Estimation of nasal cavity and conchae volumes by stereological method

Background: Studies evaluating the mean volumes of nasal cavity and concha are very rare. Since there is little date on the mentioned topic, we aimed to carry out the presented study to obtain a volumetric index showing the relation between the nasal cavity and concha. Material and methods: The volumes of the nasal cavity and concha were measured in 30 males and 30 females (18&#8211;40 years old) on computed tomography images using stereological methods. Results: The mean volumes of nasal cavity, concha nasalis media, and concha nasalis inferior were 5.95 &#177; 0.10 cm3, 0.56 &#177; 0.22 cm3, and 1.45 &#177; 0.68 cm3; 7.01 &#177; 0.18 cm3, 0.67 &#177; 0.31 cm3 and 1.59 &#177; 0.98 cm3 in females and males, respectively. There were statistically significant differences in the volume of the nasal cavity and concha nasalis media (p < 0.05) between males and females, except for concha nasalis inferior (p > 0.05). Conclusions: Our results could provide volumetric indexes for the nasal cavity and concha, which could help the physician to manage surgical procedures related to the nasal cavity and concha

Optimal motion control and vibration suppression of flexible systems with inaccessible outputs

This work addresses the optimal control problem of dynamical systems with inaccessible outputs. A case in which dynamical system outputs cannot be measured or inaccessible. This contradicts with the nature of the optimal controllers which can be considered without any loss of generality as state feedback control laws for systems with linear dynamics. Therefore, this work attempts to estimate dynamical system states through a novel state observer that does not require injecting the dynamical system outputs onto the observer structure during its design. A linear quadratic optimal control law is then realized based on the estimated states which allows controlling motion along with active vibration suppression of this class of dynamical systems with inaccessible outputs. Validity of the proposed control framework is evaluated experimentally

Metallicity and absolute magnitude calibrations for F-G type main-sequence stars in the Gaia era

In this study, photometric metallicity and absolute magnitude calibrations were derived using F-G spectral type main-sequence stars in the Solar neighbourhood with precise spectroscopic, photometric and Gaia astrometric data for UBV photometry. The sample consists of 504 main-sequence stars covering the temperature, surface gravity and colour index intervals $5300<T_{eff} < 7300$ K, $\log g > 4$ (cgs) and $0.3<(B-V)_0<0.8$ mag, respectively. Stars with relative trigonometric parallax errors $\sigma_{\pi}/\pi\leq0.01$ were preferred from Gaia DR2 data for the estimation of their $M_V$ absolute magnitudes. In order to obtain calibrations, $(U-B)_0$ and $(B-V)_0$ colour indices of stars were preferred and a multi-variable second order equation was used. Calibrations are valid for main-sequence stars in the metallicity and absolute magnitude ranges $-2<{\rm [Fe/H]}<0.5$ dex and $2.5<M_V<6$ mag, respectively. The mean value and standard deviation of the differences between original and estimated values for the metal abundance and absolute magnitude are $\langle\Delta {\rm[Fe/H]}\rangle=0.00\pm0.11$ dex and $\langle\Delta M_V \rangle=0.00\pm0.22$ mag, respectively. In this work, it has been shown that more precise iron abundance and absolute magnitude values were obtained with the new calibrations, compared to previous calibrations in the literature.Comment: 14 pages, 10 figures and 4 tables, accepted for publication in Astrophysics and Space Scienc

Test beam studies of the TRD prototype filled with different gas mixtures based on Xe, Kr, and Ar

Towards the end of LHC Run1, gas leaks were observed in some parts of the Transition Radiation Tracker (TRT) of ATLAS. Due to these leaks, primary Xenon based gas mixture was replaced with Argon based mixture in various parts. Test-beam studies with a dedicated Transition Radiation Detector (TRD) prototype were carried out in 2015 in order to understand transition radiation performance with mixtures based on Argon and Krypton. We present and discuss the results of these test-beam studies with different active gas compositions.Comment: 5 pages,12 figures, The 2nd International Conference on Particle Physics and Astrophysics (ICPPA-2016); Acknowledgments section correcte

A soft kinetic data structure for lesion border detection

Motivation: The medical imaging and image processing techniques, ranging from microscopic to macroscopic, has become one of the main components of diagnostic procedures to assist dermatologists in their medical decision-making processes. Computer-aided segmentation and border detection on dermoscopic images is one of the core components of diagnostic procedures and therapeutic interventions for skin cancer. Automated assessment tools for dermoscopic images have become an important research field mainly because of inter- and intra-observer variations in human interpretations. In this study, a novel approach—graph spanner—for automatic border detection in dermoscopic images is proposed. In this approach, a proximity graph representation of dermoscopic images in order to detect regions and borders in skin lesion is presented

