features and scattering features, which are subject to SAR parameters (e.g., sensor type, incidence angle, polarization, etc.) and environment factors (e.g., sea state, wind, wave, current, etc.), geometric features are relatively independent of SAR and environment factors, and easy to be extracted stably from SAR imagery. In this paper, the capability of geometric features to classify ships in SAR imagery with various resolution has been investigated. Firstly, the relationship between the geometric feature extraction accuracy and the SAR imagery resolution is analyzed. It shows that the minimum bounding rectangle (MBR) of ship can be extracted exactly in terms of absolute precision by the proposed automatic ship-sea segmentation method. Next, six simple but effective geometric features are extracted to build a ship representation for the subsequent classification task. These six geometric features are composed of length (f1), width (f2), area (f3), perimeter (f4), elongatedness (f5) and compactness (f6). Among them, two basic features, length (f1) and width (f2), are directly extracted based on the MBR of ship, the other four are derived from those two basic features. The capability of the utilized geometric features to classify ships are validated on two data set with different image resolutions. The results show that the performance of ship classification solely by geometric features is close to that obtained by the state-of-the-art methods, which obtained by a combination of multiple kinds of features, including scattering features and geometric features after a complex feature selection process.

A desktop system of virtual morphometric globes for Mars and the Moon

NASA Astrophysics Data System (ADS)

Florinsky, I. V.; Filippov, S. V.

2017-03-01

Global morphometric models can be useful for earth and planetary studies. Virtual globes - programs implementing interactive three-dimensional (3D) models of planets - are increasingly used in geo- and planetary sciences. We describe the development of a desktop system of virtual morphometric globes for Mars and the Moon. As the initial data, we used 15'-gridded global digital elevation models (DEMs) extracted from the Mars Orbiter Laser Altimeter (MOLA) and the Lunar Orbiter Laser Altimeter (LOLA) gridded archives. For two celestial bodies, we derived global digital models of several morphometric attributes, such as horizontal curvature, vertical curvature, minimal curvature, maximal curvature, and catchment area. To develop the system, we used Blender, the free open-source software for 3D modeling and visualization. First, a 3D sphere model was generated. Second, the global morphometric maps were imposed to the sphere surface as textures. Finally, the real-time 3D graphics Blender engine was used to implement rotation and zooming of the globes. The testing of the developed system demonstrated its good performance. Morphometric globes clearly represent peculiarities of planetary topography, according to the physical and mathematical sense of a particular morphometric variable.

Matching of renewable source of energy generation graphs and electrical load in local energy system

NASA Astrophysics Data System (ADS)

Lezhniuk, Petro; Komar, Vyacheslav; Sobchuk, Dmytro; Kravchuk, Sergiy; Kacejko, Piotr; Zavidsky, Vladislav

2017-08-01

The paper contains the method of matching generation graph of photovoltaic electric stations and consumers. Characteristic feature of this method is the application of morphometric analysis for assessment of non-uniformity of the integrated graph of energy supply, optimal coefficients of current distribution, that enables by mean of refining the powers, transferring in accordance with the graph , to provide the decrease of electric energy losses in the grid and transport task, as the optimization tool.

Kernel-based Joint Feature Selection and Max-Margin Classification for Early Diagnosis of Parkinson’s Disease

NASA Astrophysics Data System (ADS)

Adeli, Ehsan; Wu, Guorong; Saghafi, Behrouz; An, Le; Shi, Feng; Shen, Dinggang

2017-01-01

Feature selection methods usually select the most compact and relevant set of features based on their contribution to a linear regression model. Thus, these features might not be the best for a non-linear classifier. This is especially crucial for the tasks, in which the performance is heavily dependent on the feature selection techniques, like the diagnosis of neurodegenerative diseases. Parkinson’s disease (PD) is one of the most common neurodegenerative disorders, which progresses slowly while affects the quality of life dramatically. In this paper, we use the data acquired from multi-modal neuroimaging data to diagnose PD by investigating the brain regions, known to be affected at the early stages. We propose a joint kernel-based feature selection and classification framework. Unlike conventional feature selection techniques that select features based on their performance in the original input feature space, we select features that best benefit the classification scheme in the kernel space. We further propose kernel functions, specifically designed for our non-negative feature types. We use MRI and SPECT data of 538 subjects from the PPMI database, and obtain a diagnosis accuracy of 97.5%, which outperforms all baseline and state-of-the-art methods.

Kernel-based Joint Feature Selection and Max-Margin Classification for Early Diagnosis of Parkinson’s Disease

PubMed Central

Adeli, Ehsan; Wu, Guorong; Saghafi, Behrouz; An, Le; Shi, Feng; Shen, Dinggang

2017-01-01

Feature selection methods usually select the most compact and relevant set of features based on their contribution to a linear regression model. Thus, these features might not be the best for a non-linear classifier. This is especially crucial for the tasks, in which the performance is heavily dependent on the feature selection techniques, like the diagnosis of neurodegenerative diseases. Parkinson’s disease (PD) is one of the most common neurodegenerative disorders, which progresses slowly while affects the quality of life dramatically. In this paper, we use the data acquired from multi-modal neuroimaging data to diagnose PD by investigating the brain regions, known to be affected at the early stages. We propose a joint kernel-based feature selection and classification framework. Unlike conventional feature selection techniques that select features based on their performance in the original input feature space, we select features that best benefit the classification scheme in the kernel space. We further propose kernel functions, specifically designed for our non-negative feature types. We use MRI and SPECT data of 538 subjects from the PPMI database, and obtain a diagnosis accuracy of 97.5%, which outperforms all baseline and state-of-the-art methods. PMID:28120883

Application and assessment of multiscale bending energy for morphometric characterization of neural cells

NASA Astrophysics Data System (ADS)

Cesar, Roberto Marcondes; Costa, Luciano da Fontoura

1997-05-01

The estimation of the curvature of experimentally obtained curves is an important issue in many applications of image analysis including biophysics, biology, particle physics, and high energy physics. However, the accurate calculation of the curvature of digital contours has proven to be a difficult endeavor, mainly because of the noise and distortions that are always present in sampled signals. Errors ranging from 1% to 1000% have been reported with respect to the application of standard techniques in the estimation of the curvature of circular contours [M. Worring and A. W. M. Smeulders, CVGIP: Im. Understanding, 58, 366 (1993)]. This article explains how diagrams of multiscale bending energy can be easily obtained from curvegrams and used as a robust general feature for morphometric characterization of neural cells. The bending energy is an interesting global feature for shape characterization that expresses the amount of energy needed to transform the specific shape under analysis into its lowest energy state (i.e., a circle). The curvegram, which can be accurately obtained by using digital signal processing techniques (more specifically through the Fourier transform and its inverse, as described in this work), provides multiscale representation of the curvature of digital contours. The estimation of the bending energy from the curvegram is introduced and exemplified with respect to a series of neural cells. The masked high curvature effect is reported and its implications to shape analysis are discussed. It is also discussed and illustrated that, by normalizing the multiscale bending energy with respect to a standard circle of unitary perimeter, this feature becomes an effective means for expressing shape complexity in a way that is invariant to rotation, translation, and scaling, and that is robust to noise and other artifacts implied by image acquisition.