Publications by authors named "David Pokrajac"

7 Publications

  • Page 1 of 1

Modeling of laser-induced breakdown spectroscopic data analysis by an automatic classifier.

Int J Data Sci Anal 2019 8;8(2):213-220. Epub 2019 Feb 8.

1Delaware State University, Dover, DE 19901 USA.

Laser-induced breakdown spectroscopy (LIBS) is a multi-elemental and real-time analytical technique with simultaneous detection of all the elements in any type of sample matrix including solid, liquid, gas, and aerosol. LIBS produces vast amount of data which contains information on elemental composition of the material among others. Classification and discrimination of spectra produced during the LIBS process are crucial to analyze the elements for both qualitative and quantitative analysis. This work reports the design and modeling of optimal classifier for LIBS data classification and discrimination using the apparatus of statistical theory of detection. We analyzed the noise sources associated during the LIBS process and created a linear model of an echelle spectrograph system. We validated our model based on assumptions through statistical analysis of "dark signal" and laser-induced breakdown spectra from the database of National Institute of Science and Technology. The results obtained from our model suggested that the quadratic classifier provides optimal performance if the spectroscopy signal and noise can be considered Gaussian.
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http://dx.doi.org/10.1007/s41060-018-00172-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6951475PMC
February 2019

Evaluation of non-Gaussian statistical properties in virtual breast phantoms.

J Med Imaging (Bellingham) 2019 Apr 14;6(2):025502. Epub 2019 Jun 14.

University of California at Davis, Department of Radiology, Sacramento, California, United States.

Images derived from a "virtual phantom" can be useful in characterizing the performance of imaging systems. This has driven the development of virtual breast phantoms implemented in simulation environments. In breast imaging, several such phantoms have been proposed. We analyze the non-Gaussian statistical properties from three classes of virtual breast phantoms and compare them to similar statistics from a database of breast images. These include clustered-blob lumpy backgrounds (CBLBs), truncated binary textures, and the UPenn virtual breast phantoms. We use Laplacian fractional entropy (LFE) as a measure of the non-Gaussian statistical properties of each simulation procedure. Our results show that, despite similar power spectra, the simulation approaches differ considerably in LFE with very low scores for the CBLB to high values for the UPenn phantom at certain frequencies. These results suggest that LFE may have value in developing and tuning virtual phantom simulation procedures.
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http://dx.doi.org/10.1117/1.JMI.6.2.025502DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6566002PMC
April 2019

Earliest Known Hominin Calcar Femorale in Orrorin tugenensis Provides Further Internal Anatomical Evidence for Origin of Human Bipedal Locomotion.

Anat Rec (Hoboken) 2018 11 19;301(11):1834-1839. Epub 2018 Oct 19.

Laboratory for the Comparative Study of Morphology, Mechanics and Molecules, Department of Kinesiology, Pennsylvania State University, State College, Pennsylvania, 16801.

The calcar femorale (CF), a plate of dense bone internal to the lesser trochanter, is visible on computed tomographic images of the 6 million-year-old femoral fragment BAR 1003'00 (from the taxon Orrorin tugenensis), among the oldest specimens relevant to reconstructing the evolution of human bipedal locomotion. A strongly expressed CF has been used previously as an indicator of bipedality. If true, then there should be a quantifiable difference in the CF among hominoids. Absolute and normalized CF lengths were measured from computed tomographic images at five anatomical locations along the proximal portion of BAR 1003'00 in addition to samples of nine H. sapiens and ten P. troglodytes femora. The span of the CF superiorly to inferiorly within the proximal femur was measured by counting the number of cross-sections on which the CF occurred. A Bayesian approach was used to classify the BAR 1003'00 sample based on normalized lengths. The P. troglodytes femora were more variable both in the occurrence of the trait and, where present, its span in the proximal femur. The H. sapiens sample exhibited CF lengths that were consistently larger at each location than the P. troglodytes in absolute terms, but the normalized lengths overlap substantially. The Bayesian posterior probability test classifies the CF of BAR 1003'00 with H. sapiens. The BAR 1003'00's calcar femorale has a strong anatomical similarity to the H. sapiens sample, supporting the conclusion that O. tugenensis is an early bipedal hominin. Anat Rec, 301:1834-1839, 2018. © 2018 Wiley Periodicals, Inc.
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http://dx.doi.org/10.1002/ar.23939DOI Listing
November 2018

Description and characterization of a novel method for partial volume simulation in software breast phantoms.