Some results of test beam studies of Transition Radiation Detector prototypes at CERN

Operating conditions and challenging demands of present and future accelerator experiments result in new requirements on detector systems. There are many ongoing activities aimed to develop new technologies and to improve the properties of detectors based on existing technologies. Our work is dedicated to development of Transition Radiation Detectors (TRD) suitable for different applications. In this paper results obtained in beam tests at SPS accelerator at CERN with the TRD prototype based on straw technology are presented. TRD performance was studied as a function of thickness of the transition radiation radiator and working gas mixture pressure

Lesion detection in demoscopy images with novel density-based and active contour approaches

<p>Abstract</p> <p>Background</p> <p>Dermoscopy is one of the major imaging modalities used in the diagnosis of melanoma and other pigmented skin lesions. Automated assessment tools for dermoscopy images have become an important field of research mainly because of inter- and intra-observer variations in human interpretation. One of the most important steps in dermoscopy image analysis is the detection of lesion borders, since many other features, such as asymmetry, border irregularity, and abrupt border cutoff, rely on the boundary of the lesion. </p> <p>Results</p> <p>To automate the process of delineating the lesions, we employed Active Contour Model (ACM) and boundary-driven density-based clustering (BD-DBSCAN) algorithms on 50 dermoscopy images, which also have ground truths to be used for quantitative comparison. We have observed that ACM and BD-DBSCAN have the same border error of 6.6% on all images. To address noisy images, BD-DBSCAN can perform better delineation than ACM. However, when used with optimum parameters, ACM outperforms BD-DBSCAN, since ACM has a higher recall ratio.</p> <p>Conclusion</p> <p>We successfully proposed two new frameworks to delineate suspicious lesions with i) an ACM integrated approach with sharpening and ii) a fast boundary-driven density-based clustering technique. ACM shrinks a curve toward the boundary of the lesion. To guide the evolution, the model employs the exact solution <abbrgrp><abbr bid="B27">27</abbr></abbrgrp> of a specific form of the Geometric Heat Partial Differential Equation <abbrgrp><abbr bid="B28">28</abbr></abbrgrp>. To make ACM advance through noisy images, an improvement of the model’s boundary condition is under consideration. BD-DBSCAN improves regular density-based algorithm to select query points intelligently.</p

Content-Based Image Retrieval of Skin Lesions by Evolutionary Feature Synthesis

Abstract. This paper gives an example of evolved features that improve image retrieval performance. A content-based image retrieval system for skin lesion images is presented. The aim is to support decision making by retrieving and displaying relevant past cases visually similar to the one under examination. Skin lesions of five common classes, including two non-melanoma cancer types, are used. Colour and texture features are extracted from lesions. Evolutionary algorithms are used to create composite features that optimise a similarity matching function. Experiments on our database of 533 images are performed and results are compared to those obtained using simple features. The use of the evolved composite features improves the precision by about 7%.

Analysis of density based and fuzzy c-means clustering methods on lesion border extraction in dermoscopy images

<p>Abstract</p> <p>Background</p> <p>Computer-aided segmentation and border detection in dermoscopic images is one of the core components of diagnostic procedures and therapeutic interventions for skin cancer. Automated assessment tools for dermoscopy images have become an important research field mainly because of inter- and intra-observer variations in human interpretation. In this study, we compare two approaches for automatic border detection in dermoscopy images: density based clustering (DBSCAN) and Fuzzy C-Means (FCM) clustering algorithms. In the first approach, if there exists enough density –greater than certain number of points- around a point, then either a new cluster is formed around the point or an existing cluster grows by including the point and its neighbors. In the second approach FCM clustering is used. This approach has the ability to assign one data point into more than one cluster.</p> <p>Results</p> <p>Each approach is examined on a set of 100 dermoscopy images whose manually drawn borders by a dermatologist are used as the ground truth. Error rates; false positives and false negatives along with true positives and true negatives are quantified by comparing results with manually determined borders from a dermatologist. The assessments obtained from both methods are quantitatively analyzed over three accuracy measures: border error, precision, and recall. </p> <p>Conclusion</p> <p>As well as low border error, high precision and recall, visual outcome showed that the DBSCAN effectively delineated targeted lesion, and has bright future; however, the FCM had poor performance especially in border error metric.</p