IEEE Trans Med Imaging 2015 Oct 20;34(10):2146-61. Epub 2015 Apr 20.

A modification to our previous simulation of breast anatomy is proposed to improve the quality of simulated x-ray projections images. The image quality is affected by the voxel size of the simulation. Large voxels can cause notable spatial quantization artifacts; small voxels extend the generation time and increase the memory requirements. An improvement in image quality is achievable without reducing voxel size by the simulation of partial volume averaging in which voxels containing more than one simulated tissue type are allowed. The linear x-ray attenuation coefficient of voxels is, thus, the sum of the linear attenuation coefficients weighted by the voxel subvolume occupied by each tissue type. A local planar approximation of the boundary surface is employed. In the two-material case, the partial volume in each voxel is computed by decomposition into up to four simple geometric shapes. In the three-material case, by application of the Gauss-Ostrogradsky theorem, the 3D partial volume problem is converted into one of a few simpler 2D surface area problems. We illustrate the benefits of the proposed methodology on simulated x-ray projections. An efficient encoding scheme is proposed for the type and proportion of simulated tissues in each voxel. Monte Carlo simulation was used to evaluate the quantitative error of our approximation algorithms.
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http://dx.doi.org/10.1109/TMI.2015.2424854DOI Listing
October 2015

Automatic classification of laser-induced breakdown spectroscopy (LIBS) data of protein biomarker solutions.

Appl Spectrosc 2014 ;68(9):1067-75

Department of Information and Computer Sciences and Optical Science Center for Applied Research, Delaware State University, Dover, DE 19901 USA.

We perform multi-class classification of laser-induced breakdown spectroscopy data of four commercial samples of proteins diluted in phosphate-buffered saline solution at different concentrations: bovine serum albumin, osteopontin, leptin, and insulin-like growth factor II. We achieve this by using principal component analysis as a method for dimensionality reduction. In addition, we apply several different classification algorithms (K-nearest neighbor, classification and regression trees, neural networks, support vector machines, adaptive local hyperplane, and linear discriminant classifiers) to perform multi-class classification. We achieve classification accuracies above 98% by using the linear classifier with 21-31 principal components. We obtain the best detection performance for neural networks, support vector machines, and adaptive local hyperplanes for a range of the number of principal components with no significant differences in performance except for that of the linear classifier. With the optimal number of principal components, a simplistic K-nearest classifier still provided acceptable results. Our proposed approach demonstrates that highly accurate automatic classification of complex protein samples from laser-induced breakdown spectroscopy data can be successfully achieved using principal component analysis with a sufficiently large number of extracted features, followed by a wrapper technique to determine the optimal number of principal components.
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http://dx.doi.org/10.1366/14-07488DOI Listing
May 2015

Optimized generation of high resolution breast anthropomorphic software phantoms.

Med Phys 2012 Apr;39(4):2290-302

Computer and Information Sciences Department, Delaware State University, Dover, DE 19901, USA.

Purpose: The authors present an efficient method for generating anthropomorphic software breast phantoms with high spatial resolution. Employing the same region growing principles as in their previous algorithm for breast anatomy simulation, the present method has been optimized for computational complexity to allow for fast generation of the large number of phantoms required in virtual clinical trials of breast imaging.

Methods: The new breast anatomy simulation method performs a direct calculation of the Cooper's ligaments (i.e., the borders between simulated adipose compartments). The calculation corresponds to quadratic decision boundaries of a maximum a posteriori classifier. The method is multiscale due to the use of octree-based recursive partitioning of the phantom volume. The method also provides user-control of the thickness of the simulated Cooper's ligaments and skin.

Results: Using the proposed method, the authors have generated phantoms with voxel size in the range of (25-1000 μm)(3)∕voxel. The power regression of the simulation time as a function of the reciprocal voxel size yielded a log-log slope of 1.95 (compared to a slope of 4.53 of our previous region growing algorithm).

Conclusions: A new algorithm for computer simulation of breast anatomy has been proposed that allows for fast generation of high resolution anthropomorphic software phantoms.
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http://dx.doi.org/10.1118/1.3697523DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3337667PMC
April 2012
